won't have effect + // so put an additional one + buffer.push(""); + } + const bufferValue = buffer.join("
"); + dataValue["value"] = bufferValue; + useLines.push(dataValue); + buffer = []; + } + } + + for (let line of lines) { + if (line === progressLiteralStart) { + saveBuffer(); + useLines.push({ + type: "progress" + }); + } else if (line.startsWith(promptLiteralStart)) { + saveBuffer(); + const value = line.replace(promptLiteralStart, "").trimEnd(); + useLines.push({ + type: "input", + value: value + }); + } else if (line.startsWith("// ")) { + saveBuffer(); + const value = "💬 " + line.replace("// ", "").trimEnd(); + useLines.push({ + value: value, + class: "termynal-comment", + delay: 0 + }); + } else if (line.startsWith(customPromptLiteralStart)) { + saveBuffer(); + const promptStart = line.indexOf(promptLiteralStart); + if (promptStart === -1) { + console.error("Custom prompt found but no end delimiter", line) + buffer.push(line); + continue; + } + const prompt = line.slice(0, promptStart).replace(customPromptLiteralStart, "") + let value = line.slice(promptStart + promptLiteralStart.length); + useLines.push({ + type: "input", + value: value, + prompt: prompt + }); + } else { + buffer.push(line); + } + } + saveBuffer(); + + // Create new container for termynal + const div = document.createElement("div"); + container.innerHTML = ''; + container.appendChild(div); + + const termynal = new Termynal(div, { + lineData: useLines, + noInit: true, + lineDelay: 500 + }); + termynals.push(termynal); + }); + } + + function loadVisibleTermynals() { + termynals = termynals.filter(termynal => { + if (termynal.container.getBoundingClientRect().top - innerHeight <= 0) { + termynal.init(); + return false; + } + return true; + }); + } + + window.addEventListener("scroll", loadVisibleTermynals); + createTermynals(); + loadVisibleTermynals(); +} + +// Initialize when DOM is ready +if (document.readyState === 'loading') { + document.addEventListener('DOMContentLoaded', setupTermynal); +} else { + setupTermynal(); +} diff --git a/docs/_static/js/termynal.js b/docs/_static/js/termynal.js new file mode 100644 index 0000000..df6de68 --- /dev/null +++ b/docs/_static/js/termynal.js @@ -0,0 +1,265 @@ +/** + * termynal.js + * A lightweight, modern and extensible animated terminal window, using + * async/await. + * + * @author Ines Montani
+
+
+
+## Quick Start
+
+
+
+```console
+// Install the Nemotron training recipes
+$ git clone https://github.com/NVIDIA/nemotron
+$ cd nemotron && uv sync
+
+// Run the full Nano3 pipeline
+$ uv run nemotron nano3 data prep pretrain --run YOUR-CLUSTER
+$ uv run nemotron nano3 pretrain --run YOUR-CLUSTER
+$ uv run nemotron nano3 data prep sft --run YOUR-CLUSTER
+$ uv run nemotron nano3 sft --run YOUR-CLUSTER
+$ uv run nemotron nano3 data prep rl --run YOUR-CLUSTER
+$ uv run nemotron nano3 rl --run YOUR-CLUSTER
+```
+
+
+
+> **Note**: The `--run YOUR-CLUSTER` flag submits jobs to your configured Slurm cluster via [NeMo-Run](train/nemo-run.md). See [Execution through NeMo-Run](train/nemo-run.md) for setup instructions.
+
+## Usage Cookbook & Examples
+
+::::{grid} 1 2 2 2
+:gutter: 3
+
+:::{grid-item-card} Usage Cookbook
+:link: usage-cookbook/README
+:link-type: doc
+
+Deployment guides for Nemotron models: TensorRT-LLM, vLLM, SGLang, NIM, and Hugging Face.
+:::
+
+:::{grid-item-card} Use Case Examples
+:link: use-case-examples/README
+:link-type: doc
+
+End-to-end applications: RAG agents, ML agents, and multi-agent systems.
+:::
+
+::::
+
+## Available Training Recipes
+
+::::{grid} 1 2 2 2
+:gutter: 3
+
+:::{grid-item-card} Nemotron 3 Nano
+:link: train/nano3/README
+:link-type: doc
+
+31.6B total / 3.6B active parameters, 25T tokens, up to 1M context. Hybrid Mamba-Transformer with sparse MoE.
+
+**Stages:** Pretraining → SFT → RL
+:::
+
+::::
+
+## Training Pipeline
+
+The Nemotron training pipeline follows a three-stage approach with full artifact lineage tracking:
+
+| Stage | Name | Description |
+|-------|------|-------------|
+| 0 | [Pretraining](train/nano3/pretrain.md) | Base model training on large text corpus |
+| 1 | [SFT](train/nano3/sft.md) | Supervised fine-tuning for instruction following |
+| 2 | [RL](train/nano3/rl.md) | Reinforcement learning for alignment |
+
+## Why Nemotron?
+
+| | |
+|---|---|
+| **Open Models** | Transparent training data, techniques, and weights for community innovation |
+| **Compute Efficiency** | Model pruning enabling higher throughput via TensorRT-LLM |
+| **High Accuracy** | Built on frontier open models with human-aligned reasoning |
+| **Flexible Deployment** | Deploy anywhere — edge, single GPU, or data center with NIM |
+
+## Key Features
+
+- **Complete Pipelines** — From raw data to deployment-ready models
+- **[Artifact Lineage](train/artifacts.md)** — Full traceability via [W&B](train/wandb.md) from data to model
+- **Production-Grade** — Built on [NVIDIA's NeMo stack](train/nvidia-stack.md) (Megatron-Bridge, NeMo-RL)
+- **Reproducible** — Versioned configs, data blends, and checkpoints
+
+## Resources
+
+- [Tech Report](https://research.nvidia.com/labs/nemotron/files/NVIDIA-Nemotron-3-Nano-Technical-Report.pdf) — Nemotron 3 Nano methodology
+- [Model Weights](https://huggingface.co/collections/nvidia/nvidia-nemotron-v3) — Pre-trained checkpoints on HuggingFace
+- [Pre-training Datasets](https://huggingface.co/collections/nvidia/nemotron-pre-training-datasets) — Open pre-training data
+- [Post-training Datasets](https://huggingface.co/collections/nvidia/nemotron-post-training-v3) — SFT and RL data
+- [Artifact Lineage](train/artifacts.md) — W&B integration guide
+
+```{toctree}
+:caption: Usage Cookbook
+:hidden:
+
+usage-cookbook/README.md
+usage-cookbook/Nemotron-Nano2-VL/README.md
+usage-cookbook/Nemotron-Parse-v1.1/README.md
+```
+
+```{toctree}
+:caption: Use Case Examples
+:hidden:
+
+use-case-examples/README.md
+use-case-examples/Simple Nemotron-3-Nano Usage Example/README.md
+use-case-examples/Data Science ML Agent/README.md
+use-case-examples/RAG Agent with Nemotron RAG Models/README.md
+```
+
+```{toctree}
+:caption: Training Recipes
+:hidden:
+
+train/nano3/README.md
+train/artifacts.md
+```
+
+```{toctree}
+:caption: Nano3 Stages
+:hidden:
+
+train/nano3/pretrain.md
+train/nano3/sft.md
+train/nano3/rl.md
+train/nano3/import.md
+```
+
+```{toctree}
+:caption: Nemotron Kit
+:hidden:
+
+train/kit.md
+train/nvidia-stack.md
+train/nemo-run.md
+train/omegaconf.md
+train/wandb.md
+train/cli.md
+train/data-prep.md
+```
diff --git a/docs/project.json b/docs/project.json
new file mode 100644
index 0000000..bb84a0c
--- /dev/null
+++ b/docs/project.json
@@ -0,0 +1,4 @@
+{
+ "name": "nemotron",
+ "version": "latest"
+}
diff --git a/docs/train/artifacts.md b/docs/train/artifacts.md
new file mode 100644
index 0000000..35e372a
--- /dev/null
+++ b/docs/train/artifacts.md
@@ -0,0 +1,304 @@
+# Artifact Lineage & W&B Integration
+
+The Nemotron training pipeline provides complete lineage tracking from raw data to final model through [Weights & Biases](./wandb.md) artifacts. Every data transformation and model checkpoint is versioned and linked, enabling full reproducibility and traceability.
+
+> **Note**: The artifact system currently requires [W&B](./wandb.md). Backend-agnostic artifact tracking is in development.
+
+## Why Lineage Matters
+
+- **Reproducibility**: Trace any model back to its exact training data and configuration
+- **Debugging**: Identify which data or training stage caused a regression
+- **Compliance**: Audit trail for model provenance and data usage
+- **Collaboration**: Share artifacts across teams with version control
+
+## End-to-End Lineage
+
+The training pipeline produces six artifact types across three stages:
+
+```mermaid
+%%{init: {'theme': 'base', 'themeVariables': { 'primaryBorderColor': '#333333', 'lineColor': '#333333', 'primaryTextColor': '#333333', 'clusterBkg': '#ffffff', 'clusterBorder': '#333333'}}}%%
+flowchart TB
+ subgraph stage0["Stage 0: Pretraining"]
+ raw0["Raw Text Data"] --> dp0["data_prep.py"]
+ dp0 --> data0["DataBlendsArtifact-pretrain(bin/idx)"] + data0 --> train0["train.py"] + train0 --> model0["ModelArtifact-pretrain"] + end + + subgraph stage1["Stage 1: SFT"] + raw1["Instruction Data"] --> dp1["data_prep.py"] + dp1 --> data1["DataBlendsArtifact-sft
(.npy)"] + model0 --> train1["train.py"] + data1 --> train1 + train1 --> model1["ModelArtifact-sft"] + end + + subgraph stage2["Stage 2: RL"] + raw2["RL Prompts"] --> dp2["data_prep.py"] + dp2 --> data2["DataBlendsArtifact-rl
(JSONL)"] + model1 --> train2["train.py"] + data2 --> train2 + train2 --> model2["ModelArtifact-rl
(Final Model)"] + end + + style stage0 fill:#e1f5fe,stroke:#2196f3 + style stage1 fill:#f3e5f5,stroke:#9c27b0 + style stage2 fill:#e8f5e9,stroke:#4caf50 +``` + +## Artifact Types + +| Artifact | Stage | Format | Description | +|----------|-------|--------|-------------| +| `DataBlendsArtifact-pretrain` | [0](./nano3/pretrain.md) | bin/idx | Tokenized pretraining data in Megatron format | +| `ModelArtifact-pretrain` | [0](./nano3/pretrain.md) | checkpoint | Base model after pretraining | +| `DataBlendsArtifact-sft` | [1](./nano3/sft.md) | .npy | Packed SFT sequences with loss masks | +| `ModelArtifact-sft` | [1](./nano3/sft.md) | checkpoint | Instruction-tuned model | +| `DataBlendsArtifact-rl` | [2](./nano3/rl.md) | JSONL | RL prompts for [NeMo-RL](./nvidia-stack.md#nemo-rl) | +| `ModelArtifact-rl` | [2](./nano3/rl.md) | checkpoint | Final aligned model | + +## W&B Configuration + +Configure W&B in your `env.toml`: + +```toml +[wandb] +project = "nemotron" +entity = "YOUR-TEAM" +``` + +Authenticate before running: + +```bash +wandb login +``` + +## Using Artifacts + +### Semantic URIs + +Reference artifacts by semantic URI in configs and CLI: + +``` +art://DataBlendsArtifact-pretrain:latest # Latest version +art://ModelArtifact-sft:v3 # Specific version +art://ModelArtifact-rl:production # Alias +``` + +### CLI Options + +Override artifact inputs via [CLI](./cli.md#artifact-inputs): + +```bash +# Use specific data artifact +uv run nemotron nano3 pretrain --art.data DataBlendsArtifact-pretrain:v2 + +# Use imported model +uv run nemotron nano3 sft --art.model my-custom-pretrain:latest +``` + +### Config Resolvers + +Reference artifact paths in YAML configs: + +```yaml +run: + data: DataBlendsArtifact-pretrain:latest + +recipe: + per_split_data_args_path: ${art:data,path}/blend.json +``` + +The `${art:data,path}` resolver extracts the filesystem path from the artifact. + +## Viewing Lineage in W&B + +After running the pipeline, view lineage in the W&B UI: + +1. Navigate to your project's **Artifacts** tab +2. Select any artifact (e.g., `ModelArtifact-rl`) +3. Click the **Graph** view to see upstream dependencies +4. Trace back through each stage to the original data sources + +The lineage graph shows: +- Which data artifacts were used to train each model +- Which model checkpoints were inputs to each stage +- Version history and metadata for each artifact + +## Importing External Assets + +Import existing models or data into the artifact system: + +### Model Import + +```bash +# Import pretrain checkpoint +uv run nemotron nano3 model import pretrain /path/to/checkpoint --step 50000 + +# Import SFT checkpoint +uv run nemotron nano3 model import sft /path/to/sft_model --step 10000 + +# Import RL checkpoint +uv run nemotron nano3 model import rl /path/to/rl_model --step 5000 +``` + +### Data Import + +```bash +# Import pretrain data (path to blend.json) +uv run nemotron nano3 data import pretrain /path/to/blend.json + +# Import SFT data (directory with blend.json) +uv run nemotron nano3 data import sft /path/to/sft_data/ + +# Import RL data (directory with manifest.json) +uv run nemotron nano3 data import rl /path/to/rl_data/ +``` + +See [Importing Models & Data](./nano3/import.md) for detailed directory structures. + +## Troubleshooting + +### "Artifact not found" + +- Verify `project` and `entity` in `env.toml` match where artifacts were created +- Check artifact name spelling and version tag +- Ensure you're authenticated: `wandb login` + +### Version Resolution Issues + +- Use explicit versions (`artifact:v3`) instead of `:latest` for reproducibility +- Check artifact aliases in W&B UI + +### Missing Lineage Links + +- Artifacts must be created by the pipeline to have automatic lineage +- Imported artifacts start a new lineage chain +- Manual uploads via W&B UI don't create lineage links + +## Programmatic Access + +Access artifacts programmatically via the kit module: + +```python +from nemotron.kit import PretrainBlendsArtifact, ModelArtifact + +# Load from semantic URI +data = PretrainBlendsArtifact.from_uri("art://DataBlendsArtifact-pretrain:latest") +print(f"Data path: {data.path}") +print(f"Total tokens: {data.total_tokens}") + +# Load model artifact +model = ModelArtifact.from_uri("art://ModelArtifact-sft:latest") +print(f"Training step: {model.step}") +print(f"Loss: {model.loss}") +``` + +For framework details, see [Nemotron Kit](./kit.md). + +## Creating Custom Artifacts + +Create custom artifact types by subclassing `Artifact`. Typed fields are automatically synced to `metadata.json` and available via the `${art:NAME,FIELD}` resolver in configs. + +### Basic Pattern + +```python +from pathlib import Path +from typing import Annotated +from pydantic import Field +from nemotron.kit.artifacts.base import Artifact + + +class MyDataArtifact(Artifact): + """Custom data artifact with typed metadata.""" + + # Typed fields become metadata - accessible via ${art:data,num_samples} + num_samples: Annotated[int, Field(ge=0, description="Number of samples")] + source_url: Annotated[str | None, Field(default=None, description="Data source")] + compression: Annotated[str, Field(default="none", description="Compression type")] +``` + +### Saving Artifacts + +```python +# Create artifact pointing to output directory +artifact = MyDataArtifact( + path=Path("/output/my_data"), + num_samples=10000, + source_url="https://example.com/data.tar.gz", +) + +# Save metadata.json and publish to W&B (if configured) +artifact.save(name="MyDataArtifact-custom") +``` + +### Loading Artifacts + +```python +# Load from semantic URI +artifact = MyDataArtifact.from_uri("art://MyDataArtifact-custom:latest") +print(f"Path: {artifact.path}") +print(f"Samples: {artifact.num_samples}") # IDE autocomplete works + +# Load from local path +artifact = MyDataArtifact.load(path=Path("/output/my_data")) +``` + +### Using in Configs + +Once saved, custom artifacts work with the resolver system: + +```yaml +run: + data: MyDataArtifact-custom:latest + +recipe: + data_path: ${art:data,path} + num_samples: ${art:data,num_samples} # Resolves to 10000 +``` + +### Customizing W&B Uploads + +Override methods to control what gets uploaded to W&B: + +```python +class MyModelArtifact(Artifact): + step: int + + def get_wandb_files(self) -> list[tuple[str, str]]: + """Files to upload to W&B storage (small files only).""" + files = super().get_wandb_files() # Includes metadata.json + # Add config files + config_path = self.path / "config.yaml" + if config_path.exists(): + files.append((str(config_path), "config.yaml")) + return files + + def get_wandb_references(self) -> list[tuple[str, str]]: + """References to shared storage (large files - not uploaded).""" + # Reference checkpoint directory without uploading + return [(f"file://{self.path.resolve()}", "checkpoint")] +``` + +### Input Lineage + +Track input dependencies by overriding `get_input_uris()`: + +```python +class ProcessedDataArtifact(Artifact): + source_artifact: str # e.g., "art://RawDataArtifact:v1" + + def get_input_uris(self) -> list[str]: + """Input artifacts for lineage graph.""" + return [self.source_artifact] +``` + +## Further Reading + +- [Nemotron Kit](./kit.md) — Artifact system internals +- [OmegaConf Configuration](./omegaconf.md) — `${art:...}` interpolations and lineage +- [W&B Integration](./wandb.md) — Credentials and configuration +- [Importing Models & Data](./nano3/import.md) — Import commands and directory structures +- [CLI Framework](./cli.md) — CLI building and artifact inputs +- [Data Preparation](./data-prep.md) — Data preparation module +- [Nano3 Recipe](./nano3/README.md) — Complete training pipeline diff --git a/docs/train/cli.md b/docs/train/cli.md new file mode 100644 index 0000000..b7d40ec --- /dev/null +++ b/docs/train/cli.md @@ -0,0 +1,490 @@ +# CLI Framework + +The `nemotron.kit` CLI framework is built on [Typer](https://typer.tiangolo.com/) and provides tools for building hierarchical command-line interfaces for training recipes, with native integration with NeMo-Run for remote execution. + +
+
+```console
+$ uv run nemotron nano3 sft --help
+Usage: nemotron nano3 sft [OPTIONS]
+
+ Run supervised fine-tuning with Megatron-Bridge (stage1).
+
+╭─ Options ────────────────────────────────────────────────────────────────╮
+│ --help -h Show this message and exit. │
+╰──────────────────────────────────────────────────────────────────────────╯
+╭─ Global Options ─────────────────────────────────────────────────────────╮
+│ -c, --config NAME Config name or path │
+│ -r, --run PROFILE Submit to cluster (attached) │
+│ -b, --batch PROFILE Submit to cluster (detached) │
+│ -d, --dry-run Preview config without execution │
+│ --stage Stage files for interactive debugging │
+╰──────────────────────────────────────────────────────────────────────────╯
+╭─ Configs (-c/--config) ──────────────────────────────────────────────────╮
+│ Built-in: default, tiny │
+│ Custom: -c /path/to/your/config.yaml │
+╰──────────────────────────────────────────────────────────────────────────╯
+╭─ Artifact Overrides (W&B artifact references) ───────────────────────────╮
+│ run.model Base model checkpoint artifact │
+│ run.data SFT data artifact (packed .npy) │
+╰──────────────────────────────────────────────────────────────────────────╯
+╭─ Run Overrides (override env.toml settings) ─────────────────────────────╮
+│ run.env.nodes Number of nodes │
+│ run.env.nproc_per_node GPUs per node │
+│ run.env.partition Slurm partition │
+│ run.env.account Slurm account │
+│ run.env.time Job time limit (e.g., 04:00:00) │
+│ run.env.container_image Override container image │
+╰──────────────────────────────────────────────────────────────────────────╯
+╭─ env.toml Profiles ──────────────────────────────────────────────────────╮
+│ Available profiles: my-cluster, my-cluster-large │
+│ Usage: --run PROFILE or --batch PROFILE │
+╰──────────────────────────────────────────────────────────────────────────╯
+╭─ Examples ───────────────────────────────────────────────────────────────╮
+│ $ ... sft -c tiny Local execution │
+│ $ ... sft -c tiny --dry-run Preview config │
+│ $ ... sft -c tiny --run my-cluster Submit to cluster │
+│ $ ... sft -c tiny -r cluster run.env.nodes=4 │
+╰──────────────────────────────────────────────────────────────────────────╯
+```
+
+
+
+## Overview
+
+The CLI framework enables:
+
+- **Nested Commands** — Build hierarchical CLIs like `uv run nemotron nano3 data prep pretrain`
+- **Config Integration** — Automatic YAML config loading with dotlist overrides
+- **[Artifact Resolution](./artifacts.md)** — Map [W&B artifacts](./wandb.md) to config fields automatically
+- **[Remote Execution](./nemo-run.md)** — Submit jobs to Slurm via NeMo-Run with `--run` / `--batch`
+
+For artifacts and configuration, see [Nemotron Kit](./kit.md). For execution profiles, see [Execution through NeMo-Run](./nemo-run.md).
+
+## Architecture
+
+```mermaid
+%%{init: {'theme': 'base', 'themeVariables': { 'primaryBorderColor': '#333333', 'lineColor': '#333333', 'primaryTextColor': '#333333', 'clusterBkg': '#f5f5f5', 'clusterBorder': '#666666'}}}%%
+flowchart LR
+ subgraph cli["CLI Layer (Typer)"]
+ Root["nemotron"]
+ Recipe["nano3"]
+ Commands["pretrain/sft/rl"]
+ end
+
+ subgraph config["Configuration"]
+ YAML["YAML Config"]
+ Dotlist["Dotlist Overrides"]
+ Artifacts["Artifact Resolution"]
+ end
+
+ subgraph execution["Execution Modes"]
+ Local["Local (torchrun)"]
+ NemoRun["NeMo-Run"]
+ Ray["Ray Jobs"]
+ end
+
+ Root --> Recipe --> Commands
+ Commands --> config
+ config --> execution
+```
+
+## The @recipe Decorator
+
+Commands are defined using the `@recipe` decorator, which wraps Typer commands with standardized config loading and execution logic:
+
+```python
+from nemotron.kit.cli.recipe import recipe
+import typer
+
+@recipe(
+ name="nano3/pretrain",
+ script_path="src/nemotron/recipes/nano3/stage0_pretrain/train.py",
+ config_dir="src/nemotron/recipes/nano3/stage0_pretrain/config",
+ default_config="default",
+ packager="self_contained",
+ torchrun=True,
+ ray=False,
+ artifacts={
+ "data": {
+ "default": "PretrainBlendsArtifact-default",
+ "mappings": {"path": "recipe.per_split_data_args_path"},
+ },
+ },
+)
+def pretrain(ctx: typer.Context) -> None:
+ """Run pretraining with Megatron-Bridge."""
+ pass # Execution handled by decorator
+```
+
+### Decorator Parameters
+
+| Parameter | Type | Description |
+|-----------|------|-------------|
+| `name` | `str` | Recipe identifier (e.g., `"nano3/pretrain"`) |
+| `script_path` | `str` | Path to the training script |
+| `config_dir` | `str` | Directory containing YAML configs |
+| `default_config` | `str` | Default config name (without `.yaml`) |
+| `packager` | `str` | Code packaging strategy: `"pattern"`, `"code"`, `"self_contained"` |
+| `torchrun` | `bool` | Use `torch.distributed.run` launcher |
+| `ray` | `bool` | Submit as Ray job (for data prep, RL) |
+| `artifacts` | `dict` | Artifact-to-config mappings |
+| `run_command` | `str` | Custom command template for Ray jobs |
+
+### Registering Commands
+
+Commands are registered on Typer apps with specific context settings:
+
+```python
+nano3_app = typer.Typer(name="nano3", help="Nano3 training recipe")
+
+nano3_app.command(
+ name="pretrain",
+ context_settings={
+ "allow_extra_args": True, # Capture dotlist overrides
+ "ignore_unknown_options": True, # Pass through unknown flags
+ },
+)(pretrain)
+```
+
+The `allow_extra_args=True` setting is critical—it allows commands to capture Hydra-style `key=value` overrides.
+
+## Global Options
+
+All recipe commands automatically receive these global options:
+
+| Option | Short | Description |
+|--------|-------|-------------|
+| `--config` | `-c` | Config name or path (from `config_dir`) |
+| `--run` | `-r` | Attached [NeMo-Run](./nemo-run.md) execution (waits, streams logs) |
+| `--batch` | `-b` | Detached [NeMo-Run](./nemo-run.md) execution (submits, exits) |
+| `--dry-run` | `-d` | Preview config without executing |
+| `--stage` | | Stage files to remote for debugging |
+| `key=value` | | Dotlist overrides (any position) |
+
+### GlobalContext
+
+Global options are captured in a `GlobalContext` dataclass:
+
+```python
+@dataclass
+class GlobalContext:
+ config: str | None = None # -c/--config value
+ run: str | None = None # --run profile name
+ batch: str | None = None # --batch profile name
+ dry_run: bool = False # --dry-run flag
+ stage: bool = False # --stage flag
+ dotlist: list[str] # key=value overrides
+ passthrough: list[str] # Unknown args for script
+```
+
+Key properties:
+- `mode` → `"run"`, `"batch"`, or `"local"`
+- `profile` → Environment profile name (from `--run` or `--batch`)
+
+## Configuration Pipeline
+
+The `ConfigBuilder` class orchestrates config loading:
+
+```mermaid
+%%{init: {'theme': 'base', 'themeVariables': { 'primaryBorderColor': '#333333', 'lineColor': '#333333', 'primaryTextColor': '#333333'}}}%%
+flowchart LR
+ Default["default.yaml"] --> Merge
+ Config["--config"] --> Merge
+ Dotlist["key=value"] --> Merge
+ Merge --> JobConfig["job.yaml"]
+ Merge --> TrainConfig["train.yaml"]
+```
+
+### Two-Config System
+
+The CLI generates two config files:
+
+| File | Purpose |
+|------|---------|
+| `job.yaml` | Full provenance: config + CLI args + env profile |
+| `train.yaml` | Clean config for script (paths rewritten for remote) |
+
+**job.yaml structure:**
+```yaml
+recipe:
+ _target_: megatron.bridge.recipes...
+ per_split_data_args_path: /data/blend.json
+train:
+ train_iters: 1000
+run:
+ mode: "run"
+ profile: "my-cluster"
+ env:
+ executor: "slurm"
+ nodes: 4
+ gpus_per_node: 8
+ cli:
+ argv: ["nemotron", "nano3", "pretrain", "-c", "tiny", "--run", "my-cluster"]
+ dotlist: ["train.train_iters=1000"]
+ wandb:
+ entity: "nvidia"
+ project: "nemotron"
+```
+
+### Dotlist Overrides
+
+Override any config value with `key.path=value` syntax:
+
+```bash
+# Override nested values
+uv run nemotron nano3 pretrain train.train_iters=5000
+
+# Multiple overrides
+uv run nemotron nano3 pretrain \
+ train.train_iters=5000 \
+ train.micro_batch_size=2 \
+ run.data=PretrainBlendsArtifact-v2:latest
+```
+
+## Execution Modes
+
+### Local Execution
+
+Without `--run` or `--batch`, commands execute locally:
+
+```bash
+# Local execution (no NeMo-Run)
+uv run nemotron nano3 pretrain -c tiny
+
+# Equivalent to:
+python -m torch.distributed.run \
+ --nproc_per_node=1 \
+ src/nemotron/recipes/nano3/stage0_pretrain/train.py \
+ --config train.yaml
+```
+
+### NeMo-Run Attached (`--run`)
+
+Submit job and wait for completion, streaming logs:
+
+```bash
+uv run nemotron nano3 pretrain -c tiny --run MY-CLUSTER
+```
+
+### NeMo-Run Detached (`--batch`)
+
+Submit job and exit immediately:
+
+```bash
+uv run nemotron nano3 pretrain -c tiny --batch MY-CLUSTER
+```
+
+### Ray Jobs
+
+For recipes with `ray=True` (data prep, RL), jobs are submitted via Ray:
+
+```bash
+# Data prep uses Ray for distributed processing
+uv run nemotron nano3 data prep pretrain --run MY-CLUSTER
+
+# RL uses Ray for actor orchestration
+uv run nemotron nano3 rl -c tiny --run MY-CLUSTER
+```
+
+## Artifact Inputs
+
+Map W&B artifacts to config fields:
+
+```python
+@recipe(
+ ...,
+ artifacts={
+ "data": {
+ "default": "PretrainBlendsArtifact-default:latest",
+ "mappings": {"path": "recipe.per_split_data_args_path"},
+ },
+ "model": {
+ "default": "ModelArtifact-sft:latest",
+ "mappings": {"path": "model.init_from_path"},
+ },
+ },
+)
+```
+
+### CLI Override
+
+Override artifacts via dotlist:
+
+```bash
+uv run nemotron nano3 sft --run MY-CLUSTER \
+ run.data=PretrainBlendsArtifact-v2:latest \
+ run.model=ModelArtifact-pretrain:v3
+```
+
+### Config Resolver
+
+Use `${art:...}` in YAML configs:
+
+```yaml
+run:
+ data: PretrainBlendsArtifact-default:latest
+
+recipe:
+ per_split_data_args_path: ${art:data,path}/blend.json
+```
+
+## Packager Types
+
+Control how code is synced to remote:
+
+| Packager | Description | Use Case |
+|----------|-------------|----------|
+| `pattern` | Minimal sync (`main.py` + `config.yaml`) | Default |
+| `code` | Full codebase with exclusions | Ray jobs needing imports |
+| `self_contained` | Inline all `nemotron.*` imports | Isolated scripts |
+
+## CLI Examples
+
+
+
+```console
+// Preview config without executing
+$ uv run nemotron nano3 pretrain -c tiny --dry-run
+
+// Submit to cluster (attached)
+$ uv run nemotron nano3 pretrain -c tiny --run MY-CLUSTER
+
+// Submit to cluster (detached)
+$ uv run nemotron nano3 pretrain -c tiny --batch MY-CLUSTER
+
+// Override training iterations
+$ uv run nemotron nano3 pretrain -c tiny --run MY-CLUSTER train.train_iters=5000
+
+// Stage files for interactive debugging
+$ uv run nemotron nano3 pretrain -c tiny --run MY-CLUSTER --stage
+
+// Data preparation (Ray job)
+$ uv run nemotron nano3 data prep pretrain --run MY-CLUSTER
+
+// RL training (Ray job)
+$ uv run nemotron nano3 rl -c tiny --run MY-CLUSTER
+```
+
+
+
+## Building a Recipe
+
+### Step 1: Create Config Directory
+
+```
+src/nemotron/recipes/myrecipe/
+├── config/
+│ ├── default.yaml
+│ └── tiny.yaml
+├── train.py
+└── data_prep.py
+```
+
+### Step 2: Define Training Script
+
+```python
+# train.py
+import argparse
+from pathlib import Path
+from omegaconf import OmegaConf
+
+def main():
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--config", type=Path, required=True)
+ args, unknown = parser.parse_known_args()
+
+ # Load config
+ cfg = OmegaConf.load(args.config)
+
+ # Apply any remaining overrides
+ if unknown:
+ overrides = OmegaConf.from_dotlist(unknown)
+ cfg = OmegaConf.merge(cfg, overrides)
+
+ # Run training...
+ print(f"Training with {cfg.train.train_iters} iterations")
+
+if __name__ == "__main__":
+ main()
+```
+
+### Step 3: Create CLI Command
+
+```python
+# src/nemotron/cli/myrecipe/train.py
+from nemotron.kit.cli.recipe import recipe
+import typer
+
+@recipe(
+ name="myrecipe/train",
+ script_path="src/nemotron/recipes/myrecipe/train.py",
+ config_dir="src/nemotron/recipes/myrecipe/config",
+ default_config="default",
+ torchrun=True,
+)
+def train(ctx: typer.Context) -> None:
+ """Run training for my recipe."""
+ pass
+```
+
+### Step 4: Register in CLI
+
+```python
+# src/nemotron/cli/myrecipe/__init__.py
+import typer
+from .train import train
+
+app = typer.Typer(name="myrecipe", help="My training recipe")
+
+app.command(
+ name="train",
+ context_settings={"allow_extra_args": True, "ignore_unknown_options": True},
+)(train)
+```
+
+### Step 5: Add to Main CLI
+
+```python
+# src/nemotron/cli/bin/nemotron.py
+from nemotron.cli.myrecipe import app as myrecipe_app
+
+main_app.add_typer(myrecipe_app, name="myrecipe")
+```
+
+### Step 6: Run
+
+```bash
+# Test locally
+uv run nemotron myrecipe train -c tiny
+
+# Run on cluster
+uv run nemotron myrecipe train -c tiny --run MY-CLUSTER
+```
+
+## API Reference
+
+### Recipe Decorator
+
+| Export | Description |
+|--------|-------------|
+| `@recipe` | Decorator for training commands |
+| `ConfigBuilder` | Config loading and merging |
+| `GlobalContext` | Shared CLI state |
+| `split_unknown_args()` | Parse dotlist vs passthrough args |
+
+### Execution
+
+| Export | Description |
+|--------|-------------|
+| `build_executor()` | Create NeMo-Run executor from profile |
+| `load_env_profile()` | Load profile from `env.toml` |
+
+## Further Reading
+
+- [Nemotron Kit](./kit.md) — Artifacts, configuration, lineage tracking
+- [Execution through NeMo-Run](./nemo-run.md) — Execution profiles and env.toml
+- [Data Preparation](./data-prep.md) — Data preparation module
+- [Artifact Lineage](./artifacts.md) — W&B artifact system and lineage tracking
+- [W&B Integration](./wandb.md) — Credentials and configuration
+- [Nano3 Recipe](./nano3/README.md) — Complete training recipe example
diff --git a/docs/train/data-prep.md b/docs/train/data-prep.md
new file mode 100644
index 0000000..bf38536
--- /dev/null
+++ b/docs/train/data-prep.md
@@ -0,0 +1,568 @@
+# Data Preparation Module
+
+The `nemotron.data_prep` module focuses on **last-mile data processing**—transforming curated datasets into training-ready formats. It bridges the gap between data curation (handled by [NeMo Curator](https://github.com/NVIDIA-NeMo/Curator)) and model training, producing outputs compatible with the NVIDIA AI training stack.
+
+> **Coming Soon**: Native integration with [NeMo Curator](https://github.com/NVIDIA-NeMo/Curator) for data curation and [NeMo Data Designer](https://github.com/NVIDIA-NeMo/DataDesigner) for synthetic data generation. These integrations will enable end-to-end data pipelines from raw sources to training-ready formats.
+
+## Overview
+
+Built on top of [Ray](https://ray.io/), the data preparation module provides:
+
+- **Last-mile processing** — Convert curated datasets to training-ready formats (tokenization, packing, chat templating)
+- **Distributed processing** — Scale from a single machine to a cluster of workers using Ray actors
+- **Cloud-native I/O** — Read from HuggingFace Hub (`hf://`), S3 (`s3://`), GCS (`gs://`), or local paths via fsspec
+- **Deterministic output** — Frozen shard plans ensure reproducible results across runs
+- **Resumable pipelines** — Skip completed shards on restart; verify output integrity with checksums
+
+### Data Pipeline: Curator → Data Prep → Training
+
+This module is designed to work alongside [NeMo Curator](https://github.com/NVIDIA-NeMo/Curator) in a two-stage data pipeline:
+
+```mermaid
+%%{init: {'theme': 'base', 'themeVariables': { 'primaryBorderColor': '#333333', 'lineColor': '#333333', 'primaryTextColor': '#333333', 'clusterBkg': '#ffffff', 'clusterBorder': '#333333'}}}%%
+flowchart LR
+ subgraph sources["📁 Raw Data"]
+ CC["CommonCrawl"]
+ HF["HuggingFace"]
+ Custom["Custom Sources"]
+ end
+
+ subgraph curator["🔧 NeMo Curator"]
+ C1["Deduplication"]
+ C2["Quality Filtering"]
+ C3["Language ID"]
+ C4["PII Removal"]
+ C1 --- C2 --- C3 --- C4
+ end
+
+ subgraph dataprep["⚡ Data Prep"]
+ D1["Tokenization"]
+ D2["Chat Templating"]
+ D3["Sequence Packing"]
+ D4["Loss Mask Creation"]
+ D1 --- D2 --- D3 --- D4
+ end
+
+ subgraph training["🚀 Training"]
+ T1["Megatron-Bridge"]
+ T2["NeMo-RL"]
+ end
+
+ sources --> curator
+ curator -->|"Curated Data"| dataprep
+ dataprep -->|"bin/idx, .npy, JSONL"| training
+```
+
+**Typical workflow:**
+1. Use **[NeMo Curator](https://github.com/NVIDIA-NeMo/Curator)** to curate raw data at scale—deduplication, quality filtering, language identification, PII removal
+2. Use **Data Prep** to transform curated data into training-ready formats—tokenization, chat templating, sequence packing, loss mask generation
+
+This separation allows you to curate once and prepare data multiple times for different training configurations (different tokenizers, sequence lengths, or output formats).
+
+### Recipe Integration
+
+Each training stage in a recipe includes a dedicated data preparation step that transforms source data into the format required by that stage's training framework:
+
+| Stage | Data Prep Output | Training Framework | Guide |
+|-------|------------------|-------------------|-------|
+| Stage 0: Pretrain | bin/idx indexed datasets | [Megatron-Bridge](./nvidia-stack.md#megatron-bridge) | [pretrain.md](./nano3/pretrain.md#data-preparation) |
+| Stage 1: SFT | Packed .npy with loss masks | [Megatron-Bridge](./nvidia-stack.md#megatron-bridge) | [sft.md](./nano3/sft.md#data-preparation) |
+| Stage 2: RL | JSONL with OpenAI chat format | [NeMo-RL](./nvidia-stack.md#nemo-rl) | [rl.md](./nano3/rl.md#data-preparation) |
+
+Run data preparation for any stage using the [CLI](./cli.md):
+
+```bash
+uv run nemotron nano3 data prep pretrain --run YOUR-CLUSTER # Stage 0
+uv run nemotron nano3 data prep sft --run YOUR-CLUSTER # Stage 1
+uv run nemotron nano3 data prep rl --run YOUR-CLUSTER # Stage 2
+```
+
+> **Note**: The `--run YOUR-CLUSTER` flag submits jobs via [NeMo-Run](./nemo-run.md). See [Execution through NeMo-Run](./nemo-run.md) for setup.
+
+### NeMo-Run Integration
+
+The module integrates natively with [NeMo-Run](./nemo-run.md) for job orchestration. Submit data preparation jobs to various executors (Slurm, local, Docker, cloud) directly from your local machine:
+
+```bash
+# Submit to Slurm cluster
+uv run nemotron nano3 data prep pretrain --run YOUR-CLUSTER
+
+# Run locally with Ray
+uv run nemotron nano3 data prep pretrain --run local
+
+# Preview without executing
+uv run nemotron nano3 data prep pretrain --run YOUR-CLUSTER --dry-run
+```
+
+Configure execution profiles in `env.toml`:
+
+```toml
+[YOUR-CLUSTER]
+executor = "slurm"
+account = "YOUR-ACCOUNT"
+partition = "batch"
+```
+
+See [Execution through NeMo-Run](./nemo-run.md) for complete configuration options.
+
+## Choosing an API
+
+| API | Use When | Output Format |
+|-----|----------|---------------|
+| `run_data_prep()` | Simple tokenization pipelines | bin/idx |
+| `last_mile_process()` | Custom formats, transforms, chat SFT | Any |
+
+**Use high-level API (`run_data_prep`)** for:
+- Standard pretraining data tokenization
+- Per-split output (train/valid/test) with `PerSplitConfig`
+- When you want automatic Ray initialization and artifact tracking
+
+**Use low-level API (`last_mile_process`)** for:
+- JSONL output with custom transforms
+- Chat SFT with loss masking (`ChatSftOutputConfig`)
+- Packed sequences without chat templates
+- Custom pipeline orchestration
+
+## Supported Output Formats
+
+| Format | Description | Use Case |
+|--------|-------------|----------|
+| `binidx` | Tokenized `.bin/.idx` indexed datasets | Pretraining (default) |
+| `jsonl` | JSONL files with optional transforms | SFT/RL training |
+| `packed` | Packed sequences in `.npy` format | Efficient SFT training |
+
+## Quick Start
+
+### High-Level API (DataPrepConfig)
+
+For simple tokenization to binidx format:
+
+```python
+from nemotron.data_prep import DataPrepConfig, run_data_prep
+from pathlib import Path
+
+config = DataPrepConfig(
+ blend_path=Path("data_blend.json"),
+ output_dir=Path("./output"),
+ tokenizer_model="meta-llama/Llama-3.2-1B",
+)
+
+artifact = run_data_prep(config)
+print(f"Blend path: {artifact.path}")
+```
+
+### Low-Level API (last_mile_process)
+
+For more control over output format:
+
+```python
+from nemotron.data_prep import last_mile_process, DataBlend, PipelineConfig
+from nemotron.data_prep.config import OutputConfig, JsonlOutputConfig
+from nemotron.data_prep.formats.transforms import sft
+
+blend = DataBlend.load("data_blend.json")
+
+config = PipelineConfig(
+ output=OutputConfig(
+ dir=Path("./sft_data"),
+ format=JsonlOutputConfig(
+ transform=sft(input="instruction", output="response"),
+ ),
+ ),
+)
+
+result = last_mile_process(blend, config)
+```
+
+## Output Formats
+
+### BinIdx (Default)
+
+Tokenized binary format for Megatron pretraining:
+
+```python
+from nemotron.data_prep.config import BinIdxOutputConfig
+
+config = PipelineConfig(
+ tokenizer=TokenizerConfig(model="meta-llama/Llama-3.2-1B"),
+ output=OutputConfig(
+ dir=Path("./tokenized"),
+ format=BinIdxOutputConfig(
+ shard_size="256MB", # Or num_shards=128
+ dtype="int32",
+ ),
+ ),
+)
+```
+
+### JSONL
+
+Structured JSONL for SFT/RL training (no tokenization):
+
+```python
+from nemotron.data_prep.config import JsonlOutputConfig
+from nemotron.data_prep.formats.transforms import sft, openai_chat
+
+# SFT format: {"input": "...", "output": "..."}
+config = PipelineConfig(
+ output=OutputConfig(
+ dir=Path("./sft_data"),
+ format=JsonlOutputConfig(
+ transform=sft(input="instruction", output="response"),
+ compression="zstd", # Optional compression
+ ),
+ ),
+)
+
+# OpenAI chat format: {"messages": [...]}
+config = PipelineConfig(
+ output=OutputConfig(
+ dir=Path("./rl_data"),
+ format=JsonlOutputConfig(
+ transform=openai_chat(),
+ ),
+ ),
+)
+```
+
+### Packed
+
+Packed sequences for efficient SFT training:
+
+```python
+from nemotron.data_prep.config import PackedOutputConfig
+
+config = PipelineConfig(
+ tokenizer=TokenizerConfig(model="meta-llama/Llama-3.2-1B"),
+ output=OutputConfig(
+ dir=Path("./packed_data"),
+ format=PackedOutputConfig(
+ pack_size=4096,
+ algorithm="first_fit_shuffle",
+ ),
+ ),
+)
+```
+
+### Chat SFT (Packed with Loss Masking)
+
+Chat-templated SFT with role-based loss masking. This format applies chat templates to OpenAI-format messages, tokenizes them, and produces packed sequences with a loss mask that zeros out system/user tokens:
+
+```python
+from nemotron.data_prep import last_mile_process, DataBlend, PipelineConfig
+from nemotron.data_prep.config import OutputConfig, ChatSftOutputConfig, TokenizerConfig
+
+blend = DataBlend.load("chat_data.json")
+
+config = PipelineConfig(
+ tokenizer=TokenizerConfig(model="nvidia/NVIDIA-Nemotron-Nano-9B-v2"),
+ output=OutputConfig(
+ dir=Path("./chat_sft"),
+ format=ChatSftOutputConfig(
+ chat_template="nano3", # Built-in template or path to .jinja file
+ messages_field="messages", # Field containing OpenAI-format messages
+ pack_size=4096, # Maximum tokens per packed sequence
+ algorithm="first_fit_shuffle",
+ ),
+ ),
+)
+
+result = last_mile_process(blend, config)
+```
+
+**Input format** (OpenAI chat messages):
+```json
+{
+ "messages": [
+ {"role": "system", "content": "You are a helpful assistant."},
+ {"role": "user", "content": "Hello!"},
+ {"role": "assistant", "content": "Hi there!"}
+ ]
+}
+```
+
+**Output**:
+- `.npy` files with packed `input_ids` and `loss_mask` arrays
+- Loss mask: `0` for system/user tokens, `1` for assistant tokens
+- Metadata files for Megatron-Bridge compatibility
+
+## Transforms
+
+Transforms convert input records to the desired output format. They are callables that take a dict and return a dict (or `None` to skip the record).
+
+### Built-in Transform Factories
+
+```python
+from nemotron.data_prep.formats.transforms import (
+ sft, # SFT format: {input, output}
+ openai_chat, # OpenAI format: {messages: [...]}
+ sharegpt, # ShareGPT format: {conversations: [...]}
+ nemotron_rl, # Nemotron RL format: {messages, tools}
+ passthrough, # Pass records unchanged
+ select, # Select specific fields
+ rename, # Rename fields
+)
+```
+
+### sft()
+
+Creates SFT format output:
+
+```python
+transform = sft(
+ input="instruction", # Source field for input
+ output="response", # Source field for output
+ system="system_prompt" # Optional system prompt field
+)
+
+# Input: {"instruction": "Hello", "response": "Hi!", "system_prompt": "Be helpful"}
+# Output: {"input": "Hello", "output": "Hi!", "system": "Be helpful"}
+```
+
+### openai_chat()
+
+Creates OpenAI chat format:
+
+```python
+transform = openai_chat(messages="conversation")
+
+# Input: {"conversation": [{"role": "user", "content": "Hi"}]}
+# Output: {"messages": [{"role": "user", "content": "Hi"}]}
+```
+
+### sharegpt()
+
+Creates ShareGPT format:
+
+```python
+transform = sharegpt(conversations="turns")
+
+# Input: {"turns": [{"from": "human", "value": "Hi"}]}
+# Output: {"conversations": [{"from": "human", "value": "Hi"}]}
+```
+
+### nemotron_rl()
+
+Extracts messages and tools from Nemotron RL dataset format:
+
+```python
+transform = nemotron_rl()
+
+# Input: {
+# "responses_create_params": {
+# "input": [{"role": "user", "content": "Hi"}],
+# "tools": [{"name": "search", ...}]
+# }
+# }
+# Output: {"messages": [{"role": "user", "content": "Hi"}], "tools": [...]}
+```
+
+Records without valid `responses_create_params.input` are skipped.
+
+### passthrough()
+
+Passes records unchanged:
+
+```python
+transform = passthrough()
+
+# Input: {"any": "data"}
+# Output: {"any": "data"}
+```
+
+### select()
+
+Selects specific fields:
+
+```python
+transform = select("id", "text")
+
+# Input: {"id": 1, "text": "hello", "extra": "ignored"}
+# Output: {"id": 1, "text": "hello"}
+```
+
+### rename()
+
+Renames fields:
+
+```python
+transform = rename(input="question", output="answer")
+
+# Input: {"question": "What?", "answer": "This."}
+# Output: {"input": "What?", "output": "This."}
+```
+
+### Custom Transforms
+
+You can use any callable:
+
+```python
+# Lambda
+transform = lambda r: {"input": r["q"], "output": r["a"]} if r.get("valid") else None
+
+# Function
+def my_transform(record: dict) -> dict | None:
+ if len(record.get("text", "")) < 10:
+ return None # Skip short records
+ return {"input": record["question"], "output": record["answer"]}
+```
+
+### Filtering Records
+
+Return `None` from a transform to skip records. This is useful for filtering out low-quality or malformed data:
+
+```python
+def filter_by_length(min_chars: int = 100) -> Transform:
+ """Skip records with text shorter than min_chars."""
+ def transform(record: dict) -> dict | None:
+ text = record.get("text", "")
+ if len(text) < min_chars:
+ return None # Record will be skipped
+ return record
+ return transform
+
+# Usage
+config = PipelineConfig(
+ output=OutputConfig(
+ dir=Path("./filtered"),
+ format=JsonlOutputConfig(transform=filter_by_length(min_chars=200)),
+ ),
+)
+```
+
+Built-in transforms also filter: for example, `nemotron_rl()` skips records missing required fields.
+
+## Sharding Configuration
+
+Both `shard_size` and `num_shards` are supported (mutually exclusive):
+
+```python
+# Target shard size (default)
+format=JsonlOutputConfig(shard_size="256MB")
+
+# Explicit shard count
+format=JsonlOutputConfig(num_shards=64)
+```
+
+Supported size formats: `"256MB"`, `"1G"`, `"500MiB"`, etc.
+
+## Per-Split Output
+
+Generate separate train/valid/test outputs using `PerSplitConfig`:
+
+```python
+from nemotron.data_prep import DataPrepConfig, PerSplitConfig
+from pathlib import Path
+
+config = DataPrepConfig(
+ blend_path=Path("blend.json"),
+ output_dir=Path("./output"),
+ tokenizer_model="nvidia/NVIDIA-Nemotron-Nano-9B-v2",
+ per_split=PerSplitConfig(
+ enabled=True,
+ valid_shards=1, # Number of validation shards
+ test_shards=1, # Number of test shards
+ ),
+)
+```
+
+**Output structure:**
+```
+output/
+├── train/
+│ ├── shard_000000.bin
+│ ├── shard_000000.idx
+│ └── ...
+├── valid/
+│ └── shard_000000.bin/.idx
+├── test/
+│ └── shard_000000.bin/.idx
+└── blend.json
+```
+
+**blend.json format** (per-split mode):
+```json
+{
+ "train": [["1.0", "/path/to/train/shard_000000"], ["1.0", "/path/to/train/shard_000001"]],
+ "valid": [["1.0", "/path/to/valid/shard_000000"]],
+ "test": [["1.0", "/path/to/test/shard_000000"]]
+}
+```
+
+This format is directly compatible with Megatron-Bridge's per-split data loading.
+
+## Type Definitions
+
+TypedDicts are provided for type safety:
+
+```python
+from nemotron.data_prep.formats.transforms import (
+ SftRecord, # {"input": str, "output": str}
+ OpenAIChatRecord, # {"messages": list[Message]}
+ ShareGPTRecord, # {"conversations": list[Conversation]}
+ Message, # {"role": str, "content": str}
+)
+```
+
+## API Reference
+
+### Main Functions
+
+| Function | Description |
+|----------|-------------|
+| `run_data_prep(config)` | High-level entry point for tokenization |
+| `last_mile_process(blend, config)` | Low-level entry point with format dispatch |
+| `tokenize(blend, config)` | Deprecated alias for `last_mile_process` |
+
+### Configuration Classes
+
+| Class | Description |
+|-------|-------------|
+| `DataPrepConfig` | High-level configuration for `run_data_prep` |
+| `PipelineConfig` | Low-level pipeline configuration |
+| `TokenizerConfig` | Tokenizer settings (model, add_bos, add_eos) |
+| `OutputConfig` | Output directory and format |
+| `BinIdxOutputConfig` | Tokenized binary format options |
+| `JsonlOutputConfig` | JSONL format options |
+| `PackedOutputConfig` | Packed sequence format options |
+
+### Result Classes
+
+| Class | Description |
+|-------|-------------|
+| `PipelineResult` | Complete pipeline result with all splits |
+| `SplitResult` | Result for a single split (train/valid/test) |
+| `DataBlendsArtifact` | Artifact with blend.json path and metrics |
+
+## Compression
+
+JSONL output supports optional zstd compression:
+
+```python
+format=JsonlOutputConfig(
+ compression="zstd", # Output .jsonl.zst files
+)
+```
+
+Requires the `zstandard` package: `uv pip install zstandard`
+
+## Dependencies
+
+Core dependencies:
+- `ray` - Parallel processing
+- `pyarrow` - Parquet file reading
+- `xxhash` - Fast checksums
+
+Optional dependencies:
+- `orjson` - Fast JSON serialization (falls back to stdlib json)
+- `zstandard` - Zstd compression for JSONL output
+
+## Further Reading
+
+- [NeMo Curator](https://github.com/NVIDIA-NeMo/Curator) — Data curation at scale (coming soon)
+- [NeMo Data Designer](https://github.com/NVIDIA-NeMo/DataDesigner) — Synthetic data generation (coming soon)
+- [NVIDIA AI Stack](./nvidia-stack.md) — Megatron-Core, Megatron-Bridge, NeMo-RL documentation
+- [Execution through NeMo-Run](./nemo-run.md) — Job orchestration and execution profiles
+- [CLI Framework](./cli.md) — CLI building and recipe decorators
+- [Artifact Lineage](./artifacts.md) — W&B artifact system and lineage tracking
+- [Nano3 Recipe](./nano3/README.md) — Complete training recipe example
diff --git a/docs/train/kit.md b/docs/train/kit.md
new file mode 100644
index 0000000..368c417
--- /dev/null
+++ b/docs/train/kit.md
@@ -0,0 +1,211 @@
+# Nemotron Kit
+
+The `nemotron.kit` module is a lightweight framework powering Nemotron training recipes. It provides the infrastructure for building reproducible ML training pipelines with artifact versioning, configuration management, and lineage tracking.
+
+> **Lightweight by Design**: Kit handles recipe orchestration—not training. All heavy lifting is done by the [NVIDIA AI Stack](./nvidia-stack.md): [Megatron-Core](https://github.com/NVIDIA/Megatron-LM) for distributed training primitives, [Megatron-Bridge](https://github.com/NVIDIA/Megatron-Bridge) for model training, and [NeMo-RL](https://github.com/NVIDIA/NeMo-RL) for reinforcement learning.
+
+## Overview
+
+Kit handles four core responsibilities:
+
+| Component | Purpose |
+|-----------|---------|
+| **[Artifacts](./artifacts.md)** | Path-centric data and model versioning with typed metadata |
+| **Configuration** | YAML/TOML config loading with Hydra-style overrides |
+| **[CLI](./cli.md)** | Batteries-included CLI with optional MLOps execution via `--run` and `--batch` |
+| **[Lineage](./artifacts.md)** | Experiment tracking and artifact provenance via [W&B](./wandb.md) |
+| **[Execution](./nemo-run.md)** | Optional NeMo-Run integration for distributed training |
+
+## Architecture
+
+```mermaid
+%%{init: {'theme': 'base', 'themeVariables': { 'primaryBorderColor': '#333333', 'lineColor': '#333333', 'primaryTextColor': '#333333', 'clusterBkg': '#ffffff', 'clusterBorder': '#333333'}}}%%
+flowchart LR
+ subgraph cli["CLI"]
+ direction TB
+ CMD1["nemotron ... data prep"]
+ CMD2["nemotron ... train/sft/rl"]
+ end
+
+ subgraph recipes["Recipe Scripts"]
+ direction TB
+ DataPrep["data_prep.py"]
+ Train["train.py"]
+ end
+
+ subgraph kit["nemotron.kit"]
+ direction TB
+ Config["Configuration"]
+ Run["NeMo-Run"]
+ Artifact["Artifacts"]
+ Tracker["Lineage"]
+ end
+
+ subgraph execution["Execution Backends"]
+ direction TB
+ Slurm["Slurm ✓"]
+ Docker["Docker (planned)"]
+ Cloud["Cloud (planned)"]
+ end
+
+ subgraph tracking["Logging & Artifact Backends"]
+ direction LR
+ WandB["W&B ✓"]
+ More1["(planned)"]
+ More2["(planned)"]
+ end
+
+ CMD1 --> DataPrep
+ CMD2 --> Train
+ DataPrep --> kit
+ Train --> kit
+ Run --> execution
+ Tracker --> tracking
+
+ style cli fill:#fce4ec,stroke:#c2185b
+ style recipes fill:#e8f5e9,stroke:#388e3c
+ style kit fill:#e3f2fd,stroke:#1976d2
+ style execution fill:#fff3e0,stroke:#f57c00
+ style tracking fill:#f3e5f5,stroke:#7b1fa2
+```
+
+## Quick Example
+
+A minimal training recipe using kit:
+
+```python
+from nemotron.kit import cli, PretrainBlendsArtifact
+from dataclasses import dataclass
+from pathlib import Path
+
+@dataclass
+class Config:
+ data_path: Path
+ output_dir: Path
+ learning_rate: float = 1e-4
+
+# Load config with CLI override support
+config = cli(Config, config_file="config.yaml")
+
+# Load data artifact
+data = PretrainBlendsArtifact.load(config.data_path)
+print(f"Training on {data.total_tokens:,} tokens")
+
+# ... training code ...
+
+# Save model artifact
+model = ModelArtifact(path=config.output_dir, step=10000, loss=2.5)
+model.save(name="ModelArtifact-pretrain")
+```
+
+Run with overrides:
+
+```bash
+python train.py --config config.yaml learning_rate=5e-5
+```
+
+## Key Concepts
+
+### Artifacts
+
+Artifacts are path-centric objects with typed metadata. The core field is always `path`—the filesystem location of the data. See [Artifact Lineage](./artifacts.md) for details.
+
+```python
+from nemotron.kit import PretrainBlendsArtifact
+
+# Load from semantic URI
+artifact = PretrainBlendsArtifact.from_uri("art://PretrainBlendsArtifact:latest")
+print(f"Path: {artifact.path}")
+print(f"Tokens: {artifact.total_tokens:,}")
+```
+
+### Configuration
+
+Kit supports YAML, TOML, and JSON configs with automatic format detection and Hydra-style overrides:
+
+```yaml
+# config.yaml
+data_path: /data/pretrain
+output_dir: ${oc.env:PWD}/output
+learning_rate: 1e-4
+```
+
+Override from CLI:
+
+```bash
+python train.py --config config.yaml output_dir=/scratch/output
+```
+
+### Execution
+
+Kit integrates with [NeMo-Run](./nemo-run.md) for distributed execution across Slurm, Docker, and cloud environments:
+
+```bash
+# Run on Slurm cluster
+uv run nemotron nano3 pretrain --run YOUR-CLUSTER
+
+# Run in Docker
+uv run nemotron nano3 pretrain --run docker-profile
+```
+
+### Lineage Tracking
+
+Kit tracks artifact lineage through W&B. See [W&B Integration](./wandb.md) for automatic credential handling and [Artifact Lineage](./artifacts.md) for the lineage graph.
+
+## Module Structure
+
+```
+nemotron.kit
+├── artifacts # Artifact classes and registry
+├── cli # CLI building and env.toml parsing
+├── config # Configuration loading
+├── run # NeMo-Run executor building
+├── trackers # Lineage tracking backends
+└── wandb # W&B integration utilities
+```
+
+## API Reference
+
+### Artifacts
+
+| Export | Description |
+|--------|-------------|
+| `Artifact` | Base artifact class |
+| `PretrainBlendsArtifact` | Pretrain data with train/valid/test splits |
+| `SFTDataArtifact` | Packed SFT sequences |
+| `SplitJsonlDataArtifact` | RL JSONL data |
+| `ModelArtifact` | Model checkpoints |
+| `InputDatasetInfo` | Source dataset metadata |
+
+### Configuration
+
+| Export | Description |
+|--------|-------------|
+| `cli()` | Config loader with CLI support |
+| `load_omegaconf_yaml()` | Load YAML with OmegaConf |
+| `apply_hydra_overrides()` | Apply key=value overrides |
+
+### Execution
+
+| Export | Description |
+|--------|-------------|
+| `build_executor()` | Build NeMo-Run executor from profile |
+| `load_env_profile()` | Load execution profile from env.toml |
+
+### Tracking
+
+| Export | Description |
+|--------|-------------|
+| `init_wandb_if_configured()` | Conditional W&B initialization |
+| `add_wandb_tags()` | Add tags to W&B runs |
+| `finish_wandb()` | Clean W&B shutdown |
+
+## Further Reading
+
+- [NVIDIA AI Stack](./nvidia-stack.md) — Megatron-Core, Megatron-Bridge, NeMo-RL
+- [OmegaConf Configuration](./omegaconf.md) — Artifact interpolations and unified W&B logging
+- [Artifact Lineage](./artifacts.md) — Artifact versioning and W&B lineage
+- [W&B Integration](./wandb.md) — Automatic credential handling
+- [Execution through NeMo-Run](./nemo-run.md) — Execution profiles and packagers
+- [CLI Framework](./cli.md) — Building recipe CLIs
+- [Data Preparation](./data-prep.md) — Data prep module
diff --git a/docs/train/nano3/README.md b/docs/train/nano3/README.md
new file mode 100644
index 0000000..8563216
--- /dev/null
+++ b/docs/train/nano3/README.md
@@ -0,0 +1,264 @@
+# Nemotron 3 Nano Training Recipe
+
+A complete, reproducible training pipeline for Nemotron 3 Nano—an open, efficient Mixture-of-Experts hybrid Mamba-Transformer model optimized for agentic reasoning.
+
+## Quick Start
+
+### Prerequisites
+
+- **Slurm cluster** with GPU nodes (H100 recommended) — see [Execution through NeMo-Run](../nemo-run.md)
+- **[Weights & Biases](../wandb.md) account** for experiment tracking and [artifact lineage](../artifacts.md)
+- **Container images**:
+ - Training: `nvcr.io/nvidia/nemo:25.11.nemotron_3_nano`
+ - RL: `nvcr.io/nvidia/nemo-rl:v0.4.0.nemotron_3_nano`
+
+### Installation
+
+```bash
+git clone https://github.com/NVIDIA/nemotron
+cd nemotron
+uv sync
+```
+
+### Configuration
+
+Create an `env.toml` file (see [Execution through NeMo-Run](../nemo-run.md) for details):
+
+```toml
+[wandb]
+project = "nemotron"
+entity = "YOUR-TEAM"
+
+[YOUR-CLUSTER]
+executor = "slurm"
+account = "YOUR-ACCOUNT"
+partition = "batch"
+nodes = 2
+ntasks_per_node = 8
+gpus_per_node = 8
+mounts = ["/lustre:/lustre"]
+```
+
+### Run the Pipeline
+
+
+
+```console
+// Stage 0: Pretraining
+$ uv run nemotron nano3 data prep pretrain --run YOUR-CLUSTER
+$ uv run nemotron nano3 pretrain --run YOUR-CLUSTER
+
+// Stage 1: Supervised Fine-Tuning
+$ uv run nemotron nano3 data prep sft --run YOUR-CLUSTER
+$ uv run nemotron nano3 sft --run YOUR-CLUSTER
+
+// Stage 2: Reinforcement Learning
+$ uv run nemotron nano3 data prep rl --run YOUR-CLUSTER
+$ uv run nemotron nano3 rl --run YOUR-CLUSTER
+```
+
+
+
+## Resources
+
+- **Tech Report**: [Nemotron 3 Nano Technical Report](https://research.nvidia.com/labs/nemotron/files/NVIDIA-Nemotron-3-Nano-Technical-Report.pdf)
+- **Model Weights**:
+ - [NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16) (Base model)
+ - [NVIDIA-Nemotron-3-Nano-30B-A3B-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16) (Instruct model)
+ - [NVIDIA-Nemotron-3-Nano-30B-A3B-FP8](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8) (FP8 quantized)
+- **Model Collection**: [NVIDIA Nemotron v3 Collection](https://huggingface.co/collections/nvidia/nvidia-nemotron-v3)
+- **Training Datasets**:
+ - [Pre-training Datasets](https://huggingface.co/collections/nvidia/nemotron-pre-training-datasets) (Open pre-training data)
+ - [Post-training Datasets](https://huggingface.co/collections/nvidia/nemotron-post-training-v3) (SFT and RL data)
+
+## Training Pipeline
+
+| Stage | Name | Purpose | Guide |
+|-------|------|---------|-------|
+| 0 | [Pretraining](./pretrain.md) | Base model on 25T tokens with curriculum learning | [pretrain.md](./pretrain.md) |
+| 1 | [SFT](./sft.md) | Multi-domain instruction tuning with 12+ data sources | [sft.md](./sft.md) |
+| 2 | [RL](./rl.md) | GRPO alignment with multi-environment rewards | [rl.md](./rl.md) |
+
+## Model Specifications
+
+| Specification | Value |
+|---------------|-------|
+| **Total Parameters** | 31.6B |
+| **Active Parameters** | 3.6B (per forward pass) |
+| **Pretraining Tokens** | 25 trillion |
+| **Context Length** | Up to 1M tokens |
+| **Architecture** | Hybrid Mamba-Transformer with sparse MoE |
+
+> For architecture details, see [Tech Report Section 2.1](https://research.nvidia.com/labs/nemotron/files/NVIDIA-Nemotron-3-Nano-Technical-Report.pdf).
+
+## Stage Summaries
+
+### Stage 0: Pretraining
+
+Two-phase curriculum on 25 trillion tokens: Phase 1 (23.5T) focuses on diversity across web, code, math, and multilingual data; Phase 2 (1.5T) emphasizes high-quality sources. Includes long-context extension to 1M tokens.
+
+→ [Pretraining Guide](./pretrain.md)
+
+### Stage 1: Supervised Fine-Tuning
+
+Multi-domain instruction tuning covering 12+ data domains including competition math/code, InfinityByte cross-domain synthesis, STEM reasoning, conversational tool use, and multilingual support.
+
+→ [SFT Guide](./sft.md)
+
+### Stage 2: Reinforcement Learning
+
+Multi-environment RLVR training across 7 reward environments using GRPO, plus GenRM-based RLHF and DPO for reducing tool hallucination.
+
+→ [RL Guide](./rl.md)
+
+## Execution Options
+
+All commands support [NeMo-Run](../nemo-run.md) execution modes:
+
+| Option | Behavior | Use Case |
+|--------|----------|----------|
+| `--run (bin/idx)"] + data0 --> cmd0["uv run nemotron nano3 pretrain"] + cmd0 --> model0["ModelArtifact-pretrain"] + end + + subgraph sft["Stage 1: SFT"] + data1["DataBlendsArtifact-sft
(.npy)"] --> cmd1["uv run nemotron nano3 sft"] + model0 --> cmd1 + cmd1 --> model1["ModelArtifact-sft"] + end + + subgraph rl["Stage 2: RL"] + data2["DataBlendsArtifact-rl
(JSONL)"] --> cmd2["uv run nemotron nano3 rl"] + model1 --> cmd2 + cmd2 --> model2["ModelArtifact-rl
(Final Model)"] + end + + style pretrain fill:#e1f5fe,stroke:#2196f3 + style sft fill:#f3e5f5,stroke:#9c27b0 + style rl fill:#e8f5e9,stroke:#4caf50 +``` + +→ [Artifact Lineage & W&B Integration](../artifacts.md) + +## Open-Source Data + +> **Note**: These recipes train exclusively on the open-sourced subset of training data. Results will differ from the tech report benchmarks, which used additional proprietary data. Use these recipes as reference implementations to apply the methodology with your own data. + +## Coming Soon + +Native integrations with NVIDIA's NeMo ecosystem: + +| Tool | Description | Status | +|------|-------------|--------| +| [NeMo Curator](https://github.com/NVIDIA-NeMo/Curator) | Scalable data curation—deduplication, quality filtering, PII removal | Planned | +| [NeMo Data Designer](https://github.com/NVIDIA-NeMo/DataDesigner) | Synthetic data generation for instruction tuning and alignment | Planned | +| [NeMo Export-Deploy](https://github.com/NVIDIA-NeMo/Export-Deploy) | Model export to TensorRT-LLM and deployment | Planned | +| [NeMo Evaluator](https://github.com/NVIDIA-NeMo/Evaluator) | Comprehensive model evaluation and benchmarking | Planned | + +These integrations will enable end-to-end pipelines from data curation to model evaluation. + +## CLI Reference + +
+
+```console
+// Show available commands
+$ uv run nemotron nano3 --help
+Usage: nemotron nano3 [OPTIONS] COMMAND [ARGS]...
+
+ Nano3 training recipe
+
+╭─ Commands ───────────────────────────────────────────────────────────────╮
+│ data Data curation and preparation commands │
+│ model Model evaluation and import commands │
+╰──────────────────────────────────────────────────────────────────────────╯
+╭─ Training Stages ────────────────────────────────────────────────────────╮
+│ pretrain Run pretraining with Megatron-Bridge (stage0). │
+│ sft Run supervised fine-tuning with Megatron-Bridge (stage1). │
+│ rl Run reinforcement learning with NeMo-RL GRPO (stage2). │
+╰──────────────────────────────────────────────────────────────────────────╯
+
+// View training command help (SFT example with artifact overrides)
+$ uv run nemotron nano3 sft --help
+Usage: nemotron nano3 sft [OPTIONS]
+
+ Run supervised fine-tuning with Megatron-Bridge (stage1).
+
+╭─ Options ────────────────────────────────────────────────────────────────╮
+│ --help -h Show this message and exit. │
+╰──────────────────────────────────────────────────────────────────────────╯
+╭─ Global Options ─────────────────────────────────────────────────────────╮
+│ -c, --config NAME Config name or path │
+│ -r, --run PROFILE Submit to cluster (attached) │
+│ -b, --batch PROFILE Submit to cluster (detached) │
+│ -d, --dry-run Preview config without execution │
+│ --stage Stage files for interactive debugging │
+╰──────────────────────────────────────────────────────────────────────────╯
+╭─ Configs (-c/--config) ──────────────────────────────────────────────────╮
+│ Built-in: default, tiny │
+│ Custom: -c /path/to/your/config.yaml │
+╰──────────────────────────────────────────────────────────────────────────╯
+╭─ Artifact Overrides (W&B artifact references) ───────────────────────────╮
+│ run.model Base model checkpoint artifact │
+│ run.data SFT data artifact (packed .npy) │
+╰──────────────────────────────────────────────────────────────────────────╯
+╭─ Run Overrides (override env.toml settings) ─────────────────────────────╮
+│ run.env.nodes Number of nodes │
+│ run.env.nproc_per_node GPUs per node │
+│ run.env.partition Slurm partition │
+│ run.env.account Slurm account │
+│ run.env.time Job time limit (e.g., 04:00:00) │
+│ run.env.container_image Override container image │
+╰──────────────────────────────────────────────────────────────────────────╯
+╭─ env.toml Profiles ──────────────────────────────────────────────────────╮
+│ Available profiles: YOUR-CLUSTER, YOUR-CLUSTER-large │
+│ Usage: --run PROFILE or --batch PROFILE │
+╰──────────────────────────────────────────────────────────────────────────╯
+╭─ Examples ───────────────────────────────────────────────────────────────╮
+│ $ ... sft -c tiny Local execution │
+│ $ ... sft -c tiny --dry-run Preview config │
+│ $ ... sft -c tiny --run my-cluster Submit to cluster │
+│ $ ... sft -c tiny -r cluster run.env.nodes=4 │
+╰──────────────────────────────────────────────────────────────────────────╯
+```
+
+
+
+## Troubleshooting
+
+**W&B authentication**: See [W&B Integration](../wandb.md) for setup.
+```bash
+wandb login
+```
+
+**Container not found**: Verify image path in config files.
+
+**Job submission fails**: Check Slurm account and partition in `env.toml`. See [Execution through NeMo-Run](../nemo-run.md).
+
+## Further Reading
+
+- [Stage 0: Pretraining](./pretrain.md)
+- [Stage 1: SFT](./sft.md)
+- [Stage 2: RL](./rl.md)
+- [Importing Models & Data](./import.md)
+- [Artifact Lineage](../artifacts.md)
+- [Execution through NeMo-Run](../nemo-run.md)
+- [W&B Integration](../wandb.md)
+- [NVIDIA AI Stack](../nvidia-stack.md)
+- [CLI Framework](../cli.md)
+- [Data Preparation Module](../data-prep.md)
diff --git a/docs/train/nano3/import.md b/docs/train/nano3/import.md
new file mode 100644
index 0000000..4fdfdc9
--- /dev/null
+++ b/docs/train/nano3/import.md
@@ -0,0 +1,162 @@
+# Importing Models and Data
+
+This guide covers how to import existing models and data as [W&B artifacts](../artifacts.md) using the nemotron CLI. This is useful when you want to:
+
+- Use a pre-existing checkpoint from another training run
+- Import data prepared outside of the standard pipeline
+- Connect external assets to the [W&B artifact lineage](../artifacts.md) system
+
+## Prerequisites
+
+- [W&B](../wandb.md) configuration in `env.toml` (see [Execution through NeMo-Run](../nemo-run.md)):
+ ```toml
+ [wandb]
+ project = "nemotron"
+ entity = "YOUR-TEAM"
+ ```
+- Or provide `--project` and `--entity` CLI flags
+
+## Model Import
+
+Import model checkpoints as [W&B artifacts](../artifacts.md) for use in downstream training stages.
+
+### Commands
+
+```bash
+# Import pretrain model checkpoint
+uv run nemotron nano3 model import pretrain /path/to/model_dir --step 10000
+
+# Import SFT model checkpoint
+uv run nemotron nano3 model import sft /path/to/model_dir --step 5000
+
+# Import RL model checkpoint
+uv run nemotron nano3 model import rl /path/to/model_dir --step 2000
+```
+
+### Options
+
+| Option | Description |
+|--------|-------------|
+| `--step, -s` | Training step number (optional) |
+| `--name, -n` | Custom artifact name (default: `nano3/
+
+```console
+// 1. Prepare data (tokenize to bin/idx format)
+$ uv run nemotron nano3 data prep pretrain --run YOUR-CLUSTER
+
+// 2. Run pretraining
+$ uv run nemotron nano3 pretrain --run YOUR-CLUSTER
+```
+
+
+
+> **Note**: The `--run YOUR-CLUSTER` flag submits jobs via [NeMo-Run](../nemo-run.md). See [Execution through NeMo-Run](../nemo-run.md) for setup.
+
+#### Direct Script Execution (Megatron-Bridge)
+
+For direct execution outside this CLI, use the scripts in the [Megatron-Bridge](https://github.com/NVIDIA-NeMo/Megatron-Bridge) repository:
+
+```bash
+# Clone the repository and checkout the nano-v3 branch
+git clone https://github.com/NVIDIA-NeMo/Megatron-Bridge.git
+cd Megatron-Bridge
+git checkout nano-v3
+
+# Run pretraining (inside container on compute node)
+python examples/recipes/nemotron_3/pretrain_nemotron_3_nano.py \
+ --per-split-data-args-path /path/to/data_args.json \
+ --tokenizer-model /path/to/tokenizer.model
+
+# With config file overrides
+python examples/recipes/nemotron_3/pretrain_nemotron_3_nano.py \
+ --config-file /path/to/overrides.yaml \
+ --per-split-data-args-path /path/to/data_args.json \
+ --tokenizer-model /path/to/tokenizer.model
+```
+
+See the [Megatron-Bridge Nemotron 3 documentation](https://docs.nvidia.com/nemo/megatron-bridge/latest/models/llm/nemotron3.html) for detailed configuration options.
+
+### Configuration
+
+| File | Purpose |
+|------|---------|
+| `config/default.yaml` | Production configuration |
+| `config/data_prep.yaml` | Data preparation settings |
+| `config/data_blend_raw.json` | Dataset blend definition |
+
+**Blend Configuration**
+
+Data blends are defined in `config/data_prep/data_blend_raw.json`. Each entry specifies:
+
+```json
+{
+ "name": "dataset-name",
+ "path": "hf://nvidia/...",
+ "subset": "subset-name",
+ "weight": 1.0
+}
+```
+
+Weights control sampling probability during data preparation. Phase transitions are implemented by using different blend configurations.
+
+### Data Preparation
+
+The `data_prep.py` script tokenizes raw text datasets into Megatron's binary format. See [Data Preparation Module](../data-prep.md) for detailed documentation.
+
+#### CLI Command
+
+```bash
+uv run nemotron nano3 data prep pretrain [options]
+```
+
+| Option | Description |
+|--------|-------------|
+| `--run (bin/idx files + blend.json)"] + data --> train["train.py"] + train --> model["ModelArtifact-pretrain
(checkpoint)"] + model --> next["Stage 1: SFT"] + + style raw fill:#e1f5fe,stroke:#2196f3 + style dp fill:#e1f5fe,stroke:#2196f3 + style data fill:#e1f5fe,stroke:#2196f3 + style train fill:#e1f5fe,stroke:#2196f3 + style model fill:#e1f5fe,stroke:#2196f3 + style next fill:#f3e5f5,stroke:#9c27b0 +``` + +--- + +## Infrastructure + +This stage uses the following components from the [NVIDIA AI Stack](../nvidia-stack.md): + +| Component | Role | Documentation | +|-----------|------|---------------| +| [Megatron-Core](../nvidia-stack.md#megatron-core) | Distributed training primitives (TP, PP, DP, EP, CP, SP) | [GitHub](https://github.com/NVIDIA/Megatron-LM) | +| [Megatron-Bridge](../nvidia-stack.md#megatron-bridge) | Model definitions, training loop, checkpoint management | [Docs](https://docs.nvidia.com/nemo/megatron-bridge/latest/) | + +### Parallelism Configuration + +Pretraining uses multiple parallelism strategies for efficient scaling. The specific values differ between main pretraining and long-context extension: + +| Parallelism | Main Pretraining | Long-Context (LC) | Config Key | +|-------------|------------------|-------------------|------------| +| Tensor (TP) | 8 | 8 | `model.tensor_model_parallel_size` | +| Pipeline (PP) | 1 | 4 | `model.pipeline_model_parallel_size` | +| Expert (EP) | 8 | 8 | `model.expert_model_parallel_size` | +| Context (CP) | 1 | 8 | `model.context_parallel_size` | +| Sequence (SP) | Yes | Yes | `model.sequence_parallel` | +| Data (DP) | Auto | Auto | Computed from world size | + +**Why the difference?** + +- **Main pretraining** uses 4K sequences, so context parallelism (CP=1) isn't needed +- **Long-context extension** handles up to 1M tokens, requiring CP=8 to distribute sequences across GPUs +- **Pipeline parallelism** increases in LC phase (PP=4) to handle larger activation memory + +> For parallelism concepts, see [NVIDIA AI Stack: Parallelism](../nvidia-stack.md#parallelism-strategies). + +### Container + +``` +nvcr.io/nvidia/nemo:25.11.nemotron_3_nano +``` + +--- + +## Next Steps + +After pretraining completes, proceed to [Stage 1: SFT](./sft.md) for instruction tuning. + +## Reference + +- [Tech Report Section 2](https://research.nvidia.com/labs/nemotron/files/NVIDIA-Nemotron-3-Nano-Technical-Report.pdf) — Pretraining methodology +- [NVIDIA AI Stack](../nvidia-stack.md) — Megatron-Core, Megatron-Bridge documentation +- [Artifact Lineage](../artifacts.md) — W&B artifact system +- **Recipe Source**: `src/nemotron/recipes/nano3/stage0_pretrain/` — Implementation details +- [Back to Overview](./README.md) diff --git a/docs/train/nano3/rl.md b/docs/train/nano3/rl.md new file mode 100644 index 0000000..0b96fb0 --- /dev/null +++ b/docs/train/nano3/rl.md @@ -0,0 +1,506 @@ +# Stage 2: Reinforcement Learning (RL) + +This stage aligns the instruction-tuned model using GRPO (Group Relative Policy Optimization) with [NeMo-RL](../nvidia-stack.md#nemo-rl). + +> **Open-Source Data Only**: This recipe uses exclusively open-sourced RL data from the [Nemotron Post-training Datasets](https://huggingface.co/collections/nvidia/nemotron-post-training-v3) collection, which is a subset of the full data used to train the released model. The recipe uses the [Nemotron-3-Nano-RL-Training-Blend](https://huggingface.co/datasets/nvidia/Nemotron-3-Nano-RL-Training-Blend) dataset. Results will differ from the benchmarks in the [tech report](https://research.nvidia.com/labs/nemotron/files/NVIDIA-Nemotron-3-Nano-Technical-Report.pdf). Use this recipe as a reference implementation to apply the methodology with your own data. + +--- + +## Training Methodology + +> **Training Framework**: RL alignment is implemented using [NeMo-RL](https://docs.nvidia.com/nemo/rl/latest/) with Ray for distributed actor coordination and vLLM for fast rollout generation. The Megatron backend handles distributed policy training with tensor, pipeline, context, and expert parallelism. See [NeMo-RL Documentation](https://docs.nvidia.com/nemo/rl/latest/) for implementation details. +> +> For complete methodology, see [Tech Report Section 3.2](https://research.nvidia.com/labs/nemotron/files/NVIDIA-Nemotron-3-Nano-Technical-Report.pdf). + +### RL Pipeline Overview + +The RL pipeline consists of three components: +1. **RLVR** — Multi-environment training with verifiable rewards +2. **RLHF with GenRM** — Generative reward model-based alignment +3. **DPO** — Preference learning to reduce tool hallucination + +### Data Preparation Pipeline + +Before training, the RL dataset is transformed into JSONL format compatible with NeMo-Gym: + +```mermaid +%%{init: {'theme': 'base', 'themeVariables': { 'primaryBorderColor': '#333333', 'lineColor': '#333333', 'primaryTextColor': '#333333'}}}%% +flowchart LR + subgraph prep["Data Preparation"] + direction LR + hf["HuggingFace
Dataset"] --> resolve["Placeholder
Resolution"] + resolve --> jsonl["JSONL
Format"] + jsonl --> split["Train/Val/Test
Split"] + end + split --> gym["NeMo-Gym
Environment"] + gym --> reward["Reward
Computation"] + + style hf fill:#e1f5fe,stroke:#2196f3 + style resolve fill:#e1f5fe,stroke:#2196f3 + style jsonl fill:#f3e5f5,stroke:#9c27b0 + style split fill:#f3e5f5,stroke:#9c27b0 + style gym fill:#e8f5e9,stroke:#4caf50 + style reward fill:#e8f5e9,stroke:#4caf50 +``` + +| Stage | What Happens | +|-------|--------------| +| **HuggingFace Dataset** | Load [Nemotron-3-Nano-RL-Training-Blend](https://huggingface.co/datasets/nvidia/Nemotron-3-Nano-RL-Training-Blend) from HuggingFace Hub | +| **Placeholder Resolution** | Resolve `_hf_placeholder` records by fetching from external datasets (DAPO, Skywork) and applying template restoration | +| **JSONL Format** | Convert to JSONL with `question`, `expected_answer`, and `responses_create_params` fields | +| **Train/Val/Test Split** | Split into training (98%), validation (1%), and test (1%) sets | +| **NeMo-Gym Environment** | Route samples to appropriate reward environments based on task type | +| **Reward Computation** | Compute verifiable rewards (math correctness, code execution, schema adherence) | + +**Placeholder Resolution:** + +The [Nemotron-3-Nano-RL-Training-Blend](https://huggingface.co/datasets/nvidia/Nemotron-3-Nano-RL-Training-Blend) dataset contains placeholder records that reference external HuggingFace datasets. The `data_prep.py` script resolves these by: + +1. Detecting placeholder records by the presence of `_hf_placeholder` field +2. Fetching actual data from external HF datasets: + - [ByteDance-Seed/DAPO-Math-17k](https://huggingface.co/datasets/ByteDance-Seed/DAPO-Math-17k) — Math reasoning problems + - [Skywork/Skywork-OR1-RL-Data](https://huggingface.co/datasets/Skywork/Skywork-OR1-RL-Data) — Open reasoning data +3. Applying template restoration (DAPO prefix/suffix, Skywork `{question}` replacement) + +> For data preparation implementation, see **Recipe Source**: `src/nemotron/recipes/nano3/stage2_rl/data_prep.py` + +### GRPO Algorithm + +GRPO (Group Relative Policy Optimization) optimizes the policy using group-relative advantages: + +1. **Generate responses** from the current policy using vLLM +2. **Evaluate** responses using NeMo-Gym reward environments +3. **Compute group-relative advantages** across response groups per prompt +4. **Update the policy** to favor higher-reward responses with clipped gradients + +**Loss Function:** + +The GRPO loss uses clipped policy gradients with KL regularization: + +$$ +L(\theta) = E_{x \sim \pi_{\theta_{\text{old}}}} \Big[ \min \Big(\frac{\pi_\theta(x)}{\pi_{\theta_{\text{old}}}(x)}A_t, \text{clip} \big( \frac{\pi_\theta(x)}{\pi_{\theta_{\text{old}}}(x)}, 1 - \varepsilon, 1 + \varepsilon \big) A_t \Big) \Big] - \beta D_{\text{KL}} (\pi_\theta \| \pi_\text{ref}) +$$ + +Where: +- $\pi_\theta$ is the policy being optimized +- $\pi_{\theta_{\text{old}}}$ is the policy from the beginning of this step +- $A_t$ is the advantage estimate (group-relative) +- $\varepsilon$ is the clipping hyperparameter (0.2–0.28) +- $\beta$ is the KL penalty coefficient +- $\pi_{\text{ref}}$ is the reference policy (frozen SFT checkpoint) + +**Stability Improvements:** + +| Improvement | Description | +|-------------|-------------| +| **On-Policy KL Approximation** | Uses importance weights to correct for off-policy samples, providing an unbiased and guaranteed-positive KL estimator | +| **Importance Sampling Correction** | Corrects for discrepancies between inference (vLLM) and training (Megatron) token probabilities | +| **Overlong Filtering** | Excludes sequences that hit max length without EOS from loss computation, reducing noise from truncated generations | +| **Asymmetric Clipping** | Uses `ratio_clip_min=0.2` and `ratio_clip_max=0.28` for asymmetric policy update bounds | + +> For detailed loss function derivations, see the [NeMo-RL GRPO Guide](https://docs.nvidia.com/nemo/rl/latest/guides/grpo.html#loss). + +### Multi-Environment RLVR + +Training uses 6 reward environments through NeMo-Gym: + +| Environment | Description | Reward Type | +|-------------|-------------|-------------| +| **math_with_judge** | Mathematical reasoning (DAPO, Skywork math) | Answer correctness verification | +| **code_gen** | Code correctness with test case execution | Unit test pass rate | +| **mcqa** | STEM multiple choice questions | Answer matching | +| **instruction_following** | IFEval, Multi-Challenge compliance | Constraint satisfaction | +| **workplace_assistant** | Agentic tool use, multi-turn interactions | Task completion | +| **structured_outputs_json** | JSON schema adherence | Schema validation | + +Training on all environments simultaneously provides stable gains without co-reward degradation. + +> For environment implementation details, see [NeMo-RL Environments Guide](https://docs.nvidia.com/nemo/rl/latest/guides/environments.html). + +### GenRM (RLHF) + +Generative reward models use circular comparison strategy (N comparisons instead of O(N²)) with length-normalized reward adjustment: + +| Parameter | Value | +|-----------|-------| +| **Prompts per batch** | 128 | +| **Responses per prompt** | 16 | +| **Comparison strategy** | Circular | +| **Length bonus α** | 0.5 | + +> For GenRM training details, see [Tech Report Section 3.2](https://research.nvidia.com/labs/nemotron/files/NVIDIA-Nemotron-3-Nano-Technical-Report.pdf). + +### DPO for Tool Hallucination + +DPO reduces hallucinated tool usage with minimal computational overhead: + +| Metric | Before DPO | After DPO | +|--------|------------|-----------| +| **AIME25 Accuracy** | 80.88% | 84.58% | +| **Hallucination Rate** | 8.33% | 0.7% | + +> For DPO methodology, see [Tech Report Appendix C](https://research.nvidia.com/labs/nemotron/files/NVIDIA-Nemotron-3-Nano-Technical-Report.pdf) and [NeMo-RL DPO Guide](https://docs.nvidia.com/nemo/rl/latest/guides/dpo.html). + +### Reasoning Control + +The model supports: +- **Reasoning on/off control** — Strip reasoning from 10% of samples +- **Token budget control** — Truncate 3% of reasoning traces to different budgets + +### Hyperparameters + +**GRPO Settings:** + +| Parameter | Value | Description | +|-----------|-------|-------------| +| `num_prompts_per_step` | 128 | Prompts sampled per training step | +| `num_generations_per_prompt` | 16 | Rollouts generated per prompt | +| `max_total_sequence_length` | 49152 | Maximum sequence length (~49K tokens) | +| `normalize_rewards` | true | Normalize rewards across batch | +| `use_leave_one_out_baseline` | true | Variance reduction for advantage estimation | +| `val_period` | 5 | Validation every N steps | +| `max_num_epochs` | 1 | Single epoch over data | +| `seed` | 42 | Random seed for reproducibility | + +**Loss Function:** + +| Parameter | Value | Description | +|-----------|-------|-------------| +| `ratio_clip_min` | 0.2 | Lower bound for importance ratio clipping | +| `ratio_clip_max` | 0.28 | Upper bound for importance ratio clipping | +| `use_on_policy_kl_approximation` | true | Use unbiased on-policy KL estimator | +| `use_importance_sampling_correction` | true | Correct for inference/training mismatch | +| `token_level_loss` | true | Per-token loss normalization | +| `reference_policy_kl_penalty` | 0 | KL regularization weight (disabled) | + +**Optimizer:** + +| Parameter | Value | +|-----------|-------| +| `optimizer` | AdamW | +| `lr` | 3e-6 | +| `min_lr` | 3e-6 | +| `weight_decay` | 0.0 | +| `adam_beta1` | 0.9 | +| `adam_beta2` | 0.999 | +| `adam_eps` | 1e-8 | +| `clip_grad` | 1.0 | + +**Sequence Packing:** + +| Parameter | Value | +|-----------|-------| +| `enabled` | true | +| `algorithm` | modified_first_fit_decreasing | +| `sequence_length_round` | 64 | + +--- + +## Recipe Execution + +### Quick Start + +
+
+```console
+// 1. Prepare data (convert to JSONL format)
+$ uv run nemotron nano3 data prep rl --run YOUR-CLUSTER
+
+// 2. Run RL training
+$ uv run nemotron nano3 rl --run YOUR-CLUSTER
+```
+
+
+
+> **Note**: The `--run YOUR-CLUSTER` flag submits jobs via [NeMo-Run](../nemo-run.md). See [Execution through NeMo-Run](../nemo-run.md) for setup.
+
+### Running in NeMo-RL Repository
+
+For direct execution using NeMo-RL (without the nemotron CLI wrapper), follow the [NeMo-RL Nemotron 3 Nano Guide](https://docs.nvidia.com/nemo/rl/latest/guides/nemotron-3-nano.html):
+
+**1. Download and prepare the dataset:**
+
+```bash
+# Download the RL blend dataset
+huggingface-cli download nvidia/Nemotron-3-Nano-RL-Training-Blend \
+ --repo-type dataset \
+ --local-dir /path/to/rl-blend
+
+# Fill in placeholder entries (resolves DAPO, Skywork references)
+python /path/to/rl-blend/create_nanov3_jsonl.py /path/to/rl-blend/data/train.jsonl
+
+# Split into train/validation
+head -n -1000 /path/to/rl-blend/data/train.jsonl > /path/to/train.jsonl
+tail -n 1000 /path/to/rl-blend/data/train.jsonl > /path/to/validation.jsonl
+```
+
+**2. Run GRPO training:**
+
+```bash
+# From NeMo-RL repository root
+uv run python examples/nemo_gym/run_grpo_nemo_gym.py \
+ --config examples/nemo_gym/grpo_nanov3.yaml \
+ data.train_jsonl_fpath=/path/to/train.jsonl \
+ data.validation_jsonl_fpath=/path/to/validation.jsonl \
+ policy.model_name=/path/to/sft/checkpoint \
+ logger.wandb_enabled=True
+```
+
+> **Note**: The default recipe requires 32 nodes with 8 GPUs each. See the [NeMo-RL cluster documentation](https://docs.nvidia.com/nemo/rl/latest/cluster.html) for Slurm configuration.
+
+### Configuration
+
+| File | Purpose |
+|------|---------|
+| `config/default.yaml` | Production GRPO configuration |
+| `config/data_prep/default.yaml` | Data preparation settings |
+| `config/data_prep/data_blend_raw.json` | RL dataset blend |
+
+### Data Preparation
+
+The `data_prep.py` script converts datasets to JSONL format compatible with [NeMo-RL](../nvidia-stack.md#nemo-rl)'s NeMo-Gym interface. See [Data Preparation Module](../data-prep.md) for detailed documentation.
+
+#### CLI Command
+
+```bash
+uv run nemotron nano3 data prep rl [options]
+```
+
+| Option | Description |
+|--------|-------------|
+| `--run (from Stage 1)"] --> train + rl["RL Datasets
(HuggingFace)"] --> dp["data_prep.py"] + dp --> data["DataBlendsArtifact-rl
(JSONL files)"] + data --> train["train.py
(GRPO with NeMo-RL)"] + train --> model["ModelArtifact-rl
(final aligned model)"] + + style prev fill:#f3e5f5,stroke:#9c27b0 + style rl fill:#e8f5e9,stroke:#4caf50 + style dp fill:#e8f5e9,stroke:#4caf50 + style data fill:#e8f5e9,stroke:#4caf50 + style train fill:#e8f5e9,stroke:#4caf50 + style model fill:#e8f5e9,stroke:#4caf50 +``` + +--- + +## Infrastructure + +This stage uses the following components from the [NVIDIA AI Stack](../nvidia-stack.md): + +| Component | Role | Documentation | +|-----------|------|---------------| +| [NeMo-RL](../nvidia-stack.md#nemo-rl) | GRPO algorithm, policy training, reward computation | [Docs](https://docs.nvidia.com/nemo/rl/latest/) | +| [Megatron-Core](../nvidia-stack.md#megatron-core) | Distributed training primitives (TP, PP, CP, EP) | [GitHub](https://github.com/NVIDIA/Megatron-LM) | +| [Ray](https://ray.io/) | Distributed actor coordination | [Docs](https://docs.ray.io/) | +| vLLM | Fast rollout generation | [GitHub](https://github.com/vllm-project/vllm) | + +### Parallelism Configuration + +Training uses multiple parallelism strategies for efficient scaling: + +| Parallelism | Value | Config Key | +|-------------|-------|------------| +| Tensor (TP) | 2 | `policy.megatron_cfg.tensor_model_parallel_size` | +| Pipeline (PP) | 2 | `policy.megatron_cfg.pipeline_model_parallel_size` | +| Context (CP) | 4 | `policy.megatron_cfg.context_parallel_size` | +| Expert (EP) | 8 | `policy.megatron_cfg.expert_model_parallel_size` | +| Sequence (SP) | Yes | `policy.megatron_cfg.sequence_parallel` | + +**Generation (vLLM):** + +| Parameter | Value | Description | +|-----------|-------|-------------| +| `tensor_parallel_size` | 4 | TP for vLLM generation | +| `gpu_memory_utilization` | 0.5 | GPU memory fraction for KV cache | +| `colocated` | true | Share GPUs with training | +| `enforce_eager` | false | Use torch.compile | + +**Cluster:** + +| Parameter | Value | +|-----------|-------| +| `num_nodes` | 32 | +| `gpus_per_node` | 8 | + +### Key Features Used + +| Feature | Purpose | +|---------|---------| +| GRPO algorithm | Group Relative Policy Optimization with clipped gradients | +| Megatron backend | Distributed training with TP/PP/CP/EP parallelism | +| Sequence Packing | Efficient batch utilization for variable-length generations | +| vLLM Generation | Fast rollout with tensor parallelism | +| MoE Router Bias | Aux-loss-free load balancing (`freeze_moe_router=true`) | +| Per-token Loss | Consistent gradient signal (`calculate_per_token_loss=true`) | + +### NeMo-Gym Environments + +The training configuration includes these reward environment configs: + +```yaml +env: + nemo_gym: + config_paths: + - responses_api_models/vllm_model/configs/vllm_model_for_training.yaml + - resources_servers/math_with_judge/configs/math_with_judge.yaml + - resources_servers/code_gen/configs/code_gen.yaml + - resources_servers/workplace_assistant/configs/workplace_assistant.yaml + - resources_servers/mcqa/configs/mcqa.yaml + - resources_servers/instruction_following/configs/instruction_following.yaml + - resources_servers/structured_outputs/configs/structured_outputs_json.yaml +``` + +### Architecture + +NeMo-RL uses a Ray-based actor model: + +| Actor | Function | +|-------|----------| +| **Policy Model** | Trainable policy weights (Megatron backend) | +| **Generator** | vLLM-backed rollout generation (colocated) | +| **Reward Environments** | NeMo-Gym environments for reward computation | +| **Reference Model** | Frozen SFT checkpoint for KL divergence | + +### Container + +``` +nvcr.io/nvidia/nemo-rl:v0.4.0.nemotron_3_nano +``` + +--- + +## Next Steps + +After RL completes, the final aligned model (`ModelArtifact-rl`) is ready for evaluation and deployment. + +## Reference + +- [Tech Report Section 3.2](https://research.nvidia.com/labs/nemotron/files/NVIDIA-Nemotron-3-Nano-Technical-Report.pdf) — RL methodology +- [NeMo-RL Documentation](https://docs.nvidia.com/nemo/rl/latest/) — GRPO, DPO, environments +- [NeMo-RL Nemotron 3 Nano Guide](https://docs.nvidia.com/nemo/rl/latest/guides/nemotron-3-nano.html) — Upstream training guide +- [NVIDIA AI Stack](../nvidia-stack.md) — NeMo-RL, Megatron-Core documentation +- [Artifact Lineage](../artifacts.md) — W&B artifact system +- [Stage 0: Pretraining](./pretrain.md) — Pretrain the base model +- [Stage 1: SFT](./sft.md) — Instruction tuning +- **Recipe Source**: `src/nemotron/recipes/nano3/stage2_rl/` — Implementation details +- [Back to Overview](./README.md) diff --git a/docs/train/nano3/sft.md b/docs/train/nano3/sft.md new file mode 100644 index 0000000..12666c6 --- /dev/null +++ b/docs/train/nano3/sft.md @@ -0,0 +1,388 @@ +# Stage 1: Supervised Fine-Tuning (SFT) + +This stage fine-tunes the pretrained model for instruction following using [Megatron-Bridge](../nvidia-stack.md#megatron-bridge). + +> **Open-Source Data Only**: This recipe uses exclusively open-sourced SFT data from the [Nemotron Post-training Datasets](https://huggingface.co/collections/nvidia/nemotron-post-training-v3) collection, which is a subset of the full data used to train the released model. The recipe includes datasets from Nemotron-Science-v1, Nemotron-Instruction-Following-Chat-v1, Nemotron-Math-Proofs-v1, Nemotron-SWE-v1, Nemotron-Agentic-v1, and Nemotron-Competitive-Programming-v1. Results will differ from the benchmarks in the [tech report](https://research.nvidia.com/labs/nemotron/files/NVIDIA-Nemotron-3-Nano-Technical-Report.pdf). Use this recipe as a reference implementation to apply the methodology with your own data. + +--- + +## Training Methodology + +> **Training Framework**: SFT is implemented using [Megatron-Bridge](https://docs.nvidia.com/nemo/megatron-bridge/latest/)'s `finetune()` entry point, which loads a pretrained checkpoint and handles the training loop with role-based loss masking. See [Training Entry Points](https://docs.nvidia.com/nemo/megatron-bridge/latest/training/entry-points.html) for implementation details. +> +> For complete methodology, see [Tech Report Section 3.1](https://research.nvidia.com/labs/nemotron/files/NVIDIA-Nemotron-3-Nano-Technical-Report.pdf). + +### Data Preparation Pipeline + +Before training, chat conversations are transformed into training-ready sequences through several stages: + +```mermaid +%%{init: {'theme': 'base', 'themeVariables': { 'primaryBorderColor': '#333333', 'lineColor': '#333333', 'primaryTextColor': '#333333'}}}%% +flowchart LR + subgraph prep["Data Preparation"] + direction LR + chat["OpenAI Chat
Format"] --> template["Chat
Template"] + template --> chunks["Role-Labeled
Chunks"] + chunks --> tok["Tokenization"] + tok --> mask["Loss Mask
(role-based)"] + mask --> pack["Packing"] + pack --> roll["Mask Rolling"] + end + roll --> npy[".npy Output"] + + style chat fill:#e3f2fd,stroke:#2196f3 + style template fill:#e3f2fd,stroke:#2196f3 + style chunks fill:#e3f2fd,stroke:#2196f3 + style tok fill:#f3e5f5,stroke:#9c27b0 + style mask fill:#f3e5f5,stroke:#9c27b0 + style pack fill:#fff3e0,stroke:#ff9800 + style roll fill:#fff3e0,stroke:#ff9800 + style npy fill:#e8f5e9,stroke:#4caf50 +``` + +| Stage | What Happens | +|-------|--------------| +| **OpenAI Chat Format** | Input messages with `role` (system/user/assistant) and `content` fields | +| **Chat Template** | Renders messages using Nano3 Jinja template with special tokens (`<\|im_start\|>`, `<\|im_end\|>`) | +| **Role-Labeled Chunks** | Splits rendered text back into chunks, each tagged with its source role | +| **Tokenization** | Converts text chunks to token IDs | +| **Loss Mask** | Builds mask: `1` for assistant tokens, `0` for system/user tokens | +| **Packing** | Multiple sequences packed into fixed-length bins (4096 tokens) | +| **Mask Rolling** | Shifts mask by 1 position for next-token prediction alignment | + +**Multi-turn splitting**: For conversations with reasoning content (`reasoning_content` field), the pipeline creates separate training sequences at each user turn. Reasoning from previous turns is dropped when a new user message appears—this matches inference behavior where users don't see intermediate reasoning. + +> For data preparation implementation, see **Recipe Source**: `src/nemotron/recipes/nano3/stage1_sft/data_prep.py` + +### Loss Masking + +Loss masking determines which tokens contribute to the training loss. In SFT, we only want the model to learn to generate responses—not to predict prompts or system instructions. + +**Why mask non-assistant tokens?** + +The model should learn to *respond*, not to *prompt*. If we computed loss on user messages, the model would be optimized to predict "What is 2+2?" given prior context—which isn't useful for an assistant. By masking user and system tokens (setting their loss weight to 0), gradients only flow from assistant responses, teaching the model what to generate without wasting capacity on predicting inputs. + +| Role | Loss Mask | Training Signal | +|------|-----------|-----------------| +| `system` | 0 | Ignored (instructions) | +| `user` | 0 | Ignored (prompts) | +| `assistant` | 1 | Learned (responses) | + +**Why roll the mask by 1?** + +In next-token prediction, the model predicts `token[i+1]` given `tokens[0:i]`. The loss compares the prediction against the *label*, which is the input sequence shifted by one position: + +``` +Position: 0 1 2 3 4 +Input: [A] [B] [C] [D] [E] +Label: [B] [C] [D] [E] [_] <- shifted by 1 +``` + +If assistant content starts at position 2 (`[C]`), we want loss on predicting `[C]`, `[D]`, and `[E]`. But the label for position 2 is `[D]`—so we need to shift the mask to align with labels: + +``` +Original mask: [0] [0] [1] [1] [1] <- "assistant starts at C" +Rolled mask: [0] [0] [0] [1] [1] <- aligns with labels D, E +``` + +The pipeline rolls the loss mask by 1 position so it correctly masks the *predictions* (labels) rather than the *inputs*. + +**Truncation behavior (`max_doc_tokens`):** + +- **Default (null)**: No truncation—full sequences are preserved +- **When set**: Sequences exceeding the limit are truncated from the end, with the loss mask adjusted accordingly + +> For implementation details, see `src/nemotron/data_prep/chat_sft_processor.py` + +### Packed Sequences + +**Why pack sequences?** + +Individual chat conversations vary in length—some are 50 tokens, others 3000. Without packing, each training sample would require padding to the maximum sequence length, wasting compute on empty tokens. Packing concatenates multiple conversations into a single fixed-length sequence (default 4096 tokens), maximizing GPU utilization. + +The packed sequence format stores everything Megatron-Bridge needs for training: + +| Field | Description | +|-------|-------------| +| `input_ids` | Concatenated token IDs from multiple conversations | +| `loss_mask` | Rolled mask indicating which positions contribute to loss (see [Loss Masking](#loss-masking)) | +| `seq_start_id` | Boundary indices marking where each original conversation starts within the pack | + +**How `seq_start_id` works:** + +When multiple conversations are packed together, the model needs to know where one ends and another begins—otherwise attention could "leak" between unrelated conversations. The `seq_start_id` array marks these boundaries: + +``` +Pack: [Conv A tokens] [Conv B tokens] [Conv C tokens] + ^ ^ ^ +seq_start_id: [0, 128, 384] +``` + +Megatron-Bridge uses these boundaries for: +- **Variable-length attention**: Attention is masked so tokens from Conv A can't attend to Conv B +- **FlashAttention optimization**: Boundaries map to `cu_seqlens` parameter for efficient packed attention + +> For packing implementation, see `src/nemotron/data_prep/packing/builder.py` + +### Chat Template + +Nemotron 3 Nano supports both reasoning and non-reasoning modes: + +- **Multi-Step**: Existing reasoning tokens preserved for reuse in subsequent steps +- **Multi-Turn**: Reasoning from previous turns dropped when user message introduced +- **Tool Calling**: Uses XML-style special tags to reduce character escaping + +### SFT Data Domains + +| Domain | Description | +|--------|-------------| +| **Competition Math** | Tool-integrated reasoning with GPT-OSS teachers | +| **Competition Code** | OpenCodeReasoning solutions with obfuscation/complication | +| **InfinityByte** | Cross-domain code synthesis at model capability boundaries | +| **STEM Reasoning (RQA)** | Reasoning Q&A from undergraduate/graduate STEM content | +| **Conversational Tool Use** | Multi-turn trajectories with simulated tool execution | +| **Long Context** | 128k mean token length, 256k hard limit | +| **Formal Proofs** | Lean theorem proving with 300k examples | +| **Multilingual** | French, Spanish, Italian, German, Japanese | +| **Terminal Use** | Terminal operations from Terminal Bench | +| **General Chat** | Multi-turn responses from LMSYS and WildChat | +| **Instruction Following** | Tulu 3 methodology with verifier filtering | +| **Safety** | Refusal behaviors from safety datasets | +| **Software Engineering** | GitHub issue resolution trajectories | +| **Science** | Physics, chemistry, biology via NeMo Data Designer | + +> For detailed data generation pipelines, see [Tech Report Section 3.1](https://research.nvidia.com/labs/nemotron/files/NVIDIA-Nemotron-3-Nano-Technical-Report.pdf). + +### Data Filtering + +The pipeline applies: +- **Structural checks**: Discard malformed examples +- **Pathological repetition filtering**: Remove repeated n-grams +- **Consistency filtering**: Judge-based action consistency verification +- **Narrative filtering**: Remove political/nationalistic narratives + +### Troubleshooting + +Common data preparation errors and solutions: + +| Error | Cause | Solution | +|-------|-------|----------| +| "# Tools missing" validation failure | Messages contain `
+
+```console
+// 1. Prepare data (apply chat templates, tokenize to .npy)
+$ uv run nemotron nano3 data prep sft --run YOUR-CLUSTER
+
+// 2. Run SFT
+$ uv run nemotron nano3 sft --run YOUR-CLUSTER
+```
+
+
+
+> **Note**: The `--run YOUR-CLUSTER` flag submits jobs via [NeMo-Run](../nemo-run.md). See [Execution through NeMo-Run](../nemo-run.md) for setup.
+
+#### Direct Script Execution (Megatron-Bridge)
+
+For direct execution outside this CLI, use the scripts in the [Megatron-Bridge](https://github.com/NVIDIA-NeMo/Megatron-Bridge) repository:
+
+```bash
+# Clone the repository and checkout the nano-v3 branch
+git clone https://github.com/NVIDIA-NeMo/Megatron-Bridge.git
+cd Megatron-Bridge
+git checkout nano-v3
+
+# Run fine-tuning (inside container on compute node)
+python examples/recipes/nemotron_3/finetune_nemotron_3_nano.py \
+ --per-split-data-args-path /path/to/data_args.json \
+ --tokenizer-model /path/to/tokenizer.model
+
+# With config file overrides
+python examples/recipes/nemotron_3/finetune_nemotron_3_nano.py \
+ --config-file /path/to/overrides.yaml \
+ --per-split-data-args-path /path/to/data_args.json \
+ --tokenizer-model /path/to/tokenizer.model
+```
+
+See the [Megatron-Bridge Nemotron 3 documentation](https://docs.nvidia.com/nemo/megatron-bridge/latest/models/llm/nemotron3.html) for detailed configuration options.
+
+### Configuration
+
+| File | Purpose |
+|------|---------|
+| `config/default.yaml` | Production configuration |
+| `config/data_prep.yaml` | Data preparation settings |
+| `config/data_blend_raw.json` | Dataset blend definition |
+
+### Data Preparation
+
+The `data_prep.py` script processes OpenAI-format chat data into packed sequences with role-based loss masking. See [Data Preparation Module](../data-prep.md) for detailed documentation.
+
+#### CLI Command
+
+```bash
+uv run nemotron nano3 data prep sft [options]
+```
+
+| Option | Description |
+|--------|-------------|
+| `--run (from Stage 0)"] --> train + inst["Instruction Datasets
(OpenAI chat format)"] --> dp["data_prep.py"] + dp --> data["DataBlendsArtifact-sft
(packed .npy files)"] + data --> train["train.py"] + train --> model["ModelArtifact-sft
(fine-tuned checkpoint)"] + model --> next["Stage 2: RL"] + + style prev fill:#e1f5fe,stroke:#2196f3 + style inst fill:#f3e5f5,stroke:#9c27b0 + style dp fill:#f3e5f5,stroke:#9c27b0 + style data fill:#f3e5f5,stroke:#9c27b0 + style train fill:#f3e5f5,stroke:#9c27b0 + style model fill:#f3e5f5,stroke:#9c27b0 + style next fill:#e8f5e9,stroke:#4caf50 +``` + +--- + +## Infrastructure + +This stage uses the following components from the [NVIDIA AI Stack](../nvidia-stack.md): + +| Component | Role | Documentation | +|-----------|------|---------------| +| [Megatron-Core](../nvidia-stack.md#megatron-core) | Distributed training primitives (TP, PP, DP, EP) | [GitHub](https://github.com/NVIDIA/Megatron-LM) | +| [Megatron-Bridge](../nvidia-stack.md#megatron-bridge) | Fine-tuning loop, checkpoint loading, loss masking | [Docs](https://docs.nvidia.com/nemo/megatron-bridge/latest/) | + +### Key Features Used + +| Feature | Purpose | +|---------|---------| +| `finetune()` entry point | SFT training with pre-loaded checkpoint | +| Role-based loss masking | Only compute loss on assistant tokens | +| Mixed precision (BF16) | Memory-efficient training | +| Gradient checkpointing | Reduce memory footprint | + +### Container + +``` +nvcr.io/nvidia/nemo:25.11.nemotron_3_nano +``` + +--- + +## Next Steps + +After SFT completes, proceed to [Stage 2: RL](./rl.md) for alignment training. + +## Reference + +- [Tech Report Section 3.1](https://research.nvidia.com/labs/nemotron/files/NVIDIA-Nemotron-3-Nano-Technical-Report.pdf) — SFT methodology +- [NVIDIA AI Stack](../nvidia-stack.md) — Megatron-Core, Megatron-Bridge documentation +- [Artifact Lineage](../artifacts.md) — W&B artifact system +- [Stage 0: Pretraining](./pretrain.md) — Pretrain the base model +- **Recipe Source**: `src/nemotron/recipes/nano3/stage1_sft/` — Implementation details +- [Back to Overview](./README.md) diff --git a/docs/train/nemo-run.md b/docs/train/nemo-run.md new file mode 100644 index 0000000..95ea087 --- /dev/null +++ b/docs/train/nemo-run.md @@ -0,0 +1,325 @@ +# Execution through NeMo-Run + +Nemotron recipes use [NeMo-Run](https://github.com/NVIDIA-NeMo/Run) for job orchestration. NeMo-Run is an NVIDIA tool that streamlines configuration, execution, and management of ML experiments across computing environments. + +> **Note**: This release has been tested primarily with Slurm execution. Support for additional executors is planned. + +## What is NeMo-Run? + +NeMo-Run decouples **what** you want to run from **where** you run it: + +```mermaid +%%{init: {'theme': 'base', 'themeVariables': { 'primaryBorderColor': '#333333', 'lineColor': '#333333', 'primaryTextColor': '#333333', 'clusterBkg': '#ffffff', 'clusterBorder': '#333333'}}}%% +flowchart LR + subgraph config["Configuration"] + Task["Training Task"] + end + + subgraph nemorun["NeMo-Run"] + Packager["Packager"] + Executor["Executor"] + end + + subgraph targets["Execution Targets"] + Local["Local"] + Docker["Docker"] + Slurm["Slurm"] + Cloud["Cloud"] + end + + Task --> Packager + Packager --> Executor + Executor --> Local + Executor --> Docker + Executor --> Slurm + Executor --> Cloud +``` + +**Key concepts:** +- **Executor**: Where to run (local, Docker, Slurm, cloud) +- **Packager**: How to package code for the executor +- **Launcher**: How to launch the process (torchrun, direct) + +For full documentation, see the [NeMo-Run GitHub repository](https://github.com/NVIDIA-NeMo/Run). + +## Quick Start + +Add `--run
High-Level API"] + low["last_mile_process(...)
Low-Level API"] + end + + subgraph orchestration["Pipeline Orchestration"] + pipeline["pipeline.py"] + end + + subgraph processors["Ray Actor Pool (Processors)"] + shard["ShardProc.
(bin/idx)"] + jsonl["JsonlProc.
(jsonl)"] + packed["PackedProc.
(.npy)"] + chat["ChatSftProc.
(.npy + loss)"] + end + + high --> pipeline + low --> pipeline + pipeline --> shard + pipeline --> jsonl + pipeline --> packed + pipeline --> chat +``` + +## Module Structure + +``` +src/nemotron/data_prep/ +├── __init__.py # Public API exports +├── __main__.py # CLI entry point +├── cli.py # CLI commands (run, status, verify) +├── config.py # Configuration dataclasses +├── pipeline.py # Pipeline orchestration +├── planning.py # Shard planning & assignment +├── discovery.py # Dataset metadata fetching +├── providers.py # Tokenizer factories +├── blend.py # DataBlend specification +├── formats/ +│ ├── transforms.py # Transform factories (sft, openai_chat, etc.) +│ ├── indexed_dataset.py# bin/idx writer +│ └── jsonl_dataset.py # JSONL writer +├── shard_processor.py # bin/idx Ray actor +├── jsonl_processor.py # JSONL Ray actor +├── packed_processor.py # Packed .npy Ray actor +├── chat_sft_processor.py # Chat SFT Ray actor +├── chat_template.py # Chat templating utilities +├── filesystem.py # Cloud-native file I/O (fsspec) +└── console.py # Rich terminal UI +``` + +## Quick Start + +### High-Level API (Tokenization) + +For simple tokenization to bin/idx format: + +```python +from nemotron.data_prep import DataPrepConfig, run_data_prep +from pathlib import Path + +config = DataPrepConfig( + blend_path=Path("data_blend.json"), + output_dir=Path("./output"), + tokenizer_model="nvidia/NVIDIA-Nemotron-Nano-9B-v2", +) +artifact = run_data_prep(config) +``` + +### Low-Level API (Custom Formats) + +For more control over output format: + +```python +from nemotron.data_prep import last_mile_process, DataBlend, PipelineConfig +from nemotron.data_prep.config import OutputConfig, JsonlOutputConfig +from nemotron.data_prep.formats.transforms import sft + +blend = DataBlend.load("data_blend.json") +config = PipelineConfig( + output=OutputConfig( + dir=Path("./sft_data"), + format=JsonlOutputConfig( + transform=sft(input="instruction", output="response"), + ), + ), +) +result = last_mile_process(blend, config) +``` + +## CLI Usage + +```bash +# Run tokenization +python -m nemotron.data_prep run config.yaml ./output + +# Check pipeline status +python -m nemotron.data_prep status ./output + +# Verify output integrity +python -m nemotron.data_prep verify ./output
(bin/idx)"] --> train0["train.py
(Megatron-Bridge)"] --> base["Base Model"] + end + + subgraph stage1["Stage 1: SFT"] + direction LR + inst["Instruction Datasets"] --> dp1["data_prep.py
(.npy)"] --> train1["train.py
(Megatron-Bridge)"] --> instruct["Instruct Model"] + end + + subgraph stage2["Stage 2: RL"] + direction LR + pref["Preference Datasets"] --> dp2["data_prep.py
(JSONL)"] --> train2["train.py
(NeMo-RL/GRPO)"] --> aligned["Aligned Model"] + end + + base --> train1 + instruct --> train2 + + style stage0 fill:#e1f5fe + style stage1 fill:#f3e5f5 + style stage2 fill:#e8f5e9 +``` + +| Stage | Purpose | Framework | Output | +|-------|---------|-----------|--------| +| [Stage 0: Pretrain](./stage0_pretrain/) | Train on large text corpus | Megatron-Bridge | Base model checkpoint | +| [Stage 1: SFT](./stage1_sft/) | Instruction tuning | Megatron-Bridge | Instruction-following model | +| [Stage 2: RL](./stage2_rl/) | Alignment with GRPO | NeMo-RL | Final aligned model | + +## Prerequisites + +### v0 Requirements + +> **Slurm Only**: This initial release has been tested exclusively with Slurm execution. Support for additional NeMo-Run executors (local, Docker, SkyPilot, DGX Cloud) is planned for future releases. + +- **Slurm cluster**: GPU nodes (H100 recommended) +- **Weights & Biases**: Required for experiment tracking and artifact lineage (future versions will be backend-agnostic) +- **Container images**: NeMo containers with Megatron-Bridge and NeMo-RL + +### env.toml Setup + +Create an `env.toml` file in your project root: + +```toml +[wandb] +project = "nemotron" +entity = "YOUR-TEAM" + +[YOUR-CLUSTER] +executor = "slurm" +account = "YOUR-ACCOUNT" +partition = "batch" +nodes = 2 +ntasks_per_node = 8 +gpus_per_node = 8 +mounts = ["/lustre:/lustre"] +``` + +> **Note**: Container images are specified in the recipe config files (e.g., `config/tiny.yaml`), not in env.toml. + +See [docs/train/nemo-run.md](../../../docs/train/nemo-run.md) for complete configuration options. + +## Quick Start + +### Full Pipeline + +```bash +# Stage 0: Data prep + Pretraining +uv run nemotron nano3 data prep pretrain --run YOUR-CLUSTER +uv run nemotron nano3 pretrain --run YOUR-CLUSTER + +# Stage 1: Data prep + SFT +uv run nemotron nano3 data prep sft --run YOUR-CLUSTER +uv run nemotron nano3 sft --run YOUR-CLUSTER + +# Stage 2: Data prep + RL +uv run nemotron nano3 data prep rl --run YOUR-CLUSTER +uv run nemotron nano3 rl --run YOUR-CLUSTER +``` + +### Testing with Tiny Config + +Use the `tiny` config variant for quick testing: + +```bash +# Quick test with small dataset sample +uv run nemotron nano3 data prep pretrain --run YOUR-CLUSTER --sample 1000 + +# Quick training test (small model, few iterations) +uv run nemotron nano3 pretrain -c tiny --run YOUR-CLUSTER +``` + +## CLI Commands + +### Data Preparation + +```bash +# Pretrain data: tokenize to Megatron bin/idx format +uv run nemotron nano3 data prep pretrain [--run
(Final)"] + end + + style pretrain fill:#e1f5fe + style sft fill:#f3e5f5 + style rl fill:#e8f5e9 +``` + +Artifacts are automatically linked when you run stages in sequence, providing full traceability from raw data to final model. + +## Execution Methods + +### nemotron CLI (Recommended) + +The main entrypoint integrates with [NeMo-Run](https://github.com/NVIDIA-NeMo/Run) for streamlined execution: + +```bash +# Submit to Slurm cluster +uv run nemotron nano3 pretrain -c tiny --run megatron + +# Check execution plan before submitting +uv run nemotron nano3 pretrain -c tiny --run megatron --dry-run +``` + +### Direct Script Execution + +Scripts can be executed directly inside a container on a compute node (useful for debugging): + +```bash +# Inside container on compute node +cd src/nemotron/recipes/nano3/stage0_pretrain +python train.py --config config/tiny.yaml + +# With torchrun for distributed training +torchrun --nproc_per_node=8 train.py --config config/tiny.yaml +``` + +## Stage Documentation + +- [Stage 0: Pretraining](./stage0_pretrain/README.md) - Pretrain on large text corpus +- [Stage 1: SFT](./stage1_sft/README.md) - Supervised fine-tuning for instruction following +- [Stage 2: RL](./stage2_rl/README.md) - Reinforcement learning for alignment + +## Further Reading + +- [NeMo-Run Configuration](../../../docs/train/nemo-run.md) - Complete guide to env.toml and execution profiles +- [Recipes Overview](../README.md) - General information about Nemotron recipes diff --git a/src/nemotron/recipes/nano3/__init__.py b/src/nemotron/recipes/nano3/__init__.py new file mode 100644 index 0000000..8cb0e00 --- /dev/null +++ b/src/nemotron/recipes/nano3/__init__.py @@ -0,0 +1,15 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Nano3 training recipe.""" diff --git a/src/nemotron/recipes/nano3/stage0_pretrain/README.md b/src/nemotron/recipes/nano3/stage0_pretrain/README.md new file mode 100644 index 0000000..b2ff630 --- /dev/null +++ b/src/nemotron/recipes/nano3/stage0_pretrain/README.md @@ -0,0 +1,243 @@ +# Stage 0: Pretraining + +Pretrain Nemotron Nano 3 on a large text corpus using Megatron-Bridge. + +## Overview + +This stage tokenizes raw text data and trains the base language model from scratch. It produces a pretrained checkpoint that serves as the foundation for subsequent instruction tuning (SFT) and alignment (RL) stages. + +> **Open-Source Data Only**: This recipe trains exclusively on the open-sourced subset of pretraining data. Results will differ from the tech report benchmarks, which used additional proprietary data. Use this recipe as a reference implementation to apply the methodology with your own data. + +| Component | Description | +|-----------|-------------| +| `data_prep.py` | Tokenizes raw text into Megatron bin/idx format | +| `train.py` | Runs pretraining using Megatron-Bridge | +| `config/` | Configuration files for data prep and training | + +## Quick Start + +### Using nemotron CLI (Recommended) + +```bash +# 1. Prepare data (tokenize to bin/idx format) +uv run nemotron nano3 data prep pretrain --run YOUR-CLUSTER + +# 2. Run pretraining +uv run nemotron nano3 pretrain --run YOUR-CLUSTER + +# Quick test with tiny config +uv run nemotron nano3 pretrain -c tiny --run YOUR-CLUSTER +``` + +### Direct Script Execution + +Inside a container on a compute node: + +```bash +# Data preparation +python data_prep.py --config config/data_prep.yaml + +# Training (single node) +python train.py --config config/tiny.yaml + +# Training (distributed) +torchrun --nproc_per_node=8 train.py --config config/tiny.yaml +``` + +## Data Preparation + +The `data_prep.py` script tokenizes raw text datasets into Megatron's binary format. + +### CLI Command + +```bash +uv run nemotron nano3 data prep pretrain [options] +``` + +| Option | Description | +|--------|-------------| +| `--run
(bin/idx files + blend.json)"] + data --> train["train.py"] + train --> model["ModelArtifact-pretrain
(checkpoint)"] + model --> next["Stage 1: SFT"] + + style raw fill:#e1f5fe + style dp fill:#e1f5fe + style data fill:#e1f5fe + style train fill:#e1f5fe + style model fill:#e1f5fe + style next fill:#f3e5f5 +``` + +## Next Steps + +After pretraining completes, proceed to [Stage 1: SFT](../stage1_sft/README.md) for instruction tuning. diff --git a/src/nemotron/recipes/nano3/stage0_pretrain/__init__.py b/src/nemotron/recipes/nano3/stage0_pretrain/__init__.py new file mode 100644 index 0000000..9150aa2 --- /dev/null +++ b/src/nemotron/recipes/nano3/stage0_pretrain/__init__.py @@ -0,0 +1,15 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Nemotron Nano 3 stage 0 (pretraining) recipe package.""" diff --git a/src/nemotron/recipes/nano3/stage0_pretrain/config/data_prep/data_blend_raw.json b/src/nemotron/recipes/nano3/stage0_pretrain/config/data_prep/data_blend_raw.json new file mode 100644 index 0000000..67cd669 --- /dev/null +++ b/src/nemotron/recipes/nano3/stage0_pretrain/config/data_prep/data_blend_raw.json @@ -0,0 +1,165 @@ +{ + "datasets": [ + { + "name": "nemotron-math-3", + "path": "hf://nvidia/Nemotron-CC-Math-v1", + "subset": "3", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-math-4plus", + "path": "hf://nvidia/Nemotron-CC-Math-v1", + "subset": "4plus", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-math-4plus-mind", + "path": "hf://nvidia/Nemotron-CC-Math-v1", + "subset": "4plus_MIND", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-cc-v2-diverse-qa", + "path": "hf://nvidia/Nemotron-CC-v2", + "subset": "Diverse-QA", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-cc-v2-high-quality", + "path": "hf://nvidia/Nemotron-CC-v2", + "subset": "High-Quality", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-cc-v2-high-quality-synthetic", + "path": "hf://nvidia/Nemotron-CC-v2", + "subset": "High-Quality-Synthetic", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-cc-v2-medium-high-quality", + "path": "hf://nvidia/Nemotron-CC-v2", + "subset": "Medium-High-Quality", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-cc-v2-medium-quality", + "path": "hf://nvidia/Nemotron-CC-v2", + "subset": "Medium-Quality", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-cc-v2-translated-diverse-qa", + "path": "hf://nvidia/Nemotron-CC-v2", + "subset": "Translated-Diverse-QA", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-cc-v2.1-high-quality", + "path": "hf://nvidia/Nemotron-CC-v2.1", + "subset": "High-Quality", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-cc-v2.1-high-quality-synthetic", + "path": "hf://nvidia/Nemotron-CC-v2.1", + "subset": "High-Quality-Synthetic", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-cc-v2.1-high-quality-translated-to-english", + "path": "hf://nvidia/Nemotron-CC-v2.1", + "subset": "High-Quality-Translated-To-English", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-cc-v2.1-high-quality-translated-to-english-synthetic", + "path": "hf://nvidia/Nemotron-CC-v2.1", + "subset": "High-Quality-Translated-To-English-Synthetic", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-cc-v2.1-medium-high-quality", + "path": "hf://nvidia/Nemotron-CC-v2.1", + "subset": "Medium-High-Quality", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-cc-v2.1-medium-high-quality-synthetic", + "path": "hf://nvidia/Nemotron-CC-v2.1", + "subset": "Medium-High-Quality-Synthetic", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-cc-v2.1-medium-high-quality-translated-to-english", + "path": "hf://nvidia/Nemotron-CC-v2.1", + "subset": "Medium-High-Quality-Translated-To-English", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-cc-v2.1-medium-quality", + "path": "hf://nvidia/Nemotron-CC-v2.1", + "subset": "Medium-Quality", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-pretraining-infinibyte-reasoning", + "path": "hf://nvidia/Nemotron-Pretraining-Specialized-v1", + "subset": "Nemotron-Pretraining-InfiniByte-Reasoning", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-pretraining-math-textbooks", + "path": "hf://nvidia/Nemotron-Pretraining-Specialized-v1", + "subset": "Nemotron-Pretraining-Math-Textbooks", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-pretraining-rqa", + "path": "hf://nvidia/Nemotron-Pretraining-Specialized-v1", + "subset": "Nemotron-Pretraining-RQA", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-pretraining-stem-sft", + "path": "hf://nvidia/Nemotron-Pretraining-Specialized-v1", + "subset": "Nemotron-Pretraining-STEM-SFT", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-pretraining-scientific-coding", + "path": "hf://nvidia/Nemotron-Pretraining-Specialized-v1", + "subset": "Nemotron-Pretraining-Scientific-Coding", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-pretraining-wiki-rewrite", + "path": "hf://nvidia/Nemotron-Pretraining-Specialized-v1", + "subset": "Nemotron-Pretraining-Wiki-Rewrite", + "text_field": "text", + "weight": 1.0 + } + ] +} diff --git a/src/nemotron/recipes/nano3/stage0_pretrain/config/data_prep/data_blend_raw_large.json b/src/nemotron/recipes/nano3/stage0_pretrain/config/data_prep/data_blend_raw_large.json new file mode 100644 index 0000000..3be21de --- /dev/null +++ b/src/nemotron/recipes/nano3/stage0_pretrain/config/data_prep/data_blend_raw_large.json @@ -0,0 +1,166 @@ +{ + "datasets": [ + { + "name": "nemotron-math-3", + "path": "hf://nvidia/Nemotron-CC-Math-v1", + "subset": "3", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-math-4plus", + "path": "hf://nvidia/Nemotron-CC-Math-v1", + "subset": "4plus", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-math-4plus-mind", + "path": "hf://nvidia/Nemotron-CC-Math-v1", + "subset": "4plus_MIND", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-cc-v2-diverse-qa", + "path": "hf://nvidia/Nemotron-CC-v2", + "subset": "Diverse-QA", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-cc-v2-high-quality", + "path": "hf://nvidia/Nemotron-CC-v2", + "subset": "High-Quality", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-cc-v2-high-quality-synthetic", + "path": "hf://nvidia/Nemotron-CC-v2", + "subset": "High-Quality-Synthetic", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-cc-v2-medium-high-quality", + "path": "hf://nvidia/Nemotron-CC-v2", + "subset": "Medium-High-Quality", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-cc-v2-medium-quality", + "path": "hf://nvidia/Nemotron-CC-v2", + "subset": "Medium-Quality", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-cc-v2-translated-diverse-qa", + "path": "hf://nvidia/Nemotron-CC-v2", + "subset": "Translated-Diverse-QA", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-cc-v2.1-high-quality", + "path": "hf://nvidia/Nemotron-CC-v2.1", + "subset": "High-Quality", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-cc-v2.1-high-quality-synthetic", + "path": "hf://nvidia/Nemotron-CC-v2.1", + "subset": "High-Quality-Synthetic", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-cc-v2.1-high-quality-translated-to-english", + "path": "hf://nvidia/Nemotron-CC-v2.1", + "subset": "High-Quality-Translated-To-English", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-cc-v2.1-high-quality-translated-to-english-synthetic", + "path": "hf://nvidia/Nemotron-CC-v2.1", + "subset": "High-Quality-Translated-To-English-Synthetic", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-cc-v2.1-medium-high-quality", + "path": "hf://nvidia/Nemotron-CC-v2.1", + "subset": "Medium-High-Quality", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-cc-v2.1-medium-high-quality-synthetic", + "path": "hf://nvidia/Nemotron-CC-v2.1", + "subset": "Medium-High-Quality-Synthetic", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-cc-v2.1-medium-high-quality-translated-to-english", + "path": "hf://nvidia/Nemotron-CC-v2.1", + "subset": "Medium-High-Quality-Translated-To-English", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-cc-v2.1-medium-quality", + "path": "hf://nvidia/Nemotron-CC-v2.1", + "subset": "Medium-Quality", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-pretraining-infinibyte-reasoning", + "path": "hf://nvidia/Nemotron-Pretraining-Specialized-v1", + "subset": "Nemotron-Pretraining-InfiniByte-Reasoning", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-pretraining-math-textbooks", + "path": "hf://nvidia/Nemotron-Pretraining-Specialized-v1", + "subset": "Nemotron-Pretraining-Math-Textbooks", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-pretraining-rqa", + "path": "hf://nvidia/Nemotron-Pretraining-Specialized-v1", + "subset": "Nemotron-Pretraining-RQA", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-pretraining-stem-sft", + "path": "hf://nvidia/Nemotron-Pretraining-Specialized-v1", + "subset": "Nemotron-Pretraining-STEM-SFT", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-pretraining-scientific-coding", + "path": "hf://nvidia/Nemotron-Pretraining-Specialized-v1", + "subset": "Nemotron-Pretraining-Scientific-Coding", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-pretraining-wiki-rewrite", + "path": "hf://nvidia/Nemotron-Pretraining-Specialized-v1", + "subset": "Nemotron-Pretraining-Wiki-Rewrite", + "text_field": "text", + "weight": 1.0 + } + ] + } + \ No newline at end of file diff --git a/src/nemotron/recipes/nano3/stage0_pretrain/config/data_prep/data_blend_raw_small.json b/src/nemotron/recipes/nano3/stage0_pretrain/config/data_prep/data_blend_raw_small.json new file mode 100644 index 0000000..114d377 --- /dev/null +++ b/src/nemotron/recipes/nano3/stage0_pretrain/config/data_prep/data_blend_raw_small.json @@ -0,0 +1,39 @@ +{ + "datasets": [ + { + "name": "nemotron-math-3", + "path": "hf://nvidia/Nemotron-CC-Math-v1", + "subset": "3", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-math-4plus", + "path": "hf://nvidia/Nemotron-CC-Math-v1", + "subset": "4plus", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-cc-v2-high-quality-synthetic", + "path": "hf://nvidia/Nemotron-CC-v2", + "subset": "High-Quality-Synthetic", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-cc-v2-medium-high-quality", + "path": "hf://nvidia/Nemotron-CC-v2", + "subset": "Medium-High-Quality", + "text_field": "text", + "weight": 1.0 + }, + { + "name": "nemotron-cc-v2-medium-quality", + "path": "hf://nvidia/Nemotron-CC-v2", + "subset": "Medium-Quality", + "text_field": "text", + "weight": 1.0 + } + ] +} diff --git a/src/nemotron/recipes/nano3/stage0_pretrain/config/data_prep/default.yaml b/src/nemotron/recipes/nano3/stage0_pretrain/config/data_prep/default.yaml new file mode 100644 index 0000000..c6e3d3b --- /dev/null +++ b/src/nemotron/recipes/nano3/stage0_pretrain/config/data_prep/default.yaml @@ -0,0 +1,57 @@ +run: + env: + container: anyscale/ray:2.49.2-py312 + +# Default config for pretrain data preparation +# +# Usage: +# nemotron nano3 data prep pretrain +# nemotron nano3 data prep pretrain sample=100 force=true +# +# Environment variables: +# PWD: Current working directory (for path resolution) + +# Path to data blend JSON file +blend_path: ${oc.env:PWD}/src/nemotron/recipes/nano3/stage0_pretrain/config/data_prep/data_blend_raw.json + +# Output directory for tokenized data (outputs to job directory for persistence) +output_dir: ${oc.env:PWD}/../output/stage0_pretrain + +# Number of output shards for parallel loading +num_shards: 128 + +# Number of shards for validation split +valid_shards: 1 + +# Number of shards for test split +test_shards: 1 + +# HuggingFace tokenizer model name +tokenizer_model: nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16 + +# Prepend BOS token to documents +add_bos: false + +# Append EOS token to documents +add_eos: true + +# Default text field name in datasets +text_field: text + +# Skip documents shorter than this (null = no limit) +min_doc_chars: null + +# Truncate documents longer than this (null = no limit) +max_doc_tokens: null + +# Limit rows per dataset for quick tests (null = no limit) +sample: null + +# Ray actors for parallel processing (null = auto) +num_actors: null + +# Force new run, ignoring cache +force: false + +# Config name for artifact naming (produces PretrainBlendsArtifact-default) +config_name: default diff --git a/src/nemotron/recipes/nano3/stage0_pretrain/config/data_prep/tiny.yaml b/src/nemotron/recipes/nano3/stage0_pretrain/config/data_prep/tiny.yaml new file mode 100644 index 0000000..9a7a2b2 --- /dev/null +++ b/src/nemotron/recipes/nano3/stage0_pretrain/config/data_prep/tiny.yaml @@ -0,0 +1,56 @@ +run: + env: + container: anyscale/ray:2.49.2-py312 + +# Tiny config for pretrain data preparation (fast iteration/testing) +# +# Usage: +# nemotron nano3 data prep pretrain --config tiny +# nemotron nano3 data prep pretrain --config tiny --run prep +# +# This config uses sampling for quick testing. + +# Path to data blend JSON file +blend_path: ${oc.env:PWD}/src/nemotron/recipes/nano3/stage0_pretrain/config/data_prep/data_blend_raw_small.json + +# Output directory for tokenized data (outputs to job directory for persistence) +output_dir: ${oc.env:PWD}/../output/stage0_pretrain_tiny + +# Number of output shards - smaller for tiny config +num_shards: 4 + +# Number of shards for validation split +valid_shards: 1 + +# Number of shards for test split +test_shards: 1 + +# HuggingFace tokenizer model name +tokenizer_model: nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16 + +# Prepend BOS token to documents +add_bos: false + +# Append EOS token to documents +add_eos: true + +# Default text field name in datasets +text_field: text + +# Skip documents shorter than this (null = no limit) +min_doc_chars: null + +# Truncate documents longer than this (null = no limit) +max_doc_tokens: null + +# Limit rows per dataset for quick tests - small sample for tiny +sample: 1000 + +# Ray actors for parallel processing (null = auto) +num_actors: null + +# Force new run, ignoring cache +force: false + +# Config name for artifact naming (produces PretrainBlendsArtifact-tiny) +config_name: tiny diff --git a/src/nemotron/recipes/nano3/stage0_pretrain/config/default.yaml b/src/nemotron/recipes/nano3/stage0_pretrain/config/default.yaml new file mode 100644 index 0000000..7176ed7 --- /dev/null +++ b/src/nemotron/recipes/nano3/stage0_pretrain/config/default.yaml @@ -0,0 +1,17 @@ +run: + data: PretrainBlendsArtifact-tiny:latest + env: + container: nvcr.io/nvidia/nemo:25.11.nemotron_3_nano + +recipe: + _target_: megatron.bridge.recipes.nemotronh.nemotron_3_nano.nemotron_3_nano_pretrain_config + per_split_data_args_path: ${art:data,path}/blend.json + +logger: + log_interval: 10 + wandb_project: ${run.wandb.project} # Set from env.toml [wandb] + wandb_entity: ${run.wandb.entity} # Set from env.toml [wandb] + +checkpoint: + save: /nemo_run/pretrain + save_interval: 20 \ No newline at end of file diff --git a/src/nemotron/recipes/nano3/stage0_pretrain/config/tiny.yaml b/src/nemotron/recipes/nano3/stage0_pretrain/config/tiny.yaml new file mode 100644 index 0000000..5ae5389 --- /dev/null +++ b/src/nemotron/recipes/nano3/stage0_pretrain/config/tiny.yaml @@ -0,0 +1,31 @@ +run: + data: PretrainBlendsArtifact-tiny:latest + env: + container: nvcr.io/nvidian/nemo:25.11-nano-v3.rc2 + +recipe: + _target_: megatron.bridge.recipes.qwen.qwen3.qwen3_8b_pretrain_config + per_split_data_args_path: ${art:data,path}/blend.json + +train: + train_iters: 1700 + global_batch_size: 32 + +# model: +# pipeline_model_parallel_size: 2 + +# tokenizer: +# tokenizer_type: HuggingFaceTokenizer +# tokenizer_model: meta-llama/Llama-3.2-1B + +scheduler: + lr_warmup_iters: 32 + +logger: + log_interval: 10 + wandb_project: ${run.wandb.project} # Set from env.toml [wandb] + wandb_entity: ${run.wandb.entity} # Set from env.toml [wandb] + +checkpoint: + save: /nemo_run/pretrain + save_interval: 20 diff --git a/src/nemotron/recipes/nano3/stage0_pretrain/data_prep.py b/src/nemotron/recipes/nano3/stage0_pretrain/data_prep.py new file mode 100644 index 0000000..463a93c --- /dev/null +++ b/src/nemotron/recipes/nano3/stage0_pretrain/data_prep.py @@ -0,0 +1,225 @@ +#!/usr/bin/env python3 + +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Data preparation for Nano3 pretraining stage. + +Tokenizes raw text data into Megatron bin/idx format. + +Outputs blend.json with {"train": [...], "valid": [...], "test": [...]} format +compatible with Megatron-Bridge's per_split_data_args_path parameter. + +Usage: + # With default config + uv run python data_prep.py + + # With custom config file + uv run python data_prep.py --config /path/to/config.yaml + + # With CLI overrides (Hydra-style) + uv run python data_prep.py sample=100 force=true + + # Via nemotron CLI with nemo-run + nemotron nano3 data prep pretrain --run prep --sample 10000 +""" + +from __future__ import annotations + +import os +import sys +from dataclasses import dataclass, field +from pathlib import Path + +from nemotron.data_prep import DataPrepConfig, PerSplitConfig, run_data_prep +from nemotron.kit import PretrainBlendsArtifact, print_step_complete +from nemotron.kit.train_script import ( + apply_hydra_overrides, + init_wandb_from_env, + load_omegaconf_yaml, + omegaconf_to_dataclass, + parse_config_and_overrides, +) +from nemotron.kit.wandb import add_wandb_tags + +STAGE_PATH = Path(__file__).parent + +# Default config path relative to this file +DEFAULT_CONFIG_PATH = STAGE_PATH / "config" / "data_prep.yaml" + +# Use NEMO_RUN_DIR for output when running via nemo-run (avoids writing to code dir) +_OUTPUT_BASE = Path(os.environ.get("NEMO_RUN_DIR", ".")) + +# Module-level flag for Ray execution (used by nemotron CLI) +RAY = True + + +@dataclass +class PreTrainDataPrepConfig: + """Pretrain data preparation config. + + Tokenizes text into Megatron bin/idx format for pretraining. + Outputs {"train": [...], "valid": [...], "test": [...]} JSON format. + """ + + blend_path: Path = field(default_factory=lambda: STAGE_PATH / "config/data_blend_raw.json") + """Path to data blend JSON file""" + + output_dir: Path = field(default_factory=lambda: _OUTPUT_BASE / "output/nano3/stage0_pretrain") + """Output directory for tokenized data""" + + num_shards: int = 128 + """Number of output shards for parallel loading""" + + valid_shards: int = 1 + """Number of shards for validation split""" + + test_shards: int = 1 + """Number of shards for test split""" + + tokenizer_model: str = "nvidia/NVIDIA-Nemotron-Nano-9B-v2" + """HuggingFace tokenizer model name""" + + add_bos: bool = False + """Prepend BOS token to documents""" + + add_eos: bool = True + """Append EOS token to documents""" + + text_field: str = "text" + """Default text field name in datasets""" + + min_doc_chars: int | None = None + """Skip documents shorter than this""" + + max_doc_tokens: int | None = None + """Truncate documents longer than this""" + + sample: int | None = None + """Limit rows per dataset (for quick tests)""" + + num_actors: int | None = None + """Ray actors for parallel processing (None = auto)""" + + force: bool = False + """Force new run, ignoring cache""" + + config_name: str = "default" + """Config name used for artifact naming (e.g., 'default', 'tiny', 'test')""" + + def __post_init__(self) -> None: + # Ensure paths are Path objects + if isinstance(self.blend_path, str): + self.blend_path = Path(self.blend_path) + if isinstance(self.output_dir, str): + self.output_dir = Path(self.output_dir) + + # Add sample suffix to output_dir if sampling + if self.sample is not None: + self.output_dir = self.output_dir / f"sample-{self.sample}" + + +def run_data_prep_main(cfg: PreTrainDataPrepConfig) -> PretrainBlendsArtifact: + """Run pretrain data preparation. + + Args: + cfg: Data prep configuration. + + Returns: + PretrainBlendsArtifact with paths to tokenized data. + """ + # Add stage-specific tags to wandb run + add_wandb_tags(["data-prep", "pretrain", cfg.config_name]) + + # Log config to W&B + try: + import wandb + from dataclasses import asdict + + if wandb.run is not None: + # Convert config to dict, handling Path objects + config_dict = asdict(cfg) + for key, value in config_dict.items(): + if isinstance(value, Path): + config_dict[key] = str(value) + wandb.config.update(config_dict) + except ImportError: + pass + + # Build artifact name using config_name. + # Example: "nano3/default/data" or "nano3/tiny/data?sample=100". + sample_suffix = f"?sample={cfg.sample}" if cfg.sample else "" + artifact_name = f"nano3/{cfg.config_name}/data{sample_suffix}" + + data_prep_config = DataPrepConfig( + blend_path=cfg.blend_path, + output_dir=cfg.output_dir, + num_shards=cfg.num_shards, + per_split=PerSplitConfig( + enabled=True, + valid_shards=cfg.valid_shards, + test_shards=cfg.test_shards, + ), + tokenizer_model=cfg.tokenizer_model, + add_bos=cfg.add_bos, + add_eos=cfg.add_eos, + text_field=cfg.text_field, + min_doc_chars=cfg.min_doc_chars, + max_doc_tokens=cfg.max_doc_tokens, + sample=cfg.sample, + num_actors=cfg.num_actors, + force=cfg.force, + artifact_name=artifact_name, + ) + artifact = run_data_prep(data_prep_config) + print_step_complete(data_prep=artifact) + return artifact + + +def main(cfg: PreTrainDataPrepConfig | None = None) -> PretrainBlendsArtifact: + """Entry point for pretrain data preparation. + + Args: + cfg: Config from CLI framework, or None when run directly as script. + + Returns: + PretrainBlendsArtifact with paths to tokenized data. + """ + if cfg is None: + # Called directly as script - parse config ourselves + config_path, cli_overrides = parse_config_and_overrides(default_config=DEFAULT_CONFIG_PATH) + + # Load YAML config + try: + config = load_omegaconf_yaml(config_path) + except FileNotFoundError as e: + print(f"Error: {e}", file=sys.stderr) + sys.exit(1) + + # Apply CLI overrides (Hydra-style: key=value) + if cli_overrides: + config = apply_hydra_overrides(config, cli_overrides) + + # Convert to dataclass + cfg = omegaconf_to_dataclass(config, PreTrainDataPrepConfig) + + # Initialize wandb from environment variables (set by nemo-run) + init_wandb_from_env() + + # Run data prep + return run_data_prep_main(cfg) + + +if __name__ == "__main__": + main() diff --git a/src/nemotron/recipes/nano3/stage0_pretrain/train.py b/src/nemotron/recipes/nano3/stage0_pretrain/train.py new file mode 100644 index 0000000..cd8a186 --- /dev/null +++ b/src/nemotron/recipes/nano3/stage0_pretrain/train.py @@ -0,0 +1,152 @@ +#!/usr/bin/env python3 + +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Pretrain script for Nemotron Nano3. + +Uses Megatron-Bridge's ConfigContainer for full training configuration. +Dynamically loads the recipe function specified in the YAML config. + +Usage: + # With YAML config file (required) + python /path/to/train.py --config /path/to/pretrain.yaml + + # With CLI overrides (Hydra syntax) + python /path/to/train.py --config /path/to/pretrain.yaml train.train_iters=5000 + + # As module (requires nemotron package installed) + torchrun --nproc_per_node=8 -m nemotron.recipes.nano3.stage0_pretrain.train \ + --config /path/to/pretrain.yaml +""" + +from __future__ import annotations + +import logging +import sys +from pathlib import Path + +import torch +from megatron.bridge.training.config import ConfigContainer +from megatron.bridge.training.gpt_step import forward_step +from megatron.bridge.training.pretrain import pretrain +from megatron.bridge.training.utils.omegaconf_utils import ( + apply_overrides, + create_omegaconf_dict_config, + parse_hydra_overrides, +) +from omegaconf import OmegaConf + +from nemotron.kit.recipe_loader import extract_recipe_config, import_recipe_function +from nemotron.kit.resolvers import clear_artifact_cache, register_resolvers_from_config +from nemotron.kit.train_script import load_omegaconf_yaml, parse_config_and_overrides +from nemotron.kit.wandb import ( + patch_wandb_checkpoint_logging, + patch_wandb_http_handler_skip_digest_verification, + patch_wandb_init_for_lineage, + patch_wandb_local_file_handler_skip_digest_verification, + patch_wandb_runid_for_seeded_random, +) + +logger: logging.Logger = logging.getLogger(__name__) + + +# Default config path relative to this file +DEFAULT_CONFIG_PATH = Path(__file__).parent / "config" / "default.yaml" + +# Default recipe function +DEFAULT_RECIPE_TARGET = ( + "megatron.bridge.recipes.nemotronh.nemotron_next_3b_v2.nemotron_next_3b_v2_pretrain_config" +) + + +def main() -> None: + """Entry point for Nemotron Nano3 pretraining.""" + try: + config_path, cli_overrides = parse_config_and_overrides(default_config=DEFAULT_CONFIG_PATH) + config = load_omegaconf_yaml(config_path) + except FileNotFoundError as e: + logger.error(str(e)) + sys.exit(1) + + # ------------------------------------------------------------------------- + # WANDB MONKEY-PATCHES + # These patches work around bugs in wandb and Megatron-Bridge. + # See nemotron/kit/wandb.py for detailed "Why" / "Remove when" documentation. + # ------------------------------------------------------------------------- + patch_wandb_http_handler_skip_digest_verification() + patch_wandb_local_file_handler_skip_digest_verification() + patch_wandb_runid_for_seeded_random() + patch_wandb_checkpoint_logging() + + # Clear artifact cache to ensure fresh downloads (important for :latest resolution) + clear_artifact_cache() + + # Resolve artifacts before wandb.init() (Megatron-Bridge initializes wandb). + qualified_names = register_resolvers_from_config( + config, + artifacts_key="run", + mode="pre_init", + pre_init_patch_http_digest=False, + ) + + # Patch wandb.init so lineage is registered immediately once MB initializes wandb. + patch_wandb_init_for_lineage( + artifact_qualified_names=qualified_names, + tags=["pretrain"], + ) + + recipe_target, recipe_kwargs = extract_recipe_config( + config, + default_target=DEFAULT_RECIPE_TARGET, + ) + + try: + recipe_func = import_recipe_function(recipe_target) + except Exception as e: + logger.error(str(e)) + sys.exit(1) + + cfg: ConfigContainer = recipe_func(**recipe_kwargs) + + # Convert the initial Python dataclass to an OmegaConf DictConfig for merging + merged_omega_conf, excluded_fields = create_omegaconf_dict_config(cfg) + + # Merge config overrides (excluding recipe field) + config_overrides = OmegaConf.to_container(config, resolve=False) + config_overrides.pop("recipe", None) + + if config_overrides: + logger.debug(f"Merging config overrides: {list(config_overrides.keys())}") + yaml_overrides_omega = OmegaConf.create(config_overrides) + merged_omega_conf = OmegaConf.merge(merged_omega_conf, yaml_overrides_omega) + logger.debug("Config overrides merged successfully.") + + # Apply command-line overrides using Hydra-style parsing + if cli_overrides: + logger.debug(f"Applying Hydra-style command-line overrides: {cli_overrides}") + merged_omega_conf = parse_hydra_overrides(merged_omega_conf, cli_overrides) + logger.debug("Hydra-style command-line overrides applied successfully.") + + final_overrides_as_dict = OmegaConf.to_container(merged_omega_conf, resolve=True) + apply_overrides(cfg, final_overrides_as_dict, excluded_fields) + + pretrain(config=cfg, forward_step_func=forward_step) + + if torch.distributed.is_initialized(): + torch.distributed.destroy_process_group() + + +if __name__ == "__main__": + main() diff --git a/src/nemotron/recipes/nano3/stage1_sft/README.md b/src/nemotron/recipes/nano3/stage1_sft/README.md new file mode 100644 index 0000000..454b4cc --- /dev/null +++ b/src/nemotron/recipes/nano3/stage1_sft/README.md @@ -0,0 +1,254 @@ +# Stage 1: Supervised Fine-Tuning (SFT) + +Fine-tune the pretrained model to follow instructions using Megatron-Bridge. + +## Overview + +This stage takes instruction-following datasets in OpenAI chat format, applies chat templates with role-based loss masking, and fine-tunes the pretrained model. The output is an instruction-following model ready for alignment training. + +> **Open-Source Data Only**: This recipe trains exclusively on the open-sourced subset of SFT data. Results will differ from the tech report benchmarks, which used additional proprietary data. Use this recipe as a reference implementation to apply the methodology with your own data. + +| Component | Description | +|-----------|-------------| +| `data_prep.py` | Applies chat templates, tokenizes to packed .npy format | +| `train.py` | Runs supervised fine-tuning using Megatron-Bridge | +| `config/` | Configuration files for data prep and training | + +## Quick Start + +### Using nemotron CLI (Recommended) + +```bash +# 1. Prepare data (apply chat templates, tokenize to .npy) +uv run nemotron nano3 data prep sft --run YOUR-CLUSTER + +# 2. Run SFT +uv run nemotron nano3 sft --run YOUR-CLUSTER + +# Quick test with tiny config +uv run nemotron nano3 sft -c tiny --run YOUR-CLUSTER +``` + +### Direct Script Execution + +Inside a container on a compute node: + +```bash +# Data preparation +python data_prep.py --config config/data_prep.yaml + +# Training (single node) +python train.py --config config/tiny.yaml + +# Training (distributed) +torchrun --nproc_per_node=8 train.py --config config/tiny.yaml +``` + +## Data Preparation + +The `data_prep.py` script processes OpenAI-format chat data into packed sequences with role-based loss masking. + +### Pipeline + +1. **Apply chat template** → Role-labeled chunks (system, user, assistant) +2. **Tokenize** → input_ids with role boundaries +3. **Build loss_mask** → 0 for system/user tokens, 1 for assistant tokens +4. **Pack sequences** → Efficient batching up to `pack_size` tokens +5. **Split by ratio** → training.npy, validation.npy, test.npy + +### CLI Command + +```bash +uv run nemotron nano3 data prep sft [options] +``` + +| Option | Description | +|--------|-------------| +| `--run
(from Stage 0)"] --> train + inst["Instruction Datasets
(OpenAI chat format)"] --> dp["data_prep.py"] + dp --> data["DataBlendsArtifact-sft
(packed .npy files)"] + data --> train["train.py"] + train --> model["ModelArtifact-sft
(fine-tuned checkpoint)"] + model --> next["Stage 2: RL"] + + style prev fill:#e1f5fe + style inst fill:#f3e5f5 + style dp fill:#f3e5f5 + style data fill:#f3e5f5 + style train fill:#f3e5f5 + style model fill:#f3e5f5 + style next fill:#e8f5e9 +``` + +## Next Steps + +After SFT completes, proceed to [Stage 2: RL](../stage2_rl/README.md) for alignment training. + +## Previous Stage + +- [Stage 0: Pretraining](../stage0_pretrain/README.md) - Pretrain the base model diff --git a/src/nemotron/recipes/nano3/stage1_sft/__init__.py b/src/nemotron/recipes/nano3/stage1_sft/__init__.py new file mode 100644 index 0000000..d750a54 --- /dev/null +++ b/src/nemotron/recipes/nano3/stage1_sft/__init__.py @@ -0,0 +1,15 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Nemotron Nano 3 stage 1 (supervised fine-tuning) recipe package.""" diff --git a/src/nemotron/recipes/nano3/stage1_sft/config/data_prep/data_blend_raw.json b/src/nemotron/recipes/nano3/stage1_sft/config/data_prep/data_blend_raw.json new file mode 100644 index 0000000..9d5f26e --- /dev/null +++ b/src/nemotron/recipes/nano3/stage1_sft/config/data_prep/data_blend_raw.json @@ -0,0 +1,73 @@ +{ + "datasets": [ + { + "name": "science-mcq", + "path": "hf://nvidia/Nemotron-Science-v1", + "subset": "MCQ" + }, + { + "name": "science-rqa", + "path": "hf://nvidia/Nemotron-Science-v1", + "subset": "RQA" + }, + { + "name": "instruction-following-chat", + "path": "hf://nvidia/Nemotron-Instruction-Following-Chat-v1", + "subset": "chat_if" + }, + { + "name": "instruction-following-structured", + "path": "hf://nvidia/Nemotron-Instruction-Following-Chat-v1", + "subset": "structured_outputs" + }, + { + "name": "math-proofs-lean", + "path": "hf://nvidia/Nemotron-Math-Proofs-v1", + "subset": "lean" + }, + { + "name": "swe", + "path": "hf://nvidia/Nemotron-SWE-v1" + }, + { + "name": "agentic-interactive", + "path": "hf://nvidia/Nemotron-Agentic-v1", + "subset": "interactive_agent" + }, + { + "name": "agentic-tool-calling", + "path": "hf://nvidia/Nemotron-Agentic-v1", + "subset": "tool_calling" + }, + { + "name": "competitive-cpp-00", + "path": "hf://nvidia/Nemotron-Competitive-Programming-v1", + "subset": "competitive_coding_cpp.part_00" + }, + { + "name": "competitive-cpp-01", + "path": "hf://nvidia/Nemotron-Competitive-Programming-v1", + "subset": "competitive_coding_cpp.part_01" + }, + { + "name": "competitive-python-00", + "path": "hf://nvidia/Nemotron-Competitive-Programming-v1", + "subset": "competitive_coding_python.part_00" + }, + { + "name": "competitive-python-01", + "path": "hf://nvidia/Nemotron-Competitive-Programming-v1", + "subset": "competitive_coding_python.part_01" + }, + { + "name": "infinibyte-00", + "path": "hf://nvidia/Nemotron-Competitive-Programming-v1", + "subset": "infinibyte.part_00" + }, + { + "name": "infinibyte-01", + "path": "hf://nvidia/Nemotron-Competitive-Programming-v1", + "subset": "infinibyte.part_01" + } + ] +} diff --git a/src/nemotron/recipes/nano3/stage1_sft/config/data_prep/data_blend_tiny.json b/src/nemotron/recipes/nano3/stage1_sft/config/data_prep/data_blend_tiny.json new file mode 100644 index 0000000..037be06 --- /dev/null +++ b/src/nemotron/recipes/nano3/stage1_sft/config/data_prep/data_blend_tiny.json @@ -0,0 +1,14 @@ +{ + "datasets": [ + { + "name": "science-mcq", + "path": "hf://nvidia/Nemotron-Science-v1", + "subset": "MCQ" + }, + { + "name": "instruction-following-chat", + "path": "hf://nvidia/Nemotron-Instruction-Following-Chat-v1", + "subset": "chat_if" + } + ] +} diff --git a/src/nemotron/recipes/nano3/stage1_sft/config/data_prep/default.yaml b/src/nemotron/recipes/nano3/stage1_sft/config/data_prep/default.yaml new file mode 100644 index 0000000..1d77381 --- /dev/null +++ b/src/nemotron/recipes/nano3/stage1_sft/config/data_prep/default.yaml @@ -0,0 +1,59 @@ +run: + env: + container: anyscale/ray:2.49.2-py311 + +# Default config for SFT data preparation +# +# Usage: +# nemotron nano3 data prep sft +# nemotron nano3 data prep sft sample=100 force=true +# +# Environment variables: +# PWD: Current working directory (for path resolution) + +# Path to data blend JSON file +blend_path: ${oc.env:PWD}/src/nemotron/recipes/nano3/stage1_sft/config/data_prep/data_blend_raw.json + +# Output directory for packed .npy data (outputs to job directory for persistence) +output_dir: ${oc.env:PWD}/../output/stage1_sft + +# HuggingFace tokenizer model name +tokenizer_model: nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16 + +# Maximum tokens per packed sequence +pack_size: 4096 + +# Target size per shard (e.g., '256MB', '1GB') +shard_size: 256MB + +# Split ratios (must sum to 1.0) +train_ratio: 0.98 +valid_ratio: 0.01 +test_ratio: 0.01 + +# Chat template: 'nano3', path to .jinja file, or inline template +chat_template: nano3 + +# Field name for OpenAI-format messages in input records +messages_field: messages + +# Field name for tools definition in input records +tools_field: tools + +# Filter to only include records where used_in contains this value +used_in_filter: nano_v3 + +# Field name for used_in filtering +used_in_field: used_in + +# Truncate sequences longer than this (null = no limit) +max_doc_tokens: null + +# Limit rows per dataset for quick tests (null = no limit) +sample: null + +# Ray actors for parallel processing (null = auto) +num_actors: null + +# Force new run, ignoring cache +force: false diff --git a/src/nemotron/recipes/nano3/stage1_sft/config/data_prep/tiny.yaml b/src/nemotron/recipes/nano3/stage1_sft/config/data_prep/tiny.yaml new file mode 100644 index 0000000..9b29146 --- /dev/null +++ b/src/nemotron/recipes/nano3/stage1_sft/config/data_prep/tiny.yaml @@ -0,0 +1,58 @@ +run: + env: + container: anyscale/ray:2.49.2-py311 + +# Tiny config for SFT data preparation (fast iteration/testing) +# +# Usage: +# nemotron nano3 data prep sft --config tiny +# nemotron nano3 data prep sft --config tiny --run prep +# +# This config uses only 2 datasets with sampling for quick testing. + +# Path to tiny data blend JSON file (only 2 datasets) +blend_path: ${oc.env:PWD}/src/nemotron/recipes/nano3/stage1_sft/config/data_prep/data_blend_tiny.json + +# Output directory for packed .npy data (outputs to job directory for persistence) +output_dir: ${oc.env:PWD}/../output/stage1_sft_tiny + +# HuggingFace tokenizer model name +tokenizer_model: nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16 + +# Maximum tokens per packed sequence +pack_size: 4096 + +# Target size per shard - smaller for tiny config +shard_size: 64MB + +# Split ratios (must sum to 1.0) +train_ratio: 0.98 +valid_ratio: 0.01 +test_ratio: 0.01 + +# Chat template: 'nano3', path to .jinja file, or inline template +chat_template: nano3 + +# Field name for OpenAI-format messages in input records +messages_field: messages + +# Field name for tools definition in input records +tools_field: tools + +# Filter to only include records where used_in contains this value +used_in_filter: nano_v3 + +# Field name for used_in filtering +used_in_field: used_in + +# Truncate sequences longer than this (null = no limit) +max_doc_tokens: null + +# Limit rows per dataset for quick tests - small sample for tiny +sample: 1000 + +# Ray actors for parallel processing (null = auto) +num_actors: null + +# Force new run, ignoring cache +force: false diff --git a/src/nemotron/recipes/nano3/stage1_sft/config/default.yaml b/src/nemotron/recipes/nano3/stage1_sft/config/default.yaml new file mode 100644 index 0000000..95072db --- /dev/null +++ b/src/nemotron/recipes/nano3/stage1_sft/config/default.yaml @@ -0,0 +1,52 @@ +run: + data: SFTDataArtifact-sft:latest + model: pretrain:latest + env: + container: nvcr.io/nvidia/nemo:25.11.nemotron_3_nano + +recipe: + _target_: megatron.bridge.recipes.nemotronh.nemotron_3_nano.nemotron_3_nano_finetune_config + packed_sequence: true + peft: null # Disable LoRA, do full SFT + +# Dataset config for pre-packed .npy files from data_prep +# train.py builds FinetuningDatasetConfig directly (not HFDatasetConfig) to skip HF download +dataset: + dataset_root: ${art:data,path} + seq_length: ${art:data,pack_size} + packed_sequence_specs: + packed_sequence_size: ${art:data,pack_size} + packed_train_data_path: ${art:data,training_path} + packed_val_data_path: ${art:data,validation_path} + packed_metadata_path: ${art:data,metadata_path} + +train: + train_iters: 1700 + global_batch_size: 4 + +model: + seq_length: ${art:data,pack_size} + pipeline_model_parallel_size: 1 + tensor_model_parallel_size: 4 + context_parallel_size: 2 + hidden_size: 1344 + calculate_per_token_loss: True + +# tokenizer: +# tokenizer_type: HuggingFaceTokenizer +# tokenizer_model: meta-llama/Llama-3.2-1B + +scheduler: + lr_warmup_iters: 4 + +logger: + log_interval: 10 + wandb_project: ${run.wandb.project} # Set from env.toml [wandb] + wandb_entity: ${run.wandb.entity} # Set from env.toml [wandb] + +checkpoint: + save: /nemo_run/sft + save_interval: 20 + pretrained_checkpoint: ${art:model,path} + finetune: true # Skip loading optimizer state from pretrained checkpoint + # ckpt_step: ${art:model,iteration} # Optional: set to load specific iteration from artifact diff --git a/src/nemotron/recipes/nano3/stage1_sft/config/tiny.yaml b/src/nemotron/recipes/nano3/stage1_sft/config/tiny.yaml new file mode 100644 index 0000000..6b531c5 --- /dev/null +++ b/src/nemotron/recipes/nano3/stage1_sft/config/tiny.yaml @@ -0,0 +1,50 @@ +run: + data: SFTDataArtifact-sft:latest + model: pretrain:latest + env: + container: nvcr.io/nvidia/nemo:25.11.nemotron_3_nano + +recipe: + _target_: megatron.bridge.recipes.qwen.qwen3.qwen3_8b_finetune_config + packed_sequence: true + peft: null # Disable LoRA, do full SFT + +# Dataset config for pre-packed .npy files from data_prep +# train.py builds FinetuningDatasetConfig directly (not HFDatasetConfig) to skip HF download +dataset: + dataset_root: ${art:data,path} + seq_length: ${art:data,pack_size} + packed_sequence_specs: + packed_sequence_size: ${art:data,pack_size} + packed_train_data_path: ${art:data,training_path} + packed_val_data_path: ${art:data,validation_path} + packed_metadata_path: ${art:data,metadata_path} + +train: + train_iters: 1700 + global_batch_size: 32 + +model: + seq_length: ${art:data,pack_size} + # Match pretrain parallelism settings for checkpoint compatibility + tensor_model_parallel_size: 4 + pipeline_model_parallel_size: 1 + +# tokenizer: +# tokenizer_type: HuggingFaceTokenizer +# tokenizer_model: meta-llama/Llama-3.2-1B + +scheduler: + lr_warmup_iters: 32 + +logger: + log_interval: 10 + wandb_project: ${run.wandb.project} # Set from env.toml [wandb] + wandb_entity: ${run.wandb.entity} # Set from env.toml [wandb] + +checkpoint: + save: /nemo_run/sft + save_interval: 20 + pretrained_checkpoint: ${art:model,path} + finetune: true # Skip loading optimizer state from pretrained checkpoint + # ckpt_step: ${art:model,iteration} # Optional: set to load specific iteration from artifact diff --git a/src/nemotron/recipes/nano3/stage1_sft/data_prep.py b/src/nemotron/recipes/nano3/stage1_sft/data_prep.py new file mode 100644 index 0000000..a5b65d8 --- /dev/null +++ b/src/nemotron/recipes/nano3/stage1_sft/data_prep.py @@ -0,0 +1,552 @@ +#!/usr/bin/env python3 + +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Data preparation for Nano3 SFT stage. + +Applies chat templates to OpenAI-format messages, tokenizes with role-based +loss masking, and outputs packed .npy files compatible with GPTSFTPackedDataset. + +Output structure (Megatron-Bridge compatible): + output_dir/ + training_{pack_size}.npy # All training data concatenated + validation_{pack_size}.npy # All validation data concatenated + test_{pack_size}.npy # All test data concatenated + {pack_size}_metadata.jsonl # Megatron-Bridge compatible packing metadata + metadata.json # Nemotron metadata with split info + +Compatible with Megatron-Bridge's FinetuningDatasetConfig with PackedSequenceSpecs. + +Pipeline: +1. Apply nano3 chat template → role-labeled chunks +2. Tokenize chunks → input_ids +3. Build loss_mask (0=system/user, 1=assistant) +4. Pack sequences → sharded .npy files +5. Concatenate shards and split by ratio → single .npy per split + +Usage: + # With default config + python data_prep.py + + # With custom config file + python data_prep.py --config /path/to/config.yaml + + # With CLI overrides (Hydra-style) + python data_prep.py sample=100 force=true + + # Via nemotron CLI with nemo-run + nemotron nano3 data prep sft --run prep --sample 10000 +""" + +from __future__ import annotations + +import glob +import json +import logging +import os +import sys +from dataclasses import dataclass, field +from pathlib import Path + +import numpy as np + +from nemotron.data_prep import ( + ChatSftOutputConfig, + DataBlend, + OutputConfig, + PipelineConfig, + TokenizerConfig, + last_mile_process, +) +from nemotron.data_prep.config import DatasetConfig +from nemotron.data_prep.discovery import get_dataset_metadata +from nemotron.kit import SFTDataArtifact, print_step_complete +from nemotron.kit.trackers import InputDatasetInfo, tokenizer_to_uri +from nemotron.kit.train_script import ( + apply_hydra_overrides, + init_wandb_from_env, + load_omegaconf_yaml, + omegaconf_to_dataclass, + parse_config_and_overrides, +) +from nemotron.kit.wandb import add_wandb_tags, finish_wandb + +logger = logging.getLogger(__name__) + +STAGE_PATH = Path(__file__).parent + +# Default config path relative to this file +DEFAULT_CONFIG_PATH = STAGE_PATH / "config" / "data_prep.yaml" + +# Use NEMO_RUN_DIR for output when running via nemo-run (avoids writing to code dir) +_OUTPUT_BASE = Path(os.environ.get("NEMO_RUN_DIR", ".")) + +# Module-level flag for Ray execution (used by nemotron CLI) +RAY = True + + +@dataclass +class SFTDataPrepConfig: + """SFT data preparation config using chat template. + + Applies chat templates to OpenAI-format messages, tokenizes with role-based + loss masking, and outputs single packed .npy files per split (training.npy, + validation.npy, test.npy) compatible with Megatron-Bridge's FinetuningDatasetConfig. + """ + + blend_path: Path = field(default_factory=lambda: STAGE_PATH / "config/data_blend_raw.json") + """Path to data blend JSON file""" + + output_dir: Path = field(default_factory=lambda: _OUTPUT_BASE / "stage1_sft") + """Output directory for packed .npy data""" + + # Tokenizer + tokenizer_model: str = "nvidia/NVIDIA-Nemotron-Nano-9B-v2" + """HuggingFace tokenizer model name""" + + # Packing + pack_size: int = 4096 + """Maximum tokens per packed sequence""" + + shard_size: str = "256MB" + """Target size per shard (e.g., '256MB', '1GB')""" + + # Split ratios (must sum to 1.0) + train_ratio: float = 0.98 + """Fraction of data for training split""" + + valid_ratio: float = 0.01 + """Fraction of data for validation split""" + + test_ratio: float = 0.01 + """Fraction of data for test split""" + + # Chat template + chat_template: str = "nano3" + """Chat template: 'nano3', path to .jinja file, or inline template""" + + messages_field: str = "messages" + """Field name for OpenAI-format messages in input records""" + + tools_field: str = "tools" + """Field name for tools definition in input records""" + + used_in_filter: str | None = None + """Filter to only include records where used_in contains this value (e.g., 'nano_v3')""" + + used_in_field: str = "used_in" + """Field name for used_in filtering""" + + # Processing limits + max_doc_tokens: int | None = None + """Truncate sequences longer than this""" + + sample: int | None = None + """Limit rows per dataset (for quick tests)""" + + num_actors: int | None = None + """Ray actors for parallel processing (None = auto)""" + + force: bool = False + """Force new run, ignoring cache""" + + def __post_init__(self) -> None: + # Ensure paths are Path objects + if isinstance(self.blend_path, str): + self.blend_path = Path(self.blend_path) + if isinstance(self.output_dir, str): + self.output_dir = Path(self.output_dir) + + # Validate split ratios sum to 1.0 + total_ratio = self.train_ratio + self.valid_ratio + self.test_ratio + if abs(total_ratio - 1.0) > 1e-6: + raise ValueError( + f"Split ratios must sum to 1.0, got {total_ratio} " + f"(train={self.train_ratio}, valid={self.valid_ratio}, test={self.test_ratio})" + ) + + # Add sample suffix to output_dir if sampling + if self.sample is not None: + self.output_dir = self.output_dir / f"sample-{self.sample}" + + +def _concatenate_and_split_npy( + shards_dir: Path, + output_dir: Path, + train_ratio: float, + valid_ratio: float, + test_ratio: float, + pack_size: int, + seed: int = 42, + data_paths: list[str] | None = None, +) -> dict: + """Load all shards, concatenate, split by ratio, and save single files per split. + + Args: + shards_dir: Directory containing shard_*.npy files from pipeline. + output_dir: Directory to write training.npy, validation.npy, test.npy. + train_ratio: Fraction of data for training. + valid_ratio: Fraction of data for validation. + test_ratio: Fraction of data for test. + pack_size: Pack size (for metadata). + seed: Random seed for shuffling before split. + data_paths: Optional list of weight/path pairs from PipelineResult. + Format: ["weight", "path_prefix", ...] where path_prefix is the + shard path prefix (without the _XXXXXX.npy suffix). + + Returns: + Dict with split statistics: { + "train": {"sequences": N, "path": "..."}, + "valid": {"sequences": N, "path": "..."}, + "test": {"sequences": N, "path": "..."}, + "total_sequences": N, + } + """ + # Find all shard files + shard_files: list[str] = [] + + if data_paths: + # Extract shard prefixes from data_paths (format: ["weight", "path", ...]) + # and find all matching shard files + for i in range(1, len(data_paths), 2): + prefix = data_paths[i] + # Find all shard files matching this prefix + pattern = f"{prefix}_*.npy" + matching = sorted(glob.glob(pattern)) + shard_files.extend(matching) + + if not shard_files: + # Fallback: try recursive search under shards_dir + shard_pattern = str(shards_dir / "**" / "shard_*.npy") + shard_files = sorted(glob.glob(shard_pattern, recursive=True)) + + if not shard_files: + raise ValueError(f"No shard files found matching {shard_pattern}") + + logger.info(f"Loading {len(shard_files)} shard files from {shards_dir}") + + # Load and concatenate all shards + all_sequences = [] + for shard_file in shard_files: + shard_data = np.load(shard_file, allow_pickle=True) + all_sequences.extend(shard_data) + logger.debug(f"Loaded {len(shard_data)} sequences from {shard_file}") + + total_sequences = len(all_sequences) + logger.info(f"Total sequences loaded: {total_sequences}") + + # Shuffle before splitting (for reproducibility) + rng = np.random.default_rng(seed) + indices = np.arange(total_sequences) + rng.shuffle(indices) + + # Calculate split boundaries + train_end = int(total_sequences * train_ratio) + valid_end = train_end + int(total_sequences * valid_ratio) + + # Split indices + train_indices = indices[:train_end] + valid_indices = indices[train_end:valid_end] + test_indices = indices[valid_end:] + + # Extract sequences for each split + train_sequences = [all_sequences[i] for i in train_indices] + valid_sequences = [all_sequences[i] for i in valid_indices] + test_sequences = [all_sequences[i] for i in test_indices] + + # Ensure output directory exists and resolve to absolute path + # This is critical for W&B artifacts - paths must be absolute for remote access + output_dir = output_dir.resolve() + output_dir.mkdir(parents=True, exist_ok=True) + + # Save each split as a single .npy file with pack_size in filename + # Format: {split}_{pack_size}.npy (Megatron-Bridge compatible) + train_path = output_dir / f"training_{pack_size}.npy" + valid_path = output_dir / f"validation_{pack_size}.npy" + test_path = output_dir / f"test_{pack_size}.npy" + + np.save(train_path, train_sequences, allow_pickle=True) + logger.info(f"Saved {len(train_sequences)} training sequences to {train_path}") + + np.save(valid_path, valid_sequences, allow_pickle=True) + logger.info(f"Saved {len(valid_sequences)} validation sequences to {valid_path}") + + np.save(test_path, test_sequences, allow_pickle=True) + logger.info(f"Saved {len(test_sequences)} test sequences to {test_path}") + + # Compute packing statistics for Megatron-Bridge compatible metadata + def compute_packing_stats(sequences: list) -> dict: + """Compute packing statistics for a list of sequences.""" + if not sequences: + return { + "max_samples_per_bin": 0, + "dataset_max_seqlen": 0, + "packing_factor": 0.0, + "packing_efficiency": 0.0, + "pack_size": pack_size, + "min_packed_seqlen": 0, + } + # Count samples per bin and sequence lengths + samples_per_bin = [] + packed_seqlens = [] + max_seqlen = 0 + for seq in sequences: + num_samples = len(seq.get("seq_start_id", [1])) # Default to 1 if no boundaries + samples_per_bin.append(num_samples) + seq_len = len(seq.get("input_ids", [])) + packed_seqlens.append(seq_len) + # Track max sequence length across all sub-sequences + for i, start in enumerate(seq.get("seq_start_id", [0])): + if i + 1 < len(seq.get("seq_start_id", [])): + end = seq["seq_start_id"][i + 1] + else: + end = len(seq.get("input_ids", [])) + max_seqlen = max(max_seqlen, end - start) + + total_tokens = sum(packed_seqlens) + + packing_factor = round(sum(samples_per_bin) / len(sequences), 2) if sequences else 0.0 + packing_efficiency = ( + round(total_tokens / (len(sequences) * pack_size) * 100, 2) if sequences else 0.0 + ) + return { + "max_samples_per_bin": max(samples_per_bin) if samples_per_bin else 0, + "dataset_max_seqlen": max_seqlen, + "packing_factor": packing_factor, + "packing_efficiency": packing_efficiency, + "pack_size": pack_size, + "min_packed_seqlen": min(packed_seqlens) if packed_seqlens else 0, + } + + # Compute stats for each split + train_stats = compute_packing_stats(train_sequences) + valid_stats = compute_packing_stats(valid_sequences) + test_stats = compute_packing_stats(test_sequences) + + # Write Megatron-Bridge compatible metadata.jsonl + # Format: JSON list with one entry per dataset (train, valid, test) + mb_metadata_path = output_dir / f"{pack_size}_metadata.jsonl" + mb_metadata = [train_stats, valid_stats, test_stats] + with open(mb_metadata_path, "w") as f: + json.dump(mb_metadata, f, indent=2) + logger.info(f"Saved Megatron-Bridge compatible metadata to {mb_metadata_path}") + + # Write nemotron metadata.json (backward compatible) + metadata = { + "pack_size": pack_size, + "seed": seed, + "splits": { + "train": {"sequences": len(train_sequences), "path": str(train_path), **train_stats}, + "valid": {"sequences": len(valid_sequences), "path": str(valid_path), **valid_stats}, + "test": {"sequences": len(test_sequences), "path": str(test_path), **test_stats}, + }, + "total_sequences": total_sequences, + "train_ratio": train_ratio, + "valid_ratio": valid_ratio, + "test_ratio": test_ratio, + } + + metadata_path = output_dir / "metadata.json" + with open(metadata_path, "w") as f: + json.dump(metadata, f, indent=2) + logger.info(f"Saved metadata to {metadata_path}") + + return { + "train": {"sequences": len(train_sequences), "path": str(train_path)}, + "valid": {"sequences": len(valid_sequences), "path": str(valid_path)}, + "test": {"sequences": len(test_sequences), "path": str(test_path)}, + "total_sequences": total_sequences, + "training_path": str(train_path), + "validation_path": str(valid_path), + "test_path": str(test_path), + "metadata_path": str(mb_metadata_path), + } + + +def run_data_prep_main(cfg: SFTDataPrepConfig) -> SFTDataArtifact: + """Run SFT data preparation with chat template. + + Processes data through pipeline to generate shards, then concatenates + and splits into single .npy files per split (training.npy, validation.npy, test.npy). + + Args: + cfg: SFT data prep configuration. + + Returns: + SFTDataArtifact with paths to packed data. + """ + import shutil + import time + + start_time = time.time() + + # Add stage-specific tags to wandb run + add_wandb_tags(["data-prep", "sft"]) + + # Load data blend + blend = DataBlend.load(cfg.blend_path) + + # Auto-detect num_actors from CPU count + num_actors = cfg.num_actors + if num_actors is None: + cpu_count = os.cpu_count() or 4 + num_actors = max(2, min(32, cpu_count * 3 // 4)) + + # Use a temporary shards directory for pipeline output + shards_dir = cfg.output_dir / "_shards" + + # Build pipeline config with ChatSftOutputConfig (no per-split config) + format_config = ChatSftOutputConfig( + shard_size=cfg.shard_size, + pack_size=cfg.pack_size, + chat_template=cfg.chat_template, + messages_field=cfg.messages_field, + tools_field=cfg.tools_field, + used_in_filter=cfg.used_in_filter, + used_in_field=cfg.used_in_field, + ) + + pipeline_config = PipelineConfig( + output=OutputConfig( + dir=shards_dir, + format=format_config, + max_doc_tokens=cfg.max_doc_tokens, + max_rows=cfg.sample, + ), + tokenizer=TokenizerConfig(model=cfg.tokenizer_model), + num_actors=num_actors, + force=cfg.force, + # No per_split config - we handle splitting ourselves + ) + + # Run processing pipeline to generate shards + logger.info("Running pipeline to generate shards...") + result = last_mile_process(blend, pipeline_config) + + # Concatenate shards and split by ratio + # Extract data_paths from pipeline result to locate actual shard files + data_paths = result.splits["all"].data_paths if "all" in result.splits else None + logger.info("Concatenating shards and splitting by ratio...") + split_stats = _concatenate_and_split_npy( + shards_dir=shards_dir, + output_dir=cfg.output_dir, + train_ratio=cfg.train_ratio, + valid_ratio=cfg.valid_ratio, + test_ratio=cfg.test_ratio, + pack_size=cfg.pack_size, + data_paths=data_paths, + ) + + # Clean up intermediate shards directory + if shards_dir.exists(): + logger.info(f"Cleaning up intermediate shards directory: {shards_dir}") + shutil.rmtree(shards_dir) + + elapsed_sec = time.time() - start_time + + # Collect source datasets with metadata for lineage tracking + source_datasets: list[InputDatasetInfo] = [] + seen_keys: set[str] = set() + for split_datasets in blend.splits.values(): + for dataset in split_datasets: + # Use path+subset as key since same path can have different subsets + key = f"{dataset.path}|{dataset.subset or ''}" + if key not in seen_keys: + seen_keys.add(key) + ds_config = DatasetConfig( + name=dataset.name, + path=dataset.path, + split=dataset.split, + subset=dataset.subset, + text_field=dataset.text_field, + ) + hf_metadata = get_dataset_metadata(ds_config) + source_datasets.append( + InputDatasetInfo( + uri=dataset.path, + name=dataset.name, + weight=dataset.weight, + split=dataset.split, + subset=dataset.subset, + text_field=dataset.text_field, + num_rows=hf_metadata.num_rows, + size_bytes=hf_metadata.size_bytes, + ) + ) + + # Create tokenizer URI for lineage tracking + tok_uri = tokenizer_to_uri(cfg.tokenizer_model) + + # Build output artifact - path points to output_dir (contains training_{pack_size}.npy, etc.) + # Use resolved absolute path for W&B artifact storage + artifact = SFTDataArtifact( + path=cfg.output_dir.resolve(), + total_tokens=result.total_tokens, + total_sequences=split_stats["total_sequences"], + elapsed_sec=elapsed_sec, + pack_size=cfg.pack_size, + source_datasets=source_datasets, + tokenizer_uri=tok_uri, + training_path=split_stats["training_path"], + validation_path=split_stats["validation_path"], + test_path=split_stats["test_path"], + metadata_path=split_stats["metadata_path"], + ) + artifact.name = f"nano3/sft/data{'?sample=' + str(cfg.sample) if cfg.sample else ''}" + artifact.save() + + # Mark wandb run as successful + finish_wandb(exit_code=0) + + print_step_complete(data_prep=artifact) + return artifact + + +def main(cfg: SFTDataPrepConfig | None = None) -> SFTDataArtifact: + """Entry point for SFT data preparation. + + Args: + cfg: Config from CLI framework, or None when run directly as script. + + Returns: + SFTDataArtifact with paths to packed data. + """ + if cfg is None: + # Called directly as script - parse config ourselves + config_path, cli_overrides = parse_config_and_overrides(default_config=DEFAULT_CONFIG_PATH) + + # Load YAML config + try: + config = load_omegaconf_yaml(config_path) + except FileNotFoundError as e: + print(f"Error: {e}", file=sys.stderr) + sys.exit(1) + + # Apply CLI overrides (Hydra-style: key=value) + if cli_overrides: + config = apply_hydra_overrides(config, cli_overrides) + + # Convert to dataclass + cfg = omegaconf_to_dataclass(config, SFTDataPrepConfig) + + # Initialize wandb from environment variables (set by nemo-run) + init_wandb_from_env() + + # Run data prep + return run_data_prep_main(cfg) + + +if __name__ == "__main__": + main() diff --git a/src/nemotron/recipes/nano3/stage1_sft/train.py b/src/nemotron/recipes/nano3/stage1_sft/train.py new file mode 100644 index 0000000..9278a3a --- /dev/null +++ b/src/nemotron/recipes/nano3/stage1_sft/train.py @@ -0,0 +1,206 @@ +#!/usr/bin/env python3 + +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""SFT (Supervised Fine-Tuning) script for Nemotron Nano3. + +Uses Megatron-Bridge's ConfigContainer for full training configuration. +Dynamically loads the recipe function specified in the YAML config. + +Usage: + # With YAML config file (required) + python /path/to/train.py --config /path/to/sft.yaml + + # With CLI overrides (Hydra syntax) + python /path/to/train.py --config /path/to/sft.yaml train.train_iters=5000 + + # As module (requires nemotron package installed) + torchrun --nproc_per_node=8 -m nemotron.recipes.nano3.stage1_sft.train \ + --config /path/to/sft.yaml +""" + +from __future__ import annotations + +import logging +import sys +from pathlib import Path +from typing import Any + +import torch +from megatron.bridge.data.datasets.packed_sequence import PackedSequenceSpecs +from megatron.bridge.training.config import ConfigContainer, FinetuningDatasetConfig +from megatron.bridge.training.finetune import finetune +from megatron.bridge.training.gpt_step import forward_step +from megatron.bridge.training.utils.omegaconf_utils import ( + apply_overrides, + create_omegaconf_dict_config, + parse_hydra_overrides, +) +from omegaconf import DictConfig, OmegaConf + +from nemotron.kit.recipe_loader import extract_recipe_config, import_recipe_function +from nemotron.kit.resolvers import clear_artifact_cache, register_resolvers_from_config +from nemotron.kit.train_script import load_omegaconf_yaml, parse_config_and_overrides +from nemotron.kit.wandb import ( + patch_wandb_checkpoint_logging, + patch_wandb_http_handler_skip_digest_verification, + patch_wandb_init_for_lineage, + patch_wandb_local_file_handler_skip_digest_verification, + patch_wandb_runid_for_seeded_random, +) + +logger: logging.Logger = logging.getLogger(__name__) + + +# Default config path relative to this file +DEFAULT_CONFIG_PATH = Path(__file__).parent / "config" / "default.yaml" + +# Default recipe function +DEFAULT_RECIPE_TARGET = "megatron.bridge.recipes.nemotronh.nemotron_nano_9b_v2_finetune_config" + + +def _build_dataset_config(dataset_config: DictConfig, current_dataset: Any) -> FinetuningDatasetConfig: + """Build a FinetuningDatasetConfig from YAML config. + + This creates a proper FinetuningDatasetConfig (not HFDatasetConfig) to avoid + downloading HuggingFace datasets. The dataset config uses pre-packed .npy files. + + Args: + dataset_config: The dataset section from YAML config (resolved) + current_dataset: The current dataset config from the recipe (for defaults) + + Returns: + A FinetuningDatasetConfig instance + """ + # Build PackedSequenceSpecs if provided + packed_specs = None + if "packed_sequence_specs" in dataset_config: + specs_dict = dataset_config["packed_sequence_specs"] + # PackedSequenceSpecs.__post_init__ converts string paths to Path/MultiStoragePath + packed_specs = PackedSequenceSpecs( + packed_sequence_size=specs_dict.get("packed_sequence_size", -1), + packed_train_data_path=specs_dict.get("packed_train_data_path"), + packed_val_data_path=specs_dict.get("packed_val_data_path"), + packed_metadata_path=specs_dict.get("packed_metadata_path"), + ) + + # Build FinetuningDatasetConfig with values from YAML, falling back to current config + return FinetuningDatasetConfig( + dataset_root=dataset_config.get("dataset_root", getattr(current_dataset, "dataset_root", None)), + seq_length=dataset_config.get("seq_length", getattr(current_dataset, "seq_length", 4096)), + packed_sequence_specs=packed_specs, + dataloader_type=dataset_config.get("dataloader_type", getattr(current_dataset, "dataloader_type", "batch")), + ) + + +def main() -> None: + """Entry point for Nemotron Nano3 supervised fine-tuning.""" + try: + config_path, cli_overrides = parse_config_and_overrides(default_config=DEFAULT_CONFIG_PATH) + config = load_omegaconf_yaml(config_path) + except FileNotFoundError as e: + logger.error(str(e)) + sys.exit(1) + + # ------------------------------------------------------------------------- + # WANDB MONKEY-PATCHES + # These patches work around bugs in wandb and Megatron-Bridge. + # See nemotron/kit/wandb.py for detailed "Why" / "Remove when" documentation. + # ------------------------------------------------------------------------- + patch_wandb_http_handler_skip_digest_verification() + patch_wandb_local_file_handler_skip_digest_verification() + patch_wandb_runid_for_seeded_random() + patch_wandb_checkpoint_logging() + + # Clear artifact cache to ensure fresh downloads (important for :latest resolution) + clear_artifact_cache() + + # Resolve artifacts before wandb.init() (Megatron-Bridge initializes wandb). + qualified_names = register_resolvers_from_config( + config, + artifacts_key="run", + mode="pre_init", + pre_init_patch_http_digest=False, + ) + + # Patch wandb.init so lineage is registered immediately once MB initializes wandb. + patch_wandb_init_for_lineage( + artifact_qualified_names=qualified_names, + tags=["sft"], + ) + + recipe_target, recipe_kwargs = extract_recipe_config( + config, + default_target=DEFAULT_RECIPE_TARGET, + ) + try: + recipe_func = import_recipe_function(recipe_target) + except Exception as e: + logger.error(str(e)) + sys.exit(1) + + cfg: ConfigContainer = recipe_func(**recipe_kwargs) + + # Convert the initial Python dataclass to an OmegaConf DictConfig for merging + # Do this BEFORE building our custom dataset config (which contains MultiStoragePath) + merged_omega_conf, excluded_fields = create_omegaconf_dict_config(cfg) + + # Get config overrides (excluding recipe, run, and dataset) + config_overrides = OmegaConf.to_container(config, resolve=False) + config_overrides.pop("recipe", None) + config_overrides.pop("run", None) + config_overrides.pop("dataset", None) # We handle dataset separately below + + if config_overrides: + logger.debug(f"Merging config overrides: {list(config_overrides.keys())}") + yaml_overrides_omega = OmegaConf.create(config_overrides) + merged_omega_conf = OmegaConf.merge(merged_omega_conf, yaml_overrides_omega) + logger.debug("Config overrides merged successfully.") + + # Apply command-line overrides using Hydra-style parsing + if cli_overrides: + logger.debug(f"Applying Hydra-style command-line overrides: {cli_overrides}") + merged_omega_conf = parse_hydra_overrides(merged_omega_conf, cli_overrides) + logger.debug("Hydra-style command-line overrides applied successfully.") + + final_overrides_as_dict = OmegaConf.to_container(merged_omega_conf, resolve=True) + + # Don't let apply_overrides touch the dataset - we handle it separately + final_overrides_as_dict.pop("dataset", None) + apply_overrides(cfg, final_overrides_as_dict, excluded_fields) + + # Handle dataset config AFTER apply_overrides - build FinetuningDatasetConfig directly + # This avoids HFDatasetConfig which tries to download from HuggingFace + # PackedSequenceSpecs.__post_init__ converts paths to Path/MultiStoragePath automatically + if "dataset" in config: + dataset_config = OmegaConf.to_container(config.dataset, resolve=True) + dataset_config.pop("_target_", None) + cfg.dataset = _build_dataset_config(dataset_config, cfg.dataset) + logger.info(f"Built dataset config: {type(cfg.dataset).__name__}") + + # Debug: print key config values + print(f"DEBUG: checkpoint.pretrained_checkpoint = {cfg.checkpoint.pretrained_checkpoint}") + print(f"DEBUG: dataset type = {type(cfg.dataset).__name__}") + if hasattr(cfg.dataset, "packed_sequence_specs") and cfg.dataset.packed_sequence_specs: + print(f"DEBUG: packed_sequence_specs.packed_train_data_path = {cfg.dataset.packed_sequence_specs.packed_train_data_path}") + + finetune(config=cfg, forward_step_func=forward_step) + + if torch.distributed.is_initialized(): + torch.distributed.destroy_process_group() + + +if __name__ == "__main__": + main() diff --git a/src/nemotron/recipes/nano3/stage2_rl/README.md b/src/nemotron/recipes/nano3/stage2_rl/README.md new file mode 100644 index 0000000..1d87f38 --- /dev/null +++ b/src/nemotron/recipes/nano3/stage2_rl/README.md @@ -0,0 +1,257 @@ +# Stage 2: Reinforcement Learning (RL) + +Align the instruction-tuned model using GRPO (Group Relative Policy Optimization) with NeMo-RL. + +## Overview + +This stage takes the SFT model and further aligns it using reinforcement learning. The GRPO algorithm optimizes the policy based on reward signals from NeMo-Gym environments, producing a final aligned model. + +> **Open-Source Data Only**: This recipe trains exclusively on the open-sourced subset of RL data. Results will differ from the tech report benchmarks, which used additional proprietary data. Use this recipe as a reference implementation to apply the methodology with your own data. + +| Component | Description | +|-----------|-------------| +| `data_prep.py` | Converts datasets to JSONL format for NeMo-RL | +| `train.py` | Runs GRPO training using NeMo-RL with Ray | +| `config/` | Configuration files for data prep and training | + +## Quick Start + +### Using nemotron CLI (Recommended) + +```bash +# 1. Prepare data (convert to JSONL format) +uv run nemotron nano3 data prep rl --run YOUR-CLUSTER + +# 2. Run RL training +uv run nemotron nano3 rl --run YOUR-CLUSTER + +# Quick test with tiny config +uv run nemotron nano3 rl -c tiny --run YOUR-CLUSTER +``` + +### Direct Script Execution + +Inside a container on a compute node (requires NeMo-RL and Ray): + +```bash +# Data preparation +python data_prep.py --config config/data_prep.yaml + +# Training (Ray will be initialized internally) +python train.py --config config/grpo_nanov3.yaml +``` + +## Data Preparation + +The `data_prep.py` script converts datasets to JSONL format compatible with NeMo-RL's NeMo-Gym interface. + +### CLI Command + +```bash +uv run nemotron nano3 data prep rl [options] +``` + +| Option | Description | +|--------|-------------| +| `--run
(from Stage 1)"] --> train + rl["RL Datasets
(preference/reward data)"] --> dp["data_prep.py"] + dp --> data["DataBlendsArtifact-rl
(JSONL files)"] + data --> train["train.py
(GRPO with NeMo-RL)"] + train --> model["ModelArtifact-rl
(final aligned model)"] + + style prev fill:#f3e5f5 + style rl fill:#e8f5e9 + style dp fill:#e8f5e9 + style data fill:#e8f5e9 + style train fill:#e8f5e9 + style model fill:#e8f5e9 +``` + +## Requirements + +- **NeMo-RL**: Required for GRPO training +- **Ray**: Automatically initialized for distributed execution +- **NeMo-Gym**: Provides reward environments +- **GPU nodes**: Recommended 8 GPUs per node + +## Previous Stages + +- [Stage 0: Pretraining](../stage0_pretrain/README.md) - Pretrain the base model +- [Stage 1: SFT](../stage1_sft/README.md) - Instruction tuning diff --git a/src/nemotron/recipes/nano3/stage2_rl/__init__.py b/src/nemotron/recipes/nano3/stage2_rl/__init__.py new file mode 100644 index 0000000..b7e1d0c --- /dev/null +++ b/src/nemotron/recipes/nano3/stage2_rl/__init__.py @@ -0,0 +1,15 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Nemotron Nano 3 stage 2 (reinforcement learning) recipe package.""" diff --git a/src/nemotron/recipes/nano3/stage2_rl/config/data_prep/data_blend_raw.json b/src/nemotron/recipes/nano3/stage2_rl/config/data_prep/data_blend_raw.json new file mode 100644 index 0000000..5dc19b1 --- /dev/null +++ b/src/nemotron/recipes/nano3/stage2_rl/config/data_prep/data_blend_raw.json @@ -0,0 +1,10 @@ +{ + "datasets": [ + { + "name": "nemotron-3-nano-rl-training-blend", + "path": "hf://nvidia/Nemotron-3-Nano-RL-Training-Blend", + "split": "train", + "weight": 1.0 + } + ] +} diff --git a/src/nemotron/recipes/nano3/stage2_rl/config/data_prep/default.yaml b/src/nemotron/recipes/nano3/stage2_rl/config/data_prep/default.yaml new file mode 100644 index 0000000..d695f66 --- /dev/null +++ b/src/nemotron/recipes/nano3/stage2_rl/config/data_prep/default.yaml @@ -0,0 +1,41 @@ +run: + env: + container: anyscale/ray:2.49.2-py311 + +# Config for RL data preparation with HuggingFace placeholder resolution +# +# Processes nvidia/Nemotron-3-Nano-RL-Training-Blend and resolves +# placeholder entries by fetching from external datasets (DAPO, Skywork). +# +# Placeholder records have an `_hf_placeholder` field containing row indices and +# question templates. The data_prep.py script resolves these by: +# 1. Detecting placeholder records by the presence of `_hf_placeholder` field +# 2. Fetching the actual data from the external HF dataset +# 3. Applying template restoration (DAPO prefix/suffix, Skywork {question} replacement) +# +# For simple copy/passthrough (no placeholder resolution), use data_prep_copy.py instead. +# +# Usage: +# python data_prep.py +# python data_prep.py sample=100 force=true +# +# Environment variables: +# PWD: Current working directory (for path resolution) + +# Path to data blend JSON file +blend_path: ${oc.env:PWD}/src/nemotron/recipes/nano3/stage2_rl/config/data_prep/data_blend_raw.json + +# Output directory for resolved JSONL data (outputs to job directory for persistence) +output_dir: ${oc.env:PWD}/../output/stage2_rl_resolved + +# Target size per shard (e.g., '256MB', '1GB') +shard_size: 256MB + +# Limit rows per dataset for quick tests (null = no limit) +sample: null + +# Ray actors for parallel processing (null = auto) +num_actors: null + +# Force new run, ignoring cache +force: false diff --git a/src/nemotron/recipes/nano3/stage2_rl/config/data_prep/merge.yaml b/src/nemotron/recipes/nano3/stage2_rl/config/data_prep/merge.yaml new file mode 100644 index 0000000..e8fec9d --- /dev/null +++ b/src/nemotron/recipes/nano3/stage2_rl/config/data_prep/merge.yaml @@ -0,0 +1,46 @@ +run: + env: + container: anyscale/ray:2.49.2-py311 + +# Config for RL data preparation (merge mode) +# +# Merges datasets from a raw data blend into a single JSONL output, then splits +# into train/val/test based on configurable ratios. +# +# This is useful when you have multiple source datasets that need to be combined +# and then split into training splits, rather than using pre-existing HF splits. +# +# For placeholder resolution (DAPO, Skywork datasets), use data_prep.py instead. +# +# Usage: +# python data_prep_merge.py +# python data_prep_merge.py sample=100 force=true train_ratio=0.9 +# +# Environment variables: +# PWD: Current working directory (for path resolution) + +# Path to data blend JSON file +blend_path: ${oc.env:PWD}/src/nemotron/recipes/nano3/stage2_rl/config/data_prep/data_blend_raw.json + +# Output directory for JSONL data (outputs to job directory for persistence) +output_dir: ${oc.env:PWD}/../output/stage2_rl_merged + +# Target size per shard (e.g., '256MB', '1GB') +shard_size: 256MB + +# Split ratios (must sum to 1.0) +train_ratio: 0.98 +valid_ratio: 0.01 +test_ratio: 0.01 + +# Random seed for shuffling before split +seed: 42 + +# Limit rows per dataset for quick tests (null = no limit) +sample: null + +# Ray actors for parallel processing (null = auto) +num_actors: null + +# Force new run, ignoring cache +force: false diff --git a/src/nemotron/recipes/nano3/stage2_rl/config/data_prep/tiny.yaml b/src/nemotron/recipes/nano3/stage2_rl/config/data_prep/tiny.yaml new file mode 100644 index 0000000..5ec3f8b --- /dev/null +++ b/src/nemotron/recipes/nano3/stage2_rl/config/data_prep/tiny.yaml @@ -0,0 +1,38 @@ +run: + env: + container: anyscale/ray:2.49.2-py311 + +# Tiny config for RL data preparation (fast iteration/testing) +# +# Usage: +# nemotron nano3 data prep rl --config tiny +# nemotron nano3 data prep rl --config tiny --run prep +# +# This config uses sampling for quick testing. + +# Path to data blend JSON file +blend_path: ${oc.env:PWD}/src/nemotron/recipes/nano3/stage2_rl/config/data_prep/data_blend_raw.json + +# Output directory for JSONL data (outputs to job directory for persistence) +output_dir: ${oc.env:PWD}/../output/stage2_rl_tiny + +# Target size per shard - smaller for tiny config +shard_size: 64MB + +# Limit rows per dataset for quick tests - small sample for tiny +sample: 1000 + +# Ray actors for parallel processing (null = auto) +num_actors: null + +# Force new run, ignoring cache +force: false + +# Split output into train/val/test directories: 'none' or 'train_val_test' +split_output: train_val_test + +# Ratio of data for training when split_output='train_val_test' +train_ratio: 0.98 + +# Ratio of data for validation when split_output='train_val_test' +val_ratio: 0.01 diff --git a/src/nemotron/recipes/nano3/stage2_rl/config/default.yaml b/src/nemotron/recipes/nano3/stage2_rl/config/default.yaml new file mode 100644 index 0000000..05f990d --- /dev/null +++ b/src/nemotron/recipes/nano3/stage2_rl/config/default.yaml @@ -0,0 +1,303 @@ +run: + data: DataBlendsArtifact-rl:latest + model: sft:latest + env: + container: nvcr.io/nvidia/nemo-rl:v0.4.0.nemotron_3_nano + +# GRPO Algorithm Configuration for training Nano v3 +grpo: + num_prompts_per_step: 128 + num_generations_per_prompt: 16 + num_val_generations_per_prompt: 4 + max_rollout_turns: 1 # for multi-turn rollouts. Math Environments just have 1 turn (answering the question) + max_num_epochs: 1 + max_num_steps: 1000000 + normalize_rewards: true + use_leave_one_out_baseline: true + val_period: 5 + val_at_start: False + overlong_filtering: false + max_val_samples: null + val_batch_size: 256 + seed: 42 + async_grpo: + enabled: false # Set to true to enable async training mode + # Max age (in training steps) for trajectories used in training + max_trajectory_age_steps: 1 + + # Best@k training configuration (optional - set use_best_at_k to true to enable) + use_best_at_k: False # Enable Best@k training to optimize for best reward among k samples + best_at_k_k: 8 # Number of samples per bootstrap group (should be <= num_generations_per_prompt) + best_at_k_m: 1000 # Number of bootstrap groups (will use C(n,k) if smaller) + # Note: With num_generations_per_prompt=16 and k=8, C(16,8)=12870 + # So m=1000 will use bootstrap sampling (not all combinations) + + # Combined training: blend of Best@k and Pass@1 + use_combined_training: false # Enable combined training + combined_training_weight_mode: "auto" # "auto" for adaptive, "fixed" for custom weights + combined_training_best_at_k_weight: 0.2 # Used when mode is "fixed" + combined_training_pass_at_1_weight: 1.0 # Used when mode is "fixed" + # "auto": adaptive weights (N_pos/N) per prompt + # "fixed": use custom weights specified above + + # Dynamic sampling: oversample prompts, keep only those with non-zero advantages + dynamic_sampling_oversample_ratio: 1.0 # 1.0 = disabled, 2.0 = load 2x and filter, etc. + + batch_multiplier: 1 + use_dynamic_sampling: False + reward_shaping: + enabled: False + reward_scaling: + enabled: False + +loss_fn: + reference_policy_kl_penalty: 0 + reference_policy_kl_type: k3 + kl_input_clamp_value: null + kl_output_clamp_value: null + ratio_clip_min: 0.2 + ratio_clip_max: 0.28 + ratio_clip_c: null + # (default off) loss formulation improvements (docs/guides/grpo.md#loss) + use_on_policy_kl_approximation: True + use_importance_sampling_correction: True + sequence_level_importance_ratios: False + token_level_loss: True + truncated_importance_sampling_ratio: null + +checkpointing: + enabled: true + checkpoint_dir: "results/grpo" + metric_name: "val:total_reward/mean" + higher_is_better: true + keep_top_k: 1000000 + save_period: 10 + checkpoint_must_save_by: "00:03:40:00" + +policy: + model_name: "/path/to/hf_checkpoint" + tokenizer: + name: nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16 + train_global_batch_size: 2048 + train_micro_batch_size: 1 + generation_batch_size: 64 # Only used when generating using HF backend + logprob_batch_size: 1 + max_total_sequence_length: 49152 + precision: "bfloat16" + logprob_chunk_size: 2048 + + dtensor_cfg: + _v2: true + enabled: false + cpu_offload: False + sequence_parallel: false + activation_checkpointing: false + tensor_parallel_size: 1 + context_parallel_size: 1 + custom_parallel_plan: null + + megatron_cfg: + enabled: true + empty_unused_memory_level: 1 + activation_checkpointing: true + bias_activation_fusion: False + # converter_type: "Qwen2ForCausalLM" + tensor_model_parallel_size: 2 + expert_tensor_parallel_size: 1 + expert_model_parallel_size: 8 + pipeline_model_parallel_size: 2 + num_layers_in_first_pipeline_stage: null + num_layers_in_last_pipeline_stage: null + context_parallel_size: 4 + pipeline_dtype: ${policy.precision} + sequence_parallel: true + freeze_moe_router: true + # moe_router_dtype: "fp64" + moe_router_dtype: "fp32" + moe_router_load_balancing_type: "none" # "seq_aux_loss" causes logprob error divergence for grpo + moe_router_bias_update_rate: 1e-3 + moe_permute_fusion: true + moe_enable_deepep: false + moe_token_dispatcher_type: "alltoall" + moe_aux_loss_coeff: 0.0 + moe_router_enable_expert_bias: true + #gives ~20% training perf speedup with sequence packing + apply_rope_fusion: True + defer_fp32_logits: True + track_moe_metrics: True + moe_per_layer_logging: True + do_not_average_loss: true + cp_normalize: true + calculate_per_token_loss: true + scale_loss_by_dp_cp_size: false + + optimizer: + optimizer: "adam" + lr: 3e-6 + min_lr: 3e-6 + weight_decay: 0.0 + bf16: true + fp16: false + params_dtype: "float32" + + #adam + adam_beta1: 0.9 + adam_beta2: 0.999 + adam_eps: 1e-8 + + #sgd + sgd_momentum: 0.9 + + clip_grad: ${policy.max_grad_norm} + + #distributed optimizer + use_distributed_optimizer: true + use_precision_aware_optimizer: true + + optimizer_cpu_offload: False + optimizer_offload_fraction: 0 + + scheduler: + start_weight_decay: ${policy.megatron_cfg.optimizer.weight_decay} + end_weight_decay: ${policy.megatron_cfg.optimizer.weight_decay} + weight_decay_incr_style: "constant" + lr_decay_style: "constant" + lr_decay_iters: null + lr_warmup_iters: 10 + lr_warmup_init: 0.3e-7 + + distributed_data_parallel_config: + grad_reduce_in_fp32: false + overlap_grad_reduce: true + overlap_param_gather: true + average_in_collective: false + use_custom_fsdp: false + data_parallel_sharding_strategy: "optim_grads_params" + + env_vars: null + + # See docs/design-docs/sequence-packing-and-dynamic-batching.md + # for more details on dynamic batching and sequence packing. + dynamic_batching: + enabled: False + train_mb_tokens: ${mul:${policy.max_total_sequence_length}, ${policy.train_micro_batch_size}} + logprob_mb_tokens: ${mul:${policy.max_total_sequence_length}, ${policy.logprob_batch_size}} + sequence_length_round: 64 + + sequence_packing: + enabled: True + train_mb_tokens: ${mul:${policy.max_total_sequence_length}, ${policy.train_micro_batch_size}} + logprob_mb_tokens: ${mul:${policy.max_total_sequence_length}, ${policy.logprob_batch_size}} + algorithm: "modified_first_fit_decreasing" + sequence_length_round: 64 + + # makes the training sequence length divisible by the tensor parallel size + # this is useful for sequence parallel training + make_sequence_length_divisible_by: ${policy.megatron_cfg.tensor_model_parallel_size} + max_grad_norm: 1.0 + + optimizer: null # remove default FSDP optimizer + scheduler: null + + offload_optimizer_for_logprob: False + + generation: + backend: "vllm" + max_new_tokens: ${policy.max_total_sequence_length} + temperature: 1.0 + top_p: 1.0 + top_k: null + stop_token_ids: null + stop_strings: null + vllm_cfg: + # NB: can re-enable prefix cache on vllm >= 0.11.2. + # enable_prefix_caching: false + async_engine: false + kv_cache_dtype: auto + precision: ${policy.precision} + tensor_parallel_size: 4 + pipeline_parallel_size: 1 + expert_parallel_size: 1 # When EP > 1, EP must be a multiple of TP since vLLM's EP = DP * TP + gpu_memory_utilization: 0.5 + max_model_len: ${policy.max_total_sequence_length} + # when enforce_eager is False, it is optional to set ++policy.generation.vllm_kwargs.compilation_config.use_inductor=False for better accuracy, + # with the flag, vllm will use the custom CUDA kernels instead of the Triton kernels generated by torch.compile + # for more details, see convergence issue https://github.com/NVIDIA-NeMo/RL/issues/998 + enforce_eager: False + use_deep_gemm: False + num_last_layers_in_bf16: 0 + num_first_layers_in_bf16: 0 + expose_http_server: true + http_server_serving_chat_kwargs: + enable_auto_tools: true + tool_parser: qwen3_coder + reasoning_parser: deepseek_r1 + + vllm_kwargs: + mamba_ssm_cache_dtype: "float32" + compilation_config: + # when enforce_eager is False, set ++policy.generation.vllm_kwargs.compilation_config.use_inductor=False for better accuracy, + # with the flag, vllm will use the custom CUDA kernels instead of the Triton kernels generated by torch.compile + # for more details, see convergence issue https://github.com/NVIDIA-NeMo/RL/issues/998 + use_inductor: False + colocated: + # true: generation shares training GPUs + # false: uses dedicated generation resources + enabled: true + # only relevant when enabled is false + resources: + gpus_per_node: null # Decides num gpus to be dedicated to generation when there is one node in the cluster i.e cluster.num_nodes == 1 + num_nodes: null # Decides number of nodes to be dedicated to generation + +data: + train_jsonl_fpath: "/path/to/train.jsonl" + validation_jsonl_fpath: "/path/to/validation.jsonl" + shuffle: False + num_workers: 1 + +env: + should_use_nemo_gym: true + nemo_gym: + config_paths: + - responses_api_models/vllm_model/configs/vllm_model_for_training.yaml # Required! And it must be *for_training + - resources_servers/math_with_judge/configs/math_with_judge.yaml + - resources_servers/code_gen/configs/code_gen.yaml + - resources_servers/workplace_assistant/configs/workplace_assistant.yaml + - resources_servers/mcqa/configs/mcqa.yaml + - resources_servers/instruction_following/configs/instruction_following.yaml + - resources_servers/structured_outputs/configs/structured_outputs_json.yaml + math_with_judge: + resources_servers: + math_with_judge: + judge_model_server: + name: policy_model + should_use_judge: false + code_gen: + resources_servers: + code_gen: + num_processes: 1024 + unit_test_timeout_secs: 10 + debug: false + +logger: + log_dir: "logs" # Base directory for all logs + num_val_samples_to_print: 0 # Number of validation samples to pretty print on terminal + wandb_enabled: false + tensorboard_enabled: false + mlflow_enabled: false # Disable MLflow logging + monitor_gpus: true # If true, will monitor GPU usage and log to wandb and/or tensorboard + swanlab_enabled: false # Disable SwanLab logging + wandb: + project: "grpo-dev" + name: "grpo-dev-logger" + tensorboard: {} + mlflow: + experiment_name: "grpo-dev" + run_name: "grpo-dev-logger" + gpu_monitoring: + collection_interval: 10 # How often to collect GPU usage metrics (in seconds) + flush_interval: 10 # How often to flush GPU usage metrics to the loggers (in seconds) + +cluster: + gpus_per_node: 8 + num_nodes: 32 diff --git a/src/nemotron/recipes/nano3/stage2_rl/config/tiny.yaml b/src/nemotron/recipes/nano3/stage2_rl/config/tiny.yaml new file mode 100644 index 0000000..54b4936 --- /dev/null +++ b/src/nemotron/recipes/nano3/stage2_rl/config/tiny.yaml @@ -0,0 +1,303 @@ +run: + data: SplitJsonlDataArtifact-rl:latest + model: sft:latest + env: + container: nvcr.io/nvidia/nemo-rl:v0.4.0.nemotron_3_nano + +# GRPO Algorithm Configuration for training Nano v3 +grpo: + num_prompts_per_step: 128 + num_generations_per_prompt: 16 + num_val_generations_per_prompt: 4 + max_rollout_turns: 1 # for multi-turn rollouts. Math Environments just have 1 turn (answering the question) + max_num_epochs: 1 + max_num_steps: 1000000 + normalize_rewards: true + use_leave_one_out_baseline: true + val_period: 5 + val_at_start: False + overlong_filtering: false + max_val_samples: null + val_batch_size: 256 + seed: 42 + async_grpo: + enabled: false # Set to true to enable async training mode + # Max age (in training steps) for trajectories used in training + max_trajectory_age_steps: 1 + + # Best@k training configuration (optional - set use_best_at_k to true to enable) + use_best_at_k: False # Enable Best@k training to optimize for best reward among k samples + best_at_k_k: 8 # Number of samples per bootstrap group (should be <= num_generations_per_prompt) + best_at_k_m: 1000 # Number of bootstrap groups (will use C(n,k) if smaller) + # Note: With num_generations_per_prompt=16 and k=8, C(16,8)=12870 + # So m=1000 will use bootstrap sampling (not all combinations) + + # Combined training: blend of Best@k and Pass@1 + use_combined_training: false # Enable combined training + combined_training_weight_mode: "auto" # "auto" for adaptive, "fixed" for custom weights + combined_training_best_at_k_weight: 0.2 # Used when mode is "fixed" + combined_training_pass_at_1_weight: 1.0 # Used when mode is "fixed" + # "auto": adaptive weights (N_pos/N) per prompt + # "fixed": use custom weights specified above + + # Dynamic sampling: oversample prompts, keep only those with non-zero advantages + dynamic_sampling_oversample_ratio: 1.0 # 1.0 = disabled, 2.0 = load 2x and filter, etc. + + batch_multiplier: 1 + use_dynamic_sampling: False + reward_shaping: + enabled: False + reward_scaling: + enabled: False + +loss_fn: + reference_policy_kl_penalty: 0 + reference_policy_kl_type: k3 + kl_input_clamp_value: null + kl_output_clamp_value: null + ratio_clip_min: 0.2 + ratio_clip_max: 0.28 + ratio_clip_c: null + # (default off) loss formulation improvements (docs/guides/grpo.md#loss) + use_on_policy_kl_approximation: True + use_importance_sampling_correction: True + sequence_level_importance_ratios: False + token_level_loss: True + truncated_importance_sampling_ratio: null + +checkpointing: + enabled: true + checkpoint_dir: "results/grpo" + metric_name: "val:total_reward/mean" + higher_is_better: true + keep_top_k: 1000000 + save_period: 10 + checkpoint_must_save_by: "00:03:40:00" + +policy: + model_name: ${art:model,path} + tokenizer: + name: nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16 + train_global_batch_size: 2048 + train_micro_batch_size: 1 + generation_batch_size: 64 # Only used when generating using HF backend + logprob_batch_size: 1 + max_total_sequence_length: 49152 + precision: "bfloat16" + logprob_chunk_size: 2048 + + dtensor_cfg: + _v2: true + enabled: false + cpu_offload: False + sequence_parallel: false + activation_checkpointing: false + tensor_parallel_size: 1 + context_parallel_size: 1 + custom_parallel_plan: null + + megatron_cfg: + enabled: true + empty_unused_memory_level: 1 + activation_checkpointing: true + bias_activation_fusion: False + # converter_type: "Qwen2ForCausalLM" + tensor_model_parallel_size: 2 + expert_tensor_parallel_size: 1 + expert_model_parallel_size: 8 + pipeline_model_parallel_size: 2 + num_layers_in_first_pipeline_stage: null + num_layers_in_last_pipeline_stage: null + context_parallel_size: 4 + pipeline_dtype: ${policy.precision} + sequence_parallel: true + freeze_moe_router: true + # moe_router_dtype: "fp64" + moe_router_dtype: "fp32" + moe_router_load_balancing_type: "none" # "seq_aux_loss" causes logprob error divergence for grpo + moe_router_bias_update_rate: 1e-3 + moe_permute_fusion: true + moe_enable_deepep: false + moe_token_dispatcher_type: "alltoall" + moe_aux_loss_coeff: 0.0 + moe_router_enable_expert_bias: true + #gives ~20% training perf speedup with sequence packing + apply_rope_fusion: True + defer_fp32_logits: True + track_moe_metrics: True + moe_per_layer_logging: True + do_not_average_loss: true + cp_normalize: true + calculate_per_token_loss: true + scale_loss_by_dp_cp_size: false + + optimizer: + optimizer: "adam" + lr: 3e-6 + min_lr: 3e-6 + weight_decay: 0.0 + bf16: true + fp16: false + params_dtype: "float32" + + #adam + adam_beta1: 0.9 + adam_beta2: 0.999 + adam_eps: 1e-8 + + #sgd + sgd_momentum: 0.9 + + clip_grad: ${policy.max_grad_norm} + + #distributed optimizer + use_distributed_optimizer: true + use_precision_aware_optimizer: true + + optimizer_cpu_offload: False + optimizer_offload_fraction: 0 + + scheduler: + start_weight_decay: ${policy.megatron_cfg.optimizer.weight_decay} + end_weight_decay: ${policy.megatron_cfg.optimizer.weight_decay} + weight_decay_incr_style: "constant" + lr_decay_style: "constant" + lr_decay_iters: null + lr_warmup_iters: 10 + lr_warmup_init: 0.3e-7 + + distributed_data_parallel_config: + grad_reduce_in_fp32: false + overlap_grad_reduce: true + overlap_param_gather: true + average_in_collective: false + use_custom_fsdp: false + data_parallel_sharding_strategy: "optim_grads_params" + + env_vars: null + + # See docs/design-docs/sequence-packing-and-dynamic-batching.md + # for more details on dynamic batching and sequence packing. + dynamic_batching: + enabled: False + train_mb_tokens: ${mul:${policy.max_total_sequence_length}, ${policy.train_micro_batch_size}} + logprob_mb_tokens: ${mul:${policy.max_total_sequence_length}, ${policy.logprob_batch_size}} + sequence_length_round: 64 + + sequence_packing: + enabled: True + train_mb_tokens: ${mul:${policy.max_total_sequence_length}, ${policy.train_micro_batch_size}} + logprob_mb_tokens: ${mul:${policy.max_total_sequence_length}, ${policy.logprob_batch_size}} + algorithm: "modified_first_fit_decreasing" + sequence_length_round: 64 + + # makes the training sequence length divisible by the tensor parallel size + # this is useful for sequence parallel training + make_sequence_length_divisible_by: ${policy.megatron_cfg.tensor_model_parallel_size} + max_grad_norm: 1.0 + + optimizer: null # remove default FSDP optimizer + scheduler: null + + offload_optimizer_for_logprob: False + + generation: + backend: "vllm" + max_new_tokens: ${policy.max_total_sequence_length} + temperature: 1.0 + top_p: 1.0 + top_k: null + stop_token_ids: null + stop_strings: null + vllm_cfg: + # NB: can re-enable prefix cache on vllm >= 0.11.2. + # enable_prefix_caching: false + async_engine: false + kv_cache_dtype: auto + precision: ${policy.precision} + tensor_parallel_size: 4 + pipeline_parallel_size: 1 + expert_parallel_size: 1 # When EP > 1, EP must be a multiple of TP since vLLM's EP = DP * TP + gpu_memory_utilization: 0.5 + max_model_len: ${policy.max_total_sequence_length} + # when enforce_eager is False, it is optional to set ++policy.generation.vllm_kwargs.compilation_config.use_inductor=False for better accuracy, + # with the flag, vllm will use the custom CUDA kernels instead of the Triton kernels generated by torch.compile + # for more details, see convergence issue https://github.com/NVIDIA-NeMo/RL/issues/998 + enforce_eager: False + use_deep_gemm: False + num_last_layers_in_bf16: 0 + num_first_layers_in_bf16: 0 + expose_http_server: true + http_server_serving_chat_kwargs: + enable_auto_tools: true + tool_parser: qwen3_coder + reasoning_parser: deepseek_r1 + + vllm_kwargs: + mamba_ssm_cache_dtype: "float32" + compilation_config: + # when enforce_eager is False, set ++policy.generation.vllm_kwargs.compilation_config.use_inductor=False for better accuracy, + # with the flag, vllm will use the custom CUDA kernels instead of the Triton kernels generated by torch.compile + # for more details, see convergence issue https://github.com/NVIDIA-NeMo/RL/issues/998 + use_inductor: False + colocated: + # true: generation shares training GPUs + # false: uses dedicated generation resources + enabled: true + # only relevant when enabled is false + resources: + gpus_per_node: null # Decides num gpus to be dedicated to generation when there is one node in the cluster i.e cluster.num_nodes == 1 + num_nodes: null # Decides number of nodes to be dedicated to generation + +data: + train_jsonl_fpath: ${art:data,train} + validation_jsonl_fpath: ${art:data,train} + shuffle: False + num_workers: 1 + +env: + should_use_nemo_gym: true + nemo_gym: + config_paths: + - responses_api_models/vllm_model/configs/vllm_model_for_training.yaml # Required! And it must be *for_training + - resources_servers/math_with_judge/configs/math_with_judge.yaml + - resources_servers/code_gen/configs/code_gen.yaml + - resources_servers/workplace_assistant/configs/workplace_assistant.yaml + - resources_servers/mcqa/configs/mcqa.yaml + - resources_servers/instruction_following/configs/instruction_following.yaml + - resources_servers/structured_outputs/configs/structured_outputs_json.yaml + math_with_judge: + resources_servers: + math_with_judge: + judge_model_server: + name: policy_model + should_use_judge: false + code_gen: + resources_servers: + code_gen: + num_processes: 1024 + unit_test_timeout_secs: 10 + debug: false + +logger: + log_dir: "logs" # Base directory for all logs + num_val_samples_to_print: 0 # Number of validation samples to pretty print on terminal + wandb_enabled: true + tensorboard_enabled: false + mlflow_enabled: false # Disable MLflow logging + monitor_gpus: true # If true, will monitor GPU usage and log to wandb and/or tensorboard + swanlab_enabled: false # Disable SwanLab logging + wandb: + project: "${run.wandb.project}" + name: "${run.wandb.entity}" + tensorboard: {} + mlflow: + experiment_name: "grpo-dev" + run_name: "grpo-dev-logger" + gpu_monitoring: + collection_interval: 10 # How often to collect GPU usage metrics (in seconds) + flush_interval: 10 # How often to flush GPU usage metrics to the loggers (in seconds) + +cluster: + gpus_per_node: 8 + num_nodes: 1 diff --git a/src/nemotron/recipes/nano3/stage2_rl/data_prep.py b/src/nemotron/recipes/nano3/stage2_rl/data_prep.py new file mode 100644 index 0000000..2196067 --- /dev/null +++ b/src/nemotron/recipes/nano3/stage2_rl/data_prep.py @@ -0,0 +1,381 @@ +#!/usr/bin/env python3 + +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Data preparation for Nano3 RL stage. + +Processes the nvidia/Nemotron-3-Nano-RL-Training-Blend dataset and resolves +placeholder entries that reference external HuggingFace datasets (DAPO, Skywork). + +Placeholder records have an `_hf_placeholder` field containing row indices and +question templates. This script: +1. Detects placeholder records by the presence of `_hf_placeholder` field +2. Fetches the actual data from the external HF dataset +3. Applies template restoration (DAPO prefix/suffix, Skywork {question} replacement) +4. Outputs resolved JSONL with train/val/test splits + +For simple copy/passthrough (no placeholder resolution), use data_prep_copy.py instead. + +Usage: + # With default config + python data_prep.py + + # With custom config file + python data_prep.py --config /path/to/config.yaml + + # With CLI overrides (Hydra-style) + python data_prep.py sample=100 force=true + + # Via nemotron CLI with nemo-run + nemotron nano3 data prep rl --sample 10000 +""" + +from __future__ import annotations + +import json +import os +import sys +import time +from dataclasses import dataclass, field +from pathlib import Path + +from nemotron.data_prep import ( + DataBlend, + Dataset, + OutputConfig, + PipelineConfig, + last_mile_process, +) +from nemotron.data_prep.config import DatasetConfig, JsonlOutputConfig +from nemotron.data_prep.discovery import get_dataset_metadata +from nemotron.data_prep.formats.transforms import resolve_hf_placeholders +from nemotron.data_prep.hf_placeholder import HFPlaceholderResolver +from nemotron.kit import SplitJsonlDataArtifact, print_step_complete +from nemotron.kit.trackers import InputDatasetInfo +from nemotron.kit.train_script import ( + apply_hydra_overrides, + init_wandb_from_env, + load_omegaconf_yaml, + omegaconf_to_dataclass, + parse_config_and_overrides, +) +from nemotron.kit.wandb import add_wandb_tags, finish_wandb + +STAGE_PATH = Path(__file__).parent + +# Default config path relative to this file +DEFAULT_CONFIG_PATH = STAGE_PATH / "config" / "data_prep" / "default.yaml" + +# Use NEMO_RUN_DIR for output when running via nemo-run (avoids writing to code dir) +_OUTPUT_BASE = Path(os.environ.get("NEMO_RUN_DIR", ".")) + +# Module-level flag for Ray execution (used by nemotron CLI) +RAY = True + + +@dataclass +class RLDataPrepConfig: + """RL data preparation config with HuggingFace placeholder resolution. + + Processes nvidia/Nemotron-3-Nano-RL-Training-Blend and resolves placeholder + entries by fetching from external datasets (DAPO, Skywork). + + Outputs JSONL with resolved records containing: + - question: Full question text with template applied + - expected_answer: Answer from source dataset + - responses_create_params: OpenAI-format messages for RL training + + For simple copy/passthrough, use data_prep_copy.py instead. + """ + + blend_path: Path = field( + default_factory=lambda: STAGE_PATH / "config" / "data_prep" / "data_blend_raw.json" + ) + """Path to data blend JSON file""" + + output_dir: Path = field(default_factory=lambda: _OUTPUT_BASE / "output/nano3/stage2_rl_resolved") + """Output directory for resolved JSONL data""" + + shard_size: str = "256MB" + """Target size per shard (e.g., '256MB', '1GB')""" + + sample: int | None = None + """Limit rows per dataset (for quick tests)""" + + num_actors: int | None = None + """Ray actors for parallel processing (None = auto)""" + + force: bool = False + """Force new run, ignoring cache""" + + def __post_init__(self) -> None: + # Ensure paths are Path objects + if isinstance(self.blend_path, str): + self.blend_path = Path(self.blend_path) + if isinstance(self.output_dir, str): + self.output_dir = Path(self.output_dir) + + # Add sample suffix to output_dir if sampling + if self.sample is not None: + self.output_dir = self.output_dir / f"sample-{self.sample}" + + +def _run_resolve( + blend: DataBlend, + cfg: RLDataPrepConfig, + num_actors: int, + source_datasets: list[InputDatasetInfo], + resolver: HFPlaceholderResolver, +) -> SplitJsonlDataArtifact: + """Process blend with HuggingFace placeholder resolution. + + Downloads the HF dataset and outputs JSONL files for each split found + in the dataset (train, validation, test), resolving any placeholder records. + """ + from datasets import get_dataset_split_names + + start_time = time.time() + total_sequences = 0 + split_paths: dict[str, Path] = {} + + # Get the dataset from blend (expects single dataset in blend) + if len(blend.datasets) != 1: + raise ValueError( + f"Resolve mode expects exactly one dataset in blend, got {len(blend.datasets)}." + ) + + dataset = blend.datasets[0] + + # Handle hf:// prefix if present + dataset_path = dataset.path + if dataset_path.startswith("hf://"): + dataset_path = dataset_path[5:] + + # Discover available splits from HF + available_splits = get_dataset_split_names(dataset_path) + + # Normalize split names for output directories + # HF uses "validation" but we output as "val" for consistency + split_name_mapping = { + "train": "train", + "validation": "val", + "test": "test", + } + + # Create the resolve transform with pre-loaded resolver + transform = resolve_hf_placeholders(resolver=resolver) + + # Process each split + for hf_split in available_splits: + output_split_name = split_name_mapping.get(hf_split, hf_split) + split_output_dir = cfg.output_dir / output_split_name + + # Create a single-dataset blend for this split + split_blend = DataBlend( + datasets=[ + Dataset( + name=dataset.name, + path=dataset.path, + split=hf_split, # Use the HF split name + subset=dataset.subset, + weight=1.0, + text_field=dataset.text_field, + ) + ] + ) + + # Build pipeline config with resolve transform + format_config = JsonlOutputConfig( + shard_size=cfg.shard_size, + transform=transform, + ) + + pipeline_config = PipelineConfig( + output=OutputConfig( + dir=split_output_dir, + format=format_config, + max_rows=cfg.sample, + ), + tokenizer=None, + num_actors=num_actors, + force=cfg.force, + ) + + # Run processing for this split + result = last_mile_process(split_blend, pipeline_config) + total_sequences += result.total_sequences + + # Extract actual JSONL shard path from result + # data_paths format: ["weight", "shard_prefix", ...] + shard_prefix = result.splits["all"].data_paths[1] + # Find the actual JSONL file by globbing (shard naming uses variable digit padding) + shard_files = sorted(Path(shard_prefix).parent.glob("shard_*.jsonl")) + if shard_files: + jsonl_path = shard_files[0] + else: + raise FileNotFoundError(f"No JSONL shard files found at {shard_prefix}") + + split_paths[output_split_name] = str(jsonl_path.resolve()) + + # Resolve output_dir to absolute path for W&B artifact storage + output_dir = cfg.output_dir.resolve() + output_dir.mkdir(parents=True, exist_ok=True) + + # Resolve split paths to absolute paths + resolved_split_paths = {k: str(Path(v).resolve()) for k, v in split_paths.items() if v} + + # Create a combined manifest with absolute paths + manifest = { + "train": resolved_split_paths.get("train", ""), + "val": resolved_split_paths.get("val", ""), + "test": resolved_split_paths.get("test", ""), + "mode": "resolve", + "source_splits": available_splits, + } + + manifest_path = output_dir / "manifest.json" + with open(manifest_path, "w") as f: + json.dump(manifest, f, indent=2) + + elapsed = time.time() - start_time + + # Build artifact with split paths as typed fields + artifact = SplitJsonlDataArtifact( + path=manifest_path, + total_sequences=total_sequences, + elapsed_sec=elapsed, + source_datasets=source_datasets, + train=resolved_split_paths.get("train"), + val=resolved_split_paths.get("val"), + test=resolved_split_paths.get("test"), + ) + + return artifact + + +def run_data_prep_main(cfg: RLDataPrepConfig) -> SplitJsonlDataArtifact: + """Run RL data preparation with placeholder resolution. + + Args: + cfg: Resolve data prep configuration. + + Returns: + SplitJsonlDataArtifact with paths to resolved JSONL data. + """ + # Add stage-specific tags to wandb run + add_wandb_tags(["data-prep", "rl"]) + + # Load data blend + blend = DataBlend.load(cfg.blend_path) + + # Auto-detect num_actors from CPU count + num_actors = cfg.num_actors + if num_actors is None: + cpu_count = os.cpu_count() or 4 + num_actors = max(2, min(32, cpu_count * 3 // 4)) + + # Pre-load the HF placeholder resolver (loads DAPO and Skywork datasets) + print("Loading external HuggingFace datasets for placeholder resolution...") + resolver = HFPlaceholderResolver.create() + + # Collect source datasets with metadata for lineage tracking + source_datasets: list[InputDatasetInfo] = [] + seen_keys: set[str] = set() + for dataset in blend.datasets: + # Use path+subset as key since same path can have different subsets + key = f"{dataset.path}|{dataset.subset or ''}" + if key not in seen_keys: + seen_keys.add(key) + ds_config = DatasetConfig( + name=dataset.name, + path=dataset.path, + split=dataset.split, + subset=dataset.subset, + text_field=dataset.text_field, + ) + hf_metadata = get_dataset_metadata(ds_config) + source_datasets.append( + InputDatasetInfo( + uri=dataset.path, + name=dataset.name, + weight=dataset.weight, + split=dataset.split, + subset=dataset.subset, + text_field=dataset.text_field, + num_rows=hf_metadata.num_rows, + size_bytes=hf_metadata.size_bytes, + ) + ) + + # Add external placeholder datasets (DAPO, Skywork) for lineage tracking + for ext_ds_info in resolver.get_loaded_datasets_info(): + source_datasets.append( + InputDatasetInfo( + uri=ext_ds_info["uri"], + name=ext_ds_info["name"], + split=ext_ds_info["split"], + num_rows=ext_ds_info["num_rows"], + ) + ) + + # Run resolve processing + artifact = _run_resolve(blend, cfg, num_actors, source_datasets, resolver) + + artifact.name = f"nano3/rl/data-resolved{'?sample=' + str(cfg.sample) if cfg.sample else ''}" + artifact.save() + + # Mark wandb run as successful + finish_wandb(exit_code=0) + + print_step_complete(data_prep=artifact) + return artifact + + +def main(cfg: RLDataPrepConfig | None = None) -> SplitJsonlDataArtifact: + """Entry point for RL data preparation with placeholder resolution. + + Args: + cfg: Config from CLI framework, or None when run directly as script. + + Returns: + SplitJsonlDataArtifact with paths to resolved JSONL data. + """ + if cfg is None: + # Called directly as script - parse config ourselves + config_path, cli_overrides = parse_config_and_overrides(default_config=DEFAULT_CONFIG_PATH) + + # Load YAML config + try: + config = load_omegaconf_yaml(config_path) + except FileNotFoundError as e: + print(f"Error: {e}", file=sys.stderr) + sys.exit(1) + + # Apply CLI overrides (Hydra-style: key=value) + if cli_overrides: + config = apply_hydra_overrides(config, cli_overrides) + + # Convert to dataclass + cfg = omegaconf_to_dataclass(config, RLDataPrepConfig) + + # Initialize wandb from environment variables (set by nemo-run) + init_wandb_from_env() + + # Run data prep + return run_data_prep_main(cfg) + + +if __name__ == "__main__": + main() diff --git a/src/nemotron/recipes/nano3/stage2_rl/data_prep_merge.py b/src/nemotron/recipes/nano3/stage2_rl/data_prep_merge.py new file mode 100644 index 0000000..f800572 --- /dev/null +++ b/src/nemotron/recipes/nano3/stage2_rl/data_prep_merge.py @@ -0,0 +1,417 @@ +#!/usr/bin/env python3 + +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Data preparation for Nano3 RL stage (merge mode). + +Merges datasets from a raw data blend into a single JSONL output, then splits +into train/val/test based on configurable ratios. + +This is useful when you have multiple source datasets that need to be combined +and then split into training splits, rather than using pre-existing HF splits. + +For placeholder resolution (DAPO, Skywork datasets), use data_prep.py instead. + +Usage: + # With default config + python data_prep_merge.py + + # With custom config file + python data_prep_merge.py --config /path/to/config.yaml + + # With CLI overrides (Hydra-style) + python data_prep_merge.py sample=100 force=true train_ratio=0.9 + + # Via nemotron CLI with nemo-run + nemotron nano3 data prep rl --run data_prep_merge --sample 10000 +""" + +from __future__ import annotations + +import json +import os +import shutil +import sys +import time +from dataclasses import dataclass, field +from pathlib import Path + +from nemotron.data_prep import ( + DataBlend, + OutputConfig, + PipelineConfig, + last_mile_process, +) +from nemotron.data_prep.config import DatasetConfig, JsonlOutputConfig +from nemotron.data_prep.discovery import get_dataset_metadata +from nemotron.data_prep.formats.transforms import passthrough +from nemotron.kit import SplitJsonlDataArtifact, print_step_complete +from nemotron.kit.trackers import InputDatasetInfo +from nemotron.kit.train_script import ( + apply_hydra_overrides, + init_wandb_from_env, + load_omegaconf_yaml, + omegaconf_to_dataclass, + parse_config_and_overrides, +) +from nemotron.kit.wandb import add_wandb_tags, finish_wandb + +STAGE_PATH = Path(__file__).parent + +# Default config path relative to this file +DEFAULT_CONFIG_PATH = STAGE_PATH / "config" / "data_prep" / "merge.yaml" + +# Use NEMO_RUN_DIR for output when running via nemo-run (avoids writing to code dir) +_OUTPUT_BASE = Path(os.environ.get("NEMO_RUN_DIR", ".")) + +# Module-level flag for Ray execution (used by nemotron CLI) +RAY = True + + +@dataclass +class RLMergeDataPrepConfig: + """RL data preparation config (merge mode). + + Merges datasets from a raw data blend into JSONL, then splits into + train/val/test based on configurable ratios. + """ + + blend_path: Path = field( + default_factory=lambda: STAGE_PATH / "config" / "data_prep" / "data_blend_raw.json" + ) + """Path to data blend JSON file""" + + output_dir: Path = field(default_factory=lambda: _OUTPUT_BASE / "output/nano3/stage2_rl") + """Output directory for JSONL data""" + + shard_size: str = "256MB" + """Target size per shard (e.g., '256MB', '1GB')""" + + # Split ratios (must sum to 1.0) + train_ratio: float = 0.98 + """Fraction of data for training split""" + + valid_ratio: float = 0.01 + """Fraction of data for validation split""" + + test_ratio: float = 0.01 + """Fraction of data for test split""" + + sample: int | None = None + """Limit rows per dataset (for quick tests)""" + + num_actors: int | None = None + """Ray actors for parallel processing (None = auto)""" + + force: bool = False + """Force new run, ignoring cache""" + + seed: int = 42 + """Random seed for shuffling before split""" + + def __post_init__(self) -> None: + # Ensure paths are Path objects + if isinstance(self.blend_path, str): + self.blend_path = Path(self.blend_path) + if isinstance(self.output_dir, str): + self.output_dir = Path(self.output_dir) + + # Validate split ratios sum to 1.0 + total_ratio = self.train_ratio + self.valid_ratio + self.test_ratio + if abs(total_ratio - 1.0) > 1e-6: + raise ValueError( + f"Split ratios must sum to 1.0, got {total_ratio} " + f"(train={self.train_ratio}, valid={self.valid_ratio}, test={self.test_ratio})" + ) + + # Add sample suffix to output_dir if sampling + if self.sample is not None: + self.output_dir = self.output_dir / f"sample-{self.sample}" + + +def _split_jsonl_files( + shards_dir: Path, + output_dir: Path, + train_ratio: float, + valid_ratio: float, + test_ratio: float, + seed: int = 42, +) -> dict[str, dict]: + """Load all JSONL shards, shuffle, split by ratio, and save to split directories. + + Args: + shards_dir: Directory containing shard_*.jsonl files from pipeline. + output_dir: Directory to write train/, val/, test/ subdirectories. + train_ratio: Fraction of data for training. + valid_ratio: Fraction of data for validation. + test_ratio: Fraction of data for test. + seed: Random seed for shuffling before split. + + Returns: + Dict with split statistics and paths. + """ + import glob + import random + + # Find all shard files + shard_pattern = str(shards_dir / "**" / "*.jsonl") + shard_files = sorted(glob.glob(shard_pattern, recursive=True)) + + if not shard_files: + raise ValueError(f"No JSONL shard files found matching {shard_pattern}") + + print(f"Loading {len(shard_files)} shard files from {shards_dir}") + + # Load all records from shards + all_records: list[str] = [] + for shard_file in shard_files: + with open(shard_file) as f: + for line in f: + line = line.strip() + if line: + all_records.append(line) + + total_records = len(all_records) + print(f"Total records loaded: {total_records}") + + # Shuffle before splitting (for reproducibility) + random.seed(seed) + random.shuffle(all_records) + + # Calculate split boundaries + train_end = int(total_records * train_ratio) + valid_end = train_end + int(total_records * valid_ratio) + + # Split records + train_records = all_records[:train_end] + valid_records = all_records[train_end:valid_end] + test_records = all_records[valid_end:] + + # Create output directories + train_dir = output_dir / "train" + val_dir = output_dir / "val" + test_dir = output_dir / "test" + + for d in [train_dir, val_dir, test_dir]: + d.mkdir(parents=True, exist_ok=True) + + # Write split files + def write_split(records: list[str], split_dir: Path, split_name: str) -> Path: + output_path = split_dir / f"{split_name}.jsonl" + with open(output_path, "w") as f: + for record in records: + f.write(record + "\n") + print(f"Saved {len(records)} records to {output_path}") + return output_path + + train_path = write_split(train_records, train_dir, "train") + val_path = write_split(valid_records, val_dir, "val") + test_path = write_split(test_records, test_dir, "test") + + return { + "train": {"sequences": len(train_records), "path": train_path}, + "val": {"sequences": len(valid_records), "path": val_path}, + "test": {"sequences": len(test_records), "path": test_path}, + "total_sequences": total_records, + } + + +def _run_merge( + blend: DataBlend, + cfg: RLMergeDataPrepConfig, + num_actors: int, + source_datasets: list[InputDatasetInfo], +) -> SplitJsonlDataArtifact: + """Merge datasets and split into train/val/test. + + Processes all datasets in the blend, merges them together, shuffles, + and splits into train/val/test based on configured ratios. + """ + start_time = time.time() + + # Use a temporary shards directory for pipeline output + shards_dir = cfg.output_dir / "_shards" + + # Build pipeline config with passthrough transform + format_config = JsonlOutputConfig( + shard_size=cfg.shard_size, + transform=passthrough(), + ) + + pipeline_config = PipelineConfig( + output=OutputConfig( + dir=shards_dir, + format=format_config, + max_rows=cfg.sample, + ), + tokenizer=None, + num_actors=num_actors, + force=cfg.force, + ) + + # Run processing pipeline to generate merged shards + print("Running pipeline to merge datasets...") + result = last_mile_process(blend, pipeline_config) + + # Split the merged data by ratio + print("Splitting merged data by ratio...") + split_stats = _split_jsonl_files( + shards_dir=shards_dir, + output_dir=cfg.output_dir, + train_ratio=cfg.train_ratio, + valid_ratio=cfg.valid_ratio, + test_ratio=cfg.test_ratio, + seed=cfg.seed, + ) + + # Clean up intermediate shards directory + if shards_dir.exists(): + print(f"Cleaning up intermediate shards directory: {shards_dir}") + shutil.rmtree(shards_dir) + + # Create a combined manifest + manifest = { + "train": str(split_stats["train"]["path"]), + "val": str(split_stats["val"]["path"]), + "test": str(split_stats["test"]["path"]), + "mode": "merge", + "train_ratio": cfg.train_ratio, + "valid_ratio": cfg.valid_ratio, + "test_ratio": cfg.test_ratio, + "seed": cfg.seed, + "total_sequences": split_stats["total_sequences"], + } + + manifest_path = cfg.output_dir / "manifest.json" + with open(manifest_path, "w") as f: + json.dump(manifest, f, indent=2) + + elapsed = time.time() - start_time + + # Build artifact + artifact = SplitJsonlDataArtifact( + path=manifest_path, + total_sequences=split_stats["total_sequences"], + elapsed_sec=elapsed, + source_datasets=source_datasets, + ) + + # Add split paths to metadata for artifact resolution + artifact.metadata["train"] = str(split_stats["train"]["path"]) + artifact.metadata["val"] = str(split_stats["val"]["path"]) + artifact.metadata["test"] = str(split_stats["test"]["path"]) + + return artifact + + +def run_data_prep_main(cfg: RLMergeDataPrepConfig) -> SplitJsonlDataArtifact: + """Run RL data preparation (merge mode). + + Args: + cfg: RL merge data prep configuration. + + Returns: + SplitJsonlDataArtifact with paths to JSONL data. + """ + # Add stage-specific tags to wandb run + add_wandb_tags(["data-prep", "rl", "merge"]) + + # Load data blend + blend = DataBlend.load(cfg.blend_path) + + # Auto-detect num_actors from CPU count + num_actors = cfg.num_actors + if num_actors is None: + cpu_count = os.cpu_count() or 4 + num_actors = max(2, min(32, cpu_count * 3 // 4)) + + # Collect source datasets with metadata for lineage tracking + source_datasets: list[InputDatasetInfo] = [] + seen_keys: set[str] = set() + for dataset in blend.datasets: + # Use path+subset as key since same path can have different subsets + key = f"{dataset.path}|{dataset.subset or ''}" + if key not in seen_keys: + seen_keys.add(key) + ds_config = DatasetConfig( + name=dataset.name, + path=dataset.path, + split=dataset.split, + subset=dataset.subset, + text_field=dataset.text_field, + ) + hf_metadata = get_dataset_metadata(ds_config) + source_datasets.append( + InputDatasetInfo( + uri=dataset.path, + name=dataset.name, + weight=dataset.weight, + split=dataset.split, + subset=dataset.subset, + text_field=dataset.text_field, + num_rows=hf_metadata.num_rows, + size_bytes=hf_metadata.size_bytes, + ) + ) + + # Run merge processing + artifact = _run_merge(blend, cfg, num_actors, source_datasets) + + artifact.name = f"nano3/rl/data-merged{'?sample=' + str(cfg.sample) if cfg.sample else ''}" + artifact.save() + + # Mark wandb run as successful + finish_wandb(exit_code=0) + + print_step_complete(data_prep=artifact) + return artifact + + +def main(cfg: RLMergeDataPrepConfig | None = None) -> SplitJsonlDataArtifact: + """Entry point for RL data preparation (merge mode). + + Args: + cfg: Config from CLI framework, or None when run directly as script. + + Returns: + SplitJsonlDataArtifact with paths to JSONL data. + """ + if cfg is None: + # Called directly as script - parse config ourselves + config_path, cli_overrides = parse_config_and_overrides(default_config=DEFAULT_CONFIG_PATH) + + # Load YAML config + try: + config = load_omegaconf_yaml(config_path) + except FileNotFoundError as e: + print(f"Error: {e}", file=sys.stderr) + sys.exit(1) + + # Apply CLI overrides (Hydra-style: key=value) + if cli_overrides: + config = apply_hydra_overrides(config, cli_overrides) + + # Convert to dataclass + cfg = omegaconf_to_dataclass(config, RLMergeDataPrepConfig) + + # Initialize wandb from environment variables (set by nemo-run) + init_wandb_from_env() + + # Run data prep + return run_data_prep_main(cfg) + + +if __name__ == "__main__": + main() diff --git a/src/nemotron/recipes/nano3/stage2_rl/train.py b/src/nemotron/recipes/nano3/stage2_rl/train.py new file mode 100644 index 0000000..cb0511d --- /dev/null +++ b/src/nemotron/recipes/nano3/stage2_rl/train.py @@ -0,0 +1,319 @@ +#!/usr/bin/env python3 + +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""RL (Reinforcement Learning) script for Nemotron Nano3. + +Uses NeMo-RL's GRPO algorithm for reinforcement learning training. +This script is designed to run inside a container with NeMo-RL installed. + +Usage: + # Direct execution inside container (nemo-run with Ray) + python /path/to/train.py --config /path/to/grpo_config.yaml + + # With CLI overrides (Hydra syntax) + python /path/to/train.py --config /path/to/grpo_config.yaml \ + grpo.num_iterations=100 \ + policy.generation.temperature=0.7 +""" + +from __future__ import annotations + +# Flag to indicate this module requires Ray execution +RAY = True + +import argparse # noqa: E402 +import json # noqa: E402 +import os # noqa: E402 +import pprint # noqa: E402 +from itertools import chain, repeat # noqa: E402 +from typing import TYPE_CHECKING # noqa: E402 + +if TYPE_CHECKING: + from nemo_rl.algorithms.grpo import MasterConfig + + +def parse_args() -> tuple[argparse.Namespace, list[str]]: + """Parse command line arguments.""" + parser = argparse.ArgumentParser( + description="Run GRPO training for Nemotron Nano3", + formatter_class=argparse.RawTextHelpFormatter, + ) + parser.add_argument( + "--config", + type=str, + default=None, + help="Path to YAML config file", + ) + + args, overrides = parser.parse_known_args() + return args, overrides + + +def setup_single_nemo_gym_dataset(jsonl_fpath: str, tokenizer, num_repeats: int | None = None): + """Load and prepare a NeMo-Gym dataset from JSONL file.""" + from nemo_rl.data.datasets import AllTaskProcessedDataset + from nemo_rl.data.interfaces import DatumSpec + from nemo_rl.environments.nemo_gym import nemo_gym_example_to_nemo_rl_datum_spec + + with open(jsonl_fpath) as f: + nemo_gym_examples = list(map(json.loads, f)) + + print(f"Loaded data at {jsonl_fpath}. Found {len(nemo_gym_examples)} examples") + + if num_repeats: + previous_length = len(nemo_gym_examples) + nemo_gym_examples = list( + chain.from_iterable( + repeat(nemo_gym_example, num_repeats) for nemo_gym_example in nemo_gym_examples + ) + ) + print( + f"Repeating examples (in a pattern of abc to aabbcc) for {jsonl_fpath} " + f"from {previous_length} to {len(nemo_gym_examples)}!" + ) + + nemo_rl_compatible_examples: list[DatumSpec] = [ + nemo_gym_example_to_nemo_rl_datum_spec(nemo_gym_example, idx) + for idx, nemo_gym_example in enumerate(nemo_gym_examples) + ] + + def passthrough_task_processor(datum_dict, *args, **kwargs): + return datum_dict + + return AllTaskProcessedDataset( + nemo_rl_compatible_examples, + tokenizer, + None, + passthrough_task_processor, + ) + + +def main() -> None: + """Main entry point for GRPO training.""" + # ------------------------------------------------------------------------- + # WANDB MONKEY-PATCHES + # These patches work around bugs in wandb and NeMo-RL. + # See nemotron/kit/wandb.py for detailed "Why" / "Remove when" documentation. + # Applied early, before any wandb imports/init. + # ------------------------------------------------------------------------- + from nemotron.kit.wandb import ( + patch_nemo_rl_checkpoint_logging, + patch_wandb_http_handler_skip_digest_verification, + patch_wandb_local_file_handler_skip_digest_verification, + patch_wandb_runid_for_seeded_random, + ) + + patch_wandb_http_handler_skip_digest_verification() + patch_wandb_local_file_handler_skip_digest_verification() + patch_wandb_runid_for_seeded_random() + patch_nemo_rl_checkpoint_logging() + + # Increase W&B single object size warning threshold + import wandb.util + + wandb.util.VALUE_BYTES_LIMIT = 10_000_000 + + import ray + from nemo_rl.algorithms.grpo import ( + _should_use_nemo_gym, + grpo_train, + setup, + ) + from nemo_rl.algorithms.utils import get_tokenizer + from nemo_rl.distributed.ray_actor_environment_registry import get_actor_python_env + from nemo_rl.distributed.virtual_cluster import init_ray + from nemo_rl.environments.nemo_gym import ( + NemoGym, + NemoGymConfig, + setup_nemo_gym_config, + ) + from nemo_rl.models.generation import configure_generation_config + from nemo_rl.utils.config import load_config, parse_hydra_overrides + from nemo_rl.utils.logger import get_next_experiment_dir + from omegaconf import OmegaConf + + OmegaConf.register_new_resolver("mul", lambda a, b: a * b) + + # Parse arguments + args, overrides = parse_args() + + # Use default config if not specified + if not args.config: + args.config = os.path.join( + os.path.dirname(__file__), + "config", + "default.yaml", + ) + + config = load_config(args.config) + print(f"Loaded configuration from: {args.config}") + + if overrides: + print(f"Overrides: {overrides}") + config = parse_hydra_overrides(config, overrides) + + # Register nemotron artifact resolver for ${art:...} interpolations + # This must happen before OmegaConf.to_container() resolves the config + from nemotron.kit.resolvers import clear_artifact_cache, register_resolvers_from_config + + clear_artifact_cache() + register_resolvers_from_config( + config, + artifacts_key="run", + mode="pre_init", + pre_init_patch_http_digest=False, + ) + + config: MasterConfig = OmegaConf.to_container(config, resolve=True) + print("Applied CLI overrides") + + # Get the next experiment directory with incremented ID + config["logger"]["log_dir"] = get_next_experiment_dir(config["logger"]["log_dir"]) + print(f"Using log directory: {config['logger']['log_dir']}") + if config["checkpointing"]["enabled"]: + print(f"Using checkpoint directory: {config['checkpointing']['checkpoint_dir']}") + + # Setup tokenizer + tokenizer = get_tokenizer(config["policy"]["tokenizer"]) + assert config["policy"]["generation"] is not None, "A generation config is required for GRPO" + config["policy"]["generation"] = configure_generation_config( + config["policy"]["generation"], tokenizer + ) + + # NeMo-Gym specific config setup + setup_nemo_gym_config(config, tokenizer) + + # We assert here since this is right after the final config has been materialized + assert _should_use_nemo_gym(config) + + print("\nSetting up data...") + train_dataset = setup_single_nemo_gym_dataset( + jsonl_fpath=config["data"]["train_jsonl_fpath"], + tokenizer=tokenizer, + ) + val_dataset = setup_single_nemo_gym_dataset( + jsonl_fpath=config["data"]["validation_jsonl_fpath"], + tokenizer=tokenizer, + ) + + # Validation dataset config setup + if config["grpo"]["max_val_samples"] is not None: + raise ValueError( + "A non-null `grpo.max_val_samples` parameter is not supported. " + "The validation set you pass in will directly be used for validation " + "with no additional preprocessing." + ) + + print( + f"Setting `grpo.max_val_samples` and `grpo.val_batch_size` to the length " + f"of the validation dataset, which is {len(val_dataset)}" + ) + config["grpo"]["max_val_samples"] = len(val_dataset) + config["grpo"]["val_batch_size"] = config["grpo"]["max_val_samples"] + + # Print config + print("Final config:") + pprint.pprint(config) + + # Initialize Ray + init_ray() + + ( + policy, + policy_generation, + cluster, + dataloader, + val_dataloader, + loss_fn, + logger, + checkpointer, + grpo_state, + master_config, + ) = setup(config, tokenizer, train_dataset, val_dataset) + + is_trajectory_collection = ( + config["env"]["nemo_gym"].pop("is_trajectory_collection", False) or False + ) + nemo_gym_config = NemoGymConfig( + model_name=policy_generation.cfg["model_name"], + base_urls=policy_generation.dp_openai_server_base_urls, + initial_global_config_dict=config["env"]["nemo_gym"], + ) + nemo_gym = NemoGym.options( + runtime_env={ + "py_executable": get_actor_python_env("nemo_rl.environments.nemo_gym.NemoGym"), + } + ).remote(nemo_gym_config) + # Blocking wait for NeMo-Gym to spin up + ray.get(nemo_gym.health_check.remote()) + task_to_env = {"nemo_gym": nemo_gym} + val_task_to_env = task_to_env + + if is_trajectory_collection: + from nemo_rl.algorithms.grpo import refit_policy_generation + from nemo_rl.experience.rollouts import run_async_nemo_gym_rollout + from wandb import Table + + # Run trajectory collection + colocated_inference = master_config["policy"]["generation"]["colocated"]["enabled"] + refit_policy_generation(policy, policy_generation, colocated_inference) + + log_filename = "trajectory_collection.jsonl" + print("\nRunning trajectory collection...", flush=True) + generation_config = master_config["policy"]["generation"] + + for val_batch in val_dataloader: + nemo_gym_rollout_result = run_async_nemo_gym_rollout( + policy_generation=policy_generation, + input_batch=val_batch, + tokenizer=tokenizer, + task_to_env=val_task_to_env, + max_seq_len=None, + generation_config=generation_config, + max_rollout_turns=None, + greedy=False, + ) + + rows_to_log: list[str] = [] + for key, value in nemo_gym_rollout_result.rollout_metrics.items(): + if "full_result" not in key: + continue + value: Table + data: list[list[str]] = value.data + rows_to_log.extend(v[0] for v in data) + + logger.log_string_list_as_jsonl(rows_to_log, log_filename) + + policy_generation.finish_generation() + else: + grpo_train( + policy, + policy_generation, + dataloader, + val_dataloader, + tokenizer, + loss_fn, + task_to_env, + val_task_to_env, + logger, + checkpointer, + grpo_state, + master_config, + ) + + +if __name__ == "__main__": + main() diff --git a/tests/data_prep/__init__.py b/tests/data_prep/__init__.py new file mode 100644 index 0000000..8afe18a --- /dev/null +++ b/tests/data_prep/__init__.py @@ -0,0 +1,15 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Tests for data_prep module.""" diff --git a/tests/data_prep/test_ray_data.py b/tests/data_prep/test_ray_data.py new file mode 100644 index 0000000..c3b3719 --- /dev/null +++ b/tests/data_prep/test_ray_data.py @@ -0,0 +1,1336 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Tests for Ray Data shard-task executor. + +These tests verify: +1. ShardTask serialization/deserialization +2. RayDataConfig construction +3. execute_shard_tasks basic execution +4. BinIdxShardTaskUDF initialization +5. Bottleneck metrics tracking +6. process_binidx_shard_core with mocked tokenizer +7. Error handling and idempotency +8. Helper functions for tokenization and file processing +""" + +import json +import os +import tempfile +from pathlib import Path +from unittest.mock import MagicMock, patch + +import numpy as np +import pytest + + +class TestShardTask: + """Tests for ShardTask dataclass.""" + + def test_shard_task_creation(self): + """Test basic ShardTask creation.""" + from nemotron.data_prep.ray_data import ShardTask + + task = ShardTask( + dataset_name="test_dataset", + plan_hash="abc123", + shard_index=0, + assignment_json='{"shard_index": 0, "files": [], "total_bytes": 0}', + output_dir="/output", + receipts_dir="/receipts", + fs_protocol="file", + kind="binidx", + text_field="text", + ) + + assert task.dataset_name == "test_dataset" + assert task.plan_hash == "abc123" + assert task.shard_index == 0 + assert task.fs_protocol == "file" + + def test_shard_task_from_assignment(self): + """Test ShardTask.from_assignment factory method.""" + from nemotron.data_prep.ray_data import ShardTask + + assignment = { + "shard_index": 5, + "files": [{"path": "/data/file.parquet"}], + "total_bytes": 1000, + } + + task = ShardTask.from_assignment( + assignment=assignment, + dataset_name="ds1", + plan_hash="hash123", + shard_index=5, + output_dir="/out", + receipts_dir="/receipts", + fs_protocol="file", + ) + + assert task.shard_index == 5 + assert task.assignment_json == json.dumps(assignment) + + def test_shard_task_get_assignment_roundtrip(self): + """Test assignment JSON serialization roundtrip.""" + from nemotron.data_prep.ray_data import ShardTask + + original_assignment = { + "shard_index": 10, + "files": [ + {"path": "/data/file1.parquet", "bytes": 500}, + {"path": "/data/file2.parquet", "bytes": 500}, + ], + "total_bytes": 1000, + } + + task = ShardTask.from_assignment( + assignment=original_assignment, + dataset_name="ds", + plan_hash="h", + shard_index=10, + output_dir="/o", + receipts_dir="/r", + fs_protocol="file", + ) + + recovered = task.get_assignment() + assert recovered == original_assignment + + def test_shard_task_to_dict(self): + """Test ShardTask.to_dict for Ray Data serialization.""" + from nemotron.data_prep.ray_data import ShardTask + + task = ShardTask( + dataset_name="test", + plan_hash="hash", + shard_index=0, + assignment_json='{"files": []}', + output_dir="/out", + receipts_dir="/receipts", + fs_protocol="file", + ) + + d = task.to_dict() + + assert isinstance(d, dict) + assert d["dataset_name"] == "test" + assert d["plan_hash"] == "hash" + assert d["shard_index"] == 0 + assert d["assignment_json"] == '{"files": []}' + assert d["fs_protocol"] == "file" + assert d["kind"] == "binidx" + + def test_shard_task_empty_assignment(self): + """Test ShardTask with empty files list.""" + from nemotron.data_prep.ray_data import ShardTask + + assignment = {"shard_index": 0, "files": [], "total_bytes": 0} + task = ShardTask.from_assignment( + assignment=assignment, + dataset_name="empty", + plan_hash="h", + shard_index=0, + output_dir="/out", + receipts_dir="/r", + fs_protocol="file", + ) + + assert task.get_assignment()["files"] == [] + assert task.get_assignment()["total_bytes"] == 0 + + def test_shard_task_special_characters_in_paths(self): + """Test ShardTask with special characters in paths.""" + from nemotron.data_prep.ray_data import ShardTask + + task = ShardTask( + dataset_name="test-dataset_v2.0", + plan_hash="abc123def456", + shard_index=999, + assignment_json='{"files": [{"path": "/data/path with spaces/file.parquet"}]}', + output_dir="/output/path-with-dashes", + receipts_dir="/receipts/path_with_underscores", + fs_protocol="s3", + text_field="content", + ) + + assert task.dataset_name == "test-dataset_v2.0" + assert task.fs_protocol == "s3" + d = task.to_dict() + assert "path with spaces" in d["assignment_json"] + + def test_shard_task_large_shard_index(self): + """Test ShardTask with large shard index (stress test).""" + from nemotron.data_prep.ray_data import ShardTask + + task = ShardTask( + dataset_name="big_dataset", + plan_hash="hash", + shard_index=999999, + assignment_json='{"files": []}', + output_dir="/out", + receipts_dir="/r", + fs_protocol="file", + ) + + assert task.shard_index == 999999 + d = task.to_dict() + assert d["shard_index"] == 999999 + + def test_shard_task_complex_assignment_roundtrip(self): + """Test complex nested assignment JSON roundtrip.""" + from nemotron.data_prep.ray_data import ShardTask + + complex_assignment = { + "shard_index": 42, + "files": [ + { + "path": "/data/file1.parquet", + "bytes": 1000000, + "hf_repo_id": "nvidia/test-dataset", + "hf_filename": "train/data-00000.parquet", + "hf_revision": "main", + }, + { + "path": "/data/file2.parquet", + "bytes": 2000000, + "local_path": "/cache/file2.parquet", + }, + ], + "total_bytes": 3000000, + } + + task = ShardTask.from_assignment( + assignment=complex_assignment, + dataset_name="ds", + plan_hash="h", + shard_index=42, + output_dir="/o", + receipts_dir="/r", + fs_protocol="gcs", + ) + + recovered = task.get_assignment() + assert recovered == complex_assignment + assert len(recovered["files"]) == 2 + assert recovered["files"][0]["hf_repo_id"] == "nvidia/test-dataset" + + def test_shard_task_default_values(self): + """Test ShardTask default field values.""" + from nemotron.data_prep.ray_data import ShardTask + + task = ShardTask( + dataset_name="ds", + plan_hash="h", + shard_index=0, + assignment_json="{}", + output_dir="/o", + receipts_dir="/r", + fs_protocol="file", + ) + + assert task.kind == "binidx" + assert task.text_field == "text" + + def test_shard_task_custom_text_field(self): + """Test ShardTask with custom text field.""" + from nemotron.data_prep.ray_data import ShardTask + + task = ShardTask( + dataset_name="ds", + plan_hash="h", + shard_index=0, + assignment_json="{}", + output_dir="/o", + receipts_dir="/r", + fs_protocol="file", + text_field="content", + ) + + assert task.text_field == "content" + d = task.to_dict() + assert d["text_field"] == "content" + + def test_shard_task_frozen(self): + """Test that ShardTask is immutable (frozen dataclass).""" + from nemotron.data_prep.ray_data import ShardTask + + task = ShardTask( + dataset_name="ds", + plan_hash="h", + shard_index=0, + assignment_json="{}", + output_dir="/o", + receipts_dir="/r", + fs_protocol="file", + ) + + with pytest.raises(Exception): # FrozenInstanceError + task.shard_index = 1 + + +class TestRayDataExecConfig: + """Tests for RayDataExecConfig.""" + + def test_default_config(self): + """Test default config values.""" + from nemotron.data_prep.ray_data import RayDataExecConfig + + cfg = RayDataExecConfig() + + assert cfg.min_actors == 2 + assert cfg.max_actors == 32 + assert cfg.cpus_per_actor == 1.0 + assert cfg.max_tasks_in_flight_per_actor == 2 + + def test_custom_config(self): + """Test custom config values.""" + from nemotron.data_prep.ray_data import RayDataExecConfig + + cfg = RayDataExecConfig( + min_actors=4, + max_actors=64, + cpus_per_actor=2.0, + max_tasks_in_flight_per_actor=4, + ) + + assert cfg.min_actors == 4 + assert cfg.max_actors == 64 + assert cfg.cpus_per_actor == 2.0 + assert cfg.max_tasks_in_flight_per_actor == 4 + + +class TestRayDataConfig: + """Tests for RayDataConfig in config.py.""" + + def test_ray_data_config_creation(self): + """Test RayDataConfig creation.""" + from nemotron.data_prep.config import RayDataConfig + + cfg = RayDataConfig( + enabled=True, + min_actors=2, + max_actors=16, + cpus_per_actor=0.5, + max_tasks_in_flight_per_actor=3, + ) + + assert cfg.enabled is True + assert cfg.min_actors == 2 + assert cfg.max_actors == 16 + assert cfg.cpus_per_actor == 0.5 + assert cfg.max_tasks_in_flight_per_actor == 3 + + def test_pipeline_config_with_ray_data(self): + """Test PipelineConfig includes ray_data field.""" + from nemotron.data_prep.config import ( + OutputConfig, + PipelineConfig, + RayDataConfig, + ) + + ray_data_cfg = RayDataConfig(enabled=True, max_actors=8) + + pipeline_cfg = PipelineConfig( + output=OutputConfig(dir=Path("/out")), + ray_data=ray_data_cfg, + ) + + assert pipeline_cfg.ray_data is not None + assert pipeline_cfg.ray_data.enabled is True + assert pipeline_cfg.ray_data.max_actors == 8 + + +class TestBottleneckMetrics: + """Tests for BottleneckMetrics in console.py.""" + + def test_bottleneck_metrics_default(self): + """Test default values.""" + from nemotron.data_prep.console import BottleneckMetrics + + metrics = BottleneckMetrics() + + assert metrics.time_download == 0.0 + assert metrics.time_read == 0.0 + assert metrics.time_tokenize == 0.0 + assert metrics.time_write == 0.0 + assert metrics.num_shards == 0 + assert metrics.num_errors == 0 + + def test_bottleneck_metrics_add_shard_stats(self): + """Test adding shard stats to metrics.""" + from nemotron.data_prep.console import BottleneckMetrics + + metrics = BottleneckMetrics() + + stats = { + "time_download_sec": 1.0, + "time_read_sec": 2.0, + "time_tokenize_sec": 5.0, + "time_write_sec": 1.0, + "time_total_sec": 9.0, + "num_errors": 0, + } + + metrics.add_shard_stats(stats) + + assert metrics.time_download == 1.0 + assert metrics.time_read == 2.0 + assert metrics.time_tokenize == 5.0 + assert metrics.time_write == 1.0 + assert metrics.num_shards == 1 + assert metrics.num_errors == 0 + + def test_bottleneck_metrics_accumulates(self): + """Test that metrics accumulate across multiple shards.""" + from nemotron.data_prep.console import BottleneckMetrics + + metrics = BottleneckMetrics() + + # Add first shard + metrics.add_shard_stats({"time_tokenize_sec": 5.0, "num_errors": 0}) + # Add second shard + metrics.add_shard_stats({"time_tokenize_sec": 3.0, "num_errors": 1}) + + assert metrics.time_tokenize == 8.0 + assert metrics.num_shards == 2 + assert metrics.num_errors == 1 + + def test_bottleneck_metrics_percentages(self): + """Test time breakdown percentage calculation.""" + from nemotron.data_prep.console import BottleneckMetrics + + metrics = BottleneckMetrics() + metrics.time_download = 1.0 # 10% + metrics.time_read = 2.0 # 20% + metrics.time_tokenize = 5.0 # 50% + metrics.time_write = 2.0 # 20% + + pcts = metrics.get_percentages() + + assert abs(pcts["download_pct"] - 10.0) < 0.1 + assert abs(pcts["read_pct"] - 20.0) < 0.1 + assert abs(pcts["tokenize_pct"] - 50.0) < 0.1 + assert abs(pcts["write_pct"] - 20.0) < 0.1 + + def test_bottleneck_metrics_percentages_zero(self): + """Test percentage calculation with zero totals.""" + from nemotron.data_prep.console import BottleneckMetrics + + metrics = BottleneckMetrics() + pcts = metrics.get_percentages() + + assert pcts["download_pct"] == 0 + assert pcts["read_pct"] == 0 + assert pcts["tokenize_pct"] == 0 + assert pcts["write_pct"] == 0 + + +class TestDataPrepConfigRayData: + """Tests for DataPrepConfig with ray_data fields.""" + + def test_data_prep_config_ray_data_defaults(self): + """Test DataPrepConfig ray_data field defaults.""" + from nemotron.data_prep import DataPrepConfig + + cfg = DataPrepConfig() + + assert cfg.ray_data_enabled is True # Enabled by default + assert cfg.ray_data_min_actors == 16 # Start with good parallelism + assert cfg.ray_data_max_actors == 64 # Allow scaling on large nodes + assert cfg.ray_data_cpus_per_actor == 1.0 + assert cfg.ray_data_max_tasks_in_flight == 2 + + def test_data_prep_config_ray_data_custom(self): + """Test DataPrepConfig with custom ray_data settings.""" + from nemotron.data_prep import DataPrepConfig + + cfg = DataPrepConfig( + ray_data_enabled=True, + ray_data_min_actors=4, + ray_data_max_actors=16, + ray_data_cpus_per_actor=2.0, + ray_data_max_tasks_in_flight=4, + ) + + assert cfg.ray_data_enabled is True + assert cfg.ray_data_min_actors == 4 + assert cfg.ray_data_max_actors == 16 + assert cfg.ray_data_cpus_per_actor == 2.0 + assert cfg.ray_data_max_tasks_in_flight == 4 + + +class TestLiveExecutionStatusShardTiming: + """Tests for LiveExecutionStatus.report_shard_timing.""" + + def test_report_shard_timing_updates_bottleneck_metrics(self): + """Test that report_shard_timing updates internal metrics.""" + from nemotron.data_prep.console import LiveExecutionStatus + + status = LiveExecutionStatus() + + stats = { + "time_download_sec": 1.0, + "time_read_sec": 2.0, + "time_tokenize_sec": 5.0, + "time_write_sec": 1.0, + "time_total_sec": 9.0, + } + + status.report_shard_timing(stats) + + assert status._bottleneck_metrics.time_download == 1.0 + assert status._bottleneck_metrics.time_tokenize == 5.0 + assert status._bottleneck_metrics.num_shards == 1 + + +# Skip Ray tests if Ray is not available or not initialized +@pytest.fixture(scope="module") +def ray_initialized(): + """Initialize Ray for tests.""" + import ray + + if not ray.is_initialized(): + ray.init(num_cpus=2, ignore_reinit_error=True) + yield + # Don't shutdown - other tests may need it + + +class TestRayDataExecConfigEdgeCases: + """Additional edge case tests for RayDataExecConfig.""" + + def test_config_frozen(self): + """Test that config is immutable.""" + from nemotron.data_prep.ray_data import RayDataExecConfig + + cfg = RayDataExecConfig() + with pytest.raises(Exception): + cfg.min_actors = 10 + + def test_config_fractional_cpus(self): + """Test fractional CPU allocation.""" + from nemotron.data_prep.ray_data import RayDataExecConfig + + cfg = RayDataExecConfig(cpus_per_actor=0.25) + assert cfg.cpus_per_actor == 0.25 + + +class TestProcessBinidxShardCore: + """Tests for process_binidx_shard_core function.""" + + @pytest.fixture + def mock_tokenize(self): + """Create a mock tokenizer function.""" + + def tokenize(texts): + # Simple tokenizer: split on whitespace + return [[ord(c) for c in text] for text in texts] + + return tokenize + + @pytest.fixture + def temp_dirs(self): + """Create temporary directories for output and receipts.""" + with tempfile.TemporaryDirectory() as tmpdir: + output_dir = os.path.join(tmpdir, "shards") + receipts_dir = os.path.join(tmpdir, "receipts") + os.makedirs(output_dir) + os.makedirs(receipts_dir) + yield { + "output_dir": output_dir, + "receipts_dir": receipts_dir, + "tmpdir": tmpdir, + } + + def test_empty_assignment(self, mock_tokenize, temp_dirs): + """Test processing with empty assignment (no files).""" + from fsspec import filesystem + + from nemotron.data_prep.shard_processor import process_binidx_shard_core + + output_fs = filesystem("file") + assignment = {"shard_index": 0, "files": [], "total_bytes": 0} + + stats = process_binidx_shard_core( + tokenize=mock_tokenize, + text_field="text", + min_doc_chars=None, + max_doc_tokens=None, + dtype="int32", + max_rows=None, + shard_index=0, + assignment=assignment, + plan_hash="testhash", + output_dir=temp_dirs["output_dir"], + receipts_dir=temp_dirs["receipts_dir"], + output_fs=output_fs, + ) + + # Should create empty receipt + receipt_path = os.path.join(temp_dirs["receipts_dir"], "shard_000000.json") + assert os.path.exists(receipt_path) + + with open(receipt_path) as f: + receipt = json.load(f) + assert receipt["status"] == "completed" + assert receipt["stats"]["num_sequences"] == 0 + + def test_idempotency_skip_completed(self, mock_tokenize, temp_dirs): + """Test that processing skips already-completed shards.""" + from fsspec import filesystem + + from nemotron.data_prep.shard_processor import process_binidx_shard_core + + output_fs = filesystem("file") + assignment = {"shard_index": 0, "files": [], "total_bytes": 0} + + # First call - creates receipt + stats1 = process_binidx_shard_core( + tokenize=mock_tokenize, + text_field="text", + min_doc_chars=None, + max_doc_tokens=None, + dtype="int32", + max_rows=None, + shard_index=0, + assignment=assignment, + plan_hash="testhash", + output_dir=temp_dirs["output_dir"], + receipts_dir=temp_dirs["receipts_dir"], + output_fs=output_fs, + ) + + # Second call - should skip and return cached stats + stats2 = process_binidx_shard_core( + tokenize=mock_tokenize, + text_field="text", + min_doc_chars=None, + max_doc_tokens=None, + dtype="int32", + max_rows=None, + shard_index=0, + assignment=assignment, + plan_hash="testhash", + output_dir=temp_dirs["output_dir"], + receipts_dir=temp_dirs["receipts_dir"], + output_fs=output_fs, + ) + + # Both should return stats (second from receipt) + assert stats1["num_sequences"] == 0 + assert stats2["num_sequences"] == 0 + + def test_process_jsonl_file(self, mock_tokenize, temp_dirs): + """Test processing a JSONL file.""" + from fsspec import filesystem + + from nemotron.data_prep.shard_processor import process_binidx_shard_core + + # Create a test JSONL file + jsonl_path = os.path.join(temp_dirs["tmpdir"], "test.jsonl") + with open(jsonl_path, "w") as f: + for i in range(10): + f.write(json.dumps({"text": f"hello world document {i}"}) + "\n") + + output_fs = filesystem("file") + assignment = { + "shard_index": 0, + "files": [{"path": jsonl_path, "local_path": jsonl_path, "size": 100}], + "total_bytes": 100, + } + + stats = process_binidx_shard_core( + tokenize=mock_tokenize, + text_field="text", + min_doc_chars=None, + max_doc_tokens=None, + dtype="int32", + max_rows=None, + shard_index=0, + assignment=assignment, + plan_hash="hash123", + output_dir=temp_dirs["output_dir"], + receipts_dir=temp_dirs["receipts_dir"], + output_fs=output_fs, + ) + + assert stats["num_sequences"] == 10 + assert stats["num_input_rows"] == 10 + assert stats["total_tokens"] > 0 + + # Check output files + assert os.path.exists(os.path.join(temp_dirs["output_dir"], "shard_000000.bin")) + assert os.path.exists(os.path.join(temp_dirs["output_dir"], "shard_000000.idx")) + + def test_min_doc_chars_filter(self, mock_tokenize, temp_dirs): + """Test that min_doc_chars filters short documents.""" + from fsspec import filesystem + + from nemotron.data_prep.shard_processor import process_binidx_shard_core + + # Create JSONL with varying document lengths + jsonl_path = os.path.join(temp_dirs["tmpdir"], "test.jsonl") + with open(jsonl_path, "w") as f: + f.write(json.dumps({"text": "hi"}) + "\n") # 2 chars - filtered + f.write(json.dumps({"text": "hello world"}) + "\n") # 11 chars - kept + f.write(json.dumps({"text": "x"}) + "\n") # 1 char - filtered + + output_fs = filesystem("file") + assignment = { + "shard_index": 0, + "files": [{"path": jsonl_path, "local_path": jsonl_path, "size": 100}], + "total_bytes": 100, + } + + stats = process_binidx_shard_core( + tokenize=mock_tokenize, + text_field="text", + min_doc_chars=5, # Filter docs < 5 chars + max_doc_tokens=None, + dtype="int32", + max_rows=None, + shard_index=0, + assignment=assignment, + plan_hash="hash123", + output_dir=temp_dirs["output_dir"], + receipts_dir=temp_dirs["receipts_dir"], + output_fs=output_fs, + ) + + assert stats["num_sequences"] == 1 # Only "hello world" + assert stats["num_filtered"] == 2 # "hi" and "x" filtered + + def test_max_rows_limit(self, mock_tokenize, temp_dirs): + """Test that max_rows limits processing.""" + from fsspec import filesystem + + from nemotron.data_prep.shard_processor import process_binidx_shard_core + + # Create JSONL with many rows + jsonl_path = os.path.join(temp_dirs["tmpdir"], "test.jsonl") + with open(jsonl_path, "w") as f: + for i in range(100): + f.write(json.dumps({"text": f"document number {i}"}) + "\n") + + output_fs = filesystem("file") + assignment = { + "shard_index": 0, + "files": [{"path": jsonl_path, "local_path": jsonl_path, "size": 1000}], + "total_bytes": 1000, + } + + stats = process_binidx_shard_core( + tokenize=mock_tokenize, + text_field="text", + min_doc_chars=None, + max_doc_tokens=None, + dtype="int32", + max_rows=10, # Only process 10 rows + shard_index=0, + assignment=assignment, + plan_hash="hash123", + output_dir=temp_dirs["output_dir"], + receipts_dir=temp_dirs["receipts_dir"], + output_fs=output_fs, + ) + + assert stats["num_sequences"] == 10 + assert stats["num_input_rows"] == 10 + + def test_timing_metrics_present(self, mock_tokenize, temp_dirs): + """Test that timing metrics are returned.""" + from fsspec import filesystem + + from nemotron.data_prep.shard_processor import process_binidx_shard_core + + jsonl_path = os.path.join(temp_dirs["tmpdir"], "test.jsonl") + with open(jsonl_path, "w") as f: + f.write(json.dumps({"text": "test document"}) + "\n") + + output_fs = filesystem("file") + assignment = { + "shard_index": 0, + "files": [{"path": jsonl_path, "local_path": jsonl_path, "size": 50}], + "total_bytes": 50, + } + + stats = process_binidx_shard_core( + tokenize=mock_tokenize, + text_field="text", + min_doc_chars=None, + max_doc_tokens=None, + dtype="int32", + max_rows=None, + shard_index=0, + assignment=assignment, + plan_hash="hash", + output_dir=temp_dirs["output_dir"], + receipts_dir=temp_dirs["receipts_dir"], + output_fs=output_fs, + ) + + assert "time_total_sec" in stats + assert "time_read_sec" in stats + assert "time_tokenize_sec" in stats + assert "time_write_sec" in stats + assert stats["time_total_sec"] >= 0 + + +class TestTokenizeAndWriteBatchCore: + """Tests for _tokenize_and_write_batch_core helper function.""" + + def test_basic_tokenization(self): + """Test basic batch tokenization and writing.""" + from nemotron.data_prep.shard_processor import _tokenize_and_write_batch_core + + mock_builder = MagicMock() + stats = {"num_truncated": 0, "num_errors": 0} + + def tokenize(texts): + return [[1, 2, 3] for _ in texts] + + texts = ["text1", "text2", "text3"] + _tokenize_and_write_batch_core(texts, mock_builder, stats, tokenize, None) + + # Should call add_documents with 3 processed tokens + mock_builder.add_documents.assert_called_once() + args = mock_builder.add_documents.call_args[0][0] + assert len(args) == 3 + + def test_max_doc_tokens_truncation(self): + """Test that max_doc_tokens truncates long documents.""" + from nemotron.data_prep.shard_processor import _tokenize_and_write_batch_core + + mock_builder = MagicMock() + stats = {"num_truncated": 0, "num_errors": 0} + + def tokenize(texts): + return [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10] for _ in texts] + + texts = ["long text"] + _tokenize_and_write_batch_core(texts, mock_builder, stats, tokenize, max_doc_tokens=5) + + mock_builder.add_documents.assert_called_once() + args = mock_builder.add_documents.call_args[0][0] + assert len(args[0]) == 5 # Truncated to 5 tokens + assert stats["num_truncated"] == 1 + + def test_empty_tokens_filtered(self): + """Test that empty token sequences are filtered out.""" + from nemotron.data_prep.shard_processor import _tokenize_and_write_batch_core + + mock_builder = MagicMock() + stats = {"num_truncated": 0, "num_errors": 0} + + def tokenize(texts): + return [[1, 2, 3], [], [4, 5]] # Middle one is empty + + texts = ["text1", "empty", "text3"] + _tokenize_and_write_batch_core(texts, mock_builder, stats, tokenize, None) + + mock_builder.add_documents.assert_called_once() + args = mock_builder.add_documents.call_args[0][0] + assert len(args) == 2 # Only non-empty sequences + + def test_tokenization_error_triggers_bisect(self): + """Test that tokenization errors trigger bisection.""" + from nemotron.data_prep.shard_processor import _tokenize_and_write_batch_core + + mock_builder = MagicMock() + stats = {"num_truncated": 0, "num_errors": 0} + + call_count = 0 + + def tokenize(texts): + nonlocal call_count + call_count += 1 + if call_count == 1: + raise ValueError("Tokenization error") + return [[1, 2, 3] for _ in texts] + + texts = ["text1", "text2"] + _tokenize_and_write_batch_core(texts, mock_builder, stats, tokenize, None) + + # Should have been called multiple times due to bisection + assert call_count > 1 + + +class TestTokenizeWithBisectCore: + """Tests for _tokenize_with_bisect_core helper function.""" + + def test_single_text_error(self): + """Test that single text error is recorded.""" + from nemotron.data_prep.shard_processor import _tokenize_with_bisect_core + + mock_builder = MagicMock() + stats = {"num_truncated": 0, "num_errors": 0} + + def tokenize(texts): + raise ValueError("Bad text") + + _tokenize_with_bisect_core(["bad text"], mock_builder, stats, tokenize, None) + + assert stats["num_errors"] == 1 + + def test_empty_list(self): + """Test that empty list is handled.""" + from nemotron.data_prep.shard_processor import _tokenize_with_bisect_core + + mock_builder = MagicMock() + stats = {"num_truncated": 0, "num_errors": 0} + + def tokenize(texts): + return [[1, 2, 3] for _ in texts] + + _tokenize_with_bisect_core([], mock_builder, stats, tokenize, None) + + # Should return without calling builder + mock_builder.add_document.assert_not_called() + + def test_isolates_bad_text(self): + """Test that bisection isolates the bad text.""" + from nemotron.data_prep.shard_processor import _tokenize_with_bisect_core + + mock_builder = MagicMock() + stats = {"num_truncated": 0, "num_errors": 0} + + bad_index = 1 + + def tokenize(texts): + for i, t in enumerate(texts): + if t == "bad": + raise ValueError("Bad text") + return [[1, 2, 3] for _ in texts] + + texts = ["good1", "bad", "good2", "good3"] + _tokenize_with_bisect_core(texts, mock_builder, stats, tokenize, None) + + # Should have 1 error (the "bad" text) + assert stats["num_errors"] == 1 + + +class TestBinIdxShardTaskUDFUnit: + """Unit tests for BinIdxShardTaskUDF.""" + + def test_udf_batch_extraction(self): + """Test that UDF correctly extracts fields from batch dict.""" + # We can't fully test without Ray, but we can test the logic + batch = { + "dataset_name": np.array(["test_ds"], dtype=object), + "shard_index": np.array([5], dtype=np.int64), + "plan_hash": np.array(["hash123"], dtype=object), + "assignment_json": np.array(['{"files": [], "shard_index": 5}'], dtype=object), + "output_dir": np.array(["/out"], dtype=object), + "receipts_dir": np.array(["/receipts"], dtype=object), + "fs_protocol": np.array(["file"], dtype=object), + "text_field": np.array(["text"], dtype=object), + } + + # Verify extraction logic + dataset_name = str(batch["dataset_name"][0]) + shard_index = int(batch["shard_index"][0]) + assignment = json.loads(str(batch["assignment_json"][0])) + + assert dataset_name == "test_ds" + assert shard_index == 5 + assert assignment["shard_index"] == 5 + + def test_udf_error_response_format(self): + """Test that error responses have correct numpy array format.""" + # Simulate error response format + error_response = { + "dataset_name": np.array(["test"], dtype=object), + "shard_index": np.array([0], dtype=np.int64), + "plan_hash": np.array(["hash"], dtype=object), + "total_tokens": np.array([0], dtype=np.int64), + "num_sequences": np.array([0], dtype=np.int64), + "num_filtered": np.array([0], dtype=np.int64), + "num_errors": np.array([1], dtype=np.int64), + "time_total_sec": np.array([0.0], dtype=np.float64), + "time_download_sec": np.array([0.0], dtype=np.float64), + "time_read_sec": np.array([0.0], dtype=np.float64), + "time_tokenize_sec": np.array([0.0], dtype=np.float64), + "time_write_sec": np.array([0.0], dtype=np.float64), + "error": np.array(["Test error"], dtype=object), + } + + # All values should be numpy arrays + for key, value in error_response.items(): + assert isinstance(value, np.ndarray), f"{key} should be numpy array" + assert len(value) == 1, f"{key} should have length 1" + + +class TestBottleneckMetricsExtended: + """Extended tests for BottleneckMetrics.""" + + def test_bottleneck_metrics_identifies_download_bottleneck(self): + """Test identification of download as bottleneck.""" + from nemotron.data_prep.console import BottleneckMetrics + + metrics = BottleneckMetrics() + metrics.time_download = 10.0 # 62.5% + metrics.time_read = 2.0 # 12.5% + metrics.time_tokenize = 3.0 # 18.75% + metrics.time_write = 1.0 # 6.25% + + pcts = metrics.get_percentages() + assert pcts["download_pct"] > 50 # Download is bottleneck + + def test_bottleneck_metrics_identifies_tokenize_bottleneck(self): + """Test identification of tokenization as bottleneck.""" + from nemotron.data_prep.console import BottleneckMetrics + + metrics = BottleneckMetrics() + metrics.time_download = 1.0 # 10% + metrics.time_read = 1.0 # 10% + metrics.time_tokenize = 7.0 # 70% + metrics.time_write = 1.0 # 10% + + pcts = metrics.get_percentages() + assert pcts["tokenize_pct"] > 50 # Tokenize is bottleneck + + def test_bottleneck_metrics_handles_missing_keys(self): + """Test that add_shard_stats handles missing keys gracefully.""" + from nemotron.data_prep.console import BottleneckMetrics + + metrics = BottleneckMetrics() + + # Partial stats dict + stats = {"time_tokenize_sec": 5.0} + metrics.add_shard_stats(stats) + + assert metrics.time_tokenize == 5.0 + assert metrics.time_download == 0.0 # Default + assert metrics.num_shards == 1 + + def test_bottleneck_metrics_many_shards(self): + """Test accumulation across many shards.""" + from nemotron.data_prep.console import BottleneckMetrics + + metrics = BottleneckMetrics() + + for i in range(100): + metrics.add_shard_stats( + { + "time_download_sec": 0.1, + "time_read_sec": 0.2, + "time_tokenize_sec": 0.5, + "time_write_sec": 0.2, + "num_errors": 0 if i % 10 != 0 else 1, # 10% error rate + } + ) + + assert metrics.num_shards == 100 + assert abs(metrics.time_download - 10.0) < 0.01 + assert abs(metrics.time_tokenize - 50.0) < 0.01 + assert metrics.num_errors == 10 + + +class TestRayDataConfigIntegration: + """Tests for RayDataConfig integration with PipelineConfig.""" + + def test_ray_data_config_defaults_in_pipeline(self): + """Test default RayDataConfig values in PipelineConfig.""" + from nemotron.data_prep.config import OutputConfig, PipelineConfig + + pipeline_cfg = PipelineConfig(output=OutputConfig(dir=Path("/out"))) + + # ray_data is None by default (not enabled) + assert pipeline_cfg.ray_data is None + + def test_ray_data_config_with_defaults(self): + """Test RayDataConfig with defaults in PipelineConfig.""" + from nemotron.data_prep.config import ( + OutputConfig, + PipelineConfig, + RayDataConfig, + ) + + pipeline_cfg = PipelineConfig( + output=OutputConfig(dir=Path("/out")), + ray_data=RayDataConfig(), # Use defaults + ) + + assert pipeline_cfg.ray_data is not None + assert pipeline_cfg.ray_data.enabled is False + assert pipeline_cfg.ray_data.min_actors == 2 + assert pipeline_cfg.ray_data.max_actors == 32 + + def test_ray_data_config_enabled(self): + """Test enabling RayDataConfig.""" + from nemotron.data_prep.config import ( + OutputConfig, + PipelineConfig, + RayDataConfig, + ) + + pipeline_cfg = PipelineConfig( + output=OutputConfig(dir=Path("/out")), + ray_data=RayDataConfig( + enabled=True, + min_actors=4, + max_actors=16, + ), + ) + + assert pipeline_cfg.ray_data.enabled is True + assert pipeline_cfg.ray_data.min_actors == 4 + assert pipeline_cfg.ray_data.max_actors == 16 + + +class TestWriteEmptyReceiptCore: + """Tests for _write_empty_receipt_core function.""" + + def test_writes_empty_receipt(self): + """Test that empty receipt is written correctly.""" + from fsspec import filesystem + + from nemotron.data_prep.shard_processor import _write_empty_receipt_core + + with tempfile.TemporaryDirectory() as tmpdir: + receipts_dir = os.path.join(tmpdir, "receipts") + os.makedirs(receipts_dir) + receipt_path = os.path.join(receipts_dir, "shard_000000.json") + + output_fs = filesystem("file") + stats = {"num_input_rows": 0, "num_filtered": 0} + timing: dict = {} + + import time + + total_start = time.perf_counter() + + result = _write_empty_receipt_core( + shard_id="shard_000000", + shard_index=0, + plan_hash="testhash", + input_files=[], + stats=stats, + receipt_path=receipt_path, + output_fs=output_fs, + timing=timing, + total_start=total_start, + ) + + assert os.path.exists(receipt_path) + + with open(receipt_path) as f: + receipt = json.load(f) + + assert receipt["status"] == "completed" + assert receipt["stats"]["num_sequences"] == 0 + assert receipt["stats"]["total_tokens"] == 0 + assert "time_total_sec" in result + + +class TestResolveFilePathCore: + """Tests for _resolve_file_path_core function.""" + + def test_local_path_passthrough(self): + """Test that local paths pass through unchanged.""" + from nemotron.data_prep.config import FileInfo + from nemotron.data_prep.shard_processor import _resolve_file_path_core + + file_info = FileInfo(path="/data/file.parquet", local_path=None, size=100) + result = _resolve_file_path_core(file_info) + assert result == "/data/file.parquet" + + def test_local_path_preferred(self): + """Test that local_path is preferred over path.""" + from nemotron.data_prep.config import FileInfo + from nemotron.data_prep.shard_processor import _resolve_file_path_core + + file_info = FileInfo( + path="/original/path.parquet", + size=100, + local_path="/cached/path.parquet", + ) + result = _resolve_file_path_core(file_info) + assert result == "/cached/path.parquet" + + +class TestExecuteShardTasksEmptyList: + """Test execute_shard_tasks with empty task list (no Ray required).""" + + def test_empty_tasks_returns_empty_list(self): + """Test that empty task list returns empty results without Ray.""" + from nemotron.data_prep.ray_data import RayDataExecConfig + + # This tests the early return path before Ray is used + exec_cfg = RayDataExecConfig() + + # We need Ray initialized for this test + try: + import ray + + if not ray.is_initialized(): + ray.init(num_cpus=1, ignore_reinit_error=True) + + from nemotron.data_prep.ray_data import execute_shard_tasks + + results = execute_shard_tasks( + tasks=[], + udf_cls=MagicMock, + udf_constructor_kwargs={}, + exec_cfg=exec_cfg, + ) + + assert results == [] + except ImportError: + pytest.skip("Ray not available") + + +class TestProcessParquetFile: + """Tests for parquet file processing.""" + + def test_process_parquet_file(self): + """Test processing a parquet file.""" + pytest.importorskip("pyarrow") + + import pyarrow as pa + import pyarrow.parquet as pq + from fsspec import filesystem + + from nemotron.data_prep.shard_processor import process_binidx_shard_core + + with tempfile.TemporaryDirectory() as tmpdir: + # Create parquet file + parquet_path = os.path.join(tmpdir, "test.parquet") + table = pa.table({"text": ["hello world", "foo bar", "test document"]}) + pq.write_table(table, parquet_path) + + output_dir = os.path.join(tmpdir, "shards") + receipts_dir = os.path.join(tmpdir, "receipts") + os.makedirs(output_dir) + os.makedirs(receipts_dir) + + def mock_tokenize(texts): + return [[ord(c) for c in t] for t in texts] + + output_fs = filesystem("file") + assignment = { + "shard_index": 0, + "files": [{"path": parquet_path, "local_path": parquet_path, "size": 100}], + "total_bytes": 100, + } + + stats = process_binidx_shard_core( + tokenize=mock_tokenize, + text_field="text", + min_doc_chars=None, + max_doc_tokens=None, + dtype="int32", + max_rows=None, + shard_index=0, + assignment=assignment, + plan_hash="hash", + output_dir=output_dir, + receipts_dir=receipts_dir, + output_fs=output_fs, + ) + + assert stats["num_sequences"] == 3 + assert stats["num_input_rows"] == 3 + + def test_parquet_min_doc_chars_arrow_filter(self): + """Test Arrow-level min_doc_chars filtering on parquet.""" + pytest.importorskip("pyarrow") + + import pyarrow as pa + import pyarrow.parquet as pq + from fsspec import filesystem + + from nemotron.data_prep.shard_processor import process_binidx_shard_core + + with tempfile.TemporaryDirectory() as tmpdir: + # Create parquet file with varying lengths + parquet_path = os.path.join(tmpdir, "test.parquet") + table = pa.table( + { + "text": [ + "hi", # 2 chars - filtered + "hello world this is long", # 24 chars - kept + "x", # 1 char - filtered + "another long document here", # 26 chars - kept + ] + } + ) + pq.write_table(table, parquet_path) + + output_dir = os.path.join(tmpdir, "shards") + receipts_dir = os.path.join(tmpdir, "receipts") + os.makedirs(output_dir) + os.makedirs(receipts_dir) + + def mock_tokenize(texts): + return [[ord(c) for c in t] for t in texts] + + output_fs = filesystem("file") + assignment = { + "shard_index": 0, + "files": [{"path": parquet_path, "local_path": parquet_path, "size": 100}], + "total_bytes": 100, + } + + stats = process_binidx_shard_core( + tokenize=mock_tokenize, + text_field="text", + min_doc_chars=10, # Filter < 10 chars + max_doc_tokens=None, + dtype="int32", + max_rows=None, + shard_index=0, + assignment=assignment, + plan_hash="hash", + output_dir=output_dir, + receipts_dir=receipts_dir, + output_fs=output_fs, + ) + + assert stats["num_sequences"] == 2 # Only long docs + assert stats["num_filtered"] == 2 # Short docs filtered + + +@pytest.mark.skip(reason="Requires Ray cluster and tokenizer") +class TestExecuteShardTasks: + """Integration tests for execute_shard_tasks. + + These tests require: + - Ray to be initialized + - A tokenizer to be available + + Skip for now - to be run manually or in CI with proper setup. + """ + + def test_execute_empty_tasks(self, ray_initialized): + """Test executing with empty task list.""" + from nemotron.data_prep.ray_data import ( + BinIdxShardTaskUDF, + RayDataExecConfig, + execute_shard_tasks, + ) + + exec_cfg = RayDataExecConfig(min_actors=1, max_actors=2) + + results = execute_shard_tasks( + tasks=[], + udf_cls=BinIdxShardTaskUDF, + udf_constructor_kwargs={ + "resolved_tokenizer": {"type": "test"}, + "dtype": "int32", + }, + exec_cfg=exec_cfg, + ) + + assert results == [] diff --git a/tests/kit/__init__.py b/tests/kit/__init__.py new file mode 100644 index 0000000..4362c43 --- /dev/null +++ b/tests/kit/__init__.py @@ -0,0 +1,15 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Tests for nemotron.kit diff --git a/tests/kit/cli/__init__.py b/tests/kit/cli/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/kit/cli/test_utils.py b/tests/kit/cli/test_utils.py new file mode 100644 index 0000000..5722aa1 --- /dev/null +++ b/tests/kit/cli/test_utils.py @@ -0,0 +1,333 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Tests for nemotron.kit.cli.utils module.""" + +import pytest + +from nemotron.kit.cli.utils import ( + CONFIG_FILE_KEYS, + extract_run_args, + filter_config_file_args, + resolve_run_interpolations, + rewrite_paths_for_remote, +) + + +class TestResolveRunInterpolations: + """Tests for resolve_run_interpolations function.""" + + def test_resolve_simple_string(self): + """Test resolving a simple ${run.*} interpolation.""" + run_data = {"wandb": {"project": "my-project"}} + result = resolve_run_interpolations("${run.wandb.project}", run_data) + assert result == "my-project" + + def test_resolve_nested_path(self): + """Test resolving deeply nested paths.""" + run_data = {"a": {"b": {"c": {"d": "value"}}}} + result = resolve_run_interpolations("${run.a.b.c.d}", run_data) + assert result == "value" + + def test_resolve_in_dict(self): + """Test resolving interpolations in a dict.""" + run_data = {"wandb": {"project": "my-project", "entity": "my-team"}} + obj = { + "project": "${run.wandb.project}", + "entity": "${run.wandb.entity}", + "static": "unchanged", + } + result = resolve_run_interpolations(obj, run_data) + assert result == { + "project": "my-project", + "entity": "my-team", + "static": "unchanged", + } + + def test_resolve_in_list(self): + """Test resolving interpolations in a list.""" + run_data = {"values": {"a": "1", "b": "2"}} + obj = ["${run.values.a}", "${run.values.b}", "static"] + result = resolve_run_interpolations(obj, run_data) + assert result == ["1", "2", "static"] + + def test_resolve_in_nested_structure(self): + """Test resolving interpolations in nested dict/list structures.""" + run_data = {"config": {"name": "test"}} + obj = {"outer": [{"inner": "${run.config.name}"}]} + result = resolve_run_interpolations(obj, run_data) + assert result == {"outer": [{"inner": "test"}]} + + def test_preserve_non_run_interpolations(self): + """Test that non-${run.*} interpolations are preserved.""" + run_data = {"wandb": {"project": "my-project"}} + # ${art:data,path} should be preserved + result = resolve_run_interpolations("${art:data,path}", run_data) + assert result == "${art:data,path}" + + def test_preserve_unresolvable_paths(self): + """Test that unresolvable paths are preserved.""" + run_data = {"wandb": {"project": "my-project"}} + # nonexistent.path doesn't exist in run_data + result = resolve_run_interpolations("${run.nonexistent.path}", run_data) + assert result == "${run.nonexistent.path}" + + def test_preserve_scalars(self): + """Test that scalar values are preserved.""" + run_data = {} + assert resolve_run_interpolations(42, run_data) == 42 + assert resolve_run_interpolations(3.14, run_data) == 3.14 + assert resolve_run_interpolations(True, run_data) is True + assert resolve_run_interpolations(None, run_data) is None + + def test_preserve_non_interpolation_strings(self): + """Test that regular strings are preserved.""" + run_data = {"key": "value"} + assert resolve_run_interpolations("regular string", run_data) == "regular string" + assert resolve_run_interpolations("${incomplete", run_data) == "${incomplete" + + +class TestRewritePathsForRemote: + """Tests for rewrite_paths_for_remote function.""" + + def test_rewrite_pwd_env_var(self): + """Test rewriting ${oc.env:PWD}/path.""" + result = rewrite_paths_for_remote("${oc.env:PWD}/src/main.py", "/local/repo") + assert result == "/nemo_run/code/src/main.py" + + def test_rewrite_nemo_run_dir_simple(self): + """Test rewriting ${oc.env:NEMO_RUN_DIR}/path.""" + result = rewrite_paths_for_remote("${oc.env:NEMO_RUN_DIR}/output", "/local/repo") + assert result == "/nemo_run/output" + + def test_rewrite_nemo_run_dir_with_default(self): + """Test rewriting ${oc.env:NEMO_RUN_DIR,.}/path.""" + result = rewrite_paths_for_remote("${oc.env:NEMO_RUN_DIR,.}/logs", "/local/repo") + assert result == "/nemo_run/logs" + + def test_rewrite_absolute_path_under_repo(self): + """Test rewriting absolute paths under repo root.""" + result = rewrite_paths_for_remote("/local/repo/src/config.yaml", "/local/repo") + assert result == "/nemo_run/code/src/config.yaml" + + def test_preserve_paths_outside_repo(self): + """Test that paths outside repo root are preserved.""" + result = rewrite_paths_for_remote("/other/path/file.txt", "/local/repo") + assert result == "/other/path/file.txt" + + def test_rewrite_in_dict(self): + """Test rewriting paths in a dict.""" + obj = { + "config": "${oc.env:PWD}/config.yaml", + "output": "${oc.env:NEMO_RUN_DIR}/output", + "static": "/unchanged", + } + result = rewrite_paths_for_remote(obj, "/local/repo") + assert result == { + "config": "/nemo_run/code/config.yaml", + "output": "/nemo_run/output", + "static": "/unchanged", + } + + def test_rewrite_in_list(self): + """Test rewriting paths in a list.""" + obj = ["${oc.env:PWD}/a", "${oc.env:PWD}/b"] + result = rewrite_paths_for_remote(obj, "/local/repo") + assert result == ["/nemo_run/code/a", "/nemo_run/code/b"] + + def test_preserve_scalars(self): + """Test that scalar values are preserved.""" + assert rewrite_paths_for_remote(42, "/local/repo") == 42 + assert rewrite_paths_for_remote(None, "/local/repo") is None + + +class TestExtractRunArgs: + """Tests for extract_run_args function.""" + + def test_no_run_args(self): + """Test with no --run or --batch arguments.""" + profile, overrides, remaining, is_launch = extract_run_args( + ["--config", "test.yaml", "--batch-size", "32"] + ) + assert profile is None + assert overrides == {} + assert remaining == ["--config", "test.yaml", "--batch-size", "32"] + assert is_launch is False + + def test_run_profile_long_form(self): + """Test --run
![](\"http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png\"){kind=logo}
\n",
- "\n",
- "# Nemotron Nano 2 VL - A Simple Notebook Walkthrough!\n",
- "\n",
- "Explore NVIDIA's latest Vision Language Model capable of Document Intelligence and Video Understanding. It's an open 12B parameter model, with capabilities to turn reasoning on and off. You can use this notebook to try out the model.\n",
- "\n",
- "All you need to run this model is an NVIDIA API KEY. Use the instructions in docs provided to you to generate API key.\n",
- "\n",
- "\n",
- "\n",
- "> NOTE: Don't worry about any credits to use this model, although there is a rate limit of 40 requests per minute."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "Uyz3MLgW7vr6",
- "outputId": "5fcec66c-0b65-4383-ae93-1e222164d724"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Provide API Key: ··········\n"
- ]
- }
- ],
- "source": [
- "import os\n",
- "import getpass\n",
- "\n",
- "os.environ[\"NVIDIA_API_KEY\"] = getpass.getpass(\"Provide API Key: \")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "aI0BmK9tiUSX"
- },
- "source": [
- "In case, you are running this notebook in a local environment, let's ensure we install all the required libraries to run this notebook."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "collapsed": true,
- "id": "ep2tNBwLiTjS",
- "outputId": "a2cfde62-8914-4d06-8131-a15863f5ea6a"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Requirement already satisfied: pyarrow in /usr/local/lib/python3.12/dist-packages (18.1.0)\n",
- "Requirement already satisfied: matplotlib in /usr/local/lib/python3.12/dist-packages (3.10.0)\n",
- "Requirement already satisfied: pandas in /usr/local/lib/python3.12/dist-packages (2.2.2)\n",
- "Requirement already satisfied: openai in /usr/local/lib/python3.12/dist-packages (1.109.1)\n",
- "Requirement already satisfied: requests in /usr/local/lib/python3.12/dist-packages (2.32.4)\n",
- "Requirement already satisfied: Pillow in /usr/local/lib/python3.12/dist-packages (11.3.0)\n",
- "Requirement already satisfied: pymupdf in /usr/local/lib/python3.12/dist-packages (1.26.5)\n",
- "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.3.3)\n",
- "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (0.12.1)\n",
- "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (4.60.1)\n",
- "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.4.9)\n",
- "Requirement already satisfied: numpy>=1.23 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (2.0.2)\n",
- "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (25.0)\n",
- "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (3.2.5)\n",
- "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (2.9.0.post0)\n",
- "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas) (2025.2)\n",
- "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas) (2025.2)\n",
- "Requirement already satisfied: anyio<5,>=3.5.0 in /usr/local/lib/python3.12/dist-packages (from openai) (4.11.0)\n",
- "Requirement already satisfied: distro<2,>=1.7.0 in /usr/local/lib/python3.12/dist-packages (from openai) (1.9.0)\n",
- "Requirement already satisfied: httpx<1,>=0.23.0 in /usr/local/lib/python3.12/dist-packages (from openai) (0.28.1)\n",
- "Requirement already satisfied: jiter<1,>=0.4.0 in /usr/local/lib/python3.12/dist-packages (from openai) (0.11.0)\n",
- "Requirement already satisfied: pydantic<3,>=1.9.0 in /usr/local/lib/python3.12/dist-packages (from openai) (2.11.10)\n",
- "Requirement already satisfied: sniffio in /usr/local/lib/python3.12/dist-packages (from openai) (1.3.1)\n",
- "Requirement already satisfied: tqdm>4 in /usr/local/lib/python3.12/dist-packages (from openai) (4.67.1)\n",
- "Requirement already satisfied: typing-extensions<5,>=4.11 in /usr/local/lib/python3.12/dist-packages (from openai) (4.15.0)\n",
- "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests) (3.4.4)\n",
- "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests) (3.11)\n",
- "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests) (2.5.0)\n",
- "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests) (2025.10.5)\n",
- "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->openai) (1.0.9)\n",
- "Requirement already satisfied: h11>=0.16 in /usr/local/lib/python3.12/dist-packages (from httpcore==1.*->httpx<1,>=0.23.0->openai) (0.16.0)\n",
- "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.12/dist-packages (from pydantic<3,>=1.9.0->openai) (0.7.0)\n",
- "Requirement already satisfied: pydantic-core==2.33.2 in /usr/local/lib/python3.12/dist-packages (from pydantic<3,>=1.9.0->openai) (2.33.2)\n",
- "Requirement already satisfied: typing-inspection>=0.4.0 in /usr/local/lib/python3.12/dist-packages (from pydantic<3,>=1.9.0->openai) (0.4.2)\n",
- "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.7->matplotlib) (1.17.0)\n"
- ]
- }
- ],
- "source": [
- "%pip install pyarrow matplotlib pandas openai requests Pillow pymupdf\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "0RbG6pFChdaH"
- },
- "source": [
- "### Set up OpenAI Client\n",
- "First, we'll need to point our OpenAI client to the right endpoint.\n",
- "\n",
- "> NOTE: Instructions on how to perform local deployment with vLLM is attached [here]("
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "import math\n",
- "\n",
- "pages_show = pages[4:10]\n",
- "n = len(pages_show)\n",
- "cols = 3\n",
- "rows = math.ceil(n / cols)\n",
- "\n",
- "# PIL uses (width, height)\n",
- "w, h = pages_show[0].size\n",
- "\n",
- "# Match figure size to image resolution\n",
- "plt.figure(figsize=(w/100 * cols, h/100 * rows), dpi=100)\n",
- "\n",
- "for i, img in enumerate(pages_show, 1):\n",
- " plt.subplot(rows, cols, i)\n",
- " plt.imshow(img, interpolation='none') # no resampling\n",
- " plt.axis('off')\n",
- "\n",
- "plt.tight_layout()\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "BYg5RZp5F8vm"
- },
- "source": [
- "> Best Practice: For simple extractive Questions, recommend turning OFF reasoning, and setting temperature=0.0"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "KRL9R6T086oK",
- "outputId": "621979c6-2d75-4de7-9e6d-4c2a503c62b0"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Data Center business grew by 56% year-over-year (Y/Y) and 5% quarter-over-quarter (Q/Q) in Q2 FY26.\n"
- ]
- }
- ],
- "source": [
- "image_data_urls = [encode_pil_to_jpeg_data_url(page) for page in pages]\n",
- "image_data_urls = image_data_urls[4:10]\n",
- "text_prompt = \"How much did Data Center business grow in Q2 FY26?\"\n",
- "reasoning_mode = False\n",
- "temperature = 0.0\n",
- "max_tokens=1024\n",
- "\n",
- "call_nemotron_nano_2_vl(image_urls=image_data_urls, video_urls=[], text_prompt=text_prompt, reasoning_mode=reasoning_mode, temperature=temperature, max_tokens=max_tokens)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "ijwKP9GUGFbc"
- },
- "source": [
- "> Best Practice: For multi-step, multi-image reasoning, recommend turning ON reasoning, and setting temperature=0.6 (Always recommend to set temperature to 0.6 with reasoning)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "SKJlbo5VLZrJ",
- "outputId": "fc237909-8e48-409c-8897-a0c5b0615e61"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Okay, let's see. The user is asking which business unit had the most growth year-over-year (Y/Y). The text provided is from a document with different sections. Let me go through each section to find the Y/Y growth percentages.\n",
- "\n",
- "First, the Highlights section mentions \"Record quarter exceeded our outlook...\" with Data Center up 56% Y/Y and Gaming up 49% Y/Y. Then, the Data Center section says \"56% Y/Y\" again. The Gaming section states \"49% Y/Y\". The Professional Visualization section has \"32% Y/Y\" and Automotive \"69% Y/Y\". \n",
- "\n",
- "Wait, the Automotive section's Y/Y growth is 69%, which is higher than Data Center's 56% and Gaming's 49%. But I need to check if there are other business units mentioned. The Professional Visualization is 32%, so lower. The Automotive section's Y/Y is 69%, which is the highest among all listed. But wait, the user is asking for the business unit with the most growth. Let me confirm each section's Y/Y:\n",
- "\n",
- "- Data Center: 56% Y/Y\n",
- "- Gaming: 49% Y/Y\n",
- "- Professional Visualization: 32% Y/Y\n",
- "- Automotive: 69% Y/Y\n",
- "\n",
- "So Automotive has the highest Y/Y growth at 69%. But wait, the user might be referring to the main business units. The initial Highlights mention Data Center and Gaming, but the other sections are separate. The answer should be Automotive with 69% Y/Y. However, I need to make sure that the Automotive section is considered a business unit here. The text lists \"Automotive\" as a section, so it's a business unit. Therefore, the answer is Automotive with 69% Y/Y.\n",
- "] 9.96M --.-KB/s in 0.1s \n",
- "\n",
- "2025-10-20 15:41:08 (94.9 MB/s) - ‘test-00000-of-00001-56af6bd5ff7eb34d.parquet.1’ saved [10441675/10441675]\n",
- "\n"
- ]
- }
- ],
- "source": [
- "import pyarrow.parquet as pq\n",
- "import matplotlib.pyplot as plt\n",
- "import io\n",
- "\n",
- "!wget https://huggingface.co/datasets/katanaml-org/invoices-donut-data-v1/resolve/main/data/test-00000-of-00001-56af6bd5ff7eb34d.parquet\n",
- "\n",
- "table = pq.read_table(\"test-00000-of-00001-56af6bd5ff7eb34d.parquet\")\n",
- "df = table.to_pandas()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 1000
- },
- "id": "EOFp_E8E5Cwh",
- "outputId": "c8ba6815-63c1-4b7f-fc26-49d0cd460849"
- },
- "outputs": [
- {
- "data": {
- "image/png": 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- "text/plain": [
- "
"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "from PIL import Image\n",
- "import io\n",
- "\n",
- "img_bytes = df.loc[6, 'image']['bytes']\n",
- "img1 = Image.open(io.BytesIO(img_bytes))\n",
- "img1_url = encode_pil_to_jpeg_data_url(img1)\n",
- "\n",
- "img_bytes = df.loc[8, 'image']['bytes']\n",
- "img2 = Image.open(io.BytesIO(img_bytes))\n",
- "img2_url = encode_pil_to_jpeg_data_url(img2)\n",
- "\n",
- "img_bytes = df.loc[10, 'image']['bytes']\n",
- "img3 = Image.open(io.BytesIO(img_bytes))\n",
- "img3_url = encode_pil_to_jpeg_data_url(img3)\n",
- "\n",
- "img_bytes = df.loc[12, 'image']['bytes']\n",
- "img4 = Image.open(io.BytesIO(img_bytes))\n",
- "img4_url = encode_pil_to_jpeg_data_url(img4)\n",
- "\n",
- "# Display 4 images in a 2x2 grid with better quality\n",
- "plt.figure(figsize=(10, 10), dpi=200)\n",
- "\n",
- "images = [img1, img2, img3, img4]\n",
- "\n",
- "for i, img in enumerate(images, 1):\n",
- " plt.subplot(2, 2, i)\n",
- " plt.imshow(img, interpolation='nearest') # disables blurring\n",
- " plt.axis('off')\n",
- "\n",
- "plt.tight_layout()\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "O5BulBejSuZL"
- },
- "source": [
- "We are now ready to prompt the model.\n",
- "\n",
- "> Best Practice: For Multi-Image Reasoning, use reasoning with temperature 0.6"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "I8VWny905Bmb",
- "outputId": "b53d9e0e-00c9-4bb8-bd87-c687bac1c8a0"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "To solve the problem, we need to sum up all the totals across the receipts provided in the images. Let's go through each receipt step by step and extract the total gross worth.\n",
- "\n",
- "### Receipt 1:\n",
- "- **Invoice no:** 26343874\n",
- "- **Date of issue:** 07/17/2013\n",
- "- **Total Gross Worth:** $3,490.29\n",
- "\n",
- "### Receipt 2:\n",
- "- **Invoice no:** 25698672\n",
- "- **Date of issue:** 08/01/2015\n",
- "- **Total Gross Worth:** $127.00\n",
- "\n",
- "### Receipt 3:\n",
- "- **Invoice no:** 72455228\n",
- "- **Date of issue:** 05/11/2021\n",
- "- **Total Gross Worth:** $2,155.98\n",
- "\n",
- "### Receipt 4:\n",
- "- **Invoice no:** 90508672\n",
- "- **Date of issue:** 04/16/2020\n",
- "- **Total Gross Worth:** $7,052.49\n",
- "\n",
- "### Summing Up the Totals:\n",
- "Now, we add up all the total gross worth values from each receipt:\n",
- "\n",
- "\\[\n",
- "3,490.29 + 127.00 + 2,155.98 + 7,052.49\n",
- "\\]\n",
- "\n",
- "#### Step-by-Step Calculation:\n",
- "1. \\( 3,490.29 + 127.00 = 3,617.29 \\)\n",
- "2. \\( 3,617.29 + 2,155.98 = 5,773.27 \\)\n",
- "3. \\( 5,773.27 + 7,052.49 = 12,825.76 \\)\n",
- "\n",
- "### Final Answer:\n",
- "The sum of all the totals across the receipts is:\n",
- "\n",
- "\\[\n",
- "\n",
- "12825.76\n",
- "\\]12825.76\n",
- "\\\n"
- ]
- }
- ],
- "source": [
- "text_prompt = \"Sum up all the totals across the reciepts.\"\n",
- "reasoning_mode = True\n",
- "temperature = 0.6\n",
- "max_tokens=32000\n",
- "\n",
- "call_nemotron_nano_2_vl([img1_url, img2_url, img3_url, img4_url], video_urls=[], text_prompt=text_prompt, reasoning_mode=reasoning_mode, temperature=temperature, max_tokens=max_tokens)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "BaLEVDKlGfSw"
- },
- "source": [
- "You may want to audit your invoices, or insurance claims, and can ask the VLM to exactly do that."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "-Lb7TeP5RYyh"
- },
- "outputs": [],
- "source": [
- "text_prompt = \"Here are 4 invoices flagged as potential duplicates — are they actually the same document with minor layout differences?\"\n",
- "reasoning_mode = True\n",
- "temperature = 0.6\n",
- "max_tokens=32000\n",
- "\n",
- "call_nemotron_nano_2_vl([img1_url, img2_url, img3_url, img4_url], video_urls=[], text_prompt=text_prompt, reasoning_mode=reasoning_mode, temperature=temperature, max_tokens=max_tokens)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "GXgvld5bMeUe"
- },
- "source": [
- "### Video Understandig\n",
- "\n",
- "With long context, and enhanced reasoning capabilites, this model is now capable of providing high quality dense captions for videos.\n",
- "\n",
- "This model also introduces Efficient Video Sampling (EVS) method that identifies and prunes temporally static patches in video sequences. EVS reduces token redundancy, preserving essential semantics, for the model to process longer clips and deliver results more swiftly."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "J4-AWx3ZJQwO",
- "outputId": "fa7e638c-ab0c-4819-c8e7-c3edeb9c6af8"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "This video demonstrates a 3D model of a hut surrounded by trees and snow. The hut is brown with a snow-covered roof. The surrounding area is covered in snow, with scattered logs and rocks. A person is standing outside the hut, and there is a table with a pot on it. The trees are tall and dark green, and the sky is overcast. The video is shot from a top-down perspective, and the model is rotating to show different angles.\n"
- ]
- }
- ],
- "source": [
- "text_prompt = \"Describe this video in detail\"\n",
- "reasoning_mode = False\n",
- "temperature = 0.0\n",
- "max_tokens=4096\n",
- "\n",
- "call_nemotron_nano_2_vl(image_urls=[], video_urls=['https://blogs.nvidia.com/wp-content/uploads/2023/04/nvidia-studio-itns-wk53-scene-in-omniverse-1280w.mp4'], text_prompt=text_prompt, reasoning_mode=reasoning_mode, temperature=temperature, max_tokens=max_tokens)"
- ]
- }
- ],
- "metadata": {
- "colab": {
- "provenance": []
- },
- "kernelspec": {
- "display_name": "Python 3",
- "name": "python3"
- },
- "language_info": {
- "name": "python"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/usage-cookbook/Nemotron-Nano2-VL/vllm_cookbook.ipynb b/usage-cookbook/Nemotron-Nano2-VL/vllm_cookbook.ipynb
deleted file mode 100644
index 887eeb5..0000000
--- a/usage-cookbook/Nemotron-Nano2-VL/vllm_cookbook.ipynb
+++ /dev/null
@@ -1,368 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Running NVIDIA Nemotron Nano 2 VL with vLLM\n",
- "\n",
- "This notebook will walk you through how to run the `nvidia/Nemotron-Nano-12B-v2-VL-BF16` model locally with vLLM.\n",
- "\n",
- "[vLLM](https://docs.vllm.ai) is a fast and easy-to-use library for LLM inference and serving. \n",
- "\n",
- "For more details on the model [click here](TODO)\n",
- "\n",
- "Prerequisites:\n",
- "- NVIDIA GPU with recent drivers (≥ 24 GB VRAM recommended; BF16-capable) and CUDA 12.x \n",
- "- Python 3.10+"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Prerequisites & environment\n",
- "\n",
- "Set up a clean Python environment for running vLLM locally.\n",
- "\n",
- "Create and activate a virtual environment. The sample here uses Conda but feel free to choose whichever tool you prefer.\n",
- "Run these commands in a terminal before using this notebook:\n",
- "\n",
- "```bash\n",
- "conda create -n nemotron-vllm-env python=3.10 -y\n",
- "conda activate nemotron-vllm-env\n",
- "```\n",
- "\n",
- "If running notebook locally, install ipykernel and switch the kernel to this environment:\n",
- "- Installation\n",
- "```bash\n",
- "pip install ipykernel\n",
- "```\n",
- "- Kernel → Change kernel → Python (nemotron-vllm-env)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Install dependencies"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "!VLLM_USE_PRECOMPILED=1 pip install git+https://github.com/vllm-project/vllm.git@main"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Verify GPU\n",
- "\n",
- "Confirm CUDA is available and your GPU is visible to PyTorch.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# GPU environment check\n",
- "import torch\n",
- "print(f\"CUDA available: {torch.cuda.is_available()}\")\n",
- "print(f\"Num GPUs: {torch.cuda.device_count()}\")\n",
- "if torch.cuda.is_available():\n",
- " for i in range(torch.cuda.device_count()):\n",
- " print(f\"GPU[{i}]: {torch.cuda.get_device_name(i)}\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Load the model\n",
- "Initialize the Nemotron model in vLLM with BF16 for efficient GPU inference."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from vllm import LLM\n",
- "\n",
- "vlm = LLM(\n",
- " model=\"nvidia/Nemotron-Nano-12B-v2-VL-BF16\",\n",
- " trust_remote_code=True,\n",
- " dtype=\"bfloat16\",\n",
- ")\n",
- "\n",
- "print(\"Model ready\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Generate responses\n",
- "\n",
- "Once the model is loaded successfully above, you can continue with text generation:\n",
- "\n",
- "### Single or batch prompts\n",
- "\n",
- "Send one prompt or a list to run batched generation."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from vllm import SamplingParams\n",
- "from PIL import Image\n",
- "import requests\n",
- "from io import BytesIO\n",
- "\n",
- "params = SamplingParams(temperature=0.0, max_tokens=1024)\n",
- "\n",
- "image1 = Image.open(\"example_image1.png\")\n",
- "\n",
- "# Single prompt\n",
- "single = vlm.generate({\n",
- " \"prompt\": \"\\nDescribe the image in detail.\",\n",
- " \"multi_modal_data\": {\"image\": image1}}, sampling_params=params)\n",
- "print(single[0].outputs[0].text)\n",
- "\n",
- "\n",
- "# Batch prompts\n",
- "image2 = Image.open(\"example_image2.png\")\n",
- "prompts = [\n",
- " {\n",
- " \"prompt\": \"\\nDescribe the image in detail.\",\n",
- " \"multi_modal_data\": {\"image\": image1}\n",
- " },\n",
- " {\n",
- " \"prompt\": \"\\nWhat color bars are used in the image?\",\n",
- " \"multi_modal_data\": {\"image\": image2}\n",
- " }\n",
- "]\n",
- "outputs = vlm.generate(prompts, sampling_params=params)\n",
- "for i, out in enumerate(outputs):\n",
- " print(f\"\\nPrompt {i+1}: {out.prompt!r}\")\n",
- " print(out.outputs[0].text)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Streamed generation\n",
- "\n",
- "Printing characters as they are produced."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def stream_like(prompt: str, llm: LLM, sampling_params: SamplingParams) -> None:\n",
- " outputs = llm.generate([prompt], sampling_params=sampling_params)\n",
- " text = outputs[0].outputs[0].text\n",
- " print(\"Response:\", end=\" \")\n",
- " for ch in text:\n",
- " print(ch, end=\"\", flush=True)\n",
- " print()\n",
- "\n",
- "stream_like(\"Write a haiku about GPUs.\", vlm, SamplingParams(temperature=0.7, max_tokens=80))\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## OpenAI-compatible server \n",
- "\n",
- "Serve the model via an OpenAI-compatible API using vLLM.\n",
- "\n",
- "Before starting the server:\n",
- "- Restart the kernel to free GPU memory used by the in-process LLM\n",
- "- Ensure you use the same virtual environment with installed dependancies in your terminal\n",
- "- Use `--video-pruning-rate` to set EVS. The default EVS is 0."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "After restarting the kernel, run this in a terminal:\n",
- "\n",
- "```shell\n",
- "git clone https://huggingface.co/nvidia/Nemotron-Nano-12B-v2-VL-BF16\n",
- "git clone https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-9B-v2\n",
- "\n",
- "vllm serve nvidia/Nemotron-Nano-12B-v2-VL-BF16 --trust-remote-code --dtype bfloat16 --enable-auto-tool-choice --tool-parser-plugin \"NVIDIA-Nemotron-Nano-9B-v2/nemotron_toolcall_parser_no_streaming.py\" --tool-call-parser \"nemotron_json\"\n",
- "```"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Your server is now running! "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Use the API\n",
- "\n",
- "Send chat and streaming requests to your local vLLM server using the OpenAI-compatible client."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Note: The model supports two modes - Reasoning ON (default) vs OFF. These can be toggled by passing /think vs /no_think as a part of the \"system\" message. \n",
- "\n",
- "The /think or /no_think keywords can also be provided in “user” messages for turn-level reasoning control."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Client: Standard chat and streaming\n",
- "from openai import OpenAI\n",
- "\n",
- "client = OpenAI(base_url=\"http://127.0.0.1:8033/v1\", api_key=\"null\")\n",
- "\n",
- "# Simple chat completion\n",
- "resp = client.chat.completions.create(\n",
- " model=\"nvidia/Nemotron-Nano-12B-v2-VL-BF16\",\n",
- " messages=[\n",
- " {\"role\": \"system\", \"content\": \"/think\"},\n",
- " {\"role\": \"user\", \"content\": [\n",
- " {\"type\": \"text\", \"text\": \"Give me 3 interesting facts about this image.\"}, \n",
- " {\"type\": \"image_url\", \"image_url\": {\"url\": \"https://blogs.nvidia.com/wp-content/uploads/2025/08/gamescom-g-assist-nv-blog-1280x680-1.jpg\"}\n",
- " }\n",
- " ]},\n",
- " ],\n",
- " temperature=0.6,\n",
- " max_tokens=1024,\n",
- ")\n",
- "print(resp.choices[0].message.content)\n",
- "\n",
- "# Streaming chat completion\n",
- "stream = client.chat.completions.create(\n",
- " model=\"nvidia/Nemotron-Nano-12B-v2-VL-BF16\",\n",
- " messages=[\n",
- " {\"role\": \"system\", \"content\": \"/no_think\"},\n",
- " {\"role\": \"user\", \"content\": [\n",
- " {\"type\": \"text\", \"text\": \"Describe this video in detail.\"}, \n",
- " {\"type\": \"video_url\", \"video_url\": {\"url\": \"https://blogs.nvidia.com/wp-content/uploads/2023/04/nvidia-studio-itns-wk53-scene-in-omniverse-1280w.mp4\"}\n",
- " }\n",
- " ]},\n",
- " ],\n",
- " temperature=0.0,\n",
- " max_tokens=1024,\n",
- " stream=True,\n",
- ")\n",
- "for chunk in stream:\n",
- " delta = chunk.choices[0].delta\n",
- " if delta and delta.content:\n",
- " print(delta.content, end=\"\", flush=True)\n",
- "print()\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Tool calling\n",
- "\n",
- "Call functions using the OpenAI Tools schema and inspect returned tool_calls."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Tool calling via OpenAI tools schema\n",
- "TOOLS = [\n",
- " {\n",
- " \"type\": \"function\",\n",
- " \"function\": {\n",
- " \"name\": \"calculate_tip\",\n",
- " \"parameters\": {\n",
- " \"type\": \"object\",\n",
- " \"properties\": {\n",
- " \"bill_total\": {\n",
- " \"type\": \"integer\",\n",
- " \"description\": \"The total amount of the bill\"\n",
- " },\n",
- " \"tip_percentage\": {\n",
- " \"type\": \"integer\",\n",
- " \"description\": \"The percentage of tip to be applied\"\n",
- " }\n",
- " },\n",
- " \"required\": [\"bill_total\", \"tip_percentage\"]\n",
- " }\n",
- " }\n",
- " }\n",
- "]\n",
- "\n",
- "completion = client.chat.completions.create(\n",
- " model=\"nvidia/Nemotron-Nano-12B-v2-VL-BF16\",\n",
- " messages=[\n",
- " {\"role\": \"system\", \"content\": \"/think\"},\n",
- " {\"role\": \"user\", \"content\": \"My bill is $50. What will be the amount for 15% tip?\"}\n",
- " ],\n",
- " tools=TOOLS,\n",
- " temperature=0.6,\n",
- " top_p=0.95,\n",
- " max_tokens=32768,\n",
- " stream=False\n",
- ")\n",
- "\n",
- "print(completion.choices[0].message.content)\n",
- "print(completion.choices[0].message.tool_calls)"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "nemotron-vllm-env",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.19"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/usage-cookbook/Nemotron-Parse-v1.1/build_general_usage_cookbook.ipynb b/usage-cookbook/Nemotron-Parse-v1.1/build_general_usage_cookbook.ipynb
deleted file mode 100644
index 3396bc0..0000000
--- a/usage-cookbook/Nemotron-Parse-v1.1/build_general_usage_cookbook.ipynb
+++ /dev/null
@@ -1,1365 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 14,
- "id": "3596c634",
- "metadata": {
- "id": "I-rch2mj7m82"
- },
- "outputs": [],
- "source": [
- "# Copyright 2025 NVIDIA Corporation. All Rights Reserved.\n",
- "#\n",
- "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
- "# you may not use this file except in compliance with the License.\n",
- "# You may obtain a copy of the License at\n",
- "#\n",
- "# http://www.apache.org/licenses/LICENSE-2.0\n",
- "#\n",
- "# Unless required by applicable law or agreed to in writing, software\n",
- "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
- "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
- "# See the License for the specific language governing permissions and\n",
- "# limitations under the License.\n",
- "# =============================================================================="
- ]
- },
- {
- "cell_type": "markdown",
- "id": "68d0ba67",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "# Nemotron Parse: From Complex Documents to Agent-Ready Data\n",
- "\n",
- "NVIDIA Nemotron Parse (also known as NeMo Retriever Parse) is a transformer-based VLM specialized for document parsing. Think of it as the ingestion backbone for your AI agent. Its job is to:\n",
- "- Accurately extract text and formulas in correct reading order.\n",
- "- Robustly parse complex tables into a structured format (like LaTeX).\n",
- "- Perform semantic segmentation (labeling Title, Text, List-item, Figure, etc.).\n",
- "- Provide spatial grounding (a bbox for every element).\n",
- "\n",
- "It's built to preserve meaning and structure at scale, so your agent can act on the right information with confidence.\n",
- "\n",
- "\n",
- "---\n",
- "\n",
- "### What you’ll learn in this notebook:\n",
- "* How to call Nemotron Parse on a PDF page image and get structured JSON.\n",
- "* How to visualize the model's layout understanding.\n",
- "* How to convert the model's output (including complex tables) into clean HTML and Markdown for RAG or agentic workflows.\n",
- "* How this model complements a separate visual reasoning model in a full document AI agent.\n",
- "\n",
- "Note: This notebook focuses only on Nemotron Parse. Its job is to find what elements are on the page and where they are. If your workflow also needs to understand the visual content inside a Picture or Figure block (e.g., analyze a chart, describe a photo), you would pair Parse with a visual reasoning model in a second stage.\n",
- "\n",
- "Parse tells you where the image is; a reasoning model tells you what the image means."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f087b4bc",
- "metadata": {},
- "source": [
- "## 0. Setup\n",
- "\n",
- "Install minimal dependencies for PDF rendering (PyMuPDF), API calls (requests), and visualization (Pillow)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7921625c",
- "metadata": {
- "id": "install-deps"
- },
- "outputs": [],
- "source": [
- "%pip install --quiet requests pillow pymupdf pandas markdown bleach"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1f2ebcb2",
- "metadata": {},
- "source": [
- "### Configure API Endpoint\n",
- "\n",
- "This notebook uses the call_nemotron_parse function to call the NVIDIA API-compatible endpoint via the Chat Completions API.\n",
- "\n",
- "Requirements:\n",
- "1) Set your NVIDIA_API_KEY as an environment variable.\n",
- "2) Optionally set NVAI_URL to your NVIDIA API Gateway or local NIM gateway.\n",
- "\n",
- "The model returns a list of layout blocks with:\n",
- "- type: e.g., \"Title\", \"Text\", \"List-item\", \"Table\", etc.\n",
- "- text: the block's textual content (for tables, a LaTeX tabular string)\n",
- "- bbox: normalized coordinates {xmin, ymin, xmax, ymax} in [0,1]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "id": "9881a584",
- "metadata": {
- "id": "imports-config"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Using API URL: https://integrate.api.nvidia.com/v1\n",
- "Output directory: ./parse_outputs\n"
- ]
- }
- ],
- "source": [
- "import os\n",
- "import re\n",
- "import json\n",
- "import base64\n",
- "import requests\n",
- "import fitz # PyMuPDF\n",
- "from typing import List, Dict, Any, Tuple\n",
- "from PIL import Image, ImageDraw, ImageFont\n",
- "from io import BytesIO\n",
- "import pandas as pd\n",
- "from IPython.display import display, Markdown, HTML\n",
- "\n",
- "# API configuration\n",
- "API_KEY = os.environ.get(\"NVIDIA_API_KEY\", \"YOUR_API_KEY_HERE\")\n",
- "NVAI_URL = os.environ.get(\"NVAI_URL\", \"https://integrate.api.nvidia.com/v1\")\n",
- "\n",
- "if API_KEY == \"YOUR_API_KEY_HERE\":\n",
- " print(\"WARNING: NVIDIA_API_KEY not set. Please set it to run the notebook.\")\n",
- "\n",
- "# Output directory for artifacts\n",
- "OUTPUT_DIR = \"parse_outputs\"\n",
- "os.makedirs(OUTPUT_DIR, exist_ok=True)\n",
- "print(f\"Using API URL: {NVAI_URL}\")\n",
- "print(f\"Output directory: {os.path.abspath(OUTPUT_DIR)}\")\n",
- "\n",
- "# Class color mapping for visualization\n",
- "CLASS_COLORS = {\n",
- " 'Title': '#D32F2F',\n",
- " 'Section-header': '#E91E63',\n",
- " 'Text': '#4CAF50',\n",
- " 'List-item': '#1976D2',\n",
- " 'Caption': '#607D8B',\n",
- " 'Table': '#03A9F4',\n",
- " 'Figure': '#6D4C41',\n",
- " 'Picture': '#6D4C41',\n",
- " 'Formula': '#FF9800',\n",
- " 'Page-header': '#9E9E9E',\n",
- " 'Page-footer': '#9E9E9E',\n",
- " 'Footnote': '#00BCD4',\n",
- " 'Bibliography': '#512DA8',\n",
- " 'TOC': '#FFC107',\n",
- " 'DEFAULT': '#9E9E9E'\n",
- "}\n",
- "\n",
- "def get_text_color(hex_color: str) -> str:\n",
- " \"\"\"Return black or white for best contrast over a hex background color.\"\"\"\n",
- " hex_color = hex_color.lstrip('#')\n",
- " r, g, b = tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4))\n",
- " luminance = (0.2126 * r + 0.7152 * g + 0.0722 * b)\n",
- " return \"#000000\" if luminance > 140 else \"#FFFFFF\""
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e1d247be",
- "metadata": {},
- "source": [
- "## 1. Helper Functions\n",
- "\n",
- "Helpers for:\n",
- "1) PDF-to-Image rendering.\n",
- "2) API call via Chat Completions.\n",
- "3) Visualization.\n",
- "4) LaTeX tables to HTML and page-to-Markdown assembly."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "id": "4046f1fe",
- "metadata": {
- "id": "helpers"
- },
- "outputs": [],
- "source": [
- "from markdown import markdown\n",
- "import bleach\n",
- "\n",
- "# PDF page rendering\n",
- "def pdf_page_to_image(pdf_path: str, page_index: int, dpi: int = 300,\n",
- " target_size: Tuple[int, int] = (1536, 2048)) -> Image.Image:\n",
- " \"\"\"Render a PDF page to an RGB image and center it on a fixed-size canvas.\"\"\"\n",
- " doc = fitz.open(pdf_path)\n",
- " page = doc.load_page(page_index)\n",
- " zoom = dpi / 72.0\n",
- " pix = page.get_pixmap(matrix=fitz.Matrix(zoom, zoom))\n",
- " src = Image.frombytes(\"RGB\", [pix.width, pix.height], pix.samples)\n",
- " doc.close()\n",
- "\n",
- " canvas = Image.new(\"RGB\", target_size, (255, 255, 255))\n",
- " src.thumbnail(target_size, Image.Resampling.LANCZOS)\n",
- " x = (target_size[0] - src.width) // 2\n",
- " y = (target_size[1] - src.height) // 2\n",
- " canvas.paste(src, (x, y))\n",
- " return canvas\n",
- "\n",
- "# Image encoding\n",
- "def encode_image_to_base64(image: Image.Image, fmt: str = \"PNG\") -> str:\n",
- " \"\"\"Encode a PIL image to base64 for API submission.\"\"\"\n",
- " buf = BytesIO()\n",
- " image.save(buf, format=fmt)\n",
- " return base64.b64encode(buf.getvalue()).decode('utf-8')\n",
- "\n",
- "# Nemotron Parse API call\n",
- "def call_nemotron_parse(base64_image: str) -> List[Dict[str, Any]]:\n",
- " \"\"\"Call the nemotron-parse model and return a list of blocks with type/text/bbox.\"\"\"\n",
- " headers = {\"Authorization\": f\"Bearer {API_KEY}\", \"Accept\": \"application/json\"}\n",
- " messages = [{\n",
- " \"role\": \"user\",\n",
- " \"content\": [{\n",
- " \"type\": \"image_url\",\n",
- " \"image_url\": {\"url\": f\"data:image/png;base64,{base64_image}\"}\n",
- " }]\n",
- " }]\n",
- " tools = [{\"type\": \"function\", \"function\": {\"name\": \"markdown_bbox\"}}]\n",
- " payload = {\n",
- " \"model\": \"nvidia/nemotron-parse\",\n",
- " \"messages\": messages,\n",
- " \"max_tokens\": 3500,\n",
- " \"temperature\": 0,\n",
- " \"tools\": tools\n",
- " }\n",
- " try:\n",
- " resp = requests.post(f\"{NVAI_URL}/chat/completions\", headers=headers, json=payload, timeout=180)\n",
- " resp.raise_for_status()\n",
- " response_json = resp.json()\n",
- " except requests.exceptions.HTTPError as e:\n",
- " print(f\"API Error for nvidia/nemotron-parse: {e.response.status_code} - {e.response.text}\")\n",
- " return []\n",
- " except Exception as e:\n",
- " print(f\"Unexpected error calling nemotron-parse: {e}\")\n",
- " return []\n",
- "\n",
- " tool_call = response_json.get(\"choices\", [{}])[0].get(\"message\", {}).get(\"tool_calls\", [{}])[0]\n",
- " if not tool_call:\n",
- " return []\n",
- " arguments_str = tool_call.get(\"function\", {}).get(\"arguments\", \"[]\")\n",
- " try:\n",
- " parsed_args = json.loads(arguments_str)\n",
- " if isinstance(parsed_args, list) and len(parsed_args) > 0 and isinstance(parsed_args[0], list):\n",
- " return parsed_args[0]\n",
- " return parsed_args if isinstance(parsed_args, list) else []\n",
- " except json.JSONDecodeError:\n",
- " print(f\"Error decoding JSON from arguments: {arguments_str}\")\n",
- " return []\n",
- "\n",
- "# Visualization\n",
- "def draw_layout(image: Image.Image, blocks: List[Dict[str, Any]]) -> Image.Image:\n",
- " \"\"\"Draw bounding boxes and labels for each parsed block.\"\"\"\n",
- " out = image.copy()\n",
- " draw = ImageDraw.Draw(out)\n",
- " W, H = out.size\n",
- " box_thickness = max(2, int(W / 600))\n",
- " font_size = max(14, int(W / 80))\n",
- " try:\n",
- " font = ImageFont.truetype(\"Arial.ttf\", font_size)\n",
- " except Exception:\n",
- " font = ImageFont.load_default()\n",
- "\n",
- " for i, b in enumerate(blocks):\n",
- " bbox = b.get(\"bbox\") or {}\n",
- " xmin, ymin = bbox.get(\"xmin\", 0.0), bbox.get(\"ymin\", 0.0)\n",
- " xmax, ymax = bbox.get(\"xmax\", 0.0), bbox.get(\"ymax\", 0.0)\n",
- " if xmax <= xmin or ymax <= ymin:\n",
- " continue\n",
- " x0, y0, x1, y1 = xmin * W, ymin * H, xmax * W, ymax * H\n",
- " cat = b.get(\"type\", \"DEFAULT\")\n",
- " color = CLASS_COLORS.get(cat, CLASS_COLORS[\"DEFAULT\"])\n",
- " text_color = get_text_color(color)\n",
- " draw.rectangle([x0, y0, x1, y1], outline=color, width=box_thickness)\n",
- " label = f\"{i}:{cat}\"\n",
- " try:\n",
- " tb = draw.textbbox((0, 0), label, font=font)\n",
- " tw, th = tb[2] - tb[0], tb[3] - tb[1]\n",
- " except Exception:\n",
- " tw, th = draw.textsize(label, font=font)\n",
- " label_bg = (x0, max(0, y0 - th - 6), x0 + tw + 8, max(0, y0 - 6))\n",
- " draw.rectangle(label_bg, fill=color)\n",
- " draw.text((label_bg[0] + 4, label_bg[1] + 2), label, fill=text_color, font=font)\n",
- " return out\n",
- "\n",
- "# Records and DataFrame\n",
- "def blocks_to_dataframe(blocks: List[Dict[str, Any]]) -> pd.DataFrame:\n",
- " rows = []\n",
- " for i, b in enumerate(blocks):\n",
- " txt = (b.get(\"text\") or \"\").replace(\"\\n\", \" \")\n",
- " rows.append({\n",
- " \"id\": i,\n",
- " \"type\": b.get(\"type\"),\n",
- " \"text\": txt[:200],\n",
- " \"bbox\": b.get(\"bbox\")\n",
- " })\n",
- " return pd.DataFrame(rows)\n",
- "\n",
- "def blocks_to_records(blocks: List[Dict[str, Any]], page_number: int, page_size_px: Tuple[int, int]) -> List[Dict[str, Any]]:\n",
- " W, H = page_size_px\n",
- " records = []\n",
- " for i, b in enumerate(blocks):\n",
- " bbox = b.get(\"bbox\") or {\"xmin\": 0, \"ymin\": 0, \"xmax\": 0, \"ymax\": 0}\n",
- " x0, y0 = int(bbox.get(\"xmin\", 0) * W), int(bbox.get(\"ymin\", 0) * H)\n",
- " x1, y1 = int(bbox.get(\"xmax\", 0) * W), int(bbox.get(\"ymax\", 0) * H)\n",
- " records.append({\n",
- " \"page\": page_number,\n",
- " \"id\": i,\n",
- " \"type\": b.get(\"type\"),\n",
- " \"text\": b.get(\"text\"),\n",
- " \"bbox_norm\": bbox,\n",
- " \"bbox_px\": [x0, y0, x1, y1]\n",
- " })\n",
- " return records\n",
- "\n",
- "# LaTeX table parsing\n",
- "def _clean_latex_text(s: str) -> str:\n",
- " s = s.strip()\n",
- " s = re.sub(r\"\\\\textbf\\{(.*?)\\}\", r\"\\1\", s)\n",
- " s = re.sub(r\"\\\\textit\\{(.*?)\\}\", r\"\\1\", s)\n",
- " replacements = {\n",
- " r\"\\$\": \"$\",\n",
- " r\"\\%\": \"%\",\n",
- " r\"\\\\&\": \"&\",\n",
- " r\"\\\\_\": \"_\",\n",
- " r\"\\\\#\": \"#\",\n",
- " r\"\\\\textless\": \"<\",\n",
- " r\"\\\\textgreater\": \">\",\n",
- " r\"\\\\newline\": \" \"\n",
- " }\n",
- " for k, v in replacements.items():\n",
- " s = re.sub(k, v, s)\n",
- " return s\n",
- "\n",
- "def _parse_cell_spans(cell: str) -> Tuple[str, int, int]:\n",
- " \"\"\"Parse a cell for \\multicolumn and \\multirow; return (content, colspan, rowspan).\"\"\"\n",
- " content = cell.strip()\n",
- " colspan, rowspan = 1, 1\n",
- " changed = True\n",
- " while changed:\n",
- " changed = False\n",
- " m = re.match(r\"\\\\multicolumn\\{(\\d+)\\}\\{[^}]*\\}\\{(.*)\\}\\s*$\", content, flags=re.DOTALL)\n",
- " if m:\n",
- " colspan = int(m.group(1))\n",
- " content = m.group(2).strip()\n",
- " changed = True\n",
- " continue\n",
- " m = re.match(r\"\\\\multirow\\{(\\d+)\\}\\{[^}]*\\}\\{(.*)\\}\\s*$\", content, flags=re.DOTALL)\n",
- " if m:\n",
- " rowspan = int(m.group(1))\n",
- " content = m.group(2).strip()\n",
- " changed = True\n",
- " continue\n",
- " content = _clean_latex_text(content)\n",
- " return content, colspan, rowspan\n",
- "\n",
- "def latex_table_to_html(latex_str: str) -> str:\n",
- " \"\"\"Convert a LaTeX tabular into HTML. Fallbacks to raw pre if parsing fails.\"\"\"\n",
- " try:\n",
- " if not latex_str:\n",
- " return \"\"\n",
- " m = re.search(r\"\\\\begin\\{tabular\\}\\{[^}]*\\}(.*?)\\\\end\\{tabular\\}\", latex_str, flags=re.DOTALL)\n",
- " content = m.group(1) if m else latex_str\n",
- " content = re.sub(r\"\\\\hline\", \"\", content)\n",
- " rows_raw = [r.strip() for r in re.split(r\"(?\")\n",
- " tag = 'th' if r_idx == 0 else 'td'\n",
- " for cell in cells:\n",
- " attrs = []\n",
- " if cell[\"colspan\"] > 1:\n",
- " attrs.append(f\"colspan=\\\"{cell['colspan']}\\\"\")\n",
- " if cell[\"rowspan\"] > 1:\n",
- " attrs.append(f\"rowspan=\\\"{cell['rowspan']}\\\"\")\n",
- " attr_str = (\" \" + \" \".join(attrs)) if attrs else \"\"\n",
- " html.append(f\" <{tag}{attr_str}>{cell['content']}\")\n",
- " html.append(\" \")\n",
- " html.append(\"\")\n",
- " return \"\\n\".join(html)\n",
- " except Exception as e:\n",
- " print(f\"Warning: Could not parse LaTeX table. Returning raw. Error: {e}\")\n",
- " return f\""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "#### Page 1 — Annotated Layout"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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nAzwBnd+Gaz4vCdnBd3c8M80f2nzCVRY/kaQ5Zg23d1JOCSOTxTrbwpZW2i3+l+bM0V8WaZxtQglQvyhVCrwB0HPOc5oAjTxjp0iII4byS4aZoBbJDuk3qgcg4OPukHOcfjVfVPG1pb6W11p8U103kxzbhE2yNZGwu89QTzx2xzirem+E7HTbtLqOWZ5llabJ2KCxjEZ+VVAA2gcAVXfwRYNbC1jvL2G3aKOKaON1xMEJK7iVzkZ7YyODQBqahrVvp1zFbNDczzyKZPLt4jIyoCAWIHbJHuewNVZfFmmQzTrILhYojKvn+SfLd41LOinuwCt/3ycdKs6hoy315FeR3lzaXEcbRGS3K5eMkEqdwPcDkcj1qm/hKxlnkMs9zJau8sgtGceWjyBldhxuyQ7dTgbjigCe88TafZP5b+e8h8kLHFEWZjLu2AfXY3071Vi8WwT6nY2kFjeOlzHMzSGPHktG6oysPUEnP4dc0628JWsM6TzXt7czJJC4eZ1/5ZBgg4UDHznPrU48N20dzDcQ3FxFJHLPISpU7xMwZ1OR0yB0wRjrQBVXxjp728F6TJDYy28twHmhZWdE2fMo9DvHXk9qP+E10vYgCXTXDStEbdYwZAQoY8Zx0YHgnOeOamm8Jabcafa2MvnNDbWbWaAsMlG2cnjqNimoJvB1rPpf2F7ybYXZ3Ihh+bIxjb5e3gDggZHPNAGnquswaRZrdTw3Dxnk+VHkqMZJOcY/nVM+LtL3T+X9olWFYyXjhYqzSBTGinuzB1wB+OKdqnhez1S3soZJp0W0QxoQVYlSoU53g84A+bqOeeaanhSwi06WzjkuFDtC6yBxujeJUVGXjGR5annPegCW28S6fcsY/wB9HMqStJFJGQ0fl7dwYevzqR6g8VCni7TJLmOJFuijNCpm8k+WjSqrRhj2zuH0J5xUcng+2dNw1C+S5cy+dcqyh5hIFDA/LgDCIBgDG0YqxF4YsIbZrdWmKNJbynLDrCECdun7tc/jQBBZeKI9S1y0s7W3m+zXFvNOs8sZUSBGQAoe4O8/p2NVrvxi8MOpsmm3C/Yb+G0JdCRKHaMErjnOJOB9PWr+m+GLfTL2C4ju7uVbaF4LaGRlKQxsVJUYAJ+6ACSTgU6bw3bTXF3I1xcCO6uIrl4Qy7RLGUIYcZ58tQRnFAFaXxppUECvItysu6RXgMX7yPy8bywz0AZehOdwxmtC717T7KCCeaY+TPE80ciqSGVU3n8duSB3waz73wdp97cvc+bNHO8skhcKj/fCBlAdSMfu1OcZGOtaOp6JZ6tZ29rch/Lt5o5U2HBynY+xGQR3BIoApf8ACX6Y4jFstzdSSKrJHBCWZgUD9PZSpPpuA6mq994ytFsrmXTkkuZIbeO43mNhEFfBXLepGeOvFSp4QsreJBY3N1ZzJNLMs8TKWHmfeX5gRtwFAGONo9Klj8KabFp9zYx+csFxFFEwD8gRjC4Pr60ARt4ojk1yx0+1t5mjnuZbd7h4yIyY0csEbuQy4/PGcVbufEFnZ6tFp06zpJKdqSGPCM20ttB7nAPbHbOahh8M28GqRXi3d2UgnkuIbYsvlo8gYORxk53scE8Z4qOfwjYz62dUeafzDMJ9g243hNnXbuxj+HOM84oAhPjrR1tILki6EU0RnUtCQfJGP3hB528/U84FdIrK6hlIKkZBHcVz0/g3T5YtPRZJY2srYWqvtRy8QxwdykZ46jB5NdEAAAB0FABRRRQBm65/x4w/9fUH/o1a0qzdc/48Yf8Ar6g/9GrWlUr4mQvjfy/UKKKKosKKKKAKuo2EWp6fNZTM6xzLtYocMPpXA/8ACjvBu5m8u9BY7ji4xk/lXpFeK65rOtv4v1i3j8Svp1pbzYHmTMFGcABVUEn8BxSk7G9Kc7cqenodbN8I/Ds8tpK0+pCW0hEEEiXG1kjGcKCAD3PX1qzovwy0Xw6902k3OoWrXYCzlZVbzAM9dyn1P515+Lnxan2gXHieS3aKR4kElw371lGTtIGAORy2ByBVaw1fxNdwTXFx4murGCJEfzLhpCGDEgY2gk8g81POa81TuvuX+R6HpHwn8PaDdfadLm1C1kIZTsnBUgjB+UqR09qqD4K+FFge3VtRS3dt7QrdEIW9doGP0rh73VvFdm2B4gu7gN5ZiaB3YSq4JUg49sYOD7cU1NT8cPDcyC+1YfZmRZEJcMC3TjH+cil7TyDmqd19y/yPR7/4WaHqd7Fe3V3qj3cUQhSYXO1lQdBlQKl0z4ZaLo9lfWdhc38MF+CLpPMVvOyCDuLKT0J/OvKYvEfi64KiHU9VkL5ChGZs464x6ZH50SeI/F8MCzy6lqscLMVWRywUkdQD0zR7RBzVO6+5f5Hqmh/DDRfDU7TaNd6lZuylDsnBGCcngqR1Aq1rHgKw18Ww1XUNSuhayiaHfIg2OOhGEFcdqOs65J4V8KG11G5F5eiVXcSYMjbgBk/jWPfat4ks3RYvEU94GVmJt5WO3b1yCAcd89CK0R6eGy6viKcZqcVe/Ts7dvI9H174eaX4ntorfW7y/vYon3xrI6DacYyCEBq3J4Ril0ttMk1bUmsmh8gwl4ypjxt2/c6Y4rzK71HxHZ2zPJ4oY3CRrI9t57BwrYxgkYY8jgE1Rk8QeK4hEZNQ1NBNzFuLDf8A7vr+FB0RyevNXVSP3f8AAPUdC+H+m+GbN7TRb7ULKCR/MdI5EO5sAZJKE9AKLbwBp1nrd3rNvf6jHqV4oS4uBIm6QccH5Mdh27V5zDqHjKXz91/qcQgKebv35QMcA4xn3qq+v+Kkhaf+0tTNurlPOy2zOemcfpQCyeu3ZVY/d/wDsj8DPBZ3Dyb3DHJHn8H9K3dN+Hul6Po8uk6deX9tYShhJCjx4fcMHJ2ZORx1ry+fX/FlqqNcahqkKvnaZCyhsdcZ61NLrHiuOxhvBquoPBLH5hdGYhBuK4Y4wDlTQP8AsbEafvI6+X/AO9b4TeHX0W30dpb86dbTGeK3My7Vc5+b7vPU9fWtXVfBltrmmS6bqep6lc2cuN8LvHg4II6JnqBXk7+IfFcccUj6jqaxy/6tmLAP9D3/AApza34vS5S2e91ZZ3GViO4Mw9hjJoD+xcR/z8j93/APW7HwqumWEFjZ6xqkVtAgjijEkZCqOg5TNVdL8BWGi3V9c6dqGpW81/L5106yITK+ScnKe56eteZzar4uiuTbpqWozSLEsriIsxQMoPIxxjPNVm8R+KRam5/tPUhB083c23Ppnp2P5UAslxEtVUj93/APTdP+GujaVrF1q1jc31vf3YYTzI6ZfccnI2Y5Iz0rM/4Ut4UEUkKtqKQStvkhS5Kox9SoGP0ru9Md5dKs5JGLO0CMzHqSVHNWqD5+VapFtafcv8jkNW+Hel67pUOl6pe6jdWUBUxxSSp8pUYByFz0J71Df/DDQ9UOnG+nvp/7OULabnQeUBjAGF5HyjrnpXa0UE/WKnl9y/yOM1j4a6Tr97Be6lfanPdwKUim88IyDrgFVFaPhfwVpPhJrptO89nuSGleeTexP1xnn+groqKBSrzacX18kFFFFBkFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHh/xQ0/Ur/xrMLCztrtVtY98c0e8k84wMivPJtA1RCFk0O1TJ5xaDr6ffrt/i5Hoj+PA2sy6lGotkCGyXOeuc159PZ+E/lME/iB0Jzl05wP/ANdC2CW5P/YWohmVtHtQxHINoOB/33U6eH75gWGl2Hbcptlz/wCh96zbmx8M7w9tNrzR7cZdQDmoY7Dw+3zNLrYXpwozVEnRReE9Vdn26Tpzen+ir/8AF1PF4R1o2x2aJpbP15tV/L79Y9vp/g6QMz3fiUDH8EY61Jp48DWeoRXVzc+IXSGVZGiZBhgDnB9qegtRrW+pXHimDRb6O0szpqOVhjQIgL7ST1PJyPyrstbsJLzR1haeJQR1YjHp/WuKsbo634m1HVZP9dc3eQCRwpy2MewAFdZqqMdKPIyiEjn05oQM5rw/Z614h0VfI06wvF07Fqjz26uwXlgpJYcDJq+fCWsvKN2iaah25KrbL1H/AAOsvRtR0nT5Nf0TVTd/2dfbPLa1wzpKpDKQenQkU1bPwUtzslvfEqqe+wA0g1LzeF9RjLb9M04HcAB9lXj3+/VZ9B1BXIGlWLEMT8tqpGfT79U57DwoCVhufELPnPzIKq/YdBRtrSa11PQf4UMEaH9h6iULDR7baTyfsg4P/fdXLfQNY3eaug2e0DLMbQDaPXO/issWPhdbRVabXvPJyAFG3B96sQ23g9QGa58QiYjKKEG0ntn2pNeZSPqDwpuHhLSdwwfskf8A6CK2Kx/CYA8I6SFzj7JH1/3RWxSRT3Od8YXk2n2On3MEZlkXUIFCADLbm2kDJAzgmpbXxPDPd/Y5bSeC7XzRLExU7DGqMeQcHKyKRj8cU/xJZQ6hBYWtwGMT3sZO1ipyAxBBHIIIBpreE9LeFUIud4d3acXLiVy4Abc+ckEBRj/ZHpUrdmcfiZkP42QTQ3QLLYY82ZWiG9Yza+eACCcnp6envUo8U3i61Jb3dnJZxj7OFidVdjvExPzK2MYjH0IPHOa1F8KaMsYj+xgxhQm1nYgr5Xk46/3OKSHwppcMhkK3E0pZGMk1w7sdgYLyT2Dt+dUWUovGsMkEbvpl3FJcRQy2kTFN04lbavRsKc9c4wOaku/EkzeFtY1G1tTBeWHmR+Vc4IEiqDztPK8joasX/hm0n09YbaNEmit47eB5GchFRgy8qwIIIGGByKZpXhmK00K806+k+0C9lkluMM2CX6gEkt0A5JzQBlT+L74tL5FtGslna+feWzAl0ZJFEqg5/uElT3+U96s3Hia6/tD/AEZYGsWumto3IJLlLeSRmBz03BV/4C1bo0ewXVZdTFsgvJoBbySf30ByAR0PWoIPDml2thZWMNtst7Is0CBz8pZWU855yHbr60Ac6fEes2GjWl7dy2lz9u0+W5iCQGMxSLD5oB+Y7lIBHY9PWrj+LfL8UWWmsbYW7okc7F8Os8ilkAGemFwfd1q9beEdJtoGh2XEyG3a1Xz7l5PLiYYKpuPy5AHT0qZvDOjvDLG1khMsolaQ58zeCCCG6jG0YweMUAQ3Hii1zbJp8MmoS3EzwosJCgMqlzksRgYFVl8aWssSzxWV01siobqX5R9nLMVAIzkkEHO3OBzzV/WdDXWLvT5XuJYVtJHk/cuyOSyFeGBGOtRf8Ilo48kLBIkcaopjWZwkoQll8wZ+fBJOTnrzQBVTxvp0kqwiC485kQ+WVGQ5l8ox9fvBsE+3NVoPiFp9zG7wWlxNnYYViZGaUPIIxxu+U5YHDYOD7HGyvhrSEvVvFs1E63TXgbJ/1rLtLfl26Z560lv4a062j8pBOYQ6PHE87skWxt6hQTgAEDj8OlAGXJ4+023vZre5hljEG5ZWDIxR1Tey7Qc4ABG7GM8e9XNI1q91DXry0ubF7OOK0gmSOQqWJdpATlSR0UcdiDVpvD2nvdXE7JKVud3nQec3lOWXaxKZxkjj9etLp+gWWmSTyQG4aWaNYnkmneRti52gEnjG40Ac+vjV5X14RG1K21tNcWG19xcQ5V94z/fAI6ZBrR17VdTt4dGjsISZL+4WOSRdh2Dy2c4DEDJ2/wA++KtN4U0RrWG3FhGiRRNEpj+VijIUYFhycg9+/PWtCWwtpjaF0ybR/Mh5PyttK/jwxoA58eOLM3dxafZZWuYyoijjkjcykyCMDKthTuI6kYBz61LJ4lvINYt7a50mSC2azluZ5HlQmLYygnhjkYOeOeR71LH4N0aNCqxT4CeXHm4f9yocOAnPy4ZQRj0q1/wj1iWtmc3EjwK6B5J3YyK5yyvk/MCQODxxQBmnxkiIiyaTercTeU1vBmMtKkhIVs7sDkcgniobvxvbabJLFcRu84lkHl5SPaqKhblmwxy4AxyfTjNalr4W0u1ZGWOZ2jaMxtLO7lAmdigk8KMninXHhrTriZpts8MzSO7SwTvGx3hQwyD0O1ePYUAQWHim11HWm06GF1IXcHkdFJG0NkITv24I5x1zUH/CUzxXmqxXGmSBLW6jtbcxuhMzuqFR14yXzk4AHvWkNCsv7UTUGM7yxsXjR52ZEYrtLBScA4JH4mm3Ph3T7qe6mkWYNclGkCTMo3pja4APyuNq/MOeKAIW8SwRaFe6nNaXCNZOY57YbS6uMcDnBzuBBz3qv/wlm28SwfSrsag0/k/Zw0ZwNhcOW3Y27QffIxip73w1bT+G7vR7d3jW6yZJZGLszEglmJOSTj1/lU9r4e0+0uUuVWaS5WQy+dNMzuWKbOSTyNvAHagDN/4TS2EaSyWN2sVwheyb5T9q+dUAUZ+UkuuN2ODn1pNS8aQ6OIUv7CaG5eN5XhaWPKxqQCQd2GJzwByeelXB4R0cLIphlZGUoiNO5EILBj5Yz8nzAHjHQelPbwzYuISZb3zYww84XcgkdWIJVmzkjIHHbHGKAKD+N7NJL8C0nZbRSxG9A7YIGdhbcF+YHcRjGTxW1pOpJq2nR3kaBVckYWVJBwccMpINVZvDOnz3b3UjXZkIbZ/pUg8otjcU5+UnA6VJp+hwaZceZbTT7Sr70eQt5jswJdsnluMfSgDUooooAzdc/wCPGH/r6g/9GrWlWbrn/HjD/wBfUH/o1a0qlfEyF8b+X6hRRRVFhRRRQAV5pqcvguHxJdXN3pMj3qXB3ySXcaK7juFeUZH4Yr0uvIp9Mm1PxD4vW31GLTpIjHK11IgIRA8hbnqB3/DHSk9SouxqQ+IfCtvF5UdvOIy7yOp1OAiQv97f+/8AmB9DkVf0zS9JsY3a08HajJFOi5ErxTIyjkYDSkd+1Mh1aw1jSrjTm0Oe3up7SQxT3FmEjeIods7OBtQN6HkE9Ku6P4rs4NEsba0s9R1L7PbxxPNY2jSRFlUA7XOAwyO2aOUOdlG+utD0y486/wBDvbOSYhkM13DH91Cg2AzDGFcjjpms+31jwfbEGO1uDho3G/VYW5Qkr1n6Ak8Vuw3mneIvGdlMm3NrZTxy2l5CYplLvEVIjcAkfI3zDj866f8Asyw/58bb/v0v+FHKHOzmPDujeGdW0r7Rp9lNFCGmh/4+CSNxAfDK5HOByDWxceFNFurN7WayDQvGkZHmMPlXG3v2x1qHwqixw6qiKFVdTnAVRgDkVv0JKwNu5xmvJ4b8MWmmG7sZmg05JJoGSTiFQy7ixZhnll65qldjQtRu7OzutC1Se5liea3DXKl3j43EHzeV5HGcc8VV+K9ut1caFFNdQW9t5kjTGeFpUdV2MEZFIJBYL3HSuNsdfn0+309jcCe80vSb2wglUECViyeQ3PT5V5z0xW8MPVmrxi2vQ3/tGFBRjz6pfzNW1fZ6f8E6SXxT4FhsIbe5sL77LKpaFbiUEBVcqWUtJ8oDKRngfhXWf6HdiO5Tw1qLhnE6SJLFgkqQGBEvoe3tXmya/aeHr/TzpNqmowRaAumP5rGICXfuLNlSSD1OB3rIaf7Ne+H7aK6ElvpiWcbXcW9CyK375SDzjBIAUDI6nOBVfVa/8j+5kPNoPeS/8Cf+Z6lp8Gkzwmay0HVriLeELi7WQBo3J25M3Zgcj1zTdWl0TSdPlu9U0LU7azEXkOZLhAm0npt83kknsM5rzH7VZWXh2bRrOxjKNqcsryh2JliJkMbKGIAK7lBDZ9QCa6GfxHbN4J8LRPdG71fRp4LmaCUPicopUrvIxn5sgnuKPqtf+R/cx/2xC9+Zf+BP/M6qxudA8UWj3lroOo6jbvOXfdMjBZAMYKmX5SAemB24q9/Z1j5IhXwtqywiJ4hEsyBNrklhjzcd68z8VeIm8TJZ3LQmzjV5/MsbZ8vlgoSVmIClxhumSMjGauWC2d9rxvP7T3XCanbP9ourlot1mIFjmHJC5Zs5A5PWplh60VeUWl6FU80U2oQd30Sk/wDM76N9M1a2E0Hh2/nh80ESQzR4DxkrwRLwQQRxU/kaZdX1raXOhX1m06SQRSM6qMEb2GUckZ29a8vfQPs2iw6daT6csMGoTyzpDqkR+2Rvv8pgHbA2ZAw2PUZIrpfC2n/ZvHFncSaol85hhhib7aJmwlswkLBTjO4D5sc4rE6oTrO94tWTd7y0sn5noaeHdLRmYW5LMULM0jEkoNqnOewqpd+C9CvYkintHaNGdlQTOqgsSxOAfU1v0UHHHFV4u6m7+rGQxJBBHDGMRxqFUZzgAYFPoooMG76sKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPn74yIj+OEDu6/6Oh+RsZwDXD2ckcc2z9425c4Yluhx25rr/jbu/wCE6iIOB9nTv7GvM3aRZFJYj922MN7ipi7MUldnRpOizqnljBiJAKt/e+tOu9QS3W1xDv3Fxg7h/CPWuZ81w8RDkEo46/WmKcNAR3ds8+wrTm0J5NTprTVYTJbRmFVG9ssWOD8p46+9Z+tTxtYzbAvLL0B9Ky1bcsOennD+lRzc2jAD+IfyNHNdWBRs7nWaBpL2v2SV5I3+0K04C5yvy4APv1rrL2APaqjMMMpB/KsLTBL/AGXYBowhEaBTnrk1s6hGyWanPGB37ZUf1qkDPP8AWbB7KSzvmljZbkg7FzuUqMcitW/u4WklZRHwGI6+1VvGIcwWOYgqKXAYHrwKxJTgT8D7v9RU3toO17M3o9ZijLSNag/us43H+7n1q9p1xGTA7J/CuSobnp6VycjkBh/0x/8AZaNxbywzE4TI56cU1ITgnsdJbzpLAp8vOWPO1vU+hxVICOVlbzJgWYj5XIAxn/CsiF2AtxuPQd/c0+38ww2+wnOMn5ves5vQcVZn154T/wCRR0n/AK9Y/wD0EVsVjeE/+RR0n/r1j/8AQRWzTRb3MHxXevp9pYXMUHnyC/iRIt+3cWyo57ctVNPFl1JrL6dHpZdo5fs0jqzlVl8sPnOzGwEgZznnO2tTXIo520xJUDr9ujbB9QGIP4EA1Y/sfT/7S/tH7Kn2vvIM8nGMkdM44z1xUrdmcfiZx2neJ9SsNDsr7UVe6uLmzkvPLWZdrfPEoH3Bt/1nA5xzkntpXXjG4s5TZS6cn9oi5MGxJHePHlCTduVC3QgY29fat5tF014YoWsoTHDF5MaleFTKnaPbKr+Qou9E02+8w3FojtJIJGYZDbgu0HI5B28fSqLC31MS6EmqSwtbAwec8Vwdhj4yQxPTHrXP23jO5uSLePTEN411HbqpldI8PG0gbLIGwAhB+X6ZroYdHsoUuo1hBhulVJIm5TaECBQPTAqO10DSrIq1vZRoyyCQMcltwUqDk8khWI+hoA5rUfGF/PoV/Jp9ikd1bWE087POP3LK0kfyfKd53RsecDAHrW3qetXVi2n29rZpdXF3HI+ZJvLVQihjn5T1zjpU914a0W8REuNOgkVd4AI4IdtzA+oJJODxmr0llbSyRSPCjPCrLGxHKhhggfUCgDl4fGk0loskthFBJPbwXNqj3BbekpIAbahO8YJwobPrTF8cTzWP2q30kMsVnJeXAknKFVjkZGVRtyT8hIzj3xXQTeH9JnhjiksYiscaRR4BBRUOVAI5GMnp606LQtKht2t4rCBIXhaBkC8GNiSV+hLE/jQBnp4nE0kUcVrlpdQksU3SYGVjZwx46HbjHbNc9Z+MNQ0vw3b6hqSR3c88b3cqLMSyRAgfKqx8Dr97A4+9zx166BpSakNRWxiF2rbhLjkNt25HbO3jPXFMn8NaLdRRRzadA8cUZiRccbDztI7jPODQBn3Gv6jLZ6rc2NjB5FoJ445ZZyGMkYOSV2n5cgjrnjpzUa+K7i10yWW+s4/Pg0xL9xFNlW3FhtBKjH3c/jWyuiaYt/JfCyi+0Sgh3x97IweOmSBgnqaryeFNClghhk02F4oVKIrZICk529eRnnB4FAGZdeL7iBJWGmjab9rC3JlZt7ruLMwVCQuEPTJz271dOuSTaLp181tPavc3UULRMMMu6Tac7h079ASPStCfR9OuLZ7eW0jMTymcgZB8wnJYEcg57ipV06zW1gthbp5EDK0SY4UqcqR7g0Acfd+NNTOhTXdvpsEEstjLeWpe43cRsqtvG3g/MCMZz3xWjc+K7m11ddObTGlkjEP2nyC77TIxA24TBAAydxXjpW1/Y2m+QkBsoTEkLwKhXgRtjcv0OB+VRL4f0pZreYWa+ZbhVjbccgKcrnnnBJIznFAHLaf4kv8A7RbC5uJJEkEAIQKOWuJ0JPHTaijA9KtP45lt7SO6udLAhuLP7ZbeXPvZlLxoqsNvBJlUnGcc9a6NdF01GRlsoQyFSpC9NrMy/kzMfxNZGn+CrG0v7q5uPKuFnieDyhbrGuxmDHIHU5A9APTJoAhj8WahcPDbQ6OPtb+eT50rxRlYwh3KWTcQfMA+6OQfrVjTvFE2qg3NrYL9giSNp5JJsSKWiEnypgggBlHUcnjpWtbaNp9n5fkWqKYw4ViSzDfjdyeTnaPyFRp4f0mO6guUsIVlgRUjZRjAUYXjocA4BPSgCh4b8Sy6/uLafJbo0KTxSEPtKtnCksq/MOCcZHPWsvSde1oOn2m3jubjUNRuLeFPtOI4UiL5/wCWYOMJ7knniuo0/SLDS/M+w2yQ+ZjcFJ6DoBnoBk8DinpptlG8TpbRq0MjyxkD7rvncR7ncc/WgDmpvG0lvZm9fTAbaWGaa02z/PIIyAQ424UnORyfQ4qeLxRfLqRtbvS4okjvI7OWSO53/PIgdCo2jIwwBzjk8ZxWoPDmjrJcSDT4d1wCsuRwQTkjHQZPJx1NWm02yeV5Gtoy7zLOzEcmRQArfUAAfhQBzvifxDf2seowabAgNmsJmuGkwymRsDapUhuOuSOvGar6r40uof7Tt7O1gaWG1uZrabzHKEw4DbjsxnnopPIwcV0d9oOlalcrcXljFNKoC7mHUA5GfXB5Genamp4d0eOeWddPh8yUOrkjIIf74weAG7gdaAMq/wDFc+lzWcVzaQOz+SLgQzO5jMj7FI+TGO/zFc84ziuorI/4RjRd0LHT4iYQoTcScBTuXOTzg8jPTtV+xsotPtEtod3loWI3HJ5JJ/UmgCxRRRQBm65/x4w/9fUH/o1a0qzdc/48Yf8Ar6g/9GrWlUr4mQvjfy/UKKKKosKKKKACvLLrwlq2uarrstnLbfZJ7wJIj3DxMWiYsPuowK5YcH0r1OuGm8UW/hjTtanaF7u7N9cvBaRH55Aqgsc/wqByWPT6kCl1GtmZV015b215a688l/ZaPELy9hWcyC4kc/uoyxVfkULvYEYywzkCuY8T+NdQ8Tzx2elx2FvHZlrd411I7fPciOJ1aPAIByV+jEgYFdsLnVo73W1toYYtW1bT4L20j3B0LKuyRFLABio2HkAfMM8ZrynV9Om8P6vdvrVtqUYvmmljDWMDt5DbVmJ2KVDeXg8Y2FenzZpiO70zxhb+NxY6TcW8UWpxt5cOoW1yrSwXAjL+YExuCcbWzxnKnPWty10/XNf0uz1Kw8QX1jevcsl9E7rJFFtLJKsaFf7w+XJ44zmuY+HGma9olz/aU0M0Ol3TG5uJrmGGONYPJ68YkDBgMcAFRuIBNdRoetT6NZWdxd2N0bLVXurwGC2eWRJJJt8aFVBI3IxPI6igDc8H2/2Wwv4TNLMU1CcGSZsu+Gxkn1roa5zwZeRajpd5eQiRY5r+d1EiFGHzdCDyD7V0dJbDe5wXxH0u/wBQl0l7LTnvlhaQyRgEjHy8HBB5welcpqvhW/mW2S38PyWsghLzPbo7qXIyqfMx6cAn1J9K7jxvrkeh3GnzXVxqENgUmad7LBZMbMMwP8IyenPNSXrhfC1z4h07WNQuYzZtcwBpQqOCuR/Bx+XFepRxkqNOMdNL9+7+R5tXCxqzk/Tt2RwE3gvUz4iSKHSZVsi8Y3OpKAYG7POcZz3p8Pw/v1exa4SciV0M8aQH92h6/NnqO/THvXV3PiG68hGinvB9ntzPdxsyCSExyIsinCc4Vi3vgdjT7nULpdRW0hv717RJ3gWfzl3O6QNK3O3kAhV/769Kv+06lrXX4k/2fTvez/A4mLwVqp1G7j/s64+yqr+XLJEeRnggA9fTNXLr4eXhurkW3nLF5jLbq8BO4AZ+Y5+Udge/tW6+pazZ6VHqV/ezML6wkuYBbzbQjrF5mxgV6EA/MOmOnNajahKPEkWmG+vvs08axyXHmpmOdlLogGzjKKST6lfWh5nUvdNAsvp2s0ziY/h5qLQJJI8sbFFd4/szMVB7DB5YHqPTmt7wD4b1HR/E1/8AbrNxAIGjSZl+VzvXBH4Cte+g1ttcudP03UpmW3s4pm+0TYZyzOMAhfl+51wetZjapd30Euo2msS2ljDFDJK19chNzSIH2AquFwCPmIPJ6cVNTHzqQcJNWfkVTwUac1OKd15nonkxf88k/wC+RSrFGpyqKD6gVwrzXa+Io9DXUdQa5nxPDILpdhtf4nPy/eB+XHfIPTOLWoz22mX0trLqfiB/IiSaeSJlZIY2LAMxx0+U5xk4Ga8/lh/N+B3c8+34nZUVwMuuaRDJdK+u63st1kO9ZEYSeWcOFAGcg+oGcHGasJe2hv2tJNT8QQyRx+ZMZJIx5I2lvmGM9BnIBA6ZzRyQ/m/AXNL+U7aiuF/tOzCLvv8AxIkshj8qFtu+USEhGUY6Eg9cY74ouNUsbWyiu5tU8QRws7pIWkQeSyttYNkckHsueOelHJD+b8A5pfyndUVxlqGOh6hqd5rGqJHZz3KMEmXlYpGUfw9SFH4mqun3F3dR6ZHc32pQ3k949ndxC6DCF1jd+Dt5yFU/RqOWH8wc0v5TvaK4q5vLO1v721fVtdY2UZkndJEITCb8Yxu+73xjPGc0+9uLSyuBbHVdfmuC0SLHEyks0iuygcAdI2znpRyQ/m/AOaX8p2VFcDLrelQeX5mt64rEFplaRAbcByh3gjsysMLnoT0qYX9tIxWC+8SzuZpIY1QpmUxkiQrkDhSOpx1GM0ckP5vwDml/KdxRXFm7tFuvJbVfEAjDiFpyyiNZSu4RnjIbBHbGTjOaqJrVi6LIb7xMkZijuC7hQFhc4WQ8fdznjrx0xRyQ/m/AOaX8p39FcNJqdjCZml1TxDHBE00YnYrsd4txdV4yThGxxg4PNGrXi6dcRW8F7r13K00MbrHMg2iXdtPIGT8p4o5YfzfgHNL+U7miuCm1rSYJ7mOXXNcAgEuXEiEM0YJdVAGcjB7AHBwTVmK5gl1SHTvt/iJLmRFdkd4wYwxO0sMZ/hPQHHfFHJD+b8A5pfynaUVwtxdGy1y+s7rUdb+y24gJuI3UrH5mRlzjpkDoDjqeKY+p2q263f8Aa2ux2gacF5MZkESMzbBjtsPUc9qOWH834BzS/lO9oriBfW5zGb7xILvzFjS0JQSvuUspAxjG1WOSRjac80y/uJF8MLrGmX2s3LvcJAlvLOsbMxmERHK8HOcUcsP5vwDml/Kd1RXnw1FpbtXt9T1N7ANbCSUzgOgmMicjbwVdVUjtk+lV7zVL2FLqSC/v2iWC6lt2e5Hz+S8cYJwvQs7HjsB60csP5g5pfynpNFefXcmqaVO9pf6jdyXGIZInguMIyPMsTAgrkEbwepBz2xTbjU57bXJrWLUdQMUc8UcEsk6sszrJGJkK7ewkH5N6U404yaSkJzkldxPQ6KKKyNQooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPnT432t/c+N4/sVvJKFt0LbIy2Dz1x+NebjTNYZsyWFycKQNsDDr+FfVmt+Dhq2qPfxapc2jyIqOqIrKducHke5rP8A+Ffz/wDQxXf/AH5j/wAKFYb1PmT+y9SBj/4l938oYH9y3cH2oGl6l+7/AOJfd/KxJ/ct6fSvpv8A4V9P/wBDFd/9+Y/8KB8P7gf8zFd/9+I/8KNBWPmZdL1JVT/iX3XyyBv9S3+FC6RqMmIWsrld7gbjCwA6j096+mx4CugMf8JHd/8AfiP/AApj/D64kUq3iO7IPrBH/hRoFjymKDy3tYimAjxqPfGKv6hltMXI/wCWWf1Q16CPhhEWV31q6MiHcpESDB/Lmg/DPeixy67ctGEKMBCgyMAcHHtVcxPL5njHibTprzSMxRMzQvuCquSwxj+v6Vyb6bqLmQiwusMMD9y3qPavpQfDTY2YtduVH/XGMn+VTjwBcjp4iu/+/Ef+FJu5SR8yPpepMWxp91ym0fuW9MelJ/ZmpAg/2fd8Jt/1LemPSvpz/hAbnP8AyMV3/wB+I/8ACj/hALj/AKGK7/78R/4UtAsfMi6XqY8v/iX3fyjH+pb/AApsWma1HsAsZ1VcAkwNwO/OK+nP+Ff3H/QxXf8A35j/AMKR/h5M6lW8Q3ZBGD+5j/wodh2N/wAJAjwhpG4YP2WP+VbNVdOsxp2mWtkshkEESxh2GCQBjNWqBMw/FE81tbWEtuIjN9tjVBK21MtlRk/jVG88R6np8siT2lnILWxkvLpopWOQhYbUGOpx36c9a09ft4bpLCC4iSWGS7UPG6hlYbW6g1dttOsbWJUtrSCJAnlgJGANpOcfTJJ/GpW7Ij8TOVHi/UxalZLGKO7Z18ndHLtlXYWbau3dlcDJOBgg+1VP+Er1W6t76/AhSw/s6zuookciVDKW/ixjt+g966weHdFW3FuNKshCJPMCeSuN2MZxjrjj6VM2jaY7RltPtSY4/KTMS/Kn90cdPaqLOan8Y3tnHLeXNhA1kZLyKIRSnzC0G/lsjADCM/T3rV0bWLy71DULG9jtPNtIoZd9rIWVhJvOORxjb+Oc1qPYWrxbBDGmC5VlQZRnzuYcdTk59cms/QvDdpoP2loHMklxtDsY0jGFzgBUUDue3OaAOfXxvqEWmwXVzp0G68tIrm2SGRm275Ejw/GTgyKePce9On8b6hZQAXOkH7RIHECAsvnFGG/AIyPkO4A8/KR711jaXYPAIXsrdohF5IQxDAj4+XHpwOPaiHTLC2ihihsreNISTEqxgbCQQSPTIJz65oAx5PEsreFpdatbaOVWnCWqmTCyoZRGrk44Bzu+mKp/8JRq7arNYw6XHM0EgtpCu8L5piD7txGAmSBg84yfauoFnai0S1FvELdAoSIINihcbcDoMYGPpUbaZYNqC6g1nAbxRgTmMbwOnXrQByj+OZ7kW/8AZ1iHFw3lo7B3w6x75FKqCcqSq/UNnpU0Pi3UppvMfTYYbaOa0hmSSU+aGnCdMDHylx9R6V0c+kabc2v2WawtpIN5k8togV3kklsepJPPualWws1UqtrAASjECMYJXG0/hgY9MCgDlbjxy0GmWd0LNHkubNp/LVydrebHGAcDOMyc8Z4q8niO7i8OXuoXdhsntpfKVTujWTJUBvmGVXLc5HGD1rVTQ9Jjed0020DXAYTEQrmQN1B45B71LBptjbWRsoLSCO1YENCsYCHPXI75oA5w+JdV/tGPSBbWP283LQNKZW8riESggYznBwR+Oaqp42vZreW7jsrUW9rBDNcK0x3tvdkITjB5QkE9cgcVvT+FdGnW0jNhAtvbM7rbrGvlsWXBJGOT709fDWkjUzftZQvKEjSMNGpEQjzt2ccdf0FAGDc+NLy1tZbxrK3e3kjumtkWU+ZmAkEPxgZx26HA5p1/ruv6bqt0Z0sGgttJe8eBGbllLdGx7AV0g0bTBPcTDT7XzbkFZn8pcyA9Q3HOamnsLO5ninntYZJogRG7oCyg8EA+hoA5+88T3Q1T+zrG3tpJmkt41aSQhV8yOR8nA7CMcd81kXvirVpYGezWCC6QQpIHctFn7W0D7RjPO3qex9RXY2ujaZYgC00+1gAYOPLiVcEAgHgdcEj8TT30vT5I5I3sbZklUrIpiBDAsWIPHPJJ+pzQBy934pv7LUJ7CG1E12bhlyQ7ooSGJyAFGRkyfhyfardp4mvZtZtra6sVs7e4CKnm7mYu0e/buUFQQcjaSCcZ9BWzNomlXFuLebTrWSEMG2NECMgBQfrgAfTinDSNNW+W+WwthdKu1ZhEAwGMdfpx9KAOXvNV1Wz8U6nIXikt4fs1tbQmV1RXmbAZwBg8nk8ngAdauWPiPUrzWYtLFpaiVHmW6lErbAIzHygxkkiUcHoQetdDNY2lwk6zW0MizgLMHQHzAOgb1pLbTrKySNbW0hhWJSqCNAu0EgkDHqQCfWgDl/EGq6zbahrkME0CWlvozXMeMiRXxIAwPTOVH5UyfxZqdiwsbiyge+eSBY2h3yJtkSRskAbiR5TDjrkdOa6q406yu5lmubSCaVUaMO8YJCsMMuT2I6ikudLsLyORLmyt5VkChw8YO4Lnbn6ZOPTNAGTN4kls/DdprN7Z/Z4zIq3as3MKkldw9RnaeccGsu08aX9+9vaw6dDDeSSLbyiaU7IZ8SMVOBk/JGD771rrvsVobIWX2aH7KF2eTsGzb6bemKjn0vT7qGaGeyt5I53EkqtGCHYAAMfU4A59hQByN14r1O90zUBbW8FubfTZp5ZxKSVdWlj/AHfHI3R5BPanT67qd5eaekLxQWjan9iZhIfOcrE5JIxjBIzjrgA9665dOskiMS2kCxmIQlBGADGM4XH93k8e9R/2Ppv2/wC3fYLb7Xx++8pd/AwOcZ6cUAY2n61eQ66mj3iKIl/cQzybi9wyoCW3AbMnDZXIIwTXTVTXStPTUG1BLK3W8YYM4jG89uv0q5QBm65/x4w/9fUH/o1a0qzdc/48Yf8Ar6g/9GrWlUr4mQvjfy/UKKKKosKKKKACvHNQ0yxvtd8Y3F1aQTzWyx+S80Yfy90rBsZVhz/umvY65WbwPE2r3mpW2s6pZzXbZkFvKqg8kgfd9SfzpMaMOJL260DTn1iWc3E99Emm3cLxiS23KcMMRrxwQVYcg4NXdY0bXtTjhg1TSrLU/JDKl1aahJZs6sMMrLtPDDqAxBq9J4IaaWCSbxJrUpglE0YklRgrjODgpjua0f7E1D/oZNS/79wf/G6LhZdznNWmKvaW/i+7ttP01xmLTbNJJVnKYwskm0bsZB8sAZxzkAisLT1TSUjvZtWtrBL2zuIk1K3lkne+diCszR7fk8vvnoWxwK9A/sS//wChk1L/AL9wf/G6zLLwImnXYurXXdUSVRIqZMTBBI+9woKYGWAPFFwsu5H8MmR/CAZNm03M2CjMyt83UFvmIPXnnmuxqhpOlrpNrJCtxLO0krzPJLt3MzHJ+6AP0q/Qge5z2sadZan4n0eO+tIbmOOG4lRJkDhXBjAbB78n861NVs1vdHubLyTIk0ZiMavsyp4ODjjiqt3/AMjZpf8A163P84qm17Um0jQr3UEjEjwRFlVjgE9s+2a1qaxj6fqzKG8vX9EZn9lv/bM+q/2Kn2me2+zTf6UNsiZ7jHXjGfTjtUFtoBtLCws4tKcRWRkMWb0FiXVlYsdvJw5pH1+60S4ubfULr+0rhREUghtDAxLuqfKzHYy5de+R3p9x43tbHUXtL62aLZE7sY5kkKlIzIylVPHAOM9SOnSub2cv53+H+Rdn3KsPhiVLT7NPa3d1Gtq1pF598p8mNl2nbhAM44ycmpG8MwuzzNoSG7acXH2s3K+crggrhtuQBtAx6DFWL7xe+mQW/wBu0uS3uLlm8mJ7hMMqqGJLZwOoGPX25pNa1+4Gm6Jc6c00K6jMAT9kM0ioYnfGwd8qM+nNHs5fzv8AD/ILPuJqGkXt/qEl55N3bPLCsEq298qCRFLEA/ISPvNyCDzUNz4dlmV0t7KeyhlgW3mitb1VWWNRtAIKHGF4yMHHfgVV0/xjeEvJdiJlit3LIwEBLi4MYLbz8h2gEryR2zWnp/i8asYV0/TZ53Kl7geYi+SBI0fBJ+blGPHYe4FHs5fzv8P8gs+5GNDdZPNXSmE4nSdJ/tg3oVG1VB2/d25XHox7nNV7/QtR1LWru8uLeRLa4toYHt4bwKJAjOSH+Xod4HGD1p+meNY5re782N52srho7qWMBViBnaNeDycKMkjjg9+KWT4gackDSJbzsQ7JtYquD5iohJJ4D7gwPoDR7OX87/D/ACCz7jD4ZOLxBp0ggut+6EXSBVLtuYg7N3J9ScZOKm1DQ5tU1JLy80+SQJyIDdps6FeuzcBgnIBAPcVSfxjc3dxC1moiibyVdHUN8xulibDdwVJwR61tav4ng0fVLazmh3CZo1LrKu5S77F+TO4jOMnj2zR7OX87/D/ILPuZ1v4emglhlezubiWFojE818pKLHnagwo4+Y9eT3NQXvhJb6SV5NOmXzvM80JeL8wdy5HKEj5iemPQ5rQtvFs16sH2TRbmSScSSRRtLGpaONgrP1wPmIAHf2qtb+K7uOa9jvLSQQi9ntoLlduAUQuFK5yeFIz6/nR7OX87/D/ILPuWJNIkm02TT5dJ32sty1zJG12CHYyeYVPy8ru7enFR2+gLZ6gl5aaIluUlEwihuVWPeEZN20L1KuQfoPSq8HjWaGK+nvbH/RLdLYRSh1DSNKq7QRnC5LdegFXbPxedRaOKy0uee4w7TIsiAIqsFyrEgNnPGPQ5xR7OX87/AA/yCz7kGoaBLqmoG7vNPllBDDyTdptG5Chwdm4DDHjdjPOKoz+FtRaW3lhF1563MUsk8l6pk2xxyKoXCYH+s7jnnPWtXTvE91NeSwXun+Sp1J7GF0kDZwpbLD6L+opk3je2h85zY3Jgt4JLieXK4RUkePpnJJaPge/OKPZy/nf4f5BZ9yqvhho5oZksJlmTPmyfa0Zp8uXO8lDzuZjlcdSOlWH0S48mFILKe2kgmmljmhvV3qZWLOOVIwSemOw9KtWXic6lpGo3VtZn7RZqf3RkDI527hhxwff0INVYvGE66ZBcXGkymRbFL67EUi4hibPzDJ+Ynax2+g9cUezl/O/w/wAgs+4xvD8zXJlayuGjaQTNAb5SjyhdokPy53YAPXGRnGae2gs9o9sdLfy2sorI/wCmDPlxklf4evJ5rRv9YvLfxBpVhbWazW15HI7y+YFK7dvQfRs1h2Pjp7fRLe61eykR5bcyxSKyYnIkWPGM/Ly6dexz2xR7OX87/D/ILPuJbeGLsiYahayXMck9zKtv9sAiTzi+SBtzna5HXHJIqVfDdwI5d8N5JPI0Li4e+QyIYs7MfJjjJ7c55rTt/FVvNoFxqhtpR5EvkmJCG3uSoUK3Qgll5/PpWU3jWexvb2DULPy5xcrFBbmVAFAhR2JfoeW478gcUezl/O/w/wAgs+49vDTMbwHT5fKu1kEkQu0ABk++wOzdknJ64GTgVbvNHuNT1a1vLqzlAhkRwgukKgqcg/c3D3AIzjmq0vjdJ0mjtbWSFzZtPC9wyoxPlGQYQnLAdDjPIPYZpkXjO5u9O02502wF3HcXSWrytIEDNsJYqDyORjn+WDR7OX8z/D/ILPua974Ys7/UJruW4ulFx5fnwJIBHKIzlQRjP1wRnvRN4W0640+GyfzvJiEwXD8/vQwbn6OcVTufGcVok9xLp9wLJDOkc4ZT5skKsXULnI+4+CeuO3FaWl6y9/dT2txZSWdxFGkwR3V90b7gpyp65VgR2x3rUZU1jw79pka9sci/3RFXM5j27A6/KQrYJDsDkEHNO0vw4tv4btdLvZTK8U4uWdGPLibzRyeSN3HPWt6igDGbwvpbNq5ETr/ayhbkK+BkAjcv9085yO/PWm3PhTTbmzitT50ccVk1kmx8ERsUJPT72UXn61t0UAYi+F7NvMa5uLu7nkMWZp5AWAjcOqjAAA3DJwOe9ZGteE9GsdOtrmGzQXcF9BKLnA812aYBizdTncc12VZHiX/kDD/r6tv/AEclaUf4kfVGdX+HL0NeiiiszQKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAOa8b5/saDbOkB+1J+8kmMSjg8FxyuemR61y+k39x/YWiNYPd/aIJ5INn2ppo5D9jaQKD/ABLuC8HlSCO1eiX2n2upW/kXkIlizu2kkc/h9apR+GdIijSOK1KIn3FWVwF+gB4qLNN2ItJNtHDvqLQwWYsNYupra4tIX1SY3LSGDdNErPkn90SrS5AxjbnA210elTTNZ+IINNvXmtoH22Vw7mfaxiUsoYnLAMT34Jx2rVXwzpCBwloV8w5fErjcffnmlj8N6VDGI4rZkReipM4A/ANTvLt/X3BefZff/wAA5zSdZ1a5stNh+32tyh0o3dxMIjvdhgbAQ3B5OT1yOgrEk1a8WKS6EkvEDy+X5r7f+QfG+M5z1Oeuc85zXep4a0mPOy1Zc5ztmcdevel/4RvSf+fZv+/z/T1ovLt/X3BefZff/wAA5y68Wahbz3DI9o3lyzwCxKHzUEcTOJWO7kEqO2MMOc9dfRtT1OS7ntdQME7iyhvEMERTG/eCmCTnBTg+9W/+EZ0jzDJ9lPmFdpfzXyR6Zz0pw8PaYDkQSA4xnz5On/fVF5dv6+4Lz7L7/wDgHF33iPVtR8PXSxX9qJJ7BbljbRtutCXVTE3zZyQxAPByjcem/rNzN4esNKjhCO0YmGRuVSUt5XHG7plRwSfz5rTXwzpCb9toV3nL4lcbj6nnmnN4d0xvvQOfrPIf/ZqLy7f19wXn2X3/APAOcl8R6zaTw2d1cWKNcrbyfbDCVjtxIJMhgW55jABJHLjNUbDxZcaZpF7JcXVtIBDf3MMzk7ZZEnYBVyeVxj5R6iuvfw1pMiMklqzqwwVaZyCPQ80jeGNHYKGtMhDlQZXO0+o54ovLt/X3BefZff8A8A5bUfEurP8A2xaJdQQTC1uGtfIjEhUom7kh9yt14ZQM4wT3tTazqj2N6EvLS5t7bR/tbyxxsDM7CUYDK/ygbByMn6V0A8M6QsrSraESMMM4lfcR6E5oTw1pEa7UtSq4xhZnAx+dF5dv6+4Lz7L7/wDgHMjxXrB1C7ihis1SDzY1iuHVNoSPcJCxfdgnGflxtOc8c7fhjXjqcMsV1cxtdxyFdoRUJG1WONrurY3DkHuM4q3/AMIzpHmmX7KfMK7S/mvkj0znpSR+GNHhULFaGMDOAkrgDPXoaLy7f19wXn2X3/8AANeisz+wNN/55S/+BEn/AMVR/YGm/wDPKX/wIk/+KovLt/X3BefZff8A8A06KzP7A03/AJ5S/wDgRJ/8VR/YGm/88pf/AAIk/wDiqLy7f19wXn2X3/8AANOisz+wNN/55S/+BEn/AMVR/YGm/wDPKX/wIk/+KovLt/X3BefZff8A8A06KzP7A03/AJ5S/wDgRJ/8VR/YGm/88pf/AAIk/wDiqLy7f19wXn2X3/8AANOisz+wNN/55S/+BEn/AMVR/YGm/wDPKX/wIk/+KovLt/X3BefZff8A8A06KzP7A03/AJ5S/wDgRJ/8VR/YGm/88pf/AAIk/wDiqLy7f19wXn2X3/8AANOisz+wNN/55S/+BEn/AMVR/YGm/wDPKX/wIk/+KovLt/X3BefZff8A8A06KzP7A03/AJ5S/wDgRJ/8VR/YGm/88pf/AAIk/wDiqLy7f19wXn2X3/8AAF1z/jxi/wCvqD/0ataVZq6DpyyI4hcsjBl3TOQCDkHBPrWlQk73YRTu2woooqiwooooAKKKKACiiigAooooAKKKKAOI8d6/P4c1HSb63hjlkMc8e2TOMExnt9K5q8+JeqvDJa3ujWflyoVeKVXG5SO4J6EV2PjHSbTULvSri51e1sDayM6C4AIl5U45Yen61ja34Y0q/nhT/hI7UCOOQhbuQSv85yWzvBwOw6D3r18PLDezgqkbvXv3Z5ddYj2knTlZaduxyUPiuSG4C/2Kks/yBBPcTysoVg6hdzEgblU4HXApreMFku3uZNEtHd2kco80pj3OpVyE3bQWBIPHOa6u48E6fLrUepy+IbXyS8Z2cDdgAYDB++O1LF4U8PWq6fcR6tpYEUiN5ztkzMPvD/WYwT7ce9bXwVvg/BmVsZf4vyOUXxdI0IjXSwTES6yC8uDJGNu04ffuC44xnFT33j641C3toLjTIAtsweFo7iaN0IUrncrA9CR171tjwfo8eqXk8niPTgXDkwA7RHk55/eZIHoav3fhXw/dXN5cS6jpmZXLysHIMSkfLt+fAPck5z6ChvBJ/B+DBLGNfH+KOLPiyMxhRotqGXkSieYSZ3792/dndu5znNbfha7k8Q6h9ls9PtLR7eNpd/nz/vAZNxDYfLDcxPzZ6n1rRj8J+GI7ZQ2p6TI8caMZHkPzN0JYCQfKewGOe56Vb8O6VoXhjXry8TxFYNHKjRrAZVBjG4HGS3OMYqKv1Vwl7OGvTRlU/rKmueenXVGk3hu/dHVoNI2vFLC4xN8ySsWdT83IJJPtnjFM/wCEXvBHMi22kJ5qxKzIJlbEX+rwQ2QV7Ec1uf8ACR6H/wBBjT//AAJT/Gj/AISPQ/8AoMaf/wCBKf415v7z+T8D0Pc/m/Exv+Ed1I+WXXTZGQKA8rTu3yyCQZJfJ+cA/wD1qLvw5qV9fG8uF01pT5Zba06q5jbchZQ+CQemRWz/AMJHof8A0GNP/wDAlP8AGj/hI9D/AOgxYf8AgSn+NH7z+T8A9z+b8TEm8NajNBbw7dNjW3DLEYXnjZVbll3K4JB7j2FSHw/qTRmMw6QVM7XGMTf6xlKs33u4Yj8a1/8AhI9D/wCgxYf+BKf40f8ACR6H/wBBiw/8CU/xo/efyfgHufzfiYb+F759/wC70xQ8SRMEadRhMbDgP95cDDdRjrRJ4Z1KQQ72si8QYCTz7kOwY5Ks2/LAkDg8Vuf8JHof/QY0/wD8CU/xo/4SPQ/+gxYf+BKf40WqfyfgHufzfiYk3hnUZ3ndhpytPKs7GOSdNsgGN64f5WxwSMZ70sHhm9t7aS2jt9I8mSE27qwmbchZmIOW9XY5681tf8JHof8A0GLD/wACU/xo/wCEj0P/AKDFh/4Ep/jR+8/k/APc/m/EzrTS9cs7aS3jl0+SOTO/z3nlJ4xjLMTjHas8+ELtoYoWTTzHFH5QUy3HzR5yI2+f5kHZTkD8a6H/AISPQ/8AoMaf/wCBKf40f8JHof8A0GNP/wDAlP8AGj95/J+Ae5/N+Jn3Wl63ez2s8x0zzbVi0Tp5yFc9RwwyDgZB4NVX8NX0lrDbNBpHlQRNDEMTAopKscHdnOUUg9QRxW1/wkeh/wDQY0//AMCU/wAaP+Ej0P8A6DGn/wDgSn+NH7z+T8A9z+b8TL/sXV20yTTpTp01tLneJmndmJOc7i2c56c8dqqDwnfKp2/YRI0nmmcT3PmltoQnfv3cqAOvat//AISPQ/8AoMWH/gSn+NH/AAkeh/8AQY0//wACU/xotU/k/APc/m/Ew5/C+oXUoecafIAMCNpbgpnYUzt343bSRnr+NOl8NahKrgx6Wm9o3LRmdCGjGFYEPwQOM9SODmtr/hI9D/6DFh/4Ep/jR/wkeh/9BjT/APwJT/Gj95/J+Ae5/N+Jzt54Qv7tLwf8S6N7qOVCQZisZkUq7qhbaGIJycc5Pqas2Ph3VNOkWWBrDzQQd8klw5IClQDl+VAY4XoCc9a2f+Ej0P8A6DGn/wDgSn+NH/CR6H/0GNP/APAlP8aLVP5PwD3P5vxItviT/nppP/fuT/Gjb4k/56aT/wB+5P8AGpf+Ej0P/oMaf/4Ep/jR/wAJHof/AEGNP/8AAlP8aP3n8n4B7n834kW3xJ/z00n/AL9yf40bfEn/AD00n/v3J/jUv/CR6H/0GNP/APAlP8aP+Ej0P/oMWH/gSn+NH7z+T8A9z+b8SLb4k/56aT/37k/xqC7sNe1CJILmbTVh82ORjGj7sI4bjJx/DVz/AISPQ/8AoMaf/wCBKf40f8JHof8A0GNP/wDAlP8AGhOondR/AH7Nqzl+Jp0Vmf8ACR6H/wBBjT//AAJT/GrVpqFnqCs1ndwXCqcMYZA4B98Vi4SSu0aqcXomWaKKKkoKKKKACiiigAooooAKKKKACiiigAooooAKKKKACivMPGfinxJY+MhpOj3KIjQo6o0aEA4JJLMOBgZ/Csj/AISjx59qMJ1GyCiIS+efIEJQnAIfp14+tQ5o0VNtXPZqK8Ri8aeOZNXbS/tsKXSlgwkiiVV2gkktjGMDOafd+MfG1kiSSarYvE6sVli8l0Yr1UED73I4680c6H7J9z2uivB4PiJ4xuZPLhvVd9rPgW8fQDJPT0BqP/hZniz/AKCCf+A0f+FL2iD2Mj3yivA/+FmeLP8AoIJ/4DR/4V6h8PNbv9f8NyXmpTCWcXLxhggX5QFxwPqapTTdhSpuKuzrKKKKozCiiigAooooAKKKKACiua8cXNxaaAsltPJC/nqN0bFTjB4yK8sg8X30094n9pX8aWoBeSSVlUg55GT0GDzWFSuoStY5a2KVOXLa57xRXgEnjDxAupJZot++9S6SC+ABQEAtjPuOKuf8JZcGSaMa9KXgBaVRdHKAdSeeKh4pdjN41L7J7nRXhFx4yntpYYpNbuN8soiUC5PDEZGefTH5ivUvA9zcXegNJczSTSeew3SMWOMDjJq6dfnlaxpRxSqS5bWOlooorc6gooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA4jx5PrVvqWkPoFt9pv9k4WFoVkRx8mQ+WXavT5s5HHBzWlrS3P/AAgN9NdW8VrqH2B2kFs2fLk2c7Gxng9DV67/AORs0v8A69bn+cVac8RmhaNZXiJ/jTGR+YIrSo2oxt2/VmcVrL1/RHn00eqTapNpR3/2hY6c00MoXal0VlRo2OOMsFKMPr2IqWK6fUp7HWYhJHb3966wREbcQpbS4yvu4Zv++fSuuGnTNyNWvD7jZ/8AE0v9mT/9BW84/wBz/wCJrn9pP+V/ev8AMr5HnUctnH4bs5NHvGmvJNImk1BUuGl2YtyQ7ZJ2OJNoHQ8n8NG41WePX7a+W2um06w8q1nZIwYW3gea7c9VJj5xxtf1NdkNKkXIXU7oZ6gCMZ/8dp39lzYx/al5j0+T/wCJo9pP+V/ev8w+RxnjQIviMReZZQr/AGY7x/ar17VRJv8AvLs+81VF12eytLiRZDFczvG4NzCmTttImIO5lVSSckDJPOB1Nd62kysQW1O7JHQkRnH/AI7Q2lStjdqd2cHIyIzz6/do9pP+V/ev8w+RyI1yXU7S6M1zY2TG0BSxMX72XfbiTerZzwxIGAR8hzz0is/EV9aaDFc3FlErW5hikszHukEbx4ik3A/NukK9OByOoJrs/wCyZCwb+0rvcBgHEeQPT7tB02VeTqt2O2T5f/xNHtJ/yv71/mHyOLk8T6qbaLfbWtuVuBZXE/lqFSZEZpCPMZVwTtAyfXqak0ye4u/ENlNdRxq8tzC0iRHdHzZyE47YJwa7BtKldSrandsp6giMg/8AjtL/AGZN/wBBS8/8c/8AiaPaT/lf3r/MPkclqt6lvbeJbBrgR6jLdoLKHdiRlZIgvljqRuDdO+feo7Ga4g1u0zfxw2skmpDyZYgRK63PyqCT1wTgdgDiuwOlSlgx1O7LDoxEeR/47QdKlOM6ndnByMiPg/8AfNHtJ/yv71/mHyOHvvFGo6fomn3cslm95NZi+aEWqojKduEBZ8nv90E8g8DrseM5biT7JpmlwXH2lw107WiAsgT7gPI+VpCufUKwrfOkysQTqd2SvQkR8fT5a0lXaBzkgYyepqozk91Ya9DlfD14usarfXKp5ccthaOsTrkROTLuGPUEYP0rk4tUuvD3hpblbiCW+uftV25ktkHm+W5G0lnGe3C5OMYHFerAAdABmkMaNtyinacjI6GruwsjiG1S61AanHHfWVncIkqQ2PkBpuI9yyDkE569MY96pN4gubXS7Vl1LT5WTTlukd4ATfS5IMK4P3hgA4ycuOOx9E2Jv37V34xuxzj0pPKj+X92nynI+UcH1ouwsjjjrF7a6feazeJa/YLa/eGWFbf5o4FdkLk55I+UnjGFNTanqd5Y+HtIuLj7FZ3N7cxxTySxZWBXDHpkcjgZJxmukvtPttStvs90heHeGZAxUNg5w2Oo9QeDS3djbX3kfaY9/kSiWMEnhgCM+/BNF2Fkee/29KtzJeSSwki3NvFcRwr5cv8ApOxZAGYKMjuWx9RxVjSdUv8AVNV0mSa/trWOaK8h2eQpWaSOZVUj5sFiOcDPQ44NegmKMpsKKVxjaRxj0o2IcZVeDkcdDRdhZHl+mX11o3h60LXllDJPZyag09xbc3UuQPK+9yfXHPIwOK2m1XVpknmRbS3B1GGxjie13GMOIyzMc8kbyAOBxXbGNGChkU7TkZHQ+tLgeg65ouwsjjdH1HUn1W1ivmtJoZri6tMR22wgwkgPnJ+9tOR054qsfEc0es3CGaxkSLUXtDYpBmURBN3m8HJx9MY4613eB6Cq9rYWtm8zwRBXmkaV26kscZ/kOKLsLI4Cx8YXQS4up4oLqxtvIllaOFN+yUOAAEZhw6pxnOGOasRa7rK60tlcQafHNGY0khcIofMQdnGW3kAkgYUj5D36d8saIu1UVR6AYoKIXDlV3gYDY5AouwsjlPCmpzazaXsU8ts93EqHzIoo2RSynoUdgwyDwcNjGRzWBpUmpXcOiPFqtvJdrp1y08j24fDKYfkZQ33gepPPJr0pI0jBCIqgnJ2jGTQEVSSFAJ5OB1ouwsjgtP8AE2oahrdpF9ms4Y5DADbvsBdHiV2dctvOCSBhSPlOTnpa1/xDdaJrEsBsoZLaIJeO4iJItQNsmMdWD4P0PSuz8tN4fYu4DAbHIHpSkA9QKLsLI89HiHVDq9np+LQXUrLbzQtAoEUrQGTP395AbaM7QpGRnNa/g3U49YvdXu44/KO6GOWLbjZKqYdfwbI/CupeFH3ErhmXaWXhsfXrWFoFlBp+r6va2ybIYzDgEkk5TJJJ5JJJJJ65rSDfLL0/VETXvR9f0Z0FFFFZGgUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAeYePF8CL4k3eI9ZubO+aFf3acKV5wfun+dYk2pfCi4uYJJNfmMUCqkdvyIwo5x93PJJJ55zXEftEf8j3bf9ei/wA68hqeW5pztaH05b+IPhnDqTXr+KLmcs7u0UhPlsWUqeAg/hOOKkk8RfCsxiGHWvs9uEkTyIlbaS4AZjlSScADrXy/RRyi5z6l/wCEv+HwvPtK+MLxSFdY0XhYlYYwuEyAOMDOMgVXXxB8MFunuP8AhI5i7tAxBBxmLG3+DvjmvmKijlDnPqlPGPw3EUkcviOWYSsxlMm794CMYYBQCAOAO1WPDvj34deGdNawsvEBeJpDITMrE5IA7KPSvk6iiwuY+x/+FveBv+g7F/37b/Cj/hb3gb/oOxf9+2/wr44op6i0Psf/AIW94G/6DsX/AH7b/Cj/AIW94G/6DsX/AH7b/CvjiijUND7H/wCFveBv+g7F/wB+2/wpR8XvA5IA1yMk9AI3/wAK+N6ltv8Aj6i/3x/OjUND70tLuG+s4bu2cSQTIHRh3B6VNWJ4O/5E3SP+vVP5Vt0xMxvE0unxaYp1K3Wa3Mo4aQRqpwTuLEgAAA9TXBWUOhXd3qGpadY6fdRXfk2tov8AaCFROA/ysQ+ecjgZOAa6j4jWFxqfhmOztbZLmd7uJlgdgol2ksVJPHIBHNY+qWeq6tbW+qWvhA2E9jqdtdm3aWET3aIGDfdO0EbuMtz7Vk4KTdzF01OTv+hSvoPD1jrkGpXtlZWtrbxS2c8cl/GuJ2MbgZL9Qqnjr8wrA07wdHpe/WDBYXGm4lFjcTalmOR5ThAw8zYBk4+XO4+lbLaHr4vbfXn8PTzGTXpdQfTxLEZUiNv5S7iWC5yM4BOKqXngXxLrNgdL/s60srG6vbvUpIJpsxwFwEijHl/xDLScfLnFP2S/q3+Qexiv+GX+Q238HxaLJYaR9htJbiO9S6iW5uUSWaMqVkUhW65JKewA7Zr0qx83TLfyLPw/PFFuLbRPGeT9WrE0nwrLqMOk6zryzW+rW8KC5hVlZXkj6NkZ4O0Hg+nTmtTWDcappWmXUNreNaPIst1aRv5czRlGwPvDoxUkZ5x37ippO/8Al/kNUkndP8v8jR/tC/8A+gLcf9/ov/iqP7Qv/wDoC3H/AH+i/wDiq5F7TxPb6bNAtvfTS3NmscG24Um3YTSHDsW+95bINwzkqea6XRNPuLQ6nc3bTmee6mKeZMXAi3HYFGcKMelVyvuVyvu/w/yLP9oX/wD0Bbj/AL/Rf/FUf2hf/wDQFuP+/wBF/wDFVwukQazPoNvc2lnqL+bpyi6NxdlvtDsyEOmJAchPMPVc5C59NHSNH1vzLP7at35Fu93JHE1yUByYzCGAdjj7+AScd6OV9w5X3f4f5HRz6zcWojM+lSxiR1jTdPF8zHgAfN1NLb6xdXdtHcW+lTSwyqHR1niwynkEfNXI6do+vSSo09rOkH2mynMcsmdjq7+aRukcnjbzkZx0pdO0PX7e50iKb7VEkNvaIhhwyw7B+9Vv3gHPQ/K2QeOlHK+4cr7v8P8AI6221m5u7dJ7fSppYXGVdZ4sH/x6pf7Qv/8AoC3H/f6L/wCKrCGmanB4K06zEM3mRSobqCGQLI8W8llVgRzjHcZGR3qnqGnapIq/Y7LVEzbKun5u/wDj0m3sS0vz8jBT+9wCvsTlfcOV93+H+R1P9oX/AP0Bbj/v9F/8VR/aF/8A9AW4/wC/0X/xVc6PDt9carHNdG9MUt/cm4AvHCm3IbyhgNwNwU4HP61j21vrc11JayR6jJqkFvZCOYXP7uB+d5cbsHIHPBz0o5X3Dlfd/h/kdw+p3saM76PMqKMszTxAAep+amLrFy8oiXSpTIUEgUTxZ2ngH73SuaudM1zUYX0ua2u/LWO/SSZ5wI5fMJMOCGyRgjqOMYpRpGrSWtpBbwX1rai2tYpIzc4dStxmXkMf4M8g9OPajlfcOV93+H+R1P8AaF//ANAW4/7/AEX/AMVR/aF//wBAW4/7/Rf/ABVcXq0Nxo7Ot89wdNR7n7JENQEbg7Yyj7mcEqP3gxkkZHHptixvtT8PeFo5GuSv7l7/ABMyMy+Q2dxBBPzlcijlfcOV93+H+Rs/2hf/APQFuP8Av9F/8VR/aF//ANAW4/7/AEX/AMVXIpoOv22mx+Q96biS1lW633hZnImQooJbhjFvAYY68mpBp2peZG7WOqNo/wBpkaOyW6xMgMaBWJ3/AHd4kONxxuBx6HK+4cr7v8P8jpbfWLm7t47i30qaWGRdyOs8RDD1HzUW2s3N5As9vpU0sTZ2us8RBwcH+L1FcfpuheILeXSIp/tcKQ29qqGIhxCVP70MfMA57na2QeOldT4XWez02PS7i2kingj8xmOCnzyPgAg9QBk/UUcr7hyvu/w/yLE+r3VrbyXE+lSxQxqXd3niAUDkk/NVWLxR589vBHp8zS3EZliTzEDFP72M8D64rl5fD+sX2jXtnJaX3nPYPHdGa6ytzc71KsnzcDAf+6MMBjjjT1fw1eLfandaSs8czadDDbyfaXJLB33ry3XYRgnHJ6g80cr7hyvu/wAP8jo/7Qv/APoC3H/f6L/4qj+0L/8A6Atx/wB/ov8A4quc0zRNRkn08XD362SXE0rQvIYtg2KEUgSMSu4E4J6npirPhO01OzvrxLuG6MJUHz7pzvd9x4wJGU8H7wC9hj0OV9w5X3f4f5G1/aF//wBAW4/7/Rf/ABVH9oX/AP0Bbj/v9F/8VWlRRyvuHK+7/D/Izf7Qv/8AoC3H/f6L/wCKpBqs6XNvFcabNAs8nlq7SIwBwT2JPY1p1m6r/wAfOl/9fg/9FvSaa1uKSaV7/kaVFFFWaBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAGRd/wDI2aZ/163P84ql8QiZvDepi3lkim+yyFHjGWB2nGPeuP8AiWb032hxWE0kVxM0samOQoTnZxkdq4W9m1y0Ebw63c3kT7sS21zKwBX7wOcEY4PpivTpYH29OMua3/Ds86rjPYzlHlv/AMMjuYrvUNCgttPtrhojDb27WtsLVB9sZ2PmA4UYx04wR945ro9FfU7/AEO6nu7yQTzPMkQWFV8kK7quBjk4AOTXkE13rkGqDT31m88wsoLLcSMPmAPQcnr2FNhuvEdxJbrHeaoftDiOJjLIFc+xzWn9ku1+dEf2mr25Gd1po1O4t9Glt9Wujcw6NP50zQKzCRTD+7YFcZBHORuODz3q7pviDXb7xBbRSmK3RzETauMb4mhDM4G3dncSM7gBtwRmvOFvtea7mtW1PUFmi3ZQzSkkjjGB/WnXU/ia0nuYZbvVN1scSkSyEL7k56Uf2S7250H9prfkZ6TrHiHVLbxUlpY+ZIqzJE1s6KAwaMncMAtjOBuyADkYNZf/AAlOsrGyQX73MhtIZbjfbCM2rtKFkx8vRRngg46nNcMt74jkVCl1qzLIcIVklIY4zgevHNdF8PYX1fxDdRahPdyeVaYH+kyIy4cADIIOBk8e9RVyx04ObnsXTzBVJqCjudsmq6qvgyS9aaOSfztq3EaeZth8wKZCAoDFVyeBg4z0rKC3mu6tpKy3stzYwX8winNum2dViDK7ArjIYsoIAHGRzzXU/wDCNad/evv/AAPn/wDi6P8AhGtO/vX3/gfP/wDF159qfd/d/wAE7b1Oy+//AIBgeLZ9Vn1aP+y7K8nGlRi7PkuqK8pPCHcRuHlhxgZ++O4FW7a+uF0zxTqFp5hfe09rvQnP+jRsuAfft65rU/4RrTv719/4Hz//ABdH/CNaf/evv/A+f/4ui1Pu/u/4IXn2X3/8A5qbVfEVtrFrZNeIWaOB0MqKi3DOx8wYCk/KMAAEEcE5qqvijWZ5r8W9yUh8hpVe5jAMDCZVKnah2HYx4bdjAJ4zXX/8I1p/96+/8D5//i6P+Eb0/wDvX3/gfP8A/F0Wp9393/BC8+y+/wD4BycnibVGsRKl5PCyW7tbb7dX+3TrIy+XlRgjAXG3aTuzxirFzqWveTcXEkrSRSXN5a/ZDbAqqJHIyNnGScoBnOCD0710n/CN6f8A3r7/AMD5/wD4uj/hGtP/AL19/wCB8/8A8XRan3f3f8ELz7L7/wDgHPW+pa6jpcCVjCl7BarZi3AUxtChJJxnIZjznAxg0vh7xJdTavDBfai8yyWIluIntfLMFwZEXyxgA4BbGDkjqTzXQf8ACNad/evv/A+f/wCLqNfCWkJKJVS6EgTyw4vZsheu3O7p7UWp9393/BC8+y+//gG3RWR/wjWnf3r3/wAD5/8A4uj/AIRrTv717/4Hz/8AxdFqfd/d/wAEL1Oy+/8A4Br0Vkf8I1p3969/8D5//i6P+Ea07+9e/wDgfP8A/F0Wp9393/BC9Tsvv/4Br0Vkf8I1p3969/8AA+f/AOLo/wCEa07+9e/+B8//AMXRan3f3f8ABC9Tsvv/AOAa9FZH/CNad/evf/A+f/4uj/hGtO/vXv8A4Hz/APxdFqfd/d/wQvU7L7/+Aa9FZH/CNad/evf/AAPn/wDi6P8AhGtO/vXv/gfP/wDF0Wp9393/AAQvU7L7/wDgGvRWR/wjWnf3r3/wPn/+Lo/4RrTv717/AOB8/wD8XRan3f3f8EL1Oy+//gGvRWR/wjWnf3r3/wAD5/8A4uj/AIRrTv717/4Hz/8AxdFqfd/d/wAEL1Oy+/8A4Br0Vkf8I1p3969/8D5//i6P+Ea07+9e/wDgfP8A/F0Wp9393/BC9Tsvv/4Br1jab/yMeuf70H/ounf8I1p3969/8D5//i6tafpVpphmNqkgaYgyNJK0hYgYHLEmneEYtJvXy815itNtXW3n5ehdooorI1CiiigAooooAKKKKACiiigAooooAKKKKACiiigD5y+OO3/hOoy0aPi1X7yg+vrXlrCInmGL/v2K9O+OxI8cxY/59V/rXm2nQR3mp21vM5SKSRVdwQNqk8nJ4H1Nd1GMPZptIwq35yuUhJ/1EWPTYKqyaLdNdSRwopUMQuXHSu48RaDpOh2sN1Y3D3MhkAKSXMMq455ITn+lN0q1X7fIzMnmliQAvQH1/nWdSEZuPIrf0hKThe5xo8NaqRn7Nx6l1/xqrfaVeaaI2uoSiyZ2nIIOOvSvWYdOe6JW0gd1J2mVzhevb/69buiWemXtzL/a9lazQo4CGaMFQSccZpVKMYr3XqTGtK+ux8+UV9X2vhzRxfvG3hzShbgcMsCEg9s/hXnXxr0XTrO00mez022tDukDmJAm4YGM7etZTpuFr9S6eIhOTiuh4rRV23t1unZY1QEDPJNMeBMIeVJyCByODis7M2uVaKtpbI+QolYgZ+UVqQ+FLmdFdLm3Ab7qkndj6Y96ck47jWuxgVLbf8fUX++P510n/CCalNCjWksVw7Njyx8pHvzVqH4ca3Ewlmm0+PyyGZWu13ce1RzJoaTufWPg7/kTdI/69U/lW3WL4QUp4P0gHr9lj/lW1TWwPczNX/1umf8AX4v/AKC1W7a+t7tpxDJu8iXyZMjGHwDj34YVjeL7ZrvTrOBbZLoteRkwSNtWQDJKk+4B9q5mXwdqTxlhYwx273VxKLCKWMhBIkYVvnUrkbWHA43cUluzOPxM9HqvJfW8V1bWzyfvbnd5QAzu2jJ5rj7zwhqkiXdrb3u22Nqz27PKS4umjEbFjjpgE59ZGOKrDwfqTWrC2Bs2cTgI8qARlogg2iNQFyRzjnv14qiz0Dcu3duGPXNGQCBkZPT3rzq50K6h1Cxf+woBaTXwYaWkoMYC28gLE428krweDtGeTT08HaqLiyNxl41jjVBFMmbMiVnwrOhbAVlXK4J2YPGMAHoRYDqQPxoJAHJAHvXB3fg64XRII4rKOW9+0TzSkOhDFy20sJFIYAEDsV7VoeJNF1PVNK061FtazMsTLOBt+SQoACm8EBc7ucbsYx3oA6z5UXsqgfQCjIBAyMnpXGaxp92YfCtrcWI1KSElbmJ3+V2EDAkkjB+b161SXwlqyQxRPDBLMY4VguTNzp+2RmITIyRggAjGcYOBQB6DSAg5wRx1rhI/B2rLqKk3KfYvP8gp5hyLNW81AB/e3/If9k1XsfBOoCzMF0hLlrdLhjMgS5CzK7uQqhiSAeWOfmI96AO3XVLZ9VbTULvcJGJH2oSqA5xlugJwcD2qWS9givYLN3xPOrtGuDyFxu/9CFctfeEiLzVruws7SOWVbQ2xXCE+U25kyB8oIVR/+qptM0jVP+EhXVLyGKFDNcSeWJd5VXSFVHTGf3bZx+tAHSxXUE7zpFKjNA+yUA/cbAOD+BB/GpPkB3cDd39a4u78LTre6o9vplrJHc3sV22GVftKBVDQtx/eUtzwe/U1Um8Kam1qVawtp0kiuEt7Zp8Lp7O+5GU47DHK8jGBxQB2NvrVhdX7WUUxa4UyArtI5jKhufbev51N/aNr9pW3aXZM7siI4KlyoBO3PUYPUVxNx4M1ea4eQXMfyvcSFS/y3O5oCEk4yFby2zj26jIq2fDFydbg1SbT7W42ahJMI2cFo43iRQQSMfKyk4/EUAdlLDFOAssaSAHIDqDg+tQz39tbSbJ5fLOUUFgQpLttUA9CSeMVzfiTw7qeoaqLrTp0jQxLI6s5XNxCS0PT+Elju9lHWqN14NupLVRLb296yR2khjlbiSVJnkm6jjdvIB98UAdnbXtvdy3EcMm57eXypBjGG2q2Pfhh+dT5AIBIyegrkU8LSLqL6iltBFdNqcVwrhvmSARojJkfRhjoaNX8PXt5r01wltDL50lu0N48mGtFQguoHX5sHp13EHgUAdcSAMkgClyPWuBufCGqvalWkWZLa4SOCEOp8y0TeVVt4K7suOCCD5a81qeHfDDadqS3l1EGMdnHFAZJBI0Lb5S4GAABh1AwOgx0FAHQXGpWVre29nPcxx3FwHMUbHlgoyx+gFUYvFWizQTTrfKI4UEjF42XKE4VlyPmBPAK5yelYuqeE9Uvtb+1/wBo27xTPKHLQESRRNC8aop3YIBbPQckmoJtB1u/+w3EtpbQTaXDEkUYn3LcsksbnkD5VxFgZ5y3I4oA6iPXdOl0u51FJz9ntQxnJjZWj2jJBUgMDjnGO4qSLVbSa9ForsJijOFZCuVVgpOSPUj61hvo+o3ukeJGlhjgutWRlig8zcExCI13MBjJIycdOKx7rwnqDXs08um2uoI63aRpJPt8syOrI3I7AHpyO1AHoBYAZJA+tMmmSCF5XztRSx2qWOB6AcmuJfwnfRWVyzQW17dz3cRllk2s7wrEinbvBUHepOCCOp61DZeDNQTSbtLpYXvf7L+yWrmXd5bbpuhwMfK6DIHbHQUAd+GB7jPXFZuqEG40oggg3g5H/XN65JvB+oSXWotOskrS/aCJo7iOPzlc/KhOwtwMDDEqNo6g4q3psGoabJpVhdWKRRNeySo8YVQg2HCsEAXccsfl4wKmWxE9jtaKKKosKKKKACiiigAooooAKKKKACiiigAooooAwNfFtLqemxPpP2+6HmSwHzQnl7SuSCSOeR+VUdWtbFLSSW98MoljbRyTNIZo41TIyxIDdDjk1W8c+HX8T6npOnx3S2jBJ5PtGws8ePLGUwww3PB5HXg1s6rprx+CLrS1unnl+xGBZ7uQbpG24BdjxknvXR7V04xt27vu/Mw9mpylf9Oy8jInj8PwX1pdvoifbZjG8TmZOQSqqQd+DyVA9cii5udLt5IYJdFCXFvtPk/a1AjCKWU8NgkKpP0qo+hQtqN5bLqNmulPYvHaMJ1320jOrhQM/dVlDL6dOwpkmn3F1YW89zc2H9oztdTXgW5UqjSQNGig9wBsX8M1n9cXV/i/8w9jD+rf5Dba98Nyve3dtoUeFhaSSV7gIzRn+IhjnafUZraL2banLZDRWN8sZuvI+1JucN8pZhu6ZGOc1h30F3rOlRQulrZvaabLbIJLyNjNI6ov8JICjbnk9ccVZW1vh4hXXDqOmbzektbhxv8AsxHl7d+7HAAkxjrxmh4xPdr73/mCowW36f5D7rV9K06Z9On0hoHSGJDGboBUH8ILA7V+pIzWpbWz2Goz3tl4WKTzZDzC6jBcE5559aztRlnGpa7HbRWlxDqcEcUcz3kaoh2FTuBO7HOeAc1kXFq1zJqmj2eqxS29rbb4ZPtW0/aZIwhXOewVn68GUelH1uOuq+9/5h7KPf8AL/I7X+09X/6F+T/wKj/xpf7T1j/oX5P/AAKj/wAa5HVdLCS3Ntp4sn0yWZZFiFwhKHyyCwVm2jLYyTk9wCaZbaEZoYpdSu7aW7EtmHc3oJMaRosw69CQ2R/FUfWKXZfe/wDMrlf8z/D/ACOx/tPWP+hfk/8AAqP/ABo/tPWP+hfk/wDAqP8Axri7jSpljlhVradNk0VgRfqosWMzsknJzjaydMkbMYwa3fE2jahqFyJIbb7arWRht2E4j+zTk5E3X6cjJG3gc1SrQltFfj/mPlf8z/D/ACNf+09Y/wChfk/8Co/8aT+09X/6F+T/AMC4/wDGuQh0bV7+4vjZxPb3q39yP7UabGU2FQgXOcbiDjGBjPWrGl+EbwtZpe28i2guFe4tpJECMBDIu7ahO7LMuc9doJHFPnj/ACr8f8w5JfzP8P8AI6j+09Y/6F+T/wACo/8AGk/tPV/+hfk/8C4/8aoWeh6mnhxbWa+uldbaWD7NHIg3ZLBCJCpYEKVwc8YFYlv4e1RLeJLrRlubGKWQiyEqRM5ZECyMA2zIIYcEfezjNHPH+Vfj/mHJL+Z/h/kdX/aesf8AQvyf+BUf+NH9p6x/0L8n/gVH/jXOWui+I7YQ2kyG43XVrcS3f2kYCpGiuuD8xOUJ6YOc9eKbaeF7qxt9JWfTDfhLNUmj+1YMd1ld0pYnngY3DJGOBzRzx/lX4/5hyS/mf4f5HS/2nrH/AEL8n/gVH/jUcWs6pOm+PQZCu4rn7TGOQSD19wayfDmganYeIJru+aYnM26YMmycM+Uzg7jgdMgY5AyKpS+EtTuIdQedN9wLeb7ExuD+7mNxK6MOeDtZOe3Sjnj/ACr8f8w5JfzP8P8AI3bfxJfXV3c20GhTPJbMFlInTarYzt3dCcEZAPGatf2nrH/Qvyf+BUf+NcvfeD7tPtElpaJsl1OS4mhj2EzxFMLkMQpwxzgn360snhHVDp97h5XujBaxwtLOGYouDKn90FgCue/GTijnj/Kvx/zDkl/M/wAP8jpv7T1j/oX5P/AqP/Gl/tPWP+hfk/8AAqP/ABrl4vB93NayCeGQqLO4FvFLKo8mZmBTaEO1cYJBGduTzUraBrDw6kjWp/tS4ik8rVftIG0MgATAORg8dMD73Wjnj/Kvx/zDkl/M/wAP8jo/7T1j/oX5P/AqP/Gk/tPV/wDoX5P/AAKj/wAayNN0G8h8M6tatFdQtdAiO3WWOMp8oB2FcquT9c9T1rMTw9q32WNLjR0uLZBOsVqsyQlXYJslYBigIIcfKeMggZJo54/yr8f8w5JfzP8AD/I3X8V3Md5Navo7rJAqmVmuUCR7iNoLdATkYGc81e/tPWP+hfk/8Co/8a5ibwRNLpmsCe0t7q+uDbOsjNgzMiR78k9CzK3X1qXUdA143jXOkAW0UaJdW9s82AtxtETI2Mjb5YJ9Nxo54/yr8f8AMOSX8z/D/I6L+09Y/wChfk/8Co/8aP7T1j/oX5P/AAKj/wAa4+48K66t7ItrDsVIZrdZxKAZozAUTLEli2/aSOADyM9auXHhO+j1u1Nusq2aJB5TxOmYGVi0hJY7vmzyRndyDRzx/lX4/wCYckv5n+H+R0n9p6x/0L8n/gVH/jSf2nrH/Qvyf+BUf+NYuvaPrN94ngurS3RI4ZLdo7lXUEIHzIDk5HGeFGCDye1VYvD9+mlRQPo5M0ckRvj9rB/tILu3Hr3JDfNjP3elHPH+Vfj/AJhyS/mf4f5HS/2nrH/Qvyf+BUf+NQWmvahf2kV1baG8kMq7kcXUfI/OsGXw3qU2s2k6W01taqkAhSOZGNntclxuY55GPu5yPl6AVWsvCetQ3tgZxIFiSAJJC8f+j7CS65JyA3faDkHBxijnj/Kvx/zDkl/M/wAP8jrv7T1j/oX5P/AqP/Go4ta1KYyCLRC/lsUfbeRHaw6g89eRXJX/AIS1ldCsLOys0+1JZkvcCYF1uuPmLMeBxwygnjHAqzeeFb1P7Qjs9PVY5dSF1IY2Q/aYiuNmGIGQ3zYbAOOtHPH+Vfj/AJhyS/mf4f5HQ3eu6jY2slzcaFKsMS7nYXMZwPpmt+vLNc8OanZ6as4gmm8q3QPJcyxuIkDEsBghlIBxgblOBmvU6c7OCklbf9AjdSabvt+oUUUVkaBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHzj8dIlbxokjlgFtkHBHPWvNLQBb1DCGZcKxDY/vDIPbFen/HSRY/Fo3ANm3Tjv0PevMYbx2wuxI4UIVcE4HzD866aUvcsRUXvXOr8R3drJbowghgZVbHkqmW49lFR2MkFlayg4eSZ9x+npmsjV5JHQeZeSSphsglTjjrxUTzlN7gklTkZ/StaDuc+IV7I6vSHu38TsIrh1hhh3Sxr057Vd1i/RpYWDywxs5MnljAbA4B/GsDT9TaG082CNhPM+WVjnjGK6G2vJnMBe1E5kYbVJwRg54Irnqxkqd5bsqLi5KMeh2mieI7S6tjMbqMOVjVg52nO0dj06VyHxXc6rb6Vb27LKztKBsOew9K2Ztb0+3Cx3ejXJad+QIN4yPccfyrM8VwW1pf6DNp6W9m295MOVQHgcE9M81CqN2TKlBxTaPL7Hw/fW0xYxMQy9QMgdOv51VsNMfUb6O35CrvaQjqACc16e1zPPt87U9JjG4H/AI+c/ntHJxXI6AfI1K5IaN/NkeNQpzuwxJIrrhCDnH1MYVKkk+Y2NFs7LT5bhbJQ0rR7TEGLF/Y/jxXKu8zzyxKojlSVlKpncPoOwFdxZQmPU43TIiAJ6dW+o61nx6W32q4lUeWXlZgQBnr7iqx1JK1utzpoVW3yroipc3Eos4YYopI3VgGeXhW65wfWsKaaK3lbaIn8yUE7gW/n3r0LUIWW107zHRFNwq7pOVGVI5Fcj4k0Z7GFrhp4iFmRCqoASTjpjtzXiysqvKjvhZwbTPqjwrg+E9JwMf6LH/6CK16x/CnPhLSf+vWP/wBBFbFdK2ORmL4ku47GCwuZVkZEvE+WNSzMSGAAA6kkgUknia2jihJstQaeXeRbLbkyhUxuYjpt5HIPORjNR+LbR7+ws7ZFhdnvIyEmzsbGThsc4OMfj3rNsvDWq6Vd/wBoWA09J281PsZZxBCj7CAhAzwyZIwAdx6UluyI/EzTbxfpQdDG08sDLGz3McRMUQkxs3ntnI+mRnFX9T1my0g2gvJCpup1gjwufmPc+g9/cVzMPg6/s9Pm0q2urZ7K8SFbmWQMJEKKqtsUcHcFGMkYJPWtHXfC8uv30rzajLbW32RreJIFUnLnLsdyn+6mMYPB5qizZg1G2nmvYlcqbJxHMXGACUV+vphhSR6vpsrokeo2ju7lEVZlJZh1A55PtWPB4fvm0jXLe7uIHutTj2l0B2hvs6xEn23KT9DVZ/Bo3XDQ/ZY3kWxVGWPBTyH3N27jgUAdGNSsCbjF7bH7P/r8Sr+6/wB7nj8aq2/iHS7hJ5RewJDDOIPNeRQjsVVvlOcHhh+tcyfBV+8FtEXsF+wIEhYK3+l4lST99xxny+cbuWJ9qbN4L1GaeS7LWayyzzu1tFI8cYWRI1PzBckjy+eBncelAHVrrmn+ZcJLcx25guPsxM7hA77VbC5PPDCrs08VvEZZpUjjHVnYAD8TXFXXgq8WN/sMluJN7CKR5XBiQxRJg5DBxmLJVhzxyOa3L/TLzW9FFpeJAjpeRvgjKyRxzKwJHYsq9PegDVhvrO4iWWG6gkjZS6ukgIKjgkEdhUcmr6bE5STULRGC7yrTKCF6569ORzXNXPhTUIry9udPksh9q+0x7JQyrGkyxfMMDkhoyccZ3dRTrXwZ5X2cym1keK/iumYx5JVLcRY+uRmgDpjqNiptwb23BuP9QDKv73/d55/CmPq+mxIzyahaIqsEYtMoAY9AeevtXIL4EuUuI90sMsDKI5E814wiid5RtAHPDgYyMFRzVtPCVzZW1mbWPT554VuUkjnUiN/Ofdv4BO4Yx05BIyKAOsNxACQZowQwQ/MOGPQfU5H51HbX1peI72t1BOqHa5ikDBT6HHSuRi8HajbxRWMdzay2Qmtp5JJQ3mExRohXaOMHYDnPcj3q9ofhy8sLC/s7k2xtpoFgigDtIFAVlOWIVtpBAC5OMHnmgDb/ALY0w2puv7RtPs6tsM3nrsDemc4zWde+L9IsNQuLSa4X/Rrb7TcSCRNsaYJHGcknB4APb1FY1p4U1e1eC4LWMxtpCYbSZ2dNpj2EmTZuJHGMg4GRnnixa+CjBpl/ZyTQMbnTI7ISeX91lEmTj0+cYGei0AdNBqNlcxLLBdwSIxAVlkBBJ6D6mgajYm5W2F7bmdgSsXmruIGc4Gc8YP5Vyuo+D73UZILlZ7W0njtxmOIFk+0xkmCToOBubIx6elRHwZqC3tiEuoPstrLBKpyVYlB84KgfMWYsdxPGcY70AdZ/a2m4Q/2haYd/LX98vzNx8o55PI496TU9VstItXuLy4jiVUZlVnAZ9oyQoJ5PtXIy+ApVstOt4mt2SLTxY3EYkaJTyCzjaCTnByOD05rU8ReHbvU5pHtPsbiewksWW7BIjDc71wDk+o4zgc8UAbUerWD7AbuBJGiE3lPIocIRnJGeBUlxqFlaf8fN5bw/MF/eSqvJ6Dk9TXJweB3Oov8AbWjubKRzKxMzBgTD5RXZjBGM8k9D071BL4H1B4beaW7iur7bNHcsztEsquERTkAnhI1BHfLcigDq11zT91yJriO3EFx9mZp3CBn2q2FJPPDCrJ1CyF2LQ3duLk9ITIN54z93OelcmfB95a3Ms9qbK4DrJEsN3uKqjxxLuzgksPK/EHqKraf4U1BLu5tSluLaO9tpReSqfPkEMMQyvGCCVIznj5utAHYDWdLYgLqVmSXEYAnX7x6L16n0qQ6jZCeWA3luJYV3SRmVdyD1IzwOa5JfAm3TPsymzEo0ddPDiPpIDnf06Z/GobnwJc3P9oQvNC8cxuHileVycynJDJjGACRnJzgcCgDqZvEWjQfZd+pWu26lMMTCVSGcAkjOfb+XrU0mqWsWovZSv5bpAJ2dyAgUsVHPrkVh3fheb+1mv7RbMgXsVykMilVwsLRMCQDg8g9P4RT9b8MT6nr0OrQ3KLJbRRiGKTJjd1csd69xgkA9VPI5FAGlF4h0yTUV083Ucd4yu6wu4DEKxUnr6j64qK7vLW+k0uS0uYbiMXu0tFIHAPlvxkVlReFLmC5EmLKdZIruGZZAwws0rSKQQMnGcEcexqCw0rU9Hu9OhufJa2kuxtCSGQxbYWAG8qpYHng5xjrUy2InsdrRRRVFhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAGRd/8jZpf/Xrc/zirUlEZjbzQpjxlt/T8a5/XdUstI8RaXc39wsEJt7hA7AnkmPjj6GqWteMNCvdFu7a01izE8sZVPOjZk+hBUjp7H6Guh0ak4RcYtq3bzZgqsIykpSS1/RHQl9JECzlrLyW+7JlNp5xwelRX11pOneULhLcPLJHGsaopYl3CA464yRzXnWly+H4r5Z9R1TTpYRJcSCFg0m0yJEo/wCWajPyNnAHWobabS4I7SGTVNJnZJ7Kd7yTzPNTyRGGRfk5B2Eg5H3jkVn9Uqf8+39w/bUf5l96PVGj05J1gZLUTOMrGQoYj1A61GX0kRPKWshHG2x3ymFb0J7GuA1nVdGvvEBvbPWLKBZAiyTsWZgACOEMZwwB4Ksvv71jqumnS7W0juNCt/JZFk8ndmZVRl3bmhO05OcYJwSM0fVKn/Pt/cHtqP8AMvvR6Et1pBuLuFkt4za7PMeRFVfnGVwTVnZp3niDba+cV3CPC7iPXHpXk8Y02G0sgdesppLZYA0YZ1EmyExk5aNhwTkfKeM9Kv6be6LpmsWU1tqtikEQj85nkaVmCx7MANFkHoAwYDHaj6pU/wCfb+4PbUf5l96PQZbrSINYt9KkSEXlxG0kcflDlV684wO+B3wfQ0+0l0u9sheRRwCAs6bnjC8qxU9fcGuHubi01PVbjV7bxChnS6ikt4o7ZnjCRjAVm2bhuDSZx/fqyW0yXw3baVPcxyeXfi5kDW8pRk+0GXb9zk4OPTNL6tP+T8B+1pfzL7zs1GmO7IotGdVDlRtJCnofp71IL+yECTC7t/Jc7UfzBtY+gPQmvNGs7dLu+lgu4iztcPbySROR+84CNH5PQA45ZhwpxxgQNYx/Z52WeEzyTvJEGEhEQaNFOQYCjAlMkbR7HrQqE1tF/cP2tP8AmX3nq32m385ofPi81F3sm8blX1I9KjOoWx2FJFlVpDGWjIYKwBJ3EdOleaXVrDdQ3Nubm0DSNcy/bVt5hLIZYnQRkbOFBcdzwi8eli+tNNkuWayuYLeAxxjy1tpVG5YZ4ycBP+mqfgv0p+xqfyv7g9rT/mX3noovbQxSSi6hMcRxI/mDCH3Pan+fCYvNEqeX/f3DH515pcW+mwPaSWb25SCK1VrU2sqxzvH5gO/CHjDgg4PKjiprKaxuPCT6bc+Xbs+pm5aD7NL5fli58zAGzoVHAx37Uexqfyv7g9rT/mX3nocV5azsqxXMMjMm8BHBJX1+nvTRfWZtzcC6g8kHaZPMG0H0z0zXm9zp2lyQXCWt3DayTXlzN5sVrKGWOWJ0C8J/eYEjpxRttzqEeo+Vp6eW6f8AEtSKXyHCxum8nyvvfOMfL0Uc+h7Gp/K/uD2tP+ZfeehWWr2V/bwzRTKonZ1jVyFZirFTgd+QaLHV7LUIIJYZ1HnhjGjkK7AEg4HXtXmVlpdrbzWnnXcbxKsQdYllQQ7JWk+TMRJHzDoV5FWrWGKyvbCeG4ty8JTzZWikYMFdmxsaI84bAZWXBPOccnsan8r+4Pa0/wCZfeeo0Vhw+KtMMEZnnKylRvVIJSobvglRkfhUn/CU6P8A8/Mn/gPJ/wDE0exqfyv7g9rT/mX3mxRWP/wlOj/8/Mn/AIDyf/E0f8JTo/8Az8yf+A8n/wATR7Gp/K/uD2tP+ZfebFFY/wDwlOj/APPzJ/4Dyf8AxNH/AAlOj/8APzJ/4Dyf/E0exqfyv7g9rT/mX3mxRWP/AMJTo/8Az8yf+A8n/wATR/wlOj/8/Mn/AIDyf/E0exqfyv7g9rT/AJl95sUVj/8ACU6P/wA/Mn/gPJ/8TR/wlOj/APPzJ/4Dyf8AxNHsan8r+4Pa0/5l95sUVj/8JTo//PzJ/wCA8n/xNH/CU6P/AM/Mn/gPJ/8AE0exqfyv7g9rT/mX3mxRWP8A8JTo/wDz8yf+A8n/AMTR/wAJTo//AD8yf+A8n/xNHsan8r+4Pa0/5l95sUVj/wDCU6P/AM/Mn/gPJ/8AE0f8JTo//PzJ/wCA8n/xNHsan8r+4Pa0/wCZfeL4q/5FbUv+uBrXrlfEGv6de6Be21vLLJNLEVRRbycn/vmuqqpxlGmlJW1f6ChJSm2nfRfqFFFFYmoUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB80/H0t/wnEKI5Ba2QFexH1ryhHeFgdzD3HQV6j+0HgeOrfn/l1XivJxJhwUQA9h1q4SsgktTQvLqNrdY45Wd+jMVKkj86620to1gZyu5iMl2OFHt9a4owyCAz3DKN3KbjyxB5AFbcmtxs/kssrbTgKq8VrCSjLVmFVNrQ3rZULqR9zPOO/sK177UV0EW7i3E8sm7GXwFOB6VzUGu6dEFaSG6BA67AMfTmqniTxEuom1WzSWNI8l3ZQTzjtUYmpzxtEmjTcXc6OTxtqzRkRvbWy4xlIgT+bZrAvNVfVLy1a+1GWZI5PnZmL7B7Af0rn5QzxmSWSVl7M0HFUo2ZpUUqmC3QcA/WuSnBqSdzolqrHW2z6fDcIm97gh2G1Lfr1wQSep/SuYuGdJwyExsGbALYI+Y1b+x8E+SUOM7vtAGKzoEaWcDaJWORhu9dcqspdDOEFE24PFetQweUlwhIGFc7dw+hrprHxRZLEguZ5jIB8x2k5PeuLu7JYov9TGMgZdd3yn8als5LcRAtciJ84IwD+PNKdSpJe8XBKN7I6/xf4nsrzQbaHT7iT7SlwrghCMYB9a5a61/VNUigtbqUeV5qu2Idpds9SQOTVS5uES4TyrhmGcs4/wDrU+S8V3h8u9kZxIMg7uef0rnkne5onLufZfhFt3hDSCP+fWP+VbVYng7/AJE3SP8Ar1T+VbdWthPcx/EFzDZx2FxcSCOJLxCzHt8rClk8U6HFHC8mp26rMpZCW6qDtJPoAeCT0PWofFNtJeWllbxSCOR7tMMd2Bwx/hIPbHBFULLwSLWyvYGvt7XVrcW5YRY2+a7OSMsTxuxgnnHWkt2Zx+Jmnd+JtOttVttNSZZrua4EDRo3KEoW5/AdOvIqxPr2lWt1LbT30Uc0Sl5FY/dAXccnoDt5x1xzWTD4VuIb23K6gn2KC9N8sRg/eFyjKQX3fdyxI4z2zTdW8IyapqN1Kt+LaC6RlmEKMHcGMphvm2N1ByVzwBmqLNi713S7FmW5voY2BVdpPOWBKgDqSQpIHtUT+J9Dj8jfqdsPPRZIzv4KkkBvYZBGT3rBn8M6yuq2eoR3ttLdG5jeR/s+2OJUglQfLvyclx0ORn0pg8H6iLi9tUvY1tbyyENzM0AJkZ5ZXk2Dd8n+s4zkc98UAdI3iHSFuLiBtRgWS2VnlBbAUL97npx39O9Q2virSL3V49Nt7nzJpIfORlU7WXJHB/A1lXPgqS6tpLF9QUWK/aTbqIPnVpw4bc275gN7YGBnIyeK2zpcqa7DqMNwioLb7NLE0edyg7gVIIwcnuDxQBR/4S/T11vULCR0iisFXzppHI+dsYVVxz1HfqcAGr8fiDSZpLWOO/heS6BMCK2S+Dg4HseD6GqV74ce4urq7huxHcSXUN1Fvj3KjRoFwwyMgjPpjPtRpHhs6bqbajLdCaeSOUShY9qlpJN5K8nA6DHP1oAnude+yaxBYzWMyxzyiGOfehDMQT93O7bwRnHX25qtqPin+yb8wXum3CQCOWb7Qrow8uNcs20NuA5A6dWFGqaDd3+qw3v2i0H2aQSwMLbE4IB+TzN33CTzxyCR706Xw4dRiv21GfM9/YpaOYlwIsBt2zPqzZ/AUARnxYY3+yz6Tdxag5jENoWQmUPuwQwbaMbHzk8Y75FWV8SW39iT6jJBOjQSm3ktsAyCbcFCDBwSSRg5xyDVGTwzqFxdrqVxqkLanCY/IdLYrEqpvGGXcSd3mNnBGOMdObK+Gt2h3NlNeE3Vzcfa3uEjwFm3h1IUk8AqowTyB15oAJvEN3a2u650W4juGnjgjiEyEOX6ENnHGOc/rUmmeJYdR1L+zjazQXSCXzUcqQhQoCMgkHIkUjH6Gs1/Bf265nuNSntHaeaCSWO3tjGknlMx+bLElju657CtCfw99mntLjQ3trCS2jkiCNBvjZHKlsgFTnKg5z6560AXrHV7e90n+0jmGAeZuMhA2hGKkn/vkmoG8TaKtmLptQiEJkMeTnO4DJG3GeBz06c1Tj8I21vpLW9vO8eoG3aEX4HzgsSS2AcDkngfnVKz8HXmn3baha6lCt80jMd8DvGFZEQjBfcT+7U5LeooA3v7f0kXbWp1CDzlTzGG7gLt3den3efpz0qr/wAJTpjy2ognSSGZ5FeXdtEWyMuS2egwP1zVSXwvcTW+pWLahH9g1DzGlUQfvQ7ptOG3YAyM4xntnFU5fAi3lq8d1doJJBIskkMb5YNEYxy7seMk9fagDcHibRWhjl/tGELJJ5SAkgl8Z2465wQfpzVe78ZaHaWzXDXqyIs0cLCMEkF22g/7vXnpwcZqvYeFGtru3vJbmL7THI7s0UbfNmPyxy7scjk9e+MVSt/BFzEs8kmrCS6eO3VZWiZstDL5gZtzknceoBA9MUAdENf0prs2v2+ETBd5UtjA27up4zt5x1xzUOmeI7DV9QuraykEqW8McrTA8HeX4wef4M57gisabwMl1c3puLlHguzJKwCPvjkkjKMV+faBySPlzzjNamn6JewXd9eXmoxyXN1bR24aCDyxGE34YAscn58+nFAEo8UaIbd5/wC0oPLV1Qkkj5mBKgDqc4OMdccUSeKdDijhkfU7cLMu9Du6qDgk+gB4JPQ9a5pvBmp2lxaXUF9HcXQuLcu7xsVVYhL87bnJYkycgEY7VIfCeqrqU6xXsKx3dlLHc3DQZBeWVmYIu7K4B4zkeuaAN6HxNZtLdLOyW8ds0oeSSQdI2VScdcEsP5d6l/4SXRsQH+0IQJzhM5HOdvP93njnHNYx8DqpLQ37KyEvDuj3bW82ORc8/MAYgD0zntS6t4U1HWsfbNXjwyBXSOB1RSH3ZVd+MkYBLbunGKANGy8V6ZqLYtpQQtzJbOZDs2lA5J56j5D07c1KvinQ2tjcDU4PKDiPOeSxBIAHU5AJGOuOKz7jwibtJbae9zaPdT3CqkeHHnJIrqWzg4MmQcdsc0sHha4bU7bUb6/jmuLdogoit9ilI1lUDBY/MTKST04AAoA018RaQ72yJqELNcqGi2nO4E4H0yQRz3BFLqv/AB86X/1+D/0W9Yg8HTRXNvLb6gsRSZpHlSNlkIMzS7chwCPnK4YN3I61r3yyo2kLPIskouxudV2gny37ZOPzqZbET2NaiiiqLCiiigAooooAKKKKACiiigAooooAKKKKAPPPijp95fnSRa2txOFMm8wxF9oO3rj8a5VvCpS+V00zUpbSOyE5Ro2V5ZOm3gHbzg4HIFeg+Mdf/sO5sGmvbuzsnSVp5ra3ExTbswzAq2FGTkgdxVyKG/vtNhv9K8QPcpLGskBeOIRyg8gkhM8j0r1KOMlSpRhp179/Q86rhI1Kspa9O3b1PMR4Slk1m1C2F7HZTGFmieJyVDffUvjA289cdqz7Twvq08tyh0q6G2MlDJE6jO4AYOOTz0+td1Z6/rvlaZdXl1MY9RmkVIrSJZXUIGOAPLHUr+XetaTxBazPbrFrt0qTJGfNjtU2x+YcJvJU7STxg/jWizOa00/H/IzeXRff8P8AM8/TwLeJeSQXDOUWCRw8dvKQHUcL93n8OvaodF8I3t8dt7ZXNsnmqpkeGQMBtY8Db0yBz249a9EvdWvLLxUmlzahcrEbVZFmEcWwyMXARhtyCQhwc4zx6Zo2uu6k2mSzyX90bkw2s9tAVi/feeAFXOwYG/cp9gD3pf2lNpq6/H/If9nwutH+H+Zwz+CdWW2Eqxs8h2sYRDJuCk4B5XHcEjOQOoqw/gDVVLASIxBZQBFLy687fu9x0bp2zXapqfiC/vEsdPvF83N07PMUXiKbysDEZ65z0496LfX7mWOeeTUryOOCzN26mOEnfveMxfc5w0ZAPfNP+06ndfj/AJC/s6n2f4f5h8L7K7stGvUu7Wa3ZrjIWWMoSNo55ruq422vL5vC8+sXms38ElrHIbq2WOBjHJHncgPl88jg9wQazr3VNf0uxvpr7WYRPb26ziCKeEsmWAw4MWQBuB3AY/SuCvJVajqOS19f8jtoxdKCgk9PT/M9Dorzyz1bWb62v2g16FzZyIslz50QtwpUsSWMWcjgYx3HNRprutzWEt/FeXYt7S2S4vBJ5KuA2TiMeV82FG7nGcisuSP8y/H/ACNOeX8r/D/M9HorhINQ1iW7t5G1S5XTbm8eyhmAhMm9Sw3FfKwFJRh1z0/CK+1y/sLzW7WTVb4Np8RkgcpAFuSI1kZf9XwwDDjuOexwckf5l+P+Qc8v5X+H+Z6BRXE/bdT81rX+17z7aNRFkItkOCpG/wAzPl9PK+b6jFSW763e+FrbWLbWnjaWETOlz5SpGuMk7hETx9KOSP8AMvx/yDnl/K/w/wAzsqK88TU/Ea2mmXVzfSRLetGqwB4jM+9sZRPJ5wpDHOMDPpWzcTzW+qjTTr+qyXGxXYRWkThAxIXcRHxkqfy5xRyR/mX4/wCQc8v5X+H+Z1VFcXNrEUcduyeJdSma5jSSFIrWJmcPuKj/AFfBwjHB/umozrMsd/eQT6xrMMFtbR3DXD2cQUh92Bjy8g/LgDqScdqOSP8AMvx/yDnl/K/w/wAzuKK4d9cjRBnX9ZM2XDQLYxmRNgVm3AR8AKyn3B4q7ply2rGUW3iPUx5cazfvbSJN8bZ2uuY+VODz7Uckf5l+P+Qc8v5X+H+Z1dFcVHrCy2K3aa/q5jkdUgH2KLM5YEjZ+754BPsBziksNVn1LWo7C11nV3jkthOtx9liCD5ipU/u+CCpB9+KOSP8y/H/ACDnl/K/w/zO2orjZ9UEE1zEfEOrM9vMLdtllEQ0pAIRT5eGbBzx0HJog1QXUkKW3iHVpzIAX8qyjbycsU/efu/l+ZWHP909hmjkj/Mvx/yDnl/K/wAP8zsqK4g65AkMsz+JdVSJF3o7WUeJl3BN0f7v5huZRx6g9DUV3rVzFPbQwazqzNJdRW8rSWcarEXwSpPl8MFIOKOSP8y/H/IOeX8r/D/M7yiuV0yafV7SS5tNf1QRKodXltIlV1IyGU+XyOPrVG11oTG3jbxFqReRYN8kdnGYkaVQUBYx8btwAz684o5I/wAy/H/IOeX8r/D/ADO4orhtW1LUNNvr6yXU9Vkkt7B7wSm3h8s4DHaT5fH3etLBrW+zEsuv6tHP+6At2sog7mQErsBj5B2tz/snOMUckf5l+P8AkHPL+V/h/mdxRXHT3d2dP0+9s/EF7LDd3SW/zQQqVyxVsjy85BBGPUVTutfkghZo9W1uWVZ4Ymh+xRK4WVtqvgx9OD+IxxRyR/mX4/5Bzy/lf4f5ne0VxUuswQTSxz+J9SiWIMHke0i2BlXeyZ8v7wUE49iOoqlq2v3tjYwTQahrTvceaqeZZRAIUiaQE/JyDgdD0z6GmqabspL8f8hOo0ruL/D/ADPQqKZCxeCNm5JUE/lT6yNQooooAKKKKACiiigAooooAKKKKACiiigAooooA+Zf2gJCvjy3UKmDarksm7FeUTSmMBkeF89cQ4x+Yr3z4yeB/EOu+LLfUNKtbqWI2/l7rZQxBB5DcjFecN8LfGToUbTdVZSMEGAY/wDQ6m2o2rnDNeSthiVJHqg/SpVlG8OcnBycHB/Ouu/4VH4r/wCgPqX/AIDr/wDF07/hUviz/oD6l/4Dr/8AF1SaQuQ5KW7WRAoEgOeplLD8jSC428jp3rrv+FS+LP8AoD6l/wCA6/8AxdH/AAqbxYP+YPqX/gMv/wAXQ2gUbHISXO8bWUkfWq5cRuCnFdv/AMKm8Wf9AfUv/AZf/i6Q/CTxWf8AmD6l/wCAy/8AxdIdjjWvpypUyuR6bjVUNgn3ru/+FR+K/wDoD6l/4Dr/APF0f8Kj8V/9AfUv/AZf/i6dw5Thd5zmm55rvP8AhUfiv/oD6l/4DL/8XR/wqPxX/wBAfUv/AAGX/wCLouHKcHuqS3/4+ov98fzruP8AhUfiv/oD6l/4DL/8XTo/hL4rjlR/7G1I7WBx9nX/AOLpXHY+pfB3/Im6R/16p/KtusjwrbzWvhTS4LmJoZo7ZFeN+qnHQ+9a9NCe5h+KLtrG1sbhIGndb2NUiQgF2YMoGTwOSOaWPWr6K7s7TUdMFvLdXBhRopxIhAjZ9wOAf4MEEDr3p/iTSbjWNOigtZxBNHOkySE42leQeh744xzWPLofii4SKW41Sze9gnE0EwQhIjtKkbMcghmzk9+MYqL2bMr2k9C4/ie5l1M6fYaak04nniPm3HlriMRknIU9fMHHtST+Mbe3srOd7SYPNcPBcR5GbYI22R2PQqrY5HqDWavhfxFDLBc22o2Ud6jzPLOyFxKZdu75cYXGxQPYd6YfA9xMHF6ILoPC8RzdSx5LszysdgGS5IyOg2jFPmHz+TOk1jXYdGutPinjdku5HjzGrOwKoW4VQS2cUo8R6Uz2oW5LLdBDFIsTmM7zhQWxgEngAkHNZyaTre7SXlkspZNOBAdnfMpKFCW+XrzmsRfAuqrLZk3dm8dq0Tor7iQY33jacfKCeuBk+tHMHP5M7LTtZstV3/Y5HdVAIYxMquDkAqSAGHB5Gazh420BlDLeuUKeYGFvJgx5wXzt+4DwW6DuaoaL4e1rRr65uVl09xMu3yow0SdSdxUDBbnGQB9KhTwlqaactn5tmVXTX07dvbO1sfN93rxRzBz+TOgbxHpSXcls11h49wZvLbZlV3MofG0sByQDmol8WaK6wut2xSUBlfyXChSdqsx2/KpPAY4B7VhSeEtYk3wm6s/sRklnS3BbKSyKys27bkj52IHqeuBTbnwbqFxeW1wRpbeVbxW8iyoZN6xklcZX5Tyc9f0o5g5/JnSXevW9nrlvpLxXDTzwPMrLExQBSowWAwPvde3fqKqaT4w03U9Lju8yRyG3jneEQuzYbA+T5fnG44yueajvNM1261S1v45LCF4Y5IXXc7CSNypI6cHKDn61lzeENXksbO3jubSM2tmlorbnIdVZG+YYGQdmCM8gmjm8g5/Jm5J4u0SKNHe7YBkeQgQSFkVG2uWG3KhTwc4xSa/4kTRZbKMJbMbveVe4uhBGAoH8RB5O4VhWXgzUrK2uYVlsiLi2nt2wWUKJXLkgBeME8CtS907xJPaQQWtzp1v5SGNiyGUOMY6FeD+P1Bo5g5/JlibxFJbaxZWM9pEFu2VIyl0rSElSSwjxkoMEbs9s4xU994n0jTbt7W7uikqbN4ETsE3/AHMkDALHgep4FYMXhfW4jp0P2qyay08xGCJgfMBjXAHmbc47kY5yRnHFWbvw/q13c3c7NZKbmW2lYB34MLBgPu98c0c3kHP5Mt3fiy0jt0mtQs4YcoxaN1IlSJgVK8YL85wenHOaW18V2kkTyXW2ALkbFLO7HzniUBQvOSnGMnnpxmsibwjqc1xNMZbQGWR5CA7cbpYpP7vrEB+Jpv8Awh+qo4lhuLOO4Rg8UhZjsYSySZxt5H71lx6e9HMHP5M3pPF2iRRo7XjYZGkIELkoqttcsAMqFPBzjHereo61Y6YkRuJW3SgmNY42kZgBknCgnA4yegzXMxeE9WRLvfPZyTXdtNBNJuYZMrbmYDbxjoB6Crd5ouvTyWc1rcWNrcW0LW4kG5w0bbcggrwcopB9u9HMHP5Mt2Hi/TLq0sZJpDDNdQxSlArOsZkXKqXAwCewOCePUVcTxHpEmAl9GxKQyAAEnbKcRnGO5/8Ar1zNr4P1izigtoru0+xr9naaEliZXhChTu2/LnYmQPTjGTRZ+Cr2z1CC7RrQtDNLKEMrYIYYVT8vRP4aOYOfyZsS+MdMeyM9i5uWzCVUo0YdJJVj3qWHzAFu2fSrieJtJkkmRLliYlZsiF8OFO1thx8+CQDtzzXPt4S1NrWwg82zAs7aO3U72+YJJHICfl9YgPxNQyeDtbeEQfb7VYYUdLRVLAwh2DEMQMuPl29uCe/NHMHP5M6GXxXpMdoJxNIxPmARCCTepT7+5duVAyMkgAZHrS6V4mstUS0Rd63E8McjIsbMkbOgcIXxtztOcZziubtfBmt2AL2V7YwTuZg7qh2lJNm5QuMDBQEHn3zUkHg/WrW7s7i3u7CJrWFIkdUIkbbH5Y3MB8y9G2kHkdcUcwc/kd1RWWh15UUMmnMwABbzHGT6/dpd2uf88tO/7+v/APE0c3kHP5M06KzN2uf88tO/7+v/APE0btc/55ad/wB/X/8AiaObyDn8madZuq/8fOl/9fg/9FvSbtc/55ad/wB/X/8Aiaja21W6urRrlbNIoJvNPluxY/KwxyP9qk3dWsKUrqyRr0UUVZoFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAc3ruk6frPiTSLfUrSK6hSGeVY5V3LuBjwSOh6nrXRRxpFGscaKiKMKqjAA9AKyrv/AJGzS/8Ar1uf5xVPrt/LpegahfwRpJNbW7yojnCswBIBPpWk9o+n6szhvL1/REFp4dtLNdNWOSY/2e0jRbiOd4IOeP8AaNULfwRp1rd29xDI+YlRWDxxuZNhJXllJXrg7SM8VFJ4xLHQEt7UNLqEwS5Rj/x7AEq2f9oSfL+B9KdceJ7hPBen61i1glumgVjMT5UXmOqknkHAz61maGpf+H7LUpryS6DsLu2S2dQ2MKrMysp6hgWJz7CoY/C1gk2jS7pmfSofJiywxIAoAL8ckYyPQ1kQ+PrW3ilXUNkrRs5E9pjypIlCkyLvYZALbcAsSVOM1Pqni910+4k0qwuZpI7uOzErRDy97OFJALqWxn2GSOeuAC2fCyR3KXFnqV5aSjzwWjEbbhLJ5jD5lOPm6U1/BelSRxwt5xgWOKN4i+RKEdpBuPUku2T64qTVPEJ0V7M3cZMLWc9zcMq/OvlqhICgn+8eMmqGp+L7hUit9P065S+e7jt3SeJSY1dS4bG8A5AOOeCDnGMUAatr4Z0y0ttRtIYitnf/AOut8/JkrtYjuMgDP0qlP4OS8jkW91W+uG8g28TsIw0alkbOQo3HMa8tnp71H/wnujqJ8+ewiVirIqt5u1wjbVDE/eYdQM9s1MfGumpf/Y5Y7iKUMI3D7P3chTeEIDZzjHIBHIGaAGXPg5LucXU+q3kl4JEk85kiIOwOFUps2kDex6Zzg54FTXXhVLxpDNqV4RcxrFeqoQC6Vc43YX5eCQduMimQ+M7GWFX+yXySSpFJbxPGA9wshIQrzgdDncRjqaW78Z6dYzRQ3cVxDIyK8qSBQYFZioLDdzyD93dwM9KAJofDEEOoRz/a7lraK4e6iszt8tJWzlgcburMQCcAn6YTU/Cenava6lb3RmxfSrMzI2GjdUCAocccL+OSOhqPVPEraVrcttLZzTWkVkLqSSBNxjG9gxbJHAABwMnrUU3jrRoLi4iZ5SsIk/eKFIdo0LuoGd2QAeSADg4NAGmdCsz4hGtfP9pEHkbc/J1+9j+9jjPpVGDwqsWiSaNJqd5NYNEIVjZYwUUHoCFBPHHOeKUeLbYv5H2G+F6XVY7QxqJHDKzBh820DCseSMYIPNQQ+LcaxdWtzY3KW6SW8ay+XjyzKowJMnOdxxwDjjPrQBek0AnX31eHULiGV0SNowkbLsXPygspKg5OcEUmp+HINU1S2vpbiRTbsjqiomcq24YcruXJ64PIqj/wnNkU3rp+pMhiknVhCuGjjbbI4+bopx15ORgGp7jxjplrePbzLMoELzJJhSJFRPMOBnd93kEgA460AVLPweDpd5DcukMt3dtcMiKkyIuTtjxIpBUZJ6DBJxVhvBdi1obT7VdG3e0S0lRireYEJKMSV4ILHpx0BGKji8YRNrcVlJbyxi5t4pLWB0xPIzO4JxnAUKgY5xgdfStLRfENrrpmNpFMI4zje+3DckdASQeOjAH2oArWvhS0tghMztIqTIWSOOMESBQflRQOAgx+NXLDRLfTpllhklLLZxWY3Efcj3YPTr8xrTooA5M+AbB/Md7mR5mkSRHMMQCsoYAlAm1yQ7AlgSePStS08PQ2N5a3Nvcyo0EH2d1CIFlXJYZAUYOST8uOtbFFAGFfeFbK+tp4XdwZbz7aGKq+yTaF+6wIIwOhB6mktvDEdlPFLaX9xbnYkc6xpGqzhWZhkbcLyzD5ccGt6igDm18GWmxIpLy7khgUR2sbFQIE3o+0EDJ5RRk5OBipp/C8M+om5F7dRwtdJeNbIV2GZQAGzjdggDjOM81vUUAYmkeGbfSZryVLiSV7pQjkokYwM8kIoBb5jljyeKZb+E7G2szapLOU3WzZLDOYAgTt32DP49K3qKAMjU9Ah1O6kna5ni861e0mWPbiSNs+oOCCSQR+tVtR8IWGosHkkkEqpCsbFVcKYt+07WBByJGByPyroKKAMh/D1rJpNrp7SSCO2kWVWjCoSwJPRQAOT2ArNtPAtlZ280cd5cCR1gCyqkalDC5dDgLgnJ5JBzXU0UAc7F4QtIdQuLxLiQPPvZsRRbhI64ZwxTd6nGcZPTHFZmqeELWw8Mag8V1MJohLdBkSNFz5LRldiqFAKk5wM5Oc12tZniP/AJFjVf8Arzl/9ANXS+NepFT4H6F+3/49ov8AcH8qkqO3/wCPaL/cH8qkqXuUtgooopDCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDM1LTLm6vrW8tL1baWBJI/mh8wMH2k9xj7orO1XR9cvtKubQ6tbyrPGY2T7JsyDwedxxxXSUVftJcvL+i/VGbpxbb/Vnn48Ka8t9NeLJaLNNcx3LkLxvQcYGeBySR3JJ71Fa+EPE1raRWn9qCa1heN4YZQpWMxsGXGAD1A69q9ForO8/5vwj/wDIi9ku7+9nB6j4c8T6ldRXUl5bRTxoY98MYBZCQSpznuOowR608+H/ABKbd4PtFpsa7F4fk/5aBw/r0yBxXc0UXn/N+Ef/AJEPZLu/vZw2qeH/ABLq+PtdxaHEMsHypj5ZAA3fr8opsnhzxJLqH25ri08/zY5eE43IrKvGfRjXd0UXn/N+Ef8A5EPZLu/vZ50ng7xAkc8S3cAglYt5IQBUJcOdvf7w7k46DFTP4Y8SNqM96Ly3RpzuljVFCO2zZuPfOMdDjgHFd/RRef8AN+Ef/kQ9ku7+9nnsvhLxDKkA+1QRvbwxQwyIMNGIzlSDnr/MHpSnwr4ma7hum1JDOiqkkhVS0wViw3k+hJ6Y4OOleg0UXn/N+Ef/AJEPZLu/vZyGp+DtR1W6e4m8QvE8kH2aUQ2wUSRZJKkbj1yeRg+9RN4GuybwDXAkN2siyQLZqEG8YYj5sg/jjJ6V2lFa+1l5fcv8h+zXn97/AMzkLvwdqF1eG8/t7ybseXtmhtAGTYHAxliOQ7A5BzmnDwhf7ZQ+uCQzSQSyO9rlmaLbsJO//ZGfWutoo9rLy+5f5B7Nef3v/M5BPBl4kCQrrKbFtprUf6J/yzlILfx9cgc1T/4V1NvYnWyUIkAT7KMDzIzGx+9k5UnqTjtiu7oo9rLsvuX+QezXn97/AMzkbnwdeXdwLiTWIxOscUaSLZgNH5bFlKnfwcsc+o4ptt4N1O1v3vU8SSNcNgF2tVztDbtpIbJGfXJx3rsKKPay7L7l/kHs15/e/wDMx/sOu/8AQbt//AH/AOzo+w67/wBBu3/8Af8A7Otiij2svL7l/kHs4+f3v/Mx/sOu/wDQbt//AAB/+zo+w67/ANBu3/8AAH/7Otiij2svL7l/kHs4+f3v/Mx/sOu/9Bu3/wDAH/7Oj7Drv/Qbt/8AwB/+zrYoo9rLy+5f5B7OPn97/wAzH+w67/0G7f8A8Af/ALOj7Drv/Qbt/wDwB/8As62KKPay8vuX+Qezj5/e/wDMx/sOu/8AQbt//AH/AOzo+w67/wBBu3/8Af8A7Otiij2svL7l/kHs4+f3v/Mx/sOu/wDQbt//AAB/+zo+w67/ANBu3/8AAH/7Otiij2svL7l/kHs4+f3v/Mx/sOu/9Bu3/wDAH/7Oj7Drv/Qbt/8AwB/+zrYoo9rLy+5f5B7OPn97/wAzH+w67/0G7f8A8Af/ALOobvSNZvbKe0m1qDyp42jfbY4OCMHHz+9b1FNVpJ3VvuX+QOlF6O/3v/MbGnlxqmc7QBmnUUVkaBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB5H481fWYfHIsLHWZbGBoEYkzFI14JJP5fjWKL7xKbtI/+Eub7PJEJY7nzpCrgttwF27ycg8Y7ZroviFr/AIB0nxII/E1jdy3zwqQ8RbaV5wOGH8q56T4j/CyS8juxa6nHPEoWNomdPLAGAFw4x+FZOLubqSSWhDBqPi2XV5NPfX7uLy5JI3uDKzRgopY8gHPA+tLc6p4kit1ubfxXPd2zLJiWF5eGQAlSCoI4I5PHvVmP4m/C2LVDqK2V/wDaSxYkqSpJGCSu7HIJ7VLP8VvhjOylrK8RVjeMRxRbEAb73yqwGTgc9aOVj512/IzLbU/G93OkMd9qweSJpU3FxvVRkkcc/wD1xVT/AISXxbvKf2pqm4FQV3Nkbvu/n29a22+KPwze6kuGj1dpJC5bLvj5hhsDfgcHHHSkX4n/AAvSdpltNQ8xjExOG5MeNn8fbA+tHK+4c67GU2ueM0WVmvtYVYgDITvAQHkE8cfjXqXwx1G91PwtJPfXUtzMLp1DytuOAF4/WuV0b4i+A9UulsNN07Urm4JklCkEk7h85JZ/T1rc0vxVoPhuwe2sdF1KC3D72UlG+Y8d5Ce1VGEtzKpWppWbSfqj0GiuEn+KujWzbZrHUUOM4KR//F0xvi1oaD5rPUP++E/+LrSz7GHtqf8AMvvR31Fedf8AC5/DfP8Ao+oYAyT5S4/9CpYvjN4cnz5dvqDY6/u04/8AHqQ/aQ/mX3o9Eorh1+KGlNEJBp2pFD0OyP0z/fqD/hbuhE4FlqRI9IkP/s1K5tGlOSvFXO/oqtp97FqWnW97AGEU8ayIGGDgjPNWaZBznja8ubLQlltZ3hkM6ruQ4OMHivK4PHWqy3Bimu9RtVKNIkk7hVdVOCeuR1HXFev+JfsH9mKNRtpLiEyAhEbacgE5JyOMA968p8KWo1LTbjXrrw7Z3drPHIYbxdTZUGGwse1guxM8EnJzzzXJVhKUtH+Zw16c5TbT/MpyeOfECaitmo1NywLK63Ee1kBALfezjke9Xv8AhLr/AHyp/bsm6EZkX7QMoPU+n410T+HtLPiTT7o6LKkDWcsYh+1pvkdmjIKfvOQArfmK42DwlD4fub6/vLIXFtamWNC96G3SSthFlROU5YA8tn2rP2M31/Mx9hUa+L8y5ceNL22lhjl1yYPLII1HnfxEZGfw/mK9N8E3lze6C0t1PJNJ57Luc5OMDivObLwTHp0MNkNMS8ksL5LmR5blEdoShDhwHO0qT8p7gKDjrXomnajY6VDHZ2emzxrK7FE8+Ji7dDjMmT07VpShKErt/ma0acqc7t3XzOlorm7PxpY38N1NbW8zxWsrQzv5kQEbKcEEl/Xv0NO1HxfaaRYte39rcQ24Qyby8R3KO4AfLfhmunmR2c6/pM6KiseXX/s8SyT6ddRI5AVpJIVBJ6Dl+tB17Fx9nOnXIn27vKMkO7b6435x70cyDnX9JmxRWGviaFiAtpKSWCACeDljyB/rOp9Kf/wkK+cYfsFx5oBJj82HcAOpx5meKOZBzr+kzZorndR8Y2ek2Zu761uIYAUG8vEfvnC8B84J71KPFEReVfsF0oiClmZolX5hkYYvg8Ang0cyDnX9Jm7RWK3iONEid7KdUl5jYzQgP3+U+Zz+FLJr7RTQRPpN+Hnz5eBGQcDJ5D4HHrRzIOdf0mbNFY8GvfaojLb6dczRgkF45IWGR15D0kXiFLhVaGxnlVmKqUlhYEjqBiTrRzIOdf0mbNFYv/CRJ5kcf2GffKu6NfNhy49QPM5FVLLxrp+oWct5bQTPbRSNE8hkiUKysVIOXHcH69qOZBzr+kzpaKwz4lQTvC2nXisiK7FjGFw2cfMXwc4PQ0p8SRLDFM1nMIpTiNzNBtc+inzOfwo5kHOv6TNuiuet/F1rd31zZQ2dy11bSeVLCWiDBtobABfnhgeM9aLLxfaajaWt1a2ly8N1nyCzxKZMHB2hnBPT0o5kHOv6TOhorIm1027RrNpt1EZG2oHkhXcfQZfk0n/CQDzZIv7PuPMiG6RPNhyg9SN/A+tHMg51/SZsUViJ4kikdUSzmZmYoqrNASWHUD951HpSr4jjZnVbKdjGu5wJoSVHqf3nAo5kHOv6TNqiud1DxhZ6UIjfWtxB5syQJueLl2+6OH4z6nipB4phxMWsbqNYm2O0jRIM4B4JcA8EcijmQc6/pM3qKxpPEKRMiyWM6GQbkDSwgsPUfvORU/8Aalz/ANAa/wDzi/8Ai6OZBzr+kzSorN/tS5/6A1/+cX/xdS2Woi8mnha2nt5YdpZZdvRs4xgn0oUlsNTTdi7RRRVFBRRRQBFcu0dpM6nDKjEH3xXiFl4o8eX9k13b6g7xLnqsIZ9oy21SMtgdcA17hcbPs0vmZ2bDux1xjmvMfDukeBLxnuNKudRnmhRhsAd2i3AjdgLweuKiSb2NINJO6MFvEvjldMbUf7Xha2RFdikluzKG4GVHI69MUtj4j8eajapc22qRmORyke97dGdhjIVWwT1HSuk/4RbwymnJZpp/iARNL5zbYH3FhwNx29ucfU1eOi+Go1h8/SdYvGhneVHkt5SdxIJJwACCQO1Tyvuac0ex57N488X2+0S6syk5+Xy4sqQSCCNvByOhpG8feL0hjlbVJBHJkIxhjw2OuPlr0iDStE84SJa6zFO6vmd7Riy5cu3JQ4JJ9OmBSNpGh3eniylttaEB8xAjWrLs3EMSMJxyMDHrRyvuHPHsea/8LE8Vf9Bh/wDv1H/8TSp8Q/FTOo/tdyCQD+6j/wDia9U0zwxoF9aStai58syJFMsg2kmNduwgrkD19ak1D4faLqhia7a5eSMkq6uqnBx8vCjgY49KOWXcOeHYzfiPr+qaIumf2bdNAZvM34RW3Y246g+prjZfFfjWG6htpLycXEyq8cXkIWIbpxtru/Hp8NoNPbxDcXEO0v5DQtjn5c5P5Vzl/ceA4rm1lutR1GKb7OnluZCpZDnBJ75yee9anu4DEYanh4RqUrvXXlT6u2phyeMvF8d81k2oyfaVk8oxiKMnfnGOF9asX/ibxrpsayXOo4jZiodBC4DD+ElQcH2Nb7QeEHmub+O41VS94qs8QOGn3ZVV+Xrk9OvNS30XgizvGluIbiOay2zXJEDIse/O0yqQFGeccdqDpeNwl1+5Xn7q/pficZ/wn/ifGf7Wb/v1H/8AE1MnjTxbJEJRqbCM7sMyRAEqASBkcnBHHvXc2Vv4c1GeO/sv7QumeIxpqENuXUjOc5C43DpnGccVOdM0iSykge11kw75SYzasNxdcEgBfrj3NAPHYPph1/4Cv8jzn/hP/E//AEFm/wC/Uf8A8TR/wn/ifGf7Wf8A79R//E16HcQ+HY7/AE6K4t7/AM+R2ktIWtCNu1TuQDbyMHJHPSs6+0vwxrF55M1vrUt7bRASpHbkSKrEld4C9+cewoKhjsE2ubDpL0X+RyVz428WWlw9vPqbrKmNy+XGcZGf7vvUX/Cf+J/+gs3/AH6j/wDia9D1C10EwtLfWWqNBbOkvz2jBV2jrnb0/vZ61qnwfpslzFds85uI1KrLld2D2+727HqM0GbzHBQinOgl8l/l/wAN5nlUfjvxTLKkaaqxd2CqPLj5J4H8NEvjrxTDM8UmquHRirDyo+COD/DXrSeE9MivJbqJXjmmZGkI2ndtGMcjoep9TUP/AAhek/ZpbfEvkzFzKmV+csSck4zkZ454xQR/auX3/gr7l/l/XkebaL438RXWu6fbzamzxS3MaOvlpyCwBH3a9rrnH8E6VJfWd2zXG+zKGFA4CgrjsBznAzXR0Hk5nicPXlF0I8umulgooooPMCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPl79oj/AJHu2/69F/nXkNevftEf8j3bf9ei/wA68hpIb3CiiimIKKKKAPVPhpbrpHh2/wBdkAEtzJ9mgJH8K8sfzwPwp2t+IZbrbFEDHCDlhnlz6mrt7AdL8NaNpSjBhtVkkA/vv8x/nXJXDHcSc168IKnSj3Pm6kvbYmUnstDSknnvY/NkIyzfKQOtV3eVcl84PBxWhpc1vNbDGxJoVAG44z756U3VEAihukw28mNgCO2OwrlnLUlK0uWxjysx83a77enTg1ZtbO5+yNPZp5jDAIPvxU8VuBJtPlqcZLA5Bz3qe1untXDBSo3YKt8oOPUY/KsZSNk1Yv2MN+saNcOqg4yASefak1jWVspJY7fmVwIyoGMqeDUt/dfIskUiLIsZIjHY+v1rihfNLfNNK581n5OOOTWcYXvc9TDYycKbpwe9j668IknwfpBPX7LH/KtqsbwkQfCGkEf8+sf8q2ag7Gcv490qbXfD8elW9z9me7uFhMwGdqkNu/8AHcj8a5dvhzrNtby20d5YalaJqUepQ293H5aStsKyRuqrtAztYYB5HIrtfEt5HYQ6dcShigv4kwvXLZUfqwp2jeI7TXJb/wCylfJs5TEZPMU7iCQTgdBkHBPWpW7Ij8TOXh8FahB4httQ/s7QZImt7eJkZW/0AxFifs429DuyOnIrC074U6paafdW73FsZysUaT+ccTKtwspLqEGDhe5Y5J5xXpQ8R6K1m92uqWht0cRmQSgjceQPy5+lR6h4p0TTLaae41G3Iih89kSQMxTGQQM98jHrmqLPPn+Fmpvca6WuIHa8jvFt7gzEMxnOQHUJnA92PQYAqzrvw31G81DTl0saXa6fZpbeWqoI3Ro5N78hCW3dvmGDnIOa79Ne0mSa3hXULYy3KhoU8wZcHpj64P5VHF4i0pzbJJfW8M9wFKQtMpY7unQkc9vWgDgL34Z6gbbULayj0yKFtX/tGDy/3ZlQ7v3T/IQAu75eGHsKqaj8KtUl01bS2TS5RJpwtCbyR3Nm4laTdEdvOd2Oi4wO3Fep3Oq2FldQ21zeQQzzHEcbuAW5wMfjx9ajOu6SJbmI6lah7UFpwZR+7A4OfoeKAOc8W+Fb3WL7Sby2t9NvhZwywvZ6lnyW8wKN4wDyNvp0J5FZE3w91KTxI92p01YWvvtwvAG+0qPJ8v7OOMeXn36dq7j/AISHRvKt5DqdqEuCRETKPnIODj6Hj61FF4k0woDc3MdqzXEtuizOAXaNyhI9sj9RQB54PhFImmCKKPTEul0m2tVlVCCLmOXe8ucZ5GBnrSWXgbVNV8RalcyWlnY266zeTi8dGFzKjxmMKOMGM7s5z26V6RN4i0a3Mol1S0UwvskBlGVbng+/B49jUcXifR5dQu7Jb+ES2kSyykuAoQjOc+mP5igDz3/hWet32kXUGpHSGnWwsrS1xvdc27ltzZXjcCRx0ya07j4f3eoasLq6h01LV72xuJLRctGEhidGQAqARlhjjoK7GbxLo0Ngt42pW3kuWVG80fMw6ge4/Snrrtgui2mq3U6WtvcxpIvnMB95dwHucenpQB454o8E6jpGlJaRWMWpSz2d3aQQrZyzJBvnMiGNlGEbDAfNgDb14r0bWvCt1qyaEAYNtlbTxTxyOw3eZbmMAFeep5Pp0roRrWlteRWa6hbG4lQPHGJASykZBH1HP0quvinQXIC6xYtlggxOvJPTv+tAHNeEvBuo6P4c1nS777GY7xdkMMbZ2jy9h3yKiE5+mQO5rE0v4feJNKis5rd9L82zvPOht3ckbDAYm3SLGpY8gjK5wMZr0b+3tJ+1SWv9pWvnxBjJH5oyu372fp39Kh1PxLpGkLCby9iTzXjRQGBPz52k+3B59jQB5/pvw01fT9Q0CZG08myht4rqZmL71jLbgEZDyQxAYMvvmnv8OdXt9In0uyh0Vbc6nJdB9gV5YnMhVSTGwUpvAGAcjIBFd9J4j0q3Vmu723tgJGjXzJl+bbjJGD7jr071M+taXHfLZPqFst0+3bEZRuO7oMep7CgDzi3+F2pCytbW7uLKeOOHTYZVYsQ4t5XdxgjoVYAVFrXww1q70ubTbKXTEtHuL2SNGXb5ImYFNp2MQAAcqu3nHPFeny6rp8GoR2Et5Al3IMpCzgM3px+B/KoYfEOjTpO8WqWjpbpvlZZlIRfUn0oA4Jfh3qx8TC9f+y/LbUbe9a9G43SCKNVMY4xhiD36E9aoD4Uapv0oTXFvNHDbR28oExjMRSZpNyHYSc7h028jqRXpmla3aaybw2bh4raURGQMCrnYrZH/AH1j6g1TtfFmn3mmi+hWYxG8SzwVAbc7BVbGfukMrA+hFAGV4l8K3mp+KLPVoLXSr+FLX7M1vqQYiI+YH8xMA/Nxjt0HNc7J8M9Vkur0CXTkVlvyl4u7z7k3IO1ZePurn1P3RgCvTZL+zilaKS6hSRWRCrOAQz8KPqe3rUB1zShdRWp1G28+UlUj80ZYglcfmCPqDQB5+nwtNvdNcWsWnQSLNpssLom0x+QR52CBwWx+PesbRPh1q+qaVIZray0sNBqMAk2MLiczSMF80YHyjAI5PGMV6i3iXTPtcEEV1FMsjSK8scilIiiFjuOeOBTV8T6WBdPPdR28MEywiWVwFkLRrICvrw36GgDhrj4e61qUV3eahDo0l815ZXMUDFniKwIEZGYrkbgOwq6fh5Pda8t5fpp8tmdTkvXtmBZdrWqxBcEYOGGfpiu3/tjTTex2Qv7c3Mqh0iEgywIyCPqOfpVBPF2jSX0sCX0DRQwedJcCRSi/PtAznqSePWgDyTXfBWrada2Wkiyj1O8msbS1En2SWQW5ilJPly42oCDk7iOB3r3SAzFG89EVt7BQjEjbk7TyBzjGR61FHqNlNbwXEd1C0NwwWJw4w5OeAe54PHtUEmuaVFbi4fUbUQldwfzRgjdtyPXnj60AaFZln/yH9T/3If5NTzrukAxg6naAyRecn75eY8E7uvTg8+1VNHvrXUdW1K4s7iOeErCN8bZGQGyPrUy3REt16/ozboooqiwooooAgvP+PG4/65t/KvEfDdzpuqCTVNf0WzuxHp1va2NtAhLysJ5YlHPG9mXr0A+hr269/wCPC4/65N/KvI9M8Nizv4ZtCtS9xbafp+p/ZmlJEz75fMUFjhSwZiOg3Uuo+hY1rwVKmi3NxdeFdHht9m9hpkrm6tgOdy7gFkIxkrxkZHNa/hTQ/CeuWRiutA05NSt1UzLGnySKwyk0fqjjkenIPIrT1DxzBJpk6WGnai2oeW37m6s5IEgOOWlkYBFUdScngcZrI8L+EotcsbC+1L7QllaWSWenLFK8EksYxmZyhDYcjKqTgLg9TwxDdV8H+HdY1n/hH9J0e1hWHbJqd7GmDCh5ESHtI3/jq89SK6YeAPCYAH9gWXH+xVDS4l8Eax/ZMm46NqU5e0uXYs0dw3LRSMeW3dVYnPVfSuzoA5P4exR2/h24hiULHHf3KIo7ASEAV1lct4C/5Al3/wBhK6/9GmuppLYb3PNvijG76t4YZLuC08uadjcTwiVI/wB3gEoeoyQPxrltMm0OLRvBMU3kCeyv3l1ESop2gpLuPAxsLsNoHQEcV13xSsLu+Oki2tp5gpl3mGJn2g7euK5C58PfZL+ArpOrXNobZXKiFlZpDnhiAdo9uSK9bD4OjUpKUpO7v2PPxOOrRnyRirR069df1JJdU04/E1fEyJb/ANmJdLAbTcQzMEK/bdv3cjO0Drt560y81Sxnmk12cx3cp8Qm8lsCRvkto4zFFgH5SRw4BPent4TkbX4IRY3sdqzw+ZG0bsV3Y3gOFx8vqaz7Tw1qk8tyn9l3Q2xkoZInUZ3ADHHJ56VssvwzV+d/gc7x2ITtyr8S5o2tG7v9OW+1KWw04ahdTSrbhbRnjKRmPzVi4yXDepx161kSeJtaddYlivdQt/tEKkQpdOWEouVJ8t2c/wDLPdyAoxxjittPA94l5JBcOxVYJH3xwSkB1HCn5P5de1Q6L4SvL87byzubZPNVTI8UgIG1jwNpyMgc9uPWl9Rwtm+d/h/kH13E3S5V/XzK8N3banqNl9q1HUbfTIry7e3We8f7TBG0ChQzglsNIGwMnjrxSJ4h1FrWJ7/U72WIx6eL2K3uCk8yrHIJQhBHzbihOCM+tTP4L1ZLYSiMu52sYVhk3BScBuVx3BxnIHUVO/gLVVLASRsQWUARycuvO37vcdD07Zo+oYT+d/18g+u4r+Rf18yPWb6+v5IbvSZ9UuLBLOOKWG6uuIRht5ZhKNzYIyGRs4GDXui/cH0rz34b6Gg0m8Op6UBL9o+T7Vb/ADY2jpuHTNeh15eIpxp1XCLuketSre0w8LrXVv8AD/IKKKKxGFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB8vftEf8AI923/Xov868hr179oj/ke7b/AK9F/nXkNJDe4UUUUxBV3R7X7drdjaYyJriOM/iwFUq6PwBEJvH+hoRkG7Q4Psc01uKWiZ6Z4mCS6zdh38uOMldwGQgAwOn5Vx1wkclzkNiMjsM5OOQPxrsPFNzFcX9zEIUj+Y/KrYyx781zf2GOAwXF3JGsbAOsPUuOeuOgNezVbsj5jDJasp6YyNNcqsZPy7tuc8Zx+mamlUAsG4PBAHQk8GtXSdIVdPudUgjPmRSou4HGCQScAdsgYpL7RroDdNEPNkO8ljjgivNqStLU2q6TvYynZICUZNwUkFjz07UxLqeK3lWJ/kPzNuAPb3pBaeU539qUxMsJ4UL0xnNZNolSXQrSXMku13l96zHkjN0oYL5okB3gcMDjrz1FXwkcchBYbR3PSsoOH1D92Ayu4x3PWnF7ndhvi8j7H8If8ifpH/XrH/KtqsXwiMeD9IB/59Y/5VtVmeszB8V2R1G1062EmzOoQPuxn7hL/rtxU0ehAaNqemyTkrfSXDF1GCglLH8xuo8Q3MNnHp9xOxWJLxNxCljyrAcDk8mpl8QaS0Yk+3RKpMY+fKnMjFEGDzksCPqKlbsiPxMxbvwzqd/b2TT3loLqykzD5CSRIy7Ch3FX3A854OB05zT7fwclvperWKTpGt/aR2wKIf3e2MpnkkkZJOCfxq1f+LtM09mMknmRiWOHdCC5DNI0fKgdAVPPscU7UPFmmWNteukwmntYHmaIArnam8ruIwGxzjqPSqLM+88MarqWoW9zdalDsjkt5THGsgVWjcMQq7sENjqwJFJB4LaHS7i0+1oWlgtYd/ldPJYtnr3z+Fa6eJ9HktWuFvAVV1jKhG37iMgBcbjkcjA5AzVbUvGGk2FqksdwlzJII2jSMnlXcKpLYwM5OAeuDQBB4i8M3uuXoZNQEVqVjBiO/wCVkffkBWAbPA+bOMcU3/hGb/8As+TTxeWv2aOb7RasYCX3+cJQJPmwRkYOME9eDV7VfEcOm63YaWY90lyjyu7bgsca4BOQpyeenHTkjjKjxboTRwyDUYzHNyjhW2lcgbiccLkgbjx70AZWo+ErzVbtLu7ubVpJYDb3MSCVIym8twFcEnkg7uDweKiuvAzzXLyLdIyTmZJo5DIqmOSVpMDY65I3EEHg8dK2pvFuhQXEkD6gnmxsyFFRmJZThlGByR3AyQOelTX+txW1pZy2kf22S+cJapG4AkypbO48BQoJz7UAZNx4Vu/sK21nfrEpvJ7iRcOocSFiASjBsrn1wcc1X/4Qm4GlyaaL+MwS2NtbuxjYMJIPusMN909x19DWjJ4hv457e0/sST7ZIksjRNcoqhYygJDdwd4x075xSW3i+0u9G1HU4rebybK3W4KtgFw0QkwPfBxQBRi8I3tu32m2ubRLyVZo7jzEklRlkCDILOWLARr1OCOMCr7+H7qHT9DWzuYftekxiNGmjJjkHl+WcgEEHuOfbvV+91uz04xNeTRwQvC8zSSPjaFKDp35cc/T1rNvvGdhALJbP/SZbu5NsFIdAhC7ju+UkcYwMc5HbmgCNvDF493815b/AGWW6hvZgsBEnmxqowpzgKSi+pAyO9RDwVt0sWYuYwRo40wOIvQ53dentWofFeiC3acX6tCrshdEZgCv3uQOg7noO9Je+KtIsorxmuTI1rE0jrEjNnau4gEDBOCOM96AMSLwrf3/ANqgvZoYLI391cIEi/et5gdAd2cYw5PTPQVMnhXVGDzzahaG7UWnk7IGEY8hmI3Ddk7t/bp71rJ4q0Yxws94IzLAbgI6MGEYzliMcD5TyfSrN1rmm2W8XF2ishQFACzZfO0AAZJODwPQ0AZcHhZo9Qnu5LiNjKt0Nvl9DMYz69tn45rCXw/qdlf2Gl2yPNZx3ttdyzvAAMxoqsQ+/p8gwNucnHTmukTxfo7X89r9oYCG3juDKY28so+cfNj2/X64uXOvaXZ3At57yNJjKIRHgli5UNtAA64IP0oAydV8M3up69FeHUQLWOeCdYiH+UxsCVADBTnHUgkVXfwU39n6fbx3kayWVmsCN5Xys6yxygkZ+7mLBHv1roLLWtO1GWWO0ulleMbmABGVyRuXI+YZBGRkVQtPGGj3Wnm98+SOPz3gCvC+5mUsPlXGT90njoOuKAI4dAvxpmsxS30KXWqSF2kgiKiIGNUO0E5Jwp59TVVPBQtp/wDRdRmNuz2sjRz/ADkGBwVwRjGVG38B6VrajriW1hZXVlEL77bMkUGyQKrbgSDu9MCqDeKpfPisk0qQ6k1wbd7Z5lUIRGZN2/kEFen9MUAHiDwmNb1BbtLswMsBTGzOZVyYZP8AgBZjjvn2qsng2aBPssF5ELOb7O1yXiJlLREHKHOBuIycg4JJHWtHSvE8GqtMsdtLG0Nv5zhiDgiSSMrkdcNE3PQ5Faem3q6npVpfojIlzCkyq3VQyg4P50Ac3c+BobzStN0+e6ISzeaTdEu0sz7iDjOOCQcHIOOak/4RrUor/wDtOK+tW1AyFz5kB8o7oY424DZBzGCOe5HvWmfE+iiOd2v0RYCu8urL95tqkZHzAtwCMjNR3HizRoLEXX2xWVhJtXa27KfeBGMrjjJIGMigDD/4QO5LWlu2ql7K28rYCHDAKmwgANtGclskEjOOgqT/AIQ6/kjX7Rd2UjQWkVpAFhdBiNwysSrhg3H8J4PIzWzL4gI0/S5bezaa71JVaC38wKB8m9izHoAO+PTioJPEV4r2dvHo7td3CTO0RuUAURlQSG5BzvGOnvigAk8O3Vx4T/sqbUSb1W8yK72ljE4fehGTk7eBknJA561lN8P0hR/sl5tMVxHLaIdyiNFVsplSG5aSRsgjkjritS08X2l7pGoalDbzeTZ2q3JVsAuDH5mB79q34pBLEkgGAyhsfWgDk/8AhB0bTr21NwkbXVpHBujVvldZHkJyWLEFn6Zz155q34Z0+bTtR1WKdkaVzFIxRnYcgjq7Fj09a6Ssyz/5D+p/7kP8mqZboiW69f0Zp0UUVRYUUUUAQXv/AB4XH/XJv5GvImS5v9bsm0zWrezEeg2aSMdQMAZgX+XKg5I9O2fevZCAQQRkGqA0TShIZP7Ms95ABbyFzgfh70hpqx5fe6Bquo2j2t54nsp7d8b4312Qq2DnkbORkdKkl0nxDJHsj8Z2sBJH7wa07bRnnAKYPFen/wBkaZ/0DrT/AL8L/hR/ZGmf9A60/wC/C/4Uahoecap4XOpaulvL44ebw9IF+0QTamDKxAbpxj73lnOcjafWt4aRpIGP+E41X/wbL/hXU/2Rpn/QOtP+/C/4Uf2Rpn/QOtP+/C/4UahoY3giCC10e6gtpmngS/nCSs+8uN/Ut3J9a6Wo4YIbeMRwRJEg52ooUfkKkoQN3Zyvi7Xhol3p/n6jcWFlKkpmmhthNtK7MFvlbavJycelS3V3c2nh6fXLbWZNQt47QzxqscW2YBdwIYL39elYnxIuNPtrvR5NT01dRtwJv9HeQqpb5ME+o68HisW9+JNreaLPpQ0MwW0sJgAiuANikY4GzAxXoQwtepTjKnG6+Xd9zhniaMKklUlZ/PsjqF8QmSWOV9WuIIGtjO4MCFonWRYzEV2k7tzYx6jjrU03iGzNvLM/iKW1EKEyq1oFZPmC8hlJBBI4/wBoHoQa871LxRpmp3t3dtpE0b3UKxuq3KlNyuriTaUxuyig9iAM0g8T6a0dms2jFvs8kjsIpEiWXeMFWVUxgYU+uVHNV9Rxf8n/AKSL65hv5vzPSr6/mtrCe/h1eecW8otvJSCMCSYsEC5K/wB5gMg4H4VUt9Q1K0ScavrO2WKVEVLXyrgyM2fk2rGGB45GPcHrXFxeOLOPwymhnS7ho0AInN2PM8wNvEmdmN275unWs658S299JLcX1m9xeM0bLK3lhRs3YygTDZ3tnPqMYxR9Sxf8i/8AJRfXMN/M/wATvpNdvrjUoLHTtZgYSxrMJbpkhdsyMhjVDGckFCCMA9qsnXrZFmlfxLdLFFuk3tZKEkQMFOw7PnUMQMrnrXFab44stNuUuF0QPItusI/eIijDs+4BUABJc9PQVRHii2MccMtpdSW9uuy1jM6DyFLq5GdmW+4oye3vzR9Sxf8AIv8AyUPrmG/mf4noi6jumaM+JNSTYu6R3sEVIvl34djHhTt5wcH86ktbma+trqW28RagWt03vHLZxxtgglThowcHBwa5/StRuPEiapLbabfHT7t2W6tYpYSrM0YQncwDDjBwO469RWrpNpe6VZXNuugXTm4XY0iC2iO0DA+6Rk8k5Pr2rnmqkJcsrJ/9um0Z0pLmi3b/ALeM6w1zX9QgHk6kjzvZC9VY5oXVU4yrkQ5VvmGBgg4PpVj+2NYs9HsdV1bUrmCzu0DB7dIpSjMoMakeUDlsleP4sDvVjTtPm0tStp4dvokezS0lVZYAJdowshwfvYJGe+fYUTWE91Z2Fld+Hb+4s7ODyUgeeHa527Q7Yb7wGcemSeuMTzS/u/8AkpXuef8A5MOk/wCEvjEbeddP5iqSirBmMkcgnZg46cVE2q6gviG60xtfEaWzYcytCsrjyw5KR+VzwfXsa2LLUdXtLKG3fQ9QuGjUL5ss8O98dzhuvvWbqEOq6neI91pepPZrKs32Tfb43L0+bO7Gecfrjijml/d/8lD3PP8A8mE0m81q/u7WG61G6tVvbY3dqwWByyArkOPLG1vnU4GRyeeKrWWs6jc310k2u+RbW5l80O8AuIVjJ+d4jHwpxkYJ4IqxpVrqem3ME0mland/Zbc21qsstuPKjJXIyCNx+VRk+n1qtfaVqGqyFdR03VLm2AlEcTPbBk8xWUjeDkgBjgfTOcUc0v7v/koe55/+TFxdYjYRj/hJtSEskohWA6eokLFdw+Tys4KgnOMYB9KF1bYUWfxPdhyzBjFaxukYEjRgu3lfKCykc4GQeoGar6Zpt3p01vONBu2lhlMgMYtog2YymCFPPBJz6+3FVm8PSfbUuhoF4WyfMEv2aTePNaQAbj8vLsMjtjuM0c0v7v8A5KHuef8A5MbthLPqc8sVr4i1BvLLDebKNUba207WMeGwQRwaybTVNcdba6u9Rmh0+5uWto5k8lmRgzKC6mIYBK9icZFT6baX+m6zcakuiXzPMrKUVreMHcwbLbSN5GMAnnBPXNVoNK1FUjt7nTdUubGKZ5orVpLdVDMWPJBy2Nxxz6elHNL+7/5KHuef/kxbXW4Xi3r4n1EklBHH9gTfKHztKL5eWB2tyPQ0kGqXEmqy2b+IL1PlhMA+yx+ZKXDE/J5eRgLznp3xWdb+Hbi2017OPQrgqRGqs8NoflTOAw/i69eOQCMc5ks9CnsbqG8j0bVDewrEiXDTwF9qbsp1+6wYgjtxjkCjml/d/wDJQ9zz/wDJizaa611p8d22t6xEZJZI0hOnIZG2EhiFEWSBjJPbODzVgatC9wkSeK71w3lfvEsozGvmAGPc/l7RuyMZPeqaadq0Hlta2OqQSQyTNA+63by0lbc6YJ+YZwQTyMDr3qW2hanbzTQjS9RXTHNvi2EsBLiFVA3MTkZKgnHUelHNL+7/AOSh7nn/AOTHQ3ss2n3cNvc+I9QRpSqhxZRmNSzbVDOI8DJ4GTVNNZgbzWPiu+RI1dvMexRVcI219pMfzEHAwMnmq2r6XeaxqqX0+jaiNphYR+ZbttMb7xtJOVyeuOtJc6PPc2VtavomphbcTeWyzwZBkYPnr1BAx+tHNL+7/wCSh7nn/wCTFiz1DUL1dZdNYv1TTgDtltYo3Y+WHwVaMEdatNPdw6PaajPrmpBbiNXCQ2UcrDK7uixngDvVa0h1SCPU/tGk6lczaiAJpGkgTGE2fKA2Bx9eaoS6RrFzYWtndWmpTxWuFjVxa7doXbgrnBOOQT0NHNL+7/5KHuef/kxonV4fNVF8VX7giNjIlijIokAMeWEeBuyMZqGz1x7vTYbxta1iIzSPHHAdORpG2EhiFEeSBjJPbp1rOtdC1W3d4P7O1H+zitqnkCSDMggVQNzZ4yVGcdR6VcTTtWt1iNrYapDJbyStBJut2MaSnLoQThhnBBPIwPxOaX93/wAlD3PP/wAmLY1eFp0iTxXeuG8rMiWUZjXzADHufy8DdkYyaTVLjW7W+ltLLV3la3gW4uJLkwQoisSFAPlnk7W64Ax15rKttC1O3mmhXTNRXTHNv/owlgJcRBcbmJyMlRnHUelampRare3rXdvpN/ayywiCYBreVJUBJXKseoLHB9+9HNL+7/5KHuef/kxQu9e1a1urkf2pIba2WBpMvB5reaAcIojIfGf73Par39o6gNLtb59Zu1Se+azIMcHyYkZN+dn+xnHvTNJ0w6RMJYvC93NIsUMUbzSQM0YjTaMHORkcmmLp2orOgOl6obKK4e6itPMt8JKxY53ZyQGZiAe/qBijml/d/wDJQ9zz/wDJjU06S41S2e4tvEOo+UoDbpLGNNykZDLujGQR6VT/ALYTZk+JNUWTzViELacolZmUsoCeVnkKSDjsah0jTL/TbbUIY9IvwLxAjGOS3gCcEblVTgPzy2Ow44qHS/D11pl5HdrpF48qSJJhZLaNSVR0HCnuJCSepIppzfb/AMlD3PP/AMmLb63bxrGx8VX5V4hMStihEaFiu5/3fyAMpBzjGOafLqsUMs6S+Kr5BAJC0hso9jGMZcK3l4Yrg5A54PoarDQ7lba+g/sfUNt5bPbuftMGVVpJJCRz1zKR9AKZPoN5dCWCfS9RexZp3jthcW48t5gwdg2cn774B6Z78Ue/5f8Akoe55/8Akxo3l6bGZoZvE9+Zg6RiOOyjdmZlZlAAjOSQrH8KZFqQne3SHxNqEpmUMPLsEbywWKjfiP5PmBHzY5B9DVe00vU4dQW/udP1C6uRMkxZprdASsbxgYB4GHJ+tFnpWp6ddGaysNQhEjE3Cedbt5o8x3AyT8v+sYZHb35o9/y/8lD3PP8A8mJBrMLJIw8U3+FAKf6Cn74FgoMf7v58sQPlz1HrTtA1i9vNbET39xPbeZcQlJ4EjbMYjOSAqkHLkYPpWba+F57OAxQaPdRlChtpENqHhKOGXLZy/IA56gevNauiaVqMGtx3FzbXKqTPLNNPJESzyCMYAQ8D5Ka5tea1rP8Al7eQnbTlvfTv3OuooormOgKKKKACiiigAooooAKKKKAPl79oj/ke7b/r0X+deQ169+0R/wAj3bf9ei/zryGkhvcKKKKYgrpPh/J5Xj7RX/6eVH55Fc3Wp4buvsXifS7nOBHdxsT7bhmmtyZK8Wj1W/gNx4gXEQYLLulQoSOvOfatJNOsdXzC1uqTo+6MeZxg84/3an1Mi01K9mhkEkzEqExjBORgnt0H1qpdahJZkCJVV2wRhQMA89a9eq77HzeGVlZnR+FrL7M1xp160ImMgljVWyG4Ixg89P51Yl0Nrld0ls5GSqwsfuKD1BHX6e9ecf2ldw67aahuJNtMrby2TgHke/Ga9atMjUneGRlV+Rk+oyK8zExSkmfQYGh9Zg4tL3Tz/UfDElkzXcISRMlVUAgg9tw7GuRvjGkBiw3nBwX5wB7V6r4n16eBU32kUzFtrkkjI9Dj/Oa4TxNY2641W1SQ2cww2359pHBGfXv261zxTbPPxdCFCbhHddDzy9mkedkfHy8Yz/nmn6UZWvoVj6K2TgdBUF64kvJ2VGRS5wrdR9auaDcRQakvnthGGM9s9q6ZaRdjohHRI+wfCgx4S0kf9Okf/oIrYrH8KNu8JaSf+nSP/wBBFbFYLY7mYXimz/tCzs7XMY8y7QZkQuvAY8gEHt2IrOk8Cx3MFqtzqM0jwxTRs2MhixJjPzEn92WO3JP1rW8Q3C2kVhO6SOqXiHbGhZj8rDgCo4/FmmTLAYBczNKGJSK3ZmiCtsYuAOMMCPwOM0luyI/Eyr/wiCx2qRwXzJLHDbIkjRhvnhkaTewyM7mY5HH1p0nhm6ktdSsv7TVbK/ErSILf5hJIuGIbd93OSBjPbOKtN4p01UuJT9p8iElfO8htkjBwm1Gx8x3EDHc9KqnxlYi6GdyW6wSNLuQiRJVeNBHt9T5gx65HrVFjdU8IJqN816t0FmDxOivGSgKI6c4YEgiQ9CMYFQP4KYW72ttfpb2s4hNxGtsPmaNg2U+b5Qccjn2Oa0YfFmmTzw28YuWuJC4MIt2LR7Cu7cAOMbl+uRjNVbjxrZRLGIrO+llNzHbyQ+QQ8e8EqxHoQDj6GgDUv9IW+vo7kzMhS1mttoXORJs5/DZ+tZVx4Rd7VLaDUPKifT49OucwhjJEmcFeflb5mHcc9OKtSeL9HhmnjlmkjWLzAZWibYzJ99VPdhzwPQ4zil0zxGl/daqssL2sFgsbMbhCjAMpYlgegAFACW3hmO1ubWZblj9nurm5AKdfO3cfhu698Ug8NmLRdKs7a8Mdzpe029w0YYEhCh3LnkFWIxkdetIPGGl+UGZbtHZo1jia2cSSb87Cq4yQdp57Y5xRJ4y0eOJJGeccM0q+S26AK21jIP4QCCPwPagBr+Fo9QvLW61uaLUpIElXY9uoj+coeBzgDZjkknJ5puo+F5LqS+S1vltbPUIliuoRAGO1V2/IcjaSvHQ9OKnbxVp/l3EkUV7NHbytC7xWzldy53YOMEDaeenSqn/CZ20sl6sEEvk28MMyXTxt5UiydOQM9+PX8KALmt+HI9aeAm4aEQxNEAF3ZDPG3/tID8aX/hHk/tk6j9pbcbz7Xs28Z8jydufpzUcnjDSIRO0skyRRLIwlMLbJBGcPsOPmwfT8M0N4u01Fw0d2LjzvJ+zG3YS7tu/7vpt5/wDr8UAUbzwW1xo9rpsepMkMUc0bhoyVfzDndtDD5l5xnI5PFC+C8XDn7fst3gaGRYYijSgxeX853bWx1B25yBzxXRXeo29lpzX85dLdFDsdhyoPcjqMZ59KzZfF2jQiUtdE+VNJAwWNid6Y3ADHPLKB6lgKAIh4YM0Fyl5e+a9xpw092ji2fKC+GAyecP8ApVdfCt6Lz+0X1ZG1FXjeN/suI12I6YKbsnIc9+D09K1I/EOnPpdzqDSPFDbMUnWRCHjYY+Ur1ycjGOuRVUeL9MLRxhbprhndTbrAxkTaFLEqO2HU++4YoAjvvDE2oG5M2pc3dklrcEQDJZCzK688cucjnPHSi08LyprA1S81AT3BmaVgkOxeYliwBkkABc9T1oi8VxzXtzbfZZoPs9+lnvmjYLIWCn5SB1+boe3Pemr450Z4xIBeCMxCfebVwPJPHmdPug9TQBJ4c8LR+H3+WWKUJCIIW8oq4QHOGYsc9B0AHHSmL4ZuoDE9rqSJJbXM09sZLfcFWUsXVhuG7luCMYwOvNP1XxfZaWl8rW9089tBJMkfksom2DLBTjnGRz6ZPOKkPizTUeFJhcRPIqFw8JHk722pvP8ADk9Pz6UANfw0yaJpthaXvlSWEyzJNJFv3MN2cqCOu49DxUDeDbe7uYrnVJ1vpvtJuJg8ICSHyjGqhc/KFHPc5z61Y1HxJ9h1ebTVsp3kSye6E20+Xxngnt061DZ+M9Pn06KedLiG4eOFxAYGDSGQHbsBHzAkNz7c4oAmvPDsv2kzaTdxaeHtRaSRi3DqIwWKlBkbSNzeo56cVc07QtO0oIbS2RJVhWDzsfOyqABk9+AKI9e0+Wwtr1ZWEFxMIFJQgrJkrtYfwncCOe9Uk8ZaNLBBLBNLOZ1zEkULF3+ZlwBjrlW/BSegoAzrXwKYrkXFxqbTygQguYjufy5llBYljliVwcYHoBVq48JzG+u7yz1LyJrrzlk3wBwEkCAgDI5BQEHpzyDUlz4wsFtZjaLNNcpaSXIjMLgIE3AiQ4+X5kKkHnNMbxdC11p9tbwSSPPdJbSyeW3lIxQsyh+hYYHH170AWp/D7Gx0qO1u/KutMUJDM8W9WGzYwZcjII9CMHFZ9p4EsElgkvVt7pY2nkMH2ZVi3S7Purk7QPL985NbE2vWUOoyWbeeWiA86VYmMcJK7gHboOOfxGeoqvbeKtMunjjBnieVoxEs0DIXD52MMj7p2nn86AINQ8MSXMt6tnfLaWl/CsF1CIAx2qu392cjaSpx0I4GK17TS7GwlmltbWOKSbBkZRgvj1rJl8WWkV+0ZBa2WNvmVGLtKJvK2KvfLcCrehaydaS+fyGhFvdtbqrqVbAVT8wPQ5Y0Aa1Zln/yH9T/ANyH+TVp1mWf/If1P/ch/k1TLdES3j6/ozToooqiwooooAKKKKACiiigAooooAKKKKAOP8c+GbjxEdPMFzawi3LlhcMQGzt4GB7Vjy+CtUbVLS7trzS4XS3SLbHkbeuSnynBx0JzVz4h6rFo19pF3NpkGprtmT7JLy0hOziMbWy/HA+vIq/qVtYL4LudYttFt7G8WxaaJLi0TfA23IDLjqD2rvhipU6cY36Pp3bOKeGjUnKVu3XyMmTwLcP4lXURcWRVZYWwXIcbcbgQFwxOOvH0rOtvhlfwSXJudQtVSWNlHluwPLA85HTH9KtzXBNzNbx6Xp8eoWNo1zeW62qHzXjkQ5UkZ2uhbaexPqDVp57a+vobm3tbGTTZ7t7aDNsjB0SCR2bJGeZFA/4B701j6i0Uvw/4IngoPVr8f+ARJ8OoLe+cxLbNE8Ekaxy3LEkkfK33eO+evtUGifD6azbN5d2VynnK3lpKwU/K45+XrkjH40yVX07QdO1KQ2t3PfabLMBLZQhoHW3MoZSqj5QRgg5+8Pxnm1LSLXxRYWf2TS0tV8mK6j+zIHaaVcowOONvyD/tr7UfXqlmnJ6+X/BD6nC6fKv6+RBJ8MYzaKqTwJKCCZTcMQTn5k27emM4Oc8VO/w0sCSEdFyzoCbokj+62Nv3vVemO9auo6NDd+J10y2FvZW6WRucJZwuDJ5mMnep7emKi8Li01i63XGl6eFNhBcAJbIB5jPKrMDjOCEUj2o+v1P5n/XzD6lT/lX9fIt+CdDk8M6dc293eWsryzeYDE/AG0Dvj0rp/tMH/PaP/voV5wLCSFpo57G0TULiOcWqS2NubSRxkp5bqNwwozh+vPpWvoOnaQGvodQsF823RZnivrCBZI0O75t0Y2spKt7jBrnq1I1Jucm7vy/4JvThKnFQilZef/AOw+0wf89o/wDvoUfaYP8AntH/AN9CuF8MTaLr+uajGLDR2thBFNbQJbxl41LOCXOOp2qcdsgdc1Hr8NppfiR3bRrA6XY6e15JHFEgeVslcEbDn2GR1z7Vnan3f3f8Eu9Tsv6+R332mD/ntH/30KPtMH/PaP8A76FcDLqWnRWTu/hTTVuUdgI2gYCVQm4lP3O44zg/KAD3pttd2T3F7cv4b0ltOUWZgAjUSATlQNw2Y/iz17e9Fqfd/d/wQvU7L+vkegfaYP8AntH/AN9Cj7TB/wA9o/8AvoVx+/SptbGmWvhjTZH+0ywtI6ooVY1jYt905P73AHt1rNttQ077Gkw8MWE1vDBBNczSLGHxK7L8qhMEjbntRan3f3f8EL1Oy/r5HoX2mD/ntH/30KPtMH/PaP8A76Fcbp8ul3up2ULeGNMjtb4z/Z5QqF8RHB3Ls4z7E471ni6sLPV9dtbrRdNlVZvL0tRaoC7hYwYjxyd0inPoT6UWp9393/BC9Tsv6+R6F9pg/wCe0f8A30KPtMH/AD2j/wC+hXn2lXemx+HPP1DQ9Oubu3sYbiaRLeNFlZ5HQ4G3j7mfxptigfVltjoukS2u2/d5GhRZAIrjYuBsxwCB79+nJan3f3f8EL1Oy/r5Hof2mD/ntH/30KPtMH/PaP8A76FcJqdpYnR/Ds1rpcatfsnmm3soJJSDAz8Bl29QM8VITp9oL2JvD+nSLZeVGbi6SNHllkVWC7EjPPzc4HUcA0Wp9393/BC9Tsv6+R2/2mD/AJ7R/wDfQo+0wf8APaP/AL6FeeHWdMazkuIvCGnP9mt5p7lWVUKiKQxsFBTJJxkZC++Knub/AE20mNjN4U00aj5/lBEi8yPb5fmbsrGWzjjG3r3xRan3f3f8EL1Oy/r5HefaYP8AntH/AN9Cj7TB/wA9o/8AvoVx97Z6fdWGiC20e006TVJlR2ksozJAPLZyMMuNx27Rkd+lZn2zStP/ANAXRIdQvlllDfadPETKiKjYPlo2SRIuCABzzjHJan3f3f8ABC9Tsv6+R6H9pg/57R/99Cj7TB/z2j/76FcQl3pb3O4+E7GOyW4gtpHkjQSq8qIw+Tb2LgHmqlvrGmXNqk8Xg2zYXBh+zEwbFbzHCAMzRgA/MD8u4YzzxyWp9393/BC9Tsv6+R6F9pg/57R/99Cj7TB/z2j/AO+hXn2pahYW01zaQ+G9HaYQzCKRY9yLLHGXYMfLC44YYBJ45AzxIktlCbSO48PaY97c21uUhSNBF5kjsMltmRwuT19AKLU+7+7/AIIXqdl/XyO9+0wf89o/++hR9pg/57R/99CuDkvrDMkUPhHS3uLeK4luVbYFXyWCsFOz5s7gRkD3xW3qNroNl4bm1lNAsZVjtvtCxfZ0UsMZAzjii1Pu/u/4IXqdl/XyOh+0wf8APaP/AL6FH2mD/ntH/wB9CuK8yy+2nTD4U0kamZxGqfL5W0xmTcX2Z6AjG3r7VVGu+HFEPn+GbKLe8ZfMUZCREsryE7eVV0Kn6g96LU+7+7/ghep2X9fI7/7TB/z2j/76FH2mD/ntH/30K5K4GkxaDY36+HdMW4vSvl28sQzggsPuozE4HQDj8Ko22r+HriC3c+G7CKS4jjnjWSONR5TIzO5O3gLscH6D1otT7v7v+CF6nZf18ju/tMH/AD2j/wC+hR9pg/57R/8AfQrzltR0y6jeIaBY21wkm3MdupRg1vLIp+eNSeY+wHbkjIpxv9LSK3tjoemu0tuE+0pACyTfZzL8w8vYOh43Z6cYotT7v7v+CF6nZf18j0T7TB/z2j/76FKLiEkATRkntuFecRTW32PS4bvQtNguJ1s5/NjiSTzIndVcMNg2tz2yOeDxT4r3T9U08TweHLeyBa2uLe4S3I+UzoMFii4bBz8pIwTzVRjTlJRTevl/wRSlOKbsv6+R6RRRRWJqFFFFABRRRQAUUUUAFFFFABRRRQB8vftEf8j3bf8AXov868hr179oj/ke7b/r0X+deQ0kN7hRRRTEFKCVYMDgjkGkooA98e8W9g0e/VN0d1Cs8uBzIwXBH4HP41i6ndZu5A4KZf5T/Dj39Kb8PJ49X8I/Z2ObrS5jsHrG3zY/9DFU/Eck0dyZ9wIdipwOOPUV6kZKcE/I+ecHSqSh5lO+neO0Uo52g4Krjmu88C+JDrtodPuj/pNqgEchPLRjufp0/KvN2jluIAGVdrthcDp6nFXdC1UaJrMEsMYKqMSLk/OD2rGrTUlY9LA4qWHqKa+Z6jdaeHaZ3kilU88np+Brzy8uIrfVE068kEVpdnEm0nC56H2x611k3i6CSV0jtswqAS5+99MfSuC1xrbVdWmlRtsJIOTn8cD+H6VyU6Ti7s0xkoV6qmjJ8VWs6akbu4TbJOcPgcM4ABYeoPB/E1iwDNxGB/fGPzrttWMmp+D5A832i5tLldp6sYyMZPoQcA/WuKhP7+Lj+ID9a1aFB3Ps7wh/yJ2kf9eqfyrarF8If8ifpH/Xqn8q2q5zsZh+KLJdRtbK0dtqyXiDOxXxgMejAg9O4qCz8IQacY2sNQu7aQbxI6CP94rOZCpG3AwxOMAYBNTeKr+PTLOyvZVLJFeISAyr1DDqxAHXqTUFx4zsrVLGSaCRY7vbhhNCdu59gwA+W57rkYqVuyI/Ex8nhK3lsriwe9uWsZGMkVsQhWFzIJMjK5OGHAbIwSKjk8E6dPaCCaR2IjZNyxxoMmRJA21VC5BjXtjHXNJP440yCCKTy5i0ir8jbU2OzMoRixAVso+cnHyH2za/4Si0bQoNViguJkmnW3WGPaX8wv5eM5243d84xzVFjbfwraQRkedLva2mt2aNUi+WQqSQEUAEbRg/zqra+CLO0hkEV5cJOzwyLKiRrsaLO0hQu3oxzkHNMufH2m2iAT288cy+YZ4WeNWiEbbW6thj6Bck1Jd+ONOsrq5gmhm/cwyTAoyOZFQAthQ24HB43AZoAU+CNNaS8LO/l3XmFkEcYZXc7mYPt3deQCcDPpxV608PRQrqP2u6nvX1FBHcNNtGVClcAKABwapxeJmTXTaX1tJbQvHDsDhcxO/mn5yCRyIxjHc1Zu9VuZtAg1SwK28ciiUme2eZwhHGI4zkk5HfgUAZ114PlZrSSHVLl7mGeHFxLs3Rwx7sKoC4Jy3ORzUjeB7BpY5zPK1xljNLJHFIZtz7zkMhCnJONoGAcVnx+MNUutJn1SGGzSCxtIrm5iYMzSllLMqMDhcL6g88HGK3r7Wri08Q6dp0enyTQ3cMsjzK6jZtK9iQf4ufwx3oAgu/CFndW8UPnzIsdzLcDhHBMhJYbWUjjccHGRTU8HWcVt9mju7pbdrWG2ePKncIvuNnbkHsccH0qlceMpJdPhu7S2lgWe3M8K3MYy6iSJd3DcDEh4Iz347yWvi5hLNHcRNNN5rRwwQxgFz9oljUbi2OkeTnGME57UAWJfBlnOjwy3d21tiQQQ5UCDzDltp25PoMk4BNT6n4WttTe6dp5ENy6PIpjjkU7U2j5XUjpznrmqsnjW2RX26bfPJDFLNcxqEzAsbbXzlsEg8gDOR0q9q3iay0aJZLhJnR7d7iMxrnzNpUbAM8sd4wPrQBLaaOIdMuNNuJ3ubN0EMaycssXlhNpPcnBOfestfAWkC3kiLXD77WO2LO4Y5Rt3mcjBckLkng7RxTr/xvpthbPdGOWS1R2Vpg8aghVViVDMC2N2MAE8Gqtx4w1CKG+kTQ5XFvqUVkh82PDq5QZ+91+f8AUe+ADWHhmzbw/caRMxeGclnZYo4znIIICqF4IHY9Oc1SufBVrdaYtjJdME3MzlbaAbtwAyBswpAHDDBHrUureJ47a2X7Dtnu47uCGe2XDOu9wpXqBnk85xTT4xg2ME069e4hEr3MChN0CxnDEndg+wUkmgCwfDFsbuSb7Vc+W9zFdGIlSBKihQc4zyFGRmmHwlYGw+yebceX/Z39nZ3DPl+vT73+cVIvimwe3E6LK0bXLWysFHJEZkz16bRVNPGls7aeG0+7iN+gliEzRIShIAPL8k5ztGWx2oAD4H05725uJZ5388TqVwgOJhhwWC7j7ZJx0qX/AIRC2bUIb6W6kluEWNZXkhiYyhCSvVPlPODtxkfnVjVPEcGl3MsTWtxOtvCLi6kiC7YIySAxyQT91jgZOFNVtF1q9vbvWp76E29jZzNFGCFIwgyWJDEk9+g4x70AX7/Q4r++F2biaJjbvayKm3EkbdjkHBB5BGKo3vg3T76OASSS77eGGGJiFYL5ZbaSrAgn5iDkY5qtbePbC7jX7PaXMssjRCKKNo2LiTdtOQ+F+6cgkEelFx4+0y1WPzYZ0k2u08TNGrQhHKNkFvmO5W4XJOPpQBqTeHbS48NvokjyCBlwZIwsbhs7tw2gAEHkYFVf+EOsIpJprSWe2nedLiORCD5LKhQBQQRtIZ8g55c0i+LI5ZvKt9MvZna4kt4QNi+aYyQ5GWGFG3qcZyMUtl4xsL6KJkguUeZoljidAGbzCwB69BsfPptNAElv4UsoIbtPOuHa7tmt55GYbn3M7M/TG4mRvbpxTIvCdvDdwyJe3Yt4bkXa22V2ebtKlidu7ByTjOMnNPvvFEFjqFxbPZ3Tx2pi+0XCBdkQk4U8tk++AcVQHxA0o+eVhnkCAmPyyjtLiRYzhQ2VO5hgNjIoA1ZvD8cuoXNwLu4SC8x9qtV27JiE2ckjcPlABwR0FUh4Ph2Kz6leSXMIiW2uG2boVjJKgDbg9TkkEmtKfWIrNLJr2GW2F0WGXKkRMEL4YgkdFPTPSsi18eabd3kdssM6lyF+YoGDmPzNpTduHHfGM8ZoAlHgyyWID7ZeGdSzrOWUurmbzt/3cZ39sYxxitTSdKXSorhRcTXElxOZ5ZJsZLkAHAAAA+UcVjW/jizuRshsrmW5NwtuIIniclmRnB3B9uMK2eeMV1I6dMUAFZln/wAh/U/9yH+TVp1mWf8AyH9T/wByH+TVMt0RLePr+jNOiiiqLCiiigAooooAKKKKACiiigAooooA4H4jaze6HeaTd2DokxWZMsgbg7M9fpXIXvjfxQ0Rtb0xFLiMExSWy/Oje3oa77xxpOm6o2nm/wBXGntEXMWVBL/dz+WB+dYUmj+H5ryK8k8ULJcxW4iSR0yd4zhzzyefzxXtYWdD2MVOF3r0b6+h5GIjW9rLllZadfL1Oebxb4nTXNrR266oVFtzap5mM5CZ9M84qrb+Ldajt4reCOySGyDPHGtqgEXBUkD6MR+Jrrf7J0Aa2dSPiaP5pkmaMxA5K9PmPI/Cqlp4X8MQSzu/iKKcSIV2vEMLkg56+2PxrdTw1taf/kr+fQxcMRfSf/ky/wAzk7LX76BJo7S1sgHhaOQLaq37rHzLz0XHUdKux+K9ens57eKK1e3mk/exraoQ7kZ5HUnC9fau0ktfDgvnubbXbWDdA8OwW0ZwG/AZx7jPvVfTdI8LaTKJLTX4y4cPumRXIwrKQOOAdwP4VPtMPa/stf8AD/wCvZ17/wAT8f8AgnEX/ivUdUnSa+SzuJETYGe3X7pOcH1HsaSbxVqE91FcyJaefEixo6wBdqqcqOMcDJwK7uTSvCUlgtsdYtwVCneIUB3gglsgZ554zjnpUj2Xhl2O7VtO2lnyos0H7thwme2Dzu60/aYb/n1+H/AF7PEf8/Px/wCCZHhHTLzxJbXl4kun25DtC6NZK6kMAWwDwM55wOa6jT/Cus6WJBZaxawmQjewsgWbHTJJJ47DtUPhVtA8L2M9smuxXAmk8zcy7ccAY/St7/hKtC/6CcH515eJhJ1W6Ufd6af8A76HL7Ne0lr/AIv+CZp8M6vcXHm3Wtxbsxtvis0ViY23Jk+gJPHvVmbw9e3Ekkk2riR5IjC5eyiO6M8lTxyParA8W6ASQNWtiQcEB+lL/wAJVoX/AEE7f86yUayXw/8Akq/yNf3X83/kz/zMk+BlaBIWvlZEYsA1qhOSADk9SCABg8YA9KkfwaZAwe/jIaAW7D7FHgxjopHt29O1aX/CVaF/0E7f86P+Eq0L/oJ2/wCdO1b+X/yVf5DvS/m/F/5lCz8JzaeytaaksLKWIZbOPOWxuOepJ2rk98ClXwnKkDwLqMYidFRkFlFgqpJUHjoCT+dXv+Eq0L/oJ2/50f8ACVaF/wBBS3/76otW/l/8lX+QXpfzfi/8zLj8FvDqyanFqYju1LnzEtIxkv8AeJHQ5xVhvDFy0yzNqaGVZ/tCubGIkS7du8cddvGfSrn/AAlWhf8AQTt/zo/4SrQv+gnb/nRat/L/AOSr/IL0v5vxf+ZlTeCFuPJ829icQrsQGyjwFzkDHcA8jPQ81M3hOZ5IpG1Nd8Tu6MLOMEFzl/zPJHer/wDwlWhf9BO3/Oj/AISrQv8AoJ2/50Wrfy/+Sr/IL0v5vxf+ZXXw/fKlsq6xhbX/AFA+xx/u/lK/Lxx8pIqK58LXF3HOk+qh1ndXl/0OMFmXAVsjnIwMH2q7/wAJVoX/AEE7f86P+Eq0L/oJ2/50Wrfy/wDkq/yC9L+b8X/mZ0fg5oYHgjv4likjaJ0FjFhlYlmB45ySSfc0668JS3pc3Gph2dg7N9kjDFgNoORyDjj6cVf/AOEq0L/oJ2/50v8AwlOhn/mJwfnRat/L/wCSr/IL0v5vxf8AmU7jwzdXVitjPqoktVACxGzjwuOmOOMdiKqHwMptltzfJ5auZB/oibtxGCS3U5AAOTyBWv8A8JToZ6anB+dH/CU6HjP9pwY+tFq38v8A5Kv8gvS/m/F/5lP/AIRq6ww/tVcNIkxH2KLl0ACt06jauPoKgg8HNbFjDqCJudZMCzjwGU7lwO2DzgcZrT/4SnQx/wAxOD86T/hKtC/6Cdv+dFq38v8A5Kv8gvS/m/F/5maPBzC9e8/tCM3DliXNlGeWGGOOgyDz696QeDCLQ2o1BPJ2CPb9jj4UMWAB6jBJI9O1af8AwlWhf9BO3/Oj/hKtC/6Cdv8AnRat/L/5Kv8AIL0v5vxf+ZQh8JzW8Yji1JEQRNDgWUf3GOWB9cnk+ppY/Ct1Ha3FqNXJt7g5lia0jKn5QuMdhhRxV7/hKtC/6Cdv+daNpeW1/bLcWkyTQsSA6HIODg/rSk6kVeUbfJf5DSpydk/xf+Zz934SmvvMNzqYkaRlZm+yRhiyjAORyCASOOxpH8IPJEI3voWjFubUKbCLAhOMp0+6cDj2rqKKj2svL7l/kV7Nef3v/M5y88M3eoRRxXer+akTbowbSMbDjHGOnBI+hqNfCUiJEi38ISKBraNfsEWFibG5Bx904HHtXT0Ue1l5fcv8g9mvP73/AJnJweCvsy4iv4xk7iTZxkk7WUEk8n5WYfQ4oHgoLci4F9H5o6H7FHx8uzIHQHbxn0rrKKPay7L7l/kHs15/e/8AM5S18F/YgRbX8cY3I3FlHxsOUx6AHkAcCkj8ErGcfbwIzNHKyJbIuSjbgB/dGc8Djk11lFNVpLVW+5f5B7KL3v8Ae/8AMKKKKyNAooooAKKKKACiiigAooooAKKKKAPE/iz8LvEHjTxTHf6YsAt0gWPMkgBJ78Vwf/DPvjH/AKc/+/wr6oopWHfyPlf/AIZ98Y/9Of8A3+FH/DPvjH/pz/7/AAr6ooosF12Plf8A4Z98Y/8ATn/3+FH/AAz74x/6c/8Av8K+qKKLBddj548HfCXxn4W1drporae2ljMc0SzgFh1BB9QcfrWj4g+GPibWLrzobKGH5FUg3C84GM9fpXu1FaRqTirJmFTD0py55LX5nz3F8I/FaoivBCCoI3R3Cgmi6+EHiWW6W4gtYY2wNytOpDHv+B/nX0JRT9tPuCw9JdPzPDIvhp4lQtvsImUjGDcqe2O/+eKrSfC7xYQQlnaEFtx8yVD+vWvfKKXtJdyvY0+35nhdl8M/E1mLz/iX2xFwjrgXCjBb1Oea5j/hRfi/zxII7QANuwJh69K+m6KTnJ9RqnBbIzPDtlNp3hzT7O5ULPBAiOAcgEDnmtOiipLMzXdMm1Sziign8mSOZZA+4g8A9COR1rnl8EuuAPs4B2mQCWUCUiQyAtg8nexPpzXaUVLjrchwTdzlJPC9w4udqWMUlxci6eSMyhxKBgMpzx346cn1NWjpGptZW9rI9nKkEiSo0jSs29W3Ak5yea6Gijl8w5PNnJyeFrt7lrhHt4Jnd3d4ZJUL7yCwOD0yB9O2M1C/g2SWSZpBbMsplPlmWXYpl/1m0buMnn+VdlRRy+YcnmzlNX8NXusQXkc7WSNdpFHK6CTO2Niy454PJ596uXumaxewRxfabe3EZyjWzyRkcYxwcEY7Gt+ijl8w5PNnHHwdJsSNVtY4hGsckSSTBJlUkgSDd83JPXrk5yK0rvStUvbq1upHs1mti3ltGZF4bG5Tg8g4HHtW/RRy+Ycnmzlm8M3D21tbtHYeXbW/2aIZl4j3I2Pvc8xrz7VG/hSdzIwWzSR23CRHlDI3mNJuU54O52P446V1tFHL5hyebOUi8L3EMUqBbJvOgkgld2lLSLIdzknPUk9atXWjX16LIXEenP8AYpVlgyJPlYAgd+eD0NdDRRy+Ycnmzin8DlrOK0VLWKCOGSDZHLMu+NzuZWO7JGef/rVbm8NXk4uwz2yrdOksipJKAJE27XXn5W+VeR6V1VFHL5hyebMK407VbqCCGdrGRYZI5UJ8zO5CCpJzzyKzrnwncXUkkhNtG8pcytDJKhkD43K2G+6cDjt2xXXUUcvmHJ5s5E+E5zefaMWqgOZBCskoiDlDGWCBsZ2nH/16fd+GLy9tbe1lktxbQRrEIVklCOi4wGGeenXr711dFHL5hyebOX1Hw7e6pcGa4a0DPGIpVjeVFmQEkK4B+YZJ/MjoatwabqVvHdRxrp2y6kaSZWWQhmYAHv0wOlbtFHL5hyebOVtfDV7aJCiy28iQSJJCsssriPaCFAyegDH+vSmt4XuzcCeN7aCXe7O0MkqF9zlyGweRuJPtk4rrKKOXzDk82cvN4dvZYo0BtIjHPJcJJE8qOruSXwwbODuPHSquneEL3TZtLlSazd9NgkhhLq/Icg5bnkjkD/eNdlRRy+Ycnmzkbjwrf3eq3F/cXFu/2gxGSANIsbeX93coPPPPP8qcnha7RJIg9sYHYOIDJL5aEOJPlGePmAP6dOK6yijl8w5PNnOa1oup69pj2F3JYCJnRyVVwflYNgHPfGPoTUEvhm7mnupGa1CXQYTwLJKsTll2ElQeu3j9evNdVRRy+Ycnmzk7PwtdWVwlwjWsk6OsgklklY5VGRe+MBXYYrVgg1u3gSJZbJggwGk8xmP1JOTWvRRy+YcnmzMxrn9/Tv8Avh/8adp9pdw3d1c3bws84QAQggALn1+taNFHLrcFBXuFFFFUWFFFFABRRRQAUUUUAFFFFABRRRQBxPjmTXotT0hvDaM+o7Jwq7EZCP3eRJuIwvTJBz0xWxqr6yPBNw5VU1f7JlxaEkK+Pm8vPJ7479Kv6hpFvqM0M0klxFLCGVHgmaM4bGRkf7oqt/wjsP8A0EdV/wDA6T/GtW4Sirvby82Ze+m7Lf8AyOPl1m30t7z/AIRmKF4DFEXuYrl5FiBkVWaRWG1XAZjkknA+bpUiLqPiDUdJ+1RWl6nkXR3PMyoyiSIK2YxgvjI4464rq/8AhHIf+gjqv/gdJ/jR/wAI5D/0EdW/8DpP8aXLDv8Ah/wR80+34nMz+JNUtmuxBcI9xEt4p0/yObVIkcxSE9Tu2p14O/jpV+DVNRi1mCz1DWEhYRwvGhtgPtjOTuUdwF4HHI6nNa//AAjkP/QR1b/wOk/xo/4RyH/oI6t/4HSf40csO/4f8EOafb8TH8H67q2r3sov3h2+TvkhBXfbybsbMDkDGfvc5HvWNbS6taJp0bvdT2t5rTvG5LEwMJpMxt/sEAEZ4HI7iux/4RyH/oI6t/4HSf40v/COxf8AQS1b/wADpP8AGjlh3/D/AIIc0+34nH2MdpdwaVBDNcf2/cOY9VAkfzApRhN5g7KDjb6HbtqqLvXJYluWa6E06/2AVw2FlAAM+P8Ae8z5vQCu5/4RyH/oI6t/4HSf40v/AAjsX/QS1b/wOk/xo5Yd/wAP+CHNPt+If8IzpwvIboLIskLBxhyASMYyO/3R1riNSubuKC9RWaMC5lkExRmKYv4+3cYyce1dv/wjsX/QS1b/AMDpP8aP+Edi/wCglq3/AIHSf40csO/4f8EOafb8TAstb1W81SC1g1FZ7ITTn7alsCbhESJsDHy53O65A52+tZ9j4q1q/d4ba6BWZ7Xy53hVjEJJGV1KrwCAAcEkg9TXXf8ACOQ/9BHVv/A6T/Gj/hHIf+gjq3/gdJ/jRyw7/h/wQ5p9vxKdvqep/wDCUf2JIwbyj9peby8b7cphR6bvM3fgvvWFqkmo6bruv6rDMZHR7W3RpIlxBE+NzBiOAMk+meTnFdKnhSzS5kuFvNTE0ihXk+2vlgM4BOegyfzqX/hHof8AoIar/wCB0n+NHLDv+Ac0+34nHXni/WLWytnFxG84DSqECMlynm7Rz/E2AchBgcHODir1nrN3pV5ci91YyRrrDR3CzoB5EDI5Q57KW2AHp+ddH/wjsH/QQ1X/AMDpP8aP+Edh/wCghqv/AIHSf40csO/4BzT7ficxD4j1+6s7q9tyrrb2ElykIt+Zn82ZFHPIAVFOByfxqvJ4n1mPTBcf2naGATlRMrxGRwIwdoOPLyD2yCRx1BrsP+Eeh/6CGq/+B0n+NJ/wjsGMfb9U/wDA6T/Gjlh3/AOafb8TP1rWp4Y9KYX39mWt1C8sl5LACVcKpWMqeFJyxx1+XA5rL/4SXxILVk+wj7YLM6jt8k7TH5XEf+95vbrtrpP+Edh/6CGq/wDgdJ/jS/8ACPQ/9BDVf/A6T/Gjlh/N+Ac0+34nNabrWu6g1nBHqFu0U955RukRJWCeSzsDtwoO5VwffB5qfxlcWUmp2+nvcLaXjQ70vJrlo0twWxuRQfnkyOPpyccHd/4R2Ef8xDVf/A6T/Gj/AIR2H/oIar/4HSf40csO/wCAc0+34nN+I7ywuNSumvLyUQJYEWDRSMubpXcOF29ZARHgdfTvVMTTG6ZruaZfE4vrZbeLewJhIj34UfKUx5pY4xnOeQK66PwxaxLtjvdTRSxbC3sg5JyT16kmnf8ACOw/9BDVf/A6T/Gjlh3/AADmn2/E5jwvKranpH2eed9Qe3lOso8jsVk4++Dwrb8gdOM44p154pv7PVysl5tb7XPE9gtpuZYUikdHB4JLbFPXBzgYxXS/8I7D/wBBDVf/AAOk/wAagPhGwaZJmudSMkbmRGN7ISrEYJHPBwSPxo5Yd/wDmn2/E5zTPEOt6lLHZx3q4kvY4/taxJIfLaCRzjb8mQyAZ5xnByaa3iPxHbaTHdPIk8lzatIR9nCC3KzJGW+m1yxz/d9OK6z/AIR2Ef8AMQ1X/wADpP8AGl/4R6H/AKCGq/8AgdJ/jRyw7/gHNPt+JyM/ijWLfT7W5lv7cw75cNCY2knVSuMAgIx5YFUIJ4x3rrfDR3aZM3PN7cnkf9Nno/4R2D/n/wBU/wDA6T/Gr2n2EOm2gtoC5QMzkyOWYliWJJPJ5JptxUGk/wCtQSk5JtFqiiisjQKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAY0saHDSKp9CcUnnw/89Y/++hXinxKign+IIjublbeI20e+VlLbQAx6Dqewrn/+Eejm1Syt7W83Wt1Es3nzR7DEhYqSy5PccYPOR61m56mypJq9z6L8+H/nrH/30KPPh/56x/8AfQr5vstEW51eS0km8u3jlljaZVDN8isxwmR1C/T3qW40O1Nml7Y3NxPbusv+stxG4aNQTkbiNuGHIJ+lHO+w/ZLufRfnw/8APWP/AL6FHnw/89Y/++hXznb+E9VmuUhkthAXjeRTIyj7q7sHng9ODjrVAaTemZoRbEyKYwy5HBkxs798ij2j7B7Fdz6b8+H/AJ6x/wDfQp6urjKsGHqDmvmlvDerokzGxfEP38MpxgZIHPJA6gZx3r1r4RYPg6XH/P5J/wCgrTjO7sTOmoq9zvaKKKsyCiiigAooooAKKKKAI5p4bZN88scSZxudgoz+NQf2rp3/AD/2v/f5f8awPiAM+HE4z/pCfyNeMW19fpqk9peRI5EJlRIoipJBHyqzHD8Hk8YNc1Wu4SskcdbEypzcUj6G/tXTv+f+1/7/AC/40f2rp3/P/a/9/l/xr55lbVf7aitUu7dYZY2mCta5ZQrKNud3J+br7U6DxDZXV1JbwJK8gDlAAv73b1C85z9cZrP6zLojL67O11G59Cf2rp3/AD/2v/f5f8anhnhuU3wSxypnG5GDDP4V85HXUl+yPZ2ss8M9wITIFAH3STjJHQ8H6H0r2f4fjHhxuP8Al4f+QrSlXlOXK1Y1o4mVSfLJWOqooorpOwKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKj8+H/AJ6x/wDfQpl5/wAeNx/1zb+VfOGkaVY6lAEbUfKvHDmOEQllAVc5dsjaDz0B6c4qZSsaQhzH0l58P/PWP/voUefD/wA9Y/8AvoV86T+Hkh0BNRFyzSGOOQoYcRkOcALJnDMO4xxz6VJaaBZNbWxvr24gubid4EjhthMoKkDlg47t2Bqed9i/ZLufQ/nw/wDPWP8A76FHnw/89Y/++hXze3hy+a4FvbQNNIocuw2qg2yFMhiemR3xzTbjw9f22mx30kQCN5hddw3RhGCnIznqaPaPsHsV3PpLz4f+esf/AH0KPPh/56x/99CvmhND1KWOSRLRmWMIWOV43jK9+SR0A5pt5pV5pc8K3tuYS5+XJBBweRkE8juOope08g9iu59LXN9aWe37VdQQb87fNkC5x6Zqv/bmk/8AQUsv/AhP8a4H4tqG/sYHgZlycZx9yuSj0TTm1m2hV3ktGsvtDNI6wlzg8ZOQoJx68VqevhMppVsPGtObV79OzPa/7c0n/oKWX/gQn+NH9uaT/wBBSy/8CE/xrwm70u3h1cQzAWNvuj4aXziUbq6sAAw7/wCNW73w/HNLbRabZTo80pjR2nSaJxjO7eoG08Zx6UHT/YeHVr1Hr5L/AD/K57X/AG5pP/QUsv8AwIT/ABo/tzSf+gpZf+BCf414ZJ4Xu4bjZLLbJAIvNNy5YRhd23uu7OSBjFSJ4dT+zvtAuIJ5t06+XHIeiKCGB2kHr075HTnADyPDWuqr+49v/tzSf+gpZf8AgQn+NH9uaT/0FLL/AMCE/wAa8NPhW88+OBJrOSQyiGQJJnyXwTh+OOh5GRxVO80W5tTH5Wy7SSMyLJaguuASD2yMEGgqOQ4aTsq34Hv39uaT/wBBSy/8CE/xo/tzSf8AoKWX/gQn+NeJX/haVLvZYPDMrSxxCISZkQuuRu4xg8/TvUT+Fb1L2C3MluRMrFJRu2/L1GNu7PI4AoJWSYVpP234HuX9uaT/ANBSy/8AAhP8aP7c0n/oKWX/AIEJ/jXiEfhS6j1Jra8lt4EjljjZncr5hcZAXjOceoGO9NfwteNBcXMKp5SNL5cbZ3uiEgkcY7HqRnHFAf2Jhf8An9+Hc9zTWdLkkWOPUrN3YgKqzqST6AZq7XgOnaY+m+KNGSWWB5HuIHKREkoCykbuOuDXv1B5eZYGGEceSXMpIKKKKDzAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDyb4i/E2x8GeKY7G98O2+oI8AfzTtD9+MkGuTn/aF01pg0PhGIIQobzHUscHjkDtgYrD/AGiP+R7tv+vRf515DSsU3Y94i/aD06KYzL4QgWXcxDqyhsEeuOuetK37RUEzbrjwvDIQhVSXBIz25HT1FeDUUWFc95P7QWlGZJD4Phy2fObcu5ieuDt7++aUftC6aNrDwjEJMqXbevO08Y+XsOnpXhlpaSXkpjiK7gu75jircWmxRXBTUJhCm04ZTnn0osFz2aP9oTTlRi3hGDzFJMJVlAUnqenf2og/aLS1j8u38MwQpnO2N9oz9BXkEen6XcW7iO+Mc6Mf9Zja47YpV0S3a5WE6lDl0JU443dlPPFFguex/wDDSkn/AEL6f9/TR/w0pJ/0L6f9/TXhd5YzWVw8Mi52H768qfcGq1Fgue+/8NKSf9C+n/f00f8ADSkn/Qvp/wB/TXgVFFgue+/8NKSf9C+n/f00qftJSySKg0BMsQBmU14DUtt/x9Rf74/nRYLn3Zo2of2rotnqHl+WbiFZCmc4JHSr1Yng7/kTdI/69U/lW3TB7nPeNPELeGPD51JYxIFlRCu3cSG44GQM5x1Ned6PeSrqcarf36XrXTaVEl2qyf2f5kZmzGwdlYYQcksRgDpXeePtF/4SLw/FpHnLD9quo18xk3gYy3K5GRx0rG0/4ZPZ3KXP9qRRSi9W78uzsxDChWF4hsQEgMd+Se+BxUWu2ZWvJ3JF1fTZ7ttfHirSHGnQNayP5R2gSMpyRv5JKDGODzXNaZZaXbeIL1bHxBBbxS2qS21zHEnlg3LuuIiDuLZVsbi2M8AYrWtPhVeW7m7k8QedqCNbSRTSQM674WYguGclt285AIA7Yq/cfDq4utftvEMmroNZtooVimjttsYKM5fKbsFWVyvqMZzT5UPkXn97KEWl6TY20Udn4psLS3t5zfxRxpxEYf3cpG5idpJIcHuSRgmtXU/GaafYarNBrOl3k+mwPPLawwneQvUD5+3APp3qne/Ci0vNZutQOoyIs9+lz5Qj4WLkyw9fuux3H6CoT8J0KatD/aSGG7iuUt3aJ2kgM7Zb+PaQMkcKM8Z6UcqDkX9Nm1p/iLU7+w1a7WazjTTbh7dw8JG8qqtnJkwB83f0rRttRury9ls7fWNNkuIc70W2bIwcH+PnB4OOhqC18I/ZtG13T/tm7+1biSbf5ePL3Iq4xnn7v61Npvh+4sNdmvhdots/mH7PCrqrs7BtzAsVBHPKgZySaOVByLz+9kba063FzA2vaSslsrPMDAw2BfvEnzO3f070xdeLWb3a6/pRhSQRMwt3PzEZAxvySRyPUc1Wu/BU15bTWUmoRizAujbgQfOrTht29t2GA3tgADPGelT6t4Ra/wBRa/iuxHMHieNWDBflR0IJRlbkP2IxjvRyoORef3stzahdW+nLqEus6Yto4BSX7OxVs9MYfnPtWa3ia7fWtP0y01HTbh7yIzCRYWCqgOMj5zkk9uOhzWlJ4dZNJ0y2s5oYbjTpBNExjJjZtrK2V3ZwQ7fxZBxS6R4dbTb2K6e682QRTLIAm0M8solYgZOADkAc/WjlQci8/vZk33i2Wxl1KA6jZvPpwQzp9lZQAxAzkvjjPNXDr5FkLs69pXkGQxbvs7/fAyRjfnOOfpz0qXUvDUt/daiVu40t79IfMRotzK0bAgg5xgjgjH41W1Twc97qcuow3ipO85lVXVwoBiSMg7HU5/dg5z7Yo5UHIvP72W7zUrnT7aK5uta0uOGb/VObdiJOM/Lh+eBnioItfE8c8kXiHR3jgQSSMIWwFPQ58zkZ4478da0RoUaDRUiZY49LOVRU4YeU0eBzx97PfpWQfBRWzsYo74LJZWyxRsYcgusySqxGemUwR79aOVByLz+9kx1/EEEx1/SvLnJWM/Z25IODn5+MEgHPTNO0vWbrV7m9t7PU7JpbOUwyg2jjBGOR8/Tn9KqXXg69uQ841GCO/mlkkluI4nQxlgg/d7XBwAi8NuBPJrd03TJ9PvdQc3CSW93N54Uph0cqqnJzgj5Qeg60cqDkXn97MZfEu2OA3GvaRBJLGsgRoicBhkZIkxz29e1Pk8RLFbQ3L+IdJWKbd5ZNu3zbThuN+eCefTvTLXwWLewNqb0Pn7F8xi/592DevfH4VHN4c1SDxAtzp9xAqSLdu8s0O9UMrRYXaGBz8hOfbnrRyoORef3svHVphfx2P9uaUbmRQyRiBiSCMj/lp3HIHcVSPipDdWUEeuabMbx2SJ4rVmUkDPUP7inWvgx7JIbKG/U6ck0FwyPFmUyRKijD5wFPlqTx6jvxdh8Oy2lloUVrdRrJpShMvFlZFKbG4BGD3HX8aOVByLz+9kI14lLh/wDhINJC2xAlYwMAuW2jnf0zxn14qu3igC5sIF1rT5GvmdISlo5G5eoPz8HPGKig8D3H2kz3eqee+I13FGJcJOsuW3OQCduMDAGeBWkfDc0WqDULe8QSfbnuSkkRKlXjWNl4I5+XIP6UcqDkXn97EvNWm0+7S1u9c0qGdwpEb27A4JIBPz8AkEZPeo5deMIn36/pI8iTypQLdiVfJG3AfrweOvFRavoGo6pruo+VNFBYXmnw2srvFvZgHlLBeRg4YckEc+1WP+Eamt4oHsruNbq3vZ7uNpYiyHzS+VYAg8B+ue1HKg5F5/eynqnieTTLRZjrGmTPIiyRRxwMTIrEAEHfjB7euOK0Tf3SornWdLCt5mGMDAHy87+fM/hwc+mKyv8AhB7iKzms7bU41hukj+1GS33MXRy+UwwCgliMHOO1SXHgue7328upILIfajEiQfODOSx3MWwdpJxwMjrRyoORef3sG8VKs1lGut6e5vJWijKWjkBgu7n5+OMfmK0LPUbrUJ5obTWNMmkhPzqlsxxzj+/yMgjIp0mk6pcPY3U99aG8tLgyjZbsI2QoUKkb85wSc5/CmaJ4fuNJ1K4nN2htnUqltCrqmS27dhmYKe2FAHJ46YOVByLz+9lUeIZF8hbjW9Jgmm+7G0RJwWKg5EmMEgjPrxWx5Otf8/tj/wCArf8AxdY6+DAunXtp9s5ubNbXf5f3cSSPnr/00xj2rqqOVByLz+9mVFPqMGq29tdTW0sc0cjZjhZCCu31Y/3q1azbr/kYNO/64z/+yVpUR6oIdV/WwUUUVRYUUUUAR3D+XbSybQ21CcHvgVz2kQ3F1pMF2NG0dGuoA+EYqNrAEA/J781vXv8Ax4XH/XJv5Gsex1Wy0XwRp19qE6w28dlDljySdgwABySewHJpdRrYV7TVnsI7Y6dopCtnyyzmMD2XZwacsGt20Yjs7HRoUDE7Q7ge3AQc1nr49tX08X66JrrWhi84TCyypTGd3XpjmpI/HNm1ul1LpOtQWbKH+0yWRMaqRkMSpJxjviiwXJJbTV47bH9iaJcgD5oVkKbjkkYJQgdc/XNZNjqt4b+LS9V8O6Zpt1KzLEkrZjnXqRG6qVLcZKnB46V1lwZNU0oSaTqUcJlAeK6RFmUjrwOhBrFSG48UeH9R0rV4kiv7WYw+fCCq+YoDxzR55HVT1OCCMmiwXL2jGDUbeZ59PtIpbe5aIiNQy7o/lDAkD8PSr50rTioU2FqVDFseSuMnqenWud+HV9Nqfhdr65jMdxPdSvKhGMPnnjtzmutoWwPc47xv4k1XQ7vR7TSbW0uJ7+SSMLcttUFV3feyAOM/pXMab8Tpry7iivLuwsY5LdJFkkspJAHMrxFSVcgAMn3ycHcKv/FP7Il94fnvbKO9hgkmc28uNkh2gYPB9c/hXE6nrlvqP9oJFpaWkV7Yw2QhgcBYkjkLgqNvctiuylga9WCnBaPzRNTMqNG1O6ulrpfW78u1j0abxYkFv58viLSVjAmJJ06XI8pgkmRvyMMQPckYzUDeL7pJoXOr6TFZPbTXEk09jNE8YjKqf3bNn+L1HbAOa89fWY21PxBdrYKravGI/KYh1tycGTCspB3sASCO1Z8ckH2E2s0U0g8meP5ZQoj8x43yi7TtCmNSB05Naf2bif5fxRl/bFHuv/Af+AerS+MvK01NQl1yzW2ZmUs2j3GU24LFhuyoAIOTgc1b0TVtc1m61aKO70yNNPuRAJPsrsJQY1fcP3nAw3v0ryXXdYvvEllDBqt3NOyLKjsdgVg4A4QLgMuOG5PJrTtPFUljpGs2NtbNGdT+9OJfmi/dLH8vy9cLnnuaP7NxP8v4oP7Yo91/4D/wDprf4ianc+HNa1iI2eNNmVfKa0ZTPG7AJKp8z7rZJH0r0HQryTUdDsr2ZUWWaIOwQYGT6V4Az6dFb3NvYWVxawXNnHaTRm8aUMEdXRhvBwRgjA4wxrstL+Js+l6XbWK6XHIIECBzMRnHtto/s3E/y/iglmuGnTab1uulu99l6Hru0egpcc5rnPB/iaTxRYXFzJarbmKXy9qvuzwDnoPWujrjqU5U5OEt0XCpGpFSjsxCoPUClwMYxRRUFibVznaM/SloooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPl79oj/ke7b/AK9F/nXkNevftEf8j3bf9ei/zryGkhvcKKKKYi9pT7LzPX5TxXo0XhSDVbWO5t7FCvlRl5Dc4LMyAngg9680sG23a+4I/SvRtK19dPitkkZ1R7ZOQCRkEr/hWdRtbFRSe5U1LwlBZW7lrQRSKmULzK6vyARwAQcHP4VQi8MW1x5IigkkkkiEjCPBUckccg9q19U1ZL+7EaYmR4XXaemcE/0qXw/dW9rdWL3Tpap5bxgZyOGz/wCzVPNKw7K5D/Z5trCWzdZo2CbRG8QCgEHoQTn8fWvNSCCQeor3zxBNpE+lobW9jmvOSUX+6MHPT2NeFX8fk6hcR/3ZGH61cG2tRSST0K9FFFWSFS23/H1F/vj+dRVLbf8AH1F/vj+dAI+3/B3/ACJukf8AXqn8q26xPB3/ACJukf8AXqn8q26EN7mN4iuorGKwuZywjS8TOxCxOVYAADkkkgcVLb+ItMuVOydgwjkkdHjZWQRkBwwIyCNw4PrUPiS3e7gsIY53gdr1CsqAEqQGIODweRVKTwfvRnXVbmO8mEy3NwqJmVZQoYAEYXGxMY6Y71K3ZnH4ma1jrlhqN5La2kjyyRIryERMFUMqsoLEYyVYHGc1k2njS0uNOmmkQw3EazuFkV1jcROVbD7cHHGcZxmtbSdIi0hZ1ikd/OZCd2ONsaRj9EB/Gs6bwhaTafFZtcTBI0uEDDGSJjlu3bPFUWOv/GGn2en393HHdXC2SsW8u3fa5VtrBWxg4PB9Ksv4l02O4ihkedHcJu327gRbzhBIcYQk9AcVny+C7ee71CaW7k23sMsLLFEkZw5ByxA+cjHBPTnrUsvhZricvPqczLOYmvEEaAXDRnKnp8vQA46gdqALlr4l0q8ZRFcNtdpER3iZFYoCXAYgA4wfyPpSaprsNj4c/tmJfNgIidcgjKOyjOMZ6NnGKztU8LG40Cz0a2ZjGlzve4Z9rRoWJfGByWVmTHHDVo3WjS6jotxp13dMokl3RvCoBiRXDIBkY4AA6UAMPirSxFu3XJl8wxm3FtIZgQAxzHjcBtIOcYwR61J/wkul+fbx+e+y4UNFP5TeU2V3Ab8bckAnGaojwpItyb9NWnXVXdi92Ik+ZSqrt2YwBhF98jPtVeLwFZQX1rPHdz+XatG8SMqsylE2AbyMhSOdo7kn2oAs3HjPT4rS2uore+niuLiOFCtrIMh/uuMrypHTHWpZfFmnWrOly7+YHlASGKSRtse3exAXjG4Z+vU1Tt/BMVvBMsd/IkrTRTRvFBGio8ZJDbANpJzycDPHTFXo/DFvHdzXP2iUvKlwrdMfvihY/hsGPrQBIfFGk/bI7YXDsZJEiWRYnMZkcBlTeBt3EEHGehpLjxLaWuuT6ZcRzxiC1F1JctE3lKvzZy2MDG3r+HWsL/hFb+DVbK2tJJF0uC5guXLyoQ7RoFPy7dwJ2jjOO/tW5qnhyHVbuaWW4kSK4tGs7iJQMSRncRgkZBBYmgBB4r0oxbg1z5u/yxb/AGaTzidu7iPG7G0E5xiorfxZZSatcWMokjCyxRxS+U+1vMRWXccYUktgA+lQW/g5bSOOS2vvIvYpC6XEVrEgwV2lSgGDxzk9/birzeHYXWbzLmZ3mube6dztyWi2Y6DHPljP1NAEcPi/RZ45JVuXWNIjKHeB1EihgpKZHzYYgcZ5I9ajfxdYrcwjLRwbJmuGmRo3gMYQ4KEZ5Dg/ljOabJ4Os5LG0tTczgWtu0MTjGQS6SBumMho19qZJ4Mt7syy3t9cT3Mwk82YBUyWVFBAAwNojXH45zQBc/4SrS/LB3XPnGTyvs32aTzg23dzHjdjbznGMUxPFmmOnnJN5kDxxPD5SO0kpkL4ATbn+A/kc4xWde+G723kXU7W4nu9XMuXuA0cR2bNu0KVK44HvnnPaorbwFE2jWkN5cbryGKAbiiyIHj8znDD5gfNYHPseKANyDxNpd1cwwW8k0ryqrZSByI8kqN5x8hyrDBxyDVbVfFMWm319ZC0uHmtrBrwP5beW2Ax2lsYH3ev4VB/whkH2jT5Vutgs9hHl20aMSrFuGVQVDE8gcEfU1e1Tw+upXcs/wBrlhE9m9nOiqpDo2cdRwQWJoAbbeKdPubNp1W6MiCMtCLWQyfOCVIXbkqcHnpwaSy8U2Oo6xHp9rHcSLJbLcpcCFvLIJIwTjg/L378dah1HwlBfyeaLuRH2wqVKK6MI9+AynhgfMJwe4B7U/SfC0WizWb2l5Ntt7c27o6qRKm5mGcAYILHpQBZPiXS11JrFpnEqS+Q7mF/LWQqGCF8bQSCDjNMtvFWkXUkcaXDq0rKIvMhdBIGztZcgZU4IB6dPUVQj8LT3Gp38t7ev9hmvxdraIFw2I0VSWxkcrnHsPcUjeCYZrGW1udSupf9HW1tpAFV7eNWDLggctlV+Y+g98gF4eLtFe3imiuzMsyholiiZmfLFQAAMkkq3H+yT0FTWniPTL+7htbWZ5ZpYvNCrC/yLllyxxhfmRhg4ORisufwHpkkVwsTMhkuUuIwyK6RFVIChSMFfmc4PdzWnpOgwaRMZYXyzW6QMBGqL8rO2QqgAZMhoAZN4gjh8TQaOYGKyJ81xn5UlILLGR6lUc/gPWrWjal/a+lx3oi8re8i7N2cbXZev/Ac1mTeDdPnupr15J/t8l0t0LnzDlGUjaAv3cBVC8jpmtXStOj0nT0s4nZ0RnYM3X5nLH/0KgC7RRRQBm3X/Iwad/1xn/8AZK0qzbr/AJGDTv8ArjP/AOyVpVMd2RHeXr+iCiiiqLCiiigCC9/48bj/AK5N/KuE+HkKa/ptlrOpC4muLWJYbWKWBo4LdQNuY88OxA5fnrgYFd3e8WNx/wBcm/lXlen6jd6V4d0LRdRne00YyKZtUtplTMLoXRGIO6M7yFZsAYHUZpdR9Dr7TwxrmnWC6dZeIoFsYwyRRS6cJCsZJwpO8ZwDjp0FYWg6H4i1LwvLox8TJbpYzPYsEsPnURN8h3b+67D06GktdbeK41bTdHutU1DSgY/s97bXEMjwSc+ZGrzN8642f3sbiM8cZtmmt2ms6jOlx4lW2uhHKWWTT97SgbW3A8Y2qmCMdDmmI6ew8Ia/o15cXWm+IbRFm+Z7I6ftt2fuwUSZQnuV4PcE1dn8Sanocfm+ItKiislOJL6yn8yOP/adGAZR6kbsd65y8126itLCx1C41i00+W7/ANN1K5lt1dYyp2puhPyAvtBbAwCeRmkg1HT5L7UdMis5vE2gIY2haSWKdYZsNvQPK4LADaepwWIz2oA7Dwwyvb6iysGVtRuCCDkEbq3K4v4W29xaeC47a6UpPFcSo6lgxXDdMgkHAx0rtKS2G9zjvHXh3+3jp5/tC0tBAXOLg4D529Ofb9aoQ+FkOrwXcur2IkFmII1tyEIboGTrjjIBxmpfiBdahaahpEumadFqN1tmVbWS3Mok+5xwfk/3zwPxrQ1e1jj8C3l9LpNrYagtg0jIiIxhk2ZwGA5we9d8cTKnTjC/R9F1ZxSw8Z1JSt1XfsYx8GhvEaX51e0laOaFiZHzKpXqDjAYnGMkZqjD8N3tJLg3es2ypNEygKxQ/eBJPPI/TpWv9ple/ZU0/R1gfUJNPjza5cMsbOshOcHlcbcfjVTSL25k0vR/NttJvpJNNkvbmQWo3sE8v5AAcbzuOT69qax9RaKX4Il4Km9WvxY9fAun22oOYZLALJbyRpFJM5LZHDdc5xnOD9MVW0PwRDYtum1Wwux5ykIJSFPyuMEA8nJGPoanTWQfIiNtoLyXK27pdJB+6thKW+V+eT8uByuSegqDTdVtLGyvWurfSZdq6jcJMUARmhl2qq5/hwenaj69Us05PUf1OF0+VCSfDzT/ALGoF7Zxtkfv/PY5fPzJgnGMZA7+tTt4B0RmKrParl3jH+lMSOOD/vjuOmO1Trq0ZstQv20/RvLtEkIsPJAnkZIg+4HPQ9cY+7zmo7bU5JjBby6do0Dz3GxLyS3TywPKLkFFc/NxgZYZBzjij6/U/mYfUqf8qNPwZp1r4Y0+5trjV7GdpZfMBjkAA+UDufaul/tXTv8An/tf+/y/415/HbC5+HukTW32WK+ub2KMXKwq4O6cjuOVI4x6U1bqE3Wo6lPZQW0elXNuL+1aJSkaFCsvblRkSA9woNYVKkak3OV7v0NqcJU4qMbWR6F/aunf8/8Aa/8Af5f8aP7V07/n/tf+/wAv+Nea6lGYtNmuZI44WudLub0KI1XyQZYhHjjgqjY+pNW9QtrCwu7m20t47m1C2ruzFZjC7XKJtDnJG5C2VJ7e9Z/u/Mv955Hf/wBq6d/z/wBr/wB/l/xo/tXTv+f+1/7/AC/41w2mXdnP4vZ5LFTpl9JJa2weyAhVo/usrlcEviXv2SrE+naLPrmr/wBpXEenmykiW1VGWIBCitv24w+5yy8gjjFH7vzD955HY/2rp3/P/a/9/l/xo/tXTv8An/tf+/y/41w8uqokkrR6Zob73uoorZo1SSFod2GkYnG07eeBjcvNMTU2uEWC20/R/tKLcvOZrHbt8pY2C7QxGSJOoYjp9KP3fmH7zyO7/tXTv+f+1/7/AC/40f2rp3/P/a/9/l/xrh01IqkUU+m6LHLdJayxTfZv3cCzb+HyfmI8vAOVyWHSo/7aiV7dpdO0ZbZZGimnjtlkDkTGPcF3BlUgZBAfkkdsk/d+YfvPI7z+1NP/AOf+1/7/AC/40f2rp3/P/a/9/l/xrGs/BOk2V+tykasqg/uGjUpk98Y61yuo6mj2F/iz0fa7X1qkUEIE8JiSQrITn/YGeBjcOaP3fmH7zyPQ/wC1dO/5/wC1/wC/y/40f2rp3/P/AGv/AH+X/GvP7jVY2vmsLOy0xhJHNFHIbNd0cscRck/NluQR90Dpye8IgeUaJfTR6ZeRw6TNfyRQ223zpEEfHytgn5scgjJPHTB+78w/eeR6N/aunf8AP/a/9/l/xo/tXTv+f+1/7/L/AI1wy37vcQWMNl4fuJ7h4NtxFbZijEiyEqRkksPLyORkEdKlEt1calHbwafoSRz309lGXsyxTylZt55Gc7SNvHUHPaj935h+88jtP7V07/n/ALX/AL/L/jR/aunf8/8Aa/8Af5f8a4vVoY9X8EaXd21np9pLfTWqTYgyMPIqsAQVIBz65xke9V59UisIZXGmaNIkbXNulqIP3sZhjdhI5yflOzpjgOvJ7n7vzD955Hef2rp3/P8A2v8A3+X/ABo/tXTv+f8Atf8Av8v+NYF5HDpfhpNUvNN0t2iKS3Xl2wCpCWG8jOTlVJOe+OgrAh10XAEJ0PSbWZZI4ppJYAUhMrZiJ6Z/djkZHzMBmj935h+88jvv7V07/n/tf+/y/wCNH9q6d/z/ANr/AN/l/wAa4WK8Oom4tU0/R0SC1uHluI7fPmFHZAY+eAcZyc4II561TS4S4t9Ni8rS4kjvbG3eNoR5825Y3Z856HcRjHIBOe1H7vzD955Ho39q6d/z/wBr/wB/l/xo/tXTv+f+1/7/AC/41yWvTGwv7xLLS9HMFpFbyMs1tlpDLIyYyPugYB6Gqr3+y5k0+Sz0GC4gknD3c1riJxGsbBQucgkS88nAUnnsfu/MP3nkdv8A2rp3/P8A2v8A3+X/ABo/tXTv+f8Atf8Av8v+NeeTajHqQs5VsdMso/t9nE1mYR9ocOI5Dz6HdjGOQDz2pLfUTqtnBJJpNhBFLNZyq0UUasga4RTGQGYtkEjOF6EYo/d+YfvPI9E/tXTv+f8Atf8Av8v+NH9q6d/z/wBr/wB/l/xrkNYUad4mutkGl/Y4tKe6S2ktFy7qW6Nn2GeOn51UvNVXT3slm0/R5JGW3e5ijswAqzSbVAZmHQZ6BskdBR+78w/eeR3X9q6d/wA/9r/3+X/Gj+1dO/5/7X/v8v8AjXn41Fo7W7kt9L0nyrG2ku5RNAXeQC4mTaDn5eI+vOPTFTPdN595DY6ZpKiEXs7NcQGTIhkVQo5GM7vw7Cj935h+88juv7V07/n/ALX/AL/L/jR/aunf8/8Aa/8Af5f8a4eHWbe4v7Tbp2ixw3F7HafZHgBnCtGH8zPTvwMdOc9qtavqNlYeJbexh03TZIDcQW8yG1UMrSng7iR2IOAp75Io/d+YfvPI67+1dO/5/wC1/wC/y/41NBcwXIJgnjlA4JjcNj8q87XWHi062urnStE/060+0Q7bfasH72OMlyT8ygShjjGNpHvW54O41XxAm+0fZPEu60TbGf3Y6DJwfUZNPlg02r6f5hzTTSfU62iiisjQKKKKACiiigAooooAKKKKAPl79oj/AJHu2/69F/nXkNevftEf8j3bf9ei/wA68hpIb3CiiimImtDi6j+tdBDcz4jjhklGOBtkKjNc5EcTIf8AaFb1kyiQ85Ax2/GkBtWVtNdh/tN3cRbMABW37s59x6Va/s6fTonEF/dwlslQUKjP51Xt5vKeTHoDwPRh/jW74t8Wx6+LVljkjeFdpB6dulS3ZpW3HqYE2o6wIT5tzORgkgyqdw7jpz1rldcAGrTMOj4YfiK6a8l2WgkAGRg/dx2rC8SRoLm2ljOUkgU5z37j9aoRi0UUUwCpbb/j6i/3x/Ooqltv+PqL/fH86AR9v+Dv+RN0j/r1T+VbdYng7/kTdI/69U/lW3Qhvc5vxrqbaNpNtqCQGdobuMiIHBbOR1/GrEnieGOe5g+wXpktYUlnG1AI9w+VSxbGevfAxyaPFFhBqlpZ2VyG8ma7VW2tgj5WIIPYggGnT+GLK6t7mOaW4d7gwmSUuNxaLGxumM5HPGD6VK3ZnH4mUR4309oVulErQeW5aJI98m9ZkiwCGKn5nHTOeuafL4xtYQ0k0NxAIFnNzBJFmRTGqOQNrEfdcHjOc9qlj8HaXHHtzcMcsxZpcklpUlJ/76Rfw4qa68LabeTXMsqylrnzPMw+PvoiNj8I1/WqLIv+Ert9xg+wXwvvOES2ZRfMbKlw33tuNoJyT2x14qwviK2l0BdYiguHgYkCMqqOCGKnO4gDkHqaraz4d+0yte2Py37So/mNO0e3arL8pAOMhiDkEH9abaeFIV8MWOkXc8jPbP5wmiPIk3FsjdnI+YjnP50AR/8ACcaa1slxDb3k0P2c3MrxxgiGMOyMWOexRumemRmny+KfM1ixs7S1maCa9e1e6dP3bFY3ZgpznIKYyRjripoPCWmW9jcWiCcx3Fs9tIWkJJRndzz65kbmnR+F7GLUorxZrrEU7XMdv5v7pZGUqzBcdwzcZxk5oAZfeLLHT7i9imhucWbRxySBVCGRwpRASRknePYdyKryeOdLisobtknEDu0cj5TETK20g/N83J/g3cc1o3nh6xvUvFlEgN3NHO7K3KyIFCsvpjYPaqFz4J028VVnnvG/deVIfMAMi7i3OBxyT93Hp0oAsaf4hgljdLya3jumuriGCAOA0ojkZRtBPJ4H41m2vjZX0m21K+s5rSN4Z5niKB2ZY8ZKlT79CMmuks9Pt7GN0hU4eaSY7jk7nYs36k1lHwfpjWptne5eALMiI0v+rWTG5V46ccZzigBsfi+1a7+zzWN/bFZlgleaNQsTsNyBiGPUEdM4yM4qbQ/FWn6/M8VpvDCMTJvKnfGTgMME4+hweRxVibQbG4lmkkVyZriO5cbuC6ABfwwo4pNH0G10RWS1lnaPaERJGBEajoowB+ZyenNAGVY+M4Ta6V/aELJLfbB5ibQgZnKqMFtx5x0BxnmpYvGVpcW8M0Gn6hKJkeWNViXcYkxukwW+7kgep7A0J4I0mOeGRGuVERiYIJOGMbbkJ4zwe2cH0qLVfC0h06yttIYRNbQvbiR7h428tgMglQdwyASOOgwRQBLB430i4vra1jMv+keUA52gK0iB0UjO7JDDkAgZGTTrrxN9gvNTS4t2khtZIkTyiqn5o9xLF2A/WnaZ4Ss9NktZo5p/MhiiRwpASVkjCBiMZzgDgHHAqW/8LWGoXrXjvcR3DSCTfG4HITZwCCPu/j6EUAQJ4z06aWMQw3ckDCAtcLGPLQTY8vOTnnIHAOM801vGunopke2vFgaOSWKcxjZMiEBivOerDGQM9as2/hXTLaza1jWYRMLcHMhJ/cbdnP8AwEZ9ayIfBTS6jJ9skA01YZ4YreOd2wsrA8AgbMbexPPQgDFAGvqPiez043YaGeRrWRY5duxQCyBwcswHQ+vXioR4x09zE8MF3NbO0CG5jjHlo023Ypyc5w6k8YGRmmP4L0+RzLJdX0lw0nmPO0oLsSgQjpgDaAOAMY4xVK48GSC9tIrGUQadE9s7jz3y5hK7cpjDEhFGcjpyCQKANTR/Fmm63fNa2hffsaWNm24kQMFLDBJHJHDAHkVUPjizPKadqUissrxssS4dIm2yMPm6KcdcZyMZrT0nw/aaNI5tZJvLI2pE7ArGM5wvGfzJpsfhrT4ooo1Eu2KKeFcv/DMwZ/1HHpQBCPFmntcqipObYusX2zYPKEjKGC5znOCOcYycZzWa3jmGO8DzWlxBYtZrPE0qBXmLyKibfmwAd38WCO+K0o/CWmxTxsGuDCjK/wBmMmYmkVAgcj1wB3xkZxmoB4J0zyykk97LtiWGEyTZ8lFYOgXjsVHJyfXNADtK8TprOspbWqgW5tpJHDYLpIkiqVyCVI57Z7c1CPGlrFE5kt7mfykkmmkghwsUSyvGWOWzwUPTJOMgVq2OhWthcx3KSTSTpC0O+RgcqzBjkAAdR2FV18K6YkF3CFl2XUD28n7z+B3dzj0OZG/SgDLXxk8F9qTX1tJFaw3YsrVAqAyvsDE7y+OmTyAAAOSeKtDxzpLQPcKly1vHbLcyzCMbY1YsoUnP3iyFcDPPtzVy58M2FzFIpMyO919rEiMNySbAmRkEY2jGCCOTSt4Z06S1ubeUSyLcwRwSFn5IQsVOexyxOfpQBRj8bWM4VLezvJ7ktIpghCOylFVmywbb911PB7468Vat/FNldyKbaG6mtvLSSS6WMeXEGQOu7Jz90g8A4yM1Na+HrW1khlMtxNLEJAryMMkSbQ3AAH8I6CobXwpYWbILea7jgESRSW4l/dzBE2AuMZJ2gDqM4GaAKGmeJrHxB4is1tBIpS2klAcqdyMV2t8pOM4PBwR3FdXXNWOh2+ia5YR20s7obeVFWVgQirswBgD8zk+9dLUx3ZEd5ev6IKKKKosKKKKAEIDAggEHgg96zh4d0QdNH0//AMBU/wAKvXEhitpZFxuVCwz7CvIbT4heNb6ye7ttPs5YI87nWE9hk4G/JwOTjOKltLcuMW9j1A+HdEPXR9P/APAZP8KP+Ed0P/oDaf8A+Aqf4V5fL8Q/GkOnJqElhZraPgrIYTjB6HG/IB7E9alsvHXjjUbVbm00+wliZiqkKAWI6gAvk9e1LmRXs5dz0v8A4R3RB00fT/8AwFT/AAoPh3RD10fT/wDwGT/CvJpfin4ogCGWCwTfnAMRzwSDkbuOQetNb4reJ1jSRoLEI+QrGBsNjrj5qXPEfs5ns9ta29nCIbW3igiByEiQKufoKmrw/wD4W34j/wCeen/9+W/+KpyfFrxGzqPL0/BIH+pb/wCKp+0QvYyPV7v/AJGzTP8Ar1uf5xVrMiuhR1DKRggjIIrIuv8Aka9L/wCvW5/nFTvE2oT6X4Z1K+tWVZ4IGkQsu4Agdx3refwx9P1ZzQ3l6/ojT8mIEHy0zu3/AHR971+tNitbeAkwwRRkkklEAyT16euBXBm+1HVdXsba21bz4Ir8bNQjgX+K3mLpj7pKkLyem7ByRViz8R63I9jaS+WZ70Zil8nCqInYTEj3VUI9346VmaHYDT7JYZYRZ24imOZEES7XPqRjn8aG0+yeOON7O3ZIzuRTEpCn1AxxXCWXiTUrqKFXZdQuormNj5aqY8mKU7VZcY+ZRwwDAEZzmpm8TXC6NBIuvLPcXEkYd0gjQW7FGYozNwvIwAQW4x1PAB24s7UXH2gW0In27PMCDdt9M9ce1MGmWAtjbCxthAW3GLyl2k+uMYzXMeFtVmuFvtW1EGIvp9pPKNpAU7HLYB/lXPPfaxDpOsC9t9Qt31O1N5F5rA7JQw3Im1iQNhj4OPuse5oA9IuLPzYEihk+zhG3DZGp/Qggc81WOl3DB86nKfMGHzDF8w6c/LzWN4v1S4tJFtVvlsLeWzncTGIP5sowFiGfUEnA5OOOhrE0nW9de5stMinht9sEEUcU23LIbdWMuD8xIYn2+Ug81nKnGTu7/exWOzk0meUESajI4KlCGgiOVPUfd6e1Mi0R4IfJhvmjizu2Jbwhc+uAtcnc+K9cktbe52rY28k32eSSRVXy3jQl+X4wZMqM9k4+8KI9X1KC8eW6u47BbtrYXV4q7o4v9HLfLu4AZgBk+uOpFL2MfP73/mFkdeNKuAioNSl2JgqvkxYXHTHy8UyXRHnmjmmvmkli/wBXI9vEWT6ErxXH/wDCXauxPmXsUNzHbQSwWf2bm8Z3dcDPI3KqkAcjdk8VdTUfEVzcQhdSWGO5kvVwLRT5QhkITBPXI6569sUexj5/e/8AMLI6P+xH86Sb7c3myrtkf7PDucehO3kUQ6G1vGI4L1okUEBUtoVAz14C965ax8U63e6xaI5t4BIsDfZXIUyI0Id3APzEhiwGOPlwearXuqeIZNDgafVHRriyt755YrYIYD5qB1H+ztYk55+U9jij2MfP73/mFkdnJo0ssbRyX7vGyhGVreIgqOgI29PamnQdxhLXeTB/qs20P7v/AHfk4/CrthdwXUGIrtblowqvIuOSVDA8ccgg8etW8j1o9jHz+9/5hZFO3tLmKYPJqE0y/wBxkQA/kAaZZ6Np9iJTDbR75WkZ3ZQWbexZgT1xknir+R60ZHrVxioqy/zBEC2dqlw1wltCszfekCAMeMcnr0p0Nrb24AhgijAzgIgGMnJ6epqXI9aMj1qhkENla267YLaGJd+/CRhRu9eO/vUghiBBEaAhiwO0cE9T9afketGR60AMEMQjWMRpsXBVdowMdMCo/sdr58k/2aHzpF2PJsG5l9Ce4qfI9aMj1oAayI6FGUFCMFSOCPSmSW0EsckckEbpIMOrICGHuO9S5HrRketAESW0EaBEhjVQmwBVAAX0+ntTTY2hnWc2sBlRQqyeWNwA6AH0qfI9aMj1oAY0Mbli0aMWwDlQc45FRzWVrcLtntoZV378PGGG7157+9T5HrRketAEDWdq9wLhraFpwMCUoCwHXGetEdlaRNI0drCjSMHcrGAWYcgn1NT5HrRketAEUttBO6PLBHIyZ2l0BK5GDj0yKa9layypLJbQvJGNqO0YJUegPap8j1oyPWgCL7PBhh5MeGG1htHIyTg/iT+dL5EOSfKTJBB+Uc56/nUmR60ZHrQBkS+HLKbWItRkeZniYPHEWHlqwGAQMZ4yeM4zzjNaLWdq9wLh7aFpwMCQoCwGc4z161NketGR60ARG2gKbDDGVClNu0Y2nqPp7VkaPBFb69rMUESRRL5AVEUKB8nYCtzI9axtN/5GTW/+2H/oFaQ+GXp+qM5/FH1/RmzRRRWZoFFFFABRRRQAUUUUAFFFFAHy9+0R/wAj3bf9ei/zryGvXv2iP+R7tv8Ar0X+deQ0kN7hRRRTEKpwwPvWxbTAMxI42gHB+ozWNWnbxTSTZhjaXHJTqMe9AFs3FwHBaZyB0KnGacJy+CSxz1yOlFqsRS2E2F3SMrgtjAxwafG1v5SBmQFoZM/N0cHigAmlbysK7lcYCk5xVfV5RcaVYPjmJfKJ9aurc2ILEbCMQuFwTz/GKo6p5fkzLEQyGTcpHHGf/r0AYtFFFABUtt/x9Rf74/nUVS23/H1F/vj+dAI+3/B3/Im6R/16p/KtusTwd/yJukf9eqfyrboQ3ucx471C40rQY761RXuIbhTGrdNxDAfzzT7fxK8Dx213aXLiOWO1uLs+WAs7gEKQp/2lGQMZP1rQ17Rk13T1tJJAiiRZOV3A4zx1Hr61myeFJpdS+3vqCmUyLKyeSfLaRRhXKb8FgAOfYegqNU3oZaqT0LMPiaOTwvN4gksLqK1SEzpG20ySJjOQAcDPYE1XXxeEvGt7rSru2EU6W80rMjLG7qGToxJBBGcdM1KfDtydGGkG/h+wiEQ+X9mP3AMYzvz0psvhueaWWSS+hZpZ47h/9GPLoAFP3+wUcU+Z9vyHzPs/w/zIh40h2RF9OukkukSSyjJTNyrsFXHOF5ZSQ2MA1RXx19jFyuo2rLcC6nSODfGhWOMJnLFsMcuMAHnNTp4HCRNGL/IKqsZaNyYFVtyiM78oAQDx6D0pV8EsiAR6pJHIHeQzIriRi+N+W35IO1cj2FHM+35BzPs/w/zIx43SB5mmhMkcl0yW7FlhVYxFE+GZyBuPmcDqcH0rW1rxJDotxaQyQM5uQSGMiRqACBjc5ALfNwuecGqN14Plu0mR9TZFnZmmESOgk3KqkNh+QQqipdR8MXOpwJBcaoRCqGMxxRMiuhxlWAfBHFHM+35BzPs/w/zLA8SblvJk0+c2dsZE+0mSNVd4ztYfMwwMgjJ44PtnFn8b+ciz2w8qKOO5E4wspDx+URtIbDDEnr39qtS+C2m+0K2okRXBLPCqOsYYkEsqh8BsgHI789zSDwSPLkR75ZDLvMjSRMzOXCBiSX7+Wn5Ucz7fkHM+z/D/ADLEnjK2gkma5srqK0jkuIhcnaQ7whi4Cg56I2DjtV7Tdca+urq1n0+4s57eGOZklZWyr7sYKk8/Ic1SfwtJJGscl3bvGJZZtrWpILSBg+Rv5BDtx71BovhXUNKmubgaonnTokTbo3lGxN20Zdyf4jRzPsHM+z/D/Mnm8ZWkVpazraXUhubaG4jjULuIlkWNV5OM5ce1Ty+IZf7B1a8jsZFvdOVxJayMvDBA4+YHBBUg9f1rHf4fqbcxJqLAERouVf5ESQSKifP8oBAxitS18OXVpZT2iaikkdwWM5mty7SlhglmL5PGB9AKOZ9vyDmfZ/h/mRHxcYbRJrjS7jdHapd3mx0It42zg9fm+6xwMnA9eKd4m1+60J7eeJYpbd4Lh2jZTuLJGXXDZ4HGDwetVn8EtJHFG+pMyRxiEhkc+bGDkI/z/OoJOAfUjoTV+90C61Exm7vreURq6qrWnGHUqwI385BIo5n2/IOZ9n+H+Yum3+pJq9xpmovb3Eq2qXUclvGYx8xZShBY9CvBz39qpab4turqzsWuNHmW5vbmaGKOKRCMRlssSW4AC/n0qfTfDV1pTyPb6pvkkVVaSeJpW2rnaoLOSAMnj3NNtvC09pOk0OoqGjlkmjUwErGz53bQXwAcnjpzRzPt+Qcz7P8AD/Mz18VXst/BHEyGF5IFO6LB+aeaNu57RjH596u2fjGKa2tpTa3EsG23FxdAKqxvMF2gruz/ABLnGcbh15oTwgY3R0vIQyMrKfs54Ku7j+P+9Ix/Go08F+W8DJfKqQrGBEImCP5f3C678MVwME+g9BRzPt+Qcz7P8P8AMvaR4nTVrmCI2FzbLcxPNbvKVIkVGCtwCSOWGM9QaYviuI3To+n3KW4uJbVbhmTa0sYYkY3ZAIRsE4HHai28OXFo1q0F/CptI3ih/wBGJ2qxBYff55UVnjwKWkuHn1DzVnkmkaJ42MYaUEMwXfgEhiM9smjmfb8g5n2f4f5k9t44t7sLHBYTzXTXK2wiiljcZaNpAd4bbjapzz1FEnjqyinvIWtJnltwGVIpI5DIDKsWOGwp3MvB7GltPCElnOtwmo+ZOsiy+bNEzsWCMgJJfsrEVDF4GWE/LfBgFCIrxsQih1kCqN/A3KD+FHM+35BzPs/w/wAywvjOKN3+26bdWsUcskEsrMjBJEjMhX5SSRsGc+vFVG8dtcW0b6fprTSm5t4nQzRkBJTgEMGxu4Ixng4zxV6TwvLMW8y8gcNO1wwa1JBdkMZJG/oVJGOlRDwjOLBrJtWkeIsjqXV2aMoQV2MXyuMDpRzPt+Qcz7P8P8xsnjzT4ru7geCUi2EwZldGJeJSzrtzkD5WAJGCR7jNgeLUXULKym0+a3lukVwJpYkIDMQMZb5jxkhckAioT4QlJugdTYx3SOs0OxvLbeMM23fjcR1Prk9akvPC9zfSwvPqhKxbMRCJhG2w7l3LvwcHmjmfb8g5n2f4f5nS0VkQadqlvAkKashVBgF7cs34kvk1J9k1f/oKw/8AgJ/9lRzPsHM/5X+H+Zp0VmfZNX/6CsP/AICf/ZUfZNX/AOgrD/4Cf/ZUcz7BzP8Alf4f5i3X/Iwad/1xn/8AZK0qzYNPuxqEV3dXyTeUjoqrBs+9jJzk/wB2tKiN9WwhfVsKKKKosKKKKAI7hVa2lV22qUILegx1rybTfDfhaxImj8VSSuqMLdjEdsZYYLAdzivVr3/jwuP+uTfyNU/Dn/IsaT/15w/+gCpauy4ysjz+4g8EJZS2s/iW2jvJLZLaSd+GKIVKnYTgHCj61NaaZ4Ka0tzc6yt7JDM88UwUouWIPRBgjKjpVnwdbw3uk3Fxd+Go7yZ1lujfTLG5upTLIPLG7kEBQOeAMVqeBWZZtbgFi2nxLcxSLYEgi2Z4UZlGOB8xJ44yTRyhzswRpvhld0y6/D9sYNm4ezV+WkZ2O0gjJ3Y9gKSXTPDU+liwk8Qx+WElVdtoq7C7BsjA4xgj8a9C1bVbHQ9Ln1LUbhLe0gXdJI56e3ufQd6zdE8T/wBrXv2SfTLqwne3F3Cs7I3mQk43fKTggkZB9RRyj5zGsfAml3Fmk1teJNbXAhYsLdD5kaptIBPTd1JHNLq/wz0/VDAwu5IJImJ3pDGNy8YXChRgY4781veEv+RR0r/r2T+VbNCimhOck9zzP4tbv+JOEzuJlAx1P3K4STSLqC9S0vLiC0keIS5uJSFAPQEjPPtXsfii00y/urG2vtKudQnKyPCsEgTaBt3E5dfVaybvRtChWO7vPDF8kVrECZZJl2xquTz+95x+Ne7hcYqVGMGu/bv6ni4nCOpWlJPt37eh5lNo95Dq0emiaOSaQoA0chKAv0yccdaoqkjmX96FMaljljzg4wPevY4NG00XjXf/AAjWqRzNMsrMbhSGbOdxAlwcHmlTw3otk8klt4bvnaRCr4lHIyDj5pO5H6VusyitGvy/zMHl8nqn+f8AkePwWt3cSPHFHKWVGlYcj5QMk0tpZ3N8dlud8hcKI9xyTgnP6V7XJZ2z3vnHRtWLPGyGQXGNobqv+syOg6cDtWdptnpGn3ps7Tw9qVtc/LOUE65C/MobIk6csMe/Sl/aas/d1+X+Y/7Pd1rp8/8AI8ePnbdx8zBOMnOM+lKROOol6Z6Hp617ZJp1k9oIW8P6m8QIUQmZcDachsGTGcjOepzzSiW0nvJrX+xtUaWMl5UMy8I4IGR5n3TtbC9sdBT/ALTj/L+KD+zn/N+DOb+GmkafqekXkl9Zw3LpcbVaVdxA2jpmu2/4RbQc5/sizz/1yFc/pGsaNoOki403RruKwuS0iyCeJ1cqpJIJkPRUOfoa1Z/Fi2rRrPpN7G0q7kBkh+YblXI/eerqPxFeZiKlSpVcoOyfmv8AM9ChThCmoyV36P8AyLZ8LaCeDpFmf+2QoPhbQT10izP/AGyFZkHjm0ub42UOn3jXO9oxHvhGWXIYAl8EjByB6Gpv+EvjMNtN/Zd55d1J5UDGSDEj8/Kv7zk/KfyrC9b+b8f+CbWpfy/h/wAAtjwnoAkZ/wCyLTc2M5j9Pbt1p3/CLaD/ANAiz/79CopPEU0M0MMmiagskxKxKXhy5AJIH7z0BNJB4ilufM8jRNQk8qQxvteH5WHUH951ovW/m/H/AIIWpfy/h/wCb/hFtBzn+yLP/v0KP+EW0H/oEWf/AH6FQW/iSW6Mwg0PUXMMhikw0PyuACR/rPcVP/bN5/0L+pf99Q//AByi9b+b8f8Aghal/L+H/AD/AIRbQf8AoEWf/foUf8ItoP8A0CbT/v0KP7ZvP+hf1L/vqH/45VW98Uvp0cb3Wh6mokkEcYUROzscnACuSeAT9AaL1v5vx/4IWpfy/h/wC1/wi2g/9Am0/wC/Qo/4RbQf+gTaf9+hUF34klsbSS6udD1GOCJdzuWhwB/38pbfxFNdI7waHqLqkjRsQ0PDKSGH+s7EEUXrfzfj/wAELUv5fw/4BN/wi2g/9Am0/wC/Qo/4RbQf+gTaf9+hUcuvzwKrS6HqCBmCAs8AyxOAP9Z1J4qT+2bz/oX9S/76h/8AjlF63834/wDBC1L+X8P+AH/CLaD/ANAm0/79Cj/hFtB/6BNp/wB+hUMviOaGeCCTQ9RWSdisSlofmIBJ/wCWnoCam/tm8/6F/Uv++of/AI5Ret/N+P8AwQtS/l/D/gB/wi2g/wDQJtP+/Qo/4RbQf+gTaf8AfoUf2zef9C/qX/fUP/xyj+2bz/oX9S/76h/+OUXrfzfj/wAELUv5fw/4Af8ACLaD/wBAm0/79Cj/AIRbQf8AoE2n/foUf2zef9C/qX/fUP8A8co/tm8/6F/Uv++of/jlF63834/8ELUv5fw/4Af8ItoP/QJtP+/Qo/4RbQf+gTaf9+hUA8SSm9NmND1H7QIxKY90OdhJAP8ArPUGp/7ZvP8AoX9S/wC+of8A45Ret/N+P/BC1L+X8P8AgB/wi2g/9Am0/wC/Qo/4RbQf+gTaf9+hR/bN5/0L+pf99Q//AByo11+4eWSJdB1IvHjcMw8Z6f8ALSi9b+b8f+CFqX8v4f8AAJP+EW0H/oE2n/foUf8ACLaD/wBAm0/79Cj+2bz/AKF/Uv8AvqH/AOOUf2zef9C/qX/fUP8A8covW/m/H/ghal/L+H/AD/hFtB/6BNp/36FH/CLaD/0CbT/v0KrX3ih9NtvPutD1JI9wQY8pizE4AADkkn0AqV9fuIoGnk0LUY4kUuzu0ICqBkk5k4ovW/m/H/ghal/L+H/AJP8AhFtB/wCgTaf9+hR/wi2g/wDQJtP+/QpBrV2wBGgaiQeQd0P/AMcpf7ZvP+hf1L/vqH/45Ret/N+P/BC1L+X8P+AH/CLaD/0CbT/v0KP+EW0H/oE2n/foUf2zef8AQv6l/wB9Q/8Axyo11+4aZ4V0PUDKgDMgeDKg5wSPM74P5UXrfzfj/wAELUv5fw/4BJ/wi2g/9Am0/wC/Qq5Y6ZY6arrZWkNuHOWEagbj71T/ALZvP+hf1L/vqH/45UllrButQNlNp91aTeUZh52whlBAONrHuRSkqrWruvX/AII4+zT0VvkadFFFYGwUUUUAFFFFABRRRQAUUUUAfOf7RFtbWniPSrySHz3ubd1KsxUJsI6Y9d36V4z9rs/+gbH/AN/X/wAa+o/iv8MNQ+IN3pk1lf21qLOORWEysd24qeMfSvO/+GbNd/6Dunf98P8A4UrDuzyD7XZ/9A2P/v6/+NH2uz/6Bsf/AH9f/GvX/wDhmzXf+g7p3/fD/wCFH/DNmu/9B3Tv++H/AMKLBc8g+12f/QNj/wC/r/41etNchsn3w2aKSuCd7H+deo/8M2a7/wBB3Tv++H/wo/4Zs13/AKDunf8AfD/4UWC55O2pWbOz/wBnoWJyf3jf40v9q2y526en/fxv8a9X/wCGbNd/6Dunf98P/hR/wzZrv/Qd07/vh/8ACiwXPJv7Utx006P/AL+t/jQ2qQOhVtPjIP8A00f/ABr1n/hmzXf+g7p3/fD/AOFH/DNmu/8AQd07/vh/8KLBc8g+12f/AEDY/wDv6/8AjR9rs/8AoGx/9/X/AMa9f/4Zs13/AKDunf8AfD/4Uf8ADNmu/wDQd07/AL4f/CiwXPIPtdn/ANA2P/v6/wDjSre2iOGXTo8g5H71/wDGvXv+GbNd/wCg7p3/AHw/+FH/AAzZrv8A0HdO/wC+H/wosF2e5+BpDN4E0KUgAvZRMQO2VBroKy/DWlyaJ4Y0zSppEkktLaOFnToxVQMjNalMQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAQXv/Hhcf8AXJv5GsXT9Z0vRvCejyanqNrZo1nDta4mVM/IOmTzW1ef8eNx/wBc2/lXiGhX9hqui6dDqls/9p6jqFvbBp0ys9mkmzZE3QL8hDLwck5zkUuo+h0Gn+NrfR9S1PTNI1bw7caVHMJbV7zUfJKebl2CkKwdQxPoRnFavhHxXott4WOuajq1vJfalMZ7pYTvYS7R+7RFy3yoqjHXuetb90viGG5eLT9I0VrNOIjJcujbfdRGQPzrjPH9hq0drBrF3AYQ8QtLg6bNk2373crbmKfI3AblcYU5IBpiLranHrK2evX8CX8l2WXQ9EjcOOmDLL1G7GdxPEYyOWPNNPh/d6PqWlSi51Ro51eK9k0y4dTCSVMSKGbIgQ56c565BIqfRtKvvBF5J4k1KJWtr9tl8uRI9ivASTcByCcmTHGW3dAa9MVg6hlIKkZBB4IoAxvCAI8IaSCckWyc+vFbVY3hL/kUdK/69k/lWzSWw5bnE+OfD83iXU9IsLe7WzkCTyC5wxeLBjG5NrLhueCTjrwa2tT0q6bwTdaTFNLe3RsmgWWdhvmbbjLHgZJqW7/5GzS/+vW5/nFWpNNHbwSTzOEijUu7HoAOSa1ntH0/VmUN5ev6I43xJb6zrlhDDDo08O1JQPMljLrJtGxhiTaBnPzckY4HOaZHp3iISQ20kEzZvUupLn7Qu3Z9nCMuM5J3g8Yx3zXXWGoQ6jZxXUIkWOYZQSoUZh64PPvVnIJwCKzNDgR4Y1W0sbWOCKVlaytxqEQujuuJEdTIMlupXcM5APTOKlvPC817588GnS2pTTHis4mufmin8xmXkMcHoRyQM4ruQysMqwI6cGoheW5vDZiZPtAQSeXnnaTjP5g0AcLN4e8Qy6lqMqzTx3UgnMN0jqEKtGQiE7twwSOAvBXOfXX8M6XJYajql0dNk061nggSOOWcOdyb9x4Y4HzDvz1rp967S24bR1OaGKMu1tpVxjB6N/jQB59pPh+fTm0CLzLT7CoMl/AZl/dS+S6Fl7EPu+Yeoz3NSW+i3a6dcR3E9tJNDJbWtn+/Xm1hmVwxOeGIzkf7Irszp+mrnNnaDAycxLx+lKdO04MFNla5PQeUvP6Vl+98vxFqcjbwXklvBpclrHFHFqr3hu3uYyuwXDSjaAxbJBA5Axk1nHw/qt3p1ra/a9PtfsVqwgMrCQ+e0hfeu1hgrtTBOfvNXfnTdOAybK1x1/1S/wCFA03TiARZWpB6Hyl5/Sj975fiGpg6pby6xLoMzXX2J4XaS5MFwm6ImJhgE5B+Y44rmdQ0TVFsrq0t2W4Lz3U0Ny1yhkDtt8tid6heh+YAkY6DNehnTtOBANna5PAHlLz+lVITotxYyXkVpA8KFw223y3ysVPygZPKntzR+98vxDU5K40u6+0XElxAL2CWad1ghvEjIkZIgkh+YdNrj1GcgVJbwalputxX7q2oSRwg3EhmRt+2DBERDhslx9xkIySciutvYtH0+znu7m1tVhgjMkhEIJCjvgDNWY7KxjkDR2tusi8grGoI96F7W+tg1Ob8Zafqur2sEdhZs2YJSrhwHimIGzqwA7/NhiMcDmrmp6G+qS+HpbqBZns5t87M+No8pgT15+YrXQqysoZSCD0INLWozz680XxBeWJ082smIYLyMyvcLtmaSRWjwM5+6D1Ax0ro9N0rULcOTePbp9tuZmhRUYSq8rMuWIJXg9q3qKAPN4/C2oSabLb3GlyTxQ3FtOomlUTTFJMychyrHb/EdpJPI9LF1oviK41ya4soDY5WeNZRKMbDCViyd5JIfbwFAUjIz1PoFFAHB3Ph3Ubyzs49JtJtFeKV2dpZxISTCVyMMcBiQCR83U9ea383154evrC1tJNOvI7XyoSz5CyGMEbXGc7ScZ9q3aKAOIfR5ja2gTQLtLGOVjcWBu1LSsUwHHz4wGzwSM53YyKrx+E9Wmtw98ZZbtRZIji7b5UVv3wByP4SQT1au/ooA84utIu4dUTT3065vLcRXrW0EV1tEamSPyzksOmTjnK54FWZvDGtHT7ySWSSe+ee3DkTbvOgWOISKoLADLqxwcZ79a76igDzz/hEL+WCeVo5zMumyJZmSYI0Uxld0A2sQNuVwcnA4zT5vD2vSahqcpa4+0SrcGKdHUI6vGVjjJ3ZGCRxtABXOfX0CigDg7/wveQrJFawXLWhW3kMUcqyB5QJBIWV2G4HKZ+YZIB5Ip40nxMlt9shjSPUIktxFD9oJjP7to5Ack9NwbnOSo5PWu5ooA4fTvDepWXiaN5Jbp4IZVMVyGUgwiILsYlt3LZJG3qc5qTU9L1ceJ7i9sLNp/MU4aeUKijyiAFZXDL82PlKkZJOR27SigDzqz8JX9yES9siLVb+3njikdV2KEIkIVXbHOO5J60v/CJ6rdrq8F2JWeeC6j3syiKfef3WTuLfKMY+UbcEc16JRQBwqWc66zpemWsUtpBcQJJe2xly0AhfKn5SRhydvB5A+tXda0q/n8U217b20s0f7pGMjjy0QMd5BDhkO09gwbABrrcc5ooA83g0zUdW0hpy9xdxW84tbbymVlnhi3gSMrMofcW55HKAgnFWH8N6ttvZ47EQaleaXDEJ4p8iKVNwdCS2eQVAPP1716B0GBRQBwNt4VvJ/JWeG5WzzcN9mkkWMIxRAmFRzxuDHqcHnjNXPDukvpXiS2WZWFxJo6faGaUuXmDjexJJ5yeveuyrHb/kcov+we//AKMWtKfX0ZnU6eqNiiiiszQKKKKACiiigAooooAKKKKACioJby1gfZNcwxvjO15AD+tM/tKw/wCf22/7+r/jQFi1RVX+0rD/AJ/bb/v6v+NH9pWH/P7bf9/V/wAaAsWqKq/2lYf8/tt/39X/ABo/tKw/5/bb/v6v+NAWLVFVf7SsP+f22/7+r/jU0U8U6b4ZUkXONyMCM/hQBJRRRQAUUUUAFFFFABRRRQAUVWvtQtdNgE95MsMRYLub1NZ//CWaF/0EovyP+FS5xWjZDqQi7NmzRWN/wlmhf9BKL8j/AIUf8JZoX/QSi/I/4UvaQ7oXtaf8y+82aKxv+Es0L/oJRfkf8K0LG/tdSt/Ps5lmi3Fdy+opqcXomNVISdkyzRRRVFhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAEF5/x43H/XNv5V5l4atp/+FY+E08iXcusROV2HIX7Sx3fTHOa9TdFkRkYZVhgj1FYi+ENDRQqWbqqjAAuJAAPT71IatbU3K5zxTbG7vfD8LQmaBr8rOm3cpQwSghvbnvVj/hEdE/59JP8AwIk/+KoPhHRCCPssgz6XMv8A8VRqGh5fcabFLp1k2qahIulSz3cEEt8jXEVpFE5SKLYCAWbDfM2ThQo5rr/Ak15YzWukzxyxW1zpiXsNtKWJtWDbGjXd8wQ5Vgp5XkVqJ4A0GJNkaXiLknC3soHr/ep8XgTQY7gT+TcvIFK5e7lPHp96jUNC54S/5FHSv+vZP5Vs1DaWsFjaRWttGI4IlCIg6KB2qamgerOZ8R6rDout6Ze3A3R+TPHgSIpyTGf4mGehrI1nxtpOqaJfWH2csbiB4wJJoCuSCBn9564qp8Vrea4OkCKKRwDLuKIW2/c64rlI/DMcms21tFFfTQSWvnf6vy2kfBO0EjCZx36V7FDDUalCM6m+v4Nnk1sRVhWlGHl+Rfl1OwX5beztlxDbJbyi4hVrRkYl2QBu+c8EZPB4rV0zxPp+l+Hr23gtov7QuDO5kaeEq7MzlN58zJADAe3auZk8MFfEaWXl3CW5liV1ZSXXeBuAYDadvIzWbaaLeXMtzGtlOSkZZMqV53ADtz16cfWt1gMM1e/9feYvG4hO1joNL1gaZqCXEaRGGO7SdYBdW6DHkPG3CkAHLKenOOTmo7XUIIYmkaC3W+k02S1+0pcwl4pS7kMrbskYcDPB4qmng+7W8kt7maJdkEkm5NxAZR90/Ln/AB7ZqHRfDc+pttmilt181UMrhhgFWPTac/dHPb8aX1HC2bvsP65ibpWNoanpsl5ETp9tb6eJ7eR7KK6haNtiSBmI3AEkugxjkJk+lV31C3+zzw/Z4JT5LxWbvdxA2RM8jqyfNxhWTpj/AFYHSsp/C2ppbCbYjE7WMS7i4UnAYjHTke+DyKsP4L1VCw3W7EFlwrnl15KDjrjn096PqOE/mF9cxPY6XT7SHV71L6PQ2v0W+upLpleF/MSQEojfPg4ypweBjIp9rpOuxR2/n6DcTXUf2MJeNNGZIBFt3quW5Bw3cZ3nPStP4VQyw6LfCWKSMm5yA6lSflHrXe15mIjClVcErpev+Z6VBzqU1Nvf0PL/AOy9fms7WzudAu2ht7JrbKXMQMjGaNw2CSMbY+QQc5x05rVii1geFm046Fcx3cMpltXi8lEVlffGWAcAdBuwMcnAru6Kw54/yr8f8zXkl/N+X+R5ZJoWtT3cU8+k6gzGJGkKSQBknMrSSMhYsRncACMHCAH2dNoOqCxdbTQLiG7mS+WeUPEDJ5zMyZO/JAyPpjivUaKOeP8AKvx/zDkl/N+X+R5vc6dqk1nq8R8LzPd3v2kJfNLF5m2T7it83QDC4zgBQRVZ9D1eW6v55tG1OR7hrnDJcQRl0k+6rMMtgAgYJIG0cdq9Roo54/yr8f8AMOSX835f5HGeHZ9Y0iCW0m8OXHkGVnjeHyEOCFxuVWC5zu5GOg4JJrc/tm7/AOhf1L84f/jla9FHPH+Vfj/mHJL+b8v8jI/tm7/6F/Uvzh/+OUf2zd/9C/qX5w//AByteijnj/Kvx/zDkl/N+X+Rkf2zd/8AQv6l+cP/AMco/tm7/wChf1L84f8A45WvRRzx/lX4/wCYckv5vy/yMj+2bv8A6F/Uvzh/+OUf2zd/9C/qX5w//HK16KOeP8q/H/MOSX835f5GR/bN3/0L+pfnD/8AHKP7Zu/+hf1L84f/AI5WvRRzx/lX4/5hyS/m/L/IyP7Zu/8AoX9S/OH/AOOUf2zd/wDQv6l+cP8A8crXoo54/wAq/H/MOSX835f5GR/bN3/0L+pfnD/8co/tm7/6F/Uvzh/+OVr0Uc8f5V+P+Yckv5vy/wAjI/tm7/6F/Uvzh/8AjlH9s3f/AEL+pfnD/wDHK16KOeP8q/H/ADDkl/N+X+Rkf2zd/wDQv6l+cP8A8co/tm7/AOhf1L84f/jla9FHPH+Vfj/mHJL+b8v8jI/tm7/6F/Uvzh/+OUf2zd/9C/qX5w//AByteijnj/Kvx/zDkl/N+X+Rkf2zd/8AQv6l+cP/AMco/tm7/wChf1L84f8A45WvRRzx/lX4/wCYckv5vy/yMj+2bv8A6F/Uvzh/+OUf2zd/9C/qX5w//HK16KOeP8q/H/MOSX835f5GR/bN3/0L+pfnD/8AHKhs2u7zxILyXTrm0hSzaLM5T5mLqeNrHsDW7RT9oknaP5hyN7v8gooorI0CiiigAooooAKKKKACiiigDxD4li0b4hYvXdLf7NGXMYBfGG4GfXp7VmweHdMmvmwtyIfsa3BtpbiKKVGZ9oVnYbenzdAcEV0nxM+Kk/gnxKmnro9neK8KyCSUHcPauMb9oe5kJZ/DdgzE8k5Of1rNxu7m6qWSQ+x0KC48RNYzRTRIskim1Eim4+VCwH3cHOAMgd6nvfDtultHPDZ31q7xzMbW8dRINighx8oyvOMYHTrVcftFXYl3Dw7Yhx/ECc/zpX/aLvS+5vD1kzYxuJOcfnS5B+1Fg8KSG++y3N7YRMI5WcCdWMbIpbawHQ+v4+lUl0G7e5e3UQmRGhU/PxmXGz+Yq0P2hrwsWHhmx3HOTg8+tA/aKuw2f+EcsQeOee3TvRyB7UB4Sv2SR42s5AhZVCTAmRlGWCepHf345r1L4RY/4Q6XGMfbJOn+6teYr+0ZfxxkL4fs1XrgFgD+tEP7Rl+oKxeH7JB1wrMP61Shyu5MqnMrH0XRXzs/7SGpJjOhWvP+23+NIP2ktSP/ADArX/vtv8aq5lY+iqK+dv8AhpHUv+gFan/gbf40n/DSWpZx/YVr/wB9t/jRcLH0VRXzx/w0bqX/AEA7T/vtv8aRf2j9SaZYxodpkkDO9v8AGi4WPoiis/Q9QfVdCsb+RFR7iFZGVTwCR2rQpiOY8eQyzeHQIo3kKzKzBFJwADk8dq8S0bVJ9a1e7isWjux9nElvbQsGbhmBLYyQTwfYEV7X8QddvfDnhZ9QsEMk6zIgjGMuGONoyCOfpXBTa9FJ/a+raY0phhsLT7VJGqRTJC8kiyrhVG14/mPHPB54Fc1SkpybOOtQVSbf+Rzk2i66fEUFqJtUjWWGSfyRAvG1kGBlM7fm/lUGn63LqmoSWdnp00suJPKCkkuU6gjHy5wcda799Yu38U208F1LLEupDQ4LslMsrR+ZK2NuDho1XPsawNDW5lNlqN6klvYa+9xawNa3rHyXO8sPLKhU37G5Tnmo+rrqvyM/qcWtU/w/zMGK/wBS1CKwudO0uZ7a5u/s+9gcthSWA4PIIP8A3ya9p8DW81t4faOeGSJ/PY7ZFKnGB2Nea3Wo21l4X8OaqqXKtqMsWozwWzqi2kcQCtMuFHQFQR/EDzmu28Z65qPhPTLC/ivLu+juLxIHSKKLcIyrMzKAnJAXOO9XToqErpf195pSw6py5kn+H+Z3NFeaWvjG9vtRhhtNSaa1n1hdPjnRYyGja3EwkHydecYrD/4WlqH/AAg0msmWf+01u/s4stseCmN3mZ2Z27ATn1GK35n2Onmfb8v8z2eiuThg8XnWlilvEOmMzHz1WMSKvO0bdpyT8vPHfiptUvJtKuIbeTU9TnnljeVY7e0jc7ExuJ+XtuH17Ucz7BzPt+X+Z01Fcn/bCfbLS3/tq/23iq0E/wBmiEcm5S4AO3PIHpjtnNVI/EFxc3Gkra3Ory2+pFvLmNrEuFChg2CucHNHM+wcz7fl/mdvRXL6lqLaXcmGfWNQcpGJpmitY2WCMkgO528Dg+p4J6CoNHutY1TVdWhbULyG1spvJjLWsWXIVSTuxj+LgY6YOecUcz7BzPt+X+Z19FcDH4j1KaGxmWTVFW4vZrUxG2iLnYJMFeMdY+c8DJq1J4ltY1t2OvXxSWNZWb7NEPJVmKAvlcj5gRjnGD2o5n2Dmfb8v8ztKK5DVNSvtK8Q2FhNqVybW5iZpLkRxfuW3oq5Gz7pLYz2JHaqNv4h1D7JLcXl/PGv2J7qEJHGfMKSFGT7nXPl4/3/AGo5n2Dmfb8v8zvaK4NtT8RPdpYRX6peNeraMZEQop+zCZjwmeuVH51PZ6pq0uqQadc38kU/nTQ3BRY2VSiK4KkoOCrjr0o5n2Dmfb8v8ztaK5Hw7qF9rv2rdqVzAY2DwgxxHzYHz5cn3ON2Dx2xTItUvpPEV3pv9pXAgiRhDcbIv3sqAGRMbOwdfyb0o5n2Dmfb8v8AM7GiuGvdY1S28J6VrC6sY3vzbhvOjTy4vMAJPCZOKkfW5rG3i+26zdzytbm6d7K0Ro1hBwXJK5wPz9BRzPsHM+35f5na0VxQ16VJ9UW4v9SihsZkhWU28JEzOqFQvy9SXGPbniprfW0upraGDV9SeacyDy1tYiY/LYK+8hcDBZe/OeM0cz7BzPt+X+Z19FcUniWBrP7W2r6rHC0KzxF7FAZo2ZVBQbMnllGOvI4p0uuSW0zxz6rqHmmRI0t1tYhICY/MIORjOPQ+3Jo5n2Dmfb8v8zs6K5P+2YxqqaedbvhIzrEXNtEFWRl3BDlchsEdu4HWtr+zr7/oNXP/AH6i/wDiaOZ9g5n2/L/M0qKzf7Ovv+g1c/8AfqL/AOJo/s6+/wCg1c/9+ov/AImjmfYOZ9vy/wAzSorN/s6+/wCg1c/9+ov/AImn6NNNPpweeUyyCWRC5ABIV2UcDjoBQpa2sCld2aL9FFFUWFFFFABUX2q3/wCe8X/fYqWvnDR9N0/VdYmtbqWVZZJisaxFB1JyxLcYHHyjk54qZSsXCHNc+iftVv8A8/EX/fYo+1W//PxF/wB9ivnqHQbFrdIZZ5xfzpPJAAgEYEZYYYHnJ2N9OKbpWi2lxp1xdXEV5dtG8SrHpxVyA6sctwemMYqed9jT2S7n0P8Aarf/AJ+Iv++xR9qt/wDn4i/77FfPV74XeO8kt7TLhZSpkmdEEaiNXIf+6V3YJzio4/C08lnLKlxZvMk6xLGk6MHBQvuBB54H8/SjnfYPZLufRP2q3/5+Iv8AvsUfarf/AJ+Iv++xXzXa6HdXcKzJ9nSIwG43yyqihA+zJJ6fNxRqGgXunWX2i5jiCZCuqyKzRkjIDgcqSPWl7R9g9iu57j4s8QjRLrTxLqP2C0mWUyXH2YzBSu3G7H3V5PzHjpV63W/ntob6LxBbzWjRiQOtspSRcZ3ZDdMelZN7o1hrtz4ctNSg8+2FjJIYixCsQIsBgD8w56HiuhvtIgu9Dl0mL/RbaSLycQKF2x9Cqjtxke2a63LljG1tuy7s41G8pX7932Rzen+JJ7rTZNRudU+yRi5WIQyWW5vmwY8BSSd6spHfnHWrF14lhEMcg11Ig6SNhNOkZlCMFcsuSV2ng5xjPNWV8G2EF8txazXEKb4ZGiaQyBmiYlTliSOCR9MelSJ4VgSe7l+0yk3MdzGwwPlEzhjj6Y4qPaPy+5Fci8/vZXk1nbqS251+EMo2sBYkxltu7G/ON23nbnPtUcmrNZxRyT+JY2V0imX/AEDJZJCVQAA5+Y8etSx+DokZITfStYCVZ2ttgBaUIEzu644Bx698cUy28GeVJbvcanNObdbaOL90q4SBy6g46k5wT/Kn7R+X3IPZrz+9jG10fZYXGvjMjv8Ac012kUKcMGTqoUkAkgUp1nZcTRv4jiQQGR3Z7EiMhPvKHzhtvcAk1a/4Rd4NQkvrDUpLa4leUyMYlcFXYMVAPQgrwfc5BqofAdi0l6GlHk3PmkYgXzI2kO4kSdTgk4Hvg5o9o/L7l/kHs15/eyDTtS1PU7nUo4NZcJZJGxMmn+WzFlJwVbBHAH51QtvE2q3uk2l5bX0rT3EUjNataoHjdI/M2HnkkEYPcEHvXUWejTWR1K6ubyS9uLyNVfbEqYCqQAo/HuetZVro0NnqOl30VhqYmsLX7MR+7CzALtVmG77wGcEepHpUSxPLv/6Sv8heziu/3srXGtakkkP2fVzcQyR20hlW1QBRPKEj79cFm+i+9R2mr+IZvs89xdrDY3N01rFOsSOQ4dkXcvGASuOCcZFWrXQ4rSwmtIrHU9sl7HdBmERKBHVkjHzfdG0Ae1JZ6RcwNBHOupz2Vvctcw23kwqN5ZmG5g2WALEjp0Gan62v6j/wBckfP72VV8QakbfRZm1RwupKrti1Q/Z0JChm56F2Rfxz2rR1O71LSry3hutdVIpYppXmNomI1jAJyM89azx4Ss3smt7my1ibFolpG4kWPy0QHHCuATuJbnPOK1b61l1BrV5rPUDLBbyw7jFCwfzFALFSxB6Zx05o+tr+o/8AADkj5/eytJqtxDaC6l8QTpFlg27SHDJtAJLDGVABByeKke/uI737LJ4mCPtLB30/EZwu84c/KSF5wD0rHl8JPNZtbOuo+W/mBohBF5Sh1VfkQuQpG3IPPLNxzUr+FI57zzbi31OWIB1CmOLftaExFd+7O3DEgDGD60fW1/Uf+AHJHz+9mpBfXdzp9zfReIX8i2QySF9N2HaF3ZAbBIx3FZb6z4lt9Mur+7uoo447JryONREzlQMhXXjaSO43Ac8+u5aC9XTp7LUbe/vo5U8rmKKPCbcEcNyT3P5YrGl8Ny3cbRXqajOos5LOJ/s8CyBHAHzMG+YgD2HtR9bX9R/4AckfP72RjXdf+1Gw+2xPqBeNIo4VjeJ96u2TJxtwI2JGM9MZzQmu+ILh2t7S4M17AsjXUBijURbG2kBv4iT06cdcVefRFimkl06xvrFvtCXUSxQw7I5QhRm27uQynBHtkYNRLodzATNaHVIb2ZZFurjyYW84O24nbuwpB+7joOuaPra/qP8AwA5I+f3sZDqfiW71Owt7W9gktb62+1RTsiqfKGzJK7Tz+8HGe3WpLrWNSs9dudKl1Zg8VqLiOX7GmyU4cmMc8NhCfcZ9K0ra1W0u9Pmg0zUFSxtGtIo/3ZBQlOSd3UeWPzqvqelRaqdS8/T9SU3sUce5PLDQtHu2uh3fey/6UfW1/Uf+AHJHz+9jNLu9b1K9e3OqCIC0gukf7MjblkDcEZ4IKke9R/26PKmlPitVjiAYs2nYDqWCBk/vjcQMrkcj1rW0iwWG8aQ293H/AKFFa/vggUrGWwflJOTvP5Vk3vgu4/s6KC31GSRrURQWQZFXyIhNG7ZP8bYjXGfTpzmtI13JXVvuX+Q/Zx8/vf8AmKdZdYBKfEr/AH2QxjSyZFKgFtyfeAAIJJGMEeop66s7362SeKVaVnVAV0/KbmUMo3/dyVII55zTrrwOl5Ibqe+Et+8jvJLJbKyMGVFwEPAwI0wc+uc5rQXwvbIrJHM6IbyC7CqoAUxKihQB2IjH50/ay8vuX+Qeyj5/e/8AMxL7WNUsfEQ0uTWlSKK2NzcXMlgQqLzjBAI7HqR6cnirtnf3N9KkUPiXEzuUEUmneW4YLuIKtgj5eeetaep+HbfVZ7uSaaRftNoLUhMfLhiwYe+T9OKpf8IrOboagdWf+1PNDm4EC7duwpt2dOhJznr7cUe1l5fcv8g9nHz+9/5lH+28yxRjxUGMqxsGGnZVRIcIWbooJGBnHNRSa7djVYbKLXzIred5ko0/Cr5Qy209HweDtzzWnbeDbe20yWyF5MyyQ20RcqM/uWyD9TnmoZfBZkUQjV7mO3iWcWyRoqtF5uc/N1OM8dOOuetHtZeX3L/IPZx8/vf+ZSl8QTpPZxLrlzI1zcNbELpJ3ROE3/Op5HGO3Q56VJ/bU8aIbjxNGjMXzssd6oocpudhwoyCMnAznnjNW7PwatgFkt77y7hb0XgZIFCBvK8oqE9Cue+c85ok8Fp5M0EOozRQXaNFdr5akyxmR3wD/Cf3jDPofXmj2svL7l/kHs4+f3v/ADI7bUZ7vUTYQ+Jg1wHePB07Cl0OHUMeCRg5ANRXOtNaXklpL4nPnxyeUyrphb95tDBARwWKkEKOcVs2/h6G3mtZFnkP2e8nu1BA5Mu/I+g8w/lSnw9Cbs3HnyZOoC/xgY3CLy9v0xzR7WXl9y/yD2UfP73/AJmMdaIWBv8AhKlKTIHDLp2Qik7QXI+4Mgj5scg+hqC4125h1SCyXxCGDSSpLM1iFjj8tGZiGPytjbg4PH4VfPgpFimgh1GaOC6Qx3aeWpMqeY7gA/wn94wz6e/NE/gqK5zbzahM2nhrho7YIoK+crh/m6nHmMR6d80e1l5fcv8AIPZx8/vf+ZHY6vdS6lYqmrm6iluDDNDJZGB1Bid1ODg4O0YOMHmutrkIdCTRL7SFDxSSS3p3PHbrFkCCQDOOvfr69q6+nUd1F+X6sKas2v62QUUUVkaBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHy/8AtD8eO7b/AK9F/nXlQtJTBE+VCyk7RySSK9W/aGjd/HVuVRmAtFzgZxzXAaYlo9gHnuHWZD8kRUFHH16g85/A0k4rcbTvoZSWEpcjcm4dQ2Rj9KglRopTE+CVOCRXaWttKNUls5zvupJhCAHUDB4znGOQR61n6T4VbWvF13pF1dixMEcsksjIW2+WuSMcHtVydOy5Xr+nci0lJ32MVpEWNevzA1TWN5ZCqKWPoK7NPh3eq0M119pttOncJHdNEGxn7pZQ2VzWXp9uuh+NfJYrcpZXHJIwHCms1JPYtxaepi/Zrln8sRPu6be9RSxSQSGORWRx1U8EV0PiGT+1NZnvbe2aJZsMsQGSOMfnTLy0SS/dTal9sa5YOR2FVdtXYNWdkMttHiltlnnIjiKgh92cnHseKybhFiupEQgopwD6043coBQAbemOe1dR4c8DS+J9NfUBfpb/AL0x7DGT0A9/elThOUnrcmc4xV2ckW4xTQrFshSfoK9IT4QzucDWIvcmE8frU/w88I/bbyecTFJLKSRA/ZipOPl+tb06LnPlbt3N8LR+sSsnZb38jzYFgMFH/wC+abEc3sZxj51/nXu974g06Dw5Ksmlbb+4JZCAoWTaRlgOo9dtefa1oMFx4i81JXLukcjLHGNoJUE49qmpGmo3jK/yInTqQxDopaLr93T59z6n8Hf8ibpH/Xqn8q26xfCA2+D9IH/TrH/Ktqs0S9zA8W6Zb6zY2Wn3RcQy3se4ocH5csMH6gUkHgzRbbUtbvYrdg2tRiO9jz8j4BBIHYncc+tP8U38emWdleyqWSK8QkBlXOQw6sQB16k1DJ4wtY4Vm+xXTxrALi5ePYwt4yxUMxDYYfKx+XPAzUrdmcfiYll4H0iw0vRtPg+0CHSbn7VATICzSYYEucc53n0qrpXw70nSp7ZxealdR2hka1gubgNHAz53MqgDn5m5OcZquvi3V0e5nfSZZIf7TWwiiUxggbtpbdv5J+gHStSfxpplrM8M6zxyoJ96lAdpiGSDz1YHK+tUWZ4+GHhmS3t4Lu2lvYrayFjAtw+4RoCTkYA+Y55PsK2D4Ysn07RrKSa5kTSZY5bd3cFmZFKrvOOeCfSqU3jjT7ae6hntrlJIIzLsUxu7gMqkbVYkHLjhsZqRPGdidXj0yWCaGdpFhcO8ZMcrLuCFQxJ4I5AIGevWgCCw+H2haZMj2aTwqmpNqaRq42LKUKEAY+7gniqf/Cq/DZg8vbdZ+wmw3+aN3ll9+emN3bPpxW1deJ7e11Ke0azumjt5Yop7lVXy4zJjZ3yfvDOAcVm2PjWMWulrfW7faL1Ew0TIAXYkKApbdyRjOMDPJoA6W9gmnsJYLa4NvKy7UmAyUPriszVtCn1PWbS7S/mtI4baaFmgIDtvKeoIxhT7g4xWKni7Vr6w0SSHSpLZ9QuHSRiY3CIisxwN467ep6YPB4q+fG1msCSvYX6iZUe1GxSblHdUVlG7jl14bBwwNAD4vBOlw30NzE86iFo3jiBUgFI/LX5iu7G0dM4zzV0eHrZLfSooZ7iI6YAsDqwLFdu0hsgggjrx9MVTl8Xxx3RtxpOoO4nFrlRHjzzGJAmd3ofvdOOtadpqsV/ov9pQq6IUdtsi/MpXIIIz1BBHWgCvqnhy21W5kmkuLmHzoRb3CQsAs8QJIVsgkfebkYOGPNXrPT4bF7pod2bmYzvuPRtqrx7YUVj6R4sttUvobFIpDM0KSPJlEGWjD8IW34ww5wRnjNWP+EltB4jXRXjkSdyVjcuhDELuI2htwGM8kAcUAPtvDtra3EUqz3DLDcyXUMbMNsbSBgwHGcfOx5JqongzTYriGeKSZGjAVshG8xQ7OASykjl25XBwcUk3jPTra61GKaOVUsFYyybk6rjjZu3c5ABIAPrVZPGwuruwhstNmnM101tOFliPlERGQEEOVbjB4PY9+KAN660i0vbw3NwhkJtntWjb7jRuQSCP+Ais+PwjpaWGlWf79otMlEsBaTLMeThz/EM4OPVR6VJqfiS30y7khe2uJUt41lupowu23RiQC2SCehPAOAM1V0jX7q5Gt3upQta2NlLIiAqpwsedxyGJJ4z0HbGaALN34Ztrm5a6ju7u2uDdfaxJCy5D+V5XAZSMbfbrUbeEbB4QjT3ZkIm3z+aPMkaVQrMTjrgADGAMDFVYfHVhPZzTx28peIpmMzQj5XBKktv2j7pGCc57U6Lxxp9zMiWtvczB7ZbgbSgYqULgBCwZuBjIBGeM0AaNh4a0rSr9bvTrRLRxCYGSEBVdcgjcO5GOPqfWq0Xg3RoJIJ4oCt3FM0xuhjzZGbdu3NjkHccipovFOkSyYN0saHywkjkBZGddwVfU7SpP+8Ko2fi2aeF/N0e6W5a8ltYIEaMmXYWyc7sDAXnJHPTNAGhN4dtpdFsdMjuLmCOxMRgljZd6mMYUnIIPvxSP4ct50m+1XV1cSTWb2cksjKGMbEk/dUDPPXFRnxdpA003ZuMMLd7n7OcCXamdw2+oKkfhUWqeL7HSrhreSGZ5QUH3kRfnVmHzOwUcIep5OAKAH3XhOxuvtSvNcCO5MTtHlSqyRhQrjKnnCKMHIPpVi00C2spIZkkkeaKGWEMwVQQ7KxyFUDqoxgDiqa+M9NbWo9M2yCRpFhLFk+WRkDhSu7d0I5Axk4zS3viyGz1GazGnX05inS2MkaptMroGRRlgeQw5xgd6AK2l+CooNKs7fUL25uZre3ihX5xtiCMjkJ8oOCyLycnAAq/qPhi01FrpmnnjN06vKAEdThNgG11I6e2c1TTxjbvL8sE7OyIqWojAk84yPGU3btvBjb2wM5NTTeLYLeSSOfTb+N4YUlmBRf3e9mVF+9yzMuBjPUHpQA6x8Jafp1/HdW0ky7AmY2CNuZUCBixUtnAHRgCRmt+uZuPGtraERXFjcxXQlMTwSNGpQhQ33i4U5DDAByefQ02bx5pMF3dQOspFsJdzKUJZolLOoXduGAp5IAJHXpkA6iisNfE9uCEntLq3nYQlIZAu9xI5RcYJHUZPoKg03xXHfRxbLW4nUIrXNxGirHDuyRkFs9Bk4zgEUAdHWboX/IMP/XxP/wCjWrCT4iaTLbGZIpTnyzGPMiwyvnaxbftToeGIPI45rY8NTC40OOdVKiSWVwCQSMyMe2R+VS/iRD+NfP8AQ16KKKosKKKKACuFs38QajaxXtp4f8PGGX50MjsG69/lPNd1WJ4R/wCRT07/AK5f1NJ7lLRGWY/FpcOdD8PFhnBMz556/wAHfJrk5/HXi7RtWTTB4Gs4rmdlCLFcoBJksFPXvtPWvW68r8WeG9Tu/GkkUFlbTxam8TrfyWLSSWe0BTiUHCY2Bh6lzRYXMb9sfF89osj+G9BgMo3PDJOdwJ6hsKQT+JqVIfFcfCaD4dQZz8szjnpn7ldZPPFa20lxcSLHDEpd3c4CqBkk1Dp+o2eq2aXlhcxXNs5IWWJsqcHB5+oosPmKOiC21TQbK7lsLWMzwKzRrGCozzgcdM81qS28M8flzQxyJ/ddQR+RrK8Jf8ijpX/Xsn8q2aFsJ7mPcgL4q0tVAAFrcAAduYquatfHTNIvL4QvMbeFpfLTq2BnFcf8RNavdBvNJvLFkWYpNGS67hg7D/QVx8/jrxPqED2LBHF1GU2JbfM6sCPl/WvSp4GrWpxnFq1v1Z588ZTpVJQle/8AwEeh2Hi5Vsrc6lBOJyiSXLx2+2O3DkhN3zHAPqM8cnAq+niGO70bUb+zgm22iS7Wmj2rIybgcdyAVIzXjU+v63NdGBmZXdYoZbdYseb5Z+VWXrntxjI4qxB4x12PSZ9NiMZtmWUyDyckB2JY57csfpT/ALKr9194v7So9meraf4utLqO2WeC5t7ibyMRyRbd3mg7WHP3SVYc8jHIpLrxpplr5hMV3IsSyvK0cJYRpFIY3Y+wIPTJI6CvH5vE2q3MkcnmqssSQpG8cYygiLMhH/fTZ9QaZFrWqizuIwxkjmilt5ZGjySJXaRsn1JLEf8A1qf9lV+6+/8A4Af2nR8z2ZvFtmlrPM1peq8EgjkhaMK65XcGOTjBHv7deKRPGOmSzRrEl1JC3kZuFi/dp5wHl5PvuA6cZ5xXjV54j1O+vGu5ZYxMzBiViXGQmzoQR0/Glg8R6jbWZtI3j8o/Z+qAn9zt8vn/AICM+tH9k4jy+/8A4Av7ToeZ7BL4xsjCzRJNGGG6CWaE+XOokVGKYOTgsOuOueRTo/FC3euWNja20xt7h50NxJHhH8sHOw5/vDHI57ZrifB+kXninTZZJtVeGO1cwwwiFWWNSyyHb6chRzngYrqbbwZfWt7Bcx+IZv8AR3leGJrZCkZkJLYGfc4yeO1cdSh7OThKSuvX/I64VvaRUoxdn6f5mnN4qsorm4t1tr2WSGcW2I4ciSUqG2KSRk7Tk9gAcmqD+NYUv8LaXMlmLRp5SkJMkJSRkcOM8bdp4GTwcZpJfCN/LvYa88cjXX2wSJaqGWXaFyOem0Yx7mqjeALoq4XxHcoZIZIJWSBAZVdy77vcsx5GOvGKjkj/ADL8f8iud/yv8P8AM1rjxhYxed5MF1Oqb0jlSP8AdyyqpYxq2euAfbgjOeKbaeMLa4TTUksruO5vYFm8naCUViBnryM+mTjkgVRXwPcpKCNccQiVplt/sq+UrspUsBnPQnvjJJqObwJeXFlbWc/iOeWC3RUjV7WM4CkEc9QeAMjBx3o5I/zL8f8AIOd/yv8AD/M7WisOHStbhiCf8JCZMZ+aSzUk859ak/s/W/8AoOp/4BL/AI0ckf5l+P8AkHPL+V/h/mbFFY/9n63/ANB1P/AJf8aP7P1v/oOp/wCAS/40ckf5l+P+Qc8v5X+H+ZsUVj/2frf/AEHU/wDAJf8AGj+z9b/6Dqf+AS/40ckf5l+P+Qc8v5X+H+ZsUVj/ANn63/0HU/8AAJf8aP7P1v8A6Dqf+AS/40ckf5l+P+Qc8v5X+H+ZsUVj/wBn63/0HU/8Al/xo/s/W/8AoOp/4BL/AI0ckf5l+P8AkHPL+V/h/mbFFY/9n63/ANB1P/AJf8aP7P1v/oOp/wCAS/40ckf5l+P+Qc8v5X+H+ZsUVj/2frf/AEHU/wDAJf8AGj+z9b/6Dqf+AS/40ckf5l+P+Qc8v5X+H+ZsUVj/ANn63/0HU/8AAJf8aP7P1v8A6Dqf+AS/40ckf5l+P+Qc8v5X+H+ZsUVj/wBn63/0HU/8Al/xo/s/W/8AoOp/4BL/AI0ckf5l+P8AkHPL+V/h/mbFFY/9n63/ANB1P/AJf8aP7P1v/oOp/wCAS/40ckf5l+P+Qc8v5X+H+ZsUVj/2frf/AEHU/wDAJf8AGj+z9b/6Dqf+AS/40ckf5l+P+Qc8v5X+H+Yax/yFNC/6/G/9EyVsVipo9/Jf2lxe6qLhLaQyLGtsqZJVl6g+jGtqipaySd7f5sIXu21b/hgooorM0CiiigAooooAKKKKACiiigAooooAKKKKAPn74zsw8WzALnNmv8jXhkU0sJVo96EAA/L6V7h8ZFkk8epHHMIj9lRsnv14rzuSzVo44UkSRym9mOcJ7c9/rWKtGT8zaUeaKuYo8QKLhJntxIUQKEkYlQQBzjrnIB/StjwNe/bPG89xcSKrXMEwdjhRll59qvfDfwLp3jSfVP7QvJbdbQqQIyAWBLZ6j2rQ8UeCPCOg6a11bXt/dFX2Eq67Q3oTjinKrCPuGcIPdHU6dc6XI6xNdHaj/KrTkgEcnIzwOK8i12+MHjDVLmDIV7iTGw44J9axztW8GwEJuBAJzgVsSJctZfaJp28lshRjIJ9KdOHIaVantddrFYa46zCUQ5kByGZyeajm1y9kmMiSeWCMbVHFXfCunJqmrWlnJtCTTqjEgHAOK9i0v4Z+F7zQXupLpI7h1YxDYMHBIB5HIJFKeItJU0m2+i+8tYaMKH1mrNQhtd3/AMjwqMoyAtclD6YNet/C8F/DE2x8yC6chSOGGF6ep9q81vdCuLjxPf6bZLA7wM+dkq7ML1w3ANWtM1LxHpATTrG6WLEpKJlfvng4J+lNqfLeLMLQjNxk7/1ue2XN2IFTyGPlnOVP3oiT375rgvB3iaLRJblZbqNI2u5S6Mw5BPWuavvFXiiwu5E1C5aKd1XcPLXLDselUtBthrniaG33KxmD/M4A9Tmqwcp4WcqjV9NjvoY2NGaajfRrXbU63xJ4rWOE2emi2bTZI2iSSOUeagJy2e/P8qwLLxeLWeGI2YkIRYWYPjIAwK1J/BjtPI/mFY4vlPlR5BOfU4rkNUsX07W47eRWUhlYBuoBORW9Sm7e0atfoeZDERlUai/+G0/4B9neEW3+D9IOMf6LH/KtqsTwd/yJukf9eqfyrbrJFvcxvENuLuKwgMkkYe7Ub4m2sPlboarjwXpIiWJTcqhUrMqzkC4UuXIkA6jczHt94jpxU3iW4ltYLCWGDz5RexhIt4XcSGA5PQc1hL41vLN7qG8sfMu/tcypCr/KkcaRkgMqncSXGOB15xipW7M4/EzpJ/D9jPZy22JUWS5+1745CGWXdu3A9uahuPCmkXdzNcT27PNNJBLIxc/M0X3D/j696zp/GOJ5YfsUlsv2YzRvO2yRz5XmfKjLg46HnIIPGBTrfxbLI1sRp7yWr3ENm1wZVDCV1Vs7MfdG4c569sc1RZZXwZpIldyLhg2/ajTHam9w7AD3ZQf/AK1XRoVqurtqSPPHI7B5I0kIjdwu0MV9cAD04FYKeO9mnLe3elyQxTWv2m2AmDmQb0TDAD5TukX14J9MVNF4vubtVgtdKZr0iVmRpdibIwmWVmUbsmRQBgc5zjFAFtvC0M+uXuoXNxM0dxLDKLdJCqExqNu8d8MM/lnNJD4M0iCeGSIXCiJonEYmO0tGcoxHcj8vaqF5rGrt4G0a8tCrX199kSWUsqFfMKhiMqRnnHTAzntU58WvaqXmsHNorzW6z+cCzzRIzMCuBgHy3APqOgBoA07Tw7YWZgEfnMlvM8sKPIWEZdSpA/2cMePeq8HhHS4CmPtEgiKeSskzMIVRw6onou5V/IDoKlbWrmPwydXk0yTzCgkW1jfe20kYJIHYHJwDgA9axpfH0caQ+XY/aJDE0sqwSl9qhyuFwuWY4PykL05xQB0R0WyM/nFG3/axeZ3H/WbNmfpt7Uyy0SGxW4hillNrKm0QMxIQlnZiPcl/0FZ0vimaO01DUF0xn06z81fO84B3aM4I2Y4GQQDnt0FSXviV7fUn0+209ri4F1HbKPNCKS0TS7iewAUjvQBNB4YsILy0uFe5YWYUQRPMWRCE2AgH/Z98c5xmli8MafDq/wDaSGfzRM9wqGU7FkZSrMF9wT1/DFVNF8WprOqNax2UqwnzPLm5I+Rtp3fKAueowTnHarGreIv7Kvzb/Y2lijtJL2eYOB5caHnA6s3oP1oALzwnpuoXklzeG4n3q6iN5iVTcMEr3H54HbFSf8I5a+RAjXN40kE/nxzmY+YG2lTz0wVJGMY59aw18YXN6LUx2r2v+kqJDgurxtDK4AZlHzAoMjtxyQalj8aDzNOtzDv+1xRDzhIC6SPHvG5QMD8weeBigDZ1Hw7Y6ndm4nM6l1VJo45SqTqpJVXHcAk/mR0qzHpVnHa3dt5W6G7eR5kY5DF/vfga5GLxZqjeHtP+024t725t7W4jnDq4lVpYkkJXHyk+YOOevtWxovi1Na1NrWOzlWEiQxzckHY207vlAXPUYJyAelAEv/CJWPkwxm5vi0MgkilNwS6fKVwD6YJHr3znmkXwlp8H2MwtcFbIKbeCSdmjDIMKSDz+R575rOvfFlzFe2k4tTFpQubmOSbeGaUQxSlvkx8vzRnHPOOcZpbbxtLdRxLHpEhuJpo4ol80hDvV2GXKjkbDkAHqMZoA2vDuipoOkLZqys7O8sjICFLMc4AJOFAwo9gKhk8L2LvI6TXcLtcNco0U5BikbO4p6btxyOnNZA8Xz/2hk2zY8ryfsm4f8fP2jycb8fdz39OcZ4q03jJbfz47vT3iuIRIhjWQOHmXYRGpwMlhIhBx68cUAbUekWUWjHSVhzZGJomjLE7lOc5PUk5OT15rNXwhZLHMBeaj5k23zJjckswUEAHPBGD0x79earHxXevei2g0Xf5lxNaxO10qhpIsls8HC4U4PJyOneorbx5BeXtrDBYTskqwGQ8lozKARwAQQARuORj3xQBq2PhjT9Nuo5rM3EKoFHkrKfLYqgQMR3O0AfgO9WJNDsZLiSdkfzJLmO7Y7z/rEUKp+mFHFc1c+Nr5tCnvbfSlikewkvbTzZwwZUKhtwA4PzAgc59q6G91W4sNOtpJLMNe3MqwpbrL8u9s9Xx0ABOcdulAED+E9LcysqzRyO28SJKQyN5jSblPY7nb8DjpUv8Awjdg1vcRS+fN9piSKWSWZmdghZlO7qCCxOR7elYw1/VNT1/SbS2g+zQlrj7YolUtvhdFKglTlfmzxgnPbFdhQBgS+EbGa0kge5viZmLTym4O6bKhcP2IwABxxjjFOXwjpaLcxJ9oS2uI3je3WYiP512sQPUj9eetbtFAFCfR7K51CxvpYt1xZBhA248bhg5Hf8elU7fwrp1nLE1s1zEiIqPEkxCShc7d474zj3HBzW3RQBhw+FrO3smtYbu/jj+UJi4P7tVzhR2xz0Oc8egqXw1AltocdvHnZFLKi5OTgSMBmtes3Qv+QYf+vif/ANGtUv4kQ/jXz/Q0qKKKosKKKKACvNBr11F4X0fTrS31iENKi3F5a2ucR/MSEYggknavAPU+lel15LdWt/qWi2Gm3ungw2biSNZLK8DhhkclFwRyelJ7lJXRsWGuXema/EgTxRf2ctu/nR3lnl43DLsZQqg4PzA/hVIapf31vfXdxdeLLSczTbIre0XyoFDHYD8h6LtJye5rNbU/ETeK11w+F2N6n+iif7Nc7PKx/rNv1JGOvepodPurfTNT06LT4hb6lIZLgfYr4kseeDt4x/8ArzRdC5WaOq6/e63ouk6dd6fr1pHdJt1J4LTDPmIgohIOQzHqOwp/h3VIPDesy2Ttra6ROnmGTU7fHkTvIeSQo2q+7qeMjqM1zupNrOzTbWPwu11Do6LJZMlvdIC4Iwh3YP8ACpyf8a0JLG51TUru+vrZ7V9RgFveNBpt20hi+XcqgjaCQoGecYyKLoOVnoHhL/kUtK/69k/lWzWR4Whkt/CumQyxPFIluisjghl46EGtehbA9zj/ABzo+naq2nm/1iHTjCXMfmKG8zO3PBI6YH51mLo+jS6kk8viqB5ZbL7L+7KoxB/iUg/L9MdOKu+OtY1DQ9T0i70yzF7clJ0Fr5bsZQfL+VdgO08dSMDvWrrWoXq+BrnUVt5LC9Fp5zREhmhbAJBI4OOea7ViJU6cY36Pt39DjdCM5ylb8+3qc6dC0B9Yt7uPxFalrcwofMKs5ZOB824cnoeO1Uk8D6DZyTPdeJLd1ljYYIVdvIJPDfh+NdFrGu3Vn4g8uxMNwjW9uqxO37sPLPsDEgE9Mn3xUX/CW3lvK1pcWVs100z2kBjYhJbhWUAc8gFXDe21/SmsdUWzf4f5CeDpvdL8f8ykvh7w7b6izQ6ppcRlt5Ili2Agqw4J+fkgfTNVNE8N+HtOlEq+I7K6cSrtMijA+VhjG7vnOfar7eL7+4srqf8As4x2uHMci5RkKSqmCx65yTwOMYPrTIvFepW8klpBZm9kjkuJXLgksguZI1RSOFwF6njoKPrtSzV3r6f5D+qQunZaev8AmVJfCfhc2SodY09AGAEygby4PzZO/G08jGOOOanbw54Rdiov9LXc7x4HUDHAHz/eB/i/DFaFvrl/qHiDRzm3hsri6uohCjnzSIldfnHQ8rnjpx1rP1vxBqmlyeJpWuD9hSVbe2cKM2svkxsuf9lixHPRsf3uD67U7v7/APgB9Up9l93/AAS74Ubw/wCF7Ge2XxFaXPmyeZuLKmOAMdT6Vv8A/CU6D/0F7P8A7/Cscajf/wBunw+bhvtCXhujJgZ+x43j/wAfPl/QGsnTb+71Cxku7vXbm1a30e1uVdXRU8xhIWZlIw2dorGpVjUk5STu/Nf5GsKcoRUYtWXk/wDM67/hKdB/6C9n/wB/hR/wlOg/9Bez/wC/wrlpfEl+dMvRLNLb3l09pBCI4S7WzSQI8jKoBJ2je3Q8iuk8Pa1FqHh23vLuRIplb7POZB5eJQ2wjBwRk4IB/vCovT7P7/8AgFWqd193/BJf+Ep0H/oL2f8A3+FH/CU6D/0F7P8A7/Cubm8Vaja6jfWltALtzdXDRMyllWOJIsoNv+055PTnrU8Wu66JNalNvbSCGSBYLXH7yPzEjJB5+bG5jgckjAovT7P7/wDgBap3X3f8E3f+Ep0H/oL2f/f4Uf8ACU6D/wBBez/7/CsBvGV0bSeeGGzYWNsbi7D742fEjoURWGVYbDnOeSB3zV2z8RX0+rRRy2tstlNfz2KFWPmBow5DHtg7CMfTntRen2f3/wDAC1Tuvu/4Jpf8JToP/QXs/wDv8KP+Ep0H/oL2f/f4Vg6pd3Dt4kvG1aazl0r/AI9oEZVTAiVwzqR8+5iRz6YGDUo8Q6hcFnltooYI9TgsgEkIkLMU3FuMY+fGO+KL0+z+/wD4AWqd193/AATZ/wCEp0H/AKC9n/3+FH/CU6D/ANBez/7/AArO8O+Ib7VJ7IXttaxx39k13D5LElArKCrZ653g5HuPesVPEOqWzJFeXJMV3rXk2k4UDCrclHgb/gAyD3G4duS9Ps/v/wCAFqndfd/wTq/+Ep0H/oL2f/f4Uf8ACU6D/wBBez/7/CucTxveSSyxRWltKZFgktZPnRGWScRDORkj5gdwAB54qz/wk2ptPLp6wael7bmcyyyOwiZYgh+XuCRIOvTBPNF6fZ/f/wAALVO6+7/gm1/wlOg/9Bez/wC/oo/4SnQf+gvZ/wDf4VUtLO61QWerwalc20Fysdy1qfmG0hG2cnjoen941na5d3dlrVxdy3lx/Z9uYeLO4jBt84z5kTD592R6nB4GRyXp9n9//AC1Tuvu/wCCbn/CU6D/ANBez/7/AAo/4SnQf+gvZ/8Af4ViXnjR7XTFuRawNKYr6QxmTGPs7FR+fGfTNNufFmo2t5/ZrWtrNevLGEltw8kYV43flRyWHlnoeQQeOlF6fZ/f/wAALVO6+7/gm7/wlOg/9Bez/wC/wo/4SnQf+gvZ/wDf4VS0LXNR1fUHimsYLWGG3iklDOWk3uCcDtgbc596x3vruFNQ1JdWuDcRav8AZorRnUxyJ5ir5YTGc4J5HPei9Ps/v/4AWqd193/BOl/4SnQf+gvZ/wDf4Uf8JToP/QXs/wDv8KwIPGOozJPdjSgbJBJJuwysiRyBWyTwx27jx0IweuaSXxpfSTgWOmJPEYzcoQGPnQ+YUQg9FLBS2Tx8y+uaL0+z+/8A4AWqd193/BOg/wCEp0H/AKC9n/3+FH/CU6D/ANBez/7/AArmofFGoW9pebFgl+xi6u5munIZ41uJUEaY7gJjJ4HyjHNWX8YXUMs9xJaWzWUV5JaeUjnzyVjLhsdO2MfjntRen2f3/wDAC1Tuvu/4Juf8JToP/QXs/wDv8KP+Ep0H/oL2f/f4Vz9t4r1m5itVGmWyy3c0KQySbkjAkSRzx1O3YORgNu7VZ8Vatqum6nZxadE0m+znklCgERhWizJtPLFQWwo6k0Xp9n9//AC1Tuvu/wCCa/8AwlOg/wDQXs/+/wAK0LS9tr+3E9pPHPCSQHjbIyOvNcXf6zfw3UVvpN7HLGGsB9ouW3iVJWcZ4AAztGSOueMYrovDefst7nGft9xnHTPmGm1BxbS/r7gTkpJM2aKKKyNAooooAKKKKACiiigAooooAKKKKACiiigAooooA+avjze/YfiBbSbNx+yrxnHrXm3/AAlBEMsYtR+8xk7uePwrvf2iP+R7tv8Ar0X+deQ1PKnuXztbf1odL4Z1j+zxdI8jrDO6l0j+82M9D7Zz71d1HUrXzbqKRxd2ro3lFSVw/wDeI9cCuOV2X7rEfQ0vmP8A32/OuinOlCSnyJyS3+e/6DVRq2h2fgfQrTX7jUbW5kEO2NXSQxb8EZ49uP5V0mvaHp1jod2ttbhobdU2yizXcdy5BLfWuB0DW4dHa5eWCWWSVNqMkm3b1Bz65BxXUav8SotT8PzaWunPG0wBefeMkjpwBjFZOzlfuZPU5rw7dRwP0lacuDGI13H8q+jINT8H3HhqOGa4tVt7OIM0cx2shIywx35z6814dpltZLG0EEotpVgVyrDLXDEAkBu3U8HityN4iLVHQWaIu2S4YEhhk/MfUcY49q8/HRp1oqndwa3cdH/XoafWKiSi5NpbLov6+ZycNxFd+LNUubVhHA4kdWjXbtXIwQO3arM9vpd3aLdQwBZoDmaHcVUp/fznI7dKj1W7sdOmW9sLRf36y28wJwj5A+YDt1rEbWGfT47SSBXEZ+VyfmA9PcV6F1bQycbycpPctfYbrWdXhszM0r+V+7ZfnJUZPH61t6Hpv/CLa5DqF75jJCH3xmMqcYx3+ua57Qdcm0XW4NQt38qSMFQxG7APB4/GvQNY+JVhf+H7rTTcXNy0sLIJZogCxP8ALmle2pVk1Zlm4+I2gi2LQGfzSThAmOvfPSvMLy/fUtZF1IctJID1zjms6pbb/j6i/wB8fzrSdWU1ZmVOjCnK6Pt/wd/yJukf9eqfyrbrE8Hf8ibpH/Xqn8q26zRs9zG8RWkF9FYW1ym+GS8QMuSP4WI5HIOQDkUv/CK6MIBCtntUSNJuWVw5ZgAxLA7jkAZyecc1T8aTXNvp2nyWhAuP7St1Tc2AcvjBPoQSDWbc+LruzlcXNvGbm2+0ROscpELMph2scjIGJRk9sHrUrdmcfiZ0U3h3Sp7k3EtrvcjGDI2z7uzO3O3O0kZxnFZs3g63l122vxJFHBbyRyrCkRDFkXauW3Y9OducDGcVnTa9rdxq9vYQ/YluoNQ+zyFJGMUqm1aTJGM8Ejj1A5rTt/FEs2h/bjps+8W0szMuDCrR7gV3deSnHHcVRZpHQNKa2jtzZRmGOA26Lz8sZIJUfiqn8BUcnhnSJbeOGS1Z1jZmVmmcv833stncQcDIJwcCufm8Z6jbLFDLZW0lzdJbyQG3Z5EUS7uGAGSRsOMfez2qzbeI9ZvpDBHp9vbSR2TXEpnduG3yIMKBnB2BueQDjGaAOiGmWQsrezFun2e2KGGPsmwgrj6YFVn8OaS95NdPZIZZgwfLNtJYbWIXOASOCQMmuT03xFq2naDE8wguZINOj1K7knnfdKr5+WPPQ/KfbJAA5rUPiTVpWPkWdmFl1J9Pg8yRs/LvJdgB/sfdH50AdHNYW1xYfYpIs2+0KEDEYA6YI5GMDmqI8L6OFiAtCDGWO4SuGbccnc2cvkjOGzWDD4yvxbJNeWtnCs8EzQ7ZHbEkcqxEHC5O5nGABnt70W/i7Vr3ZbW9jardqboSmZnVR5PlnhcZGfMHB6Y70AdC/h3SXubid7NWa5VllUs2xtwwx2525I6nGTS2fh7SrBg9vaAOJRNvZ2di4QoGJYkk7SRz2rFtvFt1eQT6hFa26WFrCkksbynzmLQCb5BjHRgOeuD0xVzw3r15q0U731mLZEjjmSUBlUqwJx83PGOvQ5oAtt4b039+Yo5IWmbcxjlYAfOHO0ZwuSOcAZq9LY2s07TywI8jRGBiwzmMnJUj0rz6LxjeT2WuSC6lUz2cl7p+6Ap5KqSu0EgBvl8t+/3m7Cuq1/Wb/Trm3t7G3t5Hktp7h2ncqAI9vAwOp3fh70AXIPD2l28SxJbZRH3qHkZ9p2lRgsTgbWIx05qNfDGjJcRzrYqHiKFAHbapVdittzjcFAGcZxxWJH4t1J2it2s7RLq7Fs9ufNYxosyyMA/GSR5RHHUkdKpan4u1O50bVFs47W3nsrOV55TMeWDyRgxHHPMZOT6ge9AHT23hbRLSMxw2CKhCKFLswUIwZQuT8oDAHAwMirVpo9hY3UtzbQGOSTOcOxUZOThScLk8nAGa5G+8XalNa6slvZyRwQxXUSXKhg0bxIx3kkbeSvQcjIP01Ne1+fRxayxxmZ/sE0+xpNqsymIAHg/3+vb3oA0/+Eb0j7e16bJDOWZ8liVDMpViFztBIJB45zzVW48IaZNBbQIskcMNws5XzXYttVlUBi2VA3ZGOmKxdT1TXLy9t9JDW1vMmopDO8MsiiRGhaVQCPmH3cH1wOxNJF441GWznu00gGErm3LlkAPnLGFZiOSd2cr0wRQB0o8N6OLb7P8AYY/L8vysZOdu7f1znO75s9c85qCTwvYtJpyxqEtrK4N0IiNzSTYIDM5JJxkn3454rLXxXqa6tNaPpqvHbSeRO8avgP5QcsGIxt5Ax17+1RxeMNTEFsLiytFnvoLaa22ytsTzW24kJHbIOR16cdaAOoXS7FJUkW2QPHK8yn0d87j+OT+dV4vDulQTwTQ2gjeBVRAjsq4X7uQDhsds5xWJeeIdSstQW38m2lu3W2jws58kNJNIm7pngKCR+HbNRS+MNRWOeNbaxW5s4rmW4MsrLG4hbbhDjPPXnp70AdIND0wWyW/2KLyY4HtlQ8gRNjcv0OB+VOk0exm0xNOlhL2yYKhpGLKQcghs7gQe+c1k6v4jnstIsLm2jhNzdRecIXDsdoTc2Ao7ZAJOAM/hVe08V3moQTX1vbWkdnBCjuk0pErM0AlG3Axj5gPfBPagDetdH0+yMBt7ZUNurrG2SSA5Bfk9SSASTyavVxj+LdVjsvtD2diphsF1GdDM3MbZwiHHLYU5J4yQO9Ol8W6lHcXcX9nxCQbvskLbt0oV1UsGxtfhtxUHcOnJoA7Gis3Q9SbVdMW4fy/NDtHIqKy7WUkEFWAIPsf161pUAFFFFABWboX/ACDD/wBfE/8A6NatKs3Qv+QYf+vif/0a1S/iX9diH8a+f6GlRRRVFhRRRQAUUUUAFFFFABRRRQAUUUUAZ2o6Ub66t7mO9uLSaBXRWhCHIbbkHcp/uioDot6ylW8QagQRggxwYP8A5DrYorRVJJW/REOnFu/6s5u28Ix2cRjtdRmgjLiQrHbW6gsOh4j6jt6VP/wjku4N/bN3uEnmg+Tb5D4xu/1fXHGa3aKPay8vuX+QvZx8/vZzg8Ios00w1KcSz/61xbW+ZOc/MfL555ol8JJcNE02pTyNE5eMtbW52sTkkfu+CTz9a6Oij2svL7l/kHs4+f3s51PCixXjXkep3CXTnLTLb24cnGPveXnpTJfDOoSidT4glMcxzIr2cDb+APm+Xk4AH4V0tFJzb/4ZB7KPn97Ob/4R3VfPM/8Awkk3nFdnmfY4d23OcZ25xntVR/BMsksUsmrI8kShY3bTbclAOgB28Adq6+ip5mHso+f3v/M5n/hGdT8/z/8AhIpfOznzPsUO7OMddvoMfSkk8L6hLDNDJ4gkeKc5lRrGEiQ4Ayw288AdfSunoo5mHso+f3v/ADORfwVNJaRWj6urW0JzHEdPgKofUDbgU+fwfdXLyPPrfmvIgjdnsICWUHIU/LyM9q6uijmYeyj5/e/8zkf+EKn226/2uu22OYB/Z8H7o5z8vy8c81YHhnU1II8RSgq5kBFlDw5zlvu9Tk8+5rpqKOZh7KPn97/zOUuPCF3d3UV1ca2ZriL/AFcslhAzL34JXIqX/hGdSOf+Kil5kEp/0KHlx0b7vXgc+1dNRRzMPZR8/vf+ZzMfhrU4fL8rxFKnlpsTbZQjavHA+XgcDj2o/wCEZ1IoEPiGTaJPNC/YocB853fd655z6101FHMw9lHz+9/5nJQ+DLi2LGDWRGWbcxTT4Bkg7gfu+oB+op1z4PurxdtzrXnKX8wiSwgb58Y3cr1xxmuroo5mHso+f3v/ADOeGh60BgeKbnH/AF6xf4VVn8H3Vzdx3c+t+bcx42TPYQM646YJXNdXRRzMPZR8/vf+Zx0/gaW5Fz5usEtdKUndbGFWkBGDlgueacPBMotHtBq6i2d97Rf2fBtLepG3r7119FHMw9lHz+9/5nNQ+GtTt/8AUeIpYvlC/JZQrwOAOF6DtUCeDbmO9a9TWQt2xJM40+AOSep3bc11lFHMw9lHz+9/5nKx+EbyG4muItcKTTjEsi2EAaT/AHjt5pJfB91P9n87W/M+z48ndYQHy8dNvy8dB09K6uijmYeyj5/e/wDM5ObwfdXBiM2teYYnMke+wgOxickj5eCTz9aZb+Cbi1vZL2HW3W6kdnaY2UJcluvzEZGfSuvoo5mHso+f3v8AzOTt/B11ZjFtrXk/P5n7uwgX5sEbuF64JH41ZPh/VzKsp8TTmRQVVzaQ5AOMgHb04H5V0dFHMw9lHz+9/wCZyjeD7toHgbW8wuoVozp8G1gCSARt6AkkfWtzSNMXSbI26zPMzO0jyOACzMcngAAVfop8ztYcacU7r82FFFFSWFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHy9+0R/wAj3bf9ei/zryGvef2jJXsdZ0Sa1bypJoZRKy8F9pXGfpk/nXif9r6h/wA/cn50tStGUqKu/wBr6h/z9yfnR/a+of8AP3J+dGotClRV3+19Q/5+5Pzo/tfUP+fuT86NQ0OpjX7AiX9zZSyRzW0Ztp1OFjbaBkjv344q897FDHYG4lW/t5I8C2jY7kTcfkH93nt7Csvw/wCNxZKLXWLGO+tTx5gGJUHse/0NbOr+KPD9kqvpgN0XUMoKlSp54bPTHtXn1eZ1LSg7+Wz+f2fwM2lfqcx4ktpbS0tI5ojE0kkkixsckKdoGffiudrUu/EOo3c5kM5iHZI+AKg/tfUP+fuT867o81tTRWKVFXf7X1D/AJ+5Pzo/tfUP+fuT86rUNClUtt/x9Rf74/nVj+19Q/5+5Pzo/tfUP+fuX86NQ0Ptfwd/yJukf9eqfyrbrnfATM/w/wBAdiSzWEJJPc7RXRUxMytajSR9MV1DL9tQ4IzyFYj9QDVm50u1uIp18pI5JldWlRF3DcAGPIIOcDqDnArL8W3L2dhZTpPDbst7GBNOMomcjJGR0z6isS81u7tNRSaPUbW5D20cTXaKfITdcFDIV3Y4HXnGfQVK3ZEfiZ0ei+G7HRY3ESiSR5fNMjRouG27BtVQAvy8cDufWr02n28umzWCoIoJY3jKxgLgNnOPfk1yI8Z3FvFMbqezMccF4YrrG1Lh4im0rz33MCATkqcVUl8RX+oXum+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NgdEQGDAw2hgdsKAftXg0BEZDYDQERkNgNARGQ2A0BEbBaGN0NA2MhsBoCIyGwGgIjIbAaAiMhsBoCAwYGG2MDljQj1o8GgKjITAaAqMhMBoCoyEwGgKjYLQxOpoGRkNgNARGQ2A0BEZDYDQERkNgNAQGDIw2Rgcs6EctHg2B0RAYDYHREBgNgdEQGA2BUTDaGB1NA6MhMBoCo2A0BEZDYDQERkNgNAQGDIw2Rgcs6EctHg2B0RAYDYHREBgNgdEQGA2BUTDaGB1NA6MhMBoCoyEwGgKjITAaAqMhMBoCAwZGG6MDFvSjFo+GwGgIjIbAaAiMhsBoCIyGwCgYbYyOpoHREBgNgdEQGA2B0RAYDYHREBgNgQEDo43RAQv6UYtHQ2A0BEZDYDQERkNgNARGQ2AUjDZGR9PAaAiMhsBoCIyGwGgIjIbAaAiMhsCAgdHG6IAF/ajFoyEwGgKjITAaAqMhMBoCoyEwCkYbo6NpYDQERkNgNARGQ2A0BEZDYDQERkNgwMBoY3TAgn7U4tEQGA2B0RAYDYHREBgNgdEQGAWjjdHRNDAaAqMhMBoCoyEwGgKjITAaAqMhMGBgtDE6YEE/avFoCIyGwGgIjIbAaAiMhsBoCIyC0cboaBoYDYHREBgNgdEQGAWjITAaAqMhMGBgtDE6YEE/avFoCIyGwGgIjIbAaAiMhsBoCIyC0cboaBoYDYHREBgNgdEQGA2B0RAYDYHREBgwMNoYHbCgH7V4NARGQ2A0BEZDYDQERkNgNARGwWhjdDQNjIbAaAiMhsBoCIyGwGgIjIbAaAgMGBhtjA5Y0I9aPBoCoyEwGgKjITAaAqMhMBoCo2C0MTqaBkZDYDQERkNgNARGQ2A0BEZDYDQEBgyMNkYHLOhHLR4NgdEQGA2B0RAYDYHREBgNgVEw2hgdTQOjITAaAqMhMBoCoyEwGgKjITAaAgMGRhujAxb0oxaPhsBoCIyGwGgIjIbAaAiMhsAoGG2MjqaB0RAYDYHREBgNgdEQGA2B0RAYDYEBA6ON0QEL+lGLR0NgNARGQ2A0BEZDYDQERkNgFIw2RkfTwGgIjIbAaAiMhsBoCIyC0RAYDYEBA6ON0QEL+lGLR0NgNARGQ2A0BEZDYDQERkNgFIw2RkfTwGgIjIbAaAiMhsBoCIyGwGgIjIbAgIHRxuiABf2oxaMhMBoCoyEwGgKjITAaAqMhMApGG6OjaWA0BEZDYDQERkNgNARGQ2A0BEZDYMDAaGN0wIJ+1OLREBgNgdEQGA2B0RAYDYHREBgFo43R0TQwGgKjITAaAqMhMBoCoyEwGgKjITBgYLQxOmBBP2rxaAiMhsBoCIyGwGgIjIbAaAiMgtHG6GgaGA2B0RAYDYHREBgNgdEQGA2B0RAYMDDaGB2woB+1eDQERkNgNARGQ2A0BEZDYDQERsFoY3Q0DYyGwGgIjIbAaAiMhsBoCIyGwGgIDBgYbYwOWNCPWjwaAqMhMBoCoyEwGgKjITAaAqNgtDE6mgZGQ2A0BEZDYDQERkNgNARGwWgIDBgYbYwOWNCPWjwaAqMhMBoCoyEwGgKjITAaAqNgtDE6mgZGQ2A0BEZDYDQERkNgNARGQ2A0BAYMjDZGByzoRy0eDYHREBgNgdEQGA2B0RAYDYFRMNoYHU0DoyEwGgKjITAaAqMhMBoCoyEwGgIDBkYbowMW9KMWj4bAaAiMhsBoCIyGwGgIjIbAKBhtjI6mgdEQGA2B0RAYDYHREBgNgdEQGA2BAQOjjdEBC/pRi0dDYDQERkNgNARGQ2A0BEZDYBSMNkZH08BoCIyGwGgIjIbAaAiMhsBoCIyGwICB0cbogAX9qMWjITAaAqMhMBoCoyEwGgKjITAKRhujo2lgNARGQ2A0BEZDYDQERkNgNARGQ2DAwGhjdMCCftTi0RAYDYHREBgNgdEQGA2B0RAYBaON0dE0MBoCoyEwGgKjITAaAqMhMBoCoyEwYGC0MTpgQT9q8WgIjIbAaAiMhsBoCIyGwGgIjILRxuhoGhgNgdEQGA2B0RAYDYHREBgNgdEQGDAw2hgdsKAftXg0BEZDYDQERkNgNARGQ2A0BEbBaGN0NA2MhsBoCIyGwGgIjIbAaAiMhsBoCAwYGG2MDljQj1o8GgKjITAaAqMhMBoCoyEwGgKjYLQxOpoGRkNgNARGQ2A0BEZDYDQERkNgNAQGDIw2Rgcs6EctHg2B0RAYDYHREBgNgdEQGA2BUTDaGB1NA6MhMBoCoyEwGgKjITAaAqMhMBoCAwZGG6MDFvSjFo+GwGgIjIbAaAiMhsBoCIyGwCgYbYyOpoHREBgNgdEQGA2B0RAYDYHREBgNgQEDo43RAQv6UYtHQ2A0BEZDYDQERkNgNARGQ2AUjDZGR9PAaAiMhsBoCIyGwGgIjIbAaAiMhsCAgdHG6CgYDYHREBgNgdEQGA2B0RAYDYHREBgwMNoYHbCgH7V4NARGQ2A0BEZDYDQERkNgNARGwWhjdDQNjIbAaAiMhsBoCIyGwGgIjIbAaAgMGBhtjA5Y0I9aPBoCoyEwGgKjITAaAqMhMBoCo2C0MTqaBkZDYDQERkNgNARGQ2A0BEZDYDQEBgyMNkYHLOhHLR4NgdEQGA2B0RAYDYHREBgNgVEw2hgdTQOjITAaAqMhMBoCoyEwGgKjITAaAgMGRhujAxb0oxaPhsBoCIyGwGgIjIbAaAiMhsAoGG2MjqaB0RAYDYHREBgNgdEQGA2B0RAYDYEBA6ON0QEL+lGLR0NgNARGQ2A0BEZDYDQERkNgFIw2RkfTwGgIjIbAaAiMhsBoCIyGwGgIjIbAgIHRxuiABf2oxaMhMBoCoyEwGgKjITAaAqMhMApGG6OjaWAUjIbAaAiMhsBoCIyGwGgIjIbAgIHRxuiABf2oxaMhMBoCoyEwGgKjITAaAqMhMApGG6OjaWA0BEZDYDQERkNgNARGQ2A0BEZDYMDAaGN0wIJ+1OLREBgNgdEQGA2B0RAYDYHREBgFo43R0TQwGgKjITAaAqMhMBoCoyEwGgKjITBgYLQxOmBBP2rxaAiMhsBoCIyGwGgIjIbAaAiMgtHG6GgaGA2B0RAYDYHREBgNgdEQGA2B0RAYMDDaGB2woB+1eDQERkNgNARGQ2A0BEZDYDQERsFoY3Q0DYyGwGgIjIbAaAiMhsBoCIyGwGgIDBgYbYwOWNCPWjwaAqMhMBoCoyEwGgKjITAaAqNgtDE6mgZGQ2A0BEZDYDQERkNgNARGQ2A0BAYMjDZGByzoRy0eDYHREBgNgdEQGA2B0RAYDYFRMNoYHU0DoyEwCkZDYDQERkNgNARGQ2A0BAYMjDZGByzoRy0eDYHREBgNgdEQGA2B0RAYDYFRMNoYHU0DoyEwGgKjITAaAqMhMBoCoyEwGgIDBkYbowMW9KMWj4bAaAiMhsBoCIyGwGgIjIbAKBhtjI6mgdEQGA2B0RAYDYHREBgNgdEQGA2BAQOjjdEBC/pRi0dDYDQERkNgNARGQ2A0BEZDYBSMNkZH08BoCIyGwGgIjIbAaAiMhsBoCIyGwICB0cbogAX9qMWjITAaAqMhMBoCoyEwGgKjITAKRhujo2lgNARGQ2A0BEZDYDQERkNgNARGQ2DAwGhjdMCCftTi0RAYDYHREBgNgdEQGA2B0RAYBaON0dE0MBoCoyEwGgKjITAaAqMhMBoCoyEwYGC0MTpgQT9q8WgIjIbAaAiMhsBoCIyGwGgIjILRxuhoGhgNgdEQGA2BUTAaAqMhMBoCoyEwYGC0MTpgQT9q8WgIjIbAaAiMhsBoCIyGwGgIjILRxuhoGhgNgdEQGA2B0RAYDYHREBgNgdEQGDAw2hgdsKAftXg0BEZDYDQERkNgNARGQ2A0BEbBaGN0NA2MhsBoCIyGwGgIjIbAaAiMhsBoCAwYGG2MDljQj1o8GgKjITAaAqMhMBoCoyEwGgKjYLQxOpoGRkNgNARGQ2A0BEZDYDQERkNgNAQGDIw2Rgcs6EctHg2B0RAYDYHREBgNgdEQGA2BUTDaGB1NA6MhMBoCoyEwGgKjITAaAqMhMBoCAwZGG6MDFvSjFo+GwGgIjIbAaAiMhsBoCIyGwCgYbYyOpoHREBgNgdEQGA2B0RAYDYHREBgNgQEDo43RAQv6UYtHQ2A0BEZDYDQERkNgNARGQ2AUjDZGR9PAaAiMhsBoCIyGwCgYDYHREBgNgQEDo43RAQv6UYtHQ2A0BEZDYDQERkNgNARGQ2AUjDZGR9PAaAiMhsBoCIyGwGgIjIbAaAiMhsCAgdHG6IAF/ajFoyEwGgKjITAaAqMhMBoCoyEwCkYbo6NpYDQERkNgNARGQ2A0BEZDYDQERkNgwMBoY3TAgn7U4tEQGA2B0RAYDYHREBgNgdEQGAWjjdHRNDAaAqMhMBoCoyEwGgKjITAaAqMhMGBgtDE6YEE/avFoCIyGwGgIjIbAaAiMhsBoCIyC0cboaBoYDYHREBgNgdEQGA2B0RAYDYHREBgwMNoYHbCgH7V4NARGQ2A0BEZDYDQERkNgNARGwWhjdDQNjIbAaAiMhsBoCIyGwGgIjIbAaAgMGBhtjA5Y0I9aPBoCoyEwGgKjITAaAqMhMBoCo2C0MTqaBkZDYDQERkNgNARGQ2A0BEbBaAgMGBhtjA5Y0I9aPBoCoyEwGgKjITAaAqMhMBoCo2C0MTqaBkZDYDQERkNgNARGQ2A0BEZDYDQEBgyMNkYHLOhHLR4NgdEQGA2B0RAYDYHREBgNgVEw2hgdTQOjITAaAqMhMBoCoyEwGgKjITAaAgMGRhujAxb0oxaPhsBoCIyGwGgIjIbAaAiMhsAoGG2MjqaB0RAYDYHREBgNgdEQGA2B0RAYDYEBA6ON0QEL+lGLR0NgNARGQ2A0BEZDYDQERkNgFIw2RkfTwGgIjIbAaAiMhsBoCIyGwGgIjIbAgIHRxuiABf2oxaMhMBoCoyEwGgKjITAaAqMhMApGG6OjaWA0BEZDYDQERkNgNARGQ2A0BEZDYMDAaGN0wIJ+1OLREBgNgdEQGA2B0RAYDYHREBgFo43R0TQwGgKjITAaAqMhMBoCoyEwGgKjYMDAaGN0wIJ+1OLREBgNgdEQGA2B0RAYDYHREBgFo43R0TQwGgKjITAaAqMhMBoCoyEwGgKjITBgYLQxOmBBP2rxaAiMhsBoCIyGwGgIjIbAaAiMgtHG6GgaGA2B0RAYDYHREBgNgdEQGA2B0RAYMDDaGB2woB+1eDQERkNgNARGQ2A0BEZDYDQERsFoY3Q0DYyGwGgIjIbAaAiMhsBoCIyGwGgIDBgYbYwOWNCPWjwaAqMhMBoCoyEwGgKjITAaAqNgtDE6mgZGQ2A0BEZDYDQERkNgNARGQ2A0BAYMjDZGByzoRy0eDYHREBgNgdEQGA2B0RAYDYFRMNoYHU0DoyEwGgKjITAaAqMhMBoCoyEwGgIDBkYbowMW9KMWj4bAaAiMhsBoCIyGwGgIjIbAKBhtjI6mgdEQGA2B0RAYDYHREBgNgdEQGA2BAQOjjdEBC/pRi0dDYDQERkNgNARGQ2A0BEZDYBSMNkZH08BoCIyGwGgIjIbAaAiMhsBoCIyGwICB0cbogAX9qMWjITAaAqMhMBoCoyEwGgKjITAKRhujo2lgNARGQ2A0BEZDYDQERkNgNARGQ2DAwGhjdMCCftTi0RAYDYHREBgNgdEQGA2B0RAYBaON0dE0MBoCoyEwGgKjITAaAqMhMBoCoyEwYGC0MTpgQT9q8WgIjIbAaAiMhsBoCIyGwGgIjILRxuhoGhgNgdEQGA2B0RAYDYHREBgNgdEQGDAw2hgdsKAftXg0BEZDYDQERkNgNARGQ2A0BEbBaGN0NA2MhsBoCIyGwGgIjIbAaAiMhsBoCAwYGG2MDljQj1o8GgKjITAaAqMhMBoCoyEwGgKjYLQxOpoGRkNgNARGQ2A0BEZDYDQERkNgNAQGDIw2RkfBaAiMhsBoCIyGwGgIjIbAaAiMhsCAgdHG6IAF/ajFoyEwGgKjITAaAqMhMBoCoyEwCkYbo6NpYDQERkNgNARGQ2A0BEZDYDQERkNgwADLgNk8avFAhMDtD3c+//rCxMhIB8v//f/Pz8arLKBMB7tGrRgNgdEQGA2B0RAYDYHREBiigE6N0aunTxzYtJaVjX0wB9O/v39EpWWDU7MHsyMpdNv0izPPvTzPxsxGoTnEaP/195e5pFmnbRsxikfVjIbAyAyBH5tP/Vh5lJGdTkXx0AhkJibezjgmId6h4dpRV46GwGgIUAzoVAK+fvb0zIE97JxcFDuYhgb8+f1bQUOLhhYMAqNZmVjZmdnp0xhlZGBkZWIdBJ4edcJoCAzeEPh769mP9ScYuQd1R52uwff/PwMLM09DBF0tHbVsNARGQ2BAAZ0ao0zMzGzsHGzsg7rAZWJiYmWjx5DhgMY4zHJGBiYWJkZGxv8MDP///Pv/7z9MgoGJFSQO5zIwMPz7++//X4QCZCk0NiMzI8M/hv//iVKMpneUOxoCIzEEWJgZOdkYOUdMyUMwjsGNUQa6LCUi6JZRBaMhMBoC9AF0aowie+b///9/fv/+9+8vIyMTKysrIxNsE9X//79//2JhZWNELYZ+//z5798/kAmMDIyMTCysrExMTH///v339y/WtuP/f/9AJqAaAtI+iuEhwMjw/8//OxtuPT/2lE9RQC1Cg0OI4/+//4yMjH9//70848KHW+8ZmBiZ2Zj//vzLwPBf1lVByVv5319wLMANwWQwMnx99oWFi5WNj41htDmKGT6jIqMhgDcE/v/4xQDr9TFysDIww8pGBgZQD+/XHwZIHmRlZmRhYSB+4fe//wxMjAx///3//YeRg+RW7//ffxj+MzCyUVZZ/P7zn3JD8IbeqORoCIyGwNAFlJUvpPv7////rGxsNl5+ihraL58+PrZ98+eP75mYmEEtSGYmx4DQSyeOfHzzBtJChSh2DgyTkFdkYmJi+P//zYvnx3dve/30iaaxmbSi0r71q5lZ0L3Axcv3+9ev379+gpqkpLtwJOhgYmG6v/3ug233ZF3kX597eWXOJZNyMwZGBiY2pv///4voi/HK8X17+fXxvkfKAarMHCz8ivz///9nZGJkYgXVjv9+g0ZSGRkZmdiY/v369///f5A4I+Pfn3+uL7iqHKzKKcz599ffkRCSo34cDQHqhMD//wzMzNx5viwaMqCGIwPD18lb/tx8ysjCDDL/338GDlbOEGtWM1WGn79/7r7w69BVhv+MRLZHGQW5/3/5wawiyeFp/HXSZpIGHf///sPhYcTAxf5j7XGy26P///xldzVg5GL/ufk0Sgsb5LdRPBoCoyEwChhAbQt6BsP/f/98YlMc/UOYWVjMHFwi8kqYWVj//fvHxMzsHhYTkp7HzsEJmeT9++fP3z+/mVlYjOwchcUl/v/7x8DIqGtuFVdcxcnN8/fvnz9//oBc/v//3z9/fv/69e/v3////nFwcoZlFXDz8v39+/fvn98gBQwMkGFUBgYGkJkg4V///4OaUH9+//7z+xd02BWidCSQjAz///5/eviJeoyWdrKefq7R1+dfvjz58u/Xv0e7Hvx490PGUVY1TEPORYFDiFM5QEU1VF1YW4SBkfHnhx8Ptt+/v/nut1ffmFiZfnz48WDb/d/ffjOxMr2+8OrNxVfvr7/79PDT04OPf7z9zshE/LjNSAj0UT+OhgDeEPjPwMjOyhltx8jHyQCZCIIo//2X4c9fBkZGnqoQ7nwfJm4OJnEBvgkpnPFO/3+Dy7d///7//P3/1x8G+Eqb/wwMv//8//kbpPHff0ZuDt7maCZBHobff0FjnDBjQQp+w3qMf/+BFEN0QQZfIcpAheY/FiMlNiuN/99/gbTAZf8z/Ieo/wMzhIGB4c9fkGN+/kZ44d///7/+/P/+i0Vfkc1cHepIiDLwgCvInn//GX7/Bbn5N7hIBwmN4tEQGA2BkQXQhxVp6nvQSCc7Ow8f38Ke1psXzipp6SSU1YnLyD25ezs6v0xQVPzNi6cM4M7+////pRQU//758+nd+9+/fm1ftvDCsUPMzCyi0tIFHZOUtHQe3rr+6d07Riam/wwMMkoq3Hz8b54/e/3siayKurSCkqKm9t+/f3gFBJ8/uPefgUFMWubf37/vXr2UUlD6+eO7oKjY80cPfv34oaCuyczC+vzBvS+fPzEzg0cgaOr/wWE4IyPj76+///39xyvD++f7by4JbjZutm8vv7Hzs788/YJHjo9LjAvUav/19////39//mViBdUQ3998P997+teX30zMjI/2PjIqMuYQ5nx29Mn3N99lHGQvT7+gl2306dHHnx9+vDr/St5DkV2QY3B4d9QVoyEwFELg/39mGeF/z95/TJn878VHBg5WJl5ORlZmFjXp/x+//v/9l81a61PJgl87zzGwsXBG2LIYKjGysTL8+cvIz82qKcPw5++fq49Bs/zgTiCzlhyTEM+/R6//3n/FoiPHqinDaq72+8i1nzvOgcLi7z9mdWlmcf6/Lz/8vf2cgYGBSVqIEbR0lZ1RiOfv7ef/3nyCjM6CFDMwgFqx33+xqEoyK4j/ufX0/7vPoLHV/wys2nKMQrx/H7z69/gNAyszw59/zHKiTLIiIMfcePr/83cGhv+M7GysRsr/PnwFGQJpa/7+yyQjzKwg/v/D1z83HjP8/c8oxMskzMPIzMTAwvzn5lOGP6NLfKABP0qNhsDIAXRsjP7/z8jA8Pf37wXdzaDVomxs7BycP75+/fzhPTMLy/Hd2969fJFS3cTIyAgaGf3/38rT99e379uXL4JHBiMT478/f//9+/v79y99Kzt9S9updaU+Mck2Xn4srKxfPn5YPqXX0NZBVFrWOyZxz5rl5i4e/aW5//79c/QL/vL505ZFc/2TMgSEhDl5eBb3dVi4eGibWjAyMT29d2dJf8e71y8ZRsC2m19/f/348+P/t3+gcQ3G3yD2////GP99//Gdn4dfp1T/39//3358Y2Jh+vn31////3/8/fH3zz9GFsaH++7////fpNKcmY3p+qKrd7fd1UzTUk3RuDLx4vMTT8WdpXh0eDmUOF+cfKYUocImy/Htx7eff3/9+vsLHn2jjNEQGA0B7CHwH7SJkFlZ4j8TE09dBJOowI/VR37uu8TIwMCV7v7nyqNvs3b+ufKQK8mZkYvtz7XHPzac/L/yCCM7K7OcKG9PIoumDMPff78OXP1cvfj/t59cuT6ccY6MbCz/333+VLGIw9eMWUmCpyTwKyszZ6zjO98WrhRX7gxPRm72f99+fp269dv0HVxeJlzJrv///GUS5v1z5/nH5Mn/Xn5ggPTP/zMw/PrDZqbGaqTMoiL55/qTD8mT/7/7zJ3nyxnrwMDJ9u/Vx89Vi38dvMrhZ8pTFcrIw8nw///vs3c+5c9hYGTkbYtlc9X/9/7L/4/ffh2/8f/XbzZHXd62WCYR/v/ff35fsO9r3wY2fQWe2nBGNubfp+98Kl0AKodHm6PYE8qo6GgIDFtAv8YoIxMTZCUoKxv7v79/RaVk/BLST+/f9fHtG1Y2ttuXLggIizAxgfZxMzExMTEzb5w38/+/f2wcoAE2cxcPeXVNVlY2FV39b1++PLpzS0xa9tfPnxKy8jbe/iun9t26eN7A2o6Lm2fnysWS8oqLe9vEpGX///sHWmmKNE3PxcN75+qlNTMmWbp5ySip9Jflfv38KTg1x84ncOP8GQxg24dtVIM95qHgbiCmz/D7fyPbQw9pN2VN1Z/ffjzmeOCn7aOurg7fU8/IxHj/z/0n7A8j1SO4+LgYGBgWMSx8cO/e0aKD//79Y2Nj07PRj1aJZNFkmbJ38tENRxoTm8Qkxb5/+f6K65mbjCvEqL///srwSIOtHSVGQ2A0BHCEACMDeBjyP7OCGIu86N/bzxhZmfgnpX7Mmvnr0JVPZQtBjTMmpi9NKzljHLgSnZllRP7cfv512rbfJ25yprgyfP/5zrmOgYONvy+JzcPwz9l7XPGOn6qX/D5yjd3TmImf6+ukLSzq0h+L5jJLCzP8/sssJ8KV5fm1d+OPTafY/c24sr1/7brA8O///99/P6ZP/f/1h8CSIlYDpR87zkFX2jAyglqlnOyfM6Yx/P7Dv7iQRVPm35tPHNH2n0rn/z5zlzPekTvX+/e5e4wcbF9n7vhz+zmbhTpXihuTtDCznCirudqHsO5/n7/xT89kZGVhZGflLvT/uePctynbWLRkeVuif+6+wPD3HxMf14fUKX8uPwDNjDEzgQMER3CNCo+GwGgIDEcAHoakvcd+fPv25dMHRkbYEtX//9k5uXj4+d+9fPEftO+akYHhPxMTE6+A0JePH/7+/QMqkhhAMoxMTHyCgiysiB2gP759+/zhPRcPDwsb249v38CGvGRkYvz7+w8DIwMbOwcPv8CXj++ZWVg5uXk+vX/L8J+Bm4/v379/37984RcW+fb5088fP3j4+RkZGT+9f8/IyMjGwcHFw/Pp/TuG/wwsrKz8wiK0D4+Bt8HP19fK3LqipmL/rn1FZcX79+wXEBFAdtaVC1ciYiLOnjzLDj4Bsb2l7cDhgxMmTODh5jl2/JiQoJCrm+v5U+dzC3J5eHm0NbR7J/b+//Xf3du9panFzNIM2ahR9mgIjIYArhD4/+kbaBabgQG0rJOV+d+TtwxsLMyyIv8/f4eIg+bEQeer/fv/+y9o7l6Il5GVGST77guTOP//rz//f/rGwMDAKAia6f7/7SeTMO/fJ28ZGBlBe+cZGRk42ZiEef+9+czIyszIx/X/41eQgmfvQMs3GRmZpYX+vf3MyM7KwMr879VHBiZG0Jjlz1//v/yA2vv/P5MANwMT07/XHxmYmJhE+f5/+8nw/z8jP/e/Z+9Ai6qYmJglBP++/sjw7ScDKzOjIA8TNwcDM9O/F+8Z2FkZ2VlBg6wMDExCPCBDPn5jEhcAGfXrD8P//0wSgv+//gCZxsf17znoBA+opQwMzFJCo1udcKWZUfHREBh+gE6N0eEXcEPdR0cPHI2NjZWQlHjz+k12TnZ+cf7XD18LiwpLSkrUtNQYGBgun7scFh524fwFdh7Q6bDPHj6LjIr89+8fOxv7m7dv+nr7zM3NPTw8srKyAgIC7O3ty8rKQiJB8PLly9u2b5NTkhvqQTTq/tEQGA2B0RAYDYHREBgNAToAOk3Tg/a2//79n4GBmZmZhRXlVp4/v3+Dh0JBZ4iysbOD9sX/A23PZOfgZGBg+PXjBxsHB3jr/G/wcCmIYPzPwMrGxsjE9PP7dyZmZvhpo79+/vj//z9E4+9fP5lZQCeSQgIRtPDpxw92Dg7QNnzwOnoWFhb4sVA/f/yAKMNKQk4sBS0ohW8QJ31JE8IQrHYQEoRoB532x8zEijROTEgfuvyWe1sffHzIwMjALMxiXWN76OAhTT9NJifWKRemffny9TT3uXn3Fsj8kmFgYHj1+hWfr8CUq9PZ2UHD0sxMzFZVtps2bfr+/btDvMMtqTvrd278afXnhebrBQ8WqaVrTL4y7cWF13z+Al/Yv027NkPmE8gQsPWgBRoM/xn+/Sd0TClENSghMP3/B1rVChagPsHICFoNAj5FgfSIRHUOaOkJE9Pff//+Y/MdFS1CtRbBw7SCEQSYQCdUMDESH+wIE1FZTIxMDIwM4LBClYDyGJmZmBgYGf/+BZ1HCxUbxBQjAyMXK1eEehgnC6h4GRQuRdrkDjpbFHLuMkSQkRFyGD5oMBLGhrj5//dfoOl7BtAefOgI4l/QtnqQLDMTaKQTxGKAKGPkAvUnQUOhTIwgo5gY4aeNgvY8/fvPyMnOwMgAkoLYAjEKUuj8/w/Rji6LYQsDCzPy2U+gNT8/fqELfvsJGsSFuIeBAbTR/vcfRnZWRhZmiPlgVyMRoGIXzIU5BsSBsEEsMIarAfPgY6sQHjoJUYxmAlwRflm4MggDj2KIFMir8DoDoofW5H8GBmiaobVNo+aPhgB1AZ1GRo/v2rZm5mRWNlYBYVFbb3+k5uPPo9s2vX/zmpGRkZ2Dw9Ld+8yBvZ/ev2VkYjKydfzz+/fV0ydsvfxuXjz3/OF9JiZm0KwQ6EKgv4qaOuIyssd2bOUVEDC2cxIQFbt37fK1M6f+/furZ2mjoK61d+0KUUlpfWu7x3dvf//y+d///zfPn7HzCbx58dyLxw+ZGBmFJaQMbR04OblvXTx369J5cOkEWi0Ays1ga0CrBBgYIEfBM4KqdaSQhyoCFTQQZQg5EP8/6FRO0DGnIAVgwxgYGUB3HTGA1x6AFINlQAxwcwhEQMov0NGBcDlQyxukBoZ///qlaWyeVFEPEyCZzt1X8J/pv5Kg0q9fv1hZWTk4OBgZGb98/szEzMzCzMzExPzj5w/IqlAGBgZWFpa/f//++v0bPCDKxszCws7OzsjA+PPXz69fvzIzM3Nxcv36/evXz5+soJsImH//+c3JwcnEzPTly5d/oNYJyHn/GRi+fv3KyMjIzQVaewoSwsAs4G7Dr9+g+vXP37+fP3/m4eFhxThBFkMfmQI/fv789fMXDy8PEzjWyTSFgYGFheXrt29vXr8WFBTi4oI0bhhZ2Vgh1zowMDD8/Pnz+48ffHx8FFqEx4Xfvn///fs3Hy8f3Cs/f/36/u0bHx/ft+/fGRkYuLm58WiHdMl+//4NiS/IJWRwLgMDw7dv3/79+8/Dg80QRkYWFpbXr1//+P5DXFwMsj4bj13IUqCrKxgZf/3+DWlRIUsRZDMyMbGxsv3+A3UzQfXICr7+/nbu1fklXgsE2QWRxQeQ/W3Wzq/dG9i4ORkZGFgNlBgdtP59+f5j0+m/D18xsDJzBFv+e/b+18ErDGwsbFYarGaqjMzMv87c/nXoGugwJgYGVh15NndDhn//fu46/+/6U9Cye34uVm8TJmmh36dv/9x3meHPX3Y3AxZ9xV/7L7Foy31ftJ+Rm53d25RFVerP9cc/t5/9/+UHm502oxDPzw0nGdhZOQIt/t54+vvSAwYW8Kqq33/ZnHQZOdl/bjnNwsPJEWT1+9L9nxfvMwrxcPiYMsuL/bn66OeOc/9//GIS5ecIsmTi4/p76SG7pNCf+y9/Hr76V5yfM8ji3+fvfx++ZmRj+b3nEisXB7udDpOp8q+X739vPcP04iOLpiyLueqnubv+g48CgMcFKHGCN1H9/QOCoMQJHsj4/+8fKOWAS0c2NlAzGq4F1PwDc/7/ZwClEORDshgYwMUUyFN/fv/5Cx7yAKuFEqwsrEzgWwb+/f33G3YsIFQOgwKt72Jlg5Ttv3+BDl6GKwFdgwy6zAVUt/7+9RtUIcDlaM34959ZUkhwWy28s0FrC0fNHw0BagFQhqGWWXjMObhpzYo5M6WMbbm5uH7+/Pn/798fP378Bh8UysHJ9ffvn8+fP/37BxpfYufg+PPn769fP3///MXwH1Tf//z5nYmZRVpKioub+/OnT/9BZQrLn99/fv38ycsv8OvXz6/fvrKysP74+evHj+///v79A8J/mJlZQDToSidWbm5uJSXlT58+/vjxA3TnEzMTMyMTLw/v58+fHz1+DFrJ+v//n79/Qa1HBtAupr9//zAyMTGxsDD+/y8iIiIpIfHp0+eff36BRon+/v/3/5+IsPDr169fvnz55+9fUOkP2nEFGmxjABnxn52dg5+P7++fP0xMzJxcXD++ff/P8J+dnf33719//v3j4eFhZGR8/OTJ3z9/WJgh16iALuVkZ2djZWX79w/k6v///3FxcXNzcf3+8wfSruXg4GBjY393/wbzn58FPVME+fjwhDYeqZKjFY93PfSR8YpNjWVgYLhy/srSpUsLCws2bdr86tWriooKJnZQYd3X1cfLy+vn69vR2ZkQn6ClpdXY2Pjhw4e///7y8/O7ubo5uTsxMDDs2Lz9yNGj1dXVnLygptils5fmzZuXk5OjoqmCcMMfhoT4BDY2tlnzZyEEUVmP7j169+6dgYkBAwPDjcs3EhMTJ0+ZbGJhgqqKarxpk6atWL5i46aNgqIUtUgunr2Yn5//5vWb7p5uT19PiPuunL/MxcWtpK7EwMCwfPHyaVOnbt6yBW09LkQlVciu1q4TJ06sXr2KmQM6y7F1w9bWttatW7aWV5SLioi2drXisej8qXNLly7Ny8uTU5ZnYGB4fP9xb29vWmqalr4WRFdBTsHLFy+Xr1kO4aKR0yZNmzZ1mpiY2NatWzn5QGkATQEu7r1b975++aprpItLAR7xX19/nT17Vk9Pj1sAWxMZj04Ghjff36TtzprrPkuQHWWFNF5NtJX81rX+447TvbwP7169wcbC2tbYLCcvx8DKAt3E8+sPiAE5+v7P3/9/QNMLjCygU5CgzgLdq/QX1NllYf76/duXr18Z/v/n4+LhYGMHHZbECj6x7vff/3//MbIygy5wYmMBdQB+g05lBimA3OT05y9o6T5E8a8/oKFWcLMMagVY9g/j//fv3v39+ZuTm4tfSBCLIaDjQkHXLO3Zt3f2rFkMTEyBocExUdF/f/wEjVYygTr0l65d6ezq+vn1m4eLW0pqypad2xcuXsz477+2hmZdC+goFaiN4Ibmtm3bFixY+J/hv6OjY2Zm5uXLl7u6un7+/Gmgr19aWsrKxvbt69eKiopXr15BtsZC9HKws8vKytrZ29tYW3Pz8IDH7CEyDBMnTDh69CgjI1NhYYGFpSVUFEwxMjBs2rx58aJFDIyMoaGhYWFhoENdwFJYiTev31RUlH/89ElJSampsYmdAzz2DFb69cuXpubm+/fuSUlLN9TXCwhSVMiAjSSW+HPjyZfmlSJn+yED6sRqG1U3GgKDAEArMFq7hJGR6fe///dfvOXm+vrhw4cvX74wMTH//QtqaTEzs/z6/QtUVjIx8vDyCbJy/vvP9PM/8x8mVg42tj+MTO8+fvn399ebb/dZWJh/fP/x5/efP7///GdkYGNjZWd/ycbO9vfvX3EJSWZOHiZm1v9//r568YyFlfXX16//wN3fP38/M75+/+jVu79//wkJC/3+/fvnnz+/fvxgYWaRk5PlFZO+defO75/f//8HzUUygSZwGf6CtuEzMjIzS8vK/GXlOnj6/M8fP9nY2SBjgR/ev2f4f+f7jx+/foHO5GdjY2VkZmbjYGf495+fl5ePn/8/I+O3/8wvX7/+9esXIyNomPDPH9DgImSJAhsnB/iIqz9MjIxCQkLs7Bzff4BGsP4xsPz8x/D924+3b9+C70plZmdnB9UYf/4yMzODrjtiYRVh+M77/9fnT1/IboyysrLsO7Bv4/F1orKiHh4el65fmjxjclxy3MdvH6sbql29XE1NTW/dulXTUNPc3Pz01bMJUyYYmhpKyEpMnjFZWlpaX1//3KVzfZP6urq6CgoK9h3eP2fOnPTsdFleWQYGhq7+rqVLl/5j/jdp0iR4ivr9/7eNow0LbJjz69evDKjDdf/+/evq69q1a9fFixc5ODjefX534eqFd5/effv2jRUMIEb9Ao3A/oQ05SEiEPLv37/fvn3j5OSEWPHnzx8WFpYfP0Dju1ywsdhfv379/v2bm5v7L7jzoK6t7h3gzcQGulSWmZkZYgIvLy/EQAYGhi9fvrCwsHCAT3KACzIwMPz48ePPnz88PDygs8D//u2d2Hv/8f2+vj49Iz2Isu/fvyelJ6uoqCxevJiJienJiycXrl78/P0zyxcWbm5uUFcFrO779+9///6FmAMWAKW9v3//soIHfiBeAJ0gDu6KsLKCboX4+vUrmh///PnDyMh4/8n9yzcuf/n5lZ+D/wd4tcnrD6/PXz7/5ecXaXnpmzdvgre+/Pvy5Qu4PwNacQGxESLe0du5atUqLn7upqYmBgaGe4/uTZw60crOCt4YNbU0/fz5M8QxLCws379/Z2Bg4OQEtTufPXtW01Dj4eGRlJTExA5ZGMD08+dPdnZQxQzO40zIUfDjxw8eHh4mJpDKmoaaGzduHDhwgJeXF9LdYmRk/PXrFwt4MJ6FhQV0Gu7v38yg0XpQ7+jbN9AeHYhpZy+dtbK32rBhg6+vLyMjIyS4IOpBvUJmZogIxDQmJibkWPv5FzRNDA+BwcBgZGT8x8+1683dy3fPMbKzVutLs2qok+ewJTOnT502jYmJsaWlNdDblzxDcOm6e+9ORHbOz58/7ezsZk6eiksZAwPD/SsHNjy6yMDAcGz5S+PUEG0DhHdef3m4+u4Zhl9/xDisWC3UZTi/beuu+v31++EfzxJEWlXkFJCNndd+YOOdMwwMDMGtJayGSm8+Plhz7+zf7z8/KguVm6qwMrEwfP+65fm1J/ceIOsCsS8zdO1d4+Hh0dXVpa2KsP34r5cb7oJOWg2X5GA1BHUXQYrB+A8Dw+SKLQfAsi+PcQU0FXKxoGQWsCoE8es5+7qHF768+8Dw8IKkt1VpfiFc7t+XT7vf3rly94Iiy+caPVlWETG4FK0ZjKwsoHY/ra0ZNX80BGgAQAU9DYxFNxJU0/z89eD+vQf37//6CdqM+fvXzz+/f7OysAgJCvz59fvjx4+fP3169eL59atXb1y79uj+vZfPn3379vXbt69/QBcl/WJiZpKSkmJhYfn96xdolRrD/7+//3z/9vX927fv37378OHD189fXr548eHDB1ZmNnVVdTFRMQ4ODtCyUCZmhv//P378+OXLpxfPn7FzcqqpqHOwc4LaCk+fCQkJGxoYsLKy/P3z+/evnz9/fPv188c/0BwNo7KSsoGewYOHD/6AZ7Tl5ORlZGSeP3v64f279+/f//zxnZmZkY2VhZWFhZ+PX0NNg4eb58e3H69evvrw8cOnTx/fvnnz8f37z58+/fnzi5GBgYWZ+f/ff////peWlObi4PoHWij758f37z9/fn/75s3jx49v3rh+9fLlx48eff708dOHj9+/feXm4eHh5vvz58+vXz///fvDwPDv+/dvv//8AU1nowcwCXw2Nrafv3+VlpZ++PCBg4ODnZ39////kZGRvLy827dvZ2Bg2L17Nzc3d0hICGReDNIgYGNji4qKWr58+aFDhyIiIjo6Ot68ecPKysrGxgaZn33w4MGJEydUVFR279798uVLZAe9AYNfv351d3c7OTk5ODi0tLRAmjX////v7e3dsmXLs2fPQkJCQANs4GbljBkz7OzsIiIi7t27x8DAcOjQocDAQCsrq/T0dGTDb9y4ERUVZW1tHR4efvPmzV+/fhUVFZWWlnp7e7u5uW3evBm0E+vy5aCgIHt7+8LCwoyMjDt37nz79u3Zs2cMDAzTpk3LzMyMiYmxtbXt6Oj48+fPp0+fysrKbG1tPT09N23ahOyL1atXu7u729jYVFRUvH//vrm5eceOHT9//ly9evXDhw8ZGBg+fPhQUFBw+/btw4cPR0RE3LhxAxK2xcXF1tbWeXl5Hz9+ZGBgWLJkiZubm729fXNzMyQQGBgYrl+/HhERcfLkyVevXsXHx2/ZsuXr16+pqalbt269d+9edHS0lZVVaGjohQsX/v3719DQ0NjYGBYWtmnTJh4eHhYWFlZW1iNHjnh7e7u4uKxduxZiLxcXV2ho6KNHjxISEmxsbHx8fCDxC/fUvXv3Dh8+rKKismbNGkiLkxkG4GrevXsHmoj/8SMzM7O8vNzDw8PNzW3Pnj1Pnz5NSEj4+PHj7du3t2/ffujQofT09KqqqtjY2Fu3bhUVFdnY2Li6ui5YsICBgeHo0aPBwcGQaLp+/XpLS8vBgwfv378fERFx4MCBVatWZYNBc3Pz4cOH4+Pjnz59+unTp6SkJEgUTJkyxdHR0cHBYcaMGadOnSorK2NkZGxtbS0rKzt79mxUVNTdu3f//PmTlZW1dOnS169fJyQkdHR0BAcHX758ed26dR4eHra2trW1td+/fQdNg8D9NngY//+zgk5hZmBnZGaE34pEuvPePH9549KVaxcuf3rzjnTdBHT8+vLt+sXLNy5dwdL4Q9XK/Be07Ai07vzJs+LcfHgiB52u/+cfG/jaP4h/9TS0nGzsGBgY3j5/eXD3XmRj7t27d+bYCQYGBhUVFVd7RwYGBua//xF6f4Fu4mD49YedETT0y8LCoq6urqurq6OjIy8vD1oq/ePXtg2bQvwCIAUIxGSIpRBnQETg5NmTJ88ePwnhXjh1Bs6GiGCSjL//Qqxm+POvvbH55EmoXpDKX38gFoFcS0FsgowiESNu2CJR46jy0RAYcECnxijD//8sLMwCQkLsnJxsHJx8AgK8fPzcPLxM4EEsXl5eYWEhQUFBQSEhEVExMUlJCUkpCSlpNk4uNk4uPkFBIXDL8svXb7x8fKIS4qLi4qJiYuKSkpLS0lJysrLyCjy8vOycHLz8AuycnAJCgp8/f+bm5RWXkpKVl5eVU5CRlZOUkhaXlBQRF2dlY//z94+UrJy4lIyImMTX79+5eXlVVDUkpWTEJKSExcSFxcTFJKXl5ZXEJaTff/goICgkIi4pLiXNBBpC+y8mISkhLSMpLSMjpyArryinoCQjJy8sLPLt2zduHl5OXl4OTi5OLm5Wdg5pOXlJWXkxCQlJaWkRMTERUVFhMVERUZHv378xMDLyCfLzCQuxc3OxsLELi4tKSktLSEmJS0kJCgsLCAkLi4gICAr+/fP7z+8f3LzcvPz8PKDg4mEEHcAKai+Qn27+M/z9+yfYL+jLly+1tbWgJVbgsSUpKSl3d/dNmzZ9+fJl8+bN1tbWcnJyvyH3DYIt+///P2QXCzs7u7e396dPnx4+fAi5tgoyn7V169Z///7Nnj372bNne/ci6pV///7t3LnzyJEj169fLysrc3Z2Tk1Nff78OWSsi5GRUUlJSVxcnIuLy9zcXF5e/u/fv1+/gobPw8LCdu3aNWHChJcvXyYmJnJzczc3Nx84cKC9vR3sItA4ZW5u7t27d5uamt6+fVtUVPTp06cdO3bMnz/fy8vrz58/hYWFHz9+zM/Pv3HjRmxs7MmTJ+fPn//x48fLly+vWrXq+/fvFy5cmDFjhry8vJGRUWVl5ZUrV6ZNmzZv3rySkhIjI6Pk5GRIK5OBgeHUqVOpqakKCgpRUVFTp06dOHGitra2kJAQDw+Pnp6euLg4aIktK6uOjg44MQubmpqKiopC1lxyc3N7eXlNnTp169at586dy8vLs7OzKygo6OzsXLduHcQvQkJCJ06c2LFjx6VLl5YtW7Z+/foLFy5s3ryZnZ09Ozv7woULqampT58+TUlJefbs2aFDh9ra2piYmKSlpf/+BQ2cv379Oicn5+3bt0FBQQ8fPvz169efP3/S0tICAwOnTZu2adOmiooKAwODBw9QxpBWr14tJCTU09Pz5MmTXbt2QVyCRh48eHD79u1//vzZunXrkiVLfH19v3z5UlJS8vfvXwNQL45VRkZGX1///fv3c+bMWb9+vYGBwdevX1++fFlZWampqVlaWnr9+vWGhoanT582NDQICgq+ePFCU1NTWFiYm5vbxMREWlr66tWr06dPv3LlipaW1uPHjzdv3vzly5efP39u3rz54cOHW7duLSkpsbS0tLe3P3DgADMzs46ODgMDg5qamqGh4fPnzzds2PDx48f////v2LHj6tWr379/X7t2bX9/v6qq6uPHj1NSUszMzEpKSqZPn75syTJWVjrNBaEFI7W4Hz58uHDhwvHjx8+cOfP48WO4sR8/fnzz5s3Pn6BxX0ZGxm/fvr19+/bly5eQPgZc2ZMnT86cOXP8+PErV65AxtHhUhDG3bt3T506dfz48WvXrsEV/Pjx482bN+/evYNMPvz////169evXr168+YNpEyA6MVK7ty5E3meBE0NOzu7v78/ZMZgy5YtyKYdOXIE0mN0d3cXEwMNLkLKGTQTIFxBQcFVq1btB4Pjx4+vX79eQ0MDtObnxo3m5mbkmXqIekxy/fr18LD6/v376tWrMdXgEnn//n1xcfGnT59wKYCLv379+ty5c8ePH7948SKy+o8fP74EA3iYMzAwvHv37uXLl1++fIFr//Tp08uXL1+9evUHvMKNgYHh06dPly5dOn78+OnTpx8/fQJa9wVXPcoYDYEhBehUNIM2ubNzyMnJg6abwWfLg66a/Pv3z9+////+BS8W/QfaBMwI2p0MWkjOxPjv339mVlZwcwe0H+jXz59MjEz//oE63eDhjf8sLCz//zMwszCxsYLWsDMxMf0XFvn37z9o2gu8bh20Henfv99//jAzM4P2ZPz7zcjEzMHBycrKxszEDBqJ+A9SzMTIJComwcnJ/fXr1z9//7CyMLOzc3Dz8LJzsP/791dGVg50CD8TM2h89vcffn6BP+DJU9BNpP/+MTAysoGXsYPWmDKBpn0hzTvQrUbgLcY/v38HbWf5A1qEysjAAGpNMjGBSlVGRjZ2NkZGpv///4GOGvgD2o79FxwUIPP//WUCTVAyg/Y0sLIyMbOANmszMv3+8Ynl/092dtBFAGQns9+/f+vq6sYkxsbGxr5//56VFbThhoGBITw8PDMzc8uWLWfOnJkxYwb0Kixs1kDmVUEBDooZ0AGx//79W7ZsmbKyspiYmJSU1OLFiyMiIiAjppBWGgsLCz8/v6Ki4qZNm1RUVPz8/Pj5+SFmBwcHHz169MuXL3V1dQwMDMePH+fk5CwvL3d3d9+zZ8+jR4+uXr368uXL9+/fb9q06ffv36dOnYJofPHixdWrVyUlJTdt2vTp06c7d+5AqpPw8PDi4mIODo7q6uqHDx+eP3++oaEhPz9fXV0d4ioWFhY2NjZIFaijo9PS0nLjxo0FCxY8ffr0+PHjjIyMe/bsefv27fv3769fvw4aaAG7ip2dvampSV5e/ty5czt37mxoaFixYsW3b9+qqqog7uHm5s7JyVm5cqWqqmpZWRlkapuXl7e1tZWbm3vhwoVPnz79+fPn58+fb9y48ezZMyYmppMnT0ZHRzMwMEhKSjo4OJw5c+bTp0+WlpZXrlzZsmWLjIyMiorK0aNHu7q6MjIyZGRkEhISHj9+zMTEZGdnt2bNGgYGhoULF7KwsLx9+/bOnTszZsyIiYnh5+cvKChgYGCAhLCCggITE9O0adPU1NQCAwMhTmVgYPj8+fPq1auVlJQkJSVFRUWXL18eHBwMCRO4GkjcgZZZg/cqRUVFlZSU/Pv3r7+/n52dvaCgYObMmUFBQbGxsZs3b+bk5Jw8ebKLi8v379/d3Nx2795948aNnz9/vnv3TktLa9WqVdOmTTM0NFRSUnJ0dNy6deuNGzdqa2tZWVn//PkjJia2ZMkSWVnZJUuWQMbaGRkZIRvsjhw5Iisr29zcDJ/QZ2JimjlzZlxcnJub25YtW+Dq2dnZWVhYIMP5+fn5VVVVs2fP/vz58927d1++fPn379+Tp04GJgch+24Isf/+/Tt37tzp06ffu3fvy5cv7OzsUlJS/v7+DQ0NvLy85eXlhw8ffv36NQMDw////1tbW6dNm/b379+kpKSSkhIGBob379+3trZu3Ljx6dOnv379EhAQ0NPTq6urc3BwgATC/fv3Gxoa9u/f/+rVq79//woICOjo6FRUVLi7u2/durW+vv4HGDAwMBw+fNjR0fH///+qqqrz5s0TEhKCmICLbG1ttbKysrW1xarAx8enubn5+fPnR44cuX//vrKyMkTZpk2b/v//z8bGFhYWhpksIWqQSX5+fmFhYYhIQECAmJiYj4/P+/fvt23bdu/ePVVVVYgUVvLt27dr165lYGDQ1dX9/v37nTt3Nm/eXFVVJSEhgVU9puDRo0c7OztbW1sxpSAiP378mDRp0qJFix4+fPj9+3dubm5VVdWioqLIyEhGRsZVq1b19fUxMjImJiaWlpYyMDA8fPgwLi7u1atXNjY2U6ZMAW85+B0fH3/z5k1JScnVq1cLCgouW7asr68Pkh5YWVklpaUiNC3L2ERB22Qhto6SoyEwdAC9GqMMDKxsbOJS0oz/wZsLwfvF/4KOWQItGwU3wv4zgbdSMoE2DoFWjIGGfMBLx1hATbH/v379ZAQ378DtH5ApoG1IjIwsrCysrGyg3UOMjP/+/4Ms/QQ1Q8F7kv7/+//79x8WyPL///9Ae8ZZQODf/3+g3Uj/QQtsQFNKjMzcvAzsHByQA3FAs55srGysoP3joGucmEET/ZDN7pCWJbhpC2pEMjAwsLNzMDOBnAFpfIBarszMoJOJ/v3//ec3OzsHEyPT33+ge95B00Ng3zGAN2IzszCDNg38ZwAtmQU3i//9+/fnz58/f0EtUyZGJkiDCbTJHdRGZ2RkYv7w5Q3T13eQlYXkpzFGxq/fvgUEBISEhCxYsEBAQABS1js6OoqKilZVVYmLi0OqKIg4xCJILwKynnL9+vUCAgIKCgqQkQw2NrYzZ85cuHDh////VlZW379/f/HixZUrV/T0oCspQbdb//6toKCwZs0aSCc+NzdXVFTU1xe6rA0ykvfr1y82NtA6LUiTAhJKkFWAjIyM2traBgYGNjY2MjLQQ6NAW8cYGRUUFGzBQEBAgJeX99+/f5DwAfdtQA0adnb2p0+f/v///+HDh/CxXojXILUdaOccaJUwKOoYGRlFRUWtra2ZmJjCwsJ0daE7bNjY2P78+QOZcPz+/TsrKyuoCwFOwJDwgZC/f/8GL6tAXIIKWe8LWasKSQDs7OzGxsYSEhJ2dnaQQT6IXh8fn9zc3MuXL3d3d7e1tc2dOxfSsmRkZIQstIUcSsDMzPwfvK8OogviEUjsQAZRvnz5AuktQBQkJibKyMhcu3Zt3bp1SUlJx44dEwTvqDhz5sylS5du37595MiRr1+/vnv3Dj7UDdEIJ+FWQBbRQmIEclbA////f/0Cefb///+cnJwQkydPntzT01NfXy8qKnr58mVWVtbm5mZzc/Mb4Bb/w4cPV69e/RsGIH0hPj4+yHpQyBrQP3/+fPv2DbzkGhSDf/78+fnzJy8v79evX3l4eH78AJ3gBhlDgpQJv3///vXr1/fv3yE9KCYmJshYNaQwMTIykpKScnBw0NbS/gNagQP32VBidHR01NTUQFzMwcHx/fv3u3fv9vX1/f79u7e39/bt29euXYPIMjAwPAUDBgYGSIb68uVLQkICZM0DExOoYHn79u3+/fsvX768adMmS0vLL1++JCYmHjx4EJJEGRkZ37x5c+DAgVOnTu3evfvjx49Xr16FG/7lyxcIV0REBHndM1wBhAHJs7dv3/78+XNZWdnWrVuxNltlZWXd3d0XLFjw9u3b3bt3QxqjDx8+PHr0KAMDg5GRkampKcRA/CSkLIKrsQCD7du3v3r16vr16/gbo7t27YLM5mdmZj558qStre3Ro0c7duxISEiAG4iLISsr++/fv6dPn06YMMHR0dHFxQVT5f///6uqqvr7+yFSHBwcnz59Onv2bGJiIgMDQ1RUlKam5q1bt/79+7d9+/bCwkIWFpZz584dOnQItIDh7dva2lo5Oblbt27t3bv38+fPOjo6goKC27ZtS05OhoyFQ1bJ37tzd/2dl+UGqb9+/mLnBK3bhlg3So6GwJAAdGqMMvwHnTDKw8f77/cfZmYm0MDk//+ghhd4ugHcGP33//9/ZiYm0LmF4LYXqKkBPnIPMjj69zcnfKYYtMGIgZEJpJoRNCQJWhnKwszMDCmPIA2Of///gfY5/fn37z90zBVsIKhNzMLC/P8f+HAm8J7Nv//+gc6J+cUGGlMAHb0EqnyZWZjZ2NiZWVj+/IHuogANZ4Katr9ZWFmYmJn+/fvHyMAIHr5kBp1YysoKqQhBTWTQsO4/0K74v3///f3LCDpiE7QH/+/ffyzMzKCpLkbQRSfMkBlDcKP531+QepAHQacB/AOdA8rExAIes2RmZAbZwgSiv7Gz/fsGmqonP20xMkCqdgYGhtbW1qNHj969excSbsLCwm5ubhMnTiwoKBARAV1D9ffvX0jr6t+/fz9//pw3b96RI0ceP3788OHD3t5eQUFBSIvix48f8+fPFxAQWLNmDT8///3790NCQpYtW6arqwsKSgaGX79+sbOz37p1KzU11c3NTUBAABLjcF8oKytPnTo1Pj4+MTFRUFDw+/fvkHmo79+///v3T0dHR0ND4+zZs1JSUocOHcrIyIBolJCQsLGxOXv2rKmp6fnz5+3s7Dg4OCAOhnQMvn//zsPDExsbO3HixOPHj9++fRsyWPv7929Ig+YXGICCHRT8f3/9+uXl5XXgwIH79+9//Pjxz58/YWFhELscHR25uLgyMzOVlZV37tzZ29vLzMz869cvSJMIoga0rI2ZWUlJaevWrRkZGXl5eaCDcn/9gnSffv369fXrV3Nzc35+/itXrvz9+/f48eMWFhZwvTY2Nj9+/ODn53d0dIRM07u5uYmKinp5eXV1dT148GDz5s2WlpaKiorfvn2D2wtpt4mLixsbG7e0tFy+fHnLli2QcIOYPG3atLVr1wYGBnJyckKmsyFbl2bNmqWgoLB8+XJubu47d+74+/svXrzY2dkZ0siG6IW4HxJWkNYhsgikJQqZA4W01CFZ78OHDz9//vzy5cvRo0d//vz548ePioqKFy9eWFhYQPYnQSbZV65cmZKSkpOTwwTeYwQxR0lJ6efPn2VlZZD1uz9//nRzc+vv709JSeHg4Hjx4sX69etlZGQEBQWbm5svXrwYEBAAGUfn5+d/9uzZnz9/QHetff8OaSLb2toKCgpevHjx////hw8f7unquXfvHii1gzqgcC8OAcb///+lpaXV1dWZmZnb2to0NTXPnTuXm5v75s2bVatWFRQUpKam2tnZHT58GLJCJjg42MDAgJGR0dXVlYGBYfXq1ZCWqK2tbVNTk6Sk5M6dO2tqat68eQNZK3Lx4kXIqkd3d/empiYODo59+/ZNnDjRwMCAg4PDxMQEMng5e/bs379/a2pqRkZG/vnzR19fH9LYxRqC////j4+Pv3jx4pIlS06cONHR0dHV1QUpENDUBwUFLV269Pfv3zt27EhJSWFhYdm3b9+rV68YGBiCgoIgu+XQtBDkMjExQdq1DAwMT548waP+z58/q1ev/vfvn5CQkJOT0/Pnz/v6+n78+LFmzZro6GhIzxaPdjU1tdjY2OTk5G/fvpWUlOzevRuygQ9Zy7Fjx2bOnMnAwKCsrNzd3a2trX3p0qWCgoKnT5+2t7f7+vrqgcGFCxdu3779+PFjRUXFI0eOQEyAzOzLycmdOXPm69evTExMHh4ejIyMq1ev/gk+U2/ixIkODg4vX77snzLp18lboPEYiM5RcjQEhhSgV2OU4T8TMxM3F9ff378ZQZPVoKFM+MgoeJr+P+hSOGbQND3oGG3QKBVopBTUBAQdt8T0nx20ZhFcr4OUQ4aCwKpABGT7LSjk/zP8Yf0DbtH9/8sCWgAAGcgBjxWBmqOgkSroqY+M4LM/QW1iNlaWvxxs4Fl00PIA0KkloFYxCwMjAzs769+/oOYsaP7rH2hs9T/Df1Y20AZnkHGgIV7QaYuQBiOkOQJp3IDUg50LaoeC3P4PZA4oHJhBGhmgg6kgN4NawaAFCCCPgZdmMjKALosGORM8vgcZQ2UEtWNZfoMcRv5K39+/f/v4+Lhqg6ooKSmpOXPm7NixA7Iki4GBIT09nYODIyYmBuIqKSmpsrIyHR0dbm7uwsLCz58///v3z8LCwsXFxcbGhoGBwdHREXQgKCursrJyR0eHJfi0FC0trc7OTkiLB7S+lZk5MjKSlZVVWlo6PT199+7d//79mzhxooeHB8QWBgaG5OTk////X7p06devXzIyMiUlJUpKoL2ukZGRzMzMwsLCS5cunTp16uHDh/X19bW1tSEa2djYJk+ePGXKlKNHjyoqKlpZWbGysmZkZGhqajIwMBgbG5eUlPDx8TU0NGhpaX369Onz58+QuTArK6v///9zc3P7+PgYGxszMzOLiYmVlpYqKSl5eXkxMjJu376dh4cnLCwM0nhlYGDQ0NBYv379zJkz3717N2PGjKioqP///4eGhsIbhRAnMTMzt7S0KCkpPX/+/M+fP+bm5oWFhTw8PMzMzLm5uebm5hoaGitWrJg7d+65c+fc3NyQ5wElJCQ6Ozu5uLhERERSUlJUVVXNzc0ZGRmngsGZM2ciIyPz8vJERERiY2MFBKAnE7m7u0NWR8yaNau/v//Dhw/19fUPHjyAzNEzMDD4+vq+fPnywIEDSkpKzc3NkNGpnz9/amtrBwUFQYad1NXVu7q6+Pj4pKWlS0tLIevtID4KCAj49OkTKytrbm6umRnollcLC4u8vDxubm5WVtbi4mJ9fX1I47KoqEhKSoqBgSErK+v79+9nzpyJjo729vaWk5OLj4+fN2/eoUOHgoKCsrOzGRgY8vLyODg4rl279vPnT0gqgoyMmpqazpgxY9OmTTY2Nvb29vr6+jY2NitXrly2bNnfv3/z8/N5eXkFBQVXrFixfv36b9++aWhozJ07d/ny5dra2o6Ojqqqqnx8fCUlJYaGhhBXrV27ds6cOWfOnHF2dpaWkb7y5Ar4bMgh1hplZGRMSEjw8fH58+cPJM3IyMgsWrRo+/btr1+/fvHiRUREBAMDQ1tb2969exkZGYODgyMjIyEx+P//f0hLlJubu7OzE5JJ1dXVd+3aBVnEDDqiDrz0iIGBgYODg4+PT0REJD09PSwsDBKhDAwMenp6169fX7hw4e/fv9XV1SHLzSHm4yGZmJhaWloOHTr06NGjKVOmeHl5YW282tjYqKmpXb169ejRo48ePVJSUtq+ffu/f/+EhYV9fHzwmI9fCt6KhfRMcCm+efPm/v37GRgYzMzM1NTU5OXl1dXVL168CBk5NjIywqURIv7jx4+YmJiDBw/Onz//4sWL9fX1bW1taG3uXbt2ffv2jZmZubKyErJURk1N7ezZsx0dHbdu3bp06ZK1tbWtre2FCxeePXt248YNeXn5gwcPMjMzc3JyfvnyZf/+/QEBAceOHfv375+YmBik7IXM0jAwMHBxcfHy8kpLSy+cv+D36dt/8hawgY+zgDhvlBwNgaEC6HXO6MY1G1cuV/eK+PMLdLA5ZOkkMwsL+FhN6BQ2qH0JOmYZ6iRw8xE0KggZ8oS06yCz5JB1maAmHXhhGaQhCBrwYACdLA8aHfn7l4mZ6f9/hr/gYVHIzCaoGQoeSIUMm4GaeQygmX1QIxHc3AQ1FpkYQS0/BtDwIXiSixVkJNhuyAQWJF7/gM4QZQIt7vwPWjEJlv8PcQZEAYT9798/yFgRpJUJaZ5CGsfg4V7Q6i5QCPz7x8zIyMzEDDowCHTWKmjd698/fxhAgqCTA/7//w9atMDK9vT8sX8fXxf0TBXi44FYRCqZt7/QQ8ndSx7RECTVhCGnfsWKFYsXL7ayslq5ciU7O/uePXv4YctVh5xfqOVgSOailmlDyJxnX5/l7C2YO5jOGf3eveHd0ct+b7adO3qCg4Pj1KlT8MUhaAF748aN3bt3X758+dmzZy9evLh9+zZkE8z+/fsh62oaGxsbGhoYGRnnz58fHx8P0f7t2zdHR8dTp05xcnJaW1tzcHD8B5da58+ff/LkCR8f35EjR6SkpIyNjSHb9bi4uCQlJSUkJHR1dePj4y0sLCCp5eLFi1ZWVt++ffP19YW0biHmY5IzZszIzMxkYGDo7OwsKytbvnx5YmIi6HxQA4OioqKMjIxv375lZmZOmzYNrreysrKjo4OBgWHOnDleXl76+vqvX7/29/ffsGEDXM2ePXv8/PwgK5I3btzIwcHx8eNHY2Pju3fvioqKnjx5UlFREa6YgYEhJydn6lTQEVRTp07NyspiYGCIiIhYuXIlAwPDqlWrQkNDIYpbWlogbes5c+YkJSUxMDA0NzfX14MuFikvL4e4CqISmXz06JGRkdHbt2+tra2PHDny/PlzR0fHmzdvsrGxzZw5c+7cuUeOHFFXV9+/f7+kpGRCQsLChQuZmZktLCwEBQUh4X/37t1r164xMTEtXbo0IiJi69atvr6+////r6+vDwkJMTExkZeX9/LymjBhgpmZ2YYNG3x8fM6dO+fq6rpjxw4mJibIFBbESWJiYuLi4jJycr7yepFHv/Mf7xw9ZxQSMqPkEAJ0Ghn9DzrbiIWHj/f/n7//wMOEjAwMHODTCv/9BcG/f0BnzjMygVqikG4lhIRsngDNiTOCpCDTu6B2JAyzsrKCJr5Bg4ug3UigqcOfP//++8fKwgLaE/TvH2TpIWQOHVIKMDGB2pGgAVXwkYeQ+UHw7iiQYogbIAN7kBs7/v//D2lTQrRApCCC//79gzcrmZmZ2dhAp9ZDxkeZwPuZ/oLP0oc7G5IyIE1ViAf//PkDdQB4Xxe82QppcDODdzuBRnr//GFhY2fn5PgJuiwJ1GyFGEUGCbGODI2DVgukssTlPC0tLTExsSNHjhgbGxcXFw/Rlih+P+LyOy5xSNrDJTvg4qR6lnj1kJmWAfcgGQ6YNWtWVVXV27dvIXp5eHgg3W9IQQERxEpCCh8GBobv37/v2bMHTQ1kekpYWHjy5MmlpaU3b9789u3bXTA4evTomjVrVqxY4ezsjKaLJG5ERMS+ffvmzJlz4cKF9vZ2rFEQGBg4YcKEHz9+7Ny5k4uLC7ITKzw8nCSLkBX/+/cPsgyUgYFBWloaWQqZ/fXrV0h7l5GRceXKlbt27WJkZHz69ClEzZYtW0pLS+H7oiCCmOT///8lJSU7OjoiIiJ+/vxZXV2N5kfI0Ozfv38hC2GRTYDsE4CMy8rLyz948ODMmTPi4uI/f/7U19ePioqaPHnygwcPDh48ePfuXQYGBnd3d8h0TUJCwtWrV5csWfL9+/dXYHD58uUnDEeiDZJ+/vjBwQlafI9s0Sh7NAQGOaBTYxS0owhyHSb4ukvIRmxIE5OBkQHUYgPN20MbmJARRFB+Bk2Cg241YmZhAc1c/wNNl0MmwRlBZ9KDdnJAqlUICSmXmZiZQetPwQEPsQjMZGAG3yEOKZrh6iFNRlBTleE/I2hdAOgqExCGuAWsE1Low1uiEFsgTTpmZpC7wapA7gTvfwJtlofohjgSYhfcJaABYLBZcF2QwgWkgAHUnoacCg4JHJBHwMOiIMUgQ0H0KEYLAUgIownCuXp6evPnz4cchA4XHHIM/H4cct7B72BSPUuqevy2D6wspDRAc8Pdu3dramrevn2rrKycn5+vr6+voKBQVFQE2QOOphjSGYYLgi+OYGVgYBATE5s2bZqkpCSkQGMBT0xBFjNAlnPY2toeOXLk6tWrkDMoTp069ebNm76+PicnJ+QQRmbDbcHDYGRkbGhoOH78+NWrV69fv45VpY6OjoWFxYEDB44ePQppRCopKUGGe7GqxxSEDArAxU+ePHn8+HGIr7W0oNeJwWXhjCNHjly+fBlS4+zevRsuDmFcv3798OHDAQEBEC5+0t/fPz09fdKkSZATqZAVQ5agsLGxTZgwQV9fHxL+kJm6P3/+qKmpMTAwCAsL29vbP3jw4Ny5c+/egY6JdXJy0tPTU1NTu3HjRn9/P+SSZHiY8PPzz5o1q6Cg4OTJk7dv33748OHe/fuYX//+8OHjzSOHXX29kR0wyh4NgcEP6NQYBQUEaJkWaHQTxAZhRkixC2pc/v/PAh4XhIiAhh7/gubuwUc4gZZ4MTEygTfhQxt8kAFOUEsVfiE3aF86SBbS1AMbD9oGBR6E/fefCTSZzgCe1wctI/37j5kFdKcRaE8+qA3KBNoyD5IFHR3Fwsry6/dvJvBALBP4ZmTIJT2QlayM4Jn9/6BDncCOAq1/hZwQAGo3MoBak5DGKiOI/x/UrIXM70Om8iHlOLTtywC6Hw9kCihUQM1QkOGgu3ggw6P/GUHXQTGCF5GCWuGMoK3/TCArQN4bxaSFAGT4nDQ9o6pHQ4DuIfDhw4f379+DuuIwq3l4eO7evQsZLAwNDc3NzYUcagFptMFUgWjIisz////fvXsXfoADBweHiorK8ePHQReCsLJaWVmBlDIwXLhwQVBQEHJy2d+/f+/du3f06NGoqCjIMs3Xr19bWFjcu3fv/v37P3/+5ODggBetL168eP/+PWS6GVKgQQzEQ0pLS3d3dwcFBaGtsYZr4eLiCggIOHDgwDMwYGBgcHNzk5SUhCvAz/j///+7d+8gW/t//Phx+PDh+vr6Dx8+MDAweHl5QRagYzVh1apVkEMb4BPokOL6xYsXZ8+e/ffv39KlS+EnoWI1AS7IyMhYV1d34sQJ+NlzcCnIKnbInjx4+D969OjLly/whjITE5Ozs/OSJUuegwEvL6+VlRU7O7udnd3169chZmpra6urQy+U+v79+65du/j5+SFb8hkYGCZOnzo3q/o76Giqu6A9AXDrRxmjITAUAB0bo4wMf/7+Y2ZkYmVm+cfwn4mRiZ2NnYHh/59/oAl6ZhYWcPkLWjLJwAjaOc7w7x8jaEc56DBOUGsNdAURdJElqGEKGiZkBu/z+Q/eGA8pFZn+/f//999fJibQXZqg/UngY3tA7UxGRlBjlImJlZXtL/geRSbQoCboZFDQ6kwm0CArZI3mX9A2eQbQeVLM/1lYQadRgpqeDAycHBz/GZlAu+MZGEEbpP6DNiaBFP9n+MfAwAw6LxR0TBUDiMcE2owFaogy/fsLOj2AgYnpL+wAeYhDQTvxWVhAx4z+ZmZiZP3/D3SR/X9GRuZ//0Dbp/4z/AbfhQjqPTMwMIHPtGJhBZ0YAD4AYCikrFE3jobAaAgQGQJMjD8ZQWuEfvz4ERQVwcEBuhQNohVyRRloYzgbC8OvPwtWLBWWluDl5V29es358+chav6xQYtxdX0dBmYGhr8M7f09py6d5+LidnZ2ykhIjkyIW7J65a8fv5Ky0tMvnpeRkbl9+9bMmTP5+fknTZoc4uO3Zc/OpKSkd89ezl8Ouh6Mk5Pr/Plz954+YmBgEJWRYgPfiysmJy0uL/vl6vVTp095BPgqKCiKioo0NzcLcqKvX//LCt1h+Qe0BwDiRgZPT8+cksKetnZQAcnA8BtjqZFfWHB9e8vHl29AGtiYw+JA5++C2DD8j435+/+/oPMcmP4zgvabMjBwsP4AG/fm3RvPQH/IMWEfP374/Bo0ssjAwKCup1Pb2gQfNP0FdRfDP1aQ9feePV63bQtoHl9ZYePObaK80E2BoBvR7t8xMTH59u7jjgP7rty5qasKOkIf5hAwDbIa5JifjKBxBLAQaHSzZ8pELy+vL2/eg84+Y/jHwAEak/YODuiY0PvmyfO6tuZHr57r6Og+e/Zs0aKFb9++bW5uzsvMAbmGgcHW1YlfXOTdM9D1dRoGusraIEsdPFxnzp3F8Ac0lmPr6gRpcP9hYEjJyVw2byGXEH90dIyGhvrXr19XrF71j4mBnZ1NkujjUSHOHiVHQ2AwAGgpRmun/AftiGdmZPj/6NGjr58+vX/38tfP38wsoD1GDP8ZIVPSkNklUFsNdGIow/9/f////c3GzsnKyamopKyooMj49w9o7BC05ecvw1/QeCcDIxMjMyMLExPowvq/f5hBFyOysTCzPHn64Ob16z9+gO7RBs2jM4Fbh+Dt6KBJ/L//QEtMGUAjqQzgSfD///9r6erKyMn9/f+X4T/j18+fDp058+P7dw5ODtDNcqDjocCnVzIxgpq6oAObQEJ/wRzQAC0Tk6KikrqGFisbO+jc+j+/GVlA558yMjD8+PufAdRqBQ2ogmaK2dlBJSbD/w/vP1y7evX3j68/v//8z8jAzMzCwAhq0DKATi/98w98Ej6oVcoI8h9oFonhv5iExN9XL7mZmUAjpLSOsFHzR0NgNAToEgKgBa/P3jt+55VlkQYt0Xn2j4HhB7LNXPtuKP8SSZMxffTo0b9Hf3fldTKBCqLfoYIa379///v3L9+h2z/fgc6VtHzHniBp8uLFc8ZvjD/WHf/K8OfTK+afovr2f3lrDL1Onz79/8X/E3XTWJlZf//9ZcfC8+fdz1fztv9klFK69cHhC983FpY/u67s33UZdDTy/79ebJL/WP7Fy5n/3gq6IF7g//8cBaudNz8xMTH9Pnz31aEbrOLS3/U8uGBHzcPdLHvxtQcLaJmm0rUPP8F6IVJFag4vxbe/efPm////ug//IEsxMDCI//2bq2h95u0ZRkZGaSlp3Ye/f74D2QvRy8DAIHDxXgCL3A+W70bvOH6tP8HAxvb361f33yLPWX+DLjp58hfUDAep5mRklfn/n4Gfn68kskj24uuf50HNOwYGBtOXTN9ZpBkZGcVOP/nJeubuoUPW7zn+skjby1vyHrz9EzHPxiD982eWnMWVT1cYPjA8nr5RzRlxDRLIBgYGptevfRikPrJwa33i/rHlNKjaAkuYMbA2mPjv2bObkZFR5pcw49bzPwUFFRkYWi2D169fz/CN8Ubf8rvMa37//aXJzPrvH+ebBbt+SptCmssS//8niupffQU60tWdT4Nlx6WfDAxGzxjC+DQ+f/7MyMjowygNCbR///6Zv2Z9yyzF+InxzuSVDxhZ/v3/J8XAqMEhzcPN4+gIuj0V7JxRYjQEhgwAjTnSwbGHN69bOGPqWx7x50+f/vz+/ft30EGJf//9BU09MzIx/GdgZQON+zEwMTEy/GNiYvj7/z87O/ffv/+ZGBlkZGXdvb3FxCTev39/+dJF0N3iX76oqKr5BwX9/PXry+dPF86euX37zo+fPz09PQUEBU+cOH7z2tUXz56C2or/QcOuoPPimZm5uHm4OLmYQMdD/vz9+xcLCys7OzsbG9uXz59BJ0pGxWjr6f4Anzz15eOHBXPnfHj3VoCf/z8D44/vP37//sXIyMjBycnIyPTjx/evX778+fXr71/QUCh4aQGjkIiIspqGvpGxlJTUujUrv3/7KS0tbWJmKiwqysPLe+bUyf179nBycIhLSRsZGYmJi//9+3/vzu03rl75/us3MzPj358//oFOlmL49wcUJr///vnz+y/o1iUGUFOWmZmZhZWNjYtTiY9dS146q7VfXhZU1pMRcXn7i1zlnX2VRlcUkRF4o1qGfAg8/fI0d1/h4NpNv/jAt6nbOLk4IYuCMIP458+ff//+5eAEXZ/x+8/vv3/+srCCjjb+z/AftAKfgeH7j+//QB1sUN+cEzSfDhoABE80gU4FAe2eYWTgYOeAHCry+/fvv3//grd9ggbtIPcFMDODp5IYQeMCv3//Bh35zMLMygJS8Os3CEJcBbnjCtI3BvXS///7+eMnxCKIAgjJwsICOWvz169fkK2fEHEmJibIdD/kFgyQwyASMJKDA+RIkPn//38H310HkwHRzMzMkGsX/v79C5/uB4UbI8i/IBWoGLSZFXwvLlwY7v4fP3/8/QO6Rxdi4J+/f37+AN2kClfJwMjAycEJLtsZIIoRUmAWIyMjJ6g6YER2DFiGAW7Lv3//IGf0gu9GAV0PBvH4H9B0GTNoby4DI/zUZ4heTk6opXBx5ED7+fMn5ABmFAPBSQJyYxnD//8/BTkFlxUzsIPiDmLmKDkaAkMC0GlklJGJ6cvnzzcev+Pl5GIHbTn/ywQ6GR401c3AADrrnpUddI4oI7h9+OzRg79//mrqKXGJin99/+rXn197du168/rVl8+fPrx9+/PHD3ZOTk4ujuPHj964evXNy5evX738/v2blJTU8aOHfv388eTx418/f7Exs/xj/sf6n5WZBXR4KQcH+NJ4Hm7wxqO/TEygu0m4ubhBx9r//vn7LysT6EhMNta/TAxMDEzMrKwsrDxcIMAMKpH/M4HXrLJzsLGzgXbYMzMx/PrxE3IPE2i1KhPjj29fr1w49+rFM1FxsS/v3z188OjezevXrlwUFhFVU1f//Pnj44f3fv/8ce3S+QunjnPz8YmIiv9j+M/Dz8/Px8/LxXPlxJGPH9/LKSoyM7N9+fqFm40dfFoqaIEr6PooRgZmFlYmUAXE8v3bt2/fvlKStpiZmc+fPb9tx7Z///45OTlZW1tTYhrxeq9du/b06VPIKdzE6yJeJeQOT0p24GK16////1++fOHi4oKMXsDV/PnzZ+3atQ4ODpDLfuDiA8X4+fPn5MmTY2JiIOdQgo4J+/sXcvEgIyPjly9fJk+enJ6eDjlnlBaOPHz48PPnz+HXBEDuAVq9enVubi5a0NHC9qFrJmesA2esw3/cdzjCL/8Fqfn189+vX+xcXCxMzKA9nWBvwxWAeaACCc7gQFrWA9LOwPD3+7d/f/6wcHCws4I2XCPr/c/A8OcfqKH3798/FjY28DIqBmQFoGYixGiYa7lhXGQaYhGowYRkO0QBHimI4aDZaLAuTogGVPIfmMvIwAC3FyICFkYQoNkqMA/NELjtEE8hc9GXGoA9CHEMO9g9YPNQCIjVyI6BS8NN5oILgQ0EbYf4+ePfr18srKzsHJygI2UYGJCthmvkQBKHCEIUI5kHjej/v37++/mTiZmZlYODhYkZ2UZkxaPs0RAY5AB7n5IGjv7PyMzMwyvAxcfLwsHOxs3Dzs397/9/ZlZmBmamj58+vHj66OnT50xcfHKauqzcPGxcnF++fHr78MGXN++e3X946czp50+ffPz4iYOLS1hMTERE9PP7D+tXr7p4/jwrK4usnJyMnDwzK9uzZ88+fvjIyszCAGrl/gNtLmJlZWVj5+bj4xMSYufkZmZmY2ZiZfzPxMHBxc7OxsnJycLMwsnJ9evnz1+/foHurgCNFIPWrX75/ImTk/s/E2j4lIODg42Dk4mNnYmJhY2Nk42Nk09AkIePn52Li5kFdDA+w///v//+ZWFh/v3r16MHD37+/i0hJSmnIC8oKHD/3t0tmzZevXxFQkJSSEhEQEDo27fvL5+/uHLhwp0bNz6+e/fl9avnD+6xcXAys3HwScjIami/e/v+6dOn7968ZGL6z8TK+vs/aOkqFxcXJxc3MzPo8idKqnZ2drbVq1ZXV1bz8vIKCwvX1NQsW7aMBtGNxUjIFURYJKgkdOPGDcgyfyqZBzXm58+fxcXF8CN1oKJg6vPnz+C+DZgz0AQjI+OuXbsgB09C3PLs2bO8vDzIUMr379+3bduGORYFUUkV8tatW6dPn0Y26suXL7t27YLsHUYWH2VjhgAjAwMTDgRXzMjAwMnGzs/DywpuiYK22oC1wBVAGIzgxhPEQHizDLStEiT+n4eTS4CXj4OVDaIMogVCMjIwsDIx83Jx8/PwcrKxYyqAGQLeVIrNarg5EL8g205QCmI4Lo0Q7ZiyEBE0EuJyPLZDTIMEEa4qEC6LaQ5EO8RSrLJwvRCVEBLiKi52Dn5ePi4OTiZQdEBkECQujbgUQ5MELx8vFzc8VSCMG2WNhsDQAXQaGQUdHf/394eXL7+wsv/4+fvXr69/f/5jYONQNzbh5hO6fOo4B8s3Tg4Opr+/Xj66z8PJ8fP//0f37oL2pjMxsrAw8fDwcHBzc3Jwfv/2/cvXL68+veHl5eFkZxcUEGDjYH/39j0jI2gXOhMT8+9/f36Bp5kgx4syMzNy84C6x////uHg5gHd8c3IyM7B/u3bdx4egX/////48ZPhP+PHD+9ZQZd8MjKBN01xsLP/+PmdlYWVmZ3j5/dvHOzs375+A93kycT06eNH0OzMP6b///9xcXEy/P/7iwG0ZZ/pH+iA/Z+/fjEzs/z8+YuVlfXzly8CLIJiYmJfPn/+8ePHx4+fWFiY+fh4+AQF//37++XLl+9fvr149owRtB3qLycPl6iQ4Je3b3///CXIx/P3L9u7j594ZeSlVTSvHz348MF9Li4uRmYWfmmRv7zsv8G3gZOXxpiYmI8cObK2Y7WekR4DA4OTk9OjR6BtCp8+fVq2bNmjR48cHBzc3Nz+/Plz5MiRP3/+HDhwwBwMFi9e/OfPn4SEBHFx8atXr37+/Pn8+fMvXrwICQnR1dX99evX3r17jx49KiYmFhUVJSIicvr06X///h0+fPj3799xcXHS0tIi4CtGIecdrlix4s6dOxYWFt7e3kxMTMePH9+2bRsfH19wcDDy1tezZ89u2bKFiYkpIiJCVVX15s2b7969gxw9ExgYCLkc5ebNm6tWrRIWFn737h1kchAeMg8fPly+fPmPHz8CAgIMDAz+/PmzYcOGc+fOaWpqhoaGcnBwHDhwQENDQ0ICtALkzJkzLi4uFy9e/P79+6lTpz59+hQVFSUvL79s2bKjR4/OmDHD0NDQwcHh3LlzkK3H0dHRqqqqkENbrly5sn79ejY2toiICHl5+c+fPy9fvvzhw4cGBgbe3t4QNQwMDB8/fty0adP169e1tLRCQ0PZ2NiOHDnCxsZ26tSpuLg4RkbGZcuWPX782NbW1sPD49evXwcPHrS2tubi4rp///6LFy8sLS0h5xTu27ePj48vNjZWSEjo169f69atu3r1qomJCTs7O3zt2tu3bxcuXHjixIkpU6ZYW1tDnAo5yEZVVTUsLIyNje3Tp0/Lly9//PixExhAwu3///8HDhxQU1OTlpa+fPnynz9/DA0NH4KBnZ3dPjCQkJCIiYkREBC4evXq9+/fL126pKury8bGBjmy4PPnz0uWLHnz5o2Kigrc7xDD7969u27dui9fvnh6elpYWHz48OHChQu/fv16+vRpbGzs1atXN27cyMXFFRkZiedsSIhRoyQZIQCZ1idD46iW0RAYDYHREKApwNUtpLKl///9Y+fmldIxEVVQffXy9fcvP/4zMzGzMX959/bDy+ccrCyMjIy//vz59Obl/euXnz16CD7chIGbl1tIRERAUPDP/3/v379/9vTpg/v3f//+bWhspKyiIi4hISIiwsLMwsvNxcbKws7KysYKWlTExgaaSmdnZ+dg5+Dm4eXh4RMREePl5WVhZRUQFODi4mRhZRMREeHi4uHl5RMWERYWFgaPGIHWhn79CpoEB91r/+sPByeHqKiooKAgJyfohkZu8EStgKAgBycnByeHiKiYAL8AFzcvFzc3GxsHC2iJFQsnJ2i9ECcbKxsTIy8XyFUCAgLiEhLS0tLm5mZ8PDyPHjx49vjR29evfv34wcXLKyQmys3Px8zG8vnr52dPHz2+dePFvTu/fv/484eBl4uX4fO393fv//v5g4OV5e+vn69fvQSdscLNw8mDPLFDWkz9+/tXV1dHTVUVok1DQ8PNze3Hjx/l5eWPHz82MTGZPn36ihUrGBgYampqVq9ebWBgMGPGjPT0dElJyc+fP5eVlTEwMOzatSstLY2Pj09BQaGgoODu3bv37t3bunWrjY0N5DpKBgaGxYsXl5SUKCoq/vnzJzc399evX6dOnVqwYAEDA0NLS8v169ft7OzWrl27bt26mzdvtrS0WFtbQ655hIzkMTAwfPjwYcmSJXp6erKysqWlpZ8/fz516lRqaioTE5OKikpBQcHjx49fvnyZk5MjIiIiKiq6Y8cO5DHjp0+fZmVl8fHxKSoqzp079/v371OnTt2yZYuZmdmxY8daW1v///8/Y8aMW7duQSaUJ06cyMDAsHHjxvz8fHFxcV5e3szMzI8fPwoICLCzs/Pz8wsICHz69Ck3N3f58uWCgoJ///5ta2t78uTJ/fv3KyoqNDQ0xMXFS0tLP3782NXVdffuXXt7++PHj0NOMYSE9oEDBy5fvuzg4LBnz57Zs2czMjI2NjZ2dnZyc3P/+/evsrLy8ePHxsbGs2fPXrly5a9fv/r7+9+/B23LPX369OLFixkYGPrBQFdX9/79+1VVVf///588efKmTZuMjIxOnz794MEDyOE+oDEzJiZ+fn4ODg5BQUHI1Z3Pnj07duyYgYHB6tWrZ8+e/e/fv5qampcvX1pbW8+aNWv79u0QRzIyMh49enTJkiUMDAxdXV3t7e0MDAxLliw5d+7cjh07Jk6caGRk9ObNm4KCgr9//+7Zsyc5Ofnly5c8PDygjTjgA9EqKiquX7+up6e3a9eu169fwyPl169fCxcuFBISMjIyamhouHfv3ocPH7KysjZu3CgqKnrp0qXq6mo9PT0BAQFIMELcM0qOhsBoCIyGwGgIDHtAt5FRhn//Gf6xcPFwciurqYFuoOdg4WRl+f7x47cv75l+//j65ct/Bqb///4wMP7nFxTm5OLm5uHh5ub+9OkTaL7+7Vs+fgEuHm7Qyk9u7g/v33/58gU0A8/GxvD3358/v379/AlabM7IyMTC8h98NhMrCysHJ8c/hv8/f/1kZWP78ePH799/eHh5f/z4+fPXTzY2oZ+/fn/89JGFhfXRgwd/wXccvX71ip2D4+uXr8wsTGxsrDdv3ODi5v/95/vP79+5uLi//fjBwABa1Prly5c/f/5wcXOC7h3994+FhRV8rzzT/7//fv/4CTqp6j9omBa0GvU/6JCpf//+f/v27cOHDxwcHNKSoL2cf//+ffv27b9//wUEhYRFRP7+/fvx7btff368f/v2/ZvXTKANXUzcvDzfvn389OUNv5CgkJDgz58/xP/+5+BkZWRiYWQmf3H6f/BdxqAFCUhJ+/79+3fu3NmyZQs7O7ukpOSECRO8vLyEhYVzc3N1dHSegEFUVNTTp08TEhIg+xs8PT2jo0Fnr9y8eXPPnj3p6enl5eV37twRFRU9ePAg5KLUkJCQ4OBgd3f3wMDAd+/esbCwsLGxffz48ciRI9nZ2RwcHEpKSuvXr1dXV//+/fvXr19NTEw0NDQgo2ugzbMCAjU1NTdu3Pj79++LFy8gR0lbWFhAruzbt2/frVu33r17p6mpCbl78Pbt25CTBSE+O3LkiKysLOQawOjo6F+/fm3btm3SpEnq6urOzs5hYWFPnjzh4OAAJRvwuQqQZtyfP398fHwiIiL+/fu3f//+Z8+e+fv7L1q0KDw8XEJC4tmzZ1xcXM3NzRISEj9//oRsgNu5cyc/P7+oqKiQkND79+/Pnz//+/fvb9++sbCwFBQUyMnJQdwDOVdcXV390aNHwsLCkFMMmZmZc3JynJycLl26dOfOnR07doA24crIdHV1OTo6grY4gHf4MoGOJINGenp6uqurq5mZWUZGxosXL3bv3j1p0iQ1NTUXF5dDhw7Blw0ICgr6+/vv2bMnNjaWmZn53bt33NzceXl5CgoKbGxs69ate/78+cmTJ6uqqjg5OWVkZLZs2eLp6Qlxqru7e19f3+vXr1nAB67dvn376tWrzWCQmprq4+MTFBQUEhJy9epVBgYGZ2fnyspKBgaGkydPMjIyvn79GjIgzcvLKyUlVV1dDbkJgoEBtEq7rKzs+vXrz58///3795UrVwwMDPj4+FpaWgQFBdvb20VERAQFBXl5eV++fHn16lX4iYwQV42So2A0BEZDYDQEhiugU2OUkYnp74/vn99cf//nN/iqob9/vv569f37py9f//379/vXL052DhYWpv8M7Kwc7JzcPAz//n37+vXVy5e//vyVlpHS0tH98+cveB8o6O5QVhZWbl5eBgZGDjY2Jmam9x/eMf1gY2BkZGFh5mTj/PjxIys7B6ipysHBzsEBGiLl4OTl+//nzx9mJiZeHt4fP39wcfFw83D///9XT99g1fLl7968/fLlq5AYIzc395/ff96/e8sA2uP/39HB7v7Dh5+/fP716xcrBycTIyO4/cHBzMzEwsz089cvTg6u79++/fzJ+pv117//DLz8fP//M/z6+eP/fwZmNhZ+fgFWFtafP38JCAgwMDL++fuXV1CYmYWZhZlZi4vjw+t39+/ff/36NR8/HxMDIxs7h6Co2NdPXxn+//nP+P/zp4/fP39hZmH+8uk7Px8fMwvzf0Zmhv//fn//+uv7F7KTIxMj44OHD+FNFsik+a9fv0DLZ1lAiUFQUPDHjx+/fv1iZ2fn4+ODrHYQFBSEbIiB3Hf6//9/UVFRiBsEBQUhc/pTpkwxMDDg5+dnZmaGtD8g1+iBNgJzcEAWDjIyMv78Cdr4defOnUePHnFyckZFRenq6jY2Nm7btm3t2rWCgoI9PT2c4Hti7969W19fr6ysrKCgwMHBAXEzfK4fIvLlyxeIIxkYGPj4+JAbo1+/foVv1mFhYfnx4wcjIyNk1piLi4uJienHjx/w21xBaRJ0Xi3ITxAD//z5w8kJ6m9A9jJDNgX//ftXREQEbTHA9+/fIYsW/v796+Pjo6ysXFdXt2jRog0bNty7dy8rK8vLywtkLgPDrFmzjh07ZmxsDGkE//37l5eXV0xMjIGB4du3b/BJdgEBAfg2ZPiwIsQENjY2iPMYQakd5Km/f/9yc4O2c7CzsyNfEcnAwPDjx4+/f/9CIvffv388PDyQBjcbGxvodB7wruqbN28yMTGJiYmZmJhArGBgYNDX12djY1uxYoWBgcHfv3/Xrl3LxcUlKyv76xc4JYPVcXJyfv78+f///2jz6aDBew4OSAyCVteAb0IH6wCtUigtLeXn59fQ0ODi4gIfAfyPn58fEp7fv3//8OEDZBWEv7+/goICRNcoORoCoyEwGgKjITDsAaj9QQdP/v//n42dnY+Z9dHDh6BTRUG7Cv9//fqVnYODkY1VTFxcUEjo31/QWRsMDAyPHz36Cz5K6evnT5zcPKzMTE8e3v/x/RfoGA7wNfFs7Gx/QbuM/rOysjIwMP74+vU/A8O/f38ZmZi+s3H8/v3r+8/vTIyMXDw8DJ9Ah5WwsbGxs7P/+g46F4ONjZ2Zmend39eMjAysrMyP7tx7cv8BaLqfhZWPj4+Ti5ORkfHD+3eCQiJPHj9cv2YlOxf395/f//36xcjMyMTI+uv3b4b//7m4OH//+fPzxw/QXVDMTD++f//16zcrG+t/hr9/f//98/sP6AInRoZvn7+ysrCA1gAwMrCysPz+/YfhPwMbG+u3H9+YmJm4OLlYWBnevnnL8PcHEzPrfwZGSUkpKUnpHz9+gA6c+vLl7es3//7+/v71y78/v1lZWf/++y8iDNr+z8wEOSOZnHhjYmL68P79tKnTEtMSGRkZIZO/iYmJP378OHnypJaW1tq1a9XV1fn4+CDjvpA2KKQhyMAAOinm////LCws27Zti4uLY2BgOHbsWGFh4e7du42MjAoKCrZu3Qpp9v0BA9AO2f//f/369R98kdSfP38EBQUlJSUtLS1tbGwuX74sJCR0586dV69e1dfXv3nzJiQk5NmzZ6AjvhkYIGOH1dXVt2/fnjJlCiP45H/4JP5fMIBMOj969IiLi+vAgQPI6021tLRWrVr17NkzyNJMFxcXCQmJPXv2BAcH79+/n4ODQ1ZWlo+P79ixY5qamjt27Pjy5QsjIyPY1X8gIQsZ34WMDt69e1dAQAByWAz8LJs/f/78/ftXW1v7woULycnJf//+PX8edKL4+vXrbW1tU1NTp06dunXrVkhj9M+fPzt27MjNzbWysuro6AClCnB4QprpCgoKf/78OX78uJaW1rp16zQ1NUVFRX///n3x4kUTE5Pt27dD2qDwSIFcac3Pzy8vL79t27awsLCTJ08+ePAAPq4M6UV8+vTp8ePHMjIyoGUwv35B7IK0UPn4+ERFRV1dXbW1tc+cOYM8gsvGxmZkZNTd3b158+YfP35ER0dXVlaysbGpqqpu27ZNR0fn7t27L1++VFNTO378OMRMUIb+BzojV1xc/Nu3b3v27LG0tNy3b9/Lly/h7emHDx/evn17w4YN3759gywDQA5PHR2dZ8+epaam/vz589KlS4PkjAJISqAPeXT75vNHDrCwgTa508dGMmz58/u3sraeZ2Tc/19/PhfP+/fxGwP4FmUyjBqGWn7+ZnPR50oevf9oGMbtqJdoDejUGGVkZPz65evN+68YGRlYWJgY/v9jYmASl5ASFRfn4ORkYWH++OHD06ePv3//zsbMJiwqLCQsBNqs+R80XsjI8O/bl09sbGz8Anz//4HOX+Pi5Pr79++f37+5ubn/////+QsrExPTv3+gc+m5eQW///z99y/oViY2Dg4mZtDVSuzgxujX7z///vsHOW+PmeHvr5/fWZhZbt+69eXbZ0ERYdChp4yMoM3qIKNA7a3///69fPPOwc3818/vXz+++/f/PxsXL+hiJBZmTnaOnz9//vwFGmwD1cC//4Bm6v//5WBn+/vjx6+fX0AHBTAzsrNxMDIz/fjxE3yTHuPfv//+g330+TP7z5+/2dnZFZWUpWXl//5jYGZi+vr1y4tnT38//MHMzCwpIyshISEmJvb77593b968e/3m9+9fDIyg/bY/QKe0gg7zJy9l/Pz5s7Co6NryK+np6UxMTCIiItXV1YKCgtXV1X19fSwsLPz8/M3NzYyMjJqampAhKykpKX5+fsg0q6amJjMzMxMTEzs7e0NDw9OnT83MzOzt7fn5+RsbG2/evCktLW1mZvb//38lJSXI6CkLC4uGhgYbG5uwsLCioiIrK2tpaWlfX9/ChQshyxYFBAT27t27fv36P3/+BAQEyMjIQLxmZ2e3Y8eOpKQkBQUFa2trNjY2UVFReJtJRUWFn5/f0NDQw8MjNzdXVFRUXl5eVVUVopeBgcHc3NzPzy8nJ4eFhUVPT8/X17ceDHbt2vX379/a2loODo7s7Oza2tpz587BnS0nJwdp9kFCgIuLi42NLTo6uru729bWNi4uDr6QgJGRUUNDg5WV1dnZ+fTp0+np6QwMDEZGRo6OjszMzHV1dXx8fF+/fi0tLYU4iYWFJSoqasKECWvWrBEVFdXR0WFgYICMETIwMEhISJSVlXV3d7OysgoJCTU0NLCxseXk5EyePFlUVJSfnx/iNVVVVcgVLGxsbFpaWlxcXJWVlbW1tXv37pWTk3NxcYG3/BgYGGRkZDw8PMrLy8PDw/38/LS0tCAjowICAkpKSkJCQiUlJe3t7ZycnGxsbDU1NRB3QkhXV9cTJ05oaWl9/frVxcUFciN2QUFBXV1dRkbGz58/CwoKREVFpaWl4Y1RcXHxnz9/srGxVVRU9PT0LFmyREZGxtHRETJMzsDAoKqqamRklJaWJi8vb2BgICgoyM7ODklRDAwM/v7+V69eTUtL+/fvn6WlpZ2dHcQlI4d8ev/uucMH2MHXHQ1aX/8ELYgCd4b//vu559K/Vx8YkK5ZGrTOpo/D/n/9ySgOKirpY92oLaMhMJwA6CgjOvjn6NYNE9qajz95y87KxsjE/A907dL/f//+//0Pun8JNGn45w87O7uMrCwXL++bN69/fAONHTKDRit5//z+8+v7NyYWdlZ2jl8/v/1nADU6/4PuYPoL2i7EwvLh/Yf/DP9BbSRGJm4erq9fv/7+DVqsycTMBD4WFHTaMBMT05+/v0EXwf8HzddzcvFwcHImp6WvWrZ047o1VtbW1g7OquqavDzcv/78fnDv/vnTJ3fv2CoqLt47aerypYufPXr0++/vv3//sbOz/f79h4WF5d/f/3/+/AWtHQAf7Pz3/z9mZgY+Ht5f33/+/PP7H+jepb/coIlR9i9fvjIyMLCwsvz58/v/v/9sbOz//zP+/PGTlZWRi5Pzy7dvP77/+P/vPzMrq4iI8P///0GHWL1/zwS6OYoRdBMoIwsjExMTM9Pff/9Vhbg1ZCQymnu0tTTJi7W8/UXuiq7eCp6vX7/+//+/iIgIZNEkAwPD58+fv3z5IioqChkLhDQsQFEDPp2AjY3t////kEnYrq4uJiYmyCAWZJYZrl1MTAx8pDbr79+g9Rhwo9jZ2cFDmaAog9ys/enTJ/DmMNBZB3///n39+jVkvhjZXz9//nz79q2QkBALCwukv/Hv3z9Ii+rnz58sLJD7URnevHnDyMgoJCT0+/dviCzckLdv3/79+xfuyJ8/f757946fnx8yX8/AwPD169fPnz+Lior+/fuXjY0NclE1eMQdNM0NmdGG7KaCrASALGCAbFqHjGFDAvDVq1eMjIyQ9jdk4/zXr195wQDuGAYGBoh7IKOebOClzHAr4GEoIiICcQADA8P79+///PkjIiIC8drPnz9B59wyMUHiAjKz/+PHj/fv3wsLCzMyMkK6CnAbIXd2Q2bwf/z4AVEP6siBsxsDAwN4TfYXYWFhSMcDrhEy8goJzF+/fkEYkGHy169fc3JyQvonkNULENdCRm0hKj9//vz161dRUVFIFxFuLCSiucEA0kj99Qs84wFT8fLlSyYmJngwwoSpRg/CQ+/hfls7a8ru1csgd2+C1jD8/csAvpsNktgggf//3z8mUGkAbg7CdP4DXfv2D8pjZGBmYmYEpxDQXBN47Q1UihrUr58/jWwd02pb/n//9daq/N/rjwzMTCCD//77//cfIxMjAwuK20BS//8z/PkH2t/GBPIP6FwokCgp+M9f0PgrE9giUvQx/P4Dcg944TVJ+hCKQYev/gU1uIkw5P+3n5wJTnzdiQjto6zREBgNAeIAnUZG//3/z8HJaWppIyYmxszK+unD+6uXL7958Rx0LSfoVCYmEXExBSUlfkEh0Hw3O9uPbz9ADTh2FlFRia/fv3/59ImNjYWLi+vrly+///zh4uRm+M/w8+dPPn4+VtDpwS8gOzyYmJk4Obi+ff0GancyMzExM7KysnJwcLJzsLOysv78/gs0UPrv/5+/f3m4Obm5ee/dvXP2zGkWJlCL5MOHD79+/vzOzPz3/9/Pnz9/+PiBkZHx+fNnBw/uU1BU5OTg+vET1GRk52D/+eMnOzsb+FioHz9AV0n9BLXoQTfU/+fh5vn95/fvX7///v3z69dvIUEhDk6ON29fszCzsLCyfvv+nZGBiZuH+++/P58/f+bm5hXg53//9s23r1+YmJiZWVi4uXk5ublExCQfP7z75NHjf3//Mv5l+M/4m4uDR1NXT1JGnuntU7ZfX7h4eImLXKyqQAdRga6chi36hCsCN5ygJoOum4KN0ECaGqBTABkZQSslwMN4////hzRHMLVDBucgjRJkXSxgAFHPAwYQNuQaWPhR7XBB0KHZ7OxSUlJwEUizD8JFbjzBF5LCLYWoYWAA3RYNZ0MMlJSURBYBt4tAay7RnM3AwADxLEQxaNUvmIUsiMyGt3fBqhj4wQDCRiYh62ghw8xoVjAwMCBHAUQXZLUuXD3c18gRxMHBgeYpiF5I4MNthLsWdL0DM7TFwAcGcPVwBhMTEzww4QzMmIKnDciqALh2uEcgoQoXZ2Zmhkc0JDbhroKoGYGz8xCPI5P//v3l4uETkZD8+/fvqyeP/vz+zcjE9O/vX2ExcV5BoQ9vXn98+wZ+XdP///8FxcT5BIUgbdbfv369ef705/fv7JycwhJSr548gg9dw634C7oBiKol/++/TOL8TNLC/99+/vv4DbR5CrHv3z8GFhZmFUlGLvb/H77+ffgaJExEww6k7O8/0NXHjIzMKpL/P3z99+4LqEkKkiAC/2dgYGFiVpH99+j1/5+/QeYQoQldyX8GBlZmZiXxf8/e/f/xm5xmNLqJo/zREBgNAeyAqkUSditAooyMDH///Llw9vTXT58ZGRnA45R/GcF3L/0DHVDP+PLp09fPHoIvl2Pk5OLk4eH5+uULaGiTnf3///8/QHNDjBwc7L9+/vr3/x8TEzM7O+u3rz+ZWRjZWFlY2Lg/ff3699cvJmYGQUGR33//ffr06d/f3+wcHMICQh8+fwKtYmRiEuDj52TnfPPh/c8fPxj/fOfk5v7w4TPDv/88/IL/mRjff3z/4+d30F54xv/fvn758f07Px//69dvpvf0iUtJ/Pj37+cXkBU8XGw8vKJvPr798/cPCzurEB//j+8/Pn3+DGpts3MIC/C++/jh58+/DEy/uDkFnzD9//Hj659//1mYmUHrEf+BhlK5ODl/gfeOMDIwcnBwfv/+/d/fPxyc3P/+/v3w8TMD6DwApj9//4FaDEzMjIygaza+fXt9/OCBvwwMGmICRhrK3ym7gQkUH5ThqKgoygwY1T0aAqMhgD0E/v37KyIhFVtUJa2o/O/fv8snjqyaPuHb1y/Gto4ByZl8AkJvXjxb2NP65N4dSEP/758/jgGhjv7Bv36CLrT8++fP3auXFva08gkIRuUUT6sv//7tK6SdCrHv/79/po6uH16/vnPlIrxFC5Eij/z/5y+biQpvVzyzrOi/D18+ly/4ufcyIxu4Zvn3n5GPi7cxis1eh5GT7d+Hrz/WHfvav5kBNOiL9bR4hBP+//nLaqbKIif6feUR7qKAX7vOf199lJGT6AW1//4x8XLzz8j8mDLl753noPFRhNlEs/79Y+Ln5etL+lS64O/NpyiNbKLNGFU4GgKjIUAMABcZxCikUM1/hv+gscxfX758ZGNhEZOQYufg+PvnL+QSZIb/jL9+/hQTEWDnZP70+TsbOx87x29+IQlmJg42VtYPHz5w83Azs7D+/fP316+fHBycrGzMnz5+4eRkY+fgYmJkevuZg1dAmOHP719/foBmy9k4QYsz///n5ef78ukDBxfX9+8/uLm5WZhZfv34acDL8/79+1+vnrD8/3vy1Mkfv3+wcbGzsbKzsYLm3/+CZ8HAQ3gsXNw8LO/e/P79V0lTT1hB5euHjz9e3mVl/McopKTBwfrn9y8+QcFP7979/8/w9+9fFnZmbk6e929eqXFzsDMxfvv+48e375ycX7h5WP/8+v/61WsBQX5GRqZf33/8/PmDj48ftNT18yc2NnZWNlZGpn9vX35jYWD5/PXrz59/GP79ffPhK+Rm5H//QZAJPBH29OlT0OgseCsQhbFBoXbkXTIUGjWqfTQERkMAOQT+/AY1Lr9++ticFiMsIRVXUqVuaHLn0gXPqIQDG9cc3709ODXbwS946YROUC8efGElCzPLgY1rtyyay8jMJCIhlVhWZ+HiuW/9qknVRX9+//n////fv38gK5RAB9AyM+tb2V06dhi0K5ERtAIIsssQtBcTNrkPWp7OBDoTg+H/f0ZGRkj/HzKSjexUEPv/f0YONu7yoF8Hrnzt28iZ6saV5vHr6E2GP38Z/v///+cvp7cJi7bsh9i+f4/fshgp8fUk/jpy7dfh64xMjP//g8YrwdP3TKB23n/QRXaMkO1Q/xkY/v5lM1NjkhBiXHXkc9G8////MXKw/v/1B6Lg/7//jKzMIP3w5QEg1zCgNzp//2X49QfsGPB9RyzMIFf9/gs15D8DyBDQhfGgFREgA/4zgAZfmZkYQGvI/jH+AzmJ4TfILyDZ31BlINtZmEEqQcr+g8Z5MRcngDSM4tEQGA0BYgGdGqOMTIw/f/749/evnpGZuqa2orLin9+/P4FvU+Tj4WNgZHj95iXjPwZWNlY2dq5///7//PX9//+/3Dzc/Pz8P378+Pz5Mw/4ePl3794xMjDy8HL/+fPz29efUjISzEzMTx6/YGRlYWNiYWFj5mZj//nn759/DH/+/GPjYGGTEePjFwTtpedg//L5y/8//1g5Of78+ikm6qGiopwWH3v31o2/v0F7kJgYmX/9/s38D7RW89///0xMTL/+/P4F2lvEHBsXyy8meePSeRYmg/8MjL9+/+ThE2JkYPj5+8eHt2+/f//57+9fBtC0JoeytOSf/3+///j1++fP379+iogKCAoKPnr0VEKSgZOL++vXz79//+Lj42NiYn737r20IjsPL++XL1+/fv3EJ8jIwcrGwsYKuvH+x7c///4JCglycXF9/Pjx/9//PNzcvLw8T58/+3Lv+m/ItaXExu+outEQGA2BoRQCrGxs+9av+v7165ePHz68e/P80QM2NnYJOYUvnz5ePnlMVlnt0JYNXz99/P//v76lnZmj29y2uv////369fPLp4/MLMx33ry+e+2yqJS0gLBoWGb+4r52Ng4+r5gEDQOTl08erZ87XVZFTdvEXFXX4O/fv3oWNpsWzHr97Im2mYWehc2qaRNsvQPEZGTl1TRP7tl+7+pl34RUKXmlG+fPbF0y7/PH90wY53j8//efRUqIiZv9x8ojLDpyv/Zd/rH+OMOfvywGCjxlwZ8yp///++//x2//Pv/49+HLz61n3t18+v/7L9DiZlUp7pJAVl353ydufule9+/Ze0YuNq50D44Qq/9ffnybtPnvs3dc8U6MfFz/X71n4OH8c/r2z53nuZKcOaMdGJgYv684/H3+XkZONt6GyH9fvrPZ6/x79fFrx9rfZ+8ysEKXoIAa2X//svuacviZ/Xv75Uv7mj+X7jNysnNleHCEWP///P3blK0/d51nYGZi9zHhTHJhlhT6c+nBl661f++8YBTj5y0JZLNQ/7n3Iij1/Gf4/+cvu7M+V643syj/jw0nvk7bzvDjF09VKAMbC5uN5peGFT8Pg47dBSkexaMhMBoCpAM6NUb//fvPy8trZq725fOn/bu2bf78CbxCH7TonpGJmZmZEdRNB/XCQZw/f38zMjD9/QdarvOfAdTv/P3nDyvoFnjGv3/+gPYqMTEzMDL8/v0XtK0FfBwPIzPTv7//QIcvMjP9/f8fNLcNGjf4B5ofBzUvGRkYGf/9/QsaHgB1uf+k5uQLCQs9fvSQjZVVXk4edMA+DzcT6Cx+Jsb/DFwcXGwc7NIyMh8/vHvx9On9O7eUGf83V5f8+ws6Hp3h/29GRhZGBoa///8y/mf4++8faPwA5ADQdPzff3///v/PwsT07/8/ZibQQeW/f/0C3TXKyPz772/IEgVmJuafP3+xsEICn/Hv3z8MjKDF+UzMTJCdT6ABDEbQDibQnifQ6AVovp6Dg8NATkpPVYGHl/wbmEhPIaM6RkNgNAToFAKgYUIGhjcvQLcE////38TOmYeP/9qZkxpGprwCgsmVDUJiEj+/f18xpffNi+dP7t75+f07qORkYODi4RWVkmZmYZFWVNEwNNm4YCYbB7u4rPx/hv+ByRkiUjLbVyyUV9FwCYnYtWrJ0/t3Ht68+eLRA8+IWGYWln///nFycQtLSP77+5dfWMTQxmHnisWP796OKax4cPP6qb27TBxdvGOTVk7tA5WpsON4QSECmhX6xyTKx8DFwdudwCwvyvD3/5fudd/vHvj3+O232bv+//j9c9MpVh15/ukZ/199+HP7xfc1R/8+fM0kyM3XnfDn1rMvLavYvU14KkM+Zs/kjLDljHf6NmMHIycbZ5r758pFvw5fYxTj/7HjPE950N+rj9h9TLkL/b7N3MXw9x93puf/T99/bDzJaqb278OXb9O2c/iZcxf6fYib8P/7L9A03N9///m5mHg52ay1vs3dwxFqzV3g8yGmnzPNnSPQ8tu0bYz8XNxlQX8fv/n37jNnnOOvvRf/ffjGGW7DlejyqWQ+b1EAq4HC1xnb2Wy0mJUl/3//yaIhw9sc9WPNsR8PXnHGO/3/+uPrpK0smjKMInxfp+/4c+MJeNMY6PA+UMiM4tEQGA0BEgGkPUSiJtKV////j5Obh1NBiZX5v5mV9e/fP79/+/r+/WcGhr8cXJxMjEwfPrz7+es3Fzs3MyvLv38MP3/8/PP3JycnBzcXLyPjv1evXgsLijCzsn358v7v3//CwsLcXDwvXjzj5OLiYGN7//E9KxsHCyMzMyszIysb4/9/4AbnX2YmZhYWFh4+QU42VkZWlm+fvzAysfxj+Pf/96+QkLA9u3d9+vBeSEhIRFT0z7+/rKBlAH/+gzZ9/vvP+B80cc/PysXJxfCf4dDBg95BQTnFFV8+gdYYfP/3h5edi5mZ6cfPX18+f/r569ffP6Bm8X/QAU2M/0A7l0CHoP76+ZObm5uZhenDu3fCwsJMrCyvnr/8/+8vBxcXGzvHy1cv+Xh4eHh5Pn/6Alo2yvCXg4ObmYn53/+/P0FHQf0TEOBnZWH//v37nz+/Qeq4eTm4OH8+u8v46zsbGwfpMYDQAVlqhuCPskZDYDQEBkEIgMYLmZmZwOjf37/mzu4OAaFrZk7+9OE9CyuLkJj4gq7mO5cvuEfEuoVH379x7e3L56BmK7g37uQbZO3hC7rD4uuXc4f2Xzx2WFRS6u+fP3yCwlKKyiun9t26eO40x25WNrY/v3+9e/nyztVL79+8AnV6QXsnQfPdoH36LMysbKzXzpzctWqproWViJT0jQtnRSQkP755o2lsJigq/v7NK9DIAajjDA4vFmbQtDgjE7Oc6Jf6ZT/Wn+AIteZKdft16Oq/F+9/7bnIwM7C8J/hc/0yZnkxViMlVmMVgVnZn0rn///xm1lF8vfpO8zyov9efWB3N2RRkWS11Pg+d/e36dsZOVi/L9z378v3P7efMX389ufSfdB0PGgAUuv70kNf+zeC7GZkYPc2+bnlNMOfv1+aV/3cd+nvq4/cWZ5MwrzsLvpMsiIMv/78OniV4fffrz3rfx66+u/jV654JyZRfnZ7nX8vPzCJ8IEWBvz7x+5u+GXi5u+L97PZarPZiLCoSf999JpZUohFR+5Lx7qf2878OnJdQEWSkZWZ1VydgZUFtJhBSujv8/fs7kbf5u9lYGX+vvjA98UHmPg5GdhYGP78ZWAmsBYW5PhRPBoCoyGAAUAbwTEEqS9wePP6BdMn/5HVEOTn5eIS+PX7948f3759+/X7z3duTu4/f/5++vLhz++/zCws7OzM//6CRj1//f7BysImICD099/v9+8/sHFw8PLwfv389tf3n4JCYqzsbO/fveHl52P8z/Tly0eG/4yg0+xZmNg5uf/9/vP776//oAuLGFnZmAUEhJmYGP4x/vv9/ft/BlZGViZGBtCM/t6dW69fPM/LL6hrZMzCyqqipikoJMTBwfHn759PHz48uHfv+9fPd27efPbkMb+QYEhkNDsr298/P///Z/zF8I+Dg5sFdB8S48fPb799//L/738m0EAmMxsb65+fP3/9/v37z5+///5xcnKws3N9/Piei5uLhYX17Zt3f/7/ExYU+fPrx5dPnzl5ebi5OT9/+vb122dWViYWVg4W0PlNf37++M3MxMrFw8bJwfcDdK7+by4eLk52bg5urh+PbjN9+5DXOUlOGrHHnKTYKjxUIvRGsMKnjJUNNGqLS++NGzckJSW/fft2584dW1tbuLIvX76cPHnS3t7+wYMHAgIC8G3scAV4GD9+/Hj37h18d/z79+///gWd9PT582e44L9//54+fSomJvbq1StpaemfP3/evXv3+/fvTExM0tLSkI3Y////f/bsmZCQEOSan79//758+VJSUhJ5lwYeZ9BU6tevXwcOHLC2tobcigS36/Xr19zc3FxcXKCpw///nz59KiIigryR/PXr12vXruXm5g4PD0fevQ43gQzG69evP3/+jHwRABmGDDMtg/lop29fPv/49g2WjP/zCYn8+/v347s3oP2aHBwcXNzv37wGNVhZWHgFBD9/ePf/3z8GBlABzsXDywFOWqDjn/78+fT+HeRELW4+/q+fP3Hx8H799PEfaFoHNGLIyMjIzccHXkf0i1dA8Munj3///GEDX1b3+eMHLm5eBob/Xz9/Zufg5AGfLgxJAP/////0/h1oDuc/Axs7B0jq//9/Lz+AjnNiY2US4vn7/D3IdCZGZnGBf+++/P/9B9SChGkGXQb9G3Q8E7OsyP9vP////A1qDkJkQVmC4d+bj4w8nCCpH7/AqztBi2GZeDkZmJj+ffjKJMz7/8cv0JrRH7//f/vJ8J+BkZMNpP7tJ0ZRvv8g6/4ysrMy8nL+//CVSUIAYvC/D19BW/jffQY5kp0VpP79F0YhHkZ2RNH3/8v3fx+/MYnyQ3dcMTCA3PDpG6Mgz/8PX///+csIOjSO5//Hb4wcbIz8oPwLMfz/77//Xn9kEuL9//UHaCwWstT1339GHg4mAdDpHBBlo+RoCIyGAJGATiOjTMxM796+3bZ/IS8PpwCfwItnT5hBm3KYeLg5WFg43r5/zcbKxsDwn4+X//uvHz+/f2dhZmVk+s/Ly//hw0cmxv9MjCxMTIyCIiIfv377/fv7n5////7/y8nFKiYi8f7z15/fvvz++fv3n7/snOwyklLvP3/68vXrn5+/GZiYObjYpITFPn7//unj299/mf78+MHK/FdIQOjDp+8MDP9BNzNxcTGyMHNxgi5e+guax2dkYGD8+/8/Fzf3r5/fObhAB/J//fxx7vTpEqJi3359/frl6++///7+/MzOyiwlp/Lx4+fvXz7/+fPvPxsLJzubpIjY24/vv4Evrwede8/KKisp+QF0fuenf7//gRbLMzKJC4l8/vj698+/DIz/f//5JcAn/PPXl9+//jEw/v/795+AgMCXL5/+/mX89/8XK+gwK84Pn94zMjD+/feHh1dIT05KSVLk3ds3ZDdGWVlY165dm2efwy+M73Dm1atX+/n5vXnzZv78+ciN0devX3d2dlpZWW3atMnExISkk8nv3LkTHR29atUqdXV1BgaG1atXv337NiQkpLCwcPny5byg+11B9znNnz+/paUlJydn8eLFly9fzs7ONjU1/fPnz9u3b11dXbOzs//9+9fU1JSWlmZsbMzAwHD69OmCgoJFixapqakRmehpp+zHjx9tbW0LFy5Eboz++fMnJSXFwcGhsLAQcotVbW1tbm6ukZER3CWQqwf8/f2xbxOBq2NgePTo0apVq4qLi2GtFiQ5VObly5evX7+enZ2NKoyT9/v37wkTJiQmJpLUx8Bp3KgEiSHAxcOLdmobMzOziASi2ykiIQk3UkAYeh8vXATCYGZhERQFXTAL4fIJCjEwMPALi0C4cJKNHTq7wi8kDBcUEhWHsDm40JtTjIyMyCpByhgZmSRAtwSD2AwMzDIIc5jE8JUtjLycjLyg04UhGkEkIwOTKEgL1s3yzDwgp0KkGDnZGQQRi5QYpUC+YxSHtj5BZ5nBWqKgQV9wo5ARyZGMkggHg+xlYGDk4WTmQXEMIxc7Ixc7yChkYznQd/EzsjIzQ2znQDRtIWaOkqMhMBoCZAA6NUb////PzsamqCjHycbCycEqpazOxsr6788/Pl6ut+++qHCIMDCwsLGxMjOzvn3znl+A+99fRk4u1m9ff337JiAoxPf7538ern/vv7BLsiqw8/5l/PH/8+cPnNwcP778EtMQ+vcbdLQnBycXNx/vlzevhNmYGf4xffv4iYOfj0+A5+ebt7KiMn9+fvv57evHZw85GL7++/v/6/enf/79+/OPQV5BkYONnYubh4OTg4WFFTTVz8DAyc4pICD49s0bRSXlZ48f//nzi5WFkV9UTE3X5POb53+//fz8+j4P4x8WXgkJNdEfv3//+fGLR5Cfh5Pt44vXQspaf37/YWRkZOfi4uLm/vD0iTA7Mw8X1/8fvz//+sbMwvLry0clLcF///+zs7D+/PXt88dfwqKyv38ys3Myvn3znoWZnYdP7uf3v+ycTK9efRDg52NgkGb4z8TE/Of16+9cnOwcHGxcnKDSmYzIhmjhYGd/9uzZgSMHlJWVdcD3AD1+/FhISIibm/v79++vXr2Sl5dPSkoSEhJ6+fIlfOP82bNnX7x4ISEhwcnJ+fv376ioKB4enm/fvn3+/Pndu3fXr1+3sLCADHD++vXr8OHDkOuIfv/+DT+A89+/f+/fv6+rq5s7dy4PD8/Pnz+/fPmipKTExsZ2/PhxNzc3BgaGzZs3W1lZsbOzQ+49//r1q66u7uzZsxkYGCB3RSooKPj6+n748AFyL+j///83btzIzc29adOmkpISiAfh5KdPn37+/PnixYu7d+9aWlpCzrD8/fv3gQMH/vz5Y2dnx83Nfe/ePRkZGTY2thcvXvDy8nJzc799+/b3798SEhI3b968cuWKrq6umprar1+/3r59y8jI+PTpU2Nj4y9fvhw4cICbm9vGxgZy1ub169dv3bqlqqoKGa+Fu4GBgeH48ePfv38/fPhwUlIS5HBWyFH2EDV///69cOHClStXqqqqxMXFmZmZr169eu3aNS0tLW1tbdCw0f//x48ff/36tY2NDScn56FDhzZu3GhhYSEvLy8rK/v58+fDhw9zcHDY2tqysrK+Ax3v8P/FixdcXFxmZmYQExgYGE6ePPn8+XMrKytIdDx58uTUqVNSUlImJiaQKP779+/Zs2c3bNigrKysCQb//v07evTou3fvbG1thYRAtT7Ewe/evWNgYLhz587Lly/t7e35+Pg+fvz469cvyFC3pqbmq1evjh49KiUlZWZmBmkx379//9y5c+rq6tra2oyMjN+/fz948CATE5OdnR0H+PL6EydOPH/+3MTERE5O7v///6dPn37y5ImhoaGCggLEBIjVw5v88xt0SDFoazZ9/fn/PwMrGxsz6cfj///6EzQaindeGrzEFLQln75+osw28J56SJOUMoNGdY+GwGgIEAvo1hj9x87No6piws/L9fjWjYf3LnNzcbGxsL9+8YaBmf3bjw88XPyMDL/+/Pvz7+//t29ecXPx/v33m4GB9efP3x8+vOdg537+8ysLB4+wxK9/P38z/2FhZvr//+/P/3//cnJwgqi/jH/+/GBj4WVlYmDkYP/96x/o+FFmRjYmht8M/9jYmP/9Yf737z83L58gr/Crt+//M79kZvzz7z+jurqGqLgENw/P338MHFxcPNzcf//++ff3n6CgAAcnByszw/nTpz59+s3IzMzEwsTFyfrpPwMTM6uwsDg7y99Pv5i52Nn///nJysLAzPyfnY2ZlZmBmZ35758/TKAbT3/zcLN/YWL8x/jvz5+fTP/+szD+//f717/f/548fM/GzsP0/9e3b5+Ymbi+fP7Axsrz58+Xb9++c3IwvH/3hY2V59v3D//+MPz48oWZGXSryaevb7m5xXjERP4xMP1nJL8vzsjE+P7Dh77eXnEZiRkzZsTFxUVGRlZVVWVmZlpZWV29erWjo2PNmjXV1dVJSUnwI9anTp26c+dOAwODW7du/fjxg5OTs7a21tPTU0JCorCwUE9Pj4ODY8aMGdOmTVNQUCgoKPjx44eMjExfX5+hoWFLSwskMf78+dPBwUFSUnLy5MmVlZWQRgYzM7OXl9emTZvc3Nzev39/6dKl/Pz8P6AABN22wsgImoWEXNUjJSUVHh6+fft2X19fiF4GBoanT5/euHGjubm5vb09MzMTeTySgYHhxIkTDQ0NVlZWzMzMc+bMmThxoqSkZEFBARMTEwcHx7JlyyZMmFBXV5eZmWltbZ2RkeHj45OSktLb22tqasrOzj5nzhwDA4OVK1dmZmZqamrGxMSIi4vr6elJSkqWl5fLyMh8/vx59+7dTU1N69evnzt3romJyebNm9++fQtp3kF8/f///zVr1mRmZu4Gg5CQENCYDWRTHVjFnz9/du/e/eLFi82bN3/48OHmzZsLFizQ09NbvHhxZmamh4dHXV3dvXv3ZGVl16xZU1RUdPjw4devX69fv97Pzw9yo6mMjMyXL1/WrFkzYcKErVu3zp8/X1paOjY29urVq8ePH29tbe3q6rp8+bKysvKyZct6enq+fv1aW1urr6+/efPmc+fOZWVlgQ63+f0bcpX89u3bP336pKSk1NjY+OzZMzExsfnz5/f39ysqKoLdy7Bt27ZZs2bZ2Nh8+fJl/vz5c+bMOXPmTFdXl7i4uK+v79+/f6uqqrS1tR88eKCvr19RUbF79+4JEyYYGBgsXbo0PT3d2tq6oKAA0ijfunVra2vrnDlzLl26pKCgsHLlys7Ozh07dhw5ckRdXX3FihV1dXWQ/hLE6uFNbl+2cN+GVaxsbIxEnK7+HwzwBwgTaO4Yb1ORgeE/w//fv34FpWTbevvjNw1N9v+PX++8m/49f88CGkRg/v37N+R0fcj9I3AuCwsLIyPj79+/WdlYmRhBmRpiDlwBhAsnIdp//fr1H9yMhYujMRgZQReawMuBP3/+QDZyoSnD5ILOb2YGuRaP+f///mPRkhXaUIWpfVRkNARGQ4BGgE6NUVBDkJNbTt9YRELUXM/4zZsnv37+/vnj1+9fv5hYWb58/vzzO+jKJVZ2Vk4u7r9//vz68ePrl6+gW5J4+cAHl/xiZmBi4+YCbVNnZhQVFGFkYnj/7tW//6xCEvJ///35/esHKxMjJweoHOcREvr5+/e3j585ODl5+fh+f//Oxcv3n5nl3+/fDL+/8vPxfXj7uq2u5sOHD0xMDCysrNw8PEIiwh8/fuHi4mTnZP/3hwVSdAoLiX7/+omJmYmR8f+/v/+cXF0tnL3ev33348dX1n8/WZmZ3n/+zsbF+/XL578/f3ELCXBwsH96+YaRi+Pn128/fv74++sXOzubnKjYrz+/hcXEmBkY3rx6/eX7Fw42zu9fvvz9/ZuVnRl08ujPX58+f2ZlZeXm4gZdDfDv3w/QDlnQpZeQCvv/fwYuLi5WdjZeAYFvj299efqImwd9Eo34xAE6aeD///yCAh0DncuXL5eWlvr6+v75AzqMEDII9+vXLwYGhl+/foEWLTCBDjd48+bN2rVrlyxZIiUltWfPntbWVkZGRogCyA2fLS0tvLy8lZWV+/fvV1NTe/ny5Zo1axgZGRsaGt6+fQt3G0RXWVlZYmLiiRMn4Csj/fz8Vq1a9ebNmyNHjigrK0tKSj5//hyuC5khKCj4+fNnZJGNGzeamZlZWVnJyMjs2LEjODh47969z58/Z2Fh8fT0/PfvHw8PT3NzMycnZ0dHx6JFiywtLX///j1//nwGBoaCgoL169c7OTnt2bNHXl7+69evFy9e/PTp07Vr11JTU4uKipKSkiwtLXfu3Dl16tS+vr4vX7709vYaGBjMmDGDi4sL0uZOTk4+dOjQ0qVL29rajIyMbty4ERcXh+zCx48fP3r0yNvbW0BAYP78+cHBwciykEuhSktLjx8/XltbKyMj8+HDBwMDA9C5ECws69evV1BQOHv27OrVq+GDuPn5+e/fv+/t7WVgYOjp6VFSUuro6GBgYIiJidm3b9+/f/+EhITmzJnDzs6+evXqP3/+PHz4cOfOndOmTRMWFp42bdqSJUuUlJQYGRmTkpI4OTnhVTgHB0dJScnZs2fr6+tlZGROnjx58eLFdevWsbOz9/X1zZo1q729HeJyyD2fbW1tkDDctGmTlJTUv3//pk6dKiAgkJOTExoaGhsb++XLl6CgIHd393nz5pWVldnb27979+7Xr1+7d+/+/PlzU1PT////09PTDxw4cOrUKT8/P3d3958/f/Lz8x86dMjOzi4sLAx0xRofH8TSkUB++fhBUUPbOy7l798/eNujoHYU5CxkPMECbmX+/g/azYm3PcrIuHJyz7cvKNkKj7EIqf8M/159EGiPP/Hh4Y7N2zIy0iXAd5vduX178eLF0dHRaurqjAwMS5YsnTZt6sqVq/bv33/9xvV///5xsLMbGxu7ubtDBsVBjgPd7QFqezIyMHz8+OnOwwegGQYuLsjZAuAlpKBGM9xqRlAbmmHP7t2rV69+9+69kbFRcnKyuLg4VD3YNFBpBtb5n4EB1GYFGQ8y4M2bN8+fv1DXUGdjZQOfugKRhZoPcgwD46/DV78vOQBSPYpHQ2A0BOgF6NQYZWBgYGVhFuRh52b+L6WioK6t8eXL5+9fQdey//z569evH58/fv779y8XLzc3Dw8LM/OH9x8+ffjw9+8ffn5+NnY20BYW0LokFtBi0N8/mRgYOLm5//0VZWdjZ2H7z8wA2jPPysbGycHBD2p3Mv/4+fMXBwcDIyNoN72AKDMr+z9GRjYWVnYWCWFR8WtXLv/49RtUUv1n+PTx45kzZxQVlTi4uJmYmf8x/Pvz88+XL19+fP928/oNURFB8BYBxj9/QVfRq8pLveVk+vGD7w9ov/4fIZH/n79+42VnZfj3l5mNlYODXZJb4f33z99ZmH7/Yfvz8xfDfwYuFpEv376CVrwygDbb83Lz8vHxMwgJ/f33h5OLi4GR8f27d0Li4jw8fIKCoBvYv3758vnLl/+gVhQvG2hSnoODnY0L1KrmZWRlu/T++W92Vk5Kpun//+fn44eMckFm1b99+8YEG6iDM0DFNzgJMjIyfvr0iZmZGTJRq6amxsXFBWmnQgY85OXlecHLPSUkJL5///748WMZGRmIdg0NjePHj4ONgRL//v0TFhauqqrq7u42MjKCrI8UExPT1tY+BAZ+fn6gHQM4pio/fPjAwwNdMcbExPT79+/NmzerqKhMnDjxw4cP69atCwwMfPz48d27d9nY2JycnP78+aOiogKZN1dTUztw4MDLly/hI206OjqPHj2Kj4+vq6vbsWNHUFDQ9evXd+3aJSsrKygo+Pr1602bNu3du/f79+96enp///6Vl5fX0NCA3Bd/586djo6Ov3//ysnJMTExQRgMDAyysrJ8fHzwFh4DA8POnTtfv349Y8aMT58+nTp16vbt26qqqtDggFG/fv369+8fpBtw8eLFxYsX8/DwPH/+nI+P7+XLl/Ly8pARX8hWJMiQEuS++GfPnunr60OMUVZWfvjwIRsbG2RYF7ToDXQoGNP79+/fvHkzc+bMv3//fv361cHBwdfX9+bNm/n5+f/+/YuOjg4KCoLEF6SD8RN8kc/z588VFBQgQ+P6+vrLly+H2ALaIvP3LzwMNTU1nzx5IiIioq6uLiAg8OvXr5cvX0LWBvDw8IiKij569Oj79+9aWloMDAyQJPTmzZtHjx51d3dD2s1SUlKVlZXd3d2rVq0SFhauqamprq7u7e3dvXs3Hx9fTU2NsrIy3OrhzWBiZvrLwNjWP+nZkyfMTEyQphKomEL1NiMjqK8YEhKSlpqKKoPCe/P2bU5OzqdPnyDn2aHIIXGYmJmEvr/TMPkMarQhiRPDBJ1HpybR3zVx/YblQk6Gxe75DAwMD9/c6dyxwjo7WltPgYGBYcOD8391ZcXdTBdPajxz5oy5ufmHlx96Otckvro1ceJEdkbQmaA//v1hYWKBTPcc37kjpijp8OHDuuryEDf8+v8XdB0uWCVEhIGBYeW6NcktJZpaWoqmql3bVux5fXvr1q287KDVnz/+/uZghhgGVf7z/1+IRQwMDBvn7Wlsajp18qSMOHQB7s//f1kYmZErQsbXn76DNlBBtY9SoyEwGgJ0AMh5kIbWMTMzv3jxbHtfJxsLKwc7xz/G/6CbMUE3MP379+8/MzPzzx8///37x8LKysrCwszE9PvP7+/fvv9jYGBnZ2NhZvn779/vXz+ZWVhAF4r8/cvMzMgCuiaUieHvbxY2JkYG0BQ8IxPoKCdOdta//////fOPiYHh1+9foCs2WJhZ2Fj+gkUNTMyqm1ovXDgLuiKPgZGNje3Txw9fvv/8KPTx95+/rKysf/78+vPn7/v37358+/rt66dXf3+xsLL9//+PifH/5UuX//1nOHP61IJpU/+Drvdk+vfvL2hkl4GBhYX1HyMjCzMjM+io/L+/foIGJMCXfDD++/3733/Qcad//oIOpmZiZGJjZ//77z8TCwMLE+vvP6Cb7P//A22l4uLm+vv3398/f36Dxin/cbBzQMpDJtDuLRYmZqb/TEySbP9VJUVfvXghDFpLSk58MTIxfvjw/u7du3pGeo8ePWJkZOTl5f379y9kIeC9e/cgbRHIjBsDA8O/f/8EBAQgO9bl5eVv3Ljx9etXZmZm8CQhyIGgURqwQ/6C4oVZXV19+fLlnz594uHhOXHiBFgGSkC0/Pr1y8LCwsnJqampKTc3FyIXGBhYV1cnJSVlY2MDEYEYCxrqADdM//8HrYNcsWJFWloaaMzj/38WFpYzZ878///fwAB0fLe9vf2yZctu3ryZkJAAMQHUeWFmvnPnzvfv3zk5OW/duiUoKCgpKblv3z6IgsuXL6upqSkrK7Ozs69cuRLSXOvs7KyoqODn5xcREUlOTjY3N4es7/zx48ffv38hwSIoKGhkZNTR0fH//39IU4yRkfHevXsiIiIPHjz48OED/PCs79+/79y508fHB7QZmovL1NQUMvUMCQqIMyAkxL8MDAy9vb1ZWVnu7u4zZsw4fvy4hITEgwcPvnz5wsPDc//+fcihAT9+/IDokpGRuX79OqSBeOfOHUtLy6dPn8KNAt/VBYo+SUnJqqoqISGhZ8+eCQoKvn//PjExUVpa+tKlS8XFxY6OjpBmIiP4kCBIS1pSUvL+/fuQJu+FCxcgy20hlrKwsFy7dg3CvnLliomJCSRGQB1OVlYxMbGLFy8aGRl9+vTp1atXcnJy7OzsV69edXBwePv27a9fv4SEhLS1tXt6ehgYGB4/fiwhIXHr1q2ZM2cyMzNXVlbOnTs3Ojp68uTJbGxsPT098+fPr6+v//nzJ7wTArF3WJKMjIxfv329eOF8dGSksrIyJPGDzmMG7ZpH+JiZmXnevHnnzp5lwNsY/fjhw8kTJ2prawUFBeFJAmEKjMXIyHhm86rv37+DBwVhosTRrCysNy5dOX7wsIyMzNrlKzOTU7m4uJj//udgZmX+CyocXrx4ce7EqcjISC5WdpZ/DBZGJls3bGJmZu7p6amoqIgMDoWkhFOnTgkKChYVFf39+7e7rf3z2/cVRSXOzs45OTnTp0/fvn07AwNDWFhYYmIipNf08+fPnvZOA23dbdu28fHx7d69+8qVK8x//1+8eLG7u/vp06cWFhaVlZWvXr2aMmWKuLj4vn375OXlm5ubT58+PXfGrHcvXuVmZPn5+UVHR0+bNm3btm3c3NzZ2dkuLi69vb2/GP9LnHsexAYqfskIE+JCblTVaAiMhgA6oFNjlJGR8cfPH3dvX2f4x8DNw//966c/f/+CxuOYmBj/M/Ly8Hz+8gV86RzTvz//WFhZ2NnZPn/5AioLwFMyjKDr2Rh4eXm+fwMd8swKGsL8/5+RgfEfAzMbGxMT8/dvX8Hb80GXvf1nZGL8x8DLzf3xC2hUgJGJgZHxP8N/pj9//waEhDOzsF48e+bf33+sbGzcXJysbKzMP38xMYFalqBy/+8/0LwWAyPoFHpm5n////Py8b17/fI/w//bt26+ePpMS9/gyeOnb9+9YmFhAk3t/AfdMMLJxf0XtPng138mBhD8z/AXdC0pCycn59evX0CNZfA4xx/QLaUMAgICnz6CLlAB+ww08PH/PwMnBzsTE8u3b9+YwaflMzEycXFx/fr96+cvkNv+/v3LyMDAwcnFKi2qIMz//ft39Ggkmv//P4OgoNDUKVOExIXPnz+fnJzMyckZEhIyefLko0ePPnjwQExMDLTbjJ2dCTxHz8nJKSQkFB4enp2draen9/z5cxERkX//QKsIWEBXDjDBZ9shk24mJia2trbx8fHS0tL37t2DDJJBXMfExMTOzg6pTlJSUg4dOgQZGWVgYLCysvr375+FhQXk8CPI5DUDA2h9wvnz51NTU//+/fv27VsXFxfIogIeHh4mJqa1a9fGx8dHRUVBzH/37t3atWtramogXMjQ4PPnz6uqqv7//3/r1q3JkydLS0uvW7cuLS2NhYXl69evERERDAwMtra2GzZsUFJSMjMzmzt3rp2dHSMjY1xcXEdHh7Gx8Y0bNxITE7W1tSEDwAwMDAEBAYWFhRUVFb9+/eLi4mpra0tJSamrqzM0NHz37h3yVvQzZ86ws7NXVFRAnOTo6FhQUPDy5Us+Pj54gxUixcPDAwkZCwuLxYsXnzlz5tatW/z8/GpqasbGxunp6ZKSki9fvuzv75eXl+fh4cnMzAwLC4uPj88Fg+/fvwsLCzs7Oy9duhR+YhQrKys7O7u8vLyjo2N+fr6qquqtW7fa2tpevXrV1tamr6//8uVLAwMDuL+4uLgMDQ1LS0u9vb3j4+NNTEySk5NFREQgA5kQdzKAul4s9+7dq6ys/PDhw7t373x9fY8dOwZJBoyMjJmZmTU1NdeuXXv48KGbm5u+vn5ycnJ3d/fOnTtv376dmprq4eGxdevWoqIiNja2379/Nzc3b9q06c6dO6qqqrdv387Pz9+2bdvp06e1tbXPnDmTlpa2f//+devWzZgxA+6AYcxgZGDg5eH19vbm5+evq6uD9CIgYYvs61OnTiEvgEGWQmZzc3P7+PhAtqwhi6OxH586CJ7NRxMmzGViYlq3bj0fH19dRVV2dvapU6ccHBzgPRMGBoarV6++evXK0dERIghJ4czMzKGhoU1NTZcvX+bl5T1//nxwcPCGDRsgy7W5ubmZmJg4OTnFxMQ+ffp08OBBR0fHR48e5ebm6urqmpqaMoCm8j/euXMnLy+Pjw+0isMVDN6/fx8dHS0pKRkaGtrZ2cnKyurj4zNx4kQTExNHR8eJEyfy8/N7enqyg0s2Tk5OUVFRSFenoqLi/v37CQkJR44cOXDgwJatWzJUbMJlnQj7f1TFaAiMhgD1AJ0ao////+fk4JSXVfr/9z+/gOCPn6A72pmZmP7++8vCzMzJwfXt67dff36zsrH9+v2Lh5ubkYHxydPHnJzsXFwcjAwMf/78+fzlq4iwGGhy/8c3DjZ20AlMoAHUvxxc3BzsoE3EoLYR43/Q+CgT89/ffwWFhJjfvGRhAh18zwC+nVlEVNLLN+Dpo8d3bt8C7UdiYRUWE/8MOgmfiZEZNFv0588fVlbWv//+/Gdg+Pf/PwcHx/u373h4+bl4eL99/fz+7avL588ERUU7uHns372NjZ2NATRiwfjjx09hEdHfP779/v2LkYX531/QhUw/f/7i5OLm4uZ+9+41CwszqNn879+Pn78YmZgkJKUZGZnZ2VjZOFkZGBh//fz15es3YUFRfj7+dx/esbKCamgWZmZIk+Lr16+sbKy/f//iYOdg4+AQ4mJmZWXlYGcnOwH8/PkjLz/PSczh8pXLsbGxkPnTkJAQyFpPyHw0AwNDc3MzZLRMQQE015aenm5ubv769WuIAhYWlrKyMl5eXmZm5sbGRohjwsLCIIzU1NTg4GAODg7wii7QzmuIuJaWVltbGwt40y47O/vMmTPhI3wsLCwrVqyAt4pERUWnTp3Kw8NjZGS0ePHiHz9+MDIyyoABpIlZX18vKiqak5ODPGKXnZ394cMHiF0Q8sePH3Z2dikpKc+ePauqqoLUyhMmTDh58uS/f//MzMwg099hYWHu7u4MDAwGBgYbN24UFQUdmhMUFKSpqfngwYPAwEAdHZ3fv383NTVBIkVCQmL69Olnz55lYWExMTFhZGT08/NTU1N78OCBtrY26DgeEehJOpCBH4hjGBgYlJSU+vv7+fn5m5ubkdus7OzsHR0dkFUTpaWlJ0+e/PPnT1ZW1s+fP5mYmJqamk6fPv3p0yd9fX2IrmnTpl25ckVGRkZYWHjmzJmnT59mZWW1sLBgZWX18/P7+xc0rcnAwODo6Ghubs7IyFheXn769Om3b98mJibKy8srKCj09/ffunWLk5PT1NQUchoAJGBra2vPnz/PDgYNDQ2nTp36/PmzkZGRsDDi4J4fP354enoGBwe/ffvWxMREQEDAzs4OfkyVjo7O9OnTL126JCIiAjl7y83NTVlZ+fbt29HR0ZD5/UmTJp0+ffrfv3/Gxsa8vLzl5eXnzp179+5dYGCglpaWo6Pj2bNn37x54+/vr6Ojc+HCBcjiaXgwDmsGqN/88+fPhoaGjx8/nj17VlJSMj09Hc3Lf/78QRPByv3//z9k7QdWWbggnnFTuBpMBmh0n+H/pi2bjCM8jIyMuLi4li9fDmmMwhXv3btXUFAQ0oKEC0KmLCDD8Do6OsnJyZBk9vDhQ0NDw7y8vEOHDjU3N2tqav779y8nJ+fAgQNPnz79/fv3w4cP4Ub9/w+aT0M289q1a3fv3lVUVLx9+zYzM/O+ffv8/UFbshobG728vM6ePXvx4sXe3t4HDx7cvHlzwoQJYmJiISEhrKysL168+Pr1K2QrJBcXl56ZcX1ew6+pexBniiJbM8oeDYHREKANoFNj9O/fv8IiojHekfy8PAICgry8PF9Ap29+ZWBk+Pbt28cPH3/+/MnIxMjFzSMmJsrFwfHu3btHjx79+f2LhZmZhZXl9+8/v37/YmZi+/f/95+/f9jZONjYQNP3LGysHFxc3OxcoJtF/vzm4GAXFRVlY2V7+/bd129ff/z6ycbCysLCCrqbno2Vn59fSl5u/apVb1+/+vfvj6CgoKOj87Ub139DKm9Ghn+gO+r/gG4iAU0NM7Iwg+7bMDM3Z2FhOn3iCMs/tjOnTwVFRmXlF/oH+v349efXz59///z++eMnEysbFxe3kLAQExPT2zdvP3/5xAy+HfTnT9Bs/8+fP/7+Bm32/Pr9C2ianpX9798/zKyMrGwc//78+/3rNyMzk7i4pJS09J+/v969fffh/fu///6xsDDzggEzE2ivgpCw0K8/f++dOPDz9VMRpHMESU0V////FxAQkJKWkkI9Nl9PTw/ZKFlZWQgX0lxjADfUICIQEtJyggxeQkQg7aTfv3+3tLSwsbHx8fEdOXKku7sbIsvAwMDJySknJwfnCgggTgdkYGCAGwgZe4OsaoWM1cG1QBiMjIwQcyANZYgg6DBFfhCAc+F1njYYwMU5ODjs7e3hXAYGBm4wAK1LY2ODmAyRBR9wpAlhs7KyIksJCAg4OztDpCCkBhhA2HAS0qCHcxkZGVVUVBgYGJCNYmBgYGJigvuFlZUVvlYBopGJicnc3BzChpBCQkJ2dnYQNi8vr5MTYhQH2UZQcMDOLYfX4hBdimAAYSOTbGxscLuYmJgsLCyQZSFsZmbQxWaGhoYQLgMDAx8YwLmSYADnMjAwKIMBXISHhwcyWgYRYWVlhVsKCQ1k13JwcMTHx0NUjgAStFIUcsXDxYsXf/z4IQneFTQYPQ7eEvT71++tmzYf3rv/3bt327dvf//+PaS3CRkE3bt3r5WVFbwnwwQGDAwMe/fuhZza1t7ePmfOnPz8fA0NjUuXLv39+/fHjx///oEO62NgYNi0aVNSUlJWVpa+vv7u3bvhEyl8fHxycnInT56ELA26fPny7du3BQUFIeGmoqJSUFCgrKz869cvRkZGyGQLaJQBXM7//AlaD/b792/IYC03N7eSkpKqqqqNjY22tvaPHz+kJKR4eXh+gn03GIN91E2jITBMAZ0aowz//zMxswiListISwkKCPDw8P76/evjhw8/f/76+u0bF++7jx/eMzIyiopLyMnKsrGyMbM//vX337cvX//++8fEwsIOOhL+z/9/jH/+/mJmZmJl5WAFNUhZOUHNCNDZkOxsrH/+/OHm5haXlGbjYOd99uz9+/e///xmYQINMXJxcXJycbOxsfz9/+fShXMMf/+BNqv+///u/ftPnz7xCwgwgbZ6MIMn60H3gUL20DCzsHz//v358+e/fv5kBLUYGO/dvPnm5TNxCXF2Drafv/9++/rtx49vP3//YmZm4eHilpWTZWJifvni5bv375lZmP7+/v3j+3fQdqQvX37+/Pnr50/O33z//v1nYgKt+2dgZGBmYPv3H7SNnYODQ1RCQkZBgZWN5fnTp8xMLD///OXh4Qad/cnDzcHBCWqU8vD8+PXnw50rL94+Z2KhKNbIGwghMv2zsrLW1NQcO3bs27dvMTEx8vLQXQhEaqeuMjs7OwMDA+qaOWpacHAwpC6nT1BANo3Rx65BYsv///9LS0ulpKTExMR8fHx6enrMzMzg3Q/IYReQtd14HAwZFqVpZmf88cNIV2fihDoeTq4rV65kZWVt3rBWWVn53++fHKxMTx7dv3H1ckoitCPB9P/P5QsXUpMTPn78uGf37sjwEEc7m2WLF3Kys0iICW+7f5fh329WJgY5aUnGf3/amhsiIiPfvX33+8dXGSnx8+fP//z2jZkBOuTPwcGRn5OVm5cbHhKkpKy0bt06MVGxtevWqikrvnn5TNDa4sjRoxZmxszMjAz//0N0QfYIMjAwSEpKvn37try8PDw83MHBYc+ePb9///7y5cunT59ERUW/fv36l5WJpoGGJ8pGpUZDYCQDipo1xAccMwvL2zevzi6Yy8nBzsLKCjnsDdZJ/fvj+2cmhv+///xjZePg5OZkZmL68+fv169fQKfHM/xjZGL69wc0dc7IxMrI+B989CQzaFsPCwMTaMsQKzMbCzNoRz3zv7+MHFw8TIz/f//6/uPHz39/GRiZGFlYGFlY2f//A+3FYWJjeXD7Nhs729evP0F9d3D3l5eHl5WNFTTUysICWcnHAhpPZeHm5oHs1AGZDl4q9+rFs9KCPE42zt9//jIz/QftPvoDun0JtMiJjY2dg5OJkenn9x9/QZeRMvz9B3L177+gnQG/fv0GL0X9zwC6Zenv//9MTExsTEz/GBgZwGtkWbk42VnZ2BkYmL99//YLNCsNOnCEhY0ddDUUEyMLKysLM8uPn78E/n6V5ud6//aNiABosRTx4U9PlcLCwr6+oGuy6WkpVruQhwaxKhgVJCMEIEPgZGgc1UJUCICmaP6xs7MnJSUxMDAsX768o6NDTExs3rx5goKCM2bMqKioCAkJgcy/v3jx4tq1a5cvX3706NHPnz+FhYXV1NT09PTU1dWlpaXr6uoEBQWJspR0Rb/+/bsgKGrsniKh4/T/7z8NQXXrwAtHb31gFOPUcQp7+U/k/MHrjOLa3Ipml56C1riLajsK/RE8efczBydXbHFHZGTk1df/HcOzj95+X9wx19HR0URI/fLLv4IimrHF7du2bvu+5VRubq6+a1R590InJyeXqJxXDKIQoxgYGAzdojPrGFevXrX30lEdXee8/Ly3TOIFrbO6urpSqydqa2k5fedhZmLWcwp58ov/5sufHt5ekCamp7d3Z3f3wUMHb9y+lZub+/PP7w3r13NwcISHRzCyMHt4ef5jZWZiYQYfHkV6oIzqGA2B0RAgFzBCsii52onVd2TrholtjWdefHz65BkjaJSUkZGBmZHxHwcHh6SE7JOXj3m4mL9//v7z138GJkZGRiYBAW5eHqG/f/+++/Cak4Xxx9efv/7/+/OPkYudjZ2LXVhI7M279///fv3948/3X/8ZGZgYGP7x8HAxsXAICwo8ffmUg/HPj++//vxn/cvAwMLKwMHOws7K8/3bj2+/fwgJCv39++/zp4/q6lrObu5Hjh3R0tblFxIBNSg5uVhZWf79+//jx88fP74x/Ptz5tRJA33D508fb9uykY+fj4OT+/WbV5ws7PwCgl++vf//68f3n78YGZj+/v3Pwc3568cPYVEpJiamn1/ffv76498/hn+MjEwM/3l5Ob9+/6sgr/Tx/du/vz5+/Q6S+v2bgZ2TiYWZnZGFXVxM+sOHp18+v/v2k4GZgYWRmZGXj/3jp58yEjIfP394/+4dCxPLv3+gs/h1ZMUdzIwzGrvV1NCPByIyJvL2F7rKu/gqeROpflTZaAgMpxAYzHfTr5rWf/vmjd2Xb0+aMAGybOb06dNhYWFTpkx5+PBhd3c3MzOzv79/W1sbOzv7vXv3Jk2adOTIkT9//mhra8vKyrKyskIuTXj48KG8vHx0dHRcXByoy00o/iZW5AtKycbloV9ghl/ft1//DLvuvP30mwE8/c3IyMjGxgY5cYKVlfX379//Gf6Dzif5/QdyBgUr/NB70L5Uhj/glUsssP4/6Jj//6Ajq0BnMrCxQhbZ//37l5WNlQFy6NR/ht9/foNW5IOdxcjECL5BGsJh+Pv375/foBX/0Kl8RtA2A9A+S1a2bz9/qQmznMwFXdnw7w/objxGVtBifdCK/79/GFhZQe5nBO1Y/f8HdLkJAyPjj21nvvZvFD7eBTZ9lBgNgdEQoAeg08jo////eXg4lRT5WBh/ifCx/WVi/f7jLw8fm5AQ75+f/H+4+QUEmJn+M/75xfjz+xc2Tm4OXgamn+y/fv/jVtbm+PXjx5///39///33PzsrExsXB/cfBjZheUY2hv8/f/349pPh/19mTh4eXlZWFk6Gz1+4VQ0Yf3///uEjIwvjl1cvv314JSrE/p+Z/++/t7///2NlZfv75wcLC/P7j58uXLjw6+fPf/////v3hwF0PtQ/8Ok9/37/Bi0GZWJg/P//343r1969A92p8+/fP2YWVk42djY2dmExcXEJs1+f3v/6BTr09PXDe6ysv9n42AVEJDgFhH7++/ft5++/378xMjMys3Nx8zH+/sHC9vcnr7j0999//v76y/Dv1+//jMxMv7jYOH/8+inIxs4mxCPJ/O/PL5b/P74xsjGzcv7++J5DgJlBVpLr52/BL1///GP8y8PLLvSfhYWJGXN3LT1SyqgdoyEwGgJ0DIF3795lZWUlJSV5e4O6jpycnAICAoGBgf/+/evt7Z06daqVlVUPeAYfsiwS4rQ/f/68fPly48aN06ZNW7hwYV9fH+1WqrD++8v8/y8zC7h1ycDw/+8vJtByJhCDBXLX0t/fIAboIigGBvDGUIgjQeu5GRnAGv/+B7dlIeKsoH2kDAx/f0Pan6ADS/6A7uCAyILsAWEID2QLlMUAGo1gY2Fk+P8HfCYpSJgZdK4zSA0LGxPT0zdvjSb+xdUwhx2SD1pFCjrdhOHfj18sihIgU0bxKBgNAXoBOjVGwb1b0GCokJwgOw9oSJT91z9ePk5QUfTrv6S0Ajcv26+f37lYuT+9e/GfmYWXn/P35x/MP37yCkkw//3F8ec3y39GTmFhJqb/QoKCLy9d/sfIyCogwPj399f37xgZWHjExDg4/jH9/Pf5yxdBafk/f39+537D8P8Py7//EkJ8wgJsrz79fv36DSMjI+iYJND+JAYBQQEmZmYGcIf758+frKxs//6BVqiCDniCrKAHDxr//Qs6p/0u6E5O0Aqkv//+gRYHsLBKySt8esP199fPP79+sfz/x8v+i4nx/99/nMwcbOzcnFx/GL59/MzOwc4jIMTK9vf393/fXjxi4+fnYGb+/fM3EyMTr5DYP4bfUhKS965f+f/xg7Cw5N+fv7i4eb9/ev3txx8+Pi4Obsafn54y8jBzMPEy//jPzMLIwsHM8f3fX4a/fxmha6folUhG7RkNgdEQoF8IQK5RKCkp0dfXr62thVicmJjIwMDw9evXrKys69evz549G76F7vfv32vXrr1165a/v7++vr60tHRWVlZUVNSECRNCQ0M7Ojow7/2CmAkhoaOJEA7p5H8Ghr//QPuuoFoZGUCX1kE5DCCZ//9BxSdMBHRWyT8UEZgMFvof6LhSBkhrFos0MUJ//jFLCwtsqQWbRIwGkBpGDjYQNYpHQ2A0BOgF6NQYZWRi/Pb157sf/95//vHz8zdODi5WNo4Xf97/+8sgKanEwPrjz48vf7///MrwheEfAyPj3/9/P/36zgQaBeVh/fPpI9Pffwz//n549YOdjVmAk+n3v18sPHw87P+/ffjODFoxyvD32xc2FrZ/zEy/WZiY2P6xff/+9ccPJmYWdg4uJmZmJh62f+8+MDGB+t3gFiao4y0gKATaqQ9aLMr+588fFhZWyCHh//+D9jCB7kNnZOTk4GL4Czo2nI2NHeSsv3+YQN1opn//GVlYmBn+/f394wcbEyM7Kwc3D/eff/9+f/7DzsLJwcb26csnLiYmpv///3z/xMvOA9q19J+JhYOT4feXf39/MzEwfHr9lI31308+dqZ/f36Brq3/8f/fp08fvjH//s3CxPjv74fvnxk5/nM/uf322/dv7Bxc//79+fb9m4ogryyfxH+iznUhMwX9+/fv2rVrX79+NTQ0hJyjSWFdRdAd////f/z4sbi4OOS+H4LqyVZw586dly9fGhkZQe5kItscYjT+/v37169f8LMIiNECUQM5Y19UVBQSJpAD7engYMi2mIsXL7Kzs+vo6FAx0t+9e/fv37/RlaaQ+CVEgjZP/v//v7e3d/HixRUVFf39/X///pWQkIiJifn9+3d6evq7d++2bt0KOYAMYtqkSZMWLVpkZGQUGxu7YsUKyHltAgICDQ0NWlpapaWlnJycXl5eS5cuffbsGeQwBGZmZm9v7/fv3+/dv//Z/ftGUohjLiBmEkn+Z2BgZWKUFWSDjo4yMPz9///15z+//v4HnaP8n0GAE3QA9Ptv0P7zfwYGNmZGIT7WV59/g1fso9jz++9/FtAyLajgf5j2t1///v33n5UZbglUAVHU//+M7KzM6tJEKR5VNBoCoyEwQIBOjdH///+zsrJx/mP7zPCfT1CEi4v3338GRkZuhv+Mf/9+5+KU4hPmZWdn5+XiffvyOSsnJyvr36dfnn3/+UNRSukHr+Cfv//+/frOycP///cPbkG+X//vcLJzCSqocnz8/PXDO8a//wRkZbm4WH5++vXkzmM+IQlmVmlWzpfsLIxP795lZWDjE+B+8frnP4b/oJIecgI+MxMLK8uXL1/YOTk4Obl+//kFWjQEaj2CGquM4E1S7OwcfHx8L56/4Pn9l4WV9e8vcBuQkQG0epPhH7+U7H8mFsa/f/59//by/xNOfgYmFtbPn56wcLLxSCuzCXz/8uYNtyAfv4gwLw/X6xevX9+9JSIq8Y+R8f+v34x//3DzCf77+UlATPzlo+f//v3h4hPhEpXm4xX58u7VL9Cwwc//bN8+PX3My8PNw83DyMjKwPCXh5uPjekPA+gYU0oSC4ECffr06du2bfPw8JCRkWlvb29oaKB1M+Lnz595eXnt7e2amtBzlCjxHi69GzZsmDZtmoODg4aGBsG23enTpzk5OSGHYuIyEL/4+fPnd+zYUVdXh6ns1atX586d8/DwwJQCXZ+4YcPr16/LysqKi4vb2tquXbv24MGDvLw8rIqpK1hTU/PgwQN/f3/kqwoot2Lp0qVfvnyprKxEM+rPnz87d+60t7cfCbcrofkdF/c/A+j4zK9fv7558yY1NfXNmzevXr1iYGD49OkTAwNDZ2fn/fv3N27ciJYl161bV1lZGRER4eTkdPr0aUhjFGJFWFjYr1+/iouLVVVV7927d+fOHdAUOTPzgQMHjhw5cv36dVU1NYGvn7m5yTxV8+8/BmlB1t35qmwsjJDG5Zef/3yn3bn98icrM+PPP/+SrcVlBFkLVj0GXXHCwPDn7381Mfb+MJnQmfe//foHnqMCufQ/A8P//wye2nwvPv+58PgbC3gs9Oeff7HmIpL8rGXrn0UYCx67//XZ+19MYCmQHuIxxGXEqx9VORoCoyFAd0C/xig3Fxs/B9s3FgkZEX7QPnLG/39//uPjZ3v+5MOXj584Of++ePqBnZn759evjMyMAoIc379+/vHr58O71/9++fj7D6hbzMn7GTTKKSz098+/988e/WX9++fLrz/fvzIzMH3/9YObh53px6+fPz59fvfu39+fL+7d4mRj/fbuPRsr4xeWX5/evWUA7VBnZPjP8O/fH04unv9///34/kVAWBR0zvyvP6DDnkALnkDbp8CNUdDKAg4Ozt9/fn37/pWPX/Dty9fgI0iZGZkYf3z//vnN65eP7//8/ImNkeH750/fWTh/f/v6+dNHxnevfzP++/7529/v3//8+PT13XshUZ4Pb958//Xj/eN7P3//+fv7Dzsz8ze+j2yszILSst/+/Pr69Tv3xw+vv3xmY37498evP///CfCzf/r0+9/f33JqSr++f2X4/+/fLwYm1r+sH94xgW83pSSd/P/3j4GB4cOHD3x8fExMTK9fv/7x4wfkYNGPHz/u2rWrtLTU0tKSmZm5vLwcchcoMzPzmzdvfv78KS2NGGB48uQJCwuLhAT2xVWfP3+G3AYJOVMdciLgw4cPhYSE4Ifb//nz58mTJ0JCQr9//4Yf1c4AvuOAhYXl2bNnnJycgoKC3759e/36NfyUqD9//jx8+FBMTAxiDnhUm+XVq1f//v2DOOYPeJsC5GCE379/s7Cw/PjxY9OmTTExMeHh4ezs7P/+/Xvw4IEgGEBC8v///w8ePBASEuLn5//58+f69etFREQUFBQ4OTlBa4WZQcd+/f//H3ItAsTGL1++QK44+v79+7Nnz2RlZZHX8mpra8vIyCD7hY2NDXJ51ZUrVyZPnmxhYcHOzg5pFj979oyRkRFyouSfP38gG6VBx4H9+uXo6Ai/GuDz58/v3r2Tk5ODXBQJWXMCCQoeHp7/oJsd/jIzMz9+/Bj5GIHfv38/fvxYWloaPvD88uXL379/Q5wH9/7Tp0/PnTs3bdo0+Imnv3///v79O+SSmydPnrCxsUFuDYBE5YcPH75+/QpJDw8ePJCUlISbDzGTgYEBEoMMDNCNKRCRv3//QhLb27dvp0yZoqioqKCgAFn1+PTpUwYGBoiZcENGGIMRNFq5d6+xsTF8cPrv37/W1tYXL16cP3/+ihUr0Fqia9euvXHjxuzZs+/du3fhwgVmZmZbW1slJSV4uMXExBw+fLiurm7ZsmXwZZPR0dHr16+fNm1aXFzcpIoCLi5uuHqSGCxMDE/f/3bovQVK6v/+13hLfv3x98HbX0yMDD9Bx9aBZtjhLU7Q5iRmxntvfkXNffADtBnp/x/wNDwjIyN41PN/mInguvMfTt//zwIeaWVnZZp37N3///9FuFliLYTOP/727z/D37+gS+9A59GCdTEyMvz+CxpB+AducbIyM/4BdddBc1Fs5I2kkuT/UcWjITAaAlQCdGqMgtaFM/75/ucnIyfvPy72H19e/v/198enHwJCYr9/f2Xkk2bmEuT+94uNgYuRiZGVnZWJ4/d/RqY/P0HFDAcXBxcLx58f30DXtv/+8+n9m5+/vr9+/oSJ+c//P4w/fn1nY2b/+esbww/Obx/e//n19fOHt4wM/7i5eDm5ef78+Pvr+4cf374w/PoCvlbp/7//f9nYOKVk5Xn5eD9++sDNxcXCwsLIyMjCygJpNkGuwWQG7RRi5uPlY2Zh5mDnkJdX+PH959+/oN1ODAz/fv/+9fHtK+Z//3j4+FmYWH98/vb/5/efP74z/Wf49OIlM8OfTx++gmbxf31jYeNk+s326O7Dv7///vn3n4OTk5mL5f+f379A+06/fX73+ueXT/+ZGDh5uf8yM3Ewsv5i/M7MwcTA+JWZhe3r7z+f/3z/++8Dw/8/n99/kZQW4Bdg//33z5+/4DFaslIAGxvb4sVLDn448Ovfr9bW1o0bN27dupWNjU1FRaW0tHTKlCnnz5+fPHny8ePHExMT+/v76+vrd+7cefjw4d+/f7948cLKyqq0tPTPnz/d3d1Xr179//+/o6NjcnIyvOKEOGrHjh3z58/n5OT8//9/a2urlJRUbW3tr1+/3rx58+7du9LSUltb29u3b9fX10PuK/rw4QML0uGpc+fOvXbt2rdv3yC3TZ4/f/7p06fq6upNTU2PHj1qampiYGD4/PlzTk6Og4PD/Pnzr1+//vXr1xcvXvj4+KSmpi5YsICNjS0uLu7Tp0/l5eUNDQ1btmw5ePDgs2fP7ty5Ex0d3d/f//Pnz8+fPyclJXl5eb1+/bq2tvbr168/fvzIzs7+9OnT1q1beXh4Hj16VF5ePmXKlICAAFNT05s3b86ZM6enp2fSpEkvX758/PhxWVkZCwtLW1sbGxsbMzNzbW0tvCV38+bNnTt3VlZWTpo06enTp58+fXr58mVsbKyxsfHMmTOvXbtWXFwcFBTk7u4+YcKE8+fPMzAwWFpa5uTkQAbvIfchsbCwHD58+MGDB1lZWfv37584caKgoKAwaOU0U0tLy5s3b+rr6yHrAQoLC01MTBoaGr59+/b+/fs3b97k5+e7uLhcvXq1ra0NsgaxsrJSR0dn9uzZ+/btY2Vl1dbWLi4uhqT5T58+tbe337p1q6mpydXVVU1NbdmyZd+/f9fS0srOzm5ra7t169afP3+cnJwyMzOPHz++fPlyJiamx48f29rafv/+/dq1a4yMjF1dXfB25J8/fzo6Os6fPy8pKfnkyRNLS8t/4G03Fy5c+Pfvn46OTn5+fldX19WrVxsbG52cnJKSkrq7u69du/b7929jY+Pi4mJIXwKSnEYI+f//fy5OTmMjo507d+7evRvia0ZGxl+/fomJiS1fvtzHx8fU1PTHjx+dnZ07duxwd3f/+vXrwYMH5eTkLl26dOHCBVZW1n///gUFBWVkZBw8ePD169cVFRUuLi41NTXOzs7Hjh2ztraGGMvBwTFx4sSEhATwrh2SllNCDECQv//9f/T+188//101+JSF2WIXPPz5+7+RHFeeo2jh6ieQ5iZc9Z9//2UEWXMcxao2PDOS5sp3EhXlZdl+5dO0g68DDATsVHh0pTnffP5z7N4XNham33/+exjw8nEyC3Cy6EhxTo+Wi5p7X0aALd9JVISHdc2594tPvfv7j6HEVezZxz+e2nwfv//t3vUywlTQVoVnycl3q86+R16rCnfDKGM0BEZDYBACOjVGGRlAp2/8/vXtDwvz938sv/79+vfz9+/fPz9/+fTj9++Pzx/ys/9lZuFgYmP7//vj9z+/WFhA1xT9/P2bg5Pn9+e3n798ZmVhFBIX+vX9689fP7///Pb76+cfr5/9ZWD9C1q/yfHxzav/wrzvX7/+z8TByMLFy8P24uOH/9+//vj2lYePl4eP/T/bp3//PoHWgzIwhoVH6RoY3r17+/nzp1w83CygsUZWZhboIaOgoVEm0K1HoGUEvDyMDIzSMjLm5hbv376dP3fWl4+f/zD9//f3LzsP/4+v3398+8z45xPoEBM+YQ42jh/vHv78+J6X8/+PD1852Nh/fv3099+/f184Xz56JCQuxcLF+eMH6FYmPi5OfmHhPz+//fj66+fX77++fHr35h0zCwcjO/Nfhj8/vv8W5OZi+PPl9++v3358/ff/FwNotOzv12/fuf4x83AwgAY2yU1KLMzMJ0+e9A7xDA4Pfvz48bJly2bPni0kJJSSkrJjx46ioqKzZ89WVFTo6+t//PjxypUrf//+ffbs2b1795YtW/bz58+IiIjQ0NBr165dvHhxzpw5P378gFxijnwfDwMDg5CQUHNzs4KCQlVV1apVq4qKig4fPhweHt7T07N8+fIJEybY2Nh0d3fb2tpmZmaeOHFi586dyI2PW7duvXv3bu7cuXv37k1KStq1a5eysnJoaOiZM2ckJSVTU1PNzMxWrVo1adIkBweH27dvP3jwYOnSpa9evYqNjfX393/58iWkafvv378bN258+/YtPj7+yJEjvr6+Pj4+b9++jYqKsra23rdvX3d3t6ur68SJE8XExBobG69du/bmzRtfX98zZ84ICwunp6dzcHDcvn3748ePDAygq8Ju3QKNAN24ceP37989PT0iIiLRYODr6ztr1qze3t7JkydDYubDhw+3b99mYGC4fPkyJyfn1KlTL168WFRUtHv37tzc3N7e3gkTJnBxcR08ePDUqVMzZ878+/dvYmKira0tcrOeiYnpxYsXDx8+/Pr1a1dXV2VlpZ2d3bx582bOnMnCwtLb26upqZmfn793797Ozs6lS5eeOHHC3d29r69v06ZNU6ZMsbS0bG1t9fX1jYyMPHLkCCMj46VLlzZt2jR37lxubu7k5OTdu3d7eXlBrlCC3NDd1tYmJSV14MCBXbt2LV26VF9ff/Xq1Q8fPpw/f/6nT58gcf3t27cTJ05s2bKFmZnZ3Ny8BgxKS0tXrFhRXFwM8f7+/ftPnTq1aNEiRkbG2NjYv39BvUojI6OMjIzv37/HxsZ6enrW1dXdvHmzqalJUVHx9+/fFhYWBQUFHz9+jIqK8vLyomSNBMQNQ4789/efkJDQrI5+TJffv3//0KFD69evZ2BgmDt37oYNGwoLCydNmgSZvujt7a2pqTlw4IC2tvbt27eTkpJSUlLS09P19fVzcnJ2794tKyvr6em5YMECSGP0////dXV18KkGTOuIFPn++9+3X/+YmRj//WfgYGXMtBeZe+zdnVc/2VgYH7/7tenSx3df//z5x/Dn3/9vv/5Bpul//wVtXTKV52JiYGj0lbz35sepB1+9dfnPPf52/vH3h+9+XXzy/c7rnz///P/z79/33/9kBNjE+Vk3X/oYZiyw6eJHER6WiWEyh+58OXH/W7S50M8//6cfem0gw+Wpw7r23AdXTd558fLH7n49//h7gbPYwdtf7r35+f8/w4/flBSZRAbGqLLREBgNAYoAnRqjDKCd6P8Z/vz9Bz6P/tf3H+x/mf/9/vfn/1/G//8Yfv7//P4ZExvTz+9MDL//M7ODTp3/+fPH778/3r15zvz9868ff/8x/n/04R0zwz9+ERFQ8faPgY2d5T8z+4/Pv/4x/GdnYeJiY/nMyvrt5/cvn9/+/vr/x7u3/zk5fn//8uLTSxYx3p9fP4Im6P//ZWFj/feP8eXLty9evmJgZGEEtzv/g4+hB83Ow1ZUgnYfgRrQf5iZWN5//PT02Yv///6xcXCAxhBAU0V/Pr1/++ntq7+/vrMx/v/y/vUHps+/fv77++PPf0ZmdtBlpaDFnSwMzGwsLOysLEyszL///P7y/t2/H9///v73+fOHD69fc7Iz/BIS//f398/ff35+ff33378PP1gYfv9l4WX9///rl2/Mf/8w//r95/ev76z/mf7/YQAdQfUfNAXFAD5An7xoB20lERUJDgri5eW9f//+q1evpkyZ8v///w8fPrx//56Tk5OZmZmTkxMy48zKygq5QtrJyYkffLGkpKTkixcv7t69+/z589bW1n///kGGGNEcIyAgsGzZss+fP1+6dAmiUUhICHJvtZ6e3sqVKz9//vz8+fOKigoGBgZjY2NZWVnI3DTEHGZmZnt7ezY2Nnl5eSMjIzU1NXZ2dllZ2efPn2tqam7YsGHNmjXPnj37/h10mDYzM7OXlxcnJ6eUlBQfH9/Hjx9ZwABiFBsbG2jYGyzCBgb8/Py3b9/euHHju3fvvnz58vv37/v372dlZTEyMsLXSrKygva1QW6ihx+FCLqogA26xzYoKEhKSurTp0+PHj3as2fPsWPHXr58CbERQjKB0xUDAwM7O7uLiwsbG5uioiIPD8+PHz84OTlZWFh4eHgYGRnv3Lnz6tWrrq6u////f//+/fXr18iNUcjyPjY2tpcvX/7//98OfAWog4PDmjVr/vz5c+PGjVevXlVVVX39+vXXr1/fv3/n4OBwcXFhYGDQ19dnZmZ+8eLF+/fv3dzcGBgYbGxsIAtSnz171t/f/+/fv3fv3n0Cr0SEjMJycHAwMzNzgScKfv/+bWBgYGJiwsDAcO7cOVdXVw4w0NfXv379uoSEhJGRkaSk5P///7W1tSE3eero6Fy9ehXidwYGhkuXLpmbm0NufHVwcPjy5Qvk/ojm5ubfv3+/fv3606dPELsgiY2VlfXXr191dXV///59//79ly9f4EaNKAZovhmbh48dOyYtLa2mpgbp3lhZWcXFxV26dOndu3fr16/39/e3sLDYunVrTk5OdHT08+fPJSQkkpKSVFVVp02b9uLFC0hjtLW19cOHDwICAoyMjJS3RFmYGGPNhT7/+MvEyPj33385ITYdac4jd7/mOIoyMYL21//68z/WQshUnkuQmznNVgS09RO8vUmKn5WXgznRSlhFlO3vv/8Pwc1WHSlORWG2P//+//j931WTj50VtAL199//hnJcfBzMN8R+/PkHmo5PsxVhYWa89vzHj9//Lz/9nm4rwszEoCTKfuf1z2cff99/+8tXj3/R8XfMTAxsLHxptiL3Xv9kYGSQ4GPFFqijYqMhMBoCgwjQqzEK2r/O9J+R6fu3n5w/uVlAvWkGFlZQQ+Hn/78MHEwcQvIsLP/FpYU+fnjNxMTMxc3GxHr/389/v379E+AV4hbh/PH9Kw83z+/fv3mE+f7cuffn//+//xn+//v/++9fJkYQ4+9f0HL4/7//vrp1VUxG7Pe/v/9//vzx6w8XaAMQG+jUpv//mRmZ//758+bta1YOdiYmZn4BwY8fQINe/8DX0v/9A2ryMjKALgL49fOniIjwg/svuHh42FhZX7588ffP729fv4GijpHpx/evn57d+/fj599//7/9/svIysrMwfX76/ufv74wMYJajX/+/mX8z8jwj4GVgfE/4/+/f/78+fP3168/3ALCjKDmxj9WDh6m/wxc/Dygc1XZWNm4hNnYufh4BD+8e83MyszJ+v/Lu+e//3z/+5eLmYWZ5S/TP1YGJoZ/4HNSQCv9Qc4gF7MwM4MOqGJg4ODgkJWVTUlJgUzjSkhIQFqEyDUiZHki3CpIq52Dg0NPTy8jIwOyThGyBBCu5sePHxUVFfHx8cbGxkuXLoU3GSFnX4OOKWACDTyzsLBARhx//Pjx5csXUKjAjIBPVUPWa0I0/v8P2tvR2dnJycmZk5Nz/fr1adOmQXRARlUhTgX1KP7/h1yW+OfPnw8fPiCbzMDAMHHixFevXuXk5Dx//ryyshLSVP3w4QPEqK9fv3Jzc0PshYhAjvpiYGD48uXL169fIU03iJksLCx8fHzh4eHS0tL//v3DunceohI0GfoftH8Owvj37x9k9R4rK6u6unp6evr///9TUlLk5OQWLVoEsReZ5OXl/f3797t374SEhF68ePH9+3fwHQ2cHh4eVlZWf//+ZWFh4eTkhNxnC7Hi////bGxsTExMX758ERYWhtz3zcbGpqSkBIm4379/w2fVIVqQScjQMmTQ9PXr1xDHvH//npubGxIRDAzgK82YmCCxA/cRRCUfH9+zZ88g7Ddv3vDx8V29erW/v7+jo0NQUPDu3bsQ9f/+/YOsEzh69Oj06dPb29t5eXmvXbuGvIAYYsgIJ0+dOmVqagqJlNjY2OTkZAcHh2vXrv379+/t27dz5sxZtmzZ2bNnDx48KCwsXFpaKigoGBISwsvLa2FhAdkXaGho+P79+ydPnkB6CJSHJxsLY4OPJJo59d7YV5CbyqNvkGr0Bem14mGxUkZZrmooi66SgYEBogaihYGBoT8UtBobYnVvCIitKsbuqQ29lK4nRBoileMgCmGMkqMhMBoCgx/QqzEK2oT+/+/vv3///Pn75et/pn8Mfxl+//7768cfhr9/3z9/KMrD9vvvn/cMr37++Prv7z9RIZ6fH9///vfj98/P3/98/fbz99c3z759+8bIyCwqxPHtzScGRkbQbZ+gJuk/hn////xj/P//L2ijz7//7x/f42X5/ubZexZmJmYOzt/fGVj/8n77Dm5H/mf8Bbol/sff36AzoPgFBH7+/Pnm9RtWdvbPnz+zsbL+YwDdAPX169c/f/9+//YNtIGDn5+Dnf3/3z9///z58eP7f4b/jExMv3/+/PLk7u9//z9//s7Fzs7IxPiB8c/bN69//vrLwcb6/9+//6CJSQbG/wxM/5hBBzGDB1SZ/vz+8vrV399/vrx5/fP7T24Btv+MjJ/fvOLkEfr+4f2bL8/fsIEOW2X6zygsyvPm6YO/jIx/vv/4z/iXEWzej+//fjKADkyhqDXKyPDr92/Q5iwG0JAkKyvrzp07ZWRkDh06VF5ezsHBgdwShbRNIfuBIEkZsonH1dV106ZNx48f/////40bNyoqKrq7u+3s7CCDZEygCTnGJ0+e/P//H7JdGrKFBWIypN3PxcXl6+vb1NQUFRV1+fLl9+/fwxttoJ0QoLY7aF0sZOQVYjVkkxMHB8fLly/v3LmzefNmiPMgDWJIQwqygNLGxqa2tlZeXv769evv37+HNFXhbUo2Nra3b9/evXt39+7df/+CTsIOCgqaPn36ly9frl27JiMjk5qaqq6uvnjxYm5ubm9vb1tb2xkzZrx9+3br1q2QRtKfP9BLZbi4uGxtbVeuXBkQELBv3z43NzfIJiSIgyH+/f37N6S5Bgm6v3//SkpKfv78eeLEiY6Ojs7OzmvXrj1y5AgLC8vFixdbW1shjTx4iP0BpbofoqKibm5uGRkZZmZmp0+fZgZvqAoKCtqyZYuwsPD58+dlZWWjo6PhF3BDwkRERMTV1bWqqiooKOjgwYNeXl5WVlazZ8/evXu3iIjI8ePHy8vLkRvQ/8EA4gBIa56BgSEwMLC8vJyfn//Dhw+vXr1ycnI6ePAgPOR//foF8ebfv3+RL6x3d3dfvXr1lClTeHl59+3bFxwczMLC8u3bt/v37584ceLx48eQFi0PD8+ECRN8fHyYmJi+fv36AAxevHjBwcGxadOm58+fp6enQwJzxJKQLtaTJ08g4+IMDAzW1tZr1649c+bM4sWLz58/X1BQUFtbq6OjU1NTk5GR4ejo6Onpefz48cDAQDMzM2dnZ8hhBZClxm/fvh2xITnq8dEQGA2BwQzo1RhlYPz18/cX0IWfP79//s/85+/vH39YmVjfvPz15xfD7+/fv3148/3796/f/nJysf/9++8rM2gr779fPz89f8IjJvL1/cc/33/8+f6dlYX17y/mf3//gk5a/vuXCbQS9c9/Bsa/f0F3wTOARkj/gZqgrGycXJyg2SLG/+C1oKC98qCxKDANOviOkfH3378cHBysrKxv3r6RkpIG7WL+8+f/v3+QzdecHBxPnz5lZmZmZ2dnAu9pAl8hB57F/8/AyMDIzsLCxsT8989/JhaW/3/+soKsYWQCtZFBI6PgoUfGf///MYKuowcdx/T7z69f3z//Zfj37cu3P7+//vj5lec356/vf3/8Y2D9/Z3x+6dPr98xMTGzsTL9/cvAxvr708cPvAL8/37/+PXlx8/f/5kZGL98fc/CzcEohnS+NOkp6/fv34GBgdy8oNEIAQGBCRMmrFy58vLlywEBASIiIv///8/MzITss+bl5S0pKeHm5oYsK4RYlZaWpqysLCEh0dHRsXnzZkZGxrCwME5Ozo8fPz5+/BjSGGVjY2tra1uzZg0TE1NnZydk6Cs/Px8ygCorK5ubm8vAwJCUlCQgIHDp0iULCwtnZ2fITm2ILeHh4ZDJfVlZ2aKiIogJSUlJcnJyLi4uCxYsOH/+fHx8PKRpGBISAtnxzcrKWlRUJCEhoaWlVVFRcfz4cSsrKxcXF4hsQkICZGoyMzNz2bJlp0+f9vf39/X1ZWRk9Pf35+HhOXbsmISERHBwMAMDQ3BwMDMz87Nnz379+pWens7Dw3Pr1q2kpCTI0FRiYqKcHPRcxvLy8tWrVx89elRbW9vMzAzifgYGBnV19fj4eAYGhoSEBMgAJB8fX3FxMRcXl7CwcFtb2759+z5+/Kinp9fT07Nx48b///+Hh4czMTFBtgQxMDAUFhaKi4vb2dlBboYsLS3dt2/fy5cvk5KSJk+e/OvXr6ioKH5+/lOnTomKinp6ejIyMhYVFSkqgq49FBUVzc3NZWVlzc/PX7NmzZUrVywsLBwcHDg4OPr7+9euXfvy5cvAwEAhISG4g3l5efPy8iBhpa+vn5WVBZHS1dWFxDUHB8fkyZMFBQUNDAwgG7qZmJiKioog0Qp3J0SXnJzcpEmT1q1bx8zMPHnyZDY2NnV19YqKihMnTujr60+ZMkVOTo6RkbGjo2P9+vVv3rzx8/MrLCw8ffq0kZERRPbx48c3b96EmDaSSVCpxcDw48cP5G6DtrY2ZNPSuXPnIiIitm/fnpSUFBERsXXrVkNDQ2Zm5iNHjnh5eVlaWkLW20DWe7CzsyN3GCgM1d9//y88/u79N9AOJS42Jl89/r03Pr/+/IcJNIPDICPI6qjOs+bcB00JDltVHrhd5x59u/z0u5cO/+qz7wW4WFw1eYW5WS4//X707pdfoFOfODy0+XZe+3Tz5U9mJtBcv7kiNy87045rn+1UeIzkuJaffsfExOChxcfPyXLhybcT977+/ccQYMB/5dmPa89/aEtyaEtxbLjwkYWZIdBA8OjdL08//GaG3AUFd8HgYzAxMoE3Mww+lw0aF0FyAaTfO2gchc8hjAwMLMyMkaaCo0tEiAegKWniVZOt8sjWDVM7m8+9fP/l/x9RQc5/P/7//83AwgIa8vvxi/Hl6/eKcpJ//zN8+fabhQV0eIeoMP/HD58+ffj85+8fOXnJp8/fc3FwMDEzsjAx8QtwPXny6uevP5IS/MzMoJNGmZmY/vz7LyHKDzr75tP3f3/+qCrLvnj18T+oEcjEzMIoJsx35+4jBlCD+BcbB4eXt5+svMKLly9//f7NyMj46dNnEWFRDi5OVnZWJtAkOtOnT5/YWJhv3bolLCzCwsLMwcYmLCLCxMCwcunil6+es7OxM/z/Ly0t9uffv8+ffzAxM///919MRPDVqxfffvzmYGOTkRN7/vwjI3j3PgsLi4gA5407D1k52QW5ef8z/P389TcnJyvo/CYBge8fP75+/5mPi0NaWuTF68/ga+3+MjMyCwlz37pxj0eAR0KW/9fHn39AB/Ixffn2VYaH3VhbO72lR12Z/LvpPZXcPeWxH3JJXvz++vVrzZo1dnZ2kFYseYaM6sIfAgsWLPjy5YuBgcGKFSskJCSqq6shBTR+XUNXdu/evZycnFZWVtT1wmC+m375pJ4f378llmM5mzY0NNTS0rKoqIiBgeH169dZWVnXrl178uTJnz9/eHh4VFVVv3//rq2tfe7cOS4urkuXLnFwcHBycgoLC+fk5KSmpjIzM//69cvIyGjmzJlGRka/fv2CdPYgYdtflqtlbO4eHgPhEkl++/VPq/Gakgi7IBczCxOjrBDbsw+/f/z5BzrrnoGBm41JSoDt2WcGLpa/YjzMoJOYmJiZmZlfffr5+ReDGDfD/Tc/2ZiZZIXYuTlYX3/6+e47w/cfPwW5WRXFuJ+8//XxO2gp6q/fv4W4QKc+vf7GKMzJIMTFdPfNTxZmZgVRrv9/f7/9txg/bwABAABJREFU8uf1Z9DFoXJCbB++/f32l0WQi1mAi/nRu1////2T5md6+en311//2FhZmZmZIauDwDMuf1hYWJiYmCCj+0xMTJD1ygPY0Pn16xdkdT5yyLOzs/8FzSOCJogYGRnZ2NjgsxDIyoYKGzI9BRk+IMPNoInG//8hYwGY2kEJC5zCMaVwiUCCFLR4DnwbLRN45djv37+Rw5mJEXQYOeQ4v18/fzExM4HmTsEniDEyMPz+8wc0scbAyMrGCp8rY2ZiYgEvf//77//eG5/3FKiYKYDGfXA5Y1QcGdBtZJSBERS17H+//fr79y8TM+ufX78Y//5nYGJhYvzP8O/v//9/GP6zgPbngHY+MoJuQWIA3SvHDLrriImdgx2yBQp0mBxYEtSVBCkC35YE4oCmf0FTWv/+M/4DzY+DZqLBk+P//kIO1gSdMAralvTv3+s3rzl5uEETkYyMkILp7z9Q2cfwn4GZBVJugtro////Y2Zm/Pfv3/fv39+9fQu6Uef3TyZGUEf7HwOonfsPvJuI4R94LSdowT3YQeC1An///mEE9Xf/gyX//vv3///ff2ysjH8ZmVh+soBumfrLwPTvLwPDL5b///8xMP/9/58RNLvPwAjyHchc0KUATEzgUVDQCgTQYk1GRiZGZpCfkCOQdPYfcPYjXR9OHWxsbFFRUTilRyWoEQL29vZr1qxZv369vr5+dHT08G6JMjAwwO+6pEbgDVUzPn78+OnTJ1FRUSUlpUePHkG8MXXq1I8fPy5durSlpeX27dvfv3/v6ek5duzY1KlTIUcl/P79+8mTJy0tLTIyMpmZmZCLHl68eMHMzCwpKXn8+PF9+/a1tLRATKOEZGNmnBolpynOjtWQ718/rVuzOjQwnI0TNDJ6/+7tGzduaJqonzp1MiwoFqLl84d36zdsSHSwP37saFhC3LPH9+fMnSD+57cUE6OMjExYWBiPgAjDv9+rViz39PTmFRBmYGB4+ezR9u3rE5KTwV13iDEMDH9/zZ839/7lBwwMjEa8PJ6entr6xhC5R/duzZ079+3btxISEmlpaWJSSvt3b2dmZrZzAm3sY/j3a8G8eUEhQXxCYhD19Cb//CgFAzHUS7DWrFhiZGSkpKYFWrHz7dO6desiYiMZmLEHNb3dTLp9Xz++/fv3L9mBvGfHlkePHiWlQadr0Oy/ceXC+fPnI8H35aJJ4eL+/fl12bJlIeEhnLyCoA7e88enT5+ytrbesGFDfFoKAwOoGfD/9/fVq1c7ejju2rUjOj3h2aOHM2ZMhbSqlZSUwqPC2bj4GBj+rl25wtnZWUAEtFr649uXW7dujUqJZmBi12y4hsv2UXGsANS0wipBdUFQi/HvbyZm0K2b///9BXUrfv8HnfwBWgbJ9P8/aCUkqMnG+P8/6JL4v6CNQKDBRfCOEdBqNlCbD3xGMgP4FGXGv38Y/oHbev///wO1GUFNOAYGUPPtL3iZJkgPaD3p/3//Qc0+0BYqUMOPkeHjp49v37//9v0b6KxP0NalP0wsjIzMDKCGKSMjMwszuN3MwgQaJgUZ/OPnz7dv3z1//vzP7z8M0G0o4IkVUMMQtBTwP2iHEujwGpDtUI8w/Pv/F9RkBa1nBeliZmBiYGJg+MfMwPDvL6gJDfL2339M/0HN23/gFi3DP1B7FOxF0Ln0f0FbqRgZf4OPVwWtWf0HOt2ZkRG0GIDqsTNq4CAPAUVFxdLS0t7e3tTUVMgR8YPcwaPOozAErl275ubmZmdnd+bMGRsbm5MnT0IG8z5//iwmJqampsbJyeng4JCRkdHX12dmZqahofH58+f9+/f39/ezsrJKS0urqqr++fPn58+fDAwMZ86cERQUlJGR+f////Hjx5HPK6CkY/Pw8Qtc3nz/8UtJRU1bZw9EwY1bd9au38jOxTt91rx79+5BBNdu2HT67PkXr9/Nmgfat3fu4tXde/cbmVpo6Rndf/w8JiHl+fPnP379nT1/8dMX0F10cxcsKa+quXr1OsQECPn+09fZ8xdLyykZm1nyCYkVllZu2rSJgYHh9u3byenZ0vLKMQkpvIKiWXlF79+/F5WUrWlogVxG1dM36dS5Sxw8AhBz4CTmeoY/f0CDlHAFkEXqEC6aYsgwHkQKQkIumoawISQkKkHnJX//ffr85bcfQTsjIVKQuyEqahomTp0JEXn++v3MuQvfoar5CwYQBbhITDejORXZF5iKMQcy4SKYijFFkF21dOXa6bPnI1uHxyWYUvcfP791D9QZgzgAbRibg0dASg5xywOmdky3vf34tbm9u6isCmLUzbsPFy1b9Y+Jbd6iZRcvXoG4fMuOPVt27Pny48+UGXO+fft57tK146fPG5hYaOsbX7lxJywq7u3btz9//pm/ePndh08hWlas2VBSUXPsxJl/0KYLRHiUJArQc2SUkYn5//8//xjYOBl//2VmBbU22VgZfv+BNBNB9yOBhz9BLTpQoxK8qx3UFAU3PkGJBjxeCTo/H9SQAyVsuBdBt8mBb1iCiIAukAe1af+DxjFB93+CxknBZS4jAwMTGxs7MxPL/3/gG0JA5zf9ZWdnh5zgAzlt9O+fP2zcoAPbQYtTwa1PVjaWf5ycrGxs/0Cjn1CrwUXMf0Zm8EJURlBjGuRa0LJSyFQVA7g5DGpgglig652YwOct/mcAHWcFHu4HqQA1aUF7wMFuBHkTtOgKNMjLwMD4j4GJgZWJ+R/Lvz8MrGzMoKFScNBAvDlKjobAaAgMyxC4ceNGZGTkjRs3Zs2aZWpq+ubNm8+fP58/f97c3DwjIyMpKUlfX19AQODz588BAQFPnz5duXLlv3//+vr6tLS02tvbFRQUiouLv337FhERAbkddPPmzcbGxpDFuw8ePLhz5460tPSpU6devnr16eMn0DJ60sORjY0tOjrKx1wxKjrWzs4Ocp0Y3Jg/f/7o6emdPXt2586d7u7uoJX3TEySkpJGRka7du3KyMj4+/fv7t27MzMzIYsKIBpVVVX9/f0ZGBhCQkLKy8sXL14MuZoBXHqDlihcunQJsuwbstsPoouBgYGXl9fPzw+yg1BbW7u9vd3d3X3BggWOjo5paWkMDAwWFha+vr4cHBw6OjppaWmNjY2JiYl79+5duHAhfGXt+/fv161b9+vXr9OnTysqKubl5fHz89+4cWPGjBmfP392dXWNiIi4dOnS1atXb9++ra6ubm1tPXHixHfv3mlqaiYnJwsKCq5Zs2b79u3s7OzJycnGxsYHDx58/PjxlStXXr16FR0d7ezs/Pr169mzZ9+5c0dUVLSoqIiPjw9zjn716tWRkZE3b968f/++oqIiExMTOzs7JAQg/t2zZ8+aNWv+///v5ubm6+u7fPlyT09PMTGxu3fvnjhxIjo6etmyZX/+/Dl+/DgLC0tOTo66uvqHDx+mTJly7949NTW13NxcyKV0P3/+fP0adDPC6tWr9+zZw83NnZaWpqure+7cublz53779s3T0zMsLOzevXsHDx58+fLl9evXIyMj//79u2bNGjk5uZKSEmZm5tWrV//79+/YsWPCwsIFBQViYmKLFi3y9vYWFRW9e/fupUuXJCUlly9f/vfv348fP6anp/Py8k6aNOnRo0fa2tpZWVkfwdf+ffr06fv37xkZGUuWLDlz5gwfH198fLyBgQHEv2zg4+1aW1vv3LkDcdLZs2fv3r175coVAwMDQ0NDyIVwq1ev/vPnz9GjR+Xl5QsKCgQEBB49etTX1/fjxw8rKytWVlbIunwGcKUvLy//8OHDlStXRkREQKbpBQUFXV1d161bp6+v/////23btoWEhEASJyTwNTQ04IkzPz9/yZIleXl5oDEsJtCg3tevXw8dOpSWlrZy5UoLK2twRQ1qDUC8MEoSBKBAJKiIcgXgg2ZY+fm5/v3594eJ7T8HC4cg139WJh5hbg4uNibQVD1oHp8BslscNPIJWrwJisn/oO1C4JFEUMMNtCUIdPAPqDkI2aQMbQOCmqiMkGuLwTpAW4lAxoCcDroaDtQIZQaZycXFKSUtIyIiys8vyMnJxcTE/O//f15eXlZWVg4ODjZWVnZ29v+MDNzcPOD93Yzs7Oxc3Nz8gkLiElL8AkIMjMzM4BPzGBlBDgbvHAeFIdju/2AbQS1RUEsTNEYLchaohQ3C/xnB3gS5CIxBDVBQOxzU+Ab5CZLeIVJM/0EtcEamv/8YWDiY2blZWTkYJaQFePk4/4K8DWq/ghWOEqMhMBoCwy0Erly5Eh4eHhgYaGNjIyoqys7OLiUl5eTkNHXq1P///6uqqm7cuHHZsmU7duxYvHjxkSNHIK2lo0ePrl+/fv/+/QYGBjt37tywYcPGjRvb29tZWVmvXr166NAhyH1L0tLSTk5O4eHh3t7eLS0t9Q0Np8+cZmWFHqBLUlD++/c/Kjr66eMnUVFRbm5uDx48QNb+//9/YWHhurq63t7eV69eQeY3IbsDd+7cycDAcO3ate/fv1taWv78+RPUCQdrRh76cnJyunnzJmRkFyzJsGfPHjk5uezs7LNnz7569QoiCCH///8PHxIzMjL6////48ePnz59Cll2/Pbt2zNnzsDHI6OiokRFRRMSEhoaGpC3Tn7//r2+vp6BgSE/P//t27fd3d0fPnyorq52cnIqKyvbsmXLvn37njx5UllZqaioaGFh0djYqKysXFlZycbG9uLFi507dy5ZsiQrK8vb27uiouL58+dnz56FXOEWERHR1NT08uXLe/fu8fLyVlVVCQgItLe3Y66D/PLly/79+1NSUnR1dTdu3Aiq6kBVH6LMf/PmTWtra2xsbFZW1oMHDz58+LB27VrIOQkPHjzYBB4SnjNnzvHjxzMzMw0MDCoqKr59+9be3s7MzFxZWfnr168JEyb8+vWrsbHxzZs3/v7+a9asWbt2bW5uroODw9GjR+/fv19XV+fl5ZWbm7t8+fJdu3a9f/++s7MT0uAuLS09cOBAXl7e06dP58yZw8jIOGnSpCdPnuTm5goJCdXW1n758mXt2rXv3r1jYGB49OjRhg0blJSUTE1NtbS0goKCBAUFGxsbBQQEKisr3717BznGpK2t7cuXL56enrt3796/f39lZaWDg8OVK9ARStAtsqysZ86csbKyysrKWrBgwcGDB2/fvl1VVaWhoWFqanr16tVNmzb9/fu3u7v75cuXBQUFb968mT59+p8/fwoLC9XU1LKysk6dOjV9+nR4Hfv//39WVtaWlpZFixbduXMHskcWkjhPnz795csXyG0jdnZ28DQDOeYFktgYGBggd/LBDWRgYDh69Cg3N3dOTs79e/fu3H3AwswCbo/CdYwyCABQQ4qAEupIM4LuhP/3h5mB+c8fhu/ff/z69YPhH+Nf0KXkfxiZGP6BFjKCbjYCzXKD8x6kfQYa8gS17MCOAB8sColg0GQ1A2jAEdJOZQCNg4InyqEK/4HataAxUdDAJMiA/6D58P+MDOzsHAJ8AkxMoPuWWFiY//z+9fv3T2ZmZsjZjczMzKwsLAz//zMzgSbrf/36DVqsycTy7z8DDy8PLz/oKDsmZmZwoxPUiASXoaDBUpA1IKeAxndBzgcNc4L29oMVgRqWoEFTcBCAx1NBSkGM///AawGYQMqgywz+g2bn//37D1rjyvj956/P37/9AN0kygg6rPTXL1DzFlEugX07SoyGwGgIDIsQYGFhuXHzZlRUlLe3d3FxMeQAYFDRwchYUFBw/Pjx7du3Q244MzU1FRYWNjU1Xbt2rZiYWFRU1PLly6WlpZOTkzs7O3l5eVVVVfX09JiZmf/8+VNTU+Pr66urq/vt27f///9Pnz69u7t74sSJBw4cmD1rNjNoxoqcMuX//3/W1taOTo6QKxiQp/4hsfHz509jY+OgoKCWlhZwvx00vGBsDFrNefny5d27d9vb20NuQ4CoRyNBS/xBhSnIbZBaf/PmzXZ2duzs7NLS0vAbU9F0QYILckIZ5BgBBgaGmzdvzp07NyYm5uDBgxD1P3/+ZGdnR3Pz379/lZSUoqKi9PX1c3Nzb9y4cfny5UePHj1//vzIkSN//vw5c+bM79+/bWxsYmNj5eXlZWRkjh49umvXLsh5rtu3bw8ICDA2Nvb29tbU1Dx27BgDA4Ovr6+FhYWbm5ucnNy9e/dMTU319PT27t376NGjhw8fgsYWIA6CkSdOnODi4uLj47O3t9+xY8fv37/BwyIwafA9GsLCwps2bTp//nxoaKigoCB8cA5+MQcXF1dsbKyenh7kruazZ8+ePHny379/hw4d+vXr19mzZ798+SIjI1NYWKinp3fgwIHw8HBDQ8Pg4OCMjIzTp09LSkp6e3sbGRlFRUVt377958+f+vr6/v7+dnZ2SkpKQUFBhoaGnp6et27d+vfvn6CgYEpKip6eXmZm5r17954/f87BwQFxMzMzMxMTk5iYmLKyspycnImJye/fvy9evMjIyHjo0KH///8fPXr069evKioqhYWFOjo6kGPv1q9fD7nVFu7nnz9/urm5OTo6mpqauru7HzhwgIWFxcXFJSoqCnKaBysraPmfoqJicnKyvr5+SEjI9evX79+///3796SkJD09vcTERGFh0JpjuJl//vzR0tJKSkpqaGj4Dd7KzMDAoKGhISAgcP78+V27dhkZGQkICPwDbfgApUC4RggDPJaEIr527VobGxs2NjZlZZV169eDG7igBA9RP0oSBHRrjIKaW7//g449+vf7F3gZJ+h4JtC4JripCDrPHdSAA01gg9uYDAygNZcgeZAwqAUKal+CGnCgBaCgCft//0Gz9WARcHr5z/gf3Pr8/w+0jR583xJkUh20HhW0Mwi0WvMPBxc3Iwvz798//v778wu0C+4PEwNotej//wyg/MPIyMDEBMpAzKCD2SGNzn///oCuIP31i4OLE+QQ0Bal/4wMTKAWMLgRCnYlqJUIKjFBm7AgN3aCPAZaDQo67Bw0XAu6QunvH7B3IKOmIIPAMQRqzoLXmIJm5xkZQSdXMYJao/9Ai2pBh0aCHPL3LyPoGBWwhlFiNARGQ2CYhQATE9OHDx+ys3MuX768Z88eJyen8+fPw2eQlZSUysrKiouL7969Cxktg3hfXFxcQkLC0tLS29tbRkYGcsQ9RApC9vT0PH78uKioqKOjw8XFJSQk5OLFi35+fpaWluzs7Pfv3/9H7o1uLCws8XFxK1euys3NhVxGCrERmfz582dqauqPHz/mzZsHudKMnZ3d3t5+8eLF586dc3d3R1bMAF7LBG9NHj9+XF5enp0dtGuHlZX11q1b58+fX7x4cXZ29v379yEHosG1MzIygqt/kMDNmzf//v0rLS0tLCx86dIl0LH5VlbTp0+3srL6/PkzAwPD7Nmzf/36tXjx4tbWVuQR1v/gLdugioCBATKU++/fP05OThEREX5+/jAw+PnzJ/zigOrq6ri4uB8/frS0tGzevBmy1gvkArB28FAFA2T1AoTNxMQ0adKkZcuWiYmJ6enpsbCwQEp8iBYIuX79+ocPH+bl5c2YMePOnTsnT56EpwGIAl5e3tmzZxsZGT1+/Dg9Pf3BgweQ7eSQ2WdQHQResQZhQALz3z/QkYVCQkL8/PyQsdL///9zcXHBF1MiD9BCzgCG2MXMzAzZWQFxw+/fv9nZ2eHhDAkoyBw3RD2EhJsAP6Ma3qSGHGsgLCzMz89vbGxcVVXFyMgIb7xaWlr29/fz8/Pv2bOnsLAQPtQN71RAUggkMJFPpoPkCMhyO0g/BLIsBNyXATUZMRfyMjIyfv/+PSQkRFxcfOrUqRAPMjIy+vj4rFu37siRI5AZeUgAQvwFSRIQ9pUrVyBnBTKA2w/Pnz8/derU5s2bs7Kyrt+4vn379vcf3kOmaiHqR0mCgH6NUUYGJmYm1n9/fjD//8fJzsbKzMrGwsrIzAI6GB7caARP1oO274AabaCGHCgh/QM1PUHpAcQBD4WCGnuQIUbQZiJQGxfUDAS37phAU+cMjODZcLCRoFQIGuYE7fkBjbQy/Gfg4xdgAB8ywsbOAbp1lBl08yP4MFFGFlYW0H51ZiZmJhYmZpAiUB5gZWHn5GBj5/jL8F9AQBA0rc8Imu6HLEsFt5tBToO4GTSsC2pvgy6nAWcY8Ow9AyOoqQ1abgAWB8cJqHEKWjQLGiJlgDJgZoAMZfj/9x8zIyMHMxsHCxs7MwsbG+v//4z//v9nYvgP2rgPNoT+xN+/f799A18fwMBw+fLlM2fO0N8Nw8zGHz9+IM8EEfTd4cOHIfOh376BduAxMDB8//599+7dyLOZBA359evXvHnzMjMziY/Bb9++Qaqu////z5s3D34tE0G7SFXw69evBQsWIDcRGBgYli9ffvPmzStXrsydO5dUA4eKekYmxo8fP3779m3evHnd3d2QA2iNjY1//fr18+fPf//+JScn+/v7h4eHX716Fd7UYGBgSE9P7+npMTAwYGNjg5+ND6mSe3t7Fy1aNG/evKVLl65du7aiokJPTy8hIeHBgwf///+fOXPmjBkzJCQk/pPVHv3161d3d8+BgwdbWlq0tbWRnQSx/c8f0Lp8Zmbm2traU6dOwYchQ0JCNm3axMPDo66uDlEJSb3///9/+vTp8ePHjxw5MmnSpGPHjsXFxUHuU/j79+/8+fMTExMnT57c3d29ZMmSDx8+HDlyBB65X79+PQYGmzdvrqqqioqK4ubmDg+PWLdu3caNG69fv3769OkbN27w8PCcO3du7dq1tbW1FhYWfn5+lZWV8DKNjY3t3r17a9euvXnz5vTp05WUlLS0tHh5eYWEhMzNzT99+sTDw/P3798fP36AWz8M06ZN+/fvX0xMjLGx8cWLF+3t7devX3/58uUDBw5AbsT9+fMnPHdDDhK6cuWKoaGhqanp48ePP336BGkSwcdHr1+/fuvWrWXLlnV3d0+YMKG0tHTevHmQIgJcoYC8+/r168mTJ+vp6cXHx0Nui+Dn59+0adP169eXLFkCMerXr18rV668efPmsmXLfv78aWBgoK6u/vv3bysrK0i7E3IJBcg4BgZLS8uVK1ddvXp1z549ixYtMjQ0fPDgwb59+65cubJ8+XJXV1dGRiZIBEFKG8hSit+/f//8+ZOFmeXz58/z5s27efPmvHnzZGRkFBQUeHh4NmzYcP369YULF0IalMLCwhcuXLh48SI7O7uSktK3b9+srKxAVTIjI+TcMYjvDh8+vGvXLm9v77CwsAcPHsAtZWJi2rFjx8mTJy9evLh9+3YrK6vv37//As0Tgnzw798/SNEEv2EEEkcKCgpCQkITJ048cuTIzJkzP3/+DE+ikOuXIZZWVlbeuHHj+fPnILMYGDw8PPbv3//p0ydDQ0NwLP///v07qI7////Ro0fHjh07fPhwf3//uXPnYmNjf/0CjWgxMjIuXbrUy8tr6tSpPT098+fPF+Dnf4F6QTTE8FESD6DTBiZGRoYfP369fvnx7++/r5+8FuDlAS3iZGT6+vX7nz+MTEzMf/6ALqoBRTmoSQba4g5uSILaZeBxcnCjDdQYBSUP8PAlqAXHANoLBBIBtQJBM9ugue3//0Cb2yG6GEA7mP6D7h5kYGBmYfnPyMjPzw86WA50pTITKGmCGpWg8IH04UC6QDzQPUxMTKBOIXianhnUNmZg5OblZWZmAbV4QTeQgowFOZgBMtYJOi0A3DwGLUgAuRR0WBMotf//B1lLChqgBQ3hgta9grSAh4fBTdT///+BloKCBnZBpvxj/vvnLyjEfv788/oH6Eg8hj////399v2bPD/P3z/QjAd2JjkEJTMHjx49mjZtWnd3NwMDw+PHj798+QK5wZwcd4zqAYfAwoUL+fj4IiMjwTzCxK1btzg5ORUUFKqqqnJyclRUVH79+nX58mXIQBdh/WAVhw8f3rBhQ21trbKyMliAMNHQ0BATE6Onp/fv37+VK1fa2tqKitLkusU/f/6sXr3azMwMeTHf5s2beXl5ZWVljx8/ngw61oewg4eiCkZG0CW9lpaWGhoaubm59+7dg9yq9e/fv8rKSjc3t5aWFlZW1oCAgMrKyqioKMhYY0REhLq6+oMHD6ytreGBdv/+/aqqqmvXri1ZskRLSyszM7O5udnDw8PPz+/gwYOQKx46OzunTpv2+MSBX79+kxFc////d3CwFxLGvt6Ul5fPzdUNMpgkKys7bdr0J48fQ2yRlpbOzc0zNDQElcAMDJBbGxgYGJSVlRUVlebNm/f/P4OMjMy0adMUFRV//vzp5eXNxMQsJCQUExMD92BKSgrkPmEG8M3Gbm7u27ZtY2Rk4ubmysvLg4y5mpmZNjU3L5g//8f3H0zMTCEhIfb29kuWLKmoqJCTk/v//39WVlZfX9/DBw81tTQZGBh+//6tqKj47Nmz1tZWCQnJ8vJyYWHhxsbGWbNm//71S0NTg5OTU0lJCdJIYmQEXWK3aNHilStX8vPzl5SUSEtLv3v3rquri5WFtb6+XlZWVldXFxICDAwMTk5OEhISRUVFvb19ly5eMjYxUVJSYmRk9Pb2gQ+1vn37NioqGj7kFhER8ebN29+/fvv4+EJGiBkYGPj5+eXl5bu7uv8z/Hd1ddPW1q6oqOjq6rp+7bqZqRnkwi0eHh5eXt7e3t6/f/+2trby8/PX19f39vZWV1cLCAjm5eXy8vL6+PhAhgOjo6O/ffvW2dnJxsaWmJiopqZWV1c3e/bs379/e3p4+vj43Lp1y8nJCTwqyezq4grJ+EpKSra2tj9//RASEvr3919nZyc3F3dLSws7O3t1dXVPd8/169fNzc15eHj+/2fw8fG5cePm5MmTS0tLm5ubu7q6jx87LioqamZmxsTEBEonTKDTlNTU1Hbv3lNbW/v////i4mKIXyAJIy0tbf369Y8ePQoKCnJzczty5Ag8QOTk5CDz425ubpAcISUl5eDgwMzMPHXq1MmTJ69fv97IyOjjR9DV35AUCLnMBeJ9ERGRyZMnnzp1CiIlJCSUnZ2toKAAabVDAoqFhUVJSUlAQGD+/PkMDAzy8vJz5syRlpb++fOnu7u7gIAACwtLXFwcPHFmZWUd38QKquYhho6SRADQfDcRyihVcnTbxp6Guj03HrJzsH798o0FtPuMGXRx0r9/PFy8//4z8vAw8/MLvnn/iZmJ+d+/v4L8vP/+/nr34evP79+VVGRfv/4E6vAxgmbGpSQFnz179+PHL34+Nm4e/rfvPjIxM/77+19EWOA/w9/Xrz/8/vlbSUn61etPoIlv0D6j/zKSovcfPGXj4Prx9YuNo6u8surP799A7VQmxj8/f75//9bE3OLZ06dycgo/f//i5OR6cPeujIz0pYsXGf794xcUYmRi/v0HND3x7dvXnRvX//0Pag9KSQh9/fbn85dvTMyMf/7+lRQTfvnixe9/jNyc7GLiQo+evGFmBA2IsnOy8XOz33/4lIODW1pK8MPHrz9+/gNdUvL3v5iYwKsXL37+YeTl4hAWFnjx8h0j+KZvTg42Tg7mh4+fc3LyfPn8EbQegBEUTT9+/jaQlzDX1Uhr7NLX0yUvSgoOFJvym/y//Pfl65cuLi56enq3bt36////hQsXtLW1IUvmb926ZWRk5Ozs/P///3Pnzh04cICXlzcsLOzv378zZ85cu3ZtRkaGpaUlBwfHr1+/VFRULly48P///8OHD2tqanp7ezOAL4xZs2bN27dvbWxsGBkZDQwMIBM6v379unjx4t+/f48ePaqmpubt7Q1qav/6tW7dOsiFh2ZmZgcPHoTsFL5w4YKIiIiMjMyDBw8+f/6sqwv18vnz5zk5OU+cOOHj48PNzb1q1aq3b9+6ubnp6OgcOHDAxMSEh4fn27dv586ds7GxuXXr1rZt23h5eYODgyGLgZiZmQ8fPszJyQk6xZCH5+zZs5KSklJSUt++fTt//ryZmRkrK+u+ffvOnj2rpqbm5+cHqS8hoQ2ZP+Xn5w8NDeXl5X348OGnT59u3Ljx+PFjd3d3bW3tFy9ePAeDa9eu2djYWFhYMDAwvHr1at26dT9//vTx8VFWVn7//v39+/e/ffv2/v17SUnJxsZGPj4+X19fBweHu3fvGhgYcHNzv3jx4vHjx6ampmfPnmVlZT1w4ICQkBBka+f58+chk4/V1dXBwcHm5uY2NjaXLl0yMTFhZWU9fPjwyZMnFRUV/f39WVhYnj59unHjxj9//ri6umpoaED8cuvWra6urnfv3kVERNjY2AgLC69evfrly5e2trZmZmbfvn27fv065KzKwMBAyKLDffv2VVVVubq6Wlpauru7R0ZGJiQk3L9/X1JSMjAwkJWV9e/fvxs3brx7966ZmZm9vT0kuP7+/Xvs2DEDAwNeXt7Tp0/LyMhISkreuXPn79+/6urq69evv3PnDiSlMTAwnD17lpOT8+zZs/b29kVFRc3NzZqammfOnDlw4ICmpuaqVasiIiIUFBQePnzo4eFx4MCB06dPS0tL+/r68vLyQqwjhhzMh96vmtZ/++aN3ZdvT5owQU9Pb/fu3W/evIE0Zf79+2dlZQW/92vdunWNjY1iYmIRERHOzs6ysrIQZQwMDF++fLlw4cKGDRs2bdpkZWXV3NwsKyv78+fP4OBgGxubioqKx48fu7m5TZo0ydHRMTg4WEhERIWTwcze2TWUtKOCv/36Z9ByfUWKopEcltvkUeMCMrAA6QUjs0HjCGCVcCkIAywGJcB7PKFsCAUxATRdBuZDuFg1guUZQFL//v5jQr+LCUkjaKIKZODDhw9SEjM2bl7PwcEOUw91wN+/f+GBDDYXof3vn7+QA6rB4xKgshqS0SBcmGKQM8BsEIHkHqj5YMUgN4CkEa0YFF0wNWBl4OlBmCNBmkCjHkwQ9f99vQIbmuqMjAwZYSKg+UMGBiSngrTADATpQtYOUQwTgbsQogVCQgXfvHkTH5O0aMl8QSFBSCEPNhPNQIgWuAyIi+ESSGIAacQIapB6CMYjBVGARu7evVtISEhNTW3atGmPHj2aOnUqmgJSuaCBLfCILn6Nv/8x6DZdW5SgYKZAMHfgN2kEARa6+RW0soSV5T9oyw4jCwsrMzMLA9P/33/+MjAwMjEx/P0DYjP+Z4CcJvr7N+j6b1DCBF2b9Bc0MPn/P2gp6P//P3+Cpn4YGEHq//z+Cx47BSn89fs36MBR0B53hl+/foOn6UHp+/+/f99//AKtp/n9W0FZRUxS4tfPn9++fWdhZQZtnP//jwm8fv/P7z9/QRuo/v4DXXzx9/fvXywsLL/A0zE/f/z48/sX6JZRDg5dA8PLFy/8/vvr5+8/v//8Bi9SBZV2v379+fsPxPj37/+vn38gM+mgIunfv58/f4OHgRl+/QKdfg/a1vQPRIC0/P3HxMD0H+Sp3yDNIH+CAuDn77+geAEtYGIDbd5nBqkBmQ9qxIP2SIFkycLsHOz9E/rdJVxlFWX7+vp6e3s3b968evVqJycnLS2tSZMmXbp0yc3NbcGCBR8/frSyspo2bRqkkVRRUVFXV/ft27d///59/PgR0vh49+5deXl5fn6+vr6+qanp9OnT3717FxsbW1dX9+HDBysrq56eno8fP27duhXi2J8/f6anp5ubm5uYmMyYMQOu+O/fvwYGBp2dnUVFRUuXLn379m1QUFB5ebm9vX1VVdX06dONjIzgjVHIFlczM7Pv3783NTUJCgqqqKjU1dU1NDQsX77848eP/v7+e/bs2b59u5iYWGlpqZ+f34sXL8rLy6dMmdLf3//27dvAwMDTp0+fP39+8uTJs2fP9vLy8vPze/PmTU9Pz5IlS9avX79hwwYfH59t27ZBFm9BHH/mzJn6+nofH5/bt2/n5+dPnTr12LFjnZ2d8fHxLCwshYWFkydPfvv2bUpKSkxMjJCQUFVVVV1dnYmJSVZWlr6+Ph8fX15e3qRJk/78+ZOWlqavr+/s7CwkJASZhP369eu3b99aW1unTJmipKR04cKFtWvXmpqatrS0MDMze3h47Nq169atW01NTdOmTQNPnIFOwP3y5cuPHz8+fPgAmbjcvn37kiVLAgMD9+3bd+/evaysrMLCQisrK15e3ilTprS1tfHz84PO0P716xsYvH79+sePHxUVFf/+/dPR0WlqaiouLtbR0UlKSjI0NLS1tYV4nIGB4evXr//+/QNr+sbExPTy5ctVq1Y5OzuvWrXq2bNnBQUFnZ2dz549s7W1nT59+sePH/38/MDjKEzTp0+Pjo729vbOzc0NDw8vLCycMGGCq6vr5s2br169amtrO3Xq1GfPnsXGxjY3N0OmESGWsrKynjhxoq6uLjAw8PLly6dOnYqOjlZTU9PU1Ny1a9fUqVOjo6MvXbr0/fv34TdQCio3GBggyQ++iUdCQkJOTm7OnDl8fHxhYWH29vbLli1bsWJFf38/JyenhIQEKyvrx48fX758yc3NrampOX/+fGtra0hgsrOzl5WVZWZm7tmz5+nTp9bW1vb29iwsLDNmzEhISnr14oGNqydEJbXIH38+rrua9/nXO/DRIySZCirMwRogrT0wk1gCohdVI4gHOn0P1MADVTA4zWJkZPz47us/+XMLLgazsrGCm04wxaBtpaDaBMaH0QjDQSxQGQ6SAU3QgW9eAXEwMfi0QGymYSrFJwL2LLjKYAAdLAhJNaD1aozMjL9kj257nH+BSfjvX9DmA6jDQA1IkFKQqWDdIAYCg53NAJ6sA7kOHGigUEOowGAx/vj+87fM6cWXInn4OUG7NWDGYvEjKIRAR82AHAN3CUw9qslglzAy+Kg3SfLqI0uh9geQZbCzGRkZJ06c+O/fPz4+vqqqKuyKSBGFNbgJ6IEszENJQgR0jAIGOjVGQSuaWZh5eHj///vHycHDysIC7l0wgc6j/wuaomZkYvrx/TcLWJzp//9fv0GCLKzsnEzMn7/8BO3EZGYGJ+L/n7/+YGZhYmdkZ2Ri/vH7Nxs7Oyj7MDH8BM00/WdjY2NhYf367RcL+Ox68Hjq/0+fv7GwsjD8Z5SUlmNhZWNiZGJlZQHZ9Z/x35+/LCys//8x/Pn9hwHc1GOCdCUZmdjY2L59/crIyMDMDF53Ch7pFBET4+Li+vv757evoONIWEHGMrGyMPz8+ZuNnROUoBiZPn76xg4qyyAt0n/ff/3m4uFmYmT68u0nw38mVlbQ+C4jC+O37z9Y2DhA5QgT45dvP5lBl+qBlgD8Z/j/89cfHl5eZiYWdg4OZmbwQaYM/zk4f3Fzc0LWZYMsIgv//fPX0MCwKauJmZ05IiKCm5v7z58/jo6O7e3tP378KCoqKi4uVlBQ+Pjx46JFiwICAhoaGr5+/crPz9/c3MzOzh4fH3/r1q2ioiImJqa9e/dCigZWVtaqqio5OTkpKanVq1dbWlpeuXJl7dq1nJyc8vLy7e3t8EX6kHX0kOM2REVFN23a5O7ufujQod7eXkFBwZs3b65bty4gIGDHjh16enp8fHz3799///79rVu3INfZQ3z848ePyMjI2NjYS5cuXbhwYdKkSRwcHKdPn96yZUtERMTSpUv9/Px27NgRERGxdu1aeXl5a2vr79+/5+Tk3Llz5/fv3xEREbGxsZ6engkJCciriCBr175+/bpy5crg4GBDQ0MBAYHe3t7ExETI2NvSpUvDw8Pj4uIYGBjCwsJOnz7NyMioo6NTWFjIwMDw8+fPDRs22NjYqKmpVVZWMjIy8vDwrFu37uvXr4KCgrW1tZAB4+XLl0dFRbGzs/f390MugoeMTSYnJ3/58gUSnqBxD1Bi+wdasszMnJ6e7urq6uTklJGRAbk5kJ2d3d/ff+XKlfHx8dra2q9evfr379+PHz+WLFni7u5uYGAgJibW2toaGBj45s0baWlpAwMDX19fiHUMDAw6Ojqenp53797Nzs4+f/78/fv3169fz8jIKCkpOX/+/K6uLi4uLsiIGiTAWVhYAgMD16xZExkZaWFhAT6mjS0/P9/U1FRTU7OrqyspKWnHjh21tbWysrLPnz9fsWKFn58fJNK9vLyOHj0qKysrIyNz69atN2/evHr1SlpaevLkyStWrBARETEzM6usrAwKCvr//39KSkpgYOD3798hi9IgHoyOjmZgYDh+/PivX78g4fP69WtGRkYVFRVzc3O0HbIQBw91EtTvBq9dg6TPnz9//v79W0ZGhoGB4fTp0+Li4mFhYcLCwrm5uenp6Xfv3r13796bN2/+/fvHwcEhLy+vqKgoKSn59evXu3fvKoOXYfz//9/Ozm7Tpk1Hjx4VFxd3dXWFrLGTlJTs6OjsKUgnb80onnBmZmSV5Tf9/ucL9sYGHp0DKsUowGjS6AEelwA1nQbULRRYzshQVWX7B1Sx0twXjEKMerUuoBCDNIspcDWGVkYOFlDnGUOcBAEXFxcHBwdIFUaCtlGlAwHo1BgFe42RhYUNtKoS3Df7++8fE9N/8PJlRtBZSeCZaNA9SKAZE9AEN7hrxcjKyv737z9GRtB4IWSPIOhCWEZm8PlL///8/gseOWBgAA2wgo6BAo2zMjP9/P2HiYEZVMKCu8J///z5/4+RhZWdh4eHlZmNmZnpH/v//4ygLuA/0OgjM2jtCCN4Ez0jaHcSpM5j42D/8/fPf4b/zCysLKzsbOzsoFHMX784uTg/fXz35y/oalJwyxW0zhN0lRRomBTUkACfbM8Azpug4wPAzVm2///+/fn9DzQIDO50gmZA/v0HDQ+DO66gdaSMjKDdW6CdBIwMDMxsrMzguRJGBlAXG3SjKRMzGzMTK6gRD240g4OUZOLf/38yMtIQbZCFSpCTCxkYGL59+/bz589Dhw6dPHny////YWFh165da29vl5OTExYWhmyr/PHjx79//379+gVZmgNpwHFwcEDaBJycnJBxU04wYGBgEBQUZGNjg1SuEMX8/PyQlUAcHByMjIyQccFt27aB2tyMjC4uLgYGBgsXLty0aZOvr+/ly5chpytLS0PdzMDAwM3NDVlT9fPnz69fv27cuBFyvIiVlZWFhcW0adOOHTv24cMHS0vLnTt3Pnr0aPXq1X///vXy8hISEmJmZoYseGJhYWFnZ4dc9wzp7IIClpHx9+/f3759O3Xq1O3btyGBAFk5xMDA8PHjR8iR2pCzdT59+sTAwAB3mISEBGR2W0xMDDJGISoq+uvXr3fv3klIgG6KY2BgEBcXv379+p8/f8TFxSENXNCAPewmFdD64///IY6BsBnAx7hAtIM6TywskFYaJDwhGzsgUQkxB7I44enTp//+/QsNDZWTk5swYcKiRYtWrVrFycnZ3d0tLi4OUf/r1y/IxoK3b9/y8vLCHfz9+/c/f/5ISEhA0gZEMSTiQBMG4Eto/v37x8vLC1EAcdX3799//vwJuU3+379/AQEBoGwAOgGCwcbGZsuWLdu3b4+Ojj527Njq1auVlZVFRUWZmJggu4zFxMR+//7948cPHh4eBQUFiF2gLPL//5cvX0RERCBuEBQUhPidgYEhIiLi79+/M2bMePbsmaenZ1ZWFsT9EJVDnvwPnfo4ffo05ArQwsJC+B555P3aDAwMbGxsmmCA6esnT55ERETk5eW9e/fu8OHDCgoKNTU1kK7U8ePHm5qauLm5HRwcrl67/urVK0iqwzSEbBFWZi5bhRyytY9qHA0BaoUACwsLZEaIWgaOmkMjQKfG6N+/fyUkJJJ9ov/8+gnprEF6zKDjmSA1CbgxCm6qgkYHQRt6/v8FHeUFuiITtNyHETSd/Q+kFjR7ANmlDgkTkEmgwpQRvIvpH8Pff6C5e1CJDjYTYhToXGXQ0m8hJhaWX6A9noyMzIx///758esHFycXKysrCzMzaGMTMzMLM2jUloGRgZWV9d///6zs0CYgqPrk4OTl5nJydvvz+xcTIxN4sQGo9QpyFeisJ9BqIQaG/6DrTEBN3f+gw1DBbVKQRxj+M8HulAINGjEw/vkHakkzgrZRgeptkBqQYlCjiAF0NhTIX6D2Mqh1DqrcGRgZvz66yfbnm4gI+RtHWJhZjhw5+tz0uYyCzOHDhyH7USDDJDw8PCIiIl5eXra2tnfu3OHg4Fi0aJGUlFR7e/v58+eXLFkCOXz448ePkMboP9A2M9ByAsieWUgz4vfv35AF/rt27bK2tt6xY8fXr19BsQOJKwYG0Gla/0BjfpBDqnl5eUVERGJjY9XU1K5cuSIsLCwpKSkhIbFs2bINGzawsrJWVlZOmDABFMIwEyBHjUBauiIiIunp6RISEhcuXJCVleXg4HBwcMjPz09KSmJjY5OTk/v69Wttbe3379+vXLkiLi7+G3SuLcjNkA2YLCwsgoKC165d8/HxOXny5MuXL/n4+GRkZGxtbYODg588efLp0ydIm4mBgUFLS2vfvn2urq6vXr26f/++iorKxYsXT5069fHjR15e3sOHD9vY2HBwcFy6dOnRo0dycnKHDx+WkpLS0tJauXLlx48feXh4jh49amlpCTqqCzRo8Q/S52FkZHz79i2k3cnMzHz//n0FBYUDBw5AdviCW6oIB0ManaDxfvBYLOScGoiB7OzscnJy5ubmcXFxz58/f/fu3e/fv589e9bR0cHAwBAVFXX8+HFIMxESUxBD1NTUXr58+eDBAwUFBUiTBbJIFNLehQU5KDP9/fv3w4cPEJHfoCUxoHwMCUY+Pj5hYeHAwEBzc/ObN2/CQ4yBgUFWVpafnx9yAPvbt2/b2trmzJkjLCzMxMR05coVc3Pz/fv3i4mJ8fHx/fr1C97cBHc4QWOfBw8edHd3f/ny5dWrV8PCwiC2X758WV9fPy4u7ty5c/n5+bGxsfBBX4iCoU2C8j3IB1JSUqBsD+6vg/gkYnV19cjIyJ6eHm1tbUtLy02bNvX397e2tv769ausrMzU1FRCQmLPnj2vXr/Wkpf/+xeUJUm0YVT5aAiMhsBoCFAN0KkxCjrVjJtHW1v/z+9f4FoQ2pqELB0BNzVADbK///4xglpnoIvdGUGrrUE70EFXHoGacaAzmyClM6gBCJpMB1WHDAzMjEwMzMygU+wZwRfCgpcYgwYmwU070GlKf/+BLmj+8fPH509ff/z8yQg65Z7lz19QW5CBkZGHn4+Tk4uZlZWTk+vP7z8cXJzsnJzs7OwioqLPnj5jZ+f8/58RPOnPyMTKws7KpiwmwcHGzs4GWhEAGcIBtV+ZQKsIQItmGEB75/9BTrAHjeWC3PcXtHMeJA4e4AWvmwEtjgXt/wcr+c8IOmMftNnqH2iNzn8mJtCoLmR47N9f0J31oFWnLCwPfnz69uYJJycX+fH/H3QtSnlZGQsnKw8Pj5GRkZiYmKCgIGSUBbKkb/HixZ8+fSovL/f19a2trU1PT+fn5zc0NPz375+ioqKWllZycnJ8fLyIiAjksGXIhXWQMUtZWVlBQcGampq+vr4VK1ZwcXHx8PD8+wdteDExMcnKykLGGrm5ucXFxQUFBdPT06urqwUFBX/9+lVTU8PAwODp6fn161cZGRlzc3NVVVXkxYsMDAySkpKQ5o6SkhJk7Iefn5+RkbGxsZGBgcHLy2vdunW+vr4MDAyRkZE1NTVJSUm/fv0yMjIyMTGRkpLi5uaGnAynqKjIzMyckJBQUVERHx/Py8sLOfmvsLCwqalp586dHz9+jI+Ph9ymyMDAkJKSUlVVFRcX9/Xr16CgIA0NjfPnzzMyMlZXV79580ZQUDA0NPT06dPMzMy9vb3v379nYGDo7OwUFxd3cnJKSUlhZ2eXlJSMiop6+PAhfDyVgYEhICCgvr4+MzOzCrw7vre3d9myZczMzGpqagwMDHJycpBBaFZWVllZWSYmJgkJCcioalhYWFdX17Fjx6KjoxUVFVlZWQsLC+vr6w8fPvzx48eoqChVVdU9e/YsXbqUg4ODm5vb0tISnmwEBQUhGz/l5OSSk5MLCwsh7bnOzk4WFhYFBQXk/gMk08XExEyfPv3EiRPV1dXKysqQswY5ODikpKQ4OTkLCwu7u7uFhIS+fPlSXl4O32rDzMzs4uICGZC2sbHR0NAwNDTk4eEpLS2FnM3+8+fPuro6VlZWeXl5SLQyMjLKy8uzsrJCHBYdHS0kJKShoQEZUIfcg9LV1SUoKPjp06fw8HC4ONx3Q5gBKgWh52UmJSVhegQ0JQRSgymDRaSwsDAhMVEMfOiBnJzc3LnzQPeeP3/+6vXroqIiyLz/p8+f5zSBbuXBon9UaDQERkNgNAToBSCDeTS37eDGNZtWLNN2CwHdtAQd6gNbCloYDmp0MTIxsbGw/ACfHPb/338WZkYmZuZfv/6AGkkcLKCNSrBtgezsbKADbP8xMLOA1k/++fMXvN/8P2gVKHiCnuH/P3Z2NtBuIfCp9P///WNhY/727fuvP39//vzx9w/osH1GRtAQ3fcf3+/cuikjJ6+mpnHl8mVdA73Pnz7z8fHevX1bWEiQiZn11MkTqioq7FycrCws//7+YwQt62TiYGNnYWHm5eT8C277gg43ZWBg5wC5CrT0gOk/MxMT6Oom8PFOzIxMLMxMP379ZgKdycz86/dv8LAg6E4mNhaWn79Bs/2sLKC5+F+gSyCYGBj+szCBFv3/gq5AAG3EAq0r/c/AxMz8/Pq5P5/eFHRPE+TnAQcfyUTe/iJPZXcjVoM3795oaGgwMzP/+AE6PQq0UAFs2Pv37x89eiQvLw+Zh3337t3Tp09VVFRA+89YQNdW/Qffs8fLy8vNzf3v3z92dvZv375xcXExMjL++fPn169fXFxc9+/fZ2dn5+bm3rdv36pVq5YtWwbub4A2m3379o2TE7Ty9c+fP79//4a0P169evXixQtVVVUIF74S4D9oa9dPSGsM7DoQ8e3bN1A/gAXaj3r69Om7d+/U1dXhXvjx4wdcy////2/cuMHBwaGoqAhZisAKBv///4c7+9OnT48fP1ZVVYXcEQc5uu/u3buSkpKQOX2QrWD879+/GzduQEZPGRgY5s2bd/v27aKiojdvQIHJyMi4a9eudevWdXV1QQyEO+nBgwe/fv2CtC8hZ+BB2sRgU0Hbg16/fi0hIcHBwfHixYtPnz6pqqr+/Any+Ldv39jZ2ZmZmf/9+/f9+3dubu7v37+zsLBA2oKvXr2CTPr//PmTk5MTcmYh5NpryOQ+AwPD3bt3v337hhw+kD1MkCWGEAe8ePHi7du3ampqoNkAsEWQCIXIwsm3b99++/ZNWlr6+/fvkEj8+/fvz58/ubhAvaO3b98+efJESUkJ0laG6/r37x/krOz/////+PEDEsUMDAzv379/8uSJsrIyRDvcp///gw72g5yD/fv371u3bomLiwsICEBiB2Lst2/f7ty5w8fHB5nZhwgSQw7y3fQ3rl7Zdf6alYUF+OxPSGcb4S0mJqaNmzY6ODgU5BcgRHGzIOch/wX3jO/fv5+QmBgUFPT48aPnz1/MmzeXnY2dATTsyjSjsVLPwtY9PAa3SVhkSNlNj0X7qNBoCAzjEPj5579+8/VFifJmCqCxj2HsUyoCOjVGD21Zv3RiNys7Bx6ng078BA1mQpTAJqtAI4XQoUSoBGjyHcIE72gCKYBwIVogJTiKFlDTD9TihSgDkaC5cNBAJKh5BGon/Wf4++8v6Fo8BlDrGDR2y8gAOmQKvPcfNDwLPlwftFMRtKILZAJ4QSeEASLBjgcxwMNIIGPhCuBOQVYDUwbVAqYgLgfJwLgQH4F5YOL/3z9sAiJ57RMUFeTAAiQTefuLXOWcfZVBBzCRrJloDXPnzt26dauEhMTjx4+rq6shJxwRrXvIKFy9evWdO3cqKyvhLj5y5MjWrVvb29vhIqOMQRUCg7kxunbWlD1rV7CwscFXQWAGHei+YlZWZvChjJiy6CLw8gM8A/Xr10/QtYcMjOwcoO4NqOAEl03fv34JyypwDAhF146XT2Rj9Pjx46dPn+bk5PT09ISMxeI1lSjJX79+HTx4EHL7uaioqJ2dHQsLy86dOzk4OODHiuEy6Pnz57t27YqJiYEsksGlDFP8wIEDDAwMZmZmy5cvh2z9xFSDX2Tbtm1cXFwODg5wZTdu3Pj48aO5uTnklGsinQRZzUKkYrhdyIx/YACZoUIWR2P/+fMHNN4D3qKwevVqbW1t+DQRmkpc3EWLFtna2kIGAnCpYWBg2LJli4SEBPKR1chW49E4aKVGG6NkAOjwEhk6SdJi5uQuoax+/soNSPEIav+BG12gExBAGQN0YyYL6JbO3+DGHmifDyMTI3j70H9mFsa/f/+BVk+CJsUZWFmZ/vwBHfYEmr4Hjcb9BUv9Y2EGXZ0EOb2ZjZXl9++/oOWmYDlWVqafP3+DDsMH3ecEWnPKxc3Nw8PLw8PNxsbGxMz86MHDBw8eqKipfv/2jZuL++HDh0xMjCampv//gwZQv33/9uXz569fvvwFX+YJvm6XiQ28ohTsLNCGLFZWyAn5oH3/TEwMv0FnM4EWsbIwszAzM/z6DWrpsrAwgl0OWhT7n+EfGwszaOng//8soNObmP78/Qtq8f4HLTn9z/D/z99/LCzMDKDGMWhFAmg2nxF0nD4LC5ugoABJgY+qGBwiqEJU5yUlJbm4uEB2/KANLlLdrgE00M/PD7LcFu4Gc3NzfX2Us0jgUqOM0RDAHwIekXH2fkGg4gmPOnDzEY88Pim4XjgDrPo/w38ePkq3LYNNQicmT568fft2R0fHly9fJiQkQK6JQldEOv/9+/eVlZXOzs6cnJxbtmzZuHFjT0/P4cOHIfe54zfv9evXGzZsiIyMJLUxd+LECSYmJl1d3VWrVvn6+iLPbOC3ES67f/9+UVFR5Mboy5cvX7x4YW5uPm3aNGVlZcghzXD1uBirVq369OlTeno6LgUExQ8ePHjmzJnS0lL8Knt6euzt7SHLeyAtaVIbo+vXr1dQUCDYGN2/f7+2tjZyY3TixIlGRkaOjo74XTgqO5wAC308w8HFpaappaSmgWYdqMcOPowL1EhFXwsFkYTqACkAMRlBm8shDBCJrAbGBtH/Qa04mB6wQnAjDDb4CJJmZAJtJAK1DBmNDfX+/PnLysry799/JkbQIfaMDAxs7GygMVHQrn7wSCfIWHALGjrmiVRrwAd0UQY/YdaDdvRD2KChVdBwLrhVDXIV3O9gBkQETGJRCRYHESzopzeDBAcVhiz7G1ROooVj2MEA2WTwEgBWZJFR9mgIEBkC3Lx83Lx8RCoe/Mpu3LixcePGpUuXQs5wOHjwIGTBxqVLl1hYWG7evOnp6XnmzJlTp07Jy8v7+/tzcnL+/ft39+7dFy5cgDREWFlZL1y4sGfPHgkJiaCgIMhyDsgePgkJifr6eoiIi4vLkydPODk5IWtXLl68uG/fPm5u7sDAQFFR0f///x84cODUqVMaGhoeHh7MzKDjpZmYQPdbHjx4UFlZ+fXr1xYWFn///j19+rSenh4XF9epU6dUVFQYGRlXrVr18+dPPz8/yH08oJEGRkY2NjboYAo4Dm7evLljxw4WFhZ/f3/I+WUM4KuSNTU1VVVVN2zYcO/ePWswgNwSsmzZsnfv3gUFBUlJSSkrK8vKyl6+fHn79u2Quwnc3NxYWFg2btz46NEjR0dHSPvs/v37kIvvQ0JC3r9/v2XLlm/fvgkJCdnY2EDWUAkICHz8+PH27dsmJiYXL16EXB7h6OjIzc29cuXKL1+++Pj4qKiogN3L8ODBg/Xr19+4cUNOTs7CwkJeXv7AgQMnT55UV1f39vaGhOH///+PHj26c+fOt2/fvn792t3dnYuL6/Xr13Pnzv33719ISAjkdIvdu3dfvHjR2NjYyckJeZX5kSNHjh07BrlABNLov3bt2vbt20VERAIDA/n4+F6+fLl+/fofP344Ozvr6uqysrJClN25c+f58+dsbGw7dux4+PDhhw8f3N3d2dnZt2/ffuXKFWNjYxcXlz9//ly8eJGJienAgQPS0tL+/v7wRVkQD46SQxQw0dPdLKBtRqA1lHAGK1gEQrIwMaIiJmQuMxMjGDEwg/bBMzIzMoARnAERBwsygdkgEsyFqGRiBGmEGsLIwsTEzMjAAhZkYmBgY2Hh4mBnZWZmZ2VhZWHmZGfjYGdjAu2NgqphYQI7mwnqJJhLYObDjAU5Cc5mYmCGIJCDISrBXgBxwQywSpAbYAwk/8KtAzPAoQQPNHpG2ahdoyEwGgKjIUBGCJw9e1ZeXl5cXPz///9fv361srIyMjL6+vVrVlZWR0fHly9fzp07t2TJEnl5+X379nV1dTEwMEydOnXx4sViYmJLliw5ceIE5JoJCQmJGzdu1NbWQqanQeMBjIxfvnw5ceLE2bNnlyxZIiwsLCYmBrmb5/379/39/cLCwi9fviwsLPz79++SJUumTJkiKiq6efPm3bt3M4PBz58/KysrDx48yMzM3NjY+ObNm/v370OuUfj161dra+uDBw9yc3O/fPnCyclZWlr64sUL5MYWPDQg1nFzc//8+bOwsPD79++QYdezZ8/++/evsbFx9+7dIiIiU6dOffDgARcX14YNGz5//vz69ev09PTv37/v3r173rx5379/h5xu9vnz5////9fX1586dUpSUrKzs/PYsWMPHz7Mzs7+////u3fv+vr6fvz4ATl39vPnz79+/ero6Lh9+zZkaXhvby8DA0NfX196evq9e/c+ffqUl5f35s0bPj6+8vLye/fuQZz9E3Tny7c/f/5ADqdbtmzZxIkTRURENm7c2NraClHDyMj49evX379/fwEDSJgvWLCAmZn58uXLJSUl////nzVr1ooVK2RlZReDAUQjAwPD1q1bW1pahISEDhw4cPnyZV5e3suXL1dWVoqJiT148KCuru7t27dFRUXfvn0TEBCYNGnSp0+fGBkZOTg4Ll++XFRU9PPnzx8/fvz69evr169fvnxhZGSE7IUVExObOXPmnDlzGBgYIDeMCAgIbNu2rbGxEe3oD7hLRhlDC9BpZHRoBcqoa0dDYDQERkNgNAQoCYGfP39Cbg///PlzZWXlvXv3HBwc0tPTeXh4qqurdXR0/v37p6KiArlBd/fu3V+/ft2wYcPs2bOVlZVjY2P//PlTWVmpqampq6srLS1dXV397NkzWVlZyM1eb968Wbp0KTc395MnTyA7KSEjpgICAl1dXZBrqLZs2fLixYuVK1fW19ebmprGx8f//Pnz/v37nz59SkhIUFJSgrS9FBUVT548+f37d21t7XPnzv38+VNWVvYjGNjb2zMzM2/btm379u0sLCxoa3IYGBgEBARaW1tfvHjx7NmzdevWPX78mIGBwcHBoa2t7enTp+fOnVuxYgUfH190dDRkW6Srq2t6evrv37/9/f0fPXoE2ZhoZmZmYWGhpaUVFRV19+7d48ePt7W18fDw3L17d9u2bTIyMiYmJvn5+QwMDJ8/f+bl5XVycnr//n1SUhJkpSmklQw+YQY0tPTv37/w8PCMjIzDhw8/ffo0KyuLhYVl165dW7ZsycvLY2BgUFdX9/PzO3jwYGpqKgMDQ15eXk1Njampqb+/f3R09PPnzyGnKbu7u2/bts3X19fFxQVyqUd8fHxCQkJISEhERMTdu3fXrVuXnJysoaHx/fv3devWwZfhrly5Micnx8fH58+fPydOnPj9+/eWLVs0NDR0dXVVVFSKioquXr369u1bcXFxIyOjgIAAPj4+VvANzJMmTerp6YGsCjAzM7O2tg4ICPj48ePu3bvnzJkjJydnaWlZVlYWFBTEwMCQn59vYGAQEhISGhr65MkTSMKgJLmO6h1wMNoYHfAoGAgH/Gf49PkT5DSfgbB+1M7REBgNgWEeApCztxgYGHh4eOrq6g4ePLhjx46/f/8KCAhAmjt79uxZtGiRsrLyz58/QWc8//oFubcMcp4xKyvr169fX758uXHjxr9///r5+cHP8Pr796+8vPyECRMg5zakpKTs2LEDcsLGgwcP6uvrJSQkRERE2NjYIIfXQo6uY2Zm5uLiYmZmvnLlCqQt+OkTqAx0d3c/ePAgGxtbSkoK5LpUBweHL1++fPjwYfv27X///tXV1TUwMIBsYEKLsydPnjQ0NAgICECOXYO0VtXV1RkYGN68eQM52I6BgQEyj/zv3z/IioV///5xc3P/Ah8dAzHw169fEL3fvn37/v373r17/////+fPHycnp1OnTkFOnGVgYID49+fPn5BB4n///v2HXZMBXznAxsYGmZH/8OHDp0+fIGGupqZmamoKsQvSsoSY8Bt0uss/yLJ+Pj4+bm5uyEJ/iMrfv3/DHQk5VA4yRMrKygq5IeX06dM3b96EtK0hWiCGCwkJQY7P4+Pjg1wj8vjx482bN//9+9fHx0dTU7O3t3fBggXbt29nYGDo7e1lYmK6c+eOqqrq2bNnIY1ReIB8/vyZjY0Ncmq9sLAwI3hQnIODAyLCzc0NcQzc9lHG0AWgvtTQdf2oy0kPAdB5Lw/vPuzt7f369Sto8er//x8+fPjy5Qt8suPt27cvXryAFI4Q879///78+XPItA5EBD/55cuX58+fMzAwnDhx4suXLw8fPrxy5QrkTChkY/Ebgkf27NmzkydPvnPnDkTNz58/IWMSEC5W8u/fv8eOHfvy5QtWWWIE//379+TJk58/fxKjeFTNaAiM8BCwtbV9+fLljh07vn//LiAgAGn3MDIygg7mAy+yX7p0qYeHR0NDg7a29ufPn3l4eLi5uc+dO/fr16+zZ8/eBd9lqqmpWVNTk5+f7+HhISAA3bgJubntzZs3Hz58ePv27adPn0BXNP8F3Q2xe/duUVHRzs5ONzc3SDtGRETk5MmTv379ugIG//79MzU1nTBhgqamZkNDw58/fywtLY8dO/b48WNnZ+cfP37s37/f0dFRUlJSREQkPz+/trbW399fR0cHcrUH5IIJeMweP37858+fPT09/v7+kCvNII1IyAnBnz9/Pn/+/M+fP48fP/7q1SsGBgaIGsggLqTsBZ3cwsDAwsLy5s2bb9++iYqKSkhIREdHNzQ0REREGBgYyMvLHz9+/OvXrx8+fNi/fz/kdoy3b99CLhPh5eW9fv36jx8/Dh48CCmf4XeRKCgoiIiIZGdn19XVBQQE6OnpwZ3Nysr69u1byCS4sLDw4cOHf/36debMme/fvyOfeMDMzPz69evv37////8fbiwkBLi4uGRkZDw8POrr6xMTEyFDyBDzZWVljx49+vPnz5s3b969e5eDg0NOTk5JSamqqqq4uNjb25uTk/PevXuNjY2LFi36+fPnwYMHf/36lZSUNGHChE2bNh08eBDSG3n58uX3799FRUUZGRlPnz7969evI0eOcHNzi4mJff369cSJEz9//jx16hQjIyM/P//169chIQlxwyg5FMFoY3Qoxhr5bmZnZ1+yZElRcdHatWvj4uIOHDjw9evXysrK4OBgSFk2adKk+Pj47OzsrKwsSAF68eLF6OjogoKC6OhoSF8Wbv3169cXLVoE58IZ586dmzt37v///9vb258+fbp///7Fixf/+PGju7sbcho8XCUZjIsXL1ZVVUEu6YFof/bsWU9PD6SvDxHBJH/9+tXU1PTs2TNMKUyRWbNmYbZu//z5M3HiRMgNjZhaRkVGQ2A0BJBDQEREpKmpaebMmUlJSQkJCWvXro2IiIDcbgDZrRIeHr58+fKcnJzz589DdrFUV1fPnDkzLS2to6Pj69evqampDx48SElJyczMPH36NPLgHwMDQ3FxcXp6ekpKioaGhpubGz8YuLi43Lp1Kz09fdmyZXp6eqysrBUVFevWrUtLS6utrf38+TMnJ6esrOyfP39KSkrevHlz/PhxMTExa2trU1NTbm5uKysra2trISEhAwMDc3Pz1NTUlJSURYsW/f79W0REBHIrsqysLMT9DAwMlpaW379/T0lJmTFjhpqaGgsLi6ioKGRcUFBQMCsrq6GhIS0tbcqUKX/+/IFfL8LIyCgjI8POzs7Pzw8ZKw0ODt67d29hYSETE1NycnJJSUlqampHR8eHDx+Cg4NFRUWTkpJSU1PPnDkDudfj2bNnmZmZ9+/fz8rKWrRoUWZm5o0bN1RVVRkYGGRkZCA7/XV0dJydnTMyMpKTk+fOnYvci4actZeenn727NmqqqrNmzenpKR0dXWVlJTAh58ZGBiCg4NXrFhRUVHx/v17cXFxiLFMTExycnL8/PzFxcUzZsxIT0+vqKhALlfz8vJOnz6dlJQ0efJkLS0tZmbm6OjoV69eJScnp6amHjlyhIWF5eTJkxAfsbGx2djYCAkJcXFxCQgIVFRULFy48MePH6GhoVu3bi0tLf369WtNTc3UqVNTU1OXLl1aWVnJyMjIzc29Z8+erKys5ubm/Pz8Hz9+1NbWInsQOR2OsocMAJ2/OYpHTAgUHymPbI4+f/p8Y2Pj0aNHJ0yY8OfPn5s3b9ra2r59+/bo0aMuLi6PHj16//59TU1NWVnZ9+/fAwMDly9f/vHjx2PHjjk6Oj5//hwSWh8+fJg6daqXl9fFixffvHnz5cuXz58/37t37/Hjxx8+fLh79+7///+DgoJu3ry5cOHC8vLyf//+Xb9+HXId/Lt37w4ePAgZLv3////bt28/f/586tSpM2fOQOaeIFZAyOvXrx88ePD169f///9/8+ZNQ0NDVlbWrVu3IOv9IcfXX7t2DZc5jx49Onjw4P379/38/G7fvv3p06e3b99C+vrPnz+HuOf69ev79+9/+vQp5JB8e3v7JUuW3Lp16/fv32/evPn48eOlS5d+/Phx8+bNr1+//v////3794cOHbp8+TLEhb9+/Tp58uTRo0chshDBUXIQhsCTz08CN4a++/F+ELptaDnp68+/qrVXrj4DDZvhcfm/X9+ePbr39OHd399Bu3NAlwDDGKB89Pr5kwd3/v/79e/XN4ghP758eHTv1o8vHyDc//9+PXlw582LJ1AujPr68e3bl0/fvHjy6R2oWAAJ//7+/88P0I0Jn98/unfr78+vYC5oIvv398+P7t36+hGU6////wd3yf/f339/B80IgVT++wUy5P/v/78RPnr59OHTh3f///8DkvrzA6Ts/3+EdpAoiPvo3q1f3z79//Pz//+/IDV/f4JlQMSnd68e378N8R2IBLsQFgj//v/9CRIEKfz//fP7l08fQuz6+Pblo3u3/vwAuw0s+/zxfbAsmPP//58fX148eQCy6///Lx/ePnt0Dx6GYF1gB4PVvn7+GBLCYB4S8fv7iycPQKEENg0pfJDU/P//9ePbV88e/f//F6zyN0QOHgKQyIIFLEQSRP779e3x/dugSAQFBdgx4Hh8/fwxSBqMXz59+Pj+7T8/QMUpKBCg4f8fpOv/X1Cp/ukd2GqQ9u/gOAUF8v//P79+dHe2v3PjytOHdz+/h8T+X7AusLmDhtCov3ryPiIGB427Bi8YXTM6ZLoNVHEoIyPD129fP3/+/O/fPyswYGBgEBYWhtwVdOjQIQ8PD8hicMj46IULF378+BESEsLCwmJpaSknJ3fq1Ck/Pz8GBtARIbt3775///7s2bPj4uJevHgxf/58VlbW0NBQMTGxLVu2QHbIQpzNxMT0/fv3pqamKVOmfPjwoby8XExM7MWLFw4ODrm5udOmTbtw4YK0tPSDBw9MTEyqq6shq/L///8/f/78zZs3y8jIPHnypKWlBbKe/devX5MmTcrMzISce/f48eOGhoaVK1dCzJGRkYGbc/DgwZ6eHiUlpR8/frx69Yqbm3v9+vVPnz6trKz8+fNnTU1NVVXVuXPnli9frqSkNH369JKSkqNHjz569GjDhg2QwxHz8vKYmZl5eXnr6+v7+vqKi4s5OTnLyspERUVfvnzp5OSUmJhYU1Pz7t07yKhzV1cX8ugCxPuj5GgIDL8QYGFmLF/3VJCbGY/XGEFX6bExMDD8+/vu3/83DKBLnRn//X8LOcuOmZmZkZHj71/QfMV/8Nw9E0g919+/H//9B12oy8jAyMLC8Q/UGnoI0QK2C3xnNOS4PIZv//49gKxlBJ3TzPAfdNw0E9ffv2/+g07kgxzmx/TrH8e/f5///v0AuuyOkenff9CkOWSoFbTsEnSEM0g1XARkIAMDMwsLIwPLn7+PwIcAgu37/5+Rien/v9fgs/dAbmFiYmJm5vj7F9TSZYAe/wcyCiTHwMAMOpWa/e+fF/9ARwSCTIB78z/Da4gakOGgcGFiZGT89w9kF1gXx9+/r//9ewlyCeiiLFAg//kD3REPOouekfHvv2f/QedSMzMxsfz58wQy74/iBbBGRga2P39BrXmIdRASsuHp/7+X/8CrTpmZoeEDkYWTkNOs/v17yMgIuh8H6njQddWgViDE75CAhWsB7TBjZGRmYf/79/3//+/grmJhZvvP8O/v3/sg/0Icxsj+9++rf//+McHCH6QXZPg7UKSAbiKEBgjEor9/3/779/rPnz+XZdNytv3n52f99+/r37+fQHHKxPTv31tkNwws+99/hqcffg+sG4YcGG2MDrkoowz8ZxASFOzv7799//bbt2/T09Mh21ohhn78+FFeXh7CZmFh+ffv34cPH1jAAFKC8PDwQGbzGRgY9PX109LSVq9ePWnSJEZGxsWLF79+/Xrr1q18fHxbtmz58OEDxBwICS5nQab9//9/0qRJTk5OmZmZb968iYqK8vLy+vDhg5qaWltb28uXL2NjYx8/fgxxxqNHjxYvXrxo0SJZWdklS5b09fXNnTs3Pj7+6dOn9fX1kGIXsgALYh3kXtDW1taXL1/GxcXdvXt36tSp+fn5rq6u165di42NhVyY+RW8WJaBgeHt27d///7dunWrk5NTamrqy5cvOTk5jYyMjh49WlZWZmpq+vXr11u3blVVVQUEBPz69evVK1DROX/+fF1d3cLCwtevX6emphoYGBw+fHjWrFnq6ur37t2DHNQH8fUoORoCwzgEmBgZD9/5gn489CDz8L9/DKK8LGm2IgPqLlBznBoOYMdtCH4r8MviNnXwyTAyMDrpeP3+9esfuPcy+BwIchEjA4OdKo+c0PAJdjqA0cYoHQJ5EFnx89fPqOho3Qitvsn9BgYGVVVVS5cuhQxDMjAwsLGxoa28ZGdnR14Y/vv3b8gYKtxLkEYqaOQDPNQK36EPNxOuEtbr/f/q1auwsDAGBgYRERFJScmHDx8yMzObm5tDRLi4uODbjCCH5EGuWTczM1u5ciWka468YBRiPsS6////QzaNCgsLc3FxvXz58uPHj5DRUxUVFQkJCdB1iOBOOXy84e/fvzk5OT09Pfv37xcTE6usrBQSEoJ76u/fvxISEsbGxpD7Y5mYmP78+fMMDMrLy//+/QvZcJCenl5fX8/AwGBhYZGbmwtx0ig5GgLDPgTYWUBHJg9mb/7++19agLXMTXwwO3LUbaMhMApGNzCNrDTAxMR0/949dg4OUVFRX1/fT58+ff78GdKSY2Rk1NPTO3HiBCRETp8+LSkpqaOj8/v37ytXrjAyMr5///7Bgwc6OjoQBRASvjseMrECEcRFQhp5fHx8Dx6AZtZ+/fr17t07CQkJyDpRyIQOpJkIMYGfn/8rGDAwMNy5c0dAQAB5tgiiBo2EtFYhJCcYQBbXvwVfJcLMzMzBwfHx40cGBoYvX75AdilJSUnNnTt32rRpcnJykyZNgrQ44WECYUBsgUyKCQoKuru7d3V1NTc3t7S0iIuLu7q6Ll26tK+v78iRIwcPHoScYg3RMkqOhsCwD4H//xl+/P739de/n3/QR6t+//3/8w9krhwaDP/+M3z//Q/KQaX+gWbWUYR+//3/C6wdJIUiQwLnH4r9JGgcVToaAqMhQDcwOjJKt6AeFBaxs7EvW7D88r9LDx7fv33ttqerp5SU1Jf3XzhZOVkYWPy9/Xdu3ZmbmSsqKnrixImO9nZRUdGU+JSqsipLS8tLly452TlBBhohnjHWN545dWZdVV14eDg/Nz8nCwdEnIuVk4uNC3TAHjM7OxhxsHBwMLNzsnBysHAUZOfX1NQ+efDk7t27FsbmWlpanKycnKycoKPpmFlAKmHmqKio+Lh7lxSUqKmpnThxorKykoGBgZOVk4eDB2IRhARpYQZZzc3Ozc3GDToZhJmVnZldXEgsIyWjpaHFxtr65atXvBw8LAwsXm5e61ata2ts/fL1K8Pv/7ycvGtWrLl69aqRodHNqzfd3NwYGBhsLGx6O3tdXV1DQkJ4OXnZmUGzY2zMbLycvJysnMlxyZVVlb+//Xr+/IW0jHRWZlZtRY2CoqKQoBArA4uWmlZDTYOPj4+toy3EeaPk4AkBSFQOHvcMA5f8/8/AzsqYbC1qIMt16Pbndec//P3HAJm7//33v5cOvyAX84oz70BzCwwMf//9lxFki7cU6tv96uef/xBlkED4z8DAy8HEyMjw+cc/0OJKBobff//76IK0LzzxToyX5eef/99//UPWAtE4So6GwGgIDAMAWpU8DLwx6gUiQyD/QBHzT+YPl99dvnrFycnRwsKSkYnx9+/ft27eUlNTZWNn/wG6pG7P+/fv7ezslJSU/v79y8TEdO78uQvnLygpKdnZ2SFbxMjE+ODBg3Pnzunr6fML8H/5/AWy1vP9h/fv3r1TVlK+cfOGvJz858+fv//4LiMtc/PWTRUVFU4Ojjt37hw+fERSUtLZ2YmJifnBwwd8vHyQ+fGbN2/IyctzcYLasgyMDP///T946OCDBw9srK3V1TX+/v378uXLX79+ysrKwV3y/fu3+/fva2lpP3jwgI+PV0hI+P//fzdv3pSXV+Dm5jpz5uyly5csLSxYWdkkJSW4uXkePLh/4MBBXV0dQUEhMTExVlbWw4cPP3jwQFVVxcbGFnKK4dGjR//++2trY/vgwX0FBQV2do5////dvn1bVkaWl5fn3r17Bw8eEhIWcnBw4OXhff369cGDB758/WplaaWurn7x4kUJCQnIUdJwR44yBkMIfPj5fu3tDYs85wmyCw4G9wxdN3z79c+s4+brz78ZGBkr3MWDDQUuPPlursA9cd/LmYfesrIw/vn7/++//w2+UrJCrJlLH4G2o4Abo1ICrOEmgjMOvvnx5/+fv6CRVPC1yYx///5v9Zc6eOfLtsufOFhBzdHff/+7a/HxcjBtvvxpSrhM586Xd1//ZGZi/A3WxQK6bBmqjImR4e8/BgZGBjZmxj///v/9x4AsqyHBcbhEbegG9ajLR0NgJIDRxuhIiGWEH2demn3j/U02VjZmFpbfv3///QM6OAM0JAm77I6RkZGVlZWRkfHPnz/w9aMsLCyQm+t+//nDAKo+EAaCr3pm/vMXND/HBD7RGjTPzsTIxMj09+9fZhYWUHMWNCoCOuwafqUeMzMzaIPU/39/foMcADIcfNgSA3gH67+/oKM9oHYwMrKysECmziHuAc2bMzL+A59xDVHDCF4G+ufPHzRzQOr//4c4HrKc4O+/fwzg/acsYIeB7QTNGLKArfj37x/Eg4yMjCwsoEmD339+MzODvADxNTMLC8RtEPeDjmj5DTruhImJiYWFBRRof//8/fMXZPi/f///gUyGuHCUHDwhwMvGW2ZazMOKMrg+eJw3VFwCaYy+/PSbj5N5Y4Zyx84Xy0+/q/KUcNbgDZt1X5CbRVWM/czDr8UuErJCrPkrH0Mao///M3CyMcoKst1+BWpW6kpxcLEx3Xn988n730qibFMjZA/f/Trz4JuPP/4wMTL++88gwcfCyMggI8DWEyw949CbNeff//vPoCfNycXGdPPFj5ef/7AyM6qKsb/+8kdeiO3Xn/9Xnn2XE2KTFWS78fLH2y9/mZlAw6ujjdGhkqhG3TmSAajGHcn+H2l+T9cD3Uc80nw96t/REBgNAaqHAGQp5vdf/y49/WapxH371U9tKY7rz3/8/PNPRZS9zlsiZt4D8CAm6MgjiOLff/9rCLHPjJZzm3Sn0Fk80ICfg5Xp3dc/iYseWihy68twaUpynrj7dc+NT+ysjD9//4sxExLlY/n845+yKHuZu/jx+19TrYV99PhZmBjvv/mVt/rJzRffS13FudmZlEXZ2VmZZhx87aXDryHBceLel7Slj77/BnWdIVZT3fujBo6GwGgIUBGMbmCiYmCOGjUaAqMhMBoCIyUEmBlBs+F///3ffPljgqXw8XJ1Fw2+zZc+cbAyXXr6PXTW/XdfQSOXjAwMzIwwxARaTvrjz39edmZfXb6VZ96HzLy34PhbIS6W1Wff773xqXTt04O3v3CxMYG0MDGApkj+MUzZ//rso29hs++rirHbq/FGzr1v0XXr4tPv2fYiTIyMPOxMH7//deq/Pf/Y2zwnsdpNz/yn35UXYVOXAB1QCjIHNJk/UiJl1J+jITBEweg0/RCNuFFnj4bAaAiMhsCAhcD//wzPP/3+C96pzsvOLMAFOpUddHbv1z/ffoF2IEHGI/k5mZmZGN99/QNpEP5nYGBlZhTiZnn9+Y84H8v//wwfvv/9+O0vMzMjKxODEDfLt1//vv8GaQedrcHAwMcB0v7h+19RHpZ3X/9wsjKxsTC+/PyHiYGBjYVRmJvl1efforysn3/8/fzzHzcbkwAX89MPv5kZGcT5WD99//vjD2ipDCszowQf64CF1KjFoyEwGgJEgNHGKBGBNKpkNARGQ2A0BEZDYDQERkNgNARGQ4A2YHTNKG3CddTU0RAYDYHREBjWIfDtF3hclIGBiZGBkxW64uv7r3///jOwsjCyMTN+//WPnZWJCTIoCguKH7//cbAyffv1j5mJkZ0FKgcy6j8DOwsjC3g7/M/f/xkYQdd/crGBDnv68vMfDzvI/B+/QadBwXV9/fWP4T8DNzvTn7//f/z5zwHW/vUn6PgnLjamX3/+//4LNgdm9Sg9GgKDKwRgOWVwuWqAXDPaGB2ggB+1djQERkNgNASGbAh8//XPbdKdFx9BW4T4OZn89AQURdlvvvix+dLHLz//KYqw+ejyLzn9SVuC1VWTF3L7GhMT45E7n88//hmozzPv6FshbpZAQwFJAY6T9z7tv/3t24/fOlKcEeai6y98vPz0BzPj/+8/f9mr8vBxMm26/NVVg9tCgWvm4TfMzIzhpqJ8bP+P3/9y4Obnv/8YYsyFzj76dvnZT1NFPkNZriWn3jIx/I8y4dtz/fOzz////wOd14E/mFmYQfXgn79/8CvDlGVhZvnP8P/f338srCy/f/9hAO3UYgCdxcHM8vsP9GpyJiZmJkbGP3//srGy/v79+z/DfyZGpn//QesHMA3EJcLIwMjCyvoHrB10XAkjIwuSFSARBkZWNlaG/wx//vwhaDjkImXIzSC4bCRVHE8YIvuXmQl8jgrk3BJwcJFqEX3UMzIygcPnPyMD6HgZSMRR3epff/4XuogVOIlR3eShaODoNP1QjLVRN4+C0RAYDYGBDIFvv/7pN19v8pPSkOAAnXH29/+//wzMTKAloaDLgf8zfP3+89Xzp/IKiuB1mwyfQOAjHy/P548f5BSVWcF1/befv+/cuaOoIP/65XMFBcVfv35fuHz11/evLCxM/PwCOjraf/8z/vv3//HDe0LCohxcPOwsjH///btx646CvAIHOxsLeJ3qrz//WZgY3755ffP2nZ+/fnOysygoKEhIyf7++X3Tpk3Ozk5CQkKgM0hBTR/wcCs02CBsEHn82HEGBgZLK0uwMqg0pGUJFoEsfwWpBHMRCo4cOcrLy6Oqqrpjx05fHx9Qc5CB4cePH0+ePFVWUmIEjwm/f/f+67evIsIiGzdt8vHx5ubm/vP7D+jkOOhwGJQCWwe3AmIj3CKGf3//3bt3T1FJiZkZNEL8/dv3p8+eqagow1X8/8+wZcvmA/sPxMTEGBoZIrkTYhSKyeAz7xiYIcEHMgJFFlUvRAqkCCwOMQ3c+oWIQcn/J46fYGRiBN/qjOwj0Jl4f//8YWEFNfcZGBg+fPgwf978T58/l5SUcHODLigBG4Bp7H+wdZCVwxADISJwlVjcADYK4mAICVED0YImgosLNoOB4TcojpgZGRm/ffu2efNmHx8fsGvhuuAmI4vABdEYUDMxo7hm4zNjOa4mPym4ihHN+D8KRkNgNARGQ2A0BEZDgJQQ+Przr2rtlYuPv+HS9OblcyMD3Z3bNkMU7N+zqyg/5/aNq0EBvt++fIQIHj10IDUp/vmTR6HBAV8/f7hz87qVuUlJYX5JUX5YSGBpccHvH1////8fHhp0/OghiJb7d24qysscObgPwoWTE/q6He1taqrKC/Oyfb09Th4/8v/vL19vj3u3b8DV4GJ0tjV3trXgksUj3tJYN3/OzA9vX4UG+f/6/gWi8taNqwG+Xv9+/4BwVy1fWlKY/+PrZ093lw9vX//4+rmuuvLf758QWSLJG1cvyUpJnD19AqL+8oVzYcGBEDaEPHfmpJ+3595dO969eQkRwUNu3bxh0/o1eBSQIdXV0drX3YGp8fOHt/W1Vf///IJItTTVl5cUnjp+9P9f0AnNEMFBSHa2tTx5eP////8f3732dHN+/+YVLRwZv+B+3abntDB5KAJof2VEt8dHPT8aAqMhMBoCoyFAYggwM7NU1dal+Vs4u3qAx41Q9H//9ef3X4b2rl4tXQMZGZkfv/9+/vZTRV2LlZ1r994Dfn5+DAwMK1avtXVwZuPk/vLt568//798/ykmKdPV28/IyPjz58+CgoJJU2cUFRX9+PX3J+w6+xWr1xkam61au8HSxh50/wXMzo+fv3l4+ZaVlTEwMCxatGjm7HnGphbMrBx//oPGYI8dO3b16lVVVVVHR0eIjn379t25c8fc3FxfX/8/E3Sv/bt3727duiUrK/v161c1NbWvX7/ev39fR0eHgYHh4sWLGhoaP3782LZt279//7y8vAQFBf8yMP/5z/TrL8M/RtBtpRBTfv35/+3nn5+gNawgq378/gsKin8MLGycn75+373vwOZtO9S1dNTV1TU1NZ89e/b9+/fnz5+7uLicP3/+3LlzsrKyrq6uzMzgUV+QASC8bOUaE3PLZStWG5mYMzAw/Pzz7+sPUPMOMuH+6NGjeQsWM7Nx/PnP+Psvw/v373fs2PH79293d3dxcXEGBoZnz57t2bOHi4vL09Pz2bNna9dv+v//PyMLu7W1taCg4Llz586cOaOiouLk5PT////bt29zcXFduHDByMjo7du3Ojo6jIyMnz59un//vr6+Psg1DAx3795lYWE5ffr0nz9/PD09+fn5/zOy/GMEufnhw4f79+9nY2Pz8fFhYWFZt3HLlm071TS09fX1f/78uWPXXl9f3x+///5nZH775s327duZmZm9vLwEBATevHnz6dOnt2/f/v37V0lJ6fPnz/fu3Xv+/Lm/v//r168PHDigr69vamoKcQADA8OTJ0/+/Plz/fr1V69eubm5SUpKMjAw3Lhx4+jRo0JCQj4+Pi9fvvz586eyMmj8+P79+2xsbNLS0rt373748KGlpaW2tjYDA8OFCxfOnDkjLS3t7OzMxsYGGtT/9+/QoUMbNm9l5eAyNjZWU1Nj5eC+dvP2zZtblJSU7O3tQaO1///v3bv3wYMHcHMYQIOpvy9evKirq8vOzn7z5k0xMTFBQcEHDx4wMTEJCAhs27btx48frq6uEhISFy5c0NfXB42OMzC9fv3q329BJlbQjdNwr41MBmjMf2T6fNTXoyEwGgKjITAaAmSHADMz8507dzKzcqysrLq7uz9//oxs1J8/f/T09KKiompra////w9vOAYFBW3ZsoWBgeH58+f37t3z9vb+9esXpFEFaQqA1+oxsLOzJyUlHT58GFnv27dvT5061dPTc+fOnVu3biFbx8zM/OLFixs3bly8eBFS2UMGh5iZmQ8dOjRz5sx///5Nnz590aJFDAwMvb29M2fO/PTpU2tr682bN1lYWFhZWV+9epWZmfngwYNHjx5VVlYyMDAcP348Pj7+/fv3Dx486O7ufvfuXV5e3s2bNx88eJCfn//lyxe4s5FdApqjZYTMLIOEwdfPQbm/f/++ffv2169fL1269Pz585cvX8bFxXV1dd25c+f8+fP9/f1//vxZunTp5MmTIYEA0g8OqIsXL06cOPHatWsPHjyACCJb/e7duydPnrx+/frMmTMvXrxobW29devWo0ePsrOzP378+PDhw6ysrAcPHhw9erSvr+/169dPnz598eLF1atX//z5s2nTprq6uk+fPs2aNau/H9QNaGtry8rKOnTo0Pfv35uamq5evcrAwLBhw4YVK1ZArGZgYJg+fXpycvL9+/ePHz+enZ398+dPJtAVW4xfv35tbm5++/bt5cuXi4uLP378eOvWra9fv165cuXVq1cPHz788OHDjRs3Ll++/ODBg8zMzNu3b1++fDkzM/PTp09Xr16NjIycOXPmo0ePzpw5Ex0dfezYsfPnz0dEREycOPHVq1fl5eXHjh2Du2H79u3h4eHnz5+/e/duamrq8+fPHzx40Nzc/PXr1927dzc2Nj558qShoeEP+CrBxsbGp0+fTpw4cfny5d+/f6+rqzt16tTJkycbGhp+/vy5efPmxYsXQ0yGNMchLr937x4TE9OHDx+mTZv29u3b7u5uiLLe3t5ly5Z9+/atrq7uyJEjEI1///7t7Oy8ePHinz9/cnJy1q9fz8DA0NXVdeXKlc7OzqtXr758+TI3N/f58+fTpk07dOgQZCH1t+/f//37CzFhhJOjI6MjPAGMen80BEZDYDQEyAmBP39+T5s69em5bYUlFWVlZU5OTsbGxnCDGBkZf/z4ERsbe+rUqQULFigpKUHaTw4ODnPnzn3z5s3Bgwc1NDSEhISePn0K14XM4Obm/vnzJ+QiX4j4vn37VFVVFRUVnZ2dN23apKGhARFnYGBgY2Pbt2/fx48fv379evXq1dbWVkh77s+fPyYmJoqKigwMoM09O3bs8PT03LVr16JFi8TFxd++fcvOzs7IyHjz5s2EhIS4uLiIiIgfP0Az7NevX7948aKQkNCFCxeeP3+uoaFx4cKFr1+/JiYmMjIyFhUV7dmzBzy4BXcCTgbEJf/+/ePj40tLSzt58mRtbS03N/edO3e+f//e0dEhLS399evXtrY2BgYGXl7eZcuWZWZmsrNDR8t2795tYGAgKytrbW29devW7OxsNJsMDAwCAwOvXbtWVVX1/////Px8Jiamt2/f7ty589atW/v37zc3N6+srPz379/Tp09lZWX9/f1//fqVn5/PwMAwb968qqoqKyurly9fxsbGRkdHf/78OTQ0NDY2loGBwdTUdPv27To6Onv27ElPT4fb++XLFwcHh9LSUgYGhvDw8BMnToCuYv73j5OTs76+nomJ6fnz59nZ2d++fSssLLx69WpNTQ0XFxcDA8P69euTk5Otra2nTp2qqqra1NTEwMCQmZm5bds2GRkZHh6eKVOmcHBwbN68WUREpL6+noGBwdjYuLy83MHBgY+Pb/v27VZWVhBn/Pz5U09Pr6qqioGBoby8fNWqVVlZWW1tbczMzOfOnevp6SkpKfn+/fvVq1fZ2dl//folLCy8cePGnp4eUVFRBgaGpUuXmpubs7Kyenp6BgUFcXBwQIxlZmZOTU09cOBAdna2jo7O+/fv////X15erqurq6+vv3jxYmdn561bt/b09IiJiTEyMi5evNjGxoaBgYGDg8PGxmbXrl18fHysrKxXrlx59uzZixcvrKysDAwM/v///+nTp23btj18+NDT03P9+vVOjo6/fv1SlpdnYQeFzCgYbYyOpoHREBgNgdEQGA0BkkOAhYW1uKTk2fldKioq1dXVqqqqaEaA5oIZGVtaWtLS0hwcHFhZQfPYYmJiurq669atO3v2bFRUFJoWZO7bt2+5ublZWFggjTkGBoZVq1YxMjK2trbeuXPn2rVrubm5nJycEC0/fvwIDw8vLy//////lStXKioqIFO6LCwsJ0+enDt3rpyc3OfPnxkZGd+/f8/Hxwfe1cQgLCwMGcg8c+aMpqbm27dvIa0KAwODzZs3v379Oj09/fDhwx8+fIiOjr5+/frz58+nT5/+9+9fWVlZeXn5a9euQWxHJiHNMvhI8J8/oO1KkIY4AwPDr1+//vz58/PnT9BOpj9/5OXlIS65ePHi9OnTJSUl//0DbbSHkBBjV6xYISgoCBnvvH//fmZmJtw0iALIHDFoWxIDw5cvXyZPnvz+/Xs5Obk/YPDmzRtDQ0PIOJysrCxolv/nT4ji379///v3T0pKioGBQVBQkJeX9/Xr1xwcHOrq6hCTfX19a2pqrl69+vfvX+SeBjMzs5KSEkSNtLT0ixcvIOa/efOmvb2dkZFRXFz8//////79+/Hjx9+/f3/9+gVpjIJdBDq14N27d/AEo6Sk9Pr1azExMRkZGUij8O/fvzIyMgwMDBAGJJp4eHgePXoEsRTiZXl5eQhXRkbm/fv3T548aWxs5Ofnh9jFwcHh7u4OWQng4uLCxsb28ePHlStX/vv37+/fv6ampoGBga9evaqpqXn//n1kZGRcXBzEtN+/f//58+fXr1+QDgwvLy9ktQMfHx8TE9NHMFi5cuX//////v1ra2sL0cXAwODi4tLT07Nnz57Q0NDz589v3bpVTU2Ni4urp6fn+fPnCgoKv8DA09Nz0aJFF86cuHXro5oUov82wsHoNP0ITwCj3h8NgdEQGA0BckLg79+/kpKSc+bO2b17d2FhIR8fH7IpkKr679+/4uLiubm53d3dkAYQAwNDWFjYvHnzvnz5AmkvQmbnIUvx4I2wx48fT5w40cvLi5GR8e/fv6ysrNevX//48WNERISWllZQUJCQkNDu3bvhNv4DAyYmJmZm5h8/fvz794+REXRWDDMz84wZMyIiItrb283MzH7+/CkmJvbp06cbN24wMDDcvHnz9evXv379CgkJmTp16qZNm/bv38/AwODt7b18+XJubm5HR8fjx4/fu3dPS0tLUlJSXl6+paWlq6srKSlJV1cX0piDux/iGCkpKVZWVsg87K9fv/bt26enpwd23b////+zsLBA2qMQxf/+gQQZGBjmzJnj6OjY1dXl5OT0+/dveHPz+PHjf/78CQ0N1dLSCg4OZmdnP3ToEBsbGzwwIeZAWn4MDAynT5++e/fu9OnT8/PzIQO3SkpKp0+fZmBg+P79+7lz5yCtxi9fvjAwMLCysnJxcZ09e5aBgeH69eufP3+WkpKCtNUgxmpoaEhJSTU2Njo4OECaiRDx////Q8z89u3bjRs3VFVV//79y8bGtmvXrp8/f06ZMiU1NRXiNRYWlm/fQLvcIBohggwMDLKyspA59z9//pw5cwayshPuKUjigWj58wd6OBdcL0Scg4Pj4sWLv3+DjtC6ePGioqLiihUr1NXVJ06c6O/v/+PHjz9//vj4+Bw+fPjo0aOQ7fCioqKZmZnd3d3Z2dnu7u7Pnj3z9fVdunRpS0vLihUrvn37BjGZiYnpz58/379/h3AhcQdpGf/580dQUBDZHBcXF4gyBgYGTU3NP3/+rFu3zsPDQ1lZecqUKZ6enpClwDNmzCgpKWFmZv779y83N7eZmVlvX++tW7fQFgePZDA6MjqSY3/U76MhMBoCoyFAZgj8/funra3NQBo6m4xmCjMzs7i4OKRR5eLikpKS8uPHD4gaQ0NDeXl5JycnyAgWXCU3N/fnz59jYmIYGUGrD+3t7WNiYhgYGISEBNnZ2ffu3evn5xcUFAQx5N+/fwcOHPDx8YGMQYqKii5btuzOnTt///798OFDZmamsLCIgIAAIyOju7v7/PnzIQOccnJyAgICKSkpVVVVMjIyb9686erq4uPj+/PnDz8/f01NzcSJEw0MDHR0dFRUVExNTYWFhZWVlcXExDg5OW1sbHbs2JGWlsbOzs7Kytrb2ysgIAAZLYPM2EIcxsfHV1ZW1tHRsWLFio8fP0pISERFRX379k1ISBDsFyELC4vU1FRfX19HR0dBQUFIELm7uy9btuzatWvfv39XUFCADwYfPnw4MjIyICAAYvi3b9/2798fEhIiISEBEYGQXFxcvLyg81y1tLQYGRkLCgq4ubmFhISYmZmjoqJyc3NTUlK+ffumr69vZGTk4OBQVVWVnZ1dXFxcUVHR2Ni4e/fu58+fZ2VlCQoK8vPzQ7byMDCAzkz19/dPSkrq7++HWAQhWVlZb9y4UVRU9OjRIwMwOHHiBBsbm7m5+erVq/Pz89nZ2eXk5BgYGERERHR0dNLT04OCgiIiIgQEBCCGh4aGnj17NiEh4c+fPwoKCs7OzsePH4dMoDMwgFYM8/PzQ+wSFBSEtKq5uLiQOzyMjIxPnz4tLy+HrLUIDg4+e/Zse3v7q1evGBkZFRUVIZ0lHR0ddnZ2MTHQWZ6xsbFFRUVycnIvXrxoaGj4/Plza2uroqLi69ev7ezs4K1tZmZmDw+PtrY2W1vb6OhoISEhSBpjZ2cXERGRkJCIj48vKSmRlZV98eJFVVUVPC6YmJicnJwOHDggKSlpYmKyfft2U1PTHz9+8PHx5efn8/LyCgsLQ8byAwMDY1culbaP/A8eCB8FoMQGT/SjwTESQuDbn+9///2BHeFGax//Z2Fi4WSBzqPR2rJR80dDYDQE6BYC3379M2i5viJF0UgO+4q3//8YPn36wi/AA3fS50/fePmgir99/cnBwc4E2n7NwPCf4eOHL/yCPAz/GF6+evPt6zcGRgZRUVEeXmjR8fHDF25urm9fv/PwcDPBx0/+Mbx9+0FYVABi/o/vv1+/eg0ZU5SQEOfkBjWRP374wsfHw8jEcOPa7e8/vmtra//+9ZubF7Q68NmTlw8fPtTU1BQQ4v329ScDAwMXWMvrV+8EBQVYWJm+fv7Bxc3ByMTw4/tvJiYmNnaIWxkuX7z26/cvfT09Fjamb19/MjMxsXOygtyP5FMGBobPn75dv3adj49PQwu8egHiRwEe0M1SDAx3bt1nZ2eXkZVCDqK7tx+8//BeV0f395/fPDycEJVv3rwREBCAtMYgg3Nv377l5+f/8uULZPIa4v2vX79C2tMMDAwfP368dOmSiooKPz8/ExMTBwfH79+/z58/z8HBoaurC2n7vn379unTp0pKSjw8PO/evbt69aqCggJkEv/9+/fc3NyQJuO/f/9WrVq1f//+6dOnQxpkEOsyMzOtra319fX//fsH2WIPWQLBw8Pz8uXLW7duaWlpsbGxsbOzQwZxb968ycvLKysr+/79ey4uLvhy2DNnzrCwsBgYGDCAz2f9/v27oCCoyf79+/efP38KCIAi9+3bt5BVmN++ffvz5w+8Pdrf3//169eoqKg3b94YGRlBgujhw4dPnjyBOIyHh4eJien79++MjIzwhiZkg5qmpiak4fvmzZvr16/z8/Pr6elBvAYn79+//+vXLyUlpU+fPgkKCjIxMf369evr168QF6KZA9f169evnz9/8vLy/vnz5+vXr5Am9adPny5duqSgoCAsLMzIyMjOzn7w4MFF82b9dmhVEuVo9AWdAzAKQBMZo6EwckKg7ljjpdeX2ZhBZ1jQ2te//v4yFjeqtQAtMKe1XaPmj4bAaAjQMwTwN0b//v/95OOZfwy/4NPukE3lcC4TE+jEJfhQCDMT81/QnmJGZmYmUGvpP8M/0IJD0OpJ8LQy8///0Gl3uBZGBkYmJiawLpC/GRmZmJmYIA24f3///QNfccTExAy28T94uSrjnz+g6W+wCAMzGIDuK/r3j4kRtFwNogWy4hOyhR+yHx/sVIb/YAPBNyGxMDIw/v4DOiYTLAVSBbYIZU80ExMTaLXrv//IVzFB9k0zMjAyszD//w9aEMnExAQRZGBgYGFhYWJkAqtnFOJUEuAAre8E+W3g8Pz589euXdvZ2Qk5CAnukJKSEhMTk4iICLgI/RkzZsz4/PkzZBMV/W2nxMatW7dOnTq1oaZ81n1VKV6G0UPvIQDezYRwaUWeObhv6+K5bLDtgbSyhjJz//z5I6WolFzRQJkxg1r3jz8/vv7++vsfaJ0NrR366++vH3+gE3O0tmvU/NEQGA2BwRMCv/582Xaz/sOPt+g30w8eJw5il/z5+99VOdtMNmnA3ejm5ubt7Q2Z40Z2THV1NWToFFmQzuzo6GhIv4LO9lJunamp6ezZs6WlpafcesDAQI+BoSEB6NQY/fLxw6PbN9m5oNMugzNo/vz6zcxCpwAZqBBgZGRkYmSCjASAVmkwMTIyM/6DHSgNcRUzGzPkIjsIF7Q8/8+/f5BL/eBCuBiQA/XAF7AxMYIHOXCpHBUfDYHREBimIcDBwh9nuPTff7qtCBpm4fifnQVlN9hAeU9aWhqr1ZCpaqxSdBOErJGlm3VUtAjRuAdXlFQ0eUgDOrW9QHMWrKwsLKCjPcDHJfz+9/cfExMjMwsraFKGgQG0g+/Pb4b/DMwsLEyol0/8+f3r3z9opMG1/Pv799+/fyzgs0KoFgFg26lm2qA3iJGF8cebH++uv5W0kgZdIcwAWq7+78+/+1vufn3+lZGJgYmF6S+4nSqiJypmJA66fBq/pxgZQO3a/wxMbEygi6DxKx6VHQ2BUTBMQ4CRkYmbDXSa4zD136i3RkNgNASoDOjUGEV29b9//xTUNEWlZT69e3v36pX//0Grg3gFBBU1dZiYmO7fvPbh9SvoCOX//0wsLBo6JrwCQqCxuv//P3/4cO/6lV8/fojJyPILCt+5egl5VTXIFvCSIiZm5tFNaqDQwI0ZWRh/vv95ceq5P9//StnI/P8Lau4zMjMy/GH4+uLrp4cff3/+/eHue2FtESYWJl5ZXtBiLEYGJmbQ4qr/f0HLpBjA3H9//zH8ZwBpBLdEr82/IuskJ6ghBGqV4rZ9VGY0BEZDYDQERkNgNARGQ2A0BCCA3o3Rv3//WLp5e0UlMDIysLCyHd+9bcO8GYKi4olldcLiEkxMTJ8/fpjb3vD66WNGJqb///6xsbIFJmcKiUn8+P4NtHicjfXKyeNLJ3TKKqup6urfuniOiY0N3P78D9oh/v8/OyenW1jMkW0b370Gne8AGXYFKwAN+0EYoOlp8HVtYO5/RgZGBjAXEiIjgWRiZvr85PPl6Rd+f/vNKcQJ2s7KyPD/z//Pzz9xCHHqpOoxsTK/v/72bM9piwYrVl72f7/+ghqgf/9/fvrp/7//XOLczBzMf77++fz6M480DxMr04+3P0AK/v37cOs9v4oAnwI/Iwtkwn4kBOeoH0dDYDQERkNgNARGQ2A0BMgHoIEu8nWTqPP///9sbOxWbl5bFs2piQtZPqnHwMpOWFxS19zyx7evrVkJrVmJ3z5/MnN0/ffvLys7OysbGyMDw+9fvxb2tFbHBtcmhM1tq9c0NlPW1T93eN+6OdNYwGf/srKycXByg3ZW/v3DLySsYWjCycPLzMzCBrvgi5WNjZUVtEyYjZ2diZmZnZMTtPnx7182Dg52Tq7/DAwjbRgVtFeUhUktQlM1RB00IvqfgZGJ8dfnn+f7z366//H/v/9/f/z5+ws0/vn359+/P/78//v/788/V2ZePFJ+6Ej5wQuTz/3++vvv778XJpx5duTp3x9/T7efeHf97ZMDjz/e+3B5xoXPjz4xsdA1aZGYEkeVj4bAaAjQIwS+ffv26NEjyGU2JNn3798/+CHkJGmkiuLfv38/fvwYcjI8VQykiiGgCan/oAKbKqaNGjIaAoMK0LXFwMjI+O/fv5lNVWcO7Pn37/+zh/e+f/3KwsJyau+ueR2NP75+/fTu7evnT//++fPv3/+ApAyv6MTf4Cu5/oPv7/r79+/9G9d+fP/Gxs5hZOMYkV308/t3XTOrwp7JTfNXJlc1CUtIuYZFyyirptW2Gtk5xBZV/vv798/v3+7hsbY+AX///AnNzE8oramevlBGRdXOJ7B62oKGucsDkzNZ2dhHSHv0z78/v/7++vn7J4swi4CB4G/Qety/v/79/vn7FyMPk1GtKZcy6D7oX/9+//n35z/D/9//foPYTH8eHXz48d4Hmw47h8ku/37+fbDzPqsoq0qs+r2tdy5MPscpyyVqKy7lJiOoKWRQasypwPXz589ff3/9AZ1pOqgS/KhjRkNgNAToFAJXrlyJiopqaWl5+fIlMVZ++/bt/PnzkC3SDx48yM/PB09eEaOVmmrevn2bnp5eUVFx/vx5ZHO/f//e0tLy+PFjiODbt2+nTZsGv6cHIkhTcsGCBXv27KGpFaOGj4bAQAE6TdODLiv7+/ffX9BJbF8+fvz//x8HJ5dnZPytS+dfPH4EWff5799fAxsHKQWlnauWMjIy7Fmz/N+fPyCp//+lFZW/f/vKxs6ua2HDyMj4+O5tHVMLFjZ2fmGRkPTck3t3Xj93yszJTV3f6MDGtdKKKhvmTmdhY+Pm4YXsc2JjZ//96+ef379FJKS/f/k8rbZUTFrW3jdo9cyJXz589IlNsnDz3Ld+FcN/0D6qgYoJ+tirLKD07/8/VibQTjJGFkYWPoYv7J/1RHQYICf6MYFm7CFrQJ8LPL/FfENbWIudl4OBkfHlq2efX3w+2nH4////fz7/Zmdm1+bV0vPU+3X6+5VdlzNW5vCK8v7i+nWf646GlLqMuOz/P/9///utxK9IH3+N2jIaAqMhMNhCYMWKFQ4ODrm5uaBinIEBci85/PhxiGv//fsHkWVgYHjx4kVra+uSJUs4ODh+/fr1+PFjyFVM3NzcEMWgkz3+ga47h1zdBBeEML58+cLDgzhj/9evX6CNs0gHpGB1AKbeI0eO/PnzZ968eeDTSSHyIPLUqVMTJkzg5OQsLi4GHWv/+fOWLVuio6Mhd+qAVMDw////GRkZf//+/ffvX2T/fv78GXkPOOQOdGTtX79+RfYs5Ch4NjY2SBDZ2toiy6Ip/vv3758/f+DnycPcMkqPhsDQACz0cSY7B6egqBhk3hw0Wc/B4Refys7JuX35IiFRMUYmpr9//+iYWrmGRW1dPO/X9+/C4pJ/fv36//+fgKgoMytrQHImxJ3vXr3YsXzR39+/efj5WVhYVHX1f3z7emL3tl8/f25bOp+RgZFfRPTP71+fP7yXlFdkYWEVEBH99/8/GwfHnz+/BUTFWNnZj+3c8vrZU0t3bxY2Nj1zG9D9vGzseubWF48e+vvvH68A6PoHiF3DkszSz0D21/rn6xedW9Rl044sCGFf4rp0jut0h00rJy/o0pTqLYz8JnzpqemsrKyvXr+SkpLys/Z78eT50c8HFeWU9F5oZwRl/P7x+5rA5WLTAiNTY4gho+RoCIyGwMgMgfXr12/cuFFaWvrVq1clJSWnTp1atmzZ379/nZyckpKSLl26tHfv3jdv3khJSWVlZTExMT1//rytre3s2bO5ubleXl56enq/fv1qb2+/cuWKtLR0ZWWloKDgiRMnZs2a9efPHxMTk6ysLMilOwwMDCdOnJg3b97379+VlZVLSkp4eHhWrFixceNGcXFxBQUFNTU1Ly+vPXv2LFmy5P////b29gkJCZDmHaQF3NnZ+fr1a1FR0aqqqidPnkyePPnDhw9VVVVRUVHGxtCi7P///ytXrmxra9uxY0diYqKQkBAjIyMrK/Q0GMjdSPPmzfv79++FCxdKS0vv3r27bNmy////u7m5RUdHP3/+vLe399WrV5ycnGVlZcrKytu3b1+5cuX///+NjY0zMzN//vzZ2dl57949yNWRGhoac+bM+fLly9WrV3/8+JGTk2Nubn7hwgUpKSlmZuZFixb9+PHj2rVr8vLy1dXVPDw8hw4dmjVrFg8Pj5aWFh8fX0JCwshMdaO+HsIAsgyF1uTfv39///r5+9cvMAINUv7////3r5+/fvwAi/z6/esnxA0/vn+DiUAUg+7wgEiBxuT+/P7x7RtomPPPH9AU/J8/YJOhKiEa//379/vXrz+/f0MYv3/9/AvuM/7+9RMk8htk19+/oAWRcGNB4iC3QR0GFx/2jHWr1vp5+UG8+fPrz7kz57548gLCvXj2oqaq5rdP3yDcU8dOaWtoV5RUtLe021nbrVy28v/f/xGhEbVVtdcvX9PW0D53+tz///893Tyjw6Pfv34H0TVKjobAaAgMyxD4+vOvau2Vsw+/4vLd69evExMT+/v7Hz58eP78eVdX17Nnz967d8/f33/79u2HDh1SVVVdvnz5kydP/v379////58/f+7bt8/FxeXixYtv3ry5d++enp7eokWLXr16lZycPGHChA8fPvj7+x8+fPj169fJycmrV6+GW7127dqDBw++e/cuOjp6yZIlFy9edHNzu3z58p07d2xtbdvb258/f+7j43Pu3LmnT59GRETs27cPovfv37/Jycnd3d1v377t6+tLSUn5/v17X19fcnLynTt3Pn/+DFH2////mzdvBgQE/Pz5MzU1dePGjf///3/w4IGfn9/Hjx8hav7+/RsYGBgREXH69OkLFy74+PhcuXLl4cOHQUFBJ06cuHjx4vLly79+/Tpx4sTMzMzv3787ODgcPXr01atX8+fP//TpU11dXVFR0evXr5ctW+bv7//jx4+QkJCEhISnT58uXbrUw8Pjz58/xcXF8+fPf/jwoYyMzIoVK168eBEbGztz5szXr187OTnt27cP4ruIiAiIk0bJQR4CsfPu1258OsgdSTdAp5FRJhAAbSFCbrazgDcVIYswMDCwc+A7GJ+ZmYWZE+FmyG3B8D4u3Cj4+aNwBuhOOfDxpfCzTuGKIZvrkVUiSw0z9s13N9///AA6eQDssbsM9//I/T3x/BQDA8Pbt2971/f9U/ivw6TLwMBw5+ttdi2O4y9PcH0BzZExKjJEVkdNmzb969evQUGBUtbS07ZPv/7zRmxU/BfRbyZhpg2LmqqkKzW9tWfNmul0wVlLWxtswygxGgIDEAJMjIyaQhq8bLwDYPeoleAQEBERERISkpCQkJOT279/v56enpGREQMDQ3Bw8JEjRxwdHQ0MDJDvk2RjY5OXl+fh4VFXV2dnZ3/79q2kpGRkZCQLC4uXl9ehQ4du3Ljx8uXLs2Dw9evXa9euge0BEdbW1jt27Dh69Ojz58/fv39/+vRpU1NTHR0diHU/fvy4dOnShw8fjhw58u/fP4heR0dHBgaGd+/evXr1qqenR0BAIDo6OjY29v///1JSUs+ePVNWVgYZDcNbtmxRUVF59eqViYnJ8uXL/fz8YDJQ+v///+zs7CkpKSYmJmvWrPn8+fO+ffsYGBi+fft2+fLl+Pj4+/fvT5069erVqx8+fGBlZdXQ0Jg5c6aenp6joyMbG9vFixfb29tFREQCAwOXLFny5MkTdnb2kJAQKSkpZ2fnJUuWfPv2jY2NjZmZ+e/fv3p6ej4+Ptzc3HZ2dnfAQFpaGuKj6OjodevWQd00So2GwNABiIYdTd38/euXT+/fgxckMjAxQdqQUAv//fsLuhUI6XAlcEv8HyP4vuD//0EriiBL2qEawBTyTcfgxigjRBe4ZQm6+AdsLOi+IfASeNAORNAWctBFwJDVkSBTYBpB/XIQf9Djf//+c3BxCoqIke3S6Zdm3f/wQIhLCDLDxSjMIBMuP+PiTGZmZgZGRtsKh2O/Th69fAJy57JlkfWyh6CJJAYGhv///7NIMft3BDAwMv79+3fujQUsnCxWJbZrnq799/gfuwOnBCPn3OvzWQxZYqcnHPl+7ODFI2Q7kooaWViYmZiYfv/6zcbO/gu08AOUEpDNZ2Vl/f//358/oNXMyOI0ZTNCcgLYDhZm5n//Qfdwg3k0J9jY2CD3caPZxMrCwsDI8Pv3HzRxTC4jAwMLKwj8BgNmJiY2dnbIyBY4r2HqGACRR58fTXDo1RfVGwC7R62EhQBoq8A/UHnLzMz85w80af3+/ZuZmRnSdIMphNJ/wZsK4OfxMTExgcolcOEDLtgZ+fj4DA0NGRgYzMzMFBQUINo+f/5cXFxsbW3t5OT09u1byLrJr1+/wmXZ2dkZGRkFBQUNDAz+//9vbm4Ob2hCCjqIvZABWsiyVLSU/OvXrx07dnBzc9fV1f369Qsy5oq5bpWJiQmyAJSRkVFYWNjQ0PDfv39mZmZqamrt7e3v378PCgoSFxdfv349IyNjV1fXpUuXbt26VVFR0dTUxAFaSwYNor9//zIzgwoucA0FKnsh7oT4CC3omJiY2NjY4CcPfPnyBbO6hGgcJUdDYDADOjVGT+/fvbi/g4WNA1SmoIYHpHWAfCglRARVFQEecu0OsQJiCJo4pikEFWBqGSgRRgaGf39/y6hp5bb18fOReVncv///Mo3SHUTszl049/vXbx0dHWER4f///j9+8piNlU1CUoKBgeHTx0+vX7+Wl5f79ev302dPQfuZGBmFhISEhIXgfv/399/9B/fFxcXhOwY+fvx44sQJCWEJfX19uLIBZ9y+ffv169eWDpY7d+60s7fDrD/OnD7Dz8+vqqY6UE69d/ceDw+PmDj5HQySXL5//35tA23EfXQwzZcvX/7/758ecXF37uy5HTt32NvZWztbv3v3btmyZdzc3FFRUYNk88T///9jtif8Gz0EBxa5A0X//PkT0ga1srJavHjx5s2bBQQE1qxZU11d/eXLF8x96IKCgp8+fdqwYYO5ufn///8hvUdGcO/358+fmpqaPDw8r1+/1tfX37Jli4yMDMRfv379evXqlZaWFjs7++nTpwUFBcPCwhYvXjxr1ixeXt7Vq1fHxcUZGRn9////+/fvsrKy27dvV1FRgegVEhJSV1fv7OyMiYlZsWKFlpYWJyfn79+/f/78CVEAITdt2iQsLLx48WLIIEhra+vChQszMjJ+//6N3GyF7FhiYGAwMTGZO3fu79+/RUVFd+3apaur++TJE2VlZWlp6fXr13///v3r16/Nzc0+Pj62trZbtmz5+fOnra3thAkTcnNz9+3bJyQkJCUl9e3bN0jowXt6EPMhXIjDfv369f37dw0NjX///nV0dKioqMydO1dGRubLly+LFi0KDQ0VFR29BwsSVKPkYAd0aowyMzG9+vhl77VLjP8hrURC4QJvJCIzkDVBjIHLQqQgTVqsUmhNVNBYH0QPmIRoBDMHLfHv339dOan/7Nx37941Ag8PkOFUVlbWY8eONvTW3bx9k4mJiY+Pr39Cv4eHR1xcnIKCwoIFCxgYGNauW1tXV3fgwIE7d+5ER0dD+tmsrKyJiYm1tbWQ7Zz7D+wPDg7Ozc1tbm5mYGB48+ZNWVkZIyPjr1+/3FzdYuNiyXAbLbQcOXLk5MmTJiYmvX29unq6mI3RNWvXqKur07kxOm/ePHV1dWtrawYGhl27dykrK7u6utLC+5hmzpo1Kzc3F7Mxun379t+/fxPTGH329FllVWVoaKiIqAgDA0NjYyMzE7O/vz9kFAfTRvqL/B+9i5Zegc7KjK/o9PFyhzQZlZSUutpbFy1a9Ofvn8K8HGtr67u3b0SEBqE5U0REpLaqYsuWzVwcrHa2tnExkZBEpa+jxcPJzsvL293ROnfuvMMH9ukb6MtIiUO0CwsLV1eWrVm5TFpapqQwX0REWEpKavLEvuXLl/8VE/P2cGNi+CssLNzW3Lhk6ZJfP39ZWJiLCCM2qrY01U+bNm32jKmKiopZ1ZUMDAxGBrpiIggFDAwMfNychXnZkNkkBgaGtOSEHTt28nKxx0SF83KDRlgYGBiYmZnDggOUFeQYGBjk5eVbmupXLF/x588fW1tbLi6OmqryKVOmzJk5zcHW2s7akpeXx8fTbfOGdb9///Z0c7G3tbK3s547e/bcWTPExMQmT+xjY2MLDwnS1gB1koUEeONiInm5OTxcncTExcVFhKIjwrg5QcveLMyMlRXkuLi4Zs2YumD+gof37nh7uN25c5uVmeH/31+sTJC6EBJOo+SgCwEmfLlnxAE6NUYhbb+vP35C7kAfccFMJQ///PMbVDojLWkg1WB2NvaZM2cJfxJcu24tLy9vbW3t6tWrbW1tP336BJ/Y+vnz54cPH/79+/fr168PHz5MnDhRT09v//79zc3N/Pz8lZWg8nrx4sUfP35cs2ZNXl6eqKjo9u3bpaSk7t69q66ufvzEcW8fbyEhxDAqxJEXLlx49OiRnp6egoLC379/nz59KikpycrK+unTp2/fvklISLx8+ZKVlfX+/fvKysoCAgKQDaovX75kZGS8cOGCmJiYMXhn658/f86dO/fixQuIUf///3/58iUTE9PDhw/19fU/fPhw5swZLi4uc3NzTk5OZmZmSBUCCjewO968eXPmzBlubm4LCwtWVlYmJtCiDrAMlPj06dOJEydYWVktLCwgk26XLl26f/++jo4OZHbv1atX////v3jxooiIiJGR0fnz51++fGlpacnPz//hwwfIcdkvX740NTUVERH5/v37u3fvpKSkGBkZX716xc7O/v79+y1btjx69Aiy9RWy9uv3799v3779+fPnpUuXVFVVNTQ0INOFJ0+e/Pz5s56eHiMjo5iYGGQG8+/fv69fv4ZMF2ppacnJyR05coSRkdHCwoKNDVRFPXv27Pz586KioiYmJhCPP3jw4PLly5qamiwsLBBDPn78ePLkSbg3mZmZIb0OaCggUTdu3Lh586aampqmpuanT582bd7EyMhobm4uICBw+fLl8+fPFxUVSctIs7Kyvnjx4syZMyIiImZmZkxMTG/fvmVkZHz06JE4GJw6der169e6urrwCVYkS0aZQw8wMjJce/4DT3tHwcTr379/l55+Z/jPwCqtn1LVBzrziIHx0tPvzLxKOk6qIClUf4vrOqYbuP7+8/vh93/m3okQBcxCakqimiBdPEqxRe1//v5hYWa58uL3f4bvEN1i2g6ZWvYM/0H3Ff//9//knXeXrr0Oy2pgYmYqLS1z1NY6//gbi5hObHHXv79/mVmYLzz5Be+xMDKyesSV/v79m5WV5cb73//ffWMR1ZYV1z3/+BvEcAYGBilDN4b/DHARZmYxU5+k+19/GbjFXHrx6/9/iEpGTYfwN3/+vARpZGSVNowvNf777y8TE/P5xz+Z2WTiSrr+//sPvlGZ8fzjH4Ia9vGa9n///mVlZb3w7BcDA4Olf5qp9x9mFpYnv/48evxNyzH8y9+/5x9/Y2RkNvaMv/Til4Se67///+5+/mfgHnPh6a//DL9ZpY1kZBnP3P98+sx1+7AcHh7uiRMnMghrXn/L7Bie9/D7r/sgx8D9McoYXCHw+ssfOSFQiT0KGBgY6NcYHQ3uwRAC4Jrjf25OrpOTE2gQdO1aZmbm379BbVxIGwU0gszICFmqxQhmmJubm5iY2Nranjp1avXq1ZWVlffv3z948GBaWtqyZcv2798fFhYGSkksLOLi4oKCgq9fv0aeuoL4etq0afv27dPR0Zk+fXp1dbWBgUFWVtb06dNlZWX37Nlz4MCBSZMmNTQ0PH36VFxcvKysDNIY/fLlS0ZGhoCAgKqq6qlTp9zd3bOzs+fOnXvkyBF1dfUZM2a0t7fr6ellZGSwsrLKyckJCAi0trbKy8s/e/Zs8+bNPT09cE9Bhi6ePXuWn5+vrq7++vXrXbt2NTU1oTn19evXOTk5UlJSP3782L17d1tb27x58zZv3mxgYDBr1qyCggJXV9fOzs47d+4YGhqePXtWQUGBhYXl169fK1asmDlz5vbt22fMmGFlZfXz58+pU6fOmTPn+fPnXV1dixYtYmVlnTBhgpqamqCg4P379//8+cPJyammpjZhwgQzMzN7e/vY2FhFRUVJSckJEybU1NQ4Ojq2t7efP3/ewMBg7ty5bGxsixYtggTm9+/fU1JShIWFZWRkJk6cqKGhwcHBcf/+/X379jU0NFy8eLG6utrQ0PDevXtaWlpVVVUnT56srq62srLau3fvxYsXOTk5379/X1BQICkp+f37940bN/b29iIHFMQWCLl27doFCxaYmprOnTs3MTHRyMjo6NGjT548Wbp0qZeX18WLF589e7Zly5Y3b978+vWrurpaX1//4cOHhw8fLi0tXbx48datW6WkpNLT01etWnX27FkNDY0lS5Y0NzerqalBzB8lh24IsDIzZS57xER83xgyDgQug8j3Nfj6ZrRsCzIN1fD/DIKMR0E7nJhEE89cY5h25z5IDfjeZ3gzFCwCJRgZGLGKQ6XJpEBBg2QsGhdc1oKWjyNChHxn/BdnuPwItE+XxYOJiWnvrHtkOnlUGx1D4Nef/65aZK64G35gsDRGGRlBO5CGX/gS4yP6+v0/KyubiLAwxGGQOfdf4Guu4M0ROAOiBiILmXu6cePGv3//duzY8ffv3/r6+lu3bi1ZsiQ0NNTFxWXv3r2Kiornzp2zt7cXhpkPMeHly5fr16+fO3eunJzc1q1bp0yZMnv27D9//kBqlH/g67UYGBg+fPhgZWVVVlYGGcwDF9WM3759KywsdHR0fPDgQWJiYnh4eEBAQFhY2Pfv3x8+fLhr1y59ff3nz59XV1f7+fn9/PmzpaWFl5f31q1bJSUlb968gRv1//9/ZmbmlStXqqiolJaWfvnyJTEx8datW3AFEKeuWLFCWlq6r6/vz58/9+7de/369apVq6ZPn66oqLh///5Jkya5uLh8/vzZ0dGxoKBgy5Yt7e3tu3fv5uTk9PLyunPnzp8/f8TFxVtbW5mYmCorK1esWOHo6AhZ+AUZ6P39+7e/v//+/fvNzc0jIyMZGBh+/gQdPQY53LuoqEhDQ2PhwoWbNm1SUFA4fPjwihUrBAQEVq5cuXDhQni8MDIyvn37trS01N7evqmp6dq1aytWrHj27FlCQsLnz59nzpwZFBSUlJT04cOHsLCwqKio+fPnJyQkxMbGvn379tKlS8zMzNu3b2diYiorK/v3719MTMzZs2dZWVl///4NCQQ4+fPnz3nz5jU1NRkbG587d666unrz5s3Jycnz5s3r6OgAjeVYWh46dKisrExNTa2iosLCwiIzM/Pp06eZmZlJSUm/fv2Sl5efMWMGCwtLT0+Ph4dHdHT0r1+/BsnSUrg3RxlkhwArMyPzwMw1QtqeeBwOVfD7719FUY5d+dBFong0DGkpUFkK2sPLwASJDlC7d0h7aGQ4/j8DKws0oY6CwdIYBeWlkRob9PU76F6Qt+/eQQIbsgcTMg4Kb4tAmonwVhr8GpJHjx7x8/P/+fNnxYoV379/b29vf/369fnz52/cuKGpqdnb23vq1ClfX1/IdleI+RDy5cuX/Pz8UlJSDAwMqqqqkPX7oJMOmEC30cIt4uLiMjY2hnNBSzvAp6VoamoyMDCIiIhwcXF9//79+PHj27dvl5CQePLkCWQjgrCwMGRe++PHj/39/d++fePj44NsnoA34CCmvXz58sKFC62trb9//1ZUVIRss4U4EkI+fvxYSUkJMtCrpqZ269YtLi4uyLSyoqLir1+/IKPIkIE9Li4uRUVFLi6uv3//8vPz//jx49+/fyoqKhAvKCkp3b17FzRWAVsJADrfDOxl+L5diKWQLRrCwsLy8vIMDAxiYmK/f/9+8eIFFxcXZIRYWVmZm5sbNMUJrmP+/v3Lw8MDCU+43zk4OFhYWD59+vThwwdt8LlaAgICUlJSL168gBwGzsDAICwMWk4H2fBx9+7d9vb2P3/+yMjICAgIgCoyjPXcX8G39Soqgq7RUlZWZmVl/fz5M2T9AMTloBN8//6FzO+/ffv2xo0bb968+f37t5aWFiTALSwsIMskKisrp02btmfPHl5e3rq6OohPIYaMksMgBP6C94sxgQ5GQfHNv/+gzeDIDdb/DAz//v1HFkHRgMr59x/Eh7SvQCyyMBMjAzcbqKghS/fQ0vSfgWG0cTO0omzUtVAwWBqjUOeMUjQPAVCrY9rUqRo6Gjw8PK2trdzc3HPmzNHV1d20adP27dvFxMSWLl0qIyMjKSl55cqV////nzlz5sePHwcPHty1a1dLS8u5c+fOnj1rZ2f3/v17bW3tGzdurFu3rrq6WlhY2NPTE+L8v3//fvr0iZ+fH9Isk5CQ+Pjx4+PHjxUVFa9evcrDw8PPz//v379Pnz4xMDBcuXIFsncV5DLU9hATE9OvX7+uXLkiISHx4sWLHz9+fP36dd68eTNnzlRQUCgoKIAMOsJPkFm/fv2PHz+mT5/+7NmzkydPQlr5yKSEhISlpWVdXR1k2aWEhATk+gOIsyGjv7dv34aMYj548EBYWPjHjx+3b99WU1O7efMmJycn+CgokEshjS2I4fD2GQsLy61bt/78+QNhyMnJcXJyQo6bYWRkhKy8hCiGbyUGmQX2NdwX/8BAVlb269evt27dUlRUPHny5Ldv39Aa1pAmILxdCzGHk5NTRETkwoUL5ubmb968efbsmZSUFA8Pz/Xr162srF69evX48WN2dnYxMTFdXd3u7m4GBobnz59LSkpu3rwZ4qMPHz7AI46Hh+ffv3+3bt2ysLC4efPm379/eXl54TZCAg0eAiIiIgYGBtnZ2b9//37//r2wsDBc5f///7m4uGbOnMnAwNDU1LRkyZLq6mqI9lFyGITAn3//RXlYuNiY3n79+/XnX3hD8/9/BgFOZmYmhvff/kK8+Z+BgY2ZUYiP9dXn3+BUDxGGkr///mdBWsIN1/7269+///7j3ywFNWKkU6Mt0ZGeAoYuGG2MDt24I8flv379SklN2d2z083NjZmZWVRUtLOzk4ODo7q6+vbt235+fiwsLBISElOmTIFsPIdcxAw5pS8HDOrq6mRkZObPny8uDtrN6uPjs379+sLCQoh6iJtevXqVlZU1Y8YMiBoxMbHIyEjIHPSlS5dqa2s5OTkDAgJKS0v19fWvXbumqgraNMrOzg5pvEIMgZD///+fP3/+gQMHzp07FxERoaysrKSk1NHRISkpee/ePciYJRsbG6Shpqent3bt2qampo8fP0LmgpmZmSGyHBwc////Dw0Nzc3NLS8v//Tpk6ioaFNTEysrK2ToDmJdeHh4bm5uTk7Oz58/RUVF29raoqOji4uLdXV1L168CDkxALLtCTLkCdkwxMDAADmPmoWF5cGDB5WVld+/f3/y5ElpaSk/P7+CgkJGRoaoqOinT58gF1W7uLhMnjz58ePHRUVFkF1WoOse2EGnIULWtrKwsMjIyCQkJOTn5ysoKLx9+xayoALiSEZGRoinICO4EPczMjKys4PO+8zOzi4rK7t79+6DBw/c3Nzk5eVTU1PLyspu3Ljx48cPSDvb09Nz586dhYWFkLUQ3d3dLCwsTExMnz9/jomJmTdvHiTi2NjY0tPTW1pa9PX1L168mJ6eDtn/BAlbiF746HJSUlJJScmzZ89evHihoaFRWlrKysoKGXRnYGBYvXr17du3tbW1r1y5kp2dDfHIKDkMQuDPv/8+uvy13pISvCxnH30rX//s9qsfLODBzJ9//iVbi8sIshasegyaHmBg+PP3v5oYe3+YTOjM+99+/QMP9IPC4D/oMFEGT22+F5//XHj8Da491lxEkp+1bP2zCGPBY/e/Pnv/CzoNDdI0ikdDYDQEhg+g00rNo9s2dtZVbT53HbabHtKBA8/BoAQmRBwiBJJlZGAGLwAHnZwMER3JpIGitIWudmpjp5GBAXnhkLe/0FPZw0bA6vKVy79//1ZTU5OUlIQY9fnz52vXrn3//l1dXR0i+PXr1xcvXkBmh3l4eCANlCdPnjAyMkpLS0N0vX379sOHD3JycvDZfNBJpZ8+VVdXV1RUwJUxMDBcu3YNspseMr/879+/8+fPf/36VV9f/+/fv0JCQq9eveLl5YVsYIcY/vHjx6ioqJ6enufPn4uLi0Nmnz9//nz69GnQ4aCqqv/+/RMQEHj+/LmIiAjEAXfAQF9fn4WFRUBA4MePHz9//hQWFn7x4oWoqCgLC8vHjx/PnDnDyclpaGjIycn57t07VlZWXl5eiI0MDAxfv349c+YMMzOziYkJpO1448aN+/fv6+rqQs6pef36NTc3N2TNwKdPnyDB8vLlSyEhoQULFty9ezcxMfHJkyempqZ84ONgf/z4ceLECUirlIWFBWLX3bt33717p6en9/nzZw4weP36tYSEBCMjaJns169fRUVF37x58+3bNxYWFsjWsaVLl0Ic+e/fP4h3WFlZP378CAm9v3//vnr1SkxMjJmZ+dWrV5DzB/T19SHN9OfPn0P26fPz83NxcXFycn758uXMmTNMTEyGhoa8vLwfPnyANG3Ly8urq6shcQSx7u7du7du3VJTU4McJvD9+/fPnz9DDoeCHGUgJCQEaZS/ffv23Llz/Pz8+vr6kHMDmJmZIYHw58+fy5cvP3/+XEVFBbLIAWI4Lch////Fbk8sMSkyFBtER97SwqcDaOa3X//MOm6++PRbgo91W47yqrMf1p1/n2kvys/BlLHs8d9////8A03H13hJSguy5q+ENkZBfS0mRm52ps/f//7++/8PeBoedMk76Iio/3Ni5ded/7D+/AdO8Kz6fwYGLlam////s7EwLUyQL1r95Parn/8ZGP6CdTExMrIyg1L3b9DVzgzgZQIMrMyMf/6BFgYwMTGygcxk+P33v4YEx+GS0Q1zA5hYRq0eDQHCgJ4jo4wM/yFtzf/gdS2gtibYgYyMsCYqA0wFWAHjfwam/wz/QGtg/jMxMIy2R8GhRTHx588fXj5eKysrNJN4eXnNzc2RBbm5uSHtD2RBSIMMLiIMBnAuhPH379/IyEhIixYiwsDAoAUGcC4TExPkqCa4CKR9A+dCGFxcXIKCgpBloxARXl5eyFEAEC4DAwOyRSpgAJeCNzThavj5+Z2dneEKMI+g4ubmtre3hytgYGDQAAO4CPwcaU4wgIhDmqTMzMzs7OzqYAARZ2Bg4ODgcHBwgHMhDGUwgAyIQkTgLuQCAwYGhkWLFt28eVNeXv7YsWM5OTkQZZARWXhjkZ+fHyLOzMwMN0FMTMzNzQ0iDiElwQDChpA8PDzIroIsTv348WN0dDTELxBlDAwMYJcirkYEexp6Zy8jI6OEBOiiBIhiYWFh5ANTBQURhzWysLAYggFE5Sg5PELg/38GWQHWb7/+TT/4+sXn35P3v54fJyfJz8rHwVzkIla4+gmkuQn37J9//2UEWXMcxao2PDOS5sp3EhXlZdl+5dO0g68DDATsVHh0pTnffP5z7N4XNham33/+exjw8nEyC3Cy6EhxTo+Wi5p7X0aALd9JVISHdc2594tPvfv7j6HEVezZxz+e2nwfv//t3vUywlTQVoVnycl3q86+hy8YgDtglDEaAqMhMDgB/RqjTMwMHDz/QYfeg9qe/0AHafxn+PsPND3zH7TIHSl8QM1U0IIixv+glUbg++RAQkgqRpmDOgQEBQUxG7tkuJiHh6etrQ1tbz4Z5tBNC2RHP7Wsy87OPnjw4OvXr/38/CAXbVPLZFzm8PPzUyXicJk/Kj6cQuD773/ff/+7//YXKzOjjx7/rmufAvT5xXhZGRkZHr37tenSx3df//z5x/Dn3/9vv/5Bpul//wVtXTKV52JiYGj0lbz35sepB1+9dfnPPf52/vH3h+9+XXzy/c7rnz///P/zD2S4jACbOD/r5ksfw4wFNl38KMLDMjFM5tCdLyfuf4s2F/r55//0Q68NZLg8dVjXnvvgqsk7L17+2N2v5x9/L3AWO3j7y703P///Z/jxe3QgYzilu1G/DE9Ap8bo/3//OHi55cz0mBiZmdk5GJl+//v94/+fv39/M/z78/vfn99///1jYgIdS/Gf8f+/v//+//sD2v0AIliYGH5+//Dz3dNvoPbp8IyFUV9hDwFmZmbIclLs0oNPVEQEdCkRtdzFzs6ONsBJLZNHzRkNAQpDgIWJMdZc6POPv4yMjO++/e0Lkf76C7Tg598/hnBjwacffv/99z/WQshUnkuQmznNVgRyHOnf//+l+Fl5OZgTrYRVRNn+/vv/ENxs1ZHiVBRm+/Pv/4/f/101+dhZQaMWv//+N5Tj4uNgviH2488/0IR7mq0ICzPjtec/fvz+f/np93RbEWYmBiVR9juvfz77+Pv+21++evyLjr9jZmJgY+FLsxW59/onAyODBB8rhZ4d1T4aAqMhQGtAp8YoI+P/X7+4n7/WZmJmY+Hi/8/A+Ofbe8Z/35gY/v7/Dz4V5P8/RiYGRiZmBibW/39/M/z/8///HwbGfwz/mZkZf4sov2Bkv/bm7k9G8EV/8Gl+8G2g+GfwwUrAM/20DspR80dDYDQERkNghIQAGwtjgw+o9Qn3Lx8HM4Rd6gba2ghhQ0hTeS4IA042+oL0WvGwWClzwwUZGBgMZdFVMjAwQNRAtDAwMPSHQm+lZ2Bg6A0BsVXF2D21oYeH94RAl7PnOIxey44ctKPs0RAY1IBOjVFwGPz//+/Pfwamv79///37+//P74z/fv+FrAT9/+8/qL3I+J+JhZGJGbSsHXRQDWiNKSPD7x9/mf/8k5SUffnx8bPfvxkY/0OKvL+MTKAlppBVqCDzQUYwgqfzEQ3Q/+DtmowMoFXwo6tOQaE0ikdDYDQERkNgNARGQ2A0BEZDYDABOjZG//9n/P/n/98/f779Yvj7h+H/H9DaUcggJ+iGN9AkPCPDX4Z//xgZ/zAygJqn/xn+MPxnYGZi+PuHhYmJlYn5P8Mvhv8MfxlAV7fB25aI9UDgffeg0xLBIQxql4IPC2D6DzIf0k4Fy4wSoyEwGgKjITAaAhSEwO+//xcef/f+G2iHEicbo6c2vxgvy9G7Xy4//fH/P4OMIKujOs+acx80JThsVXng9px79O3y0+9eOvyrz74X4GJx1eQV5ma5/PT70btffoFOfeLw0Obbee3TzZc/QcX+v//mity87Ew7rn22U+ExkuNafvodExODhxYfPyfLhSffTtz7+vcfQ4AB/5VnP649/6EtyaEtxbHhwkcWZoZAA8Gjd788/fCbeYScdg8PYmowQBdBMzH9/fcPfoowNUylyAwm0KJjxn9//4LqdYpMoolmJkZQA4NGwSUryBZpitgMShMPDAJD6dgYBV288Y+J4R+oGfof1IIEpSrQCCjowuD/IBHG/6B5eZAQaHsTIzh+/zP/Y/jHyPCTieU/Jy+oRcrICNLLwMgEamGCJvIZGf8zgbZAMYDWyP///5+JiYGVhRmUjZiY/v79+/3Dr/9//4OGSEF7+UF2DoJgHxRO+Pz588ePH9F2xxPjstevXzMwMMA3lROjZfCrefXq1aVLlzQ0NDAD5PHjxwICApAjmcjwyOvXr//9+yciIvLw4UN5eXn40Zu/f//++fMnDw+iqiZo+P///798+cLNzY18IOuPHz9evnxJ/zuN/v///+fPH8iJWnCX//z5E34KKUTw379/jx49kpaWhtwF8OPHj0+fPiGf+QVRRpD89+/f169feXh4QMf5EFQ9qoDGIfD77/+W7c+VRNgFuZiZmRgfvPnFycb08tPv999AzYWrz5kuP/vx7DPDtRcfD9/5Aj4CgpmZmen1p18ffzLcff36/tufbMxMt1//5mRjfvvl95tv/378+HXv7d/rb/49effr44+/jAwMP3/9evD2Fzsr88sv/59+eLf7+qe7b35ysLPfe/ue6f/ft1//vv78+z8Dw8vPvz//Yvzyi/Hp5x+nn/x++O7P39+/n3/88+rz76+//iGmzmgcIEPLeNDBWGxsv379wmw/MTEx/f/3/8WLF3z8fMgH7Q2gByF3v338+FFERISdje3f//+QKz8G0EloVv/+/RuUvsEX7KFJ4eJCmtd//4J2aeMBj9//ZmJkGG2M4gkikqVAt8CByiRW0DlOf39BWobg3gRoRz1oKBQ0uw5at84ALj9APAYmBgZQ0fbvLzMnL4+shtyPn6z//zGycbOycDFzc/L9YRD68Y///29Gbi4OBhZGFg62j5+/sHGwMzP9+/uf4ddfZiG+/98en76w68Lf36CBWfCKU5JdjkMD2IEgtw7VBu7p06fXr18/adIkUmv3lStX/v//Pzc3F0fIDD3h58+fZ2ZmSklJJSQkYDZGOzo6AgMDXVxcIB778uXL3r17vby80NphEFlMcvny5R8/fszNzc3Ly1u2bBnk3E0GBobTp09v3769ubn54sWL3759s7S0xNSLJvLp06fOzs6CggJWVtYjR474+PgwMjLeunWro6Nj2bJlaIqpzl2/fv2DBw8KCgoYGBjWr1+/Zs2af//+GRkZ5efns7OzP3r0qKOj4/379zw8PMXFxRoaGhAHfPnypaioaPr06cuWLdPT02NgYNiwYcPkyZMhssSTr1+/njBhQlVVFdm9AuLtGlVJTAiwMTNOjZLXFGfDqvjbl0/r1qwKCwhn4wId4nv/7u1bt25Ja0jeu3vXLygUouXpowcnT53UM9C7evmyf0LY8cMHNmzaLM7OKgU+/tY3IICBkfX5k4dHjxwOiYiBaNm4dqWUmKypBcrJdBfOnly/bh3oQEBGxiBtrZCYUEYWDoj6URJrCHx692r+/Pn5BQUMzOxoCn58+VBTUyMILrL0jLTRZAeEe/n86a6uLj0ZmbrCuh8/fnBycnLwCAyIS3BZ2t/dbmdnZ2xujUsBpvj3z+9//PghKApaPI0HbLn8sXX7CzwKho0U3eYwGBn+g+7bYOEUZuIU+M8E4jL8//uP4Q/jf1AnmOE/4z/Q2cX/QAtF/4EuNAadXAxmMDIwMzL8+/uH48t7wc/vhL5+5Pv+SejbB5HXHxW//THkZNXh4NISFtDj4NH9/ldRUEiPjVXr4xeFvwzqHOzq3ByG0vpOIgocoJFXUPuXyIgD3UkHumYZ3CwG6YEyoBQDaDCXEdyGYwYxQSqGDGZiYvry6cu7d+8gQ01gXzD8+PHj6dOnv379YmBggF9Sz8DAALmnnoGB4d27d69evYJ48sePH/DbLD9+/PjiBTSrQDrZP378eP78OeSiToj6T58+PX/+HNKXhZA/f/6EmPDjx48nT55A2BDFEPLnz59Pnz5FE4fo/fbtG9w0iOL379+/fPkSwoY7HnQaA2jpBygdQRzz7du3p0+fQtiQuzf//fv37du3379/Hzx4UFBQsLe319jYGH5BKGTkj4GB4cuXLxBdkJs8nz59OnPmzDdv3kAuNGJgYHj16tXbt28hDoCQyCK/wOD////fv3+HBBFY4JeOjk5ycvKvX7/27Nmzdu1aZFv+/AEdJAExCuJrSFxwc3MnJyfz8/PfuXNnxowZnz59+vnz579//yAGPnv2DO59iF4I+eLFC8iZ9v/+/YN3xP///w9RDAlq+I2s4LtI/3358gXiVIgJDAwMN27caGxsPHPmDCMj46lTpyZPnlxYWNjS0nL58uWZM2f+/fu3tLRUVVW1p6fH2tq6pKTk69evcL2QqwfCwsJMTEx+/vz5+/fvHz9+vHjxAuI1SEz9+fMHouXbt29PnjyBuwcSU1++fBEQEEhOToZc9PXt2zdkz/78+fPJkyffvn2D2zjKoE8IPHj8DJdFHz59Ka2sbe3sgSi4cevO2vUb//xn6p887ePHjxDB2fMW3H/45O79R0tWrGZgYNi8fderN+9MLWxUNXXXbdqWX1DCwMBw5/6jeYugHa13797VNrb29E+EJF2IIQwMDLv2Hrxx54GppY22nuGBIyeS07K+fAENx8IVIDPQ9KJxIRkNoh6S9iBsCImpGCIOLx8gXEhBAUnecBFI6QrnopW0yOIQ7Whm4rIaTSOEi1yIQUyDZ3yIAgYWDlVN3V/guhfZywwMDBu37nz9/vPUmXN1DU0hetFcAloJBy5asXoBTTG8BoHYCy9UIVw4iek7ZJGJU2c6u3u3dvZy8gq2dPSs37wdrhEXA1k7mhrkYhBZClMLmgia15DNuXLjzpMXbyCmoemCCEJIZKlN23b1TZqGHJhoKQTC/Qs6ZB2ie5iTdJ2mZ/z3l+HPDxD57y8DaF7+P2h3PCNo1REDaBAUtOYTNOcOmpdnYGT4z8zA9JfxL/Pf/yzsHIws3P8YP/5j+sPwj4GF+Q8zK4+0mCwjG8+Pr79ZOJleffry7/9/Dub/fLx8b758ZmL6Ky7KwcrEwPj99/9fLAKiXJ9f/GJiZWRiZgbd/sjGzMrMzMDKzsTAwM7ExMTOysLMwsLKwszMzMjI8OTB8xeP3oLm9UEtT/DwJ5iAjYGCRBlAQ6yMDCChPwygtMIIX8E6yNMLOzv7smXL5p+aKywu/O3bN0jVfv78+a6uLk5Ozv///7e3t69evVpSUjIkJOTbt2+VlZUVFRXbtm3bvXs3CwuLjo5OeXk5IyMjZJp42bJl69atY2NjExMTa21tffPmTVNTEx8f3+vXrxkZGdvb22VkZLZt2zZv3jxubm5hYeHGxsaLFy+uWbPm7du3NjY2dnZ2ra2tHBwcX758KS4uNjUFlX0MDAxnz57t6uri5ub+8uVLQ0ODlpYWJFRPnTo1b948Tk7OV69ecXJy9vT0CAkJzZgxY+/evczMzCoqKvX19ZWVlRERESYmJhUVFRYWFkFBQYsWLeLh4YFcIgq5XL65uZmBgaGjo+Pv379cXFxBQUGzZs168eJFdnZ2enr6li1bIiMjtbS0bt++vXDhwtbWVlC3A7wTjoGB4c2bN93d3VeuXCkpKfHw8AgNDW1ra7t169bv379dXFwyMzN//PgBF3F2ds7KyoJMx4BmBZiYODg4bty40dnZWVNT8+bNm+3bt1tZWa1evRrSPsvJyYEMKG7duvXGjRvl5eXHjh1btmzZxIkTnz9/3tPTU1FRMXv27KioqIkTJ169ejU3Nzc0NFRVVfXNmzelpaVv3rxhZGTs6emBH0T/+fPn+vr658+f//r1KzQ01NTUdMKECX19faysrAsXLmRhYbG3t6+qqmJhYfn9+3dtba2IiEhzczOk3oIkCUjIf/z4sbOzMysr6/Tp0wwMDDt27PDx8YHEV35+fnt7u62t7ZcvX3Jzc1lYWBISEjZt2nTu3DlbW1uIdsj9pYsWLTIwMODk5Lx582Z5eTnkzqr29vYXL15MmjTp9+/fCgoKNjY206ZN4+bm/vr1a1tbm6CgYGNj458/f5iYmCorK6dPn97e3n727Nmenh4uLi5WVtaWlpZv3741NDSwsLB8+vQpLy8PbinE6lGSdiHAxsYWHRXlY6YYFRNrZ2cHKUzg1v3580dfX//cuXM7duzw8PCAlBgGBgaSkpKHDh3y9fX98uXLuXPnpk6dev36dch0MBMTk6mpqY+PDwMDQ2hoaFBQ0PHjxzk5OeEm79ixw8vL6/Hjx1evXjVAuoWOiYlJT08PrjEyMvLQoUMfP340NjZWU1P78uXL4sWLLS0tL1269PLly0uXLllZWaWmpv78+XPevHnnz5/n5eXNyclRVVVdtWrV9+/fjxw5IicnFxwcvHjx4pcvXyYmJtra2n779m3q1KlXrlyRlJQsKCiAZ7F///4tWrTo0KFD3NzcWVlZmpqa27dvf/v27YULFz58+BATE+Pg4PD169fp06dfvXpVVla2oKDg379/mzdvhlyBpqenl5OTg7ay5dixY4sXL/7z54+dnV1kZOTfv3+nT59+4cIFUVHRgoICcXHx2bNnh4aGioiInD9//u7du56enpDZqvv371dUVOzZs2fLli2srKxxcXGWlpaHDh1aunTpv3//IiIi4Pd9/Pr1C3KX3urVq3/8+HH06FEWFpbS0tLXr1/PnTv327dvTU1NcXFxHz9+nD9//q9fv4KCgry8vA4cOPD69evTp0+7u7tra2tPmDDh1atXJiYm6enp169fP3v27IMHD+7evevj4xMWFsbAwLBixYqdO3dycXElJiaamJicPXt23rx5v3798vb2DggIgCSVX79+LVy48Pjx4zw8PLGxsaampnfu3Jk6der79++trKySk5OXLFly5MiRb9++ffjwQUhI6MCBA7du3Xry5ImHh8eJEydSUlI+fvw4b9689PR0Li6uJUuWWFpabtmy5dKlS/z8/Hl5eYqKisuWLWNiYjpz5kxhYeGDBw8WL178////kJAQ+A0dnz9/nj9//sWLF3l4eLKzs9XU1F6+fDl58uQnT55oaGhkZGTw8fHNnz//yJEjfHx8aWlp2traBw8eXL58OdwcyHXKDAwMy5cv3717Ny8vb2ZmpoqKyuLFi5mZmW/cuJGSkrJt27bz589zcXEVFxe/efNmwYIFHz58+PPnT1JSkpiY2MSJE+/fv6+mppabm8vMzDxz5sxLly5JiQuzaQewMCMuFoEE2rAkmejmK9BY6N/vv7+9+vv9DcPf34z//oIQaIEoaJzx/38GJkYmpn8M4PPv///7//8vaEjrP9Of/xwywlpupkJivEz/GLk5mUX42fg4WLk4eKVEJJiYWd7/+vvjFwMLw9/3X77/Y2L68u3H7z9/hbhYNcR5BblYxIS5pMW5QiIT6zp72yZN6Js5a+LcRVPnr5y2eN3chasXLFg7Y9G6afPXT5u/furcdVNmr502b0PrhA5JeWFQcxPkCvAUPJhg+A8aA2VkBJGg5gUzaBkquA0N6tvQLRgptIiRgfH5s2ft7e2QltyvX79+//7d0dERExMze/ZsOzu7zs5ObW3tdaA5L4YDBw78/v375cuX69evnzhx4owZMy5durR3715Ixrt///6iRYva29tnzpzJwMAwe/bsP3/+7NmzJz4+fvbs2ZqamhMmTHjx4sXkyZNbWlpmzZrFxsa2fPnyHz9+7Nu3r6CgICYmZsmSJWpqajNnzqyrq0NeOsnGxlZRUTF9+nRzc/NZs2bBvQwpvjMzM+fMmSMmJjZz5szz589v3bq1v79/+vTpT58+XblypYSExO7duz9//nz06NE9e/b8//9/586dAgICbW1t2dnZs2fP1tbWnjhxIiMj48GDBy0tLUtLSw0NDePi4mxtbXt6enR0dK5duwYfort37x7kBna4G0RERIqLi/X19dvb24ODg9evX//48eMZM2ZMmTJly5Ytp0+f3rhx48OHD2fMmDF16tQtW7acP38eclUm6CJEZubDhw9XV1dHR0crKyu/efPm2rVr9vb2oaGhrq6uzc3NKioqEIu0tbUPHDjw9evXg2Dw9OnTQ4cOcXBw8PLyXrlyRVBQMD8/X19fv6enx9XV9f///58+fQoLC5szZ46oqOjy5cshhjAwMMyZM+fXr1+zZs3q6uqaO3fu9+/f3717d+rUqb9//27btk1TU7OxsdHb23v27Nn+/v5dXV1//vzZuXOnlZVVfX095CpUUOL+/7+jo8PV1dXKygrSWX/9+jX8liZBQcFfv349e/aMm5ubhQXaueXl5X3zBjpOAHEMExPTgwcP3r17x8jI+OHDh6Kiojlz5rCysi5atOjv378HDx4MCQnJzs7m5+evq6ubNm0aJGEwMzNv27YN4p6/f//evHnzzZs3PT09kKjU09ObNWvW1q1bxcTEZsyY0dzcDLlHCmLjKEnrEPj3739MTMyz58+io6Pd3Nzu37+PbOP///+FhYXr6+v7+vpevXoFXyodEBCwefNmBgaG48ePCwsLy8rKIg+ewQefWFhY1NTUrl27xszMDBmk////P6SjaG9vv2rVKmS7kMf2GBkZTU1NL1y48OTJE8gNuqdPnz558uSnT59aW1uVlJSKi4s3b968Z8+eFy9e/P79u7Ky0sTEpLGx8ffv34sXLz5w4EBOTs7bt2+TkpJcXFwCAgLq6+u/fv3a09Pz8uXLkpISCQmJ8vJyuDuXLl26a9eu/Px8W1vb4uLiT58+HTp0aNGiRTExMeHh4e3t7bdv354yZcq7d++qqqpERUXb29u/fv1aX1/PyclZVFQEaXcyMDB8//7927dvP3/+fPfuXX19fURERFFR0cuXL799+zZx4sQ7d+4UFRUpKSmVl5d/+vRp9erVkLmOmzdv7tmz5+/fv729vbdv3w4KCjp8+DCkZRYQEHDy5MlLly51d3cnJSVlZ2dPmjTp8uXLkHB7+/YtJOstXbp07969WVlZKioqZWVlCgoKFhYW6urqgYGB3759a2tri4yMLCwsnDt37rVr1y5cuNDW1mZpaammplZdXa2mplZVVXXt2rXFixc/fvy4sbHR0tIyKytr9uzZly9f3r59+4oVK/Ly8lxcXI4fP/7s2bPGxsbQ0NCCgoKlS5ceO3YM4pLdu3dv3769qqrK09Pz+vXrHz58KC4uNjIyKiwsPHbs2OzZs62srJSUlGxtbd3c3KysrNTV1c3MzDw8PISEhJYvX/7ixYsbN240NTVdunTp8ePH69atg5Q8lZWV6urqLS0tf//+XbJkycaNGwMDA1+/ft3Z2ZmUlJSenj5lypRr165B3PD06dPfv39XVFQYGxs3NTV9+/atpqaGj4+vqKiIkZHx3r17K1eu3LdvX35+vrm5+alTp+7evdvT05OYmJienj558uSbN28yMzOzsbHt3Llz8+bN5eXl7u7uDQ0Nz58/nzdv3vHjx4OCgr59+/bjx4/KykpTU9OKigp5eXkrKytNTc3g4GBxcfHW1lZ2dvbq6mpIx2PDhg1nzpyprq62tLS6fv06aIMMxKHDmoRWHvTw43/w1iPmXwwMjEwMoMOcQJaCxhnBA6JMDP/+/vr/5ze4NQq+mImBkYmJhZ1LiFtQ+v2rTz8Z3v/9x/Dj2y8BMT4uNqbfjJz/mZn+/PmuLM714y/z77+/xZiZlKUEf/78zsvK9ebbr5/ff/z4/PPtz59SnAwS4qpygqY8/JycXNxsrCysrCwf3336xMb7lI/L+P9P8IQDyA2gsStmNm19WyUdqbcvPrGysjKzMbFzsP4DtUr/MTL+Z2T68R/EBjmciYmZkZH1188/n978BZsAEhz8+M9fUG9bRUWFkZXR29t71apVHz58ePr06Z49e44cOQLZamNjYzNjxoyrV6/u2bMnJCTk6dOnL168mDhx4v///9+9e/f+/XvIIMelS5eUlJQgh9I7Oztv2bLFx8dHX19fR0eHhYXF29u7o6Pj3r17T58+XbhwIQMDw71798TExH78+GFqagq5CNTHx6etrS01NVVeXj4tLQ0eevz8/IsXL16+fPmdO3cEBAT+w4apf/36pa2tDRk7dHZ2Xrly5dmzZ/X19SGrPO3s7C5evJiamlpfX797925PT88XL16cPn2ajY1NUVHx/v37Gzdu3L59+7Nnz/j5+b99+yYlJRUYGAgZkuHl5WVlZRUSEvr16xd4gByULuEDwHCHgVIIExM/Pz8zM7OQkBAXF9fZs2ddXFwEwEBXV/fKlSsPHz6EiDAwMBgbG1+9ehWyEIKFheXDB9BiLAUFBcjyUyYmJhYWFnZ2dsgYMPLlmcrKyqKiokePHn337l1wcPChQ4fOnDkTEBDABAasrKz8/PxMTEyioqKMjIy/fv2Sl5e3tgYtVzIxMbl69SrcwdeuXQsICOAFAw0NjcePH/v6+u7YsYONjY2Pj09XV/fGjRssLCwXL178+PHjz58/v3z5Iicn5+3tDV/YCrmV9NmzZwUFBefPn//27dvnz59ZWFggo6eQpiojIyMLCwvypOS/f//gjQ+4YyB9mN+/f5uYmEC2W3l6em7atMna2lpTUxNyvD8/P/+yZcs+fPhw+fJlJSWlv3//KigoQCqep0+fsrKyvn379sGDB5s3b96xY8fz58+FhIR8fHx27dqVnp4uLS2dmpoKt26UQesQ+P//n6W1pTjDq9Nnzt26dQvShUO29MePH0ZGRsHBwc3NzYGBgZCM4ODgMG/evFevXm3btg0ylomsBZnNyAg6DQWii4GB4dy5c9+/f5eUlIRMJrx+/VpUFPtJooyMjL9//05ISEhLS/v+/fv27dshOV1XVzc4OJiBgcHFxeXatWseHh42Njb79u17/Pjxo0ePvn79ysvLGx0dra+v7+7u/vnzZ2dn51+/fs2bN+/JkyfHjx/v7+8HXwysERgYeP/+fUjRt3v3bogWfX39rVu3nj17lpGRMTQ0FDJwu3Hjxv379x8/flxLS+vgwYOfP3++evXq169fNTQ0IiMjubm53dzc7ty58+HDh5KSko8fP2poaBQXF0tKSm7YsEFbW9vf35+Pj+/o0aMNDQ16YLBr166bN29ycXFBCmFWVlY2NrZ///4JCgoWFRWJiYlVVFR4eHhAZi3c3d2XLVv25cuXy5cvQ/Y+3rhxQ1dXF1KOQXqbnJycMTExenp6goKC27dvFxERUVZW/v//v7Gx8Zo1a968eXPz5k0GBoafP39Cmm7e3t6BgYEvX748e/asjo7O/v37//z5c+rUqcDAQE9PT0gu1tLSunTp0sWLF6Ojo8F3ABtCplM+fPhw+/btu3fv/vjx49SpU5DL3sTFxb98+bJmzRoVFZWQkJDr168zMzPHxsYyMDBkZGRMnDgxIyNDXFxcW1sbMkUmJSWlqqoK8YWxsfHBgwefP3+emJh44sQJBQUFfX19CwuLf//+7du37+7duw8fPvzy5Qs/P39ubq6VldXSpUt//Phx6dIlBgaGb9++nT9/HmKmurr6p0+fICnh2bNnkJHXyZMnc3BwQJa59/b2JicnG4ABAwPD4sWLf/78eeXKlf/////48ePChQuQ4u7w4cOsrKyHDx/+/fv3nTt3Hj58CBm8gIwyfP/+ff/+/RBXiYiIqKur//z508TE5PPnz6dPn3Zzcztw4MD379/Pnj2bmZn57t27devW6Wiqe3l5zTiGc80Jcn4Z6mw6NkYhQQUZ+/wPugUUIsD4//8/Joa/f/8K8bGW58VIign//P77198//xn+vnv3c87KE0/vPmNi+qSo/u03aF0pM8Of33/YBf+xib3/9P0/AwMzO/vvL9+UpPj5uDnefvn19jNINwcL+9efDD/+/fv1m/HRq2+ast++//zO+oPlH/P3n785Pn58+/nJ8+3/tL5IMRjJ/f7FwPL/H9M/5u+M/1mY//3+8ec3EwsjMwsnMzMrMxPT3z//fv/+/fffH8hY6d+////+Yfr7h4nhPysLK5OYPPOvX99+ffvPyMTAzMQEGjX9/5+FlZ2Zhfn/fwY2Nk4WFqZff0GnAXz/8e37h78gQyDeBi1L+Aee6IeMu0JFQUOtoCMFIFzQ3DC0mQwWoLzVy8jICFr6Cbbz1atX//79g9zeHhsbKyUl9ffvX25ubjY2NldX11mzZn369MnKyurAgQPKysr5+fmQ1oaoqOjEiROZmZkFBQU/ffoEdhfD+/fv2dnZGRkZ379/D9k0/fbtWxYWFi4uLjExsaysLFZW1r9//woLC+/YsQO+78fAwGDJkiWQEc36+vpZs2aBBsr//6+rq7OwsMjOzt4BBvDaiJmZGV7hffz4kY2NTVBQ8Pz583A3sLGxKSkpsbGxrVmzpqio6ODBg11dXQ4ODkJCQry8vImJiaKion/+/OHl5X379i0zMzNyiwoy+gIp4iHst2/fIi/xgdjCwABKq////2djA+3b4Ofnh3TEIctqeXh4uLi44AtYX79+bWxsDAklSFDPnTt34sSJkyZNysvLQzYQzoYwGBkZnZ2dZ86caWRk5Onp2dnZycbGZm5uDhmYhLgBefEAaAMsuMn+9y/oRhyIIQwMDBwcHKDoBvPfvn3LwcFhYWGxadOmxYsXe3t7s7KycnFxhYaG6ujoQFYsfPr0iYWFBT7qA2lr3rhx4+fPn3V1dS9evLh48eLevXu1tbWvXLkCNpXh5s2b/Pz8ampqHz9+fPfunZCQ0Pfv31+/fq2oqAhRgEYyMjLC19e+ffuWi4sL3uj/8+dPaWmpp6dnTk7O8uXLz5079+/fP7RmLgsLCz8/f1JSkpCQ0N+/f3l4eNjY2JYuXfr06dP169dXV1cvXryYCXQEMZq1o1zqhwBoSUZ8orrQn7y8vMjISE1NTUw7fv78mZqampmZOW/ePMi9vmJiYvr6+vPmzXv58qW9vT1ECzyPs8AG1xkYGO7evevl5QVP0pA5h5KSkv///9+9e/fQoUOQliXEBEhrAMK+cOGCr6+vlJSUjIzMjh07Xrx44ezsDJkhhSiAZI21a9euW7fOz89PRkbmxIkTkBwKmTH/8+cPNzfoNP4/4CUikOkRSOEAmhYDt5IhRoHPb4FOMDIyMkIKSbhjIBmTiYlJRERESEhIUFDQwcGBkZGRh4cHUshAzOTj46uqqvr37x8HB4eAgMCUKVP27t178+bN7Ozs9vZ2ZjCAWAdpoP/9+xcSUL9//4Ys8oYUv5AMC7cdwuXj44OUe5CBPYg5EB9ByjFIUQY5GeP///9///6F+AISCBC9KSkpVlZWixYtgmwf/PPnDxsbm7i4OOSWOA0Njbt378JXU0Ac8P//fwgDYuO/f/84OTlFRUX//v2bmpqqr68PETcxMZk6deqhQ4eOHDmye/fu2NhYuC74oPi/f6BaGKL+z58/8NTi6Oi4Zs0aPj4+SLP15s2bcXFxixcv3r17t6+vr4WFxfXr1yEdY8jM258/f3h4eCBuyM/Ph4yJMDAwLFu2bNu2bX5+frKysmfPnoXEKSRqIGEImdeCOABS/PLy8oqIiPz9+zcvL8/U1PTAgQMQlcLCwiIiIv/+/Wtra1NSUvr37x8kWBYvXrx9+/aAgAAjI6OTJ0/++fPn169fEI9AXCgqKiokJCQsLOzv729mZiYjI3PkyJFdO7dvu71D2qscbvUwZkBzEV18CGqBMoI2tYPWhYObZf8Z/v/7zfCH4e9/pj9/mDjYfzNzfvz4jYHxDzsLE8Nv5v8Mf3/8evfj6xOmv7+ZGVmYWRjEBFjEBPkVJUQMlPm+M/wXFOQUFeCQFRVk4eA6d+/FtYcv33/9/fzD799/fr378vPLt78///zhZPnD+Jfx/99/v//9ZP7HyPr7z6PrT7eJSRxnF7n15V+lpMBZsXdCPBf//2Fm/PP//7//TP/+/vzy4/vXvz++/v/64cend9+/f/r98/Ofn1/+//nOzvSPi52Zh5Odh4mR9fdPhn8M//hEeTn5+Nl5hDn4RNl5Rdj5xNi55ZjZpf8zSfz9J/b7n9jv/2K/fssKSwlx8zNxcrHxCnDzCLAJiLGIyfBIyHApaQrpGMsbmilbOGip68gwMaE2Phn/MzAxMLMysHMys3OxsHOwIEuTEWssLCwnTpxo72hfs2bN0qVL//79C7mOfO7cuVeuXJk8eTKkbeft7b1t2zYDAwMODg5DQ8OfP39u3Ljx5MmTPT093759+/Pnz48fP8zNzSFZbuXKlcuWLYuMjGRkZHzw4EFfX9+GDRu6u7u9vLx0dHQUFRUhy3F6enqePn0KGcmDuHzZsmUNDQ2QyTJIjoU0sFhYWJ4/f37y5Mn169dDCl+IejY2tqtXr06YMGH16tUzZ84MCAhwdnZ+9uzZxIkTFy1atGvXrvDwcBYWFgMDgxs3bmhpaVlZWe3Zs8fZ2Zmfn9/CwmLu3LmXL1+eMGHC1atXWVlZIVtkICZDigYGBgYWFhZTU1OIF2bMmAHZQfXr1y94s5WBgUFISIiJiam7u/vMmTOBgYE7duxYtGjRhAkTXr9+7eTk5O3tvW/fvoULF06cOPHVq1d2dnbfv38H9WfAzUQZGZmampotW7YcO3aMkZER4gZlZeUjR44sX778+fPnEPcwMDA4OjqeOXNGW1vb0NDw8ePHCgoKnJyckL1Kf//+FRcX//HjR39/P6R3DjEHUkoiN6AjIiLWrFmzYsWK9vb2f//+GRoaCgoKysnJHTx40NHRkZGR0c/Pb8mSJZcvX54xY8ahQ4dYWVnhu7IgLmFkZGxqapo3b15/f39JSYmFhYWXl5enp+eVK1c6OzsXLFjQ19cXHx+vqKhobW2dk5OzZs2avLw8DQ0NbW3oPtz//////PkTsl8K0lG5evXqlClTVqxYMW/evMDAQMi4C6R2ZGdnf/ny5ZEjR7Zt2wapDCB6IQX99+/fZWVlzczMZs+efeXKlf7+/hs3bmzZsqWqqurZs2c/f/7k5eW9fft2fUP979+/mZhAw9sQX4yStAiBX79+9fR0Hzh4sLm5WUtLC1Kzwi369+/fnz9/IPV6TU3N6dOn4ZuKIiIi+vv7tbW1Idfn/v37F5Lq/v37d/369WPHjh04cKCoqEhQUNDGxgayxRCSKubPn9/d3d3b29vZ2bl06VJ43+zfv3/3798/duzYwYMHa2trf/78CVkOGBwcXFtbq6mpycPD8+vXL0h2hmQTJiamGzduQqYU7t+/D3Hbr1+/ID0xeOsHkm5ZWFiMjIzmzZt348aNWbNm8fLywvta9vb2S5cuvXLlyubNm58/f25kZMTCwrJmzZrLly8fOnToxo0btra2enp6X758MTMz4+Tk/PnzJwsLC3yzHWSxOBMTk5KSkoqKioyMzIcPH/r7+xUUFGJjY7m5uZ88eWJqajp//vwbN27Mnz+fhYVFV1eXl5d327ZtV69eXbt2LSTvwzdH2tjYbNu27eLFi4cOHZo1a5aGhsbfv3+VlJQMDAzevHkDmQiCbEuCaPnx4wfEy5BBPsiCB0jAGhgYsLCwSEtLGxoavn37loeHB15OCgoKKigoMDMzm5ubf//+HdKDhZgDyc7///+3s7NbunTp5cuXd+/ePXfuXB0dHUZGRnFxcSMjo1evXkFahwwMDEePHt20aZObm1tsbOzdu3dFRER+/PixevXqa9euzZo1C3LGyI8fP+AlMC8v74kTJ65du/bz508HBwfIwgN1dfVfv35dvHjRxMTk4sWLampqFhYW9+7dgwwEwPeDGhsb//jxQ0FBQU9P7+XLl5CGNQMDw/Xr1xUUFKytre/duwc57lBcXHzGjBk3btyYAV6c5uzsPG/evGvXrm3atGn58uUWFhYQc3R1dV+8eCEgIADZSGpmZvbmzRs9PT0FBYV3795xcnJCegsMDAxXrlxRVFS0srKCbCdlZGQUFBS8cuXK+fPnGRkZNTQ0vnz5Ym5uzsjIyMzMvH///n379nl7ewcHB7989RI8dgTPWMOWAZoHoYPnjm5d39vWu/MxLyNo3xJoKh5kKTiM/4EOsWf68/eflLacv4f5zycvOViZmf4zvn//8du33/vP3Pr4+Ss7F4+20dt/r38pi4s9/c7HwsLDJyH//i+vjDCPKC/npUdvn73/8uX7T0FuTgUhdj4eTlFBjp+//r//+O3j5///OR/oWahKCZnz8/Lw8bKxMzC8uvd2j4T8y+dc7xl/XHTli+W4nfHy9YVnun8YGblY2P8yPZxbH//g6I9//z/+/P6FgZHp37/ff//8+s/wX0BIWFhE9P9f5n8MjG9evHrz9qW4Ksu3z7xf3rGCVh6A9jOB1r+CNjOB9jwxMzAwMzIyM/xn+ff/j6LqDyWRf/y8Sjyiij++fOBk/Mz07+O337+YWLm5+aW5uYTZOAW+/3+6a/++H+9YObk4eHm5uLnZhUX4ebk4ONk5uNjZmZgY/7/9+P3T59z2fg1VHVAAko4LD5ZoMKv/v/D3++/v9vb2kOmYX79+rVu37tatWxoaGl5eXpBi4vDhw2pqapClgY8fP167du33798dHR0h3c3///9raWm9fft2xYoVHz58cHd3NzExuXLlSlVVVXJy8qVLl0xNTT08PCDjhWvXrn3x4oW5ubmzs/Pz58+fPXtmZmYG2aW+efPm27dvS0hIBAcHQwZOGBgYnj9/vmLFChYWFmtr63///hkbG0Pque3bty9cuDA6OvrChQu2trYODg4MDAzPnj1btWrVz58/fXx8IA2gFy9e3Lt3z8rK6vv370ePHrWzs2NjY/v27duGDRvu3Lmjq6vr4eHx79+/06dP29jYQAYYHj58+PLlS4irIEXh06dPIeZbWFgcP35cQUFBUhJxBsft27d37typqqrq7u5+9erVTZs2cXFxBQcHQxYMXL16dfPmzezs7CEhIbKystevX//165empubhw4dtbW3Z2NguX77848cPWVnZO3fu2NjY/P37d+/evbdu3fLx8VFQUIDH6oEDB/T19QUFBU+ePCkpKSknJ/f79++jR4+amZlxcXFdvnx5//79RkZGOjo6ly5dsrOzg6yF+PTpE2SKEGLOmTNndu3axc/PHxYWBpnWfPTo0dOnTyGl/N+/f7ds2XLx4kVFRUVfX192dvYTJ05YW1tDBksgJsDJt2/f3rp1y8LCgpGR8eHDh+vWrfv27ZuLi4u5uTmkDlu3bt3169eVlJRCQkLg1d7v37+PHDliaWl5+fJlMTExdnb2x48f37t378aNG5B1qG/fvr127Rpk49GzZ89WrFjBxsZmYWHBwsKiqal57NgxKysrdnb2b9++nTp1ytbW9tevX+vXr4dEpaen579//yD7vURFRUNCQt6/e9/a3vre83Ozc4OeMGhGEu7+UQYVQ+Dbr38GLdfXZKjqSYGmCDBNfvPm7apVK1NSUiBpae/efU+ePI6Pj4cklc7OzvDwcMj05cWLF48fP5GRkb5585bVq1dxcHAwMjIZGOjHxMTw8vLeunVrz5499vYOx44dhS/D+P79+9SpU2NjYyEF1L59+5cuXQJZYKOpoRkTGwNp5v7+/dvc3HzGjBlmZmY3btw4sP9ARmYGAwPDzp27mJmZtLW1W1tbf/36ZWZm9vPHz7j4uJUrVtra2aqrq1+8ePHKlavR0VE/f/6cM2dOWFgYJydnb2/vndt3hISFi4uL5OTkIF7+/fv37Nmzjx8/zsHBkZmZaWRkVFNTA5m3efXqVXR0tIeHx8ePH/v6+h49esTDw5OZmSkqKrp06dLMzEx2dvb9+/e/evUqPDwcYhokcNatW7djx45///6pq6sXFBT8//+/v7//+vXrAgIChYWFysrK169f7+rqYmNjMzQ0FBYW9vT0nDx5cnZ2Nh8f3////5csWbJnzx5mZuaIiAg3N7dt27atWbMG0jqMjo6GRMeLFy8WLFhQVFQ0b948BwcHDQ2NN2/eLFq0qLCw8PDhw69evQoJCWFgYNi7d++yZcv+/ftnbm6enJy8e/fuf//+QRZX3L17d+LEiZ8/f5aWls7Pz3/69OmtW7cg+5aWLl2qoKBgZWU1e/ZsyMx1fHy8vb39gQMHlixZ8vfvXxMTk6SkJEgR8ebNm+nTp9+7dw+yGcjHx+f69euTJk36/PmzsbFxVlYWOzv7jBkz7OzsIFPqT58+7e3tZWJigqxnmD9/vpqamrW19d69ex8/fpyQkHD37t3u7u7///9bWFh8//49Li5u+fLlHh4esrKyDAwMW7ZsgeyIsLOzi46OhkzTPX78uKOj4/fv32ZmZj9+/EhOTn779m1vb+/bt2/l5OTy8/MFBASmTp169uxZTk7O1NRUU1PTLVu2rF+/HhKqsbGxkN2ZBgYGM2fOPHHiBCsra1hYmK2t7axZs2JjYwUFBR89etTV1QWZl//z509ycvLfv38nTpx47969vLw8SUnJ7u7u169fCwkJQU7KmzRp0vPnzzlYmVg0fS781ztUqARPIcOVQdfG6K5HXKBbl0CDov/+g27vBLdK/zP8Z2L88/O/tKRQf2uGjJQI0z9mBoZ/v//+ePL8TWnNtKfP37PxCWiYvBP4ys7EyP3omxAHtySPoCgfH6egCNeDd58/fADN4KtI8Etx/uZl/CMkJCglJnrpwetzd56zsHAJiL5RMpL7x6H96edHbVVlMSFJRgbmZ5+/sbGy83Oy3uNiecH51+wrs9CPv7//M0mznvv3b2Nr3b5b+7i+vHny5tMzBlDrGXQSKgPDX0FhUUkpuf//WRgZGd+8fPXq1VMxVeavn7m+vmNnZmJgZADtaQKN5DGAdzmBTpJi+s/IyvCP8R/jX2WV34YiXLwcouziah9+ggZtBL7cZuTl+fn+CQM3KxuvIC+vPJPwr5eff/L+U5WREZYQExTi5eXh4GJlY2RhZvj/4+f/v78vHDl8+8HFwu5JMhJq5KXIvP1FHkpuXvKgZiJ5JuDRdeXKlbKysq1bt0LajnhUkie1ZcuWpUuXLkfaoEOeOaO6hnEIXLt67frN6+s5N5eYFBqIQqcCh7F/B8prkMboihRFIzkst8mjugo86gDpq4POIQENgsMUoElhDmZD7uiDKQfRyFpAI+YgMajhYCaU+P/j+5+1q9fv27d72szJ7OzwY0fRtDP8+/uPCf2aJkxLoYZiUwyS+vv3L3xyuaioyMDAIDY2FjIqDJIG49+/f0OaPmAeAQJybBCyesisArI2ZEuRxeFDv/ByGHJ6GtyFaIrxcyHLACCddkyVBD319+9fyMIGiF5cpkGWQ0AmQyAqMf0LEYeQaMZCBJFJPNpxhQamFjQRNC4ec8Br+7FMO2MGF7JH0GTBXJaNl792bH96vEwd2XfDkk3XNaPgnSgMjAyQ/Uv/QKUCqMX2n+Evo4gABwsrQ1P7HKb/jCLCvExMTC9evf3//w8DMxsPD/e3X7/5WBg42VmO32H4xc0ryc/GyvyXhYvpzfffn7/9YeJiN5IStBD/K8rN9vPnn78MDL++ff/z+58QHw8PJ5uAIK/sL8Zfb+49Pb2a0dSFV99eUED8641rP3mVhKV5Vb78+c7M9O8/IyvTPwZmFl5GDqZ//Ky/2X79/8/Iygw6XQpUckLmxhn/gW4xBSUDcJEJWvHJ+J8FhEALQMENa9BxT5D1ROD1MixcooIC8rJaN+7d+vn/1p+/7H+/f/j28NTj90zcsob/fjDwiIow/H/48+c3DoF/f378ZfnFIcknK8PKJsX//c+nV/evPXv/5sP3nwz8XJzf/vz9+Z/h56dPrJzMWApekKOIxP/h8x1EaiBemZSUVHZ2NmQFDPG6iFepo6OTlJREvPpRlSMwBEBbTLQ11m/f/B9UvozAABgUXv7x5+O6q7mffr5jAq17HwAnMbMwXj/zYtfG076xeouvhv/7C763j0oO+fPvr6tykaqIC9w85HaesbGxjIwMZMURXAEDAwNyyxJZHCsbszWD2RxEthTNEDQp5EYemkqCXEyXIGsh6ClMl2B1DKbvMEWQ7UUzFlkKwsajHasDIGu0IHrhJJohaFzizYEbiBlcyB5Bk4Vw4Ysf4IYMVwY9G6Ogi0D//weXTv/+gLfWMzL8ZwKtJP3/X1SY79fv739+/eXiYGP4/+ffn/////z88uMvLzePIK/Yu89f/jB+O3X797ffsjxi3MJCAhIifK9+/Hn79eM/dhZODrb3r57cev3qOiurnqqalCTvvSefxIQ4WdmYX3/4+ez1FxH+vzx/Jfh4hO7dPSWtbPKfm/Hf1+//2H/8/Mb9l+knEwMDLBT+v2WU+PbH88fvA4yM3xhBS+lBzU7G/6DbSRlAZ/T/+/vvH+iyKCYmWA//PwMjCIGUMDAwgi4m/f+fAbT04D8zk5ORcpyz3od/Nts4L1x++fLdh08/GH4z/P/17ctfNuYrH/9/+M/CxPWX4fMvQd5fHP853v/++e/2pVtffnx9yvHzxXvuKzeefXn7TVyEV05G4P27l5de/RUWl7G30/7+8/fgTI5CQkLe3t60c5sCGNDO/FGTh0EIMIH6lqDLgIeBX4auF5gZWeX4zb//+QIu7gfAH/8ZGBRdGAK8wv/8+f/nD5UvBf377x8PuzguX0VHR+OSGhUfBaMhgAvAmmG45KknDj5A9A/odPj/oNYdE2gnE8M/xr8MTMwMfxh+/vz14uXr8CD3QG/L79++/fj588+vP3uPXT526rYwj+D/z99AC0B/iLEIsIkJcplqir///efOo2+/WJiEBTm/ffz8582LI68evHv96cvH3yqKEsJiCiLsnFxs7IzM7D+Z2O/fPPzp8Y+vv//w8nF8+fhSVET2D2gN6J/P//8K/f31l4EJtGgA7FNGRob/f77/+/vr37/fjKBrmEB3lzKAhiIZGRhY/v75/+8v+Iwn0Dw8MwMDE2j0BTTeC7rrlAnUEv377+9vXn4xLj6xNx/eyPMy/n305DXjAy15Qzb+m9ePrRFg+SMkIMDD+efLu2cfGf/eenBcQ0GQX1ackVX184+b//7d//3jK8N/ltfPf3/6zcrMxPaH/TcLBy8bN7cYp4KRGBuviMTff/8ZSbkAF+ytUWI0BEZDYDQE6BcCrMxcNgrZ9LNv1KbREBgNgSEO6NgY/f//z5/f0LvnQUdHMDEwMTMyMYPOYWH6w8jIwMTEfPPWre1sPzm4uP79Z/j6+cvDR29YWDm+/Xj/4eMXXlkuDl7hP9wc/9hYv3z9/u0X49c/v5TkBL7+ZOD/9uXfxzcPH71gYWS+8+gxEzsjt7Ai29+/XBzsDB++c7GyPn71/vaNpxy8vMIipndunZaS1vr7++9/ht9fGf79/vX7L2jzEXRKj5Hx/+/fv37//vXn328WJiZGJhbwgaj/GUGDp//+M4C2LoGWGfxj+vcf1BhlZAQd5wQ6gZSB+f/ff1xCErIqepyCCuzcKpz3z/9i4Xr+Q4qVW1ZMQOjlf25G1t/8fKLSArz3Xr35y8XM8vvXzz8/f/0ETe2z8rIxfhF++/PFu79//n7kE2RlYmFl5hPkYGFh4udj//yP7deP359+/2H5+VWSU4KLi3OIp7pR54+GwGgIjIbAaAiMhsBoCIyGABTQqTH6/z8DBxurojQPwz8GZtApgIwMHFycfIIP7z74/vU7I+Pv///+/P/3X0pcRF9D+eeff7///vsrwvfp888Xr77y8AoIiDAxcbxh5WJjZGfk4WVi5WB/8uqjOC/Xxy//v396rcP4/CnLV052rp8/fnz8/JWNnesfE+vbzz+ZGX99/PiFl4VBUFSaV+ifioqGjILcowe3Pn368Pffr/9/f3xj+Pvr149/oIYmePQTtBX+/58/oHPW/v5nYGNjZWJmZWJmAp2XxsTM8J+BlY0ddGbo/39MzGzgxigL6FjQ/4z///7/z8zAxMGjZREiIGv99cefz+8+6cmZcbLxvGL6K8b06ditm1eeX2L+xfLx3dc7X768/fbt6/ff3KzMInwc/BJSfMKSbOycLP+5WRh43n198+j+c3NlJh5ujm9MLP9Z/335853pO8u9xy+/M3JI/PitoCT+G7QsFhp/o9RoCIyGwGgIjIbAaAiMhsBoCAxpQK/GKCiQ/v3///P/P9Zvv34xMrEz/f/HyfWXn5fz26cv/xj//v//j5GZ+cS5a48eP+cT4P3/j+HT5y+C4uIqGorfv35i+veD6f9PZk4WLi52bUmBj5+/vf/6jf0327vP71WZnv9jfsHKyijAx/mV6Z++loqMpMzf/39/gY52+sLKwvL/3+/Pn16oKqsLC0m9eHJPTELh/79fv37/5/r78xfDv99//v1n+P2fEXQUE2jxJyPDb9BFUH///v//n5WDXVjq95/fTMzMf8Cn2v/j5vjLy8vAzPaPgfX/5y+MDP9AM/WgU8b/MTFzKek6MfGovnj0TEhShJ39J8O7d0xCP6TY7shzfd77heXr9x9sjMzPv/9m/vaTnZOVnZ2FnZOJl4/r+8cPX1+/4vnPwPD1A/P/n0x//n778/v2GzZlpi9/GJg+MHMx/v/x8+e3r/8ZuVh+MDHx/Pr958cvitaMMoJWHYDiY7BhyCGakHtBkN2GSxxZzZBmv3nzhoWFhQ5XWf769Qty7RNJwTWw4f/z509mZma0rQMkuX9U8WgIjIbAaAiMhsAgB1hOH6CFi0FXuv9nZPrPwsvL/ufPn1ev3z19+uzpo0d/fv34//8vaO4etGXpt7gIn6qypLQEn6QYt6KcKNO//y+ePX7z4umbVy8Y/v4U4eUwkBZ8+fLT1ftvORn+v/r0mffvR0HGd1zcHDw8Aq/fvhUT4lGUkfz1n/XF+y/vP35i+P/364/f7z/8ePr0xc1r1759+8LKxn7/0cObd69++fqZ4dc/ToY/f/4x/P0HGgsFYdCKUBD9m+EXw/9ff/iEWFV02aTVWaVVWSTkGSRl/jFx/P/JwMjG/pub9R8zCwMDKxszCy8H638mFn5hOVkN+5+/mfj4mX79+c7++6uJ2D07vgOCTE/vfGZ/+JmbmVOA4d/fP7+/qEgIuigqywry/vn39/3n31+Y2P++fvX+0JFHBw/+vfNAkPW/owaHoyanJB+ztdgXS4FPcuy/JX7/UOH4zgA6FeM/w7+/jP/+kh1HTExMnz5/mj9nfkVFxZw5cyBHPX///n327NkQEfhpzIcOHaqpqWlsbES+XhJu74YNGw4ePAjh/vz5c968efBrdSCCZJCPHj1qaGiAnOGMrP3u3bv19fXDclPhv3//Jk+enJiYuGLFiv///9+4ceP79+/IfieSDbnNhaBiyM0uBJWhKbh69Wpraysdwv/fv383btyAnH8Od8PMmTMht5tARF69etXZ2fnr16937949ePAAIjhKDvUQePPmzcyZMzHzPpH+2rt37/79+4lUDFH258+f48ePw218/fr1uXPnIFK0Jn///r19+/b379/T2iIGBobz58+vX78e06J9+/YhZytMBaMioyFAZ0Cnxui/f/+EhHmFhXlfvfjAxsLGyMDIwsTAyvQXdJ78v19///9lYmbn5BTg4RXg4mRmZvjLyszAzsrw6d2z929f//rx5+f3H////uNgYnr/9tPdB6/+fv787dPnPz9+Cfz/zMf1k4GZ6/bd599+/OHh5RHk4RDi4fn6+ffTF5/ff/n+4c0Hpv//eXk5fvz6cP/B2QtnD588uvvm1dNPHt/9+Pbp3x/f3n/9+u3b97+/f/3/8xe0kPQfA+M/5r//2RiY2NglJP8KcrDJiigb6itoqKipynPy87PJS7MIibHLKLGLSf9n4Pv7j/0fEwszM5eyrAILF9/vvwz/WfmZGNklPlz7/vjc/hdiB/67HvpjJaCsLSkqzMLyi/Efw/cvPx69ff/s49evDDz/OAUEhET+///9+v3rLywsPzk4ODm4hIV4Gb5/v/vw491XDG9f//785vPTD79vf2R9/uX/379MjAz/wSOyZKYTdnaOvt6+u3fuurq6Xr58edu2bf/+/auurj5z5oy1tfXZs2crKysZGBj279/f2NiopaUlJiaWl5cHuZ4YbuXnz59bW1v7+vr+/PkDuW9j/vz5lDdG//z5c/36dcyTp759+3bz5k0i21twRw4JxrNnzzZs2DB16tSkpKTfv39XV1fDrxIlyf3wAwXx63r06NHr16/xq8GU/fTp061btzDFqS7y9+/f8vJyNBc+ePAAfuEqAwMDJyenoaEhGxvb0aNHp02bRnU3jBo4ICHw+fPnFStWwJuGpLrh2LFjJ0+eJEnXjRs3/Pz89u3bB9F17ty5jo4OCJvW5N+/f8nudpLqths3buzZswdTFxkhhmnIqMhoCFAR0GmanpGB8c+ffz//MP4DjYP+BC3E/Pvn7+/vLKyszEzM7BwcAvwCjMzfvn37/PjxV0YGFiYmph8/f3x4++nPr7/s3EwMDL8YGZj//vr9+st3Vla2z38Yvv77/e83E+PvjxxCrO++MX34+ImPi0NCQpSRhYWdlU1cgIWFkfP1xx+cvJzMzB+EeP4LK4o8f/FOlIdDRkTq25uzT57++vDmkgjbj++Xj7P/+MwvIsfAyMrGzsPPK8LC9uvfn/9/WViEJIX0lNWuvHr9+xcDMzOfqKwkq5gkn4zwu0ev/3Fx/33wmIGB9c/fv19//OTgkRBQNeITYP344fuH1x9+f/nM9ejtsfe/P2kKsvJLCUmwWepIvn546+CZf79/Mz/+8vfhry88/CLS0pL8HNw8HJwi7LLMogYCvKJvOG/cvnr+6/NvchKMrJyct1///vKLRZzxPxsX068fTF9/f/8DWhgA2sFPdgr4+/ePkrJSSXbJ159fzc3N2dnZr1+/DrlDiJ2d3cHBITg4+N69ewsXLszKyoJc/fzmzZs1a9ZUV1fDLd21a5etre379+9PnTplZWXFyMgIvjQFdAwWRM2KFSu+fv169uxZAQGB6OjoFStWPH78OCoqys3N7cePH9OmTbt27ZqgoGBeXp6srOzt27cnTZrEwMCgqKjIysrKwsLy9u3bCRMmvHjxQlVVNS8vD3wzM4jYv3//qlWr/v//7+DgEBISgjx1++HDB4jUqVOnpKWlS0pKfv/+vXLlyrS0NBYWlv379//580dfX3/Dhg2fP3++fPmyp6enkJDQqlWrBAQESktLxcTE7t+/P3Xq1I8fPxobG6ekpJw7d+727dvXr183MjJydXWFXKAiKytbVFTEyck5b968kJAQYWHhS5cuXb16NSQkZO7cuWfPnuXi4oqJiTE1NYWEw6NHj6ZMmfL+/XsFBYXMzEwhIaH169dv27aNhYUlLi5OWVm5s7Pz4cOHEydOtLS0fPHixblz5xoaGmxtbUVERLi5uV1cXI4ePfro0aPIyMgnT57s3bvX3t5+5syZb968kZaWLi4u/v///9q1a3/8+PHs2bOamprVq1cfPHiQi4srLS0NchkV5N6/ZcuW7d27V0VF5dWrV5Cz6/bs2bNmzRoGBobo6GgZGZndu3enpaUxMDDs2rWLiYlJV1e3r6/v3bt3enp6GRkZoKAHT5Tfv39/8uTJHz9+1NfXz8jI+PHjx6pVq379+nXu3DlxcfGSkhJWVtaVK1d+/fr1/Pnz9vb26urqixYtYmVlLS4ulpeXf/HixcSJE9+8eaOlpZWVlXXnzp1Tp049ffr0zp07Li4ugYGBPT09ly5damxstLe3j46OhhzjBz/mcMWKFczMzM7Ozk+fPj127Njs2bOfPn1aXl4OueBk4sSJ79+/V1RUzMrK4hPgg4T/KDmAIfDnz5+TJ08ePXpUXFw8KCiIl5f3/PnzrKys+/bt4+fnDwkJ4ebm/vnz56ZNmx4/fqympsbNzQ3vU/3582ffvn3nzp1TVVV1c3Pj5eW9f//+xo0b2dnZ/f39paSk7ty5s3379n///vn7+ysoKLCCAQMDw5MnTyB3CAcFBUlJSb1582bt2rVfvnxxdHQ0MDCApChImKxatcrPz2/9+vXu7u6QU9nhtjMwMLx9+/bp06fv3r379u2bh4fHzp07L126ZGho6OLiwsTEdPHixd27d/Pw8AQFBYmJiX3+/HnNmjWvX7+2s7MzNTVlZmY+fPjwsWPHFBUV/f392djYzp8/z8TEtHfvXmlp6cDAQDY2Njs7OwEBgZs3b/78+fPMmTNfv34NCgqSlpZmYGA4cODAyZMnjY2NhYSEFBUVBQUFGRgYfvz4cfXq1V+/fh06dMjW1lZTU3PlypUsLCwRERGQm063bt168+ZNMzMzyDW/z58/X7duHTc397dv3yALn169erV+/fqfP3/6+vrCS1rIbZxHjhyRkpLy8vKC34EHCaJRcjQE6AnoNDIKOvro779fX38w/Gfg4eJnZmD8/ffX33//VRXlrEyM5CSlWZkY3r97xsP5T0NNWklRSFaaT0NNXEZW8D9oShrkyH//GP/+/v3t+493335+/vnz3y8Gls/f+dkZXn38/vb9ZzYmJk1lOXFB/ndf/r788OnNu+9c7JyKkrzyknzc7GySomoSokpycuoODgE+7olWJiHmJm7aqroKPDwSArzs/39+fHz+wfWdN0+vOL5n+qnHl36y/v/HxiEnLmmsIeVlpGtupM4jKqChIGmlp8zz57eghBCvrCg7LxcTMzsTI+g0ACklLWZhhbeff7Bys/AK///H+u+DmJGWUQg/t8DvX7/4hVg/fPn88fOPP/84GXl42QVFpWSVZWVlBbj4+DkFBDnE7Ph9vWXi9KVtxUSV2Fj+68pyybL/42H+oSH8V4nvOwfDP45fP2XZvkmwgQ6eAm2ZomDRJyMjEwszS0NDQ1FRUUJCwp07d169esXLy8vOzs7AwMDLyysqKnr37t1Pnz7B77vT19d/8eIFPFH+//9/06ZNoaGhTk5Oq1atAl2BgtE8XrVq1b59+9LT03/9+hUeHm5raxsREdHV1fX27dvJkyffvXu3sLBQQUGhoqLixYsXpaWl2tra6enpd+7cgUxSNzQ0CAkJ1dbWfv78ecqUKZDG0MuXLxsbG8PCwiBXz8GXE0Ac9vPnz/7+/nfv3hUVFb148WLSpElfvnxZt24dZOz2/Pnzp06d+vr1a39/v4iISFZWVl9f39q1a7OzsxkZGSdMmPDr16+KigrIVX5nz55dtWrVvXv3mpqaNDQ0jIyM6urq/vz5U1JSwsjIWFVV9f37902bNn369ImBgeHu3bv79+/fs2fP1q1bq6qq/Pz8rl27BnESAwNDR0eHsLBwY2OjiIjI06dPDxw4MGvWrOTk5KCgoIaGhlevXjk5OUlKSnp7exsZGVlaWsrIyLi7u0NuvFyyZAkDA8OaNWumTJny69evrVu3Pn78+P79+zo6OnV1da9fv54xY8b////b29ufPHni7e29ZcuWrVu3lpWVeXh41NbWfvjwAeKMnTt3Llu2LCsry8bG5uzZs2xsbDdv3pw0aVJaWlpqampvb++LFy82btz46NGj////L1iwgImJqbKyUk1Nrba29vbt24sXL2ZlZWVkZPz48WNFRYWmpmZhYeGVK1emT5/+58+fjo6O79+/FxUV/f79u7m5+e/fv+3t7SwsLDk5OQsXLuzv709OThYREWlvb//582dDQ4OcnFxNTc3z588XLlz47NmztrY2Q0PDnJycefPmnTlzxtvbW0pKysPDA3LjKMT9kK7OokWLNm3aZGZm9unTp+XLl0MuG1RTUwsMDJSVlW1ra+Pn56+vrxcWFr5//z4T+BIKiPZRcqBC4ObNm3PnzpWVlb148WJ9fT0krVZWVvLw8Bw9erShoeH///+tra0bN24UEhLasGHDu3fv4AeAr169eu7cuYqKiocPH961a9ejR48yMzOZmZkh3dTv37/39fWxsLCwsrLm5OR8+vQJUkS8evUqLy+PhQV0Q15paemjR49KS0s/fvwoLi4+depU5DmHFy9eXLhwoa2t7dmzZ1euXEEuxCAzMPfu3QsPD1+5cuXfv39nz569atUqOTm5pWDw5s2bKVOmSEhIvH37try8/PPnz1VVVQ8fPpSVlZ09e/aTJ0/WrVvX09MjKiq6d+/e+vp6BgaGysrK7u5uiDe7u7v//fsHyQVr165NTU398+fPmzdvMjMzf/78uWHDht7eXhERkcOHD8fHxz98+BASfd++fcvNzV28eLGQkFBVVVVGRsb///8hfdf///93dHRs3rxZTExs2rRpS5Ys+fjxY05Oztu3b////79s2TJWVtbPnz/n5eV9+fKFm5sbUkIyMzOzsbGdOnWqs7NTXl7+5s2by5Ytg9g1So6GwIAAOo2M/v//n5OLTU5W8Pfvv79//GT484OXnVVaSJyZmfnV61cfP3z785P539//79/9eHD/1b9/f3//+ffr16/nb77+//uLmYmBmZHhz5+fH7/++P6LjYntPxMb+///jD9/fmJg+P3t6+eP374zMDHx8vH8/f33+fufwiKMDGxMn358E+bl/PPr79vvP5XF5Vk4WX58u8vPyCArKsDFwizMzS4oIvJHUPyfkRvTtze//jD++Pbzx/9fDMKS3zh/37l09cfvn9/ef/z+/denn38lhXiUNGR/sSRhBgwAAQAASURBVPy98+TR988/mdk5mdhY/jH8Y2RmZWBkZmT8z8MvzP73j+C/n1+ZWB59+c3Nwq6qKPaH5e+/Vx+42JlevvgozSPFwsbBI8jPzy3Bw83OxcrNzMTG/JuNj1uAieH/Lbabnzg/MrMyfGd8z8bGoaIpqygkIPrwzbv3jKzPv7EK/uVh/nfzyac3PxgY/v0H7aRiZCY7rTAyMrx6/ao6pVJBVWHLli1tbW1hYWHIAwaMjIygc7eQLEATuXHjxvv379XU1CQkJObNm/f8+XN+fn4k5SAmGxtbRESEvr7++/fvHzx44ObmxsDAMGfOnFu3bh07dqy1tVVLS0tbW3v37t1Hjx799+9fYmIiOzt7VlZWfX39ixcvLl26pK6ufvDgQSYmpmPHjjk5OYHSDyenuLj4li1bDAwMfH19+fhQRr/+/fsnIiKSlJQkLi4eFha2bNmyP3/+cHFxQUY7WFlZf//+/ffvX1VV1ejoaBYWFg0NDU9PTwMDg48fP86aNev58+d37979/Pnz4cOHGRgYTp8+bWlp6e7uHhMT8+rVqytXrqxdu5aPjy8zMzM6Ovrt27fs7OyQQGNlZWVnZ5eQkPj+/fvq1atVVVX9/PxAQQDGSkpKp06dEhAQ0NbW1tXVraqq8vX1tbCwYGBg2L1797lz5xwdHQUFBW1sbNjY2H78+CEkJGRubq6kpCQqKrpo0aLz58//+/dPS0vr+vXrFy9ejIuLMzQ0PHDgwPbt29+8eQO5jE5SUrKgoEBUVLSwsBBys/zPnz/v37//8uVLAQEBBgaGw4cPBwcHQwZrISF57NixP3/+3Lhx4+/fv5CxH1tb2x07dtjb2zMzM2tqal6+fNnU1PTgwYP//v07cuSIgYEBMzPztWvXvn//npqaysDAkJmZ2dzcHBQUpKysHB8fLyIikp2dnZ+f/+XLFwUFhdDQUFFRUVNTUwUFBRMTEx4enpqamkePHl25csXAwODgwYOMjIwnTpwQFxe3tLT0Bt+SYGRk9PDhw7i4OEFBQVNTU3l5eXD4gQhmZmbIMPmuXbtkZGTu37/Pzs4uKSmpoaHx8uVLSGDKy8ufOnVKREREW1tbX18f0p4AaR7FAxcCmpqaLS0tz58///Xr19KlSz99+sTCwpKYmBgUFOTl5RUbG3v9+vWzZ88uWrRIWFjYyMgoMzMTHnFv375lZWWVl5e3tLSUkpKaOHGira1tbm4uAwPD58+fOTk5W1tbnz59+uzZsxcvXjx79gzStDp69CgTE5OZmRmkw3z48OH3798LCwsbGhp6eXlBhhgh4bFnzx49PT1xcXFXV9dNmzbp6elBxOHk////BQUFe3t7WVlZ/fz8UlJSFBUVf/78uXLlyoiICEgrlo+Pb8eOHc+fP3/16pWqqqqOjo6rq6uQkFBRUVFxcbGdnV14eHhERMSdO3cYGBiys7OtrKwMDQ3r6+t//PjBwcHBxMT0588fT0/PtLS0////BwQEXL9+fdmyZYWFhU5OTj9//jx+/DhyUczCwlJUVKSiovLgwYOvX79mZmY+f/48KSnp8uXLx44dW7NmDQ8Pj7GxcUNDAwsLCw8PT11dHSS4Hj16dPTo0W/fvtnb2zMxMe3Zs2f37t2QCYcPHz78//9fRkbG3NxcSkoK7v1RxmgI0B/QqTHKxMT4+fO3Dz84uNjYv/35KiMqKCol/vvn3w8fvnz/+YODm0FIiIOTV/zTh4/n3n/4/effv/8Mr1+//cfAKCbI8//3HwZGpn9MjL//gsRBe97/Mf5n+PuPmefx628KgkzcrIxvWRhlJQQYWfievfz99vNTLh6Of4yMX77/+v+X6RfLd1ZuISlxPUZR1T9cDP+ZWZj//xQV5+fgZPnF/o2B+T/H//+MDEx/BTi/8igzs7J9fX6J7cdPxi9/Hz9/IvFE8tGrT09ZmYQE+W49fcXOycEpKPT970/29x85mZmZQKOijH8ZGD68fGIo+5n7J6MoIxs3I/tT9v+/P3459+7tZz4B1q8fleWF//z6zcjAIiIoyPyfnYmZg52L9f2Ht89ef77376ahrukH8Q/MXP/ZWfl/M7z59PPXxStf2FW+Pbjz48VXrm+fvv368Z2Dg/XeD6anP34L/P8PugeVGbRSk7y08ufPX309PW0dbQYWBhcXl3nz5kGaQRDTfv369fHjRykpKVZWVvj6+ocPHwoJCUEUMDAwbNy48enTp6Wlpf///3/8+PHevXsjIiLgshAGExMTZA79////vLy8kIEHSKMW7b5m5NIW0nAEHaTFzAyZpzY3Nw8LC/vz58+/f/94eXlnzZp14MCBK1euLF26tK+vT0tLC2IdxHw2NjYIFzLp9h90ru0fyKz09+/f/4MBGxsbxBZmZmbIYPC/f/+YmJj+/fvHzs4uIiLCxsYWHBysrq5+6NAhiMv//gUdlwDRBWmAMjIyQgQZGEC3Cf/69cvQ0HDmzJlHjhw5cODAxo0bp06dys3NzcDAUFBQcOzYsUuXLvX29j5+/BhSAUAcyczM/O/fvz9//kDcycbG9hcMILKCgoKamppLly5VVFSUlJRctGjRr1+/jI2NIYslPDw83r59++jRo3///vHw8EBc9f//fwEBASEhof///0+YMAE+sA26kwF0WATEYBD5798/bm5uISGh379/19TUGBsbi4uL9/X1vX//3sXFhQ0MhIWFOTk5vb29lZWVISkBOaYgIYYcleDQ/f/v3z9OTsQhuBA2xAGQe7RFRUXZ2Njs7e1VVVWvXr3KxQW91hySWiB3aoOciIR//vyppaXl7u4+YcKEnp4eSERANvjDVRUVFZ04ceLixYv9/f23bt1KSkpiZEIsGoErG2XQMwROnjw5efJkJSUlXl7Q9c6QO7glJSUZQCfoMbKxsUGap5BcxsvLy8XFBU9jKSkpbGxskL5uTEzMt2/f4E0lXl7ely9f1tXVcXNzy8rKsrKyQlaZMzIyfv/+/e3bt9u3b//7968FDMyePfvgwYM/f/5sb29XUlKClBWrV69mYGAoKip69uzZrVu3ysrKIJkIHj5///5VVFTk4OB49+7d169fT58+feXKlX///oWFhd26daurq0tcXFxKSoqREeSR3t7emTNn9vb2fvz4sampiZGRUUREhIGBgZubm5OT8/3797y8vJDOM2QZEiTX//8Pyi9iYmKQtTRcXFw/wACikp2dnZeXF+I1iAJeXl5IqcLFxQXp/zMyMjIzM3/48IGZmZmHh4eBgYGfn//fv39v376FGMLAwCAuLv7o0aPPnz+/e/du586df/78UVNT09PT2717948fP1xcXN69e7d06dJnz57Z2dkVFRVB8hc8HEYZoyFANwCaAaeHZf8ZmBgZf3z99uXTe3ZWRmZGhk/fvt19/Pj2nfuvXr5//vzDq1efH9x/zMbyX0mGT0tZVEtFzMJYWVVR/P9/hp9/f7GwMDOBDICckc/49/+f/z9/MzIyvv70l5GNgZPjv5ykJA+XwIMP/z/+ZHz2/tuN+28/ffn17dvvTz9+cTAwvX748PzZk49fvfzNK/GMS+q3iDwTCy/jzz9M/xlZWHlYmNn+cXD+F1bgZPrP//EJH/t/Zt7/LMy//3z//OzFCy52hne3H14/dRV0Fuq3n+/uPHh16eaXzx9/fPv698/v////szCxCLJwfn/z+cWPPx/+/RPnZDXi52Rk+POd8ff3b7+52Nl5+Zh//PjKysLFwMLMwMjEw8zOys4iKiBmp+9gaGzCJsXCyMbIw8LNxsrExMzK9uev0O/7rK/usX59a8X7XI39u9DvP8yvvov/+ckNGiBm/P0HhMiOMlZWlkOHD2/evPnZs2fTpk1TUVGxtrbm4eGBrImcOnUqFxeXpqamn5/f9OnTr169euTIkQ0bNnh4eEBsfPXq1c6dO+fPn9/X1zdx4sTJkycvX77848ePP3/+hI9qMDAw/Pz5EzI//vfvX/imhB8/fnBxcZmbm8+ZM+fp06eLFi1iZ2e3s7NjZmZetWrV06dP582b9+XLF3FxcVVV1c+fP1tbW3///v3Xr18sLCx//vx5+/btxIkTNTQ00tPTeXh4nj59umbNmmfPnkEc9v//f0iLk4GB4c+fP9+/fxcVFf3////27dtv3Lixfv16SNH/48cPiDt//foFd+GPHz+kpKTExcVZWFisrKwg22X+/Pnz69cvBgYGUVFRNTW1qVOnQlyopKQkKSnJzs6+devWhw8fQlZunThxYvPmzb6+vnFxcffu3fv69SvEVbNmzfr69Wt0dLSJicm5c+ccHBy2bNly7dq148ePnz171sbG5vfv3/CgY2Vl5eTkPHLkCMRTbm5uy5cvNzQ0dHZ2Xrlypbq6OgsLy6VLl2xtbbW1tW/duvX7Nyj5/fjxA1KFW1pavn//3tjYWE5O7vnz5/CmuY2Nzdq1a2/dunXy5Mm9e/f+///f1NT0169fioqKurq6T58+hYwT//37d+XKlR4eHgICArKysv/+/bO2tobM9f/////bt286OjpsbGwrVqx4+vTp3Llzra2t+fj4Hjx4sHLlyqdPn86fP19BQYGTkxMeC7/BAFKPfvv2TVpaWlFR8fv371ZWVp8/f/79+zczMzM8bcATzL9//06dOvXq1StINEG0Ozs7FxcX3717d8mSJUxMTJAQExQUvHXr1vXr179+/Tp79uzPnz9HRUVZWVndvXf3xIkTb9+8Ga1WIYlwoMgNGzaYmZk1NzcbGhpCIvrfv3+Q1tX///9///4tKSnJyMh49OjRnz9/nj59+u3bt/A+yblz5ywsLGbNmlVUVLRlyxYtLa3Dhw9//fr19evXBw8ePHLkyPv373t6esLCwiB57e/fv79//5aRkZGSkiotLYUsmBEREbl69WpVVdXChQu5ubnhe5WOHz/+58+fwsJCHx+frKwsBQWFLVu2wBu1kOCCdBH//fvHx8cnIyPj4uJSX18fFxfn6Oh4+PBhPj6+zs5OZ2fn379/f//+/cKFC4WFhQsWLFBUVNy5c6e0tPShQ4cgi0EhcwW/fv2CZFLQgSj//sE7XZDuKMTGv3//srGxGRgYbN269e3btwcPHrxz5w6kwwxpQEOasBC9kLLr379/EF8zMzNDgvHYsWMiIiKGhob3799/9erVt2/fDhw48P//f8hkS3Z2dnV1tbe3t5aW1p8/f5iYmO7cuSMvLz99+vTOzk7IKO+tW7cgToW4apQcDQG6ATqNjP5nYGBmYeLhZv/6+fvHT19evPnA/IKZhZnt1/+/oGtCQRMW/37/+cXFz8UvJPDn1+////6ys7J+/8b4iZmFiYVVWlqak/3939+/GZg5/jH8+8/A9P//HyaGP58+/jl/+auQEKuUFO/T93/ff2N6/RFUz/1nYmD7zC7IwcDMyMjCwvjiyX3mz6///P5/9sZ1ATkZbhklYVlpeXaeD/fevv/6kpGLVU5JnPP9O4F/X/4xsfz5w8jGzcKvyPPn7YdH9558fvuRjY2dkZv7x6N7f779ZmRnZfr9j5uV+cufX//+/v775xcXO6+IoMiLzx8/cf8TEuT8zcIs/ZtBWkrwBT/P33e/f3z99p9BkOEv88cP3zjZ2VlYOP6y//n3k0Xlj9qPXz9/cf9i/s76m/nXFyYmTtDk+z8Wpr+8woLCMuKKnG+FmD/xs/2XFxVkZOO/+fjj/buf/v//9//ff1CYkZtAfv/+ExwcfHjfodXrV8vJyTU2NvLw8LS3t/f391dUVIiKivb09LCwsERGRn79+rWtrY2RkTE3N9fa2hpi4bt370JCQnR0dCADCS4uLpcvX3779q2fnx+ksw5R5urqChmZk5aWdnJyggi6ubkJCwsXFhb29vZWVFTw8/O3traKi4u3trZOmDDhwIEDJiYm6urqrKystbW1/f39RUVFYmJi5ubmTExMLi4ugoKCysrKXV1dkGEPW1vbrKwsYWFhyJAJNze3n58fZChORkbGycmJn5+/urp6xowZIiIi8fHxysrK/Pz8kM0KDAwMjo6OyC5kZ2dvbW2dMmXK+vXrFRQUfH19NTQ0ID5iYWGpq6vr6+srLy+XkZFpbm5mZ2evq6vr7++/cOGCra2tmJiYmpranj17IOMKZWVlkNEOBgYGMzOzefPmrVixQkBAALJMFnIyERMTU3l5uYqKyvPnzz09PSHDt2xsbOXl5bNmzXr27Fl5ebmJiUl+fr6Ojo6wsHBeXp6Pjw8jI2NlZeW8efP27NljZWXFz8/Pzs7u7e0N8XVoaOi7d+8qKytZWFhCQ0MhEcTAwODl5fX8+fOGhgYFBYWCggJ5eXk9Pb2EhISurq5///45OjpycHCwsLDEx8ffvHlTXFycgYGhpaVl0qRJO3bskJaWdnV1ZWFhcXZ2hsTXxIkTt27dqqOjk5ub+/HjR0VFxY8fP1ZXVwsLC9fW1nJwcHh5eUHcY2lpCRkGExQU9PDw4Obmrq+vnzBhwp49eyQlJR0dHX///m1vbw9JG5DZWAYGhoqKiqVLl7569QqycYqBgcHKykpaWpqFhaWzsxPSqfDy8vr//7+tre2FCxf6+vrS0tLMzMzmzJmzcuVKfn7+srKyHbt2fv75ZXRoFBK2A0X6+fm1trbeuHGDl5dXXV2dkZFRTk4OMhbOzMwsJSUFSdiQ5ZW8vLympqbwHsifP3/q6+vFxMTevHkTGBjo7e19/Pjx5OTkP3/+WFtbR0dHr1mzBrIcWVdXl52dXQg8dWNra7t79+7ExERmZmZ5efmysrKLFy8uWbKEj4/v+/fvLi4ukKA4depUSEiIg4MDhPvr16/9+/e7u7tDShKIIBcXF4TLxsaWl5fX3d29cuXKjx8/lpWVubm5bd++PS0tTVhYWENDg4OD4/79+wsWLBAWFv7w4UNqaioXF1dlZeWxY8c+fPiQl5cnJiYmJSUFaVays7PLyckxMTFJS0uzsrKKiIhAXM7AwCAnJ8fOzl5UVFRfX5+SkqKqqiokJARpdDIwMLCwsMjIyEDW1AoJCUFKDBYWFkgvuri4uLu7m4+P7+fPn01NTerq6vb29ikpKRISEoyMjFJSUsbGxpaWlunp6ZycnHx8fLq6usLCwmxsbP///+/v7583b97Hjx99fX3fvn07derUCRMmQPY8QYJilBwNAfoARnj+p6l9h7esWzhtyoXXzC9evvnz//+nT79Y2VhB/cL/DExMrJwiQoKsTO/evFVRlxXiY/73+xfD/39//vx98+7Luw8/uXiF3rx7Lazx+8079d8M3MysrIwMTIxsLH+//Pz94uGPzy+4hQQ01FQkZOSfff/9/PXXH79/s7IxCPPx8LIwyYoIcPC9+nT3qBiL5Lc/PwQFBH79/vn8zQcxOan//HJP3rH9+PyBg5eT+d9vLf5/AU4m/xmZv/x53zJz6nc+TRk+YR4Rsf9fvovKSLHzc7J8/S4oKcTCxsj+m0FcTuzI/vNTahdLKP6UFtHiZ9H8xsX3V0rq/99/HDzcLN9+//n47TMj349ff9T1ZUSkBZ4++8D05QzTvf1MzNwCrILyjDJf2b5+Zv/MwsLEwc7Ow8vDycnBxsr6+f/zPds2WCsb2dnK/3l35emzH6+e/eFmZ5JVUth+8s6i7af15WWcbDTSmro0FNEXOREZfXn7Cz2V3D3lPb59+wapGOAav3//DmlGwEUwz0iHTLnCFZDNwLQdso4K2cCvX79CpqWQBcHDbb8hU1pTpkwJDQ1FXl+IrBLChpTm8BEXiCAeEqulEPVobv7///+vX78gdQxcAXhTLyuECye/fPkCmUSDiBA8xR0yow1RjElCBkThA59oCr5//w6ZCkQT//HjBzs7O/Jg4a9fv/7//4/sfjQtuIICnk6ePHmSlpa2fPlydnZ24msvXMYi244/BNBUMjIyQlre8HBetWb1zE9z+vy69UXIzCbIVoyysYbAt1//DFqub8hU1pLkwKqAgYHhy4c3Hz9+lJaVZfj3j4GF4+/Pr8zsHAwMoCXvf358YeEAzSx//fj2w4cP0jIy///9Z2RFGPXz68dXr17x8PAIioJm9hkY/r948pCZmVlUUhY0Ovjz2/Pnz8TExNg4OBiYWBj+/ga5gRm0C/Pl04f//v2TlFWA3Kr35sWTHz9+SEhIQKwDzZyArOYC7wQFaWJgYPj17RMbF9ffnz+Z2UGra8Ci//7+/A7nQhwjIiLCyQva2/7z68fXr19LSUkxMTMzMLMxMDC+e/Xs69evYmJi7NygBfT/f39/+vSpoKAgN78w2LVfmdm5wO759+fHNxYOHnBQcDP8+cHAxMTABFpfBBbh/PLh3evXr4WFRb58+ZyVlTV79myIf2HOBoUYw9+fIBeCPQsPxm+f3r17905SUhLu5jcvnvz//19UUprhzy8GFlDAvn7++Pfv31Jy8qAogBny79e3Z8+esbOzgy36++vbVzYulOX4ILtG8cCFwJbLH1u3vzhepj5wTqCTzfQaGf3PwMLCxMz65/efb8zMbCwsDJISoq9evf798xczOzc7CzsTE2g09N+Pr7yifH+YGP/9Y2bm5vz+8+/HLz8YGX99+vSW/zfn//8M//79ZWJmYWL8x/D7J8P3P+ws/1gExf7zS35l5rr/9se7nz//MPzh5GDl5eXkYPnPxc7ExsbAwf7vL9///19+cnEwaqhK/v37V1SYh5ud+/r9W1dP3/j5/TsLC5OmmqKch8P3P0x//zB9+vXn55e//399sfd15WJh+Ae6np7x71+G71ws37//+vP661cGprtP3t64/YyJg4ODm1FcRPrLy1/v/zyRUJX//+MvDxf7Hz6Od79/8HCxcv9j/vblzZMHX3j4xX7/BK1OEGDgl/0v9Z7n4y+2bxzMnMwsLGwcrOycnFycXKzsbH9+s3KxcNx++FZfV0aEjYeRX+rxFzZLkS+ffjO/+srM8P8fI8N/ytPFn7+gM/PRWqKQExzRDMds8SC3ZtAUk8TFtB2zNYPZEmVgYIC39lhYWFJSUiB7dPBYTXwzFGIIVkshUmhuZmRkRGvJoSmA6GJgYEBuiTIwMKDpgiuDMyDjH3AuGgMyKIImCOeidSfg4pjBixm5cMUQBq6ggFvBzc3t6OjIxATKBxAtxJC4jEXWiz8EcKmEh7Ozk9PaIxvo081GdsxIY7MwM5aveyrIDWpcYvU7MxMzIxPH3z/PGRgZ/v9nAJUeoFj5z8DAyMTI+O//WwYGBmYmJpCavy8ZGEByDJAiDjRIwcTMzPXv35+//x4ygA5XZmQG3TPC8Ocv6KYDJkYmZmbOv38/gCbW/kOPF/kPVcbMwMj898+j/yCjGFmYmRkZOf/+fffv/xuII8HOePvj978//0DFKSMY/P//lhG8QRSuBpnLxMTEzMzx9+/Hf/9AJ9WDuex//ryETKAzMjAwMTMzMXH8/fv+3z+QpxhBM3Ls//59+fv3IwMDAxMT03/QfgewO0FnorxiZGL6//8VxC6It5kYGf8zMDx79uzD+/e6ulyPHj97KBKbufnP///3QKtsQUHG9O8fSAvI/eDAYmRgZGRi/P/v1X+wFczM7H//vv73D+QqyGAqqB38F7QfHzKIwMzMzMjA8ucv6NwMuCGgkGRh/////98/90AxBNrACg0oiPNGyYENgcfvf4+QBfB0aowyMjL++PHr3buvoEFNRqa/f76/ef3i589fHOwcisoKT549+8n85/ef76A9wn++sbGzMjIyf/vx7tOHb4yMLKCs/u8fEwPosvh/vxgZ/vxiZedk+Pf/358fzCys/zj4Gdh5X7Ew///1g+XfP1F+Dk42pn+MjP/+/uHl4+Pn5GJkZfrO9u8vC6Oeji4vLz8rG6uAgDQj439JOQVlRYm3b97ycPHKSkvy8bF//vKRlZWTm52FkYXhxZWHkyZs+MMv/PPXb2bQ9inmvxzsDH9+MX78wszGxcLK+ev1m/+gu5tY3n+6/+YD07v/v6W+awvKqNy/8UhUUlBMQpSVkfH3f8bfP34ICnCxsrOw8jJxCYpLs8r9BU3LM3H+4wFdcvifme0/G+s/ViZGZtCZSwygc1ifP3t++/h7Xj3Bb7/4X794/k9S+v7jp58/fmJgQB9yG9hMMrC20+f+zIH14yC3XVBQsLS0dBA6EjyPyYJx4NggdOnQdhITI+PhO19Qd8cNDR/9/vs/0lRIVQw0kkqBi9ko0ItdK7OSGhMT0+8/f8wUNVhYdCBrbbErpb4ohaFBfQeNmsjAwGCuyCArSP2UBjJ6kGE6NkZ//mRkZJKUEn/58s2/v39/fP/xD7TX5O+zJw++ff7EJ8IrJy/97+fPr98ZGZhY///7+/btR0YGJj5+nk9fPvxn+MPE8I+VhfnXr39sbKzcrEzMf/7xSHEzMfA9Z2D6ycHCxs7xn/U3JxMbFzs7C9P/X3/+CnKzcbAyPX37gfvPP3ZGHhE+Uca/DF+/fWP6wcTKwsIE6lT+lZMS19LS5OJk//XrHy8PHwsr27fv319/evv7z/dff1h+vHzHLiT+6+Pbn/efswgKcCgp/Pn8/fv1+zwK0myyXP++fP7/69fPH78uXz/19R3rf1a+Swe3CcmpCkur//vPyAIaLPj3/y8bMyMfG6cAFy/rfwZmAVY+Rlbm/2x/+L/zsvxjZ2L8z/KfheMXJ98PHk4GDo4fnK8Y3n36/vPX98+8/AosjEx/n1zi/fab5b+UrrLi1dNnGSg+8X6Qpb1R54yGAE1CALy1nyYmjxqKFgLsLKCTltEEBz/3//9/MWaC9mqggz4Gv2tHXTgaAiMB0KkxCp4v+P/5yzfQWU7fvjODJin+MTD8//P7+/u33xgY/r989YOT8ysnJ8///4x//31h+P+TjZWBkZHl0ZOnP36CNjWzsLAw/fnPwvifh52VkeG/lCg3JxvLt3//eP+DzsVkYfjHzsLM8O//u2/fWf4zC3GzCPNxvnn3mYmFlYfxLycHF+tftu/fvzEycv37/49HREJAUJAVZAEzKzvHl6+fnr95dOzMkdfvHj96/OTLr/ef3v1kZJJk/fedn4Hl8++//OKCPKLC3//84OVi03e24xbifvblGws3+8d/v//9Be2pB53++ffzu6e33r588frxI3VzG04eXkExCU4mzl/MDD8/fLh35hLTv9NKDP8U/gi8EHzBzMrGzsHCxMzAwsLOxcn1k/sHJyc7Nzvn1z+f337+/PTZ143X/n77+1lakN1f04j5/5c7z74//sYOmqti/AdeeDS6N2Mk5M1RP46GwFAKgd9/QYPRbMzQ0unP3/+//v5nZGBgQ22w/v/P8OPPP3YWJuTJx59//oOmkGF64d7+D5qPZkBWCdHOwQI6BwY0PQ21Da6DWMbPP6A5emJVj6obDYHREKAxoFNjlJGR4ffvP+/efQCtemRiZmL4/5/x/9///0F7D/4zMoDGKZl//Pj57fv3P79+ysiIiojyszIz//3D9OzVW4a//xiZ/v/9/+/vz5+cHFzsbGxSguz8vDzPv/4EbS//x8DLyPSf6S/oYtE/jP///+dm+y/Ox/bn528W5n/CvOz8XOws39h5eUSkpES5uLm/fPvMxsP17PXzJ8+fvHn74sPHFy9ePf786cuvXz8YGH+xsXGycLP9/ff337+/vLw8giz/X7Mw8fAJMDOwsP/5/+fHl0+MjL/+fBXi4n739xcDwz/Gf38Z/v1lAC1c+vf/z19Ghr8fnn27sO0hIxufqIw8r7D8n1/fvn58+eL+TVm5rxIqKr8Zfor+FH3D9v4fw38mRhYGRkYGxv//mf79Z2b4w8zw++//H7///Pzz59CxKxcusuqoy8lL3X/07Pntpx/efwCtPfrPwAJa1wQaIqVxuhg1fjQERkNgNASIDoGfv//bqnIL87BuvvSBhYnxz7//xvJcESaCn378XXD83ZP3v5jBLcp///4L87ImWAotPvHu1effTIygtuTvv/8z7URuvPx54OZnNtCyLIStLIwM/NzMH779+wdq6DL8/f9fmIsly0Fi8v7X33/94+Fg+vANtAIeoWGUNRoCoyEwNAGdGqOgRdygFifotEfQom8G0Hm/IMZ/BjYWFhZm1t+gA8B/sDAxcrAx/fn56/WL139//2VlY/vz+yd41fd/0OpuVlYhLnZdJQE2DrZ33//8/vuPmYmZgeH3f4Z/PMxsf/78//rrF+Of//LigkwM/3/+ZRLk55AW5PrP8omZgZuJmeXZ6+fXDl959uLhf6Yf796/+fHjFycHMzsnM8N/0NHoXJy8jEwM//8ysbL/Y2JmBK16Z2N5/+n9H0b2N3fu8opJ/OAT4GRjf//xs5yCjISY2IfXb59dvwdaMv//H2hjJujwuN8Mf74wMv788fcH4/cvjz88YmU5+/f/37//fjP+/c3wl4ORiekt+1vNX+rf/n39z8DIDN5ICV2czgBaA/vv77//INP+MzEyfvr669i5O8fPP/j/H1TaMoFC4S8DaA8TqAE7NBPbqKtHQ2A0BIZhCPz6+99ckXtyhOzWK5/Wn//A8P+/hgTHtCi5+69/8nMxm8rzRM+///UnqHj7++8/LwdTsKHgxosfXn4CT/OAzsAHjZX+AQ+j/vkLOrmOkZGBlZnxz9//xkrckSZCRWueMIKVgTcVMXz/9e/H73/BRgLivKydu15wsjL9+ff/zz/QOcwszKDm7T/QnSj/GRkZ/v4DmcPIwPDr738msJnDMPRHvTQaAsMC0Kkx+u/ff35+XiVF5tt37jMxsoD3D/5nYWblZAftl/zx59e//0z///6Vk5MQFeEHL45kYGRgfv/hKwPDP1A77P9fZsb/fBxMQtysX3/9uf3my29m1p9/Gf4z/WH495+Lg5nj3/9Pf//8/v1bhIfr7+8/v1jZ/v7/JSkoyMLM9OXPr2sXT318fOTn3w+/f/1mZWZmZ2NiZ2Xk5uJiZWb9//8/MxPzv/8Mf/78Z/r3lwnUKPzJzMb25ds3LRVlfSODh4/eXjj3z83P8dcvxus37vz8/UdTQ/Xpk6ef3r1nYGD+D96xCdq1CRqtZPr/789/hr8M/3+Czs5gYvz9+xsDqIRk+P/nz19Gpv/Mv74zf/3I9En6n+Rz1pdMLAygg0GYmUHm/GNl+s/OyPiPk+cfjyBo3ynDfyaQ/xj//P/D8J+B+f8/xr9/GJlBi2lBQ7F0S3vLli0zNjZWV6ffuRLfv3+HXO6H7McPHz5Mnjz59evXtbW1oqKiyFJY2W/fvj179uyfP38MDAwg5wUyMDDcvHnz5cuXkGuQeHl51dTU2NnZL1++fO/ePRkZGWNjY4hRT548uXjxIjc3t4WFBdom9GfPnu3atevjx4/GxsbW1taMjIzPnz8/ffr0nz9/2NjY1NXVVVVVv379evPmTX19fciW8BcvXrx//15TUxNiOFXIS5cu3bx5MzQ0lHjT/v//D7mZGnIKEi6NHz9+XL58eWxsLDF73nEZsmvXLn5+fiUlpStXrjg6OuJSBhf/+vUrO+gIXpavX78eOXLEzs4OvmEfrmaUMWhD4Oef/z66/Fn2Iq8+//nxG9Qp//X3f6iJ4IXH35IXPZTgZ12eomipyL3z2icDWc43n//8/ff/x2/IQCfUT8yMjEfufP3yE9QaVRRhlxdi+/D9z9VnP9iYGS0VudXE2Q1kOc8/+s4MGs5g/P773+ZLHzlYmSyVuDlYmFTF2O++/qkkwq4gzP72659rz3/8+/9fjJeFl4P555//8kJsV559//H7v5ki18fv/269/AHayQ+1dpQaDYHREBhEgE6NUfDFiX9+fP/OzgxqwLGwMP9nZGJhYf31+9fPn18ZmZlB59gz/vv85SsTaEL/NyMzCyMD47ev3xn+/wMf3PGXiYlBQYj31z/G808+cXGy8nOyMLP8YmNm+veX5eePv59+//j57xc/FzsvJ/uP33++/fqtIMrNycz46y8DCwvz23fPvn3+y83NzcrBzMr6n/H/f3Y2zn////398wc04AhqKoIuB2FgYQLZ9Zf5/7//LNycz5+91DP4JyQjwHSW9djhc8I8Au9ef/j04eOWRy++fv7y48kLBob/TEwMLFzMbH8YGRjBPXrQWXmgxiIjuPkIHvplBM8vMf37/f7+nRv/mFmesNyVZhJ5x/eBiZ2Fi5ODm4uLh1uAh5ubg4Pl7v3bLx/+/PH5PwMzAwsziGRgYmBkYmDlAHn/zx8Gdm7Gn9/+/fwGKvHJTkT4myNoxm7dulVISIiejdG5c+eqqKjA73yCuGfWrFnv3r1LSUmBX3MHEcdKvnr1qqCgQFxcnI2Nbdq0aT09PRoaGv///z969OixY8fY2Njevn17//797du3r1q1asmSJSYmJnPmzHFyciosLDxz5kxlZaWent6bN2+WLl06adIkeMPo2rVrhYWFxsbGMjIyvb29x44dKysr27Vr16xZs3x9fT99+jRp0qSSkhJFRcWampq1a9dCNB44cODw4cNTp07F6lTyBG/durVjxw6SGqNfv34tKSnp6emB3L6Iy97v37+vXLkyJCSEksbosWPHpKWlRUVFb926RUxjtLW1NT4+Xl1d/devX9euXbO0tIQEHS5HjooPkhAALegEnc3EcPf1j4xlj6PMBLlYmf4z/GdlZpQTZDt+78vffwwvP/05/+ibjhTnzmufsuxFj939svv6Z1DBCD7DCbJs8+ef/3XeEntvfD736PvkCBkRblCttODEu0XH3wYZCiiJsmfbi8YvfMjEBBorleRjnRkj27XzpaM6Ly8704Wn3849/NYTIiPExfz99/+Zh970731pqsCd6yj2888/TQmOvTc+v/7yJ8CAn4mRMWf54z03QCsBIPYOkmAcdcZoCIwC0GFk9AkFJiamz5+/Pnr8jI2ZhZODU15G5uOnzx8+fWJh+v+fleXv37///v9lZGR8+frDm9cfQSUFM9Of33+YGEC3iP7794+R6R/zv7/ff/789JeNlemfrAgvExszCwvfyzefPn398+n7T3ZWZjkxYSbm/5+//WD8/4ebjVWYm/sfIyMz07//oPvc2RgZ/jIw/wDvo2f+8+ffj5/fWFiYmZhBjU9Qw5eRifEv58+fHJ8+MDKy/fz774sAv/ibx/dnd8/lkFNi+fzp1aOHd7//YmJnZ/wPug1cgIXlGy/nu2/fGf4zM7H8Zuf+DzKKgQl8pt3/3//+MzGDjmoCNWpZGBn/M//7+4fxP9OzR1///mN6//fHfaanP5h/MDGyMDIzMrP8Z2VhZmFlZWb59/7tx6/vwF33P//+gprJTOBBYkZwuQ0iv3H9Z5AH88iNNmZmlmvXrj3ccv8/439/f38ZGZmbN2/++vXrzJkznz9/Dg4OlpaWZmBgOHXq1KFDhzQ0NFhZWSEjfBALHz9+vHHjxj9//ri6umpoaBw9etTExISLi+vBgweQ8b/Lly///PnzxIkT6urqvr6+f/78OXfuHBMT05EjR8TExIKDg9nZ2X/8+LF27dpnz55ZWVlZW1v/+PHj6tWrf/78efjwobS09NatW2VkZN69e+fj48PHx/f///9jx45t3brV2Nj49u3b6urqd+7c2bx5MycnZ1BQkJiY2N27d3/8+HHt2jU5OTlzc3MGBgYODo7a2lrIYGRTU9P69esrKysZGRmTwICBgaGzs1NbW5uZmXnmzJlz585VV1d//vx5WFhYSEjIjBkzYmNj4+Li/v79m5CQsHHjxoiICIjfu7u7w8LCkpOTGRgYwsLCDh06xMDA8PfvX1dX14qKCgYGhl27ds2dO7e6uhq5uQ8/kh1iCIS8fv36zp07WVhY/P39ZWVlGRgYjhw5cvz4cVlZWX9/f05OzocPH27atImJiSkgIEBaWvrbt29r1qx5/fq1g4ODsbExMzMz5JTQjx8/rlmz5uPHjx4eHlpaWg8fPvzy5cvNmzelpKQUFRXXrVv348cPLy8vOTm5JUuWHD58eP78+SYmJlZWVtevX9+xYwcvL29ISIiAgMDfv383bNjw6NEjNTU15Nbq////z5w5c+jQIV5e3uDgYGFh4UuXLjEyMh47duzfv38hISGioqLXr1//9evX+fPnP3/+7O/vLycnx8LCwsTEJCQkZGBgAGp2/P+/cePGW7duQa42/fbt28aNG589e2ZkZOTo6LgLDJiZmU1MTFxdXc3MzCDnsB48ePDUqVPy8vLBwcFMTEznzp1jZGQ8dOiQmJhYSEgIGxvbw4cPN2/e/O/fPzc3N3XwBT+QsB0l6RYCzIygeR9mJsY7r3/9+P2PnQV0eCUTIwMHCyMrM8O3X/8gC0B//vnPw870////glVPfv/5Ly8COqcGqhe0ZJSBmQm0XP733/+Oajz//jOkLHnEwcKoLMb+8fvf3t2vQowE8lc94QQb/h90KzRoZdPph98XHn/HxcY069CbjZnKq89+mHXkjZEsZ4OP5I5rH//++y/Jx5K+7PGLj7935KksPfnOuutWs5+khzbfvpufIVaDbaZbUI1aNBoCoyFAAID6oASUUEmaiYmRi4OdmRF0k+fTl09//PjNwszMxcnx5w/znz9//v0FXVv8999fdja2fwz/f/74CZ6UAU3RMzEz/mdgYWRiff/tLx8fi4QIJwsr26dvvz58//by3edf/xiYWVl4uVj+/P/7+cPPP/9+i/NzsTOBzhn99Zvhz6//f5h+MTOyMP79y/SP5f9fxj8Mf1iYmRgZWX/9YmZi5vj1i+XH939fvnz/8f3X9+9ff/74yynwU1SO9e+3Z6zM3yWlOP8w3PrF9JWXi4mFj42B4df3798UVATlZGUe3X30+++j//9+fnn1/w9ouz8DA+NfRsZ/DKCTVBmYmP4xMP4CnZDKwsgImo//++sLGwsLBycjCycD6+f/v5j+sTEx/v33n/Hff9a//5kY/rD8ZfzHzsbByfvtzx8GHj4GNtDqJ2YWNqYfv/8wMP7/9ePv33//ufhAR0aBVkWRGyksLCz79u0LUw959+ldQUHBkiVLNm/evGrVqsTExLdv3+bm5i5btuzMmTNNTU3BwcHXr18/ePBgYmIixLZPnz4VFxfb2dkJCAjMmjWrpKSkpaVl9uzZ8vLy+/fvP3PmTEtLS3p6up2dnaGh4cKFCx8/fpyWllZZWQm5n3PPnj2XL19ubGysra39/v27sbFxR0dHRkaGtbV1enq6pqamvb29qKgo5FL479+/g2o2sMWQi8s/g8HVq1fr6uo8PDzevn2bk5MzZ86cgwcPTp482dnZGT4dzwcG586de/Xq1Y0bN9LT08HGQIn79+/v2rVrxYoVjx8/5ufnh1zgJCEhoaSkdPPmzU+fPllZWYHO4mZmNjAwuHjxIqQx+u3bt6dPn9rZ2TEwMPz8+ZOdnT0gIABqIoyCXK2E3BKFyIAXpUCYIPLdu3fTpk0zNDT8+PFjcXHxwoUL9+zZM2vWrKCgoMOHD//+/dvNzS0nJ8fJyenXr189PT0dHR3V1dUcHBy6urptbW3V1dWsrKyMjIzfv38vKSlRUlKSkZGpqanp6ek5c+ZMR0eHvb19QEDA5MmTRURE+Pj4SktLOzo6fv/+zcjI+OXLl79//0Iu7A4MDHz58mVpaenEiROnTp167tw5T0/PTZs2PX/+HH5TwKNHj+bMmWNvb3/r1q3KysppYHDv3r3w8PB79+7l5OQsXLhw/fr1mzdvTkxM/PbtW1ZW1qJFiyALIW7evDlp0qTFixd3d3dfvXrVxcVl6dKlfHx8t27dunr1qrGx8cSJE3/+/AnqiP779/37958/f3748KG9vX3p0qVbtmxZtGhRYGDg/v37L1y40NDQUFVVJSws7ObmtmXLlgcPHuTl5ZWUlDg5OXFycs6aNau+vh5ydysofEcxXUKAk5VpR54KqAgDW/efgYGfE7QZ3lePn5GBQZiHxUKRu9QNdLusACfLf4b//gb8YIUMLEyMIjwsq9OUfoOm5UFi/xkYhLlZbFV4mBgZBbiYlyUrvP/299ef/zn2opxsTNxsTAeLVSFtx/8MUO0781R42EHbn/z1+cV5WST4WaPMBP//Z5DgY9mWo8LAwMDLwTwpTAZ05QQ7U4ixgLcePz8HEwcrk4smL6hb/59BhJd+dR/Ik6N4NARGQwAvoFuGBK0f///376+/v5lZmL78/MoA2pLE+Pn7FxZmZiYGJibQKCYzNyenkJDgx0+f/v0B7dr5+5/hH8Pf//8YGNlY/4GuAWUU4GJnYGB69ubrq4/fvv5j+MfAyMnBxMPGzsrw/82HL19+/eVhZfz9+xcvP++PXz9ZGVn/MzD9/fefmeUfNw8DEysD+DB8zt8/+BgZuT+8//Dp85efP3//+vHrz58/oE1IoLWff5mYmL58+Pvu+WsWZoZP/z/8Z2BlZPrLxPCXGTRjzszAyPDw5tcXD67+/v2DmeX75/e///xiYmT4B1o7Cl5A+p+B8S8DaN0odADz538Ghj+ghfqcf9i4f7P+Yf/O9JuZ/xcb019OfiY2zp+M//5+ecv56/svJgaWv/+ZhCVYOPn+CYmxsbMw/vzxj5WD7d8/5i+f/n/69JONi0mYjwm0dx80dIw3VnFL/v71Kyw0zF7E9s7dO+vWrXv06NG/f/+8vb0zMzMZGBj8/f1v3LixYcOGqKiohIQEBgaGffv2/f4NvmqPgeH79+/v37+XkJAwMDDw9vaGrPODNBmZmJhYWEBLgdnZ2cvKyqSkpKytrXNzc4OCgri4uAoLCw0MDLy8vKKjo0+fPn39+vX169ezsrLKy8vPnDnT3NyckZGxrq5OVVUVYqOmpmZUVBTEE4yMjE5OTuvWrfPw8PDx8Wlra3NwcMjJyWFgYEhNTT1w4MD///+1tbV7enog6iHkv3//Dh8+fPr06cePH0tISEAEIaN0U6ZM8fX1FRUVvXfvHvyGTEZGRg4Ojm/fvv379w9+xRErK+tf8G1VoAsDf/1iYmKCDNqtXbt23bp1P3/+XLJkCRsb29atW799+/b169eHDx9WVlZC1MBthFzUdObMmdWrVzMxMXl4eNjZ2VVUVHz9+vXx48dr1669fv360qVLS0pK7O3tY2Njv379umzZMl1d3cLCQgYGhnfv3t29e/f8+fO9vb28vLzXr19ftWqVjY0NKyvrtWvX7ty5U1BQwMLCcujQoW3btklJSSkrK/f39zMwMGhoaHz69Only5dv3759/fp1SkrKjh07MjMzBQUFm5ub5eXlraysvn37tnnz5pMnTx44cGD27NlSUlKWlpbx8fHw1rOsrGx1dTXkms3du3e/f//+79+/QUFB8OHhS5cu/f79293dPS0tjYGB4cmTJ9u2bYO0ZcGX0LC8evVq7969S5cuFRUVDQ4O/v//v5aWlpWV1adPn6SlpU+cONHU1LRixYqoqCgjI6MXL14wMTF9+/Zt5cqVFRUVlpaWQUFBgYGBjx8/ZmVlTU9Pt7e3NzQ0rKysTEhIgCRFPT09f39/YhZvwGNklEGVEGBkZJDix3INBy8HqMMMutGNlUkIfrkmAwMfTBxiuwQful4uNtBpTQxglWiKZdlAg6kQjRBSWgCqHaISzgVNjLBCzZEXhupCtosLwyiIgaPkaAiMhsDAAro1Rhn//vv34/dPZmbQxWigE5hAt7ux/Pv95++f3/9Bt8axsrOzC4mKvP/46cu370ws7H///fnz6xeoyfr3HwcrFwc7ixAP96sPXz9///uHkenH39///jGzszDxMLOwMoD2+HAwM7OyMwhxc7BwsHz89p2b9b8oBysHO+tPBvaf/xi+/mNi+sPMxMz268/fbz9egW62+P+Phe8fy38mXoZ/4O1TDKAW6X9Wdi6mj29+/f/77x8jJzsn938G9p8/v//68ZPh73/IpPmXTz+Zmf7+/fuD4d8/Zrb/jEx//4PGLFlZOf9//wqaR2Jm+/f3N6hABC1y+v339w/Q2QGgk5yYWP8yMPxm/Sko+ped7c8/0JXajN8//vv9689/0DT+f4Z//z+8+P3x5f+fL1g4WBl//GD4+us7NwfHn7//P335++PPX17539KaoH2lZCcaNna21WtWX/1zWU5RjokJ5I3////LyICGEED1Byfn9+/fv379Cm/AiYuL//sHXaIqLi7e3d29ePHi1atXc3Jy1tTUMIIB6KgEJtA03N+/fwUEBCC3MgoJCf379+/Hjx9cXFyQLUfs7Ozc3Nxv374FLd5lBdUlwsLCv379+v37t6SkpKAg6NJnSLMP3vyFe/Pv37/gDgOocWZkZAQRFxUV/fz589+/fxUVFSEicJKJiSk/P5+BgWHGjBmTJk2aCluyeffu3YsXL9bU1DAwMHBzc3/58gXe8Pr48aOgoCAzM/P3798h5nz79g3esuTm5v7379+nT58YGBgCAwMdHBySk5O/fv36798/FRUVyK2YBgYG4uLid+/e/QcGcEOYmJgEBAS0tbUZGRmFhYWfPHlSV1cnKCiopKTExMT08+fPb9++iYiIgBbNsLDw8/O/ffsWHv5CQkIPHjz4/v37jh07/vz5w8LC4ujo+PnzZ9A1Lb9/f/z4cf369X///pWWlraxsbl+/bqysjIDA8OHDx+am5v///+vrq7OzMz879+/379/Q0gGcKfi8ePHq1at+vv3r5+fH2SaXkhICNQOAA8qw5vg169fb2trk5OTk5CQYGRk/Pv3Lysrq6Qk5K5wBhERkU+fPv37909MTAziWRERkQ8fPsDHhiHDtywsLJAkAVmH2t/ff/HiRQMDg+/fv0MC//fv379+QSYXQPcR/vjx49evXxAz2djYuLm5f/z4wcvLKywMuuYb0u2RkJBob29fBgbc3Nw9PT2QAIQ4Y5SkTwh8+wW+TxNi2X8GbnZQoff1J2h2iIGBgZ2ZkQU0VMrw4/e/v/9Ap9hxsTH9+8/w888/Tlh7EaL1269/XGygUYMfv/9DDAHNP/z5D9pfz8gAaaR+/QUthZgZGThYmb7//sfIAGIwMDB8/fWPG9aQ/fnn/++//zlYGVmYGCEu4WZj+vUHdOIpFxvoZFOIIMTeUXI0BAZXCPxn4ASn0sHlKnq5hm6NUdCwFGi7DxPDn79//v37z8knLCom9ebpA4Y/P0GDav8Yf/36+ejxo5+/fzL++8fIyPKX4S+fiCQrB9u7F8///Wf//u3Pl//fX7/5+4eZmZGdgZWBiZ2NiZkJpIuXm+Pnz9/83Myc7Gx/Gf6BvPT3Nwc737/ffz/8/M7I9uv9q5/Pbn1nZGZg+M8COteT4R/oXCXQUCcDIyNotJQRtBXp////oFUBn9/+/vEVdFoTnyCXgKjQ378/Prz5wSbKx8XF9P3Hj58/frMwMnx884sBtG/0H+gyZNB4KjOPwH9+SdDwIOOf/+wCDF/eg5Y4sbIx/fn1+8mNn98/MjL+B4n8Y/3z7w/jq1uMjH9YGMCnrYIHkP6CrhhmYfz37//fX4zMDIy/WVn+/fz3n4mVk4OTkeHfz++f//wE7ff/+4uCQVFwkvrz+4+lhWVtcPWDBw+WLFnCwMDwD3So1h+wJMOfP6BTrpSVlffs2ePh4fHixYtTp07FxsZCZD98+PD8+fOuri4GBoaIiAjILvKnT5/KyMgcP36ciYmJjY3t6dOnFy9etLW1PXDggICAgLCw8Nu3b/ft2xcbG3vp0qVv377p6up++vTp2rVrWlpahw4dkpWVZWZm/v3793/wPi/I7fPv34MugIZYCiEhk7kMDAy6urr79+8PCwv7+vXr5cuXAwMDT58+jdZ4ffHixaVLl5ydnZmZmV+/fs3Ozv7q1auvX78qKiqeP39eQUEB0vCVlZX99+/foUOH3NzcTp069fz5c21tbVlZ2RUrVtTX17958+bgwYMlJSUQB7CyslpZWc2YMaOlpUVAQODDhw8/fvxgZGT8/fu3srKyp6cnRBkDA4OsrCwLC8uWLVvCw8Pfv3+/devWgIAAFTCAqFm9evWvX7/6+vqePHmyZMkSTk5OBQWFAwcOaGtr37t379OnT/r6+suWLUtLS/v79+/p06eVlJSEhYXj4+NlZGQuXLggJyd36NChnz9/CgkJSUhIZGVlCQkJnTt3TkVF5eLFi5Am+8WLF+/evQsZsl28eDHoyAhm5i9fvnz9+hXiwi9fvtTU1Pz48ePKlSvq6uocHBwHDx50d3c/e/bskydPIEObDAwMmzdvlpGRaW9vv3DhwtKlSyH+3bt3r7+///Pnz69du1ZcXHzixInjx4+npKT8/v373LlzeXl5J06c+Ac6F/L/r1+/IG3Es2fP2tjYnDt3jpOTc+PGjTNmzNDQ0MjPz4e4lpmZ+cOHD5DA+f37Nz8/v7S09K5duzIzM69fv/7161dBQcGfP39COkWgnYZ//7569ert27d9fX2/f//28/O7evWqpKSkqKgoJGYhRo2SNA2B77/+uU268+Ljb8iqoT9//7tq8jExMuy89hl8/QiDrgxPgKHQh29/155//+bLXyaG/6FGfPff/r7w6HOqtQgfJ3QA9duvv1P2v/bS4X/w7ufpB989tPlM5bnef/uz9dq3h2++c7AwxJoL//nPsPT0x79//zAxMXOz/o80FVx04h0XK1OqvcT5h5/33PzmpMZppsD9/gfjpksfn374rSvFoSPJsuj4O1ZmhhQ7iZ1X3t96+cNWlUeSj3XL1a////4BTWShhg4LMwsk2f/99xdSnoCua2YBVSYMoIOioJ1hRkYmVlZQSf73DwiClq8yMkKWzfz5A+oyQ0wFHVfIDNoICyrZQBUERJiBlQU02fIPdH4fqN/FwswC6mGysvz5jXASKwvrn79/QFJ//4JP+gPpZWVhZQQf1AqqR//9//3nNzMTyMXgncG/IQaC1JGMGZkYGSnQTtg+0NQZKLgprbnw28TMxAzqn/+BTuKhKWZkZGQF7Zb+DZ2uRJMGc5HdCZkTgxQ4YEmcBDNoHI0RUo6hKiIpYKGK//9nWJ+hpCvNiWrUSOGBMxudPMvI9J/x/59/oAWbzMwMv398ePP0/9/v//79+fXzFyPoWnbQNX6gE/AZQBeFMv1n/v0NtJKMkZmDgREUW3++/fr7/S+PAP/v/3///mFkBB2Zz8jOyqYkLvDzz++fv//wcnN/+/VLWpCXnUWABbSu7j8vN8/LNw++ffwFMpLxPyPT799/GJkYWRkZGP/8/svIwPQXNN7J9Bd0gicomzMy/AJtKwLdFsr06+evDx9eC0n8kxH+xczO9P8vM9c/RnYOZmYGjntX/n958/Xv7y+MDMyQ4dK3Txnfv2Bk5fzzj4GTlZPz7x9GVlYmRpCPQfdMMTJ//Mvw59ef3yICoqA7SRgY2MDFEBNoBe0/RkYmJhbGdx8+/vnzl+E/AwsbGwMT449fv7l4OOXk5V+/Ap0NBC0vwKEDWT5FXqSxsLIePHjw6fbH/ML8WlpaLCwsYmJi8FlOGRkZNja2lJSUwsLCmJgYISEhIyMjXl7opXlsbGwHDx5cunQpJycnHx+fq6srCwtLS0uLjIzMv3//NDQ0/vz5IygouHTp0nnz5r19+7auro6Dg4Odnf3w4cMHDx58/fp1YWGhoqJiZmZmdXU1Hx/fv3//2tvbWVlZIU1SiI+Cg4MbGxtv375dVVUF2U3FwMAgISEBcWRYWNjVq1fj4uJ+//7t5ORkYmJy/fp1yCgaRDukeli5cuWSJUsYGUHFRGdn56FDh06ePNnV1cXKyqqjowNRyc/P39DQ0N3dvWjRoo8fP1ZUVAgJCRUXF1dWVkZHR3/9+tXZ2Rl5M3hpaWlDQ0NSUhIfH9+nT59sbW1FRESEhIS+fPkCMRBCsrGxNTY2trS0bNmy5cuXLwYGBiEhIRApCGlubr5mzZqUlBQRERFNTU1OTs7S0tKKiorTp09/+vQpLS3Nw8Pj+PHjiYmJ////19XVtbOzi4uLKywsFBQU/PXrV3t7Oz8/v6ioqKqqakBAQGZmJi8vLxsbW2dnp4CAACQoDAwMpKWlk5KSxMXFtbS0uLm5ubi4PDw8ioqK/P39Y2Njq6urk5KSfv36ZWpqamJiUlRU1NXVtWLFCn5+fsiSWYhTPT09Gxoa0tPTBQQE9PT0mJiYuLi4nj17lp2d/fTp0+DgYCUlpf///79586a0tPTZs2d6enoODg537twRFBTk5OSUkpLi5eUtLS3t6+ubO3fuz58/6+vrfXx8amtrZWVlf/36BTmlISoqaurUqUePHs3IyJCXl2dhYSktLa2urj5x4sSXL1/KysokJSWlpaUhx2xxcHDIyspycHDs3r172bJlbGxscnJyGhoaEydOhCyBgLh8lKR1CPxnYHj58Xezn5SGBAfErj+gspQh3Q40xs/wn+HKtWtPr17hYGePkGb6/fOnqJjoj1/vWL4/LYj3+PkH3vcEjZguTlL4DToHlJEZNFTB8Offf0aG/9KMz5WUFFlZ2X79AU2waHD+kpSUePLkibKyyp//jM4avH///b9+43aonoy9+HtpGfHvP399eXzVT/gHqxgzv6CQuoZmoIHA////b96+V+sqKsAn/eff/1+/fpsLvlFRUYE37CAuZ2Fmef78+dFjR79//2FkZKStrfXnz5+XL1/dvXv3z5/fLCws8vLycnKq//7+/f3nz+XLl75//66qqiopIQm+HuXf5cuXv3z+oqqmKikpBVH/4MHDBw/uCwkJ6ejo/P8HXsQF6vb/v3fvrqysHAc7+3+G/z9//nzy+ImMrMy9e/fVVFWZwVdIMzAw3rlzR1xM7NXrVxISEpDJBIb/DHfB3VQmJtBsGi8vr5KS8tu3727cuM7Kyqqrq8vBwQlvtoKWhIGav5BaArwdFsQFlYtgz8IbheCls///QwYgkHRh1QIxDWwA1DQIG2IsRAuoAgWLgkyGGQga4AC5mhk0ag6WhRBwAyF64SRcFlkExUCwyRBlEDUge69fv/7o4SN3D3ckWZA4RN2nT5+vXb1qYmrKAgpkNNMgShhApy8yMkB2665fv15XV09FRRlsGkqIoYowXLp46d379w4O9jBxiOEM/xEBCzEfIg4hIYEGEQc58v///3///P3LyBwx5/5IvhgMNGEODxXaMY5s3TCprWXPtSe/QTfUgyz9Axo8+cfE8A80gcPMzASeBATdz/Sf6S8jCwPTv/9/GVk42IREZb58+Pb923MVcy7Wfwq///9jZWf7z8zy/ffff//+MTMxcXGyyYvysrCwcbL8/8vAyM78j4mN/Qtoz89vcT7On7//nD5x7urpm6CFnKxMSrqggdBf35k4uP5/+/afT4jl338GVpa/bx//eXKLgYnx73/QDnwGZmbGv/+YGFnYWFi5/jP8YGL5w8DI9ucX438G0D1P7JzM/36y/vr2/ef3L6xszExMLL9/Mfxj/M3KzMfIysrOxcnCzM7I8P83qIP97z/jf2YGxn//fvz//5WdkUFKQpaPh+fv/39sbOy///xl52D/8/vX37+gbVt379779v3rj2+fOdjYZGUkObk4v377wcoKuhPgz69fn798e/j0ua6chKW+Vlpjl6G+AXmRlX+g2FzAxIjdUFZOlo2NDTJTD+o4gufNv3//zsbGBpnYvXnzpqSkJD8//9+/fyFjBhAb79+/DymLWcFanj179uPHDyUlpd+/fz9//jwzM3Pu3Lnv37+XlZXl4eH58uVLQkJCX1/fjx8/hMEAYsjbt29fvnypoqLCxsb2////Hz9+cHBwgPqmYOmvX7++fftWUlISYgUDA8OPHz9AAxeQgQoGhtu3b7Ozs8vJyUGm9f///w+fTwcbACoLwBXJHxUVFRYWlv3791+8eBEyFPfv3z9kxZC1ntLS0vz80A0W//79u3PnDjc3N7wpDDETQj579uz9+/diYmKQtQeQ6W9kAyHKfv78ef/+fV5eXqyGfP369cGDB/Ly8uzs7ExMTJCx4du3b4uJiUGGEhkYGO7du8fIyAhfgfDy5cs3b94oKytzcHD8AQNI4+zp06cfP35UU1NjYWFBdszv379v3boF8Rc8Bl+8eMHCwgKx4ubNm+zs7AoKChAHf/z48enTp6qqqqDTJ0AdOWht8eHDh2fPnimBVxSwsbGlp6d7e3sbGBgwMjJCzgGorKyUkpIKDQ399u2bkpISJEYYQR0spt+/f0Mc+enTp8ePHysqKnJxcTEwMNy9e5eVlVVGRgZyPitkXQFkccivX78gYfL79+/bt2/D1298//4dIv7v37+fP39Czn66d+/ejx8/IH7/9u0bGxsos4AG+///i92eWGJSZCimD/HdKEn1EPj2659By/U1aYp6MqA4xTR/xdLFly5fOnPmzN8/fy0tLXR0dBkYGA4fOTx1+ixMxZgijvY2wUHBOfmgldN3bl2vq62dOXNmVFRUX1+/qoYWAwPDowd383Lz5syZk5WdNXPGjHv37ufk5jjYO/z5++fB/fs6urr19fUMTKyJcTEpqSnWtg4MDAznz5wMCgraunWrli5K+Xnj2uXysjI1NXVBIcFjR48lJCSEhEf2dXdu3bbVxtrm85fPd+7cqa6uNre0rCgre/H8hZCQ4OPHjzs6OpXV1Otrqm/duiksIvL82fPW1hYNbb1D+/d2dXWqqKg+fvzYxcUlMycP6rt/vwP8A6ysrMoqqxkYGB4/uFdSWrJw4cKQ4ODOri5tsJNePHuclZk1d+7cjMyMyopKA2NTsN7//T1dFy5eZGdjf/fu3devXyZMmNjU1CQiIvL161ceXp7uri5WDqT1uWA9xBDvXr+YMnVqXUMzMYrJUzN7xlRNTS0be8LnDZNnPkTX2lUr9u/fP2X6TAgXjXzx7PGC+fNLy8qYWaEdJzQFDAwMSxctkJOXs7V3As37hQUnJSW5eXhjKkMTWTR/7u3bt5vbOtDE3795NWPG9MqaOnAjFU0Snfv8ycNZM2fWN7dpNFxbkqhgIo89Q6FrG3Z8+o2MgvYoMTD++fP7PyMj6OIiUFD+/8vIBBr1/P8fdL07qMv19x8TBwe3xJ9/X/7/+ysip6Suo3r+6NH/X77//vTn5bPbP36CDor/B1rlCVpCxMjEyML0/wU7CwMjMxMTA2Tem5kRtE39P9M/dlZW0B2kbz78//Ofgfkf83+GN08Z2ThZfn79+5uD+ceP39/e/WYCX5H84+t/0Lr2fyz/mBj+M/4FjdD++8fGzMrDwwNaBsrI+5fhPxf7fxZGJgYmtv9/f7Oysv1m+AYZ82dhZgNtnmdiZmBjZ2T5LyUuKiYq9O//vyfPXj1+8oSJhYWJmZWJmZXhH8fPH18/fvwoxC/A9P/fj58/GEGn4v8HLcX79/v5i9dfv3359/8v+Pj9P79+/WZlYwe1EZlZGEADwMygs6LA82HgFaPQhgIoCEnE////4+Pn11DUgOuD9AUhXEgdz8DAwMTEBDkaCbKQESILIeHNIwgXvoedlZWVhYVFVFSUk5MTvuSRiYlJXFychYVFTU0Noh5CgtuloCWAoE4iIyPcXogsNxhA2BAS0qaBsBkYGCBbnSBcyCFHEDacZGRkVFEBbaqFiMjLy+vo6IDCE9yAhghCSG5ubi0tUN0G4UL8juZauBQDA4MUGMBF4M1luAiEwc7OrqGBCGSIIJzk5ubW1taGcyGLE9CcAWnYwdWIgwGEi9wulwYDiDiyY1hZWeFWwPsS8HhhYGCAjEpCNDIwMPCDAZwLZwiAAZwrKioK7wb8B13nyygkJMTNzY1sMjxG4EmLj48P7hgGBgbIwlYGBga4SrAlApBVyxC7WFlZkQMEnkKYmJjgbOQggjRzIXpHSfqEADMzS2VNXZqfubObJ2RZMLK9EdGxEQwMXV1dP3/+rK2tZWBgWLx4MSMz2/79++/fv+/q6grpzLx69WrHjh1sbGxeXl6Q2Q/IiWnMrByz5s43tbAyNzf/+fvf919/eQWE9Y1MN27ZVgJujG7eukPXwIiHX/DLt58///z/9PW7kop6e1cPaBXp16/p6ekLFy+Lj4//8fvvT9CSKpDTVq5Zb2xmuXzV2mZwyw8kxMDw69evppb20IjomJgYBgaGq1evXrhwgYGB4dPX7+6ePmVlZQwMDPPnz589d4GegbGJmaWnpyc3N3dzc/OS5StdXV1Pn7sAOVR4yZIlrR3dM2bM6OrtLywpd3Z2fvHiRWxsrI29o64uqCH+49dfRha2hUuWmVvZ2Nvb//zz7+v3XxxcvFa2DqvWrG8EO2n7zj2KKmqCImLff/75+QdUx4EdyZhbUPz//38WFpa6ujoeHp7/TCwJyakuLi6MjIw+Pj6nzl6wtrYGq2R4+PDhvn37uLi4PD09v3z58unTJ0hB9OTJkz9//ggJCW3ZsuXHjx9OTk4iIiLrNm7Zsm2npraempqavr7+/fv39+/fLyQk5OXlxcbGdvPmTcipfMrKypaWlrt27Xr79q2Pjw9kfTnEuufPn0NOqXN1dRUXBx2ecOjQoRs3bhgaGpqamp47d27D5m3Xbt759vO3lZUVJJG8fv3669evz549Y2RktLS0vHr16rFjx+Tk5Nzd3SFRv3PnzqdPn1pZWUEKjYsXL546dUpBQcHV1ZWBgeHWrVtcXFznz5+3sLDg4uLavHkzCwvLpy/fGJhB+xCeP3++a9cuZmZmuHtARyvwC3l4+zGxsN+9e5eFheXYsWOsrKze3t7wkuTSpUtrN2xSUFD4/PWHvb09Gwf3m3eg6z/Y2dkhW3UZGBjOgoGampqDA6hjA/H+n/+MfxlAg7537949ePAgFxeXj4/P////V6/bsGnrDiVVDV1dXS0trTt37hw4cEBcXNzb25uJient27eQPQDOzs68vLzrN23dtG2HhoHZjx/y8BEZiPkjigSFIx08zMjI+PPnz89fP/1j+P8PfI49eOs5I8N/0LDhP9DF83///f/3j4GdkZnj968vEpJiOhbmf/4z3jh//uu7J0z/QUOY//4x/PzC9P0768+vrD+/sP38wvrjA+vX96zfPrF9fs/44Q3Tpw9sH98yfv/M9vsb299vnN8/svz7ycPNyc/GycLMxPoftD3o78v7Pz+++v3m6Z/Pb/9/fMH47sn/t0/+f30PXjzK/JeZ5S8zM+gs5///GDk4WEXFhBSVFdRUlLTV1RSVFVU1VJSVZdVUVFTVFFlYQRP0oFtN//1lYmZgZGKRlhSVlhD/8/vXq1dvXr98y8zAqCAjy8vFDjpC4O/ff39+//v368vXT6/fvXnz9u3bV2/evX334f2H169efXj/Dnzmzp9/4MVM/xgYnj59du3qjTu3716+dOXipctXrl6/++AheGkCw3/Qmf3wiQNyog4xQ0aObnx6JCQkJk6cCK9RGBgYODk529rakFsq+PTTTE5JSQkykEkzG0aKwRUVFfCyGFJuZmVlRUZGjhT/j/oTKQSYmZnv3b2XnZtnbW3d1dX1+TPoNHskeRDzL2jhI7S84uTkhNwB8fjx49TU1BcvXrx69So3N/fZs2fXrl0rKSmB7x38+/cvLy9vYWFha2vrp0+fIL2pf//+BQQEHDhw4A94eeaBAwf8/f0hiRDSp4VvvOPm5o6Ojt63bx9EHOQOBoZHjx7duXOnp6fn1KlTz549gwgyMDC8fPnyxYsXvr6+oDH1f/+0tLSio6MhndJnz55dv379woULFy9e1NXV5eTkhNwH8fXrV8jBxmfOnLG3t4e0aezs7F6+fHnjxg0mJiZLS0vI4iIlJaXz589D7IJM4JSXl3d3d797944ZdOU0aFjB39//9OnTkPXcu3btgpwZB/cXRC8LCwsrK+uNGzdOnDiRlJSkoaHh6urKyMgIOY0OsrcPNNr6+HFBQcGHDx+uXLnS09Pz4sWL+vp6SKj29PScP3++qqrq5s2bHz9+7OjoePHixf379798+XL58uU3b97cuHGjqKjo48ePBw8erK+v//PnT1tbW2Vl5Zs3b7q7u1NTU0+cOHH+/PmioqIfP35AXPXmzZuCgoJXr149fvy4vb39z58/c8Dg79+/3d3dO3bseP78+du3b58+fXrr1i34wspz586FhobOnTv32bNnR44cqa6u/vXr14YNGyBLwJuamlatWvXx48eWlpaHDx/u3r27pqbm48ePixYt6ujo+P//f3d3d3Jy8oEDByBnER47duzhw4eQE+U+f/5cUFDw8uXLR48etbe3w7dFPnnypKmp6d+/f5MmTcrIyHjy5MmOHTsqKirgCebZs2evX79+/PjxjRs3IEtFFy5c+OzZsw0bNoDG1xkYtm7dCvHgvHnz5s6dC/E+JHUxMjK+ffsWklDPnj1bUVHx+TNoKP3z589Xrlx59+7dpUuXSktLv379umfPHoiy4uLiO3fuvHnzprW19fXr1w8fPvz+7fu1a9dAQQRKEXDjRxaDfiOj/0FDoKBNQ6BxR9AgKGhgkRHUHP33H3TdOhMjEwePoDQLB8fXjy+lVGR/ffv74d61Pz++gtaLsoF2rrMwc7Kx//r97xfo5E7Gf8KSv/lFWb99ZmJl/fXn9z8WFpa//378+Mj2/fMfRoa/HOxsoLuJf/9mZGISFBRk/Mf069ePr9++cXBwSMtI///3n5ubm4kJlIwePnwIchKowAQ1Q0H9HPCW3W8/vr148UJYSJjpH+OXr1//MzN++v+PnY3t96/fL1+//v71OwMD0/////78+cPECApGFmYmAQHQ6M7fP79//vjJxPSfj4+PiZnh48eHLCz//v/58//f/2/fvt69ext0FCl4SPgfaFkAaBEp6HgrBtCAMXgJyT/QxijQzaSg/VGgjT3wpSbgxAlyKZgx2AgmJib4ZDfEbYyMjGgiEPFRcoiGAGRsA9nx3NzkzA8imzDKHqIh8OfP72lTpz49t62wpLy8HDQWCL9TF6uPfv/+rampWVdXx8DAkJycfPr06bdv3/Lx8UVHR//58yc7O/vkyZPwrs6PHz9sbW3fv3/f2tqalJQE2aljYGDAxcV1+vRpdnZ2VlZWIyMjSBuOAQPw8vJ++/YNWXjr1q2mpqYKCgrGxsbbtm1LSUmByP78CdpBCxnFnzVrFmTn5YQJEzjAu/og2/6uXLnS3Ayay/779++WLVsWLFigpaUVGRnZ09MDL98g620g7oGYBpnxgDeJQEcE/PxpY2Pz9evXhoaGgoICBgbQkkptbW0BAYHjx4/Lycn9/fvXzMwM1CiBOA6VnDx5MuSmCQYGho8fP86ZM2f37t25ubmQsU8GBob379+/ffvW0dERMh8lLCzMyMh49uxZDQ2Nhw8famtrz5w508/PT1dXl5GRUUxMLCUl5ebNmw0NDUxMTDU1Nbq6uiEhId++fUtPT79x48aPHz+ioqKCgoKkpaWXLl26YMGC379/e3l5PXjwAGLj+/fvnz17VlxcLCcnBzmUbfHixTU1NRoaGpycnHPnzl29evWBAwdMTU3DwsLgXvn37x8/P//06dPZ2NgSExOdnZ39/PwMDQ0rKipMTExOnz69fv16yK5TVlbWOXPmlJaW2tnZffjwISQkJDo6+ufPn15eXvn5+UePHv3w4cO8efPAleb/e/fuvX///unTp0VFRfLy8kxMTMgzRZAm5rdv39zc3AoLC1+9ehUSEvLhwwdIO97Dw+PEiRMaGhqQU6V//PgRHh6elJT0+PHjxMREiC1BQUF2dnZKSkq9vb3R0dGQyTpwy+G/gIAAJAwfPHhQUVHx79+//Pz8hw8f1tfXQ1bAGxsbBwUFffr0KSsry9HR8eHDhzExMdra2pCZw/j4+Du3b9fUVK1svA4aqYOH1AhjgFpR9PEyZEkvEyNoE+D/v39Bi8oYGP/++8vIyMLCxPYPvDb7189Pv1kYmPklH9999uHVg7//vzBzghQIiP//94v5z29m0BrNfwx//jL+/cXy988/Zpa/XHz/GFn+sYG2Iv1mZfr/6+f/vx//MzOBrsYBzZAzsfwDtS7/sbOxs7GysbFxMjEx/vr55/u3bz++//z77+/3b9///2MAbWMDt49BM+GgE5b+s7CzcHGxc7KzcnOx/f/3j0+Am4MDdOwRGyvb9x/fGRkY/v7/ywDemf/3z29GFlYGRqb7d+8yMjIyMzL++ws6duQP6PBR0EZLZsb///78Yvj3F7SQnYGBEXTACLj7A9mNxAhaX/cf1PwEJ2zQ4amgA1NAI8fgtjEodgZt8xPkuFE8GgKjITASQ4CFhbWoqOjZxV2qqmo1NTXIi2ewBsefP3/gkyQCAgI/fvx4//79gwcPpk+f/ufPH21tbcg8L1zv79+/c3Nz4+PjN2zYwMnJCZmndnd33717Nycnp4uLCxP4RDm4emTG69evIUMDkOErBgaGtWvXysjItLa23rlz5/Tp0wkJCZABVy4urr9/QQv8GRgYgoODHR0d09PTv3///uvXr5CQkKqqqn///kEGbu3t7adNm3blypXy8nILCwvQmaYcHPD9i5BzHnh5eSE1HWQdOWQNNLLDfv78mZaWlpCQsGbNGkiDBnLA8549e8TFxW1sbNjZ2bE2Rq9evXrv3r2ODtDyRMiIsqqq6vz58+FHrUEOG8nNzZ0wYcKbN29MTEzq6+t9fX23b9/+8OFDXV1dNTW1hoaGefPmTZs2TUxMrK2tDbz4/A9kETZk1fi0adP+/v1rY2PDw8PDzMwMOfUPsoUL0nTm4OCADyiqqqpWVVVNnz79zZs3mpqaBQUFX79+3bRp0+7du3///g3Z/fn792807/z580dVVRWyW+Dz588nT5588uTJv3//3N3dv337xsnJyc7O/v//fzExMcj5zZC9ATw8PFxcXJ8+feLi4oJM37948QK+SExBQeHu3btycnLV1dUzZsx48+aNhoZGQ0MDWlcZvkqeiYmJh4cH2WG/wAASUywsLBCTQS0GTs4fP358/fp1//79ly5d+vPnD2R1BEQlZAT9+fPnra2tbGxsYmJikNNpIInh169foCUEnz5BRqD//v1ra2uroaHR0tKyYMGCV69eSUtLt7e3QxT//g1aHwg3dgQy6NQY/f//PwcXi5KuEOP/f38Z/v7+Ddoow8LM8u3rFwZWJibW/wz/v4HGAxn+MrH+Zfz76ve3Nzxif/mkWJgZ/zD8Z/37k/vLG9Zff36xs7Oy/Wf+8efv//9/P7xkef/sFwMTuDUHWofK9O/fX2aWf+BDMBgZGf//A7f//jP+/sPw78cfBtBGEdA2qX9fP33+9///j2/fQXugQHc+sYOyy98/4JWroN2doOYhC/O///++f/v64N4H0DlP4O2Xf//+AR3KwQjams/GyvzrJ2hND7jt+psJdDMIaIwTdLA+IwNoOPM/aBvNn18/QHtEGUFnlELmxyEkJKmB52JA27lAXJDRIC2gpigD1ARQexc0PIo6NApSPYpHQ2A0BEZDYCBD4O/fvwoy0i358x0cnTCHzCEuAx+5CyonIcNX8HYMZPpeVlZWVVUV0iq6du0acnMWopGVlbWhoSE4OFhSUhLSdvTw8IiLi2NmZobMljIyMkIGvUAbYkHFLuisurt3786ePRtykQdk996xY8fY2dmDgoL+/funp6fX19d34sQJGxsbBgYGcXFxFRWV2bNnl5SUiIqKsrGxgSqP/6AVWEygjQ6MzMzM3759Y2ZmfvDgwdatWzdt2iQiIgJZMG1sbNze3p6dnc3Hx7dz504pKSkNDQ1GRsa9e/f6+fk9fvz4zp07RUVFkKCALIgE7btlZm5qagoJCeHg4ICMoTo6Os6dO/ffv3+zZ8+GKIaEDyTQwNUEw4EDB7S0tCCLoDZs2MDFxdXS0oKsgIGB4fXr18rKygsWLHj9+nVkZOTdu3fd3d03bNhw8+bN2tpayFDx9OnTf//+HR8fv3fvXltb2x8/fkACUFpaWlxcvKam5uvXr/fv34ecfAeJL0jzGuIweDgzgK/kEBUVnT9//tevX8PCwi5fviwjIxMTE2Nubv748WOI+v///8Pb63ARiLGMjIwSEhJ6enppaWkfPnx4+fIlBwfHx48fnz9/LikpeeXKFfA6efGTJ08qKCg8evTo69evYmJiv3//hmhXUlKaOXPmx48f+fn5jx8//vfv30+fPomIiMDdc+7cOVtbW0gQQfwISVTIIhAnQdqUkPCBrNaAWAHa5w7evysuLu7l5RUUFPTq1atPnz5BehoQc9jZ2Xfv3s3Kyjp58uQHDx5s374dfFwjy9evXyF1vZSUlIqKSmlp6ZcvXx4/fszOzs7MzDxr1qwfP37Ex8cfPXpUVVX127dvf0EjdOBRKribRhiDTo1RRgbGv3//vH/34f8/0Db6f38Y/v//wQDK8KDjQP7/YwK1yEC7mH78Z3j//y8j6LwkUMnyGzQ4+O8rI/NPLg5Bpv8sf379/sv4nwl0N/c/Hh4ufj4pRsa/zCzMP378YmYFyb599x7UBv3PwMjKwgS6CQm0EvXP/98MjKBb5P8wMID2ToFalozMLMxM/5mYGFn//v/3B9Ia/fvnz++/fxn+MIDOj/zz9w8jC9Nf0Kgp6HYo0LAl6IZSUDMRvKwANIgKGlEFD2H+A5+sAVr0CrYBlAhBmAHckAQ1V8Gjr6ABThCGFC6QlAZW9g+kkBEUBqC1q6BloSBJkFJooxTMBImN4tEQGA2B0RAYFCHw9++f1tZWfWl2PK7h5eX9DbvCjZOTEz5aKSwszMHB4erqunfv3rS0tH///omKikKmwiFjmUJCQpDWp5qaWkVFxZo1ayDFppSUlK6u7v///yH7nyANGsgCoWfPnsXExPz79+/bt29BQUHe3qDd0IKCgqysrPv27YuJifHz84M49e3bt/v374c0RpmZmWtra6uqqqKjo/n4+N68eWNjY8PHxycsLLxixYp79+79Bt8ukZmZycPD8+PHj/z8fMiBuyEhIQEBAfb29gkJCXx8fO/fv+/q6uLk5CwvL29vb9+wYcOLFy/Cw8MhM9pwT0HmjhUVFaurq+fMmQNxj5iYmKmp6cePHyGnWzAyMoqLi7e1tQkLC0NmpWNiYv7+/QsZI4S0gW7fvh0XF/f3719mZuaamhrInstfv351d3cLCQn9+fNHTU1NWlqak5NTXV390qVLenp6P3/+XLVq1YwZM/j5+f///29iYgI5+i0uLi44ODgxMbGkpCQ7O/vz58/6+vqQxQMQ13JwcEBWIzAyMsLjBdJomzZtGmQ9q5iYmIWFBSMjY3d3t7S09IsXL3Jzc2VlZX18fHp7e+/evVtQUAAZxOXk5BQB3/HBwMCQnZ1dX19/+fLld+/eeXp6xsTEhIeHZ2RkSEpKfv78uaenB3LK2759+16+fJmWliYqKgpJOZBhYGtr68TERBkZmXfv3kFCYNq0aZBTNURFRSEDqJDFEpDpeH5+fsgCX8j9I/DrORgYGDw9Pdva2q5fv15RUSEsLAxpbkKm0dnZ2fPz89va2g4ePPjixYvIyEj47lgeHh5ubm5TU9P169fn5+ezsbFJS0v/+/dPVlZWWVk5KSkpIiIiJSWlrKwsOzv706dPFhYWSUlJixYtmjVrFg8PDwsLi76+PuicPjXVlJSUzxI5jLDTZCEJY0SR4AYQ7X18dNvGzrqqzWevgU86ALc1GSCdAPDucNAUOai9xcjI/B80q87Exs765+/vf79/gVqloKltBk5eHlYWgb//fv/89ZeFjfX//3/8/HxioqJfPn9hZ2Pj4OT8B7ook+Hho8ffvn9lYWJkZ2dlAq3CBB1Z+v/vd+b/TCwsrAxMTAzg65Ag+Rm8PoORAVTIQY/l/fvn/5//v3/8+vXnz38mFtBJMf//QQbPGf8z/AOfKgoa8AQZ/Offnz+gA5///2dgYmJhYgY160EtVgbQkCwjqCUN8hXoqnqQpQz//4NOU4aENMhCCAtMwrnghimoNQseCAUFCMidYDUQwkBR2kJXO7Wx08gA5WgSiCwxZN7+QjcFVx9Fr8+fP///D1rVCrIC5O7/8JwJ5oG4oEOc/4COBGRlZYX04PFb8ePHj+/fvwvC7lLCr5iesn///n3//j1kBRW17P3zB9Stgu8HJ2js////f//+zcbG9v79e05OTtCts1++CAkJQaZy4IFP0BxkBf/+/Xv37p2QkBB52iFGvXnzho+Pj3iPQA5v+vz5M6R8hxgyqMh/o0c70T4+IEc7rUhRNJLDdxLNF/AlZ5CziiFz35BmzadPn1hZWTk5Of/+/XvhwgVGRkY9PT1I6xNSIr17946fnx8i8v////fv34OW/oOGABggI1iQ8xMgUpBbxF69evX9+3dIAwI+PwvJbt++fePn54cXYr9///7w4QPypkbIXPzHjx+lpaUhLcKvX7++fv369+/fkPYuD3hW9+XLlz9+/IAU1IKCgpAscPPmzffv3+vq6iJbev36dSkpKYhRkNiAOJWPjw/iKcgST3hp+f3793///sFNePfu3YcPHyDjeQICAiIiIpDxRcgI9Ldv3169egWZZWZkZISfwsvAAAqcy5cvMzExGRoaQo57Ky0tNTAwgFzv/P///0uXLn3+/FlHRwfSMfj379+NGzd4eXllZWV//Phx4cIFHh4eyHVx79+/5+bmZmNjg0ccphd+/foFuRNYX18fEiPPnj27e/euiooKpOnJwMDw4sWLt2/fQo6lg5zT9/PnT0gyYGBg+Pz586VLl0RFRSGtScipdi9evNDW1oao+fjx45UrV2RlZSFt8Q8fPkCm8iGhevny5b9//2pra3///p2Pjw/uHgMDA0i7E7K64NOnT0JCQh8/fmRnZ+fg4Pj379+HDx8EBASQS85Xr169fPlSVVX1x48fECuQlb19+/b69evS0tLI58l8+/btz58/fHx8z58/v3fvHuTobg4ODlZW1j9//ty6dUtAQEBKSur79+8XLlzg5wed7Q0ZIIdcAaOnpwfJGn/+/Lly/VbQ8t+r0lVH7NFO9GuMdtVVbTp3A9RNYQeddMTBzcDCxvgPdHYnw5d3/359Ax1gywRujP5n+MPGycnEzPrz6zcGBtC+HwaGf+w8nGxsAgygGp3hH+NfBmbQ0kxQsQU9URg0q8IInokHjV/+/we6P4MZdNbT//+///37zvSfmZmRCTRjzsDwB3ThOOP//6DrDcG5nZEZdHQS6Ix60MUx//5+/wFaCMDCys4MnqyHNM5AdoHm4UE8UEP6D2hEFTyhwwBaKcrEDNqMBWrTglq7oH1ZoEFN0BICaFsTdMHoX0j+gfSSISUaVBYmARGE8dBpyhujBQeLLYXMH25+cPTkUUjnsry8/MuXL9OmTauqqoLk3gMHDly7di0rK6urq+vcuXOQC9wtLS2TkpIgHWUGBoYJEyZISUkhL0tnYGDYuXPnnj17uru70d09EPyvX7+ePXvW2tqamZn5yZMnpaWlM2fOhExykeocyNwcCwvLr1+/nJycIiIi1q5du3379j9//pibm6ekpBDTkrt379706dM7OzvLysq8vLwEBARmz549derUxsbGwMBAA7J6F69fv87NzZ08eTJytUqq7+Li4vLz8/FvPUEz8+LFi5MmTZo5cya8WkVTMLDc0cYoHcKfyMYoHVwyagWuEPj161dtbe3r168nTJhAXtGHy2RaiENWPtDC5CFh5q+/DLpNV5cmKY7YxihoPI9eUQUeBP3/j1eQmU+MkY0TNFjIys7y//f/fz8Zf337CxqzBK2b/MvAyPjn5y8Gpj9MzEwsbFy/v/8AtTT////58xcrMxM7G9ufv////ANPfDMygdqwjP/Bw5ugIUnQiCQzaFs6qEfLALpvAbw6GLRJ6c//v0yQc0RBl60jN0YZ/v9n+Qfa0w9aFABaOwBq4P5nBB1CBTIK1AwFL+RkBM3Zg3ctge6LQgQbaLHpv/+g65RASsH+AJ0OCnIaw3/QclGQUpDLQDQEwxudcAZEnNYkOztHT2+vLa91X18fExPThAkT5s6dGxAQcOTIEUg/G3Sa9KNH586dY2BgOHnyZHh4uL6+/tevX6dMmQI5IIORkfHFixfLly8XERHx8fGB9IYhzoaMjP758+f9+/fIzaOXL19CpjMgyr59+/bp0yf4VgaIIIR8/fq1oKAgCwvLly9f/vz5A+m7Q6RevnwpICAAmT2BFFugq/b+/2djY/vx48fHjx+Rdz/8////2bNnkFNC+Pj4/v79+/Hjx3///r1+/VpERATeAXj//v3fv38hc0b//v1jYmL6+vUr5Hx+iKUQcu/evcrKyk5OTr9//5aUlHz58uWZM2fS0tJ4eXlbW1v5+PhiwCcUgiIffPTm58+fITtGGRgYXr16BbkVSVJSMj09nYmJ6ePHj79AQ+9/Pn78yMTEFB8fDw+rt2/fsrKyQqoNiB+/ffv2/ft3yAAMxDEQEhLI//79+/jxI2R5E2QEQkhIiI2NDTJ5B1EJJ0G9qP+gAW+Iq+DTbR8+fIBv+H3z5g07Ozuksw4ZUXj9+rW4uDh8/OD79+8/fvyAhCfEZIh34F6ACI6SoyEwGgIDHgIsLCzp6ekSEhLIpfSAuwqXA+DFMi4Fw1v8L6hZMry9SMB3dGyMglploMWR71/++vwe1FD7+5fx//8/oMlsyAJ30Lw9aOyQ8T/L/79/Qde0M7GCLur8DzoV6j8DE2g3+j/QmflMjP9ZQGs3QTPo4C0+kCWbDOAzS///A+9pYmAAzYr+A63AANkFul/4L2hRKgMT438GRiZG0AGrjKDL7cGDmKC72v6BlgSA98KDJshBN/aCxk5B45sgO0BiDIz/QFzQSZ8g6yGND2gAg8ZEwS1W0KgpaLsSeEECoqkJtQaqesCo/6DDpBhra2sFxQQZGBg6Ozs/fvz46dMnyLJ9iLOYQUty2SDH3SsrK0NOR58wYUJQUNC9e/eUlZW3bdvm5+f37Nmz/fv3e4MXZkE0srGx3blzp7y8/NGjR1paWnV1dZ8/f25tbX3x4sWvX78SEhI8PT0PHTo0a9YsJvB5W9XV1ZCGGgMDQ3t7+48fP+7duwc5N3jz5s2vXr2KjIyE3PDe3Nz84sULBgaGzMxMW1vb5cuX37179/bt2wkJCby8vJMmTWJgYJCWlq6uroY0pF6/ft3f33/jxo3CwkIPDw9HR8efP3+2tLQ8f/6cg4OjsbFRWlp6xowZhw8f/vv3r6WlZX5+/s6dOyFnhTx79szBwSE7Oxs+qff37199fX1lZWUBAQFIiQnZ08rAwGBsbPzy5UuI9xkYGHbt2nXw4MGPHz++ePEiKCjo/v37ly5dkpaWbmtre/369dKlS2tra+HnCzIzM////3/NmjW+vr5qamoTJkw4ffr0/////fz8oqOjd+3adejQoU+fPj19+tTR0TErKwvunsuXL3d2djIxMYmJif39+5eVlfXt27cNDQ1v375lYmIqKSlhYGDYtGlTXV3dw4cPe3t7W1paIBe3JiYmLlu2DHLF1P///+vr69XV1ZnA4M+fPz09PRcuXPj//39ISEhoaOi5c+cmTZr0588fDg6OmpoaBQWF3bt3z5w5U0BAADIPxcTEtHz58s2bNzMxMamqqpaWlg6JOg8eWaOM0RAY3iHAxMSEfDHE8PbsqO+GOgC1yejjh/8MDCwsoFEfbi5uFkZOxn/s7CwcnGycXByg+3Y4ONhBJx39ZwCRjH8ZGP+BZsD//f378xd4bxADA/is/N9///36AdrFBBrHYvjH9P8v6Mgk8EZ4RoZ/jKCBVdD+JfCOP9B0/K+/oNuDGRlAVzQxMjIzMIBvnWcAbXdnZPjPxPifiRG8jhTUIgaPHIG224H2T4Han6Cz+H//A+24+vP/P2j/PmiNKbjRCWq2grbUw0MO3PqEjn2Cm62gJilEFtzGRVEMER8Y8t+//yoqKhyc0FvRODg4xMXFEU1mDEfBR934+Pj4+fkhh0Xv2rUrMDDQy8tr06ZNyHqZmZkfPnyYkJAwa9as8+fP79mz58ePH9bW1nPmzElLS5s4ceK3b9/6+vrCw8PnzZvn6en58+dPuIW7d+9mZGScPXu2iIhIU1NTbW1tXV3d7NmzP3/+3NfXx8vLO2/evKysrLa2tk+fPt25c2fXrl2lpaU6OjpNTU2pqalz58798+cPfC+qiIhIWlqahoZGc3Ozv7//////X758aWlpOXfuXFFR0ZkzZ/77909CQmLSpEkTJ07ctm3brVu3nj17tnPnzvLy8smTJ69du/bKlStwt/348WPChAlZWVlhYWEnT56EHHk4ZcqUpKSkVatWhYaGwlU+ffr0wIEDlZWVVVVVxcXFKioq8+bNe/PmzebNm3/+/Hny5Enk4IJ0Zs6cOfPx48etW7eePXt22rRp7e3tixYtunz58pMnTyDumTJlyrp16y5fvgyx5cePH01NTR4eHrNmzdLT03v16hUnJ2dHR4eYmNi8efNiYmKqqqqEhYWPHz/++vXrkydPbtq06d69e1evXn3y5ImQkND27dslJCTmzp1ramo6ceJEyIoRNja2VatWXb16debMma2trTNnzrx+/fqfP3/S0tLmzJkjLS09f/789+/fd3d3FxYWTp48WUhI6PPnz9++fZs+fXpZWdns2bMNDQ3hw6sQd46SoyEwGgKjITAaAqMhQCSg38jo/3+gC8SVlVV4eXl+/vz15s1bISEhBob/P3/+gKxKvHTpEvxqB4jrwTt/QOOloE09oKM4/0I34P/7958J1K4E7Y0HDUeCtrf/B7UwQZPi4JM6GZjAzdr/oMbUfybQdD14pBKkH9TohLQDILaA+NB2JOhkJTAXNPAJblSiNB5A7WOYSoheZBLUTAXrgQgidIIFQbIQiQElGRkYfv/6BQlTNIdAxvwgrROE45EU/f37l4uL68KFC+/evfv586eAgMC5c+cga9Uhqn7+/Glrawu5+05TU/PBgwcuLi5///5tbm5+8+YNZELZ3d191qxZ+/bts7KyQp6p5+Xl9fDw4ODgMDQ0/PDhg6ysrLCwMA8Pz9OnTy9evNjW1sbBwWFjY8PNzX3//n1GRkZfX19dXd07d+48fvx4x44d+/bte/z4MXw2GTLyys7OLiYmxsHB8ebNGykpKTc3Nw4ODmtr6y1btkCO0Js2bRpkmwJkEt/FxQWyRVdRUfHNmzcQTzEwMNTW1v7+/ZuPjw9yX8i6dev4+PicnZ0NDAwWLVq0a9cu+AHaf/78sbW1lZGR4eLi0tLSsrOz4+Hh0dHRefz4sbGxMebSUsgNpYyMjMePH/fy8hICA319/QsXLjAzM7u5uUHO+VNQUHj16hXEPW/evHn37p2/vz8HB4eXl9eSJUvev39/79695uZmDg4OZ2fn2bNnf//+XV1d/fjx4zdu3IiPjz9y5Mi3b98gV8WIiIhA9Nra2h47dgxi5v///y9cuODv7w++E5RfX1//4sWLrq6uK1as2Lp166VLl1RUVG7fvs3FxQW5dTAgIODq1atcXFxWVlYtLS1KSkqurq78/PwQ00bJ0RAYDYHREBgNgdEQIAnQrzHKxMj0/fu369evgWcbGf/8+fPixXPIDUbMzMzs7OygVZ6gzZKIlhK4SQRqHIJOEQU1Rv+DGokMoO3t///8/8cIOlkJ3MYErdQDNVhBGLx+FNz0A93rBBL5B53rBwfMf/CaUEiTCywAbYBC2JDjKsANTtCOdpDd4KYkRBbsHggTKwlRinA/VkUDK8jExHTl6tUXL18q8CpADop78uSJmJjYr1+/vnz5AtnL+fTpU/gSQMgaTQYGhuvXr3/79k1ZWbm7u/v///8zZsyANF63bNkCuUoE4i9w5EKYDOzs7CtWrDhy5EhKSsqPHz/u3r3758+fuLg4Z2fnJ0+ezJgx4+3btxkZGRDV8Mnr/+BloJCIYGBggKwZgNxrArnChIOD4////5CtVJAjXSBLVwMCApBbt6Ax7n//IMogZoI6JqAEBOoUXblypaGhoaKiQlxc/Nq1axAp+HYciNcgDvv//7+QkBBkAtrV1bWvr+/9+/dcXFyampqQVZUzZsyAN0YhjUvIfklWVlbw9jhQAkM2EGIsMsnIyMjJyfnp0yeI4NevXznAh2lDHA9Jq3ATWFlZIZfr8vLy/vjx4+fPnxARyKV/f//+/fXrFzc3t6en54IFC6SkpBISEnp7e3/9+tXR0fHz50/I0YkQF8Lb7oyMjGxsbJ9h1zl++fKFg4OjoaFBS0srKipKSkrq7t27HBwc8FXFX79+hZyDWF1d/ejRowcPHvT09HBwcEBO9YP4YpQcDYHREBgNgdEQGA0BIgH9GqOQ5gXS2Cfjz5/QK24ZGBi+f/8O2XnOADraCVR/g1uE4Olv0DAmaJae4R/kZnsGUNsUNMb5D9RYBNfVDExMoPbn///gI/BBcqBWKLhdysAAGh4FqQSbCA8XTBGQPUhqkJhwTQQZoOYyxAsElQ6Igv///2traFeVVYVGhrKysi5ZvNjZxSU1M9XTxaOmvCY0LPT58+cH9hyYPHkyqCH4n3nz+s3XLl599+7djp07s9KyGP4wnD52avWq1QKioItPr18GncqWkZzOwcvJwMDAxsjGwgBNUYx/GZn/M//98Zf5HzMrI+veA3s+v//Mysg6oXuCjKyMqampEJ8QI2RBBCQgfjOwgvWyMLBAxNmZ2VkYWAR5BKLDo6dOnPoj+cfJkydlJGTUVdX//foH2ovGwKAkp6Spqnn+1HkbW5vt27eHhIQwSECMY5AQkWBnZp8/e76drR2/AP//X//YmUEHIrIysLIxsTH9ZWL4w8DBwnHlwpXnj59zsHAw/2dmgrvnDwMrIyvEINDJee3dAvwCpmamu3fv1lLV/P/rX2lBiSt4nHXe3Lmebp4QlZAQY/wD4rEzs////Z+NCbT0lukvI9M/JhD7D6htzfiXEeQGRlamf6DUysrIysrIGh4cXl5eriCjAFpv+uSFu7P72rVrIX4EGYfkHtA1LRY2LQ0twcHBu3ft+vnlpyCvoL+3/+T+yUlJSQcPHlSUUZCWlhbiE6our7Yys9LV1f378y87C7ucnNznd5+Y/zGzMYJcxcbEBglnhj8MLAwsoQEhdfX1EiISr1+/fvP8jY2FzYY1G7hYOb99/Hpw70EpKSk9LT1xYfGu1k4LS8tFixaxMLD8+fGnoabB0cFBWkaGn5ufg4Vj6cKlysrKFjagy2lAzh4gzAQ6rG9Q9wkHKGAGu7X//oFWZ8H7XQSd+/z5c2ZmZvi6c/zqP3/+/P79ewkJCcwJCvwayZP9/v07KysrvH9LniE/fvxgZmaGd0rJMwSi69u3b5Dj1iHcUXI0BAYbgDYd6OAs0LAhSvsOJACzF1RzgE+Ph7U+URdZgpp4oDr+N3T/EIgCbVoHtTgZwDdp/gNNyzP8ZwBtcgIZBjMYtHYTtGsKxEexHSSAFyM7D69CLJKQDVlYJCgUggwFU2LIj58/9H0N3ki9rl/b+P//f3Nrcwk3qa33tqmHap5ecrZgXhEHO3tIRsgNtltX71znMufeeHET4wMmLi4ul1QXDmPOJZeWKfqrHPl8jPELKJT/c//nsxFYfGmZlKQUA8P/O2x3GfWYtt7bBhoy1Pr3RPCpqqrq9Qs3Y7vjraysDOKMdj7fzWfDP3P5rJ5dfXJyck72LhDFDAwMAvZCZ39feH7v5T3uB/91Gbfe2/br928+G4FdL/fyGfMxP2DNmpEjIiKSlJa09eH27yo//nN+2HpvGyMjo0Wa1YIFC2Ycn6WlpaX6T/3mvVvQ8GFkNIo3nrp++olvp1xcXEScxbc92cHBzn6b485vjT93ee6LOosn9Cbq6umZp1qe/H76rejbH2y/IO7hMOO6zHj1870vIKMYGcWcJZavWD7n1FxRMbGEjIRLDFd/a/2rWFb1/98/PX19IXthiC4GBobnoi9/sv3Zem/bt+/fxV0l977ez/eD96PCFxaWH4feHxa0F95ydyuLEdtVluucvzg4zbm33NvGYcZ16vtpWVE5rXDtiqWVbGxsUZnRRz8ffyr07DvbD4jJHKacV5iufb0HvmubkVErXHvu3LmZ07OtrKz04wzX39kgaCf859m/zOnZEhISoCC6v52RgcEk0fSPxr+t97bLeMmxsrJuvb/927dvAnaCe17t5fnG8+zXc14r/q33tgnaCx//elJWRkYtRKN8SQU7B0d0TvTpn2e1I3X7l0ziOcVj7mXGxMS07fF2yzSrufPmrbi2ysbGluMf1+4Xe/is+evXNf3+/VvPWPeF2Ks9h/fe5br39t47ULgNHP7P8P/jr48DZ/+ozaAQ+PDhw6RJk+Li4pDP2pw9e7a8vLybm9vUqVO9vLzgRzY+ffq0ra0NMhwgKyubnJyMv4n5////7u7ugwcP+vn5paWlQdqvc+bMMTc3hywTArkACZ89e7ahoUFRUbG8vFxaWhpJhlbM9vZ2e3t7Z2dnSizo6OjQ19cPDAykxBDIwcAFYKClpUWhUaPaR0OARoBO54we2LhmfnfrP1bQ0BRoKBPcigRNhIO9BWFASLAAFgK0XhNNGNxcBK0UZQCfWw+WBYkhNTrBhRRozBQkCWo+gWgIBu/Rh7RqIQJDgORiY2Fk50pr6iL70PtpF2dee3udnY2NmQXUD/kHntWFnCLExs4OmbeF3ib8HzSdzcTMDAmjP39A11QxMTGxsrD8/PULIsjAyMjOxvb792/IfDQzGEA2srCysUHuY+VgB0U65K6zX79+MTExQUYm/v////PXL8ilVQwMoDl9iDnMLCzMTEy/fv2CzB3/+vWbgeE/GxsbEzNo7/mvX7/+gfeP/2dg+AO+2QUyjw8Zd//169d/pNgHjUywsoLu1QKfNg+RBbuR+dfv3+xgM//9Bd0B9ufPH9AoJejmrd+gIV7w6UiQuXvQeCfseAHQOgGwm6G3AIDOXgBNi8MtZWFhYQQbAnM8yD2srKz/GRj+/v3Lxsr68+dPyNFLoIu2WVl//fzJBr6K+t+/f6BRFFZWBtBJuqALweBGYboHFIbs7IyMjH///GFkZIQEOCKIfv6ERAfokmvwvQWQkRXIMhg2NjZIOICiEuweyAqZf+CVD6CBHCQHsLGxQVY7/AcvNYYHNSRkfv3+DXIzJCH9+/fr1y9WNrZ/f//CZ/MHMEcxMTLlGeUo8ysNoBuGt9UEzxl9/vy5trZ2dnY2/F6lBw8e6Onp1dfXFxcXe3l51dXVQW54Z2BgOHPmTH5+fn9/PzMz87Zt227fvj1r1iwODo7v37/DUx08PP/////q1auEhIQ5c+ZISEjAlwaFh4fHxMT4+vpCVH758gVyRDwDA0NRUZGiomJWVhZkmQpk6yR8DRJEPWhTLPiy+69fv8I1QgqW79+/Q5YwQVRiDlgi2wVRk5aWFhQUBGmMQjIgRPzz58+QEz8gXAYGhq9fvyIb/v///2/fvkFEIIeHQLZIIhsCKUhBuRVDO6b5oFLr58/AwMCWlhYjIyPIEh1IoGFVDHfYKIOeIfD99z+Dluuj54zSPMy1TCysAiO6u7pAV79DlnlisxO1uQhSAWpcgmicGKIFfHoTaEs8hAtXTVA7XOWQYIDWPnJxp1Pg1ix9SnRTYPGo1tEQGA2BkRQCf/780dDQOHbs2NOnTyGDkUuWLJGTk4M0oZDPkgMNT4AvwDQzM2NgYFBVVfXw8Pjw4cP+/fu3bdvGxMQUHh7u5eW1YsWKd+/eXblyJSAgYOvWrdevX29sbLS1tY2NjYWEKxsbGysr65s3b+bPn//z589r167Jyck1NDSsX79+27ZtKioqb968yc3N3b9//5YtWxgYGAIDAwMCAlavXv3q1auLFy8mJiZycnJOmTLl58+fBgYG+fn5ly9f3rdv3+vXr+/fv29paZmbm8vExDRnzpyDBw8yMTFFRUV5eHhcunRpypQp379/19bWLiwshDdwOTg4du3atXXr1jdv3iQkJLi7u3/+/Lmrq+vhw4cCAgKFhYWSkpLTpk2D7B3k5uaurq6WlZU9ffr09OnTf/36paurW1hYyMXFdeTIkePHj7958yY5OdnFxWXbtm1Xr159+PDhx48f4+PjT506dfHiRVNT06KiopcvX/b19b18+ZKTk7OkpERFRWX27Nl///69dOlSZmYm5JLM379/d3Z22tjYSElJ9fX1ff/+XUJCorCwEHm1PSQwR8nREKAzAA2P0cFKMWkZr/CYt7//MzExg4oe8Mgopr2gpiQIw2RgY5qgq9tBV3DC5RCNTNj5naAjoUCtUZhWNBphEprE0OL+Z+Di4pShyzTT0AqYUdeOhsBoCAyqEPj796+KioqqquqSJUvKy8sfP3586dKliIgIpG0DCPdC9uS9fPmShYVlx44d4uLi58+fX7NmzcSJE79+/VpcXKylpXXq1KkLFy60tLRoaWlxcXFdvXo1NzcX+aoLUM3CyPj9+/dJkyb19vampaWVlJTMnz8/IiJiy5YtVlZWfn5+N2/eXLp0aW9v7////4uLi3V0dM6fP3/ixInGxkYpKan8/Py8vDwjI6PKysp169YJCwvPnj17wYIF0tLSCQkJpqamnz592rp1a39//9u3b8+dO/fhw4fGxsbU1FRLS8uGhoYFCxakp0N7+79//37+/PmECRMePnxYU1Ojo6OzcOHC////T5s2bcuWLY2Njb29vcuWLQsJCZkwYcLUqVP7+/tra2sbGhpycnKMjIyWL1/+6dOnf//+PXr0aOrUqbdu3WptbbW2tr558+aWLVsWLFhw7ty5tLS0zs7OuLi49PR0MzMzQUFBU1NTb2/vmTNn9vb2zpgxY8eOHZycnEVFRfLy8pCLN6uqqri5ua2srLKzs3V1dVNSUrZt2/bmzZvRxigiLY6yBgjQqTHKwMAgKyNdVVExQN4ctXY0BEZDYDQERkOAfiEAaV+Gh4fn5eVlZ2evWbPG2tqai4sL+dA0uGtYWFhu3rxZVFT0//9/bm7ujo6O3bt3f/nyZc2aNf/////48ePjx4+5uLgSExOtrKxAtYmsLDc3t6qqKgcH9MhkuFF//vzR09Pz9vbm5ua2s7O7fv26IBhISkrKyspu3Ljx+/fv27Zt+/v379evX+/evfv////w8HBbW9sLFy48fPjw/PnzFy5c+Pz58+3bt3l5eS3AAHK9xbNnz65fv+7p6akIBiYmJtevX3/06NHVq1evXbv24cOHW7dgC9bBp3ZERUVJg4GsrOz169evXr0qJiY2a9asT58+3bt37/3799LS0lFRUcLCwh4eHv39/RcuXBAQEPD0BG2IhBxR8uvXr5CQEHl5eSEhIU5OTsht9V6wtbbKysq+vr4cHBwaGhp3796NjY29d+/elClTrl27BmnIsrOzp6amGhsb//r1i5mZubq6mpGRcevWrWxsbEZGRtu2bXv//r2xsTHkWhN4AI4yRkNgQAD9GqMD4r1RS0dDYDQERkNgNAQGJAT+/PmjpKRkYGAwa9as06dPT5gwYdWqVXCXoC2C1NTUnDJlCisrK2S95s6dO2VkQMdu/Pv3z9bWVktLa8uWLZAT1iDrOOGH9MENhDPgc+UMDAyQdfCQpc+QoVMREREjI6P///9bWVlpamru378fYuz///95eXlNTU0ZGBgsLCyUlJROnz4Nb+yCtx+ATsOALJiG28XFxWVsbMzMzGxhYQE5pRgu9Ru8qB20m/PPH8hSdS0tLQ0NDSYmprCwMMi5vJC1m5DlqiwsLGiGMzIyQlY1/Pv3D3JhGwMDAyTcIFevQVarg+9QA93t/OrVKz8/P0lJSch1JIyMjPCggNyBd+PGjf7+/urq6uTkZGtr69u3b69cufLhw4fZ2dlwZ48yRkNgQAD9bmAaEO+NWjoaAqMhMApGQ4BGIcDBCl86hW4DB+iYtz8sLAw5makTezsNdTXFxcWZ//9hZQTd+czK+O/UscP7d2/ftW3To3u3uNmZWRj+CAryQVqiDAwMzvbWXz68EeDhEODlvHDmJBcXJwsDVC8DAwPEcE5U25n+/WZn/s/BwsD8/zdEioXhD/N/0JZEVsZ/7MygxV0eLg4/v37kYmcW5OU8f/o4Ly83C8MfFrCTdDRUFWWl3r9+riwvffXiWVbGvxwsjEz/fkE8xsr4l/HvTw9Xx13bN586dmjPji0zpkxQU5JTU5J79eyRsrz0tcvn//78ClEM2nTI9G/VskUXz57cuHbllw9vLEwNbSxMb127pCwv/eHtyycP7gjwcLAw/OFgAQUgG/P//7+/W5oaMf//vWTBnKsXz7Y3139+/5qN6T/EbSDv/PnBycoI9hHoZBlOVkbGvz85WUE1ONO/XywMf96/fq4kJ6UoK3n10rk/P78yMzOxMv5lA/uaDeSR33ZWZi0NNWdOHDlyYM/CuTNvXLlgZ2Wmq6n69ePby+dPb1y7Eu74UQb9Q4CTFXR3Of3tHTw2jo6MDp64GHXJaAiMhsBoCAyZEPjzj2HDhY8XnyCOi0Z2+ocP/79Iuyw5/oGNTVTDv/KfqsPKM5+u/VX7zfRr5ZlP32TdmlddYGG5/vvPb1eXr8bGJt/l3Jed/MjMDK+SVLkMo93zpjMwMrq6uPKc/vSI05jhg9T3M6C7Id69Y/wm677k9Fc2NlBbE2LvB3GHQy+Fr/9j/CLtuvTMNza2Pxd/KL3h4V155tMLfosT7yW/nfnEyCgnZJnsVzKPgYHBzs6OH2zszy/iTCApRjXfsuJZ835M2qOsrMyux3r3tfBrIZuVYBsfcRr9/CDxj1mHyyAqoHwhMzNzSEjI2iv/VH3KqmfP/j7zsLy8HKM6L9f79xDHvBW1+cf9PaBi/vfvP2JjSjfdZGTXDz1+avbSmBZuLq64+Ljn575+lnZeffkn/6P3Dx9yfJCwX3v1t0ZARc3cOd9nH9bX15O+/v8pr8mZz9L/T7//8ePHd3m3VZd+Xf+vxsDAuPz0+7dv//1QcF98+iMLC+tjbuN/36VEzU1aJ0/u2nTT2MiYWUtr6cm378Rsdz3kvMX4/s+fP9/kXNZd+yf7+Y+SV1H7+mumpqbtCxb9mnWQh5snNy93+v6r9+49/yYHdTzEC6MkPUPg999/H3/Q6lBIenqEbLvodLQT2e4b1TgaAqMhMBoCoyEw2ELgx+9/8QsePvuIaAuiuvD/739fWVmZf4KPgWPn4Pjz+/ffv39ZWEGDmb9//2YHHSTH/J/hP+iC4j9/QAefsbH9/IHUrmVkhB6gBj4W7Tfk4LB///7+AY0LMjIxgQ5KAxsOt5eNnf3vnz///v+HS4HOR2Ni+v3rF+g8NZheVjY2yOT4379/0YxlZmEBTYKDtrv+//nzJzMTEzMLy6+fP0GT4+BjziDuhEz9/wEdd/cbdCg9GxvoYmqG/79//WFl4gafFsMAOS6ACXQNIOi6wd+/fzMxMrKxs4MGQsHHsf3795edjR10vN3//8xMTCzgc9ZYIA5gYPgHOivtJysr6Gy1P+AT3NjZ2X7+/AWZtQeZxgS6Ne3nj5//GUB2/fsHugKGjY0NdJnLv38MjAy/fv4CHRv3+/fff/8YwWfn/QKfwQe5sA10QB5YMWRpLzMTExMz08+f0GFgeJCOMugWAowMDCzMjNOjZNXF0ZdB080NA2vRaGN0YMN/1PbREBgNgdEQGG4h8Pf/+4XnYz5+/8gEmkamwHegqXVI644CQ0jVCjkpEI8uTAX/Gf4xMvCz8ycarWBk4MWjdVRqNARGQwArgM+JYJUdFRwNgdEQGA2B0RAYDQHSQuD/fy4Lmcxff0GXPJOmc8iq/s/wn42J699/dmbQ4OeQ9caow0dDYIDA6MjoAAX8qLWjITAaAqMhMBoCoyEwGgKjITAKGBgonEQZDcLREBgNgdEQGA2B0RAYDYHREBgNgdEQIB+MNkbJD7tRnaMhMBoCoyEwGgKjITAaAqMhMBoCFILRxiiFATiqfTQERkNgNARGQ2A0BEZDYDQERkOAfDDaGCU/7EZ1jobAaAiMhsBoCIyGwGgIjIbAaAhQCEYboxQG4Kj20RAYDYHREBgNgdEQGA2B0RAYDQHywWhjlPywG9U5GgKjITAaAqMhMBoCoyEwGgKjIUAhGG2MUhiAo9pHQ2A0BEZDYBSMhsBoCIyGwGgIkA9GG6Pkh92oztEQGA2B0RAYDYHREBgNgdEQGA0BCsFoY5TCABzVPhoCoyEwGgKjITAaAqMhMBoCoyFAPhhtjJIfdqM6R0NgNARGQ2A0BEZDYDQERkNgNAQoBHS6m/7yyWP71q9iZWOj0Lk01f73719xGdmwzAKa2jKwhs+9Mv/uh3ssTPSI9z///qgJqiZoxw2sl0dtHw2BQR4CP398//DmNSPj4L7U/D8DEwuziLgkA8Sd////ffSa4c8/hsHt6oGPekZGZjlRBubRcZ+Bj4pRFwxmQI9GCQMDw9sXzy8cPcTBxTmYw+LPr98KmlqD2YWUu+3q22vnXp5nZ2an3CiCJvz8+/Pv/78ElY0qGA2BER4Cty6cm1JTzMbBMZjD4d/fv0JiEg1zlzOzgGqN/z9+v/dr+/f642gzC1+s/f/PyM4qfLyLSYwfn7JRudEQGPEAVKzQIRCYmJnZ2NlZ2aBtoL9///7/94+RkRFSrjEwMPz////vnz8MjAzMzCxoIwR///z5//8f2JGMjIyMTMzMjIyM//79+///HzMzNd3PyMjEwjqox27BgUARwcrEys7MzsYM8iYjMyMjEyPDf4Z/f/8x/EcYy8TChDba8f/f//9/kVQg1BJgsTKxElAxKj0aAqMhwMAAyoqMg3rwjJHxP1rJzMDECEWjMYgrBP4zgIIIl+yo+GgIjIYADFCzMQczkwD97+9fYXFJfmHhb58/v3ryiJGR8f///6zs7LIqav///n3x5NGfX78YmaDlMiMTk5SCEic3NwOoffT/+9evL588+vfvL5+gMCc3D0g7TCXU1v////77x8LC8v8/OY0nqCEjgGJkYvzx5vv3199ZuFh4ZHjBoQv29n+GTw8+/v72m5EBBMGh+J9DiJNTlBO5wQpWioVgZGb8/+c/yDQskqNCoyEwGgKEQuA/qOvHAGqegvreDAwM//5BuuJQjUxMjODCEMT9DwYgFhiDNIDn0CFdfeh8OliKHgS4sEC39N9/7K2xf/8ZIOqZGNG1EOPW//9BxREoKIhRjaSGbI1IZkBdDg5qZOFR9mgIjIYA2YDejdG/f//omtsEp2XzCQj9/PF995rle9etYOfgDM8p0jOz/vvv7+UTR9fMnPzr5w9QI/XfPzYOjpjCcmlFlT+/fzMwMPz+9fPYzq3r507TNDLVMDSe197Iyg4dbYUMr7Kxszt4+J0/sv/9m9dMaO1UsgNp2GlkYmF6fen15ekXfrz9zsTGrOilpBah8f8/aOTj7++/1xddfXvlzf+////++svCwcLAyKAcpKoeofnvD0qliB4qjAz///1/uO2BkKYwnyIfeSOp6GaO8kdDYCSFAKQLzcPHz8jI+O3rl79//jAzM/PyC4BmMEAFHKgB9uPr179//0BChZWNjYOTC9L3+//v//dvX//8/s3ExMTFy/frx/e/f9EXyfwDizAxM0O0U5P8/5+BmYmRnfX/t18Q94AM//efkYfj/49fDP9Qhwb+/GXkYmfk5vj/79//j98Y/v4jdq7/PwPDn78MLEwMbKyMnKz/P30H2UI8/s/AyMXBwMjw/+sPclrAEItAhrAzMDD+//4TIjBKjobAaAhQDujcGP3PysrmGRV3ZNvGozu2GNs6OgdHnDu0T1lbT0JGvi07iY2DPb9j4ond225fvsDMwgpqHjEw/v3zZ0FX85XTx1lYWDQMTYPTci4eO3Ry745zh/ezsrODy2XQiACoXPv/n0tYxMjW/tKJI+Ahg3+M4JkvSBEPat3+///v719GJhD4++cPaNaJkfH/v3+QqX/KQ3NomABuNd5eeUPSSlolSOXV2ZfXF1+TsJTiV+D///8/EyuTcakZIxPj+1vvLk07b9lkw8rDxsAImspnYmH6D65UGJkYQTP7/xgYmBggocrIxPj/3/9/v/89P/KUV44XxCVrWn9oBOCoK0dDgDYhwMjI6BkZb+Plx8bOfvnksVXTJjAyMZVPms3Dz/8ftJzm758/v6fVld2/fpWFlfXP71/6FjYxRZXg4ovhz+/fd65eWjml7+O7t6nVTZsWzrlz5SILK2KdzL9//yTlFQVERG+eP0N+Uwyrx//8+//vH29lCMPvv5/bVjOygyz9//0nq7EKb1P0h+TJ/99/gY+P/v/zl81CnbssiFVb7v/P3z8PXP7atvbfy/eE26P/GRjYWNg9DH8dvsZqpMyV5vYhqpeBhYSG9f+fv3iK/BlYmD43rmDkQoxiYPUTLsH/v35z53ozsLJ86VzLyEbnChSXo0bFR0NgyAM65yXQjPyCruZ3r17+/P796tmTVh6+HJxcNy+cvXP5AicPj6659ZN7d96+fMHIyOiflP7r+/fdq5czMDD8/PHt25fPzMwsF48d8o5J5Obj1zWzUjcwXj6lV1XX0CsqXkxG9vq505sXznELj5FRVkutadmxfIGWicXSCZ3///93D4/5/vXroS3rQzLymJiYVHQNlk7oFBIVdwuPZufgOrZzy4GNa9DmwoZ8xOLxwH/QEKZeliGnCCcLJ4uogdi9TXf///n38+PPc72n1aM1hTSFGRkYmdmZGRgZWThYWDhY/v37z8jI8Hjvw3sb7/7981fBU1HRW/n7u+8XJp/VjNEW1BS6MOmcoLrQ78+/3t18e7brlEWjNb+SAAP6uAweN41KjYbASA+B379+GdrYmzu7L5nQ+evHj7DMfFufgJ0rl0ypKWFiYvrz57eDX4igqOjzh/eZmJh+//zJwMjAxsn59P6d1TMm/fn9i4OLOzgtxy0sesWUvgXdLaACk4Xlz+/f//6B8iEzC+vfP781jc0EhcUunTjCzMwC6pEzM//////P79+srKygZeF//jAw/ActywdrhMz1M7OyEphi+vefUZSPJ9uLM8r+24I9oMHb/6B1BBx+ZjxlwYwCXAywZQb/f/5mZGBgEuHjqQ79feH+l4bljDwcPBUh3OVBn/Jng3qwv/8ygMZuGRlZmaGtzL///v/+AxpYZWZiZGJkEuXjSnb9ffrO7xM3P914AlIDV8DExMjGDGrR/v77H7QI/j9IFwszenuRjYWBien/D9A8GwNcFm4IMxNIPSMjw7//IHv//gO12lmYGFnBFeXvv////P3/6zd4aBYs8v///19/QCO7TIwgNcxMDH///f/zFzSPD3IPWM1IT9ej/h8NAaIA/XILeKgSRLx68piRgYGVlc05KOLp/TsvnjxiBM+/BwfkWrp6PXt4H7STiYHx3KH9f8FTTgwMDALCouIysmzsHPpWtqzs7C8eP9QwMOYXEeXlF4wpKL937fLJvTtN7J2N7RwvHDmgqmOwb92Kf//+iUpJ/////9+/f/xCwsysrH///pVVVmNkZNi8cDa/oLBfQuqBzeu+fvzoGBD65eOH47u2/getYkKdTiIqDIeSIpAXQSXlfy4pbkZwq/TupjscIhx8inx/f/1TClDhFOP69+cfaOkneEUXaMTj/z8mZqaXZ57fXnVTyV+FhZPl7sY77AIcMo6yokZiN1dcFzUU+/zkk2aC9tfnX3mkeSWsJDlFOf/9hSwKG+bhOZTiftStgzsEmFlYXjx6MKe9/v71K39+/1bW0hEWl/z/7+/zh/f//Pkjq6yiqKG1sKf1+9cv4jJynpFxGxfM/vf37/dvX5/cu/Pn9+/fP38c3LzO0T+UmYXFzifw1L5dzx89MHV0MXFw/fnt6551K3/9/GHp6sXBzf3iyQMuHt5716/evHBWRELSwS9416qlIlLSWsZm3Hz8P7593bdulVWgj5aR2fNHD/asXfHhzSs8M/v/f/3hsNFi+Pvvx/rjoGXmjKDtqIx8XOyeJt8W7uUIsWZkZgLNTTEx8dSG/7n44NeJm4wcbL92X/hz7t6/7z//3n7OrCTOyM7C8O8/h58ZR4j1/68/vs3Z9efCPQYGRmYFca4UF2ZF8Z/7Lv1YtJ8r2ZVFU5avO/7brF2s5upfetczSwtzpbiyaMv9Pnfv+7zdf5+95wi0YJYXZZIUZFEU/7H2+I9Np0DtY/j5U//+MfJx8taFsxorf1955Of2swx//zErinOluoFs2X3h+9KDDL/+MPJzccU6sFlq/v/24/uSA78OXmX4z8BmqcaZ5Pr3yVtGDtZ/n76BylF2Vq4UN3YHnb93X3ybsf3P/Vdsluqs1lpMIrz/P377NmnLf1D7fnAnu1HXjYbA4ADQfUK0dgwjExMLKysEsbKzcfLyBqfnSMjKb10yn4WFhZWdnYuXb9vSBc3pcX9//3bwC2ZiZnpy59azB/dY2NkYGRmj8sua5q+qnr7QxtNvz5rlr589ZWZh+ffnr4yK6p8/v9bNmXZm/+657fUndm9/9fTJl08fblw4C1pj+v8/xEZwR52BhZWVmYXlwKZ1p/ftllNT//fvHxcXt7CE5JePH0BtXDZ2iAJaB8XAms/CyMLKxMrKxMrGxMr0j+nSvAufbnzUTzFiZWVj52CXNpPhFuRmYWBmZWJhYWRhZGBkYWJhZWJhZmJ+e/HNP6Z/H15+ePPwzR/mP48OPWT+z6wRqMXKznpj8TWjVBNuQW4hBSFOQU5JIykuAYghrCyM9OvtDGzAjto+GgJkh8C/f/9+/fjx5/fvJ/fu3Lp47t+/fyKS0mr6RmcP7QOJ//nDyMDgGhp16cSR+9ev/Pv79+unj/evX/v6+dOfP3+YmJjYOTnZODjE5eT1LGwe3bn168cPDUMTZhZWfUvbgKSM10+ffPv6JSgli+H//9fPn7x/9fL5w/vKWrq8/ALfv35lYmJS1TP49+8vJxe3g38IBxfX3WuXPSLjjO2c7l2/IiAqFpaZz8jI+PPH918/f/z+hbJK8v+PX/+//2L4///7mqOfiub9ffEBNOfy/df/n7//vf74MXXKz+3nQStdwcr+f/v158rDv0/e/Hvw6tvMHdyVIbyTUnnKg5gkhX4dvvb/3Rd2bxOeqpC/d5////SNrzOeWVqYgY2Fb2IKk7Tw77N3OfzM2H1M/1x79O/j199n7jLycLBaqTMwMPBNSGHRUfh95i6bjSZvSwzD37/MMsI8xQGMbKx/H73haYxiVhD7//n7/+8gp/7/9vP/t1/sbgaMQrx/X7znbYtlEhdg5GTjm5DCJML3+9xdjlBrriSXf++/cMY5sdnp/D5/7/+XH7z1EQwcbMxywry9yaA2KCszu5sBw8/f/z9/5ynw4wyy/H3uLpMYP29rLGjPJy8nV4orIyf773N3/32B2gtqtpKdOEY1jobAyAB0aiuYObnqmlmC1mgyMPxn+M/ExMwnKPTty+fivmnMTKBFP5ABtP///rFzcqrqGZq7eIAWI4K3lHLz8UE0MjIy8vALuIfHeETEsXFwsrCyahiZsLFz1M5cBNYO2l/JxMzMxcNbPmk2MzMzOydX4/wVDP8ZOLl5/v//Z+Xhw8MnEJ5VEJyazcnNw87J6ROXAonlP79/N85fyfD/PzPSEiuI1DAjy81Kf//9zcjI+OXL56KCImdBx5a9LbzCvD9//gKdacDK+vfPH9CKBUbGiwIXsniyJjtO4OPnY2BgqN5ec/DaAUMmg/8MDMzGzNoG2mmO6T9+/oibFvuW+U2Geqqzk8uXz5/jpsVVmpabmJuClvAyMLAxg1aPDbMwHPXOaAhQNwQERcUs3bxY2ECZ5f9/0Dpsa09fDk5Obl4+S3ef////8QoIqhuYPLu/2srdh4mZ+f+/fx/fv9UxtVDW0lHR1q+dASoAWVhZuXh4d61aaurkxsHFpW1ipm1i8eTe3VuXzrOysb15/kxaSeX7ly+Q8lBAWERZW/cv6GATCQ4ubiNbJ1Ep6T+/f9+8cJaTi8fI1vHU3p33b1x7+/K5W1iMW3jMqyePGRgZePgEIEUxyPvMTBy+ZqDGGWRL+68/rNpyDCyMnGE2DKygIp3h7z8mCUEmIR4OP7N/X8Abhn7+ZpYWZo6xBy0V3Xya1VKd3dOIu8Dv+7JDv0/c5Ep1+3P1MWjclJWZRVOGu9D/z/2XzApi31cc/vf647+XHxi52Rn+/v//7effZ+9YDBSZBHi4Mz1ZtGS/Ttz89+6L/99+cKV5cGV5serK/7n38tehK4zMzGw2mpwRdr/V74Im9BkYGH79YdGX//f60++TNxnZWdmsNDmj7Bn+/WOSFvq+9OC/t58ZGBk5I+z+vf70/9O3n7svMAnxMEkKMkmLcEbasqhKMfz99+vAlf+//7IZKbHoyHMmOHH4m3/feOL3ubt/H7zizvXhLvRj5OVi+Pn799FrjKzMHKHWDP//M7KyMHKAjtIDBdooHg2B0RDAAUCLOHFIjQoP5xAoKSy5eu1qU3MTFycXIwOjiooKGzvb3Tt3ZWRl2DlBS/svnrsQFRN96uQpbl5uBgaGVStWdnZ1dXd3i4iILFiwwMLCIiw8rKO149z5c17eXrNmzNqyZYugoKCru2tgQGBGejrz6NL+4Zx8Rv02GgKjITAaAqMhMBoCVAN0Ghn98/v3r5/QKR5E3xrsC9ByIvAIKJgHImAiEDZonAC0Cgk8+AkSAmPIEW/gUQTQkXxgMdCMEFgEsj4IMjkPkwEdq49iFJIzUI2G6wAx4EaBOHAM0QBaHAUXIp0BMZokff//M7CwsrCxk39Ty48/P/7+//v69evVW1Y/f/58p93O///+c3Fzbd68RVVVJSAysL+/39LSgoGB4cvfr0ycTJ9+fWIAr/V39XU/e/V8cFTw9+/f3dzcMvIzj5w6snjNkkWLFuro6Gzft72+o6Gjo8PA3CCvLF/XVNfY2BiX10CHJEDiD5cKmDjxKmE6qEAjWwpeNwvaw0CSueTpImgFssMwFeOXRVOPqZhGbkazlxgustuQ2cToJUMNMyMzBwv5GYoMG0e1jIbAaAiMhsBoCKABOo2MHtu5ZfX0iRycXGjWDzgXtL/p319YuxC+/h60GxTEgSz2AU2cQRqycPeCNv8wgMRBDLBKUFMYog2kCN4ShjMQh+9Bm9YwSxkg1x+Bz/D8z8TABN5jxMDEyAhq8sIVMTAw/mf4++ePhJxSen0bLz+Zl8vVH2+6+OoiMyPLjx8//oM2jP4Ge5GRh4ebiYn5y9cvnJycLOB7rf7+/fvt2zceHh5Iqx109xUT08ePH//9+8fLy8vCwvLz58/fv39zc3P9+/f/z58/X79+4ecX+Pv375cvX7i5uVnAdwaCggIDQ64kwDwEEU0hExMTMzPzH9D9W6CQQJOlHZeFleX/v39//4IiBNYvQooGIiwmTxd+gxkZGVlYWHCHBmhrIDimCDuViYmRmRndKLCv/xOMFPyOpFyWGXy/2h/wtg/QshvQ2bc0XNf+598fEwnjGvNKyl1OiQnvXr64chq0+0dFV19SXhEUl+DOwYtHD25eOMvMyqprbv3gxlXI2cn///3n5uPTMja7cuo4r6CQuIzs1dMn///7C+0z/f/PyMSkb2l77/qVD29eSykoKWvr/vrx48b5Mx/fveXm5dO3tnvz/Nn961e0TMx5+AXuXrkoKi3HxMgoLCFJmhf+/Pux4QTjl5+QfuXfX39YTJSZmJn+nLnLCLmH/d9/Rn5uVlvNX3svgebWGRnYnfT+v/zw7/Kjf8yMzHryLBoyjGysDLee/zh189/fv0yMjOwasoymagy/fv08duPvw1cMDAxMvJxstlqMInx/rj76fe4ew79/rJbqLIriv8/dY1YQ+7n9HCM3O5u1JrOM8J+7LxjO3gNtk9eTY+Rk+3XkGgMbK5eLwf97r37cfgI6DYSBgeHvX1YzNQZGxt8nboCm6R11/9x8+vfhayYJQQ5PI+hUPmmhMKp6NARGQ4A6gE4jo//+/Hnw9PmhGw9AS0ZRXQ6uQcHjlqjixPNAq0vBZTcJWhhADcj///9LKClF1bW85hL6/vv/n58/BX7/ffv9rYog1+tvX48/fcjLw8vLxgZqcv1lvPvuxa+fP5j+/f/67/f3D594xPkl+QQ/ff7EysH+48vXHwz/vn77yvT/H8unn18/f2H695/x268/jP+4+PnYWJg/fPn47/cPxr//GThY/nMwc3NxyPMJfPrz+x8Ti8Dv34ZMXMxsfFpMkoIMDIdf3NDklmH6+ffWlwfM/34KP2Lg/MPKwsL45NULVkGBv5KcT+/fe/fsJdmN0fc/PgRpBAm/FoxPjldRUVmyZAms1fifhYWVlZX1NxhArlplY2MDNVh///779w8TIxMrGxsLC8vfv39//fr15s3rhIRET0/PzMxMJiYmNjY2JiamX79+/f37l5kZ1Iz89+8f6AAa8HkILCygK17//Pnz988fJmbmoKAgMzOzmpqaX6D9EKA2FjMz879/f3///g066JSJmRV0mgzjokWLJ0+evHz5MmlpaUjMMjIysbKyMjMzg45b/PMbdMw2MzMr6NQVxj9/fkM2czAzgy7fgrjz379/rKyg02p//fr1//8/FhbQmjxmZiZGRqY/YMDMDHLsr9+gRbSs4PMWPn/+FBYW7ufnl5ub+/379ylTpvz8+aO0tAxiDqSp9BscIIyMjKysbJDmMria+ws5Q4eBgWHp0mU3btyora3l5ORgYWFlZGT8+xfkZEgjFVTFQv3I9Pfvn9+/Qefdwsz5z8rK+ufP33///rGxsTIzg0Ib0jK7dOlibGzcwoULTExMf/36xQzyOMg7YH/8uXjx4vTp0wsKCrS1tZnB5/WAQukvKEgZGBjQFB85crisuHzunDlGxsZ/wWq+fv2anJysqalZV1fLxMTMwgI69wfiNniGBe1mY2WBBO9v8IFBSCqhFrGyskAiBXQ/xe/fEHMgwQU5S4gRfN/vnz+gzd/gAAS1iSHRBD4+jLGzs/P48WPr1q1nZGQ8dOjgsmXLy8vLFRUVmZmZICZDHAz3FKjbAD456M8f8AA+JKEQTe5+uPfW+9tEK6eVwsd3bi3sbhWQkhEWlxQUFhEUFGRjZ//////nD+9fP3vCxMz8+M6t969efv/6BVJasrFz3Lt6+cXjh+ycnLwCAi8fP4InLZATGRmf3rvz7tWL71+/3r50/srJo3/+/Hn97MmPb9/Y2Nkf3rrx9fOnd69ePLx1g52D4+2L5y+ePLbzCXAKDAPpJRr///3nc9WSz+z///KwMzEwCvDxc56/9+Xr14/XH4CP6GcE9XI5WLl2yn2+dJeHnZObl/fPhft/Pnx+deUOLz8fj5I0kxj/8xcvbhw+ZSitzM/L++v375vfPvyXEpSRkuJ4+/3X6w+gOS82ll8nbzJycfx7+eHv07cM////PnuHSZD377O3TII8f24+ZWRl+X3yFiM/17+3n+8cPPXz63dtZysGVpa/D14yMDPf33Tw7Z1H6uIyrGzgVZv///8+fYeBkeHvo9eMLMy/z9/79+rT3yev/778yG6vw8jHSbTvRxWOhsBoCFAZ0KkxysjI+Of//3dfvlLZ+RQbZ2nrzKVn/uXT729//vz88vnDz2/f2ZnExXlfvH39+7fCw58/tMWlGP//uPvm1e3XDIxMbH///GT+w/SLlesnG9cvZqZvP/6x//7198f/7xyMzAKC3999/vXl878/jPycPP85mb7/+M0kIvb61atvP/4x/GNk/PUPdGkHJ9Mfbu7P7Ox3OP6y/GH+yM6ixCkhxC/I8l1A6B8z7+/3guxCH54/Y/rGoPFFgPfz328///yR5BF0VPnHwfjhzUMO5r/s4JvlyfQ6I4Mkt8S5lWc+3Hp38/mNtzfemFuag1b2f/+1atWqS5cvmZuZBwQGMLMy37x+c+naJb9+/gwNDdPW0/798/fKFSsvXLhgbGwcEREuwMf/49F3zi8cSvyKz58+X758+auXr7y8vOwc7V69eHXk8BFZWdlz585Fx0Q/uv9ozdo1P3/89PH1sbSwXLZs2bMLT2/9urF90bbg4GB+Pv7Vq1efPn1aT18vMiKClYPt3et3C2ct/Pnj57t3b38++aHApSDLLwvx7Pcv31csW3H9+nVrK2sfXx9mVuYb124sXbv21+/fgYGB+ob6Tx89PXr0KB8/38GDBx3sHVRUVRYsni8hIREXF8vJw7Vrxy42NrabN24+e/4sODhYz0DvzMkzDx8+CAoK/vDxw6Y1m6xtrE8ePPny8ourvFcOShzw9vH2MvH4/fu3Ep/ils1bBPgFzp079+Xrl+joaCVFJYa/DGvWrDl77qydnd3vX791dHSUVJUg7nTSdVQVUNEQVX/z6s2SJUuePH5iY2Pj4+PDwg7Nbu/fvF8yb8mDBw8sLS0DAgLu37t/7PTpwKBAJkamVatWmZubq6mpbdiw4cjhI/Ly8pGRET9//Tq6/sin+x+Prj/K+IbRP9D/+tXrq1et/vnzp4+Pj6WNJacS+yu7l+aKZh+ffzx38Sw7B/vhQ4etra19/H0YGBiuXLqybu26n7/Aiq0tbzPe+vn4x72T905tPaWkpBQVFfWf7f+f57/ZpdiU+JXevXm3YsGKBw8eODk7eXh5QHwEIQ/uP7hz505xMbGY2FhhUeE3L1+vXLnyyZMnTs7Oru6uf37+2bp1q6CQ4LFjx/h4+UJCQzZt2vTkyZPo6GhVddVzZ849f/78x48fZ06fdnZ2cXF3eff63db1W52dnaVkpDau3yghLvH3399rB68+v/58x+LttjY2dhp2P21/msgZP7z38O7du//+/Ttx8oSrq6uzqzMDA8PlC5dXr14tIyujqqL69+9fF3cXiCNJIsW5rgyGxiio58DCevLJuz/3X/z9+3fCxIkGBgbgjhlodoDhPwOoN8jEDGmJgi4QAp249hfS6wDf2QFNV3C/Q9UzgW70AN26xMjIzAw6Npjh/3/QZR/gI0b//v3z//9/Dg6OieV54G4hXDdxjP//2bg5Kv+fvsHwk/HffwlOiea0xqdPnzY0LQdPGTH8Z2T49+1vnkXOMaYfr988XjB/gQAPX01j/f7HVxcsWCAtp8DAwFBTWLD825Gb2+a9/fChuLj4/L1zfx//UVVV7ejqNDUwIs4dCFX9yScePX51aG4uXKi7pnrTg/N71/QKCQjDBdEYf+88f+fdhCY4yh0NgdEQoDNAL8VoZz1o+pAR3F0mzg4yxjuJMxikignsEHEVZcfQiFc//nz/9fvd908v3715/+WdqaTQ04+fn7/+9fTikx9/PjP//f2X+e+rD++/fvny79tXpj+/mb784OTlY3nz4/WD18wszL++/WJiZfr3j/nf+8//vv1kZ2VnYmP//OULEwMzv5gUGwvnz0/fmX4z/v/LwPDzH2gfLAfzz/dfXvxmZOBnZ2b4y8vC8vTr6x8/vjJz/bj76fPPv1+fvfkj/eGPI6cyx9+/DMIf2Jn+3hNnFXHS/v/646tXlxk4GH+xkT9tzczI9PLVy+UrlxWWFO3evXvJsiXmluY/f/7MyctZvXq1kZHRvAXz/vz/Y2pq6uruKigoyMrKumHzxp07d7a2tm7YsMHZ2bm0ovTqjavFxcWMLIxMLEyfPn0KCApgYGBQVlYOCQ9ZtmwZJydnYkoiKyurkpKShbVFfn4+FxdoecasubPWrl27buO6j18+3rh9c8WqFW4ebq3trUuXLvXw8Ghoarh89XJTU1NsQuzx48fNzMyOHTvGy8v75z/05sOfP3+mZqTu2LFDT09v9tzZCxYs0NDQcHFzERUVZWNjmzZj2tatW9+8eRMeFa6trc3BwTFl2hQpKSlZWdlDhw49ef6kubm5b0LfwYMHTU1NX7x4MXf+3GPHjq1cs3LDhg2uHm63795OzUidPXv2th3bvv74eunqJfaN7HaOdtNmTv/8+bOji1NTS9ONGzeMjY2vXbu2Y9eOHTt2zJ07t7Cw0NbWdv3G9Xfu3Jk1axa8Mbpi1Yo9e/YEhQQlpSTduXPH1NS0rLKMg5vDwwPUtvv9+3daZtr58+dtbGxq62t5+XkfPXpUUVFhYW3BysqamZ05ceLEU2dO5eTk+Pj4HDl+5OmLp76+vtt37WBgYNi5Z+fvf78dnBxS0lIEBAT+//8/fdb0AwcOvHz5MisnS99Q/9atW/Hx8bq6utzc3P2T+teuXWtvb5+ZncnDw8PAwDBz9swDBw6wsLN8//W9u69bXV29vav95p2bjY2NoMu0WBh//vwZERXx4sULfX392PjYnp6e+Ph4UIZhYFixYkVycrKRkdGTJ08uXrnU19fnG+D34sULJSWliVMm9fX1hYWFVVRXvHjxQk9P78KFC9193XJycvfv39+wacPRo0fXbVjX3t5uYGDAwsIyccqk9evXCwsLZ+VkLVu2TEpGqq6hzsLCQlVV9catGx+/fFy2Ypm0rPTXr1/zCvNs7G2279peWlpqbGz8////aTOm7d69W1RU1NvPm5ubW1RU9MSJE05OTuQ1Rv+CsiXEfwNJ/v33V0RENDol9/Pnz7m5uelpadzc3CiDnTDXQQRZWFj+/fsHYcNksNOM4GboH/ARGVjPrufl5ZNn+2NkbYddP37R/wz3bt52zotVlJGbPXt2fFTM/PnzMxKT796929PTk5qaqq+vb2NpJS8j6+7uPlm7z9bWtrWhqaGhQUVWnoGB4fv374cPHnKxd+RmYQ9PSL58+XJjYyMXF1dHR8fcGbMMp0x59+4dDw/Pp0+fGBkZxcXFnz59ev/+fVlZWXl5+f///798+ZKfn//u3bv////X0dFhZGT89/0X06+/T548ef78ub6+PhsbW3hIqKWZuQAHaAvmixcv7t27p6SkJCEhwcDA8OrVq1u3bolLSSr+YmMcvTgaf0SPyo6GAO0B/RqjmH6haXMT0zpkkX/g5pyerYOgrNKVN5/e/f3x7uuHP3+/6wnxfv/z69bTt88uPHvz5JWgnLDEf+5nnz//e8fMzMHBwvzv38vvoMPqWH59Y+L48fM/O8O/Pz9/sPxnY/nG+O3Dh3+/frOycbJzczAyMPx68/br37/fuXn+/f/L+O8v44+/DH/+/WdjYBbiZPz8/S3rPy4Ojv+fvv9k+PPw86d3n/89ZXkow8HO/ecvC5OIMDsHPw8Lt5jwx9s/mf//4Pvw5d/NJw95vrxheC/CIPL/P6hhj+wd4tmsrKy7d+9+/vx5WloaExPT8uXLGxsbv3z5snr16oqKisLCwrt370pLS8+YMePTp0/79+8XEBB4+fLljx8/li9f7uDgEBAQ8OzZs1WrVsXExDCCVzGeAYO6ujpdXd2DBw+uWLEiPj7++/fvxcXF+fn5fHx8EyZMePTo0dWrV/fv33/37t2lS5caGRnZ29v39fW9f/9+1apVJiYmAQEBHz9+XLFiRWho6KFDhzo7O1NSUurq6mbPng332r1799asWdPd3Z2Wlnbnzh1ZWdnu7u6/f/9u27aNlZXVyspq8eLFXl5eHBwc3d3dmpqaBgYG7u7u3d3dfn5+R48ehazDMzIy2rFjx9WrV+3s7A4dOsTDwwMaMWJgYGJi+vfvn4SExIwZM8zMzCIjI+vq6iCrFCBT83///rWzs1u9evXUqVPb2tpu3769atUqd3f3devWHT9+3M3NDWIOxLVMTEyQ5sLDhw95eXltbW29vb2tra0hsn///n348CEXF5eVlZWXl5e1tfWDBw9YwDPjTExM4Mlr1mfPnv3+/VtbW9vFxcXa2lpZWbm/v9/V1bWnp8fR0ZGVlXXixInPnz+/fv36zp07z58/LysLGjyGjJyxsLDMmDFDTk7OzMzs8OHD3t7ekydPhkTB3r17L1y4ICUl9f//f1Br0tc3MzNz1apVeXl5kFbL9evX9+zZU1hYaGNjc/r06WXLlkVHR0MWcixYsMDMzGzbtm0vX75kZmY+dOjQyZMnd+/ebWNjEx0dPXfuXDc3t79//wYFBU2ZMiUuLu7MmTPr1q3buHFjamrqo0ePGBkZhYSE1qxZw8/Pb2FhsW7dusTERBYWFki4QdZI5OfnP3jwYN++fVu2bOHl5V26dCloopaREbJMedGiRUxMTGZmZhcvXmRkZPz8+fPmzZtVVFQcHR2xNrMgoT0kyH///vHwcLu6ur5//56HhweSKpDX70JilpGR8fXr12VlZZ6entHR0T9hW0Lx+JGNjW3Hjh3z5s1rb2+Xl5cHndqGpJqZmfnEyVMXt62FxAKSDFHM////cXFyxsbFGWrr/v79u7KyUlhYOCMj4+zZs/39/b5gwMDAoKGhUVJS0tPTM2fOHCcnp8LCQoh3bt++fe/evby8vDt37hw9ehSStRkYGPz8/BgYGFhYWDIzM9+9e3f79u2CggJFRcXi4mJeXt737993dXX5+flFRET8+PHjLRgkJyd3dHSwsrJeunTJ3d391atXjo6OCxYs2LFjx/bt2+3t7bds2VJSUvL3719WVtZZs2YJCQlFR0ezsbG9+/Ip1cIjnxV0fghRfh5VNBoCoyFAGzCQjVHINBNymYvsR2L6/cjq4WzIVhuCIwecPDza9k6vfv169fP3px+/Pv7+py8iJMj69+StR08uPHr//A0jC8u3P8y3br749OHTdxYmFh4GJlamPz///mNi+P71K8sfJgaGv3/+/WP49+ffrz+//7MxMnEys7H9//fv28u3f3//YmRj/v7+3f+vn5kYmRl+/ALtUuJmZ2D99//bj58s/1h//2X5/efDp9e/OLn4uTn4mP99//brDRMrLxv7c6Zftzk/BPz+L/hPiIGN7fu3j/xcPHce373G+5aPhfvrz9+ff/6Ce5ZUBgsLy/4D+99+eBcZGfn27dvHjx/v2rXL0tISNBmtpMTOzq6lpcXAwPDhwwd+fn5paWkODg5hYeF79+79+vXrwYMHCxcuFBQU1NPTg9j7//9/yJam48ePX7x40cHBwcTE5M+fP9zc3La2tvz8/NevX09LS2NhYZGQkIA0+NjBi+HY2Ng4ODggm6iePXu2aNEidnb2kJCQb9++MTExKSoqsrGxKSlBZ70hdn358uXPnz+Kiors7Oza2toMDAzv378XFhaWlARtvBASEvr0CXQGOAcHh7S0NGjVHRubjIwMOzu7oKDg8+fPIY1RSUlJLi4uBQUFDg6Or19Bi0b+//8PWdIK2bjNycn5//9/Tk5ODg4OtJpbVlaWk5NTVFQUshb2169fIiIi7OzscnJyEE9B3AkhwYs+2WbPnt3T09Pb2/vu3buGhobMzEwGBgYODo7p06f39fVNmjTp9evX7969g2iHtHr/////7du39PT0t2/frlu37tGjR1ZWVitWrIC0jLm5uVlZWa9fv56ZmQnxKegiR/CiSWR7FRQUeHl5IaNrd+7cycjIYGJikpKSYmJi+vv37////9nZ2ZWVldnZ2WVlZX/8+PH9+3dGMPj06dP///8vXLhw//59IyMjXV1deDb8/PmzoqIiJyenggJognX//v0cHBwKCgoQQ65du/bz508mJiYZGRlOTk5eXl5xcXEhMIAsqP3//z8fH5+srCwLC4uwsPDnz58hm7EgzSBIIcDKClpfy8jIyMcHOtoW4iPQxkBwjMjKyn7//p2DgwOybJSDg0NSUpKbm1tISAjuSLiWocvg5OT09fW1tbXF5QVOTs7KysrCwkJTU1NcauDit2/fLikpaW1tzc/PhwsiM6SkZW4f2P4P0jtHliCCzcTE9OPHj9qaGnEhEUgxAsmMkFbyr1+IYqq4uHjFihWPHz9euHAhPHJPnDjx588fOzu7x48f////X1lZmYGBISMj4+7duxISElOmTPn8+fPJkye7uroCAgLevXvX2NiopKRUXFw8Y8YMBweHBw8eSEpKrgKD/v7+qKgoSPd44sSJly5dqqysvHjx4pcvXyADpdXV1YaGhnV1dXv37uXl5YUsIFm0aNG3P7+k3/39e+E4Ed4dVTIaAqMhQEMwkI1RSA1ERc9BOtzgTSmgHbj4zVfW05XXNb385de1Fx+efX0vw8N87f3HP9/+vL/5/N3TNwz//jAx/vnz7NVL5v//udiYP335y8D4/99PZmYWBg4OUOvz6xcGxv9/GP6yMP778/8vw+9/LBycLFzc3398Z/j9j5GJlZGVkeHPP4afoO3q/xhB5x4zifAwMP/48+87AyPT78/fPjx+8/f5658MLN90JK4y/+P/xcjIyn6U9dsXhk86Yqrvv/F/ePbuB/uft5x/33B+vv7vBecdxle/vkkI8f3+A7psmrxw+8/A8OPHj+CAYANTgz9//swDA0dHRxkZmXnz5vHx8e3ZsycwMNDY2Lirq2vmzJmioqIXL17MzMyUk5NTUFCoqqo6e/asmpqaqKgoeBfLPzU1NW5ubkdHRycnp2PHjvn4+Ny/fx/SXGBgYDgNBhs3bnzy5MnGjRshjQYuLq6jR49u3LjR1NRUTExMRkamurr64sWL8vLympqaHBwcM2fOZGBgmDt3LkQ9xKdycnKSkpJz5sxhYWHZtWtXRESEhYXFrFmzZs6cyc7Ofvny5aioKMh+c8ikJHjPEGiKH8KFbHk5ePDgqlWrbty48eHDB1VV1R8/fjx48GDdunUHDx6END2ZmZnZ2NgOHDhgBgb/QNvqQaEN2bbFwMAA2TAEGd5bsGBBa2vrqVOnvnyB7iyBOPUfGPz8+fP48ePe3t7FxcUxMTEHDhyANEZ//fp14sQJR0fHoqKiuLi4ffv2hYaGQpqeb968+fr1KwsLy4ULF3h4eGbOnDl79uwVK1Z8/PgRMs++di1oEOv69evnzp3bsGHDy5cvV65cCel3Qcj///9DPA66c/zPH1ZW1suXL58+fXrt2rUvXrxYt24dpFH++fPn/v7+iIiI1atXKykpiYiI/P4N2gGmrKzMz89vZmYWGhp69OhRZ2dnVtg1EHp6epCRzlu3bvHw8FhYWPz9+3fq1KnOzs4bN240MTEREhICb34DbSSCbFGChB7EYczMzI8ePZowYYK4uPjFixe9vLxERUX//fu3devWFy9eXL9+3cgItEaQl5f34cOHixcvtrGxgWz8gtzoC9r99hcE/vz58+vXLyMjo/fv35eWlkpLSx85csTR0fH///+XLl1SV1fn4Bh65zQxMzO/evUqKzv7xXMQQE72z58/37x58+nTp3/8+KGqqurj4xMTE3P9+vWEhISdO3fKyMhAkhxW8sOHD7GxsRYWFnl5eVgVgPaZ/fkNOrUAlzRe8f8MDP/+/f/4/sO5k6f//v07Y8YMbm7QhDimpq1bt7569YqVlXXlypUODg6QHsju3bs1NTUVFRWfPn3KwMAAacKqqKjcuHFj8+bNPT09////d3Z2zsnJYWBguHr16pYtW548efLgwQMlJSVIbvX29jY0NGRhYZk6deqjR6BdXOrq6i4uLvz8/JycnN++fWNhYWFnZ//y5cvr16/9/PwMwQDSO1qzZk16erq0rGyCuYsBE+gME/LnmzA9PCoyGgKjIUAiYCJRPTWVQ+pFKpoIKcQh1RX+ligDA4OurcMfHoEbn/98+sfM8OvHpw+fPt7/cu/EnTeP3jH9/8v4//+//3///f7E8P8X8/cv/39++Pfz8382VgZ+HgYmRjYODgbGvwzM/1m42f8zM/9jAu0W//vj698/v7lFRBm52Bj+/vv76x8THy8TO/s/hn8M7Ez/mf8zszGx8nKzC/Bx8fL++/Tt7+0XHAy87P8Yfv74/vcfExsPOwvLv5///xmIyYn8+8fCy8vKzXqP9eUFaYbDokyvvrIcvffu0atXzKD7TsCLDMgKuL9//+hoa3d1dVVWVtbW1hYUFHz48OHr16+TJk168+ZNVlbWwYMHGcAzZQUFBVOmTKmpqXnz5o2IiEhvb++DBw/Cw8OnTp365MkTVlZWTU1NQUFBLS2t+vr6RYsWRUdHr1u37sOHDzw8PDo6OpBqydbWFtLqggycQAZFIPNl1dXVb9686e3tffbsWXh4+IQJE+7duycuLt7W1nbt2rXi4mIJCQlLS0vIHDEDA4O4uPjEiRMfPXqUlZV15MgRBgaG0NDQzMzMnp6elpaW6Ojo5ORkDg4OHR0dDg4OZmZmPT09cXFxBgYGeXl5yKALExMTBwdHb2/v3Llzs7OzbWxs/Pz8rKysamtrX7x4YWpqys3NzcnJWVxc/Pbt28bGxidPnigrKysoKDAyMqqpqUEqfgEBAW1tbTY2ttra2ujo6N27dwsICEA8C48QKSkpNTU1JiamV69etbe3x8bGCgoKJicnQxQwMTG9fv26t7c3MjKSi4srMTHR2dnZ39+/r6/vzJkzFhYWkKbh+vXrIyIiDh06lJOTIyQkpK6unpOTs2PHjv7+fgcHBzs7u6KiovXr1zs5OQkKCvLw8BgYGHBxcQkKChoYGECGGLW1tUVERExMTBwdHUtLS3fs2GFmZgZxrbOz89u3b/Py8lhZWZubmzk5OdXU1MTFxaWlpbu7u7du3RoREbF06dI3b95A3MzAwFBeXm5mZlZYWDh37tyfP38aGBj09fXt3LkzNzdXTU2tpaWFg4NDQ0NDTEyMgYFBRkZGVVWVgYGBn5/f0NCQkxO0T5mLi2vDhg01NTWOjo6JiYlqampZWVkbN26EDHTJyckxMDBERkaamZm1tbXt3LlTXFxcX1+fnZ1dXFxcT08PsvhBT0+Pn5/f1tYWkhju378PiaDfv383Nze/fPkS7uAhxGBiYnr//v2CBQs4ODhYwec/QBy/dOlSGxubuXPn/v79m4eH58iRI4GBgbm5uSUlJWpqaunp6d+/f4eoxCT//PlTVFTExMTU19cHafxhqqFQ5P+//5xcnF29PbNmzXr//v3ly5fhBkKKYgj3xo0bpaWlMTExHR0ds2fPXrt2LWRa4/jx405OToyMjIqKivz8/LNmzbp//35GRgZEEKIXMiHwAby96f///+vXrzcwAPWiIX3L8+fPf/v27fz58z9+/EAeIP/9G9QjgoxN/P37l42NjZOT89KlSz9+/Dh58uTTp0/l5eUhqVfPUL93ei9k9B1i4yg5GgKjITAggE7njB7dtrGzrmrzueug8z4GxKNgS0HnOYHO6/zHLyxcPGfhGyXDNTfevPjwguXXx9+P3jK8/sTw4+v/Xz8Zfn1j+P2T+c9Phr+/WRiY/7Ix/+P8zyDGw8DLzfTlE/Pf/wx/f/z99f3fj1+MjAxMfxj//v3HzPCflZHhN8N/ZhGxn69fM3z69J+RgVmAn4Hx//8Pnxn+/WXkZGUT4PzLxcIuwCPBJfT7yadPdx7++PVTVIT1l6Iwv6SU9Jevgl8/sbIyS3AIv/j1z/aTgPjXH+tZH1z4/59PQPnh5TtPr14wEeU1UVdMa+o21NYEe4hkIv9AkY2YdahWMETn79+/ITPybGxsHz58ePfunYiICKTJyMDA8OTJk79//8rKykLW5H348OHt27cCAgLCwsL/////8OEDOzs7ZHPS8+fPv3//Li4uzs3N/fv378+fP/Py8kIG1T59+vTmzRtJSck/4IE6yMDVu3fvfvz4ISIiwsbG9vHjxzdv3vDx8YmKikJc9fz58z9//khISEAOOkWuSt+DgaioKC8vL6QKefLkyf///2VkZCBnS3358oWPj4+Zmfn9+/ecYPD58+f///9zcXG5u7vz8fHNnTv306dPsrKyEGM/f/4Mcd7Pnz85OTnZwEfAvH37FjILD1lIwMvL+/HjRxYWFh4enp8/f379+pWfn//Jkydbt241NDQ8cuRIVVXVgQMH4KtCv379+vv3bwEBAQYGhmfPnn3//l1QUFBISAjiOwj5/Pnzb9++QQITspnjxYsXYmJi//79Y2Fh4eTkhAQLOzs7pBEM8SxkvaaoqOjnz59fv34tKSn59+9fyMrLjx8/CghAz3kVFBRkZGT88OEDGxsbNzc3PAp+//7NxgY6jgoy3/3s2TM+Pj5ILQ73IGRvx+fPn0VFReEpAeLmX79+PXv2jJ2dHTIby8DA8PLly2/fvklKSkJWNXz8+BGSJL58+fL3719+fv5fv359+vRJSEioqqpq5cqVJ06c+Pbtm4SEBKR5+u/fv0ePHkHGsf78+QMZ/f327dv79+/5+fnZ2Ng+f/4sICDw69cvSBhCUh0nJyczM/OKFSsgrfAoMOjt7X3//j0k6iGuJYZce3v9sWfHe+27iFFMOzVXTh5tLc49/PD1oYMHw8LCurq6nJyc5syZU1NT09DQEBERAUlL//79O3XqVHp6upqaWkdHR0BAgLu7e09PD1aHdXV1TZ06defOnRoaGlgVQASPnTjRW5SZW1blEBAKESGS/P/t5y/T4oDX21p2LDM2sggN9L13797hw4e5+IQunD5u72C/YsVKT9/Afz+/hoaF3b59e/++fcISkoG+Pjdv3Dh8+PC169fd3d137Nhh5+TGwMCwbOHcyspKVlZWdg72L5+/6Orpbtyw0T/AX0hIaNGy1Qz/f0WFRxw7elRPT+/u3btS0lIrVqy0tbF58/atoqLCo0eP7O0dVqxalZqc9PDhw137Dl84fdzL22vtmrUHDx1avnz5sWPHZs6cMXHiRAEBga9fv3Z2djEyMhSXVkrKyD598UztD/dqPhe+o21MfKB9lkR6f1TZKBgNAeqCgZymR/MJpCP7n8QTQ9EMwc8FTUiB701SUNcWV1Y7//n323c//336/vvr5/8fvjP++fOf4R/Dv78M//4z/v3LAFpd9+8fAyPTfxZmEcE/vCxcLIw/v31jYGH+y/yPk4eb4T/TP0ZGIRnxN6/f/Pv28/eXz/+Z//15/57x+4//jP+Y/v9j+PWTgYv1H9M/xj//mX79/vPuFxeX6J/ffzhZuBQVpG+/+yXDx8AvwfyVh5dTQOTPl79CEvzMHz98/P31DwsP78efd9k+XeZgffTgrcC7x58/f2NgYfvHxgFe2kf+eDZkQSQ8lFhZWeFNQAEwgEtBxreQuWB5UAMLsq1EUFAQLgtvnTAwMLCysiI3vPjAAK4SwkBWwA8GEHEICTeNH+Nsf0EwgCiDOAOydwciwsbGBjcZzoA0W//8+SMtLc3FxSUEBhD1DAwMvGAAWcoJFxQWhh4EA2lPMzAwQFoDDAwM7GDAwMDw4sWL6dOnv3v37t+/f7W1tWZmZnDtyAOlUlJScHFkBtyPEEFOTk5FRUUIG0KCQwXlagNGRkbIRmBkZ0MUMzAwiIiIQLZ9sLNDd2PAIwgcA6BVmJAmICSOGBgYIKs/IcEI9yADA4MYGMBNhjPY2NjgWiCCkLFnCJuJiQluI6RZycDAwMbGBnGYsLCwrKwsZC0pRD34vFUmNAMZGBi4wACiBhIR4D4FaGwVsguKgYHh69ev27dv37dvHwMDg4GBAWQaGm47RO+QI//////jxw/IauMHDx40NTX19PTExMTAPcLExGRhYbFx40YXF5fdu3cvWbLE09NTR0cnISEBrgbC2LZtW29v77x58/C3RCGKySN/MTNXWbi++WvSf5pb+M6TP9ZV73kvpi55JCz89f0HHvGwafPvyW5f+fjz58/n+XwVQxRbjjD9+/eC2anhK++ZjJXPv35h4fXpmntHes3rxwwMjCw8XqqpMlevXv3w+7eIiIiQmVnRpg+vtbM+srLmrnjMxMT426ri+59DFzk4VCNVP33+XL796xeTYgkB/ucfPjAp/2N1csxf8/qWZMQPwR95Kx+//8Aj4N835YrQO0Z7BnvVki2fWOTjBAMUHjx8KCcru++v9feff/O7ZrF/fcQnJuogqPg3dQ54nIK8kBjVNRoCoyFABTCIGqPgk8+h55XQaqM9I2ik8j8Dg7a5xV9evqdPP/7+9IXp7ad/X78x/vnL8OcXw+/fDH9+M/z9w/j/H+j0Jxbmv/8YWf79ZWFjFuLj//b6+Z+vX/jFBf/wcDH8+MPAwcbwj+Hjm7dM7Kx/WFmY//369+/PHybGf2wszFx8/5n/M7Kz///1i4WNjfH/z3//fzOysnDxcvz881NaiJeXhV1EmkVVQerOjzecLGxyv/6zsXNx/+P48eHjTX4mrX/8/3mZdzM/ffudhfkt4/uvD/n4+P7LqbIxfmVmYmaBLeMjL/5p2twnz0l00AVZWAYZ4qWKdebm5seOHXvx4gUPDw9ay5Iq5g8zQ3JyctLS0uCtYQp9x83NvXjx4qdPn0JG7uF9BgqNHVjtjIyM7OzskBMVli5dqqqqGhkZiekkBQWF4uLi+fPnJyUldXd3FxUVqaqqwkflGRgYbt26lZ2dXVpa6u3tDdmWh2kI5SJ/GZk2Kmt9+PT37q3f//69ZWHhY5Vx2n7357/b75iYmNgl7fY+/vX3wTsmJkZ2WcfLP/+ePfoa3FniYVVw3ff4FyMjF5um/+abP/79ewfqDoE6sbIs6opsDIwf/v3bevv3379v2dm1/v9nuHwSpICFBaT+1///l77/YWRjunXpB4ei26v//xlBcwAM2+78+vv3DRubMiMb480TIAewiVjtuPuLiVmMmV9yxdnPoG4krzGXvtmrf3+Xnfn07T9jqDyvJ5fivxdM309e/frjFwP5S58oD8tRE0ZDYBQwDKLG6F/QSCS0SKBFgwm0Ph00NMrAxcevbGby9Afjk7ff/339xPzrL+MfBoa/v///+8Xw+xfDv98M//8y/P/3n+E/I2jX0n8GMe4/zP9+vnv//f0nNgF+HnFeRl6Ozx+/CvKyfXv39f3rl3+/M7Byc//jZGX+zfj3+y/QSdPffzPwcjIy/Gf+x8gvKfHr08fvPz4yC7C9/fGBn5Hz75cPP7i//JP49/vvW0kGpvdfv7MwcP5gZnz0/O6Lnz8F/0sJs7A85P97/yPLpxuvf3z/xs7K8YuFk1NUhOv7c0bQ9UbMoymXjBCADJGSoRGXFvCgKmi1AC4Fo+LwEICPbsJFKGSwsLDIy4OOq6TQnMGj/fu3bwcOHPz69eu3b9/OnTvn7u4OWUmC6UJHR8cJEybcuXMnOjr6ypUrWVlZLS0tkPH4X79+tba2Qk5QAjXyIJd1YhpBDRGOP3+Y//5mY2FkYGBiYPjH8PcnB6g+Ac/b/P3JzszAALkX9O8vFgYGFjawOFgZSIrhP8OfHwj1IPf8ZQBvzWRiYAApYGZiAJ8xDNMIUs/IwACy7f9fTlZGhr8/QUU6SCNMPcMfhv8MXBCL/v0CXzHxj+HfP5Bi0DIX0EADEwMDiMvE/P3sra+fn/1iYmZg/M/uasDACnEe2LhRYjQERkOA7gBUeNDdUuwWUrEBimPGH1J2/ZdQUhJSVTv07sfbt18Yfv78+/cPAxyBDsH+x/gfVIT9Y/jHAioxGXnERDikeV/ff8jFx8fA+PcPM8Mfhl88XBz8HPxvfn77/fY/w9efrAJ8P/4x/fj3n4mfnenHH+b/zAysDCyM/0ETx1w8H758FpaQ5ubnuv/hlSAP77Wr17ikeFSERASZmYV+M37k4f3DJ3j//YNfv74oCot+Z/l5+9ddNlYBhrf/P//5y8bG/u/vn+8/v/H8Y/zHwMTIDELYQ3BUdDQERkNgKIbAfwYODk45efnW1pYPHz78////69ev8PUzmB7i5eX98+fPt2/fGBgYkpOTZ8+enZmZCVm9/fv372fPnnV3d+NqyGKaRi2RP3///2NgYGOGFLMMv//+//0XNLjAxsLIAtp2CbXn/3+GH3/+sbMwIYkx/Pzzn5mRgQWmF6oUTP3/z4DcooZo52BhgtyggiwFVk4U8f/Hb976cG4VLuyb/4kyY1TRaAiMhgA1wQjqDv6HXbOtpG/wl0f4wYfvvz5/Zvjzm/HPL/A60X8M//6DVov++8cIWin6n+k/aLiUkYGB7R8zJysLnwCvnIiYgoT4f8b/n79+//P77+tXb1n5ublEBRmY/v/69pWNi52Tn4eDjUlQXIRDnJ+djYmBiYGZm/Hdj7egmzy/fb536y43E9unTx++/f/95vP3m18+vuUVuMnJyC0p8vHPpyevn15m/Xnr7/cnH94de//iwM2bz+4+Zvj5m4mBgeXff6Y/f3++e/PjyzfI8TbUTAKjZo2GwGgIDGgI/P7zW1ZG5sjhw/v27ZOWlmZmZhYREbl9+zYuR7148QJygO6vX78KCgqsra3PnTt3FgzOnDmTlJRUUFDw9u1bXNppIf7zz399WU43Tb4/4CNL//77b6HI3eIvVestqSbGARFkAB8FJcDFnOMgKszD/A+2PeDPv/+x5oLmStyQxiuy8xgZGHg4QFdBQAT//f/Pz8lc4irOywFqyvKwk19//f8FOvcNYuwoORoCoyEw4ID8zDzgTsfjAByHRoGao0wszAo6Ou/+sr768O3/j5+gSXnosOhfhn//QLPzfxn+MTL8Y2ViZGRi+PufnZ2Jk/E/09dfLIz/fv//KccnyPb5558/v7/+/ffr719xXn5hEQEGbua/Pz79evP6/7dvf1+///v+CxsnBxMb04+/X78wff/5/fOfnz95+QXFeYRZmZl+//ujqq7Jxsf/4vffW+8+vmPjOv/q2YMPL2X4xVjZeB9wszzm5n79hfXtk/d///5h5OH6zcLIwcHJwcf7/dfP/3//MLOAVo3i8TtBKSqumyRo16iC0RAYDQFiQoCRiYkbDJiYmDg5OR0cHLZu3frlyxesetesWSMtLa2srFxTU/Pw4cNp06aJiYlBFo0ICwt3dHSwsbHl5+f/+UOn9tavP/91pTinR8nZq/H8+cvw6+9/O1WeGdFyBjKczuo8ixLllUXY//z9/wc8VirAxZxkJSLExQxri4JOWBHgYuFiY/r/n+Hvv/+//oCGVP8zgMZW9WU5qz0k/kK3EoAWdjIxMojysvz+yxBgIBBnIfzj9z/QuaFgXX/AA7GgJu9/0G13IKP+gpdb/Qc5CS6LNUhHBUdDYDQEBhYMn8YoeJs5dIYIT5jyCgtLa+q++PX/05dfoN3uf3////+X4e9vyDpR0OAp43/QCigGhn+M/xgZGf/8+v7+weOv919wMfz/zfL7+udnb5l+Mfz+94+VmZWTlfXvb242VmZWJtCxor9//v765eevH5+/gE4z+vrpIzc/DxcHNxsPN6+46IfP3/7+BRW7v3/+vnn75tfX7368enP79u3r794/+P6Zk4eXh1tAklecmZX717//fz9++8/A9o/hD8OPb4x/mX4ws3z/zfCbheUP6PRT0EJWPB7EL8XIyPju3bs/v0G11Pfv39evX79kyRLIXUT4NZIq+/TpU8g0IqkaiVT/////48eP46qtiTQEl7J3796dOnUKlyytxd+AAe1s+fXr17Fjx378+IFmxdu3byFniYPOQv/9+82bNxSunHn06NGVK1fgtkDsxXM0JlwlMuPu3bvXr19HFhnGbEgv+s+fPxEREYyMjNXV1fAYgft6x44dc+fOLSsrWwQGc+bMkZaWhstCznadM2fO0aNHu7rocWTVz7//3bT4uoKlfvz+/x3cNPz3/7+JPPfC4+98p94NmnH/2Yc/zhq8//4zqIixi/Ox/v3H8P03ypVPLEyM6y98uPD4GyMjgzgfq5Ecl6oYO8N/BhYmRhM5Lg0JDhUxdkjLlYmR8fPPfzMPvWFkZLBQ4taQ4JARZPv1578YL4uRHJeiCPvff6DWpwAns7QAmygPi4EMJxcr6OZ5PWlOBRGoIchhNcoeDYHREBgkYPg0RiHlOMFgFZKT45GWffzx67ev3yDnN4Fm5xn/gRqjoEOdQKOizP/+M/76DdpZ//8/47//P99/+Xj/KT8Tm7SYmLSkhKSYGDczG+PHz78/f/7355cQPx8XN9ffv7//fv/x9/dvKX0NcUXpX39+/eNg/fn37/c/vwT4+HgF+D//+fry9cvnb98JCwqxsrH9ZfwnKSjKysLK+Z9BlYvLlIFRguEX95+fUhw87P//cYLsZfr9++//n7/+/vzx6ef3b9++/v30mYWL4+9/BtCNQAT9iUMBOzvH5EmT7ty8w8DA0NfXt2bNGh4eHvLGSt+/fz958mTIvSlotv3792/69Ok3btxAE6ci9+/fv62trc+ePSPJzHPnzq1cuZKglhs3bnR0dBBURkUFyA7btGnTtm3bqGg4mlGfPn1qbGx89w60SRkude/ePX9/f3jb8ebNmzk5Ocg3OsJVEs/Yu3fvggUL4Oo/f/7c0NCAfJA+XAoPY9OmTcuWLcOjYPhJ/f37l4eHZ8aMGWvXro2Ojj5z5szXr1+/f//+7Nmzrq6u+Pj4qqoqPj6+8vLynp4ec3NzzBBQU1ObNWvWxIkTN27ciClLXRFmRoaXn38Xrn664+pHVtDkOQMHC1P/3lf9e1+ysjD9AQ1q/n/64TczE0O1l6S/Pv+vP6CxTGQ3/Pr7v9VfyluXX1WMfWGCwspUxXUZSmm2IkLczPGWwqYKXDVeEr9gs/+iPCyLEuUd1Xl8dPkDDPijzASN5DhXpSptyFRam6EUYy709edfK2XuGVGyM2PkNmUpN/pJlbmJr01X2pipZKHI/Qs2eorsgFH2aAiMhsCAg0G0gYnWYQE61onhv6yqOiMX38snn/99//Gf4R8DExMDMwMDIxMjIzPDf4b///8xgO4f/M/y7z/ojCdGhn8sbCzMLL++fLp2+da/j5ySsqLiAiLC/9juPH7My8vHxMikraj69MmTj89eMLD9Z+Bg+sHyh1dEkPH7W9a//wS5eP6zsfz494/hy1fuv/9///kjzSHx8fe3rz8+szAw/eMEnbL+5c/3Z38ZWb78vvj6GRO/sMDPX5z/Gf6LC/x6+/rXt+//Wf+ADmdhZvnHwc7w8dMfdp7/Qlz/voM2LpAXXLCR0d8PHjw4cuRIfHy8sbEx5KR3iIGvX7++cOGCgoIC5AYdyDnbf/78sbCwYGFhef/+PQcHx+PHjzk4OO7du7dy5UpjY2MFBQUpKam3b9+eO3dOHHZZTlpamoiIyLdv3/79+/fixYuHDx+amZmh7Wc/f/7827dvTU1N4eeJnjx58tevX2ZmZpDDMu+Cgb6+PuQ8y/fv3585c0ZWVhZyeiLkTnkGBoYPHz6cOXNGSkpKS0vr////b9++5eLiunfvnoqKys+fP0+fPi0sLGxoaPjx48d9+/adO3dOS0tLVlZWQEDg3bt3Z8+ehRvIwMBw6dKlT58+/fv3D+IASJiAJv7A543//fvX1NQUEly3bt16+PChvr6+mJjY379/P378+OfPn8uXLxsZGQkKCh47doybm1tfX5+BgQHinvPnz7OzsxsbGzMwMLx584aTkxNyQcD79+9ZWVn3799/5swZLS0tOTk5X19f0Mzj37+fPn368+fPxYsXNTQ04Effnz59+s+fP/r6+j9//kQ+WfPFixeXLl2SlZXV1ARdiPDu3TsWFpazZ89KSkpCgouBgeHevXsPHjxQUFCAXD0A9x0DA8OmTZv+/fu3YcMGPT09yK2n7969g4+MvnsHOi4Hcv7858+fIbcePH/+XFJS8gf4VhteXl6I1yDn3j958oSfn58FDCCh9/DhQ05OTsjVUAwMDB8/fjxz5oy4uLiOjg7EGVeuXHn27JmhoSFk487v379PnjwJOfqehQVUTH3+/Pn06dN8fHyGhobw3TkvXrwQFBRkZ2f/9OkTAwMDHx/fr1+/Pnz4ICYm9uzZsytXrqirq8vLy//58+fTp0+QK0l1dXW/f/9+/PhxMRExJu7B2xu3sLDYvn17Y2NjQECAgIAABwfHy5cvpaWlp0yZYmdn5+TklJiYiHwKKSQY4aSrq2tVVVV2draioiIkTuFS1GL8+QeaeWdmZjz/6Nv33/9CjARApebf/3/AgfqfgYGViaHQRez997+7r3/++48ha9mjH7//yQiy/WcATaP/+QtNX5Dp+x+//jlr8DIyMsTMu8/PyawsxvH685/OnS9DjAQzlz1mYmAATfT/+//n339GBsajd7/OO/qWm41p4t7X69KVtl39NOPQaxN57gp38V3XP/34/U9eiD1n5eMXH39vzlZecfqdZdfNZl9JTx2+vTc/Q4yCDLVSKyhGzRkNgdEQoBCASnkKjRgq2sHHOjHKq2l+YmB+++X7/7//mdhYGRjZQDPuDNz///759wPUJv3PzMzAxPj3/29mVra/DKAD8P/8+Qnanv/l53827scf3zx++pz3239WJpZHrx8KiYte4bz379sfBtAiUeZ/DH/evnj2g+EvHyfPp9evPzF84ufn//z3x/ufv2RV5P+K/fr58iOvEA8n4//3nz68f/n6Hwe7CKfkl1+/zn/99Onbr58/Xn3h/S7449/fn78YWVkZWZiZ/oE29jOzM/37/u3/r19/v/9kAAlQMjbKwMjIyMzMvH379lu3bq1du/bRo0fZ2dnc4Eulz5w509zcLC8v/+TJk9TUVGdn5+rq6o8fP7Kysq5evbqrq2vhwoWHDx/m4uLy9vY+fvz4s2fPFixY4O3traenV11dLSkp+fjxYycnp4yMjK6uruTk5C9fvrS1tUFugZ85c+a0adMgR6D///9/ypQpZ8+eFRISmjlz5sSJE0VERKqqql6+fMnBwbF69eq+vr61a9cuW7ZMXl5+xowZnZ2d//79q66uVlBQePr0qZ+fX3h4+P///1lYWO7fv19ZWSklJfX8+XMPD4/IyMiysrLfv3/z8/PHx8dPnDhRQkLiyZMnNjY21tbWu3fvfvXq1dy5c1NSUl6+fIlsYGRkZF9f38GDB1VVVR88eIA8Wvzjx4/S0tKvX78yMjKuW7euq6tr2bJla9eulZWVnTZtWn19vaamZlxcnJSUFDMz86xZs9TV1d+/f3///v3g4ODExETICK6UlNSjR4/09fWrq6s7OjpsbW39/f2fPXtWU1OTmpq6Z8+eFy9ezJs3LyUl5cCBA0xMTOHh4YmJiRISEmxsbL29vY2NjaamprW1tbdv35aTk+vt7VVXV29tbYXkvkuXLjU1NSkpKd25cyccDIqLi////y8gIHD9+vWkpKTw8PCNGzfOmTNHWVn506dPHz58gLfnIE3DAwcOdHd3d3R0vH37FnLUPHIIHASDCRMm7N69e86cOStXrnz48GFHR0d9fX1NTQ03N/fnz5+VlZVra2vXrFmzfft2VlbWjIwMVlZWyLVYtbW1QkJCkAtRWVhY7t69W15eLikp+fz5c3t7+9zc3AULFuzcuVNaWnr69OldXV1ycnJ5eXk/f/4UExM7efKkj4/Phw8fcnJyxMTEPnz4oKqqWlFRAcqSDAzTp09XUVGJjY1tampiYGDo6elZtWrVixcv7O3tOzs7FRUVZ8+enZ2dra+vn5qaysnJKSMjk52dXV1dLSws/O3jt8fiTw0DjCBhOAhJXV3dlStX3gKDX79+ycjI6OrqcnJyenl5KSsr19fX43dzbm7ulStXkpOTd+zYAYlT/OpJkuVgZdqcpQxuGoL0/fvPIM7LwsDI4KzBCx4eBa3vZGViVBZlf/7x9848FWYm0BQU6LxPFkYZQbZlSYq//v6HLKv6959BRpDVXJGbGbQelHV+vMLrz39+/f0faSLIy8EkwMm8NVsZsmse3MBllBVi25SpLMAFuhrZQZ1HSYRdhJfFXYv3/38GJRH2rdkqTIwM/JxMDT6S/xn+c7MzeerwWynziPGClqXaqPAwMYJcIifEBnL3KB4NgdEQGBxgBDVGQeUgN5eogvLLXwyfv4MWTTL8+/Pv1y8G8KwRCzvbbza2///+/P/79z/oqnm2P4z/mP8w/P/37/+/v/8Y/zP+YWRiZP375zcTC+e3v9/Z//37z8j48unzX5++/fr+g/E/0z8Gxv9/GRl+/Pz17hMbF/f/fwzffv349YWB4cNXFhbWhz8e8XLy/v3yQYFXXIif/zufwHOGv2/fveNhY3n/+QsDA5OYsPizNy/fvXn959s/AXZ+DknBzx9//v32jfHXn//MP5n+/v/HzcnMxQEq4xkgZTiZKQg0LPH3b2Zm5pEjR/Lz8yFXB4HGXxkZ+/v7o6KiwsPDX7x48eXLl3379t25c2fmzJmsrKzp6elHjx799OkTJyfnnDlz2NnZbWxsHjx40N/fz83N/f79+4aGBklJybNnz3Z0dERHR79///73798/fvz48OFDW1sbDw9PTEzMrl27oqKiIO52d3eHsAsLC/fv3y8jI3P//v1Vq1YxMzNfuHDh3bt3s2fPnjhxora29q1btwQEBBobG62trZOTk69evVpVVeXm5sbMzMzIyDhjxgwzM7OUlJRHjx7l5eXZ2to+f/48NDQ0KSnpy5cvNTU10tLSFy9erKyszMjIyMrKOnToUG8v6DbqrKwsKyurlJSUq1ev1tbW6unp7dixY+nSpaKiovPmzYPcoA1x6tatW9+9e7dkyZL///9fuHDh2bNnS5cunT17tqys7Lp167q7u2fNmvXo0aPq6mpLS8vs7OyXL1/OnDnz9OnTLS0tcXFxb968MTY2Li0tff/+fXBwcHR09I8fPyDLG/79+/fmzRtra+v09PTDhw9DHLZ582Y2NjZGRsaXL1/W1dUZGxvPnTt3+fLlkJBZs2YNBwdHY2Pjw4cPIc5jYGCQkpLq6OgQExPbtWvXvHnzQkJCHjx4kJ6eHhERsXfv3okTJ/r5+U2bNq25udnMzOzo0aPwxhzEhH379ikqKlpbW6upqW3ZsiU+Ph4iDietra1nz579+vXrM2fOPH/+/N69ewcPHtTQ0NiwYYOiomJjY+OvX7/CwsLOnDnz7du3Hz9+LF68mJOTc/HixV++fCkqKhIRESkuLobcy8rCwjJp0iRHR8fs7Oy3b99GR0d7eXnZ2dl5enqysrLW19dv375dTk7u27dvixcvZmBgSEtL+/v37717954/f97f38/Ozv7q1StIcmVgYDAxMdmwYYOvr++DBw8gd9sePnw4JCSkr68vPDzc3d39wIEDU6dOnTFjxvXr12fNmmVjY9Pe3i4mJlZXV/fy6cv0WVngDAX36KBgQNrZEKcwMzNrggGEy8DAUFxc/O7dux07dqAN3sMVwBlMTExTpkwJDAwsLCxcsGABcu8CoYaRzBuhmRgZQCs74QbBGMLc6HWKlACrlAArTB5KK4tC7wmD8hkY+DigxyeL8bKI8aIYoikJlYIrVhOHahfhAalEdgk3O7SVqSPFAVGvIAwVAS2o5UQ3CqJmlBwNgdEQGFgAyskD6wJ62i4kKyOhrHr868+fP34z/P31/88f0NFOv37/+/2NnZP9PzsbIzPj7w8/GP7+ZWRm/v+PgekfIws/D8PnL4zff//59+vvh6+MvMxMzMzMHCz/2JmZWTmZn73/8+fvz/9/QVdG/WNg+PmP8T/zn0+fPvz8yc7Hy8rMwMzCxsjPxvTv/6cvH99//fn351eBT1+F1RQ4vrD///6J6w/3t9/fvn34xPT7/4dPn//9+c3w7SeriMi7H19F/nGIS8m+e/GUg5n5w6tXzFycTJxcLJwc/0EnWVMnzP7+/fv792+IWYyMjH///v327Zuuri4DA4OEhAQDA8PBgwefP3/e0tLy9y9oLJadnf3Pnz8mJiaQWvD379///v2DmPD169c5c+Z8/PgRMhL2+/dvyEUy//7909PTg1wOCZnShVjHwMBw8eLFnTt38vLy3r9//8ePHw8ePNDR0YEM1xkYGDx8+JCfn19JSYmBgUFNTQ1y/eaLFy/u3bv369cvBQUFiMpfv369evXq7t279+/f//v3r4yMzN+/f7m4uCAX0nz58mXRokVv374FrbwA3Y715/fv3xC/MDAwvH79+uXLl/fv3//165eiouLLly8FBAQgc8Ta2to7d+6EO/Xu3bsaGhqQHXJGRkbnzp3j4eGBXEOqqan56dOnnz9/SklJQW62lJCQgIxCCQoKMjMz//79m5OTE7KwT1BQUE5O7vnz54yMoLsLQDEJZkAiAu4wiEV//vwRExODLJaQkZE5c+bM3bt3FRUVITPsOjo6T58+hbvww4cPU6ZM+f3796dPnyCeFRIS0tLSgtztycrK+uXLl9+/f0NOidfS0uLj44Nb9////zVr1nBxcc2fP//3799r166Ni4uDuAFuvpiYmLy8/O7du798+RIeHn7o0KHLly8nJibOnTvXy8sLcu2niorK48ePmZmZbW1tITctsbGxbd26VUZGJioqioWFBbIX5/fv32/fvjU0NGRgYBAWFpaQkHj8+PHv379XrFjBy8t748YNBQWFe/fuKSsrQ2zX19d/+/atrq6ug4NDWloaExNTaGioiooKRNbOzm7BggX79u0zMjL69u3bvn37vn37Zm5u3tTUtHnz5oMHD37//l1OTg6SZiALGJ4/f37z5s3q6uq/P//yiHFT1rODuIJqJCMj47dv31pbW1evXg2PILjpTExMb9++XbVqlb29PaQDAJeCpChILoakK0ja4+LiYmJiWrx48adPn6SlpSFRANfFzMx85+5dxu/fUc78hEsTYoCXcSIUQQZEIYIQNkKOATQYCRFEOzcUdBo97CTRf/8ZIGogGv/9B0UOZEwUYizIawygY0eRDUHXBV57BVocAioroQb+/fcfNI4KdgbE8FFyNARGAS7AyAjKerhkaSQ+shqjknJKnMKib5/9+v3jJ8Pfv3yc7H+Yfv5h/PPz+28GZnY+UZGPjx7+//MTNFjy5w/z779sXBxcvDxfP3/9w8rCxyvIysjxg+m/DJ/IM8a3X37/Yvj2m+Hf3z//f7OwMv/8+4+HnefXL4bf/7/9Z2Zi+P1bTFpKgJX9ycMnrFw8XFycv//++fHxEyMb8/2Xjx9/fvnv/99/X38y/vrDyMbI8o+B4Q8T6HRTZiYGFubP37/9+/vv1dvvvH95REVFVNRUju3Y/fvPn////v368Z2RiZeREbwmi9wUAV2oBdaOPAbDxMTEwsLy+PFjLS0tyIimkJCQqqpqa2srExPT8+fP5eXlt23bBtfOxMT0+/dvyHq+mTNnCggI1NXV3b17t6SkBFzBgKsCsC0QAq6RgYHh2bNnU6ZMmTt3rqysbE5Ozt+/f+Xk5Pbs2QNR+eTJEw4Ojk+fPr19+1ZGRubdu3esrKwCAgJ2dnYhISE/f/789OkTNzf3379/WVhYeHl5bW1tw8PDf/78+fHjR1FR0X9gwMDAMGfOHAYGht7e3tu3b2dlZUE8C2k9Q9YX2tnZhYaG/vjx4/Pnz5C1hl++fOHh4Xnw4MEXpFN1pKSkDh06BHHb48ePBQUFv3z58u7dOyEhoSdPnrCzs7OwsMD3z8EZ//6B9m4wMDD8+vXr5s2bdnZ2P378ePbsmbCwMDMz88ePHxkYGF69egVZ3MnIyAh3GMQiSBhCAg1ilJKS0sKFC79//87JyXn58mXkhkVXV5e+vn5iYuLBgwchw6ug3hF4WRw4MP5xcHAwMjK+efNGXFz8yZMnX758gbTmGRgYrl+//vjxYz8/v7dv38rLy58/f/7KlSsQH8FdwsDA4OHh0d/f7wMG+fn5IiIiBgYGEhIS169f9/HxYWBgePTokY+Pz8uXL+G6fv786enpWV9fHxkZqaenB1keComyu3fvWllZff/+/e3bt7y8vNXV1e3t7To6Og0NDX/+/JGXl4fvoL99+7aQkNC3b9+SkpIKCwsfPHiQk5Pj4OAA6S/x8/Nra2tPmzaturr627dvXV1dXl5evLy8goKCcXFxVlZWkBXA8K4RaGyMn9/Ozq6wsPD/3//zzy98+OsR3MGDgcHGxiYgIMAMBpjukZSUhKxC+fPnD2TtMkQNExPTwYMH37175+3tDYmLI0eO+Pv7z5o1KyIioqio6MePH3//gvrMEPUQ8vfv33p6en8FOTDTHkQBHvL773/+0+89//CLAVxt/fn330Obj4mRYevlT5xsTHHmQooioKtN//0D7RjafuXTsXtf4y1Fbr38fvnp9ww7EX7YCOW3X//697zy1eO///bnyfvfvHX5zRW43n39u/b8hyfvf3GxMSVYCf/5x7DgxAcmJsY/f/7ysjPGmgvNP/aGk5Up1Vb04pNv2y5/8tThs1Tmff3l7zqQrt/6MhwGMuzzj71lZWZMsRbZee3jrZc/HdR5JfhY15x7j3wCPx4Pghq+jIzMzExgD/4H5aP//5lAKYkJdOgKSCcjeJcB6NCVv6BjAUFC4GY9qKvJCFoMADr9BGIOqGvByMAE7uSB1IGPXAXlbkYGZtA5qowQ80FVD0Qa3OZmZmFhZARJgbSjF6gwdWAa0hv5++8vsglgGewEeLEW3Gv/QcvB8JoPMYWZGXTiIcqRYZBZOiL0Qkwgj2RhYf7////fv+BJTAYGSCAjF4CEjSXbnYyghW0szMwMjIz//v6FRDQTEyiKIQnjL7icZ2RgZGJihMgykOfCgQagtA3OYv8Z/k+JkHNQ46Gzi0ZWY1RcVvI3K8unbz/+//7Bz8rw8+Pn///+8LD+//vnJzsDj5wg7+Xrn8G5muHvr98MrGzCUpICrMz3Prxn+cXw4/cfjtdfRcVF3/z48uXXtz9//rD8Z/rLxvDv1w9BIUEm5r/fPnwUFxH/xsb96/dvbmb2t2/evf7359f3b3xMTAyMf3/9/cXEycbIxfqH6c+v719Bu6l+/2b89pfx1/+fjP9BZ+uBbpwHFW4/f3xn/v//x7+/3568ZmYGnXIqLinx7NHjP18+/+fkBJ0OBW5kkJtQ/nNycEIm7CANFLg5jIyMycnJEydOPHXq1M2bNwMDAz08PDZv3tzS0sLDw/Ps2bP+/n7IzDhEi5iYmLCwcGFhYUhIiKam5ooVKzg5Oe/cucPGxvb//38ODg6ILRASnD9BqR2il4eHR1RUdOHChZCRUUtLSxsbmyVLluTn53NxcX348GHChAk+Pj6FhYX6+vqXLl2qq6tLSkpqb29/+fIlZOVlcnIyJycnMzNzXFxcY2Pjq1evnj59qqqqmp6ezs7ODml3ampqLliwADKHzsXF9efPH11d3RkzZpSXl8fGxiYlJXV2dr569erhw4cGBgbJycnm5uapqana2tqXL1+G76liYGDw8fHZvn17YWEhKyvr9+/f+/v7PTw80tPTdXV1T58+XVRUxMbGBhkLZABdsc0KaecxMjJCLsVhYmJas2bNq1evbt26paGhoaKi4uvr29ra+vjx40ePHvHz8//+/VtXV3fmzJkVFRVxcXHwjVlwMyHeMTIyMjU1jY2NVVBQuHLlCmTYGBKeurq6hw4d+v37N8Tl8FlsSEXIzMzMy8sbExNTXl5uYWHx6NEjSBxB9G7YsAEykwvhsrCwLF26ND4+HuILiCADA4OVlVV7e7uenh5ksNbAwICZmTk2NrawsPDDhw9v374VFxe3tLQ8f/48xLUQq/n5+UVFRauqqvr6+lpbWyHjo+np6TU1NXfv3r13756RkZGOjo6oqOiSJUukpKTOnTvn6urq6+u7cePGgoICQUHBy5cve3t7v3nzpqSkxMzM7Nu3b5KSkpCxdojb7O3tt23bZmBgAOml2NraMjIyJiQkTJs27dKlS5cvXw4MDLS2tga1IsCDbNHR0RUVFf39/d8/fT/194xpqBnEnMFAglonDAzy8vJqamok1bXMzMxXrlzR09ObNm0aAwPD4cOHISmEh4envb0dMiKOy4MTK/L/gO/hxKUAq/j//wz3Xv8scxNXE+cAN84Y/4JHL331+EFHg/1l+Pv37/fvX3h4ef//+xduIhhqyPvt+08jWcEoU0HwFnuoqdzsTD0h0n/+Mdiq8CRYivwFddP/C3CxVHqIg6OL4c8/BkZGpiKz11+/fpWSkhIWFv7+83dHkDTDf4bf//67a/F56fD9+cfw8dNnzl+fY3UYGBn+s7H/FxDkNIuSA7vkv440BxMjyHn/GUBLWsGuBSVPsAvgzShIawXUBwSLg4g/f/5++PD+58+fTEzMYqISbOzs7969+/z5MzMzMwsz889fv9jY2Hh4eP7//8fDw/v/P6hV+vPnr2/fvl6/fgMyJC8mJvr////Pnz9DNiD++vXr589fEAdwcID287Gysn78+OHjx09iYqDV4aBGJ8hmkPOYmZkfPnz4+fNncXFxMTGxP38gs1gQd6I5+/+vX6BZEWFhYXCgYVWD4rXfv/+8f//u9+8/TExMQkLCPDw84A4JxFis2kFSL1+++vzpk4oqZF6CkYHhPxNoohA0/wPxFLh9BvEASD2EBRNEFoHIoFkE8jVYAlklyJY7d+7w8fGLiYmCZRl+/Pj5//8/ePEIEUS1BaQLWYSZmQU8evILphjTLohjUEIJpIiR6d+/fw8e3P/167esrAwfH9+fP39+/Pj58eOHP3/+sLKyiouDDlb7/fvXjx8/4OXSjx8///37x8XFCbYOYjKyp0AGg6Xg1qE7GCaLphfCRdMFUQs34T+yx8FymLrAwoiOC0jv27dv379/r6KikrH08cfvoLlQiCK6kWQuGCLVfUe3beysq9p87jqoLUWqZmqpZ2JKamhSCkuaefXD41uPxZl+/v7y6cfHd54GKhev3nj0+LmphsKNqzc/fP/G9OsHBzMzv5TM71/fP9+/A9oc+vvfv99/FZWEPymyvfz+mfHfP8aff9i4Of//+vPzzXsFKVkRPsGzF88xc7DzCAowfvvDxMz0lfH3758/2RmZRAUEXrx8+ouVmYmd9f+fv6ALn/6C5oz/MzExvv/NyMLAwMPGyPD339//oC7wr1/MvDz/WRj/f/3D+YH5x4NXXOycqvq6b16/fPzgkZEIt6m8RFpzj5EOaAaWjIApOFis/Ecx1TKFg4vj5s2bUlJSaDvcr1+/fvbsWXl5eUj9/fXr1927d//69cvW1lZSUvLhw4esrKxSUlIQqz98+HDkyBEZGRkDA4ODBw8+e/bM2tr69+/fcnJyDx48kJSU/P3797t37yBTrg8ePODi4hITE4Poffny5b59+6SkpCCz8JKSkl+/ft2zZ8/Pnz+dnZ0hM91Hjx69f/++gYEBZFDt1q1bp0+fFhMTs7e3Z2Nju3Hjhry8PCcn5927d0+cOCEsLOzk5MTKynrz5k2IOAMDw5EjRx4+fGhlZfX//39ZWVlWVta7d+9evnzZ2NhYVlb29u3bp06dEhUVdXBwYGNj+/v37969ez98+GBtbf3z50/k1t6nT5/27t37798/BwcHiNsOHz788OFDU1NTdXX1v3//3rx5U0VFhY2N7fHjxywsLJKSkt+/f3/06JGamlpWVpanpydku5WrqytkQOvcuXPXr1+3sLBgZGSUk5ODbOu5fPmyiYkJpPkuKip6584dNTU1Zmbm9+/fQ4Lxx48ft27dYmFhgbQ2Jk+eDAnMf//+7d279927d9bW1pAp6Vu3bsnKynJzc3/9+vXx48eQDfUnTpy4d++epaXl////paWlIcstrl+/LikpKSAgADHq06dPT58+lZWVffTokYaGBsQxECmImZycnPfv3+fn5xcSEmJgYHj58uX+/ft5eXnd3NxYWVmfPXsGMRwy7vv9+3dIS+jy5cvi4uLv379XUFBgZ2d/+PDh0aNHxcXFnZycICc87NmzR0BAQF9f/8+fP9LS0p8+fdq9ezc3N7eBgcGfP39kZGQePHhw/PhxFhYWJycnSBRAXPX9+/fHjx9DUtHNmzch5jMwMFy4cOHy5csKCgpWVlb//v27c+eOiooKK6jLBxqYP3jwID8f/2upt5c/X+mx64QYNVDk+SMHNsyb2Thv+atXr2xtbUVFRcXExEhqjEI6AC7Fy0O7AAEAAElEQVQuLtnZ2QwMDFeuXGlubv758yc7O/uECRMkJSXxeK2/LFfL2Nw9PAaPGkypb7/+GbRc35yrri6KfUTj47tXuTm59Q31ymqgwurMyWN79uyuqCaw6QrTIoa/v5qbmi5cvMjDw/P169e8vDw7RxcMZX8ry0rv3r3Hx8f76+cvcwuL7LxCDDUkC5w/c7K8vFxWVvbXr1+cnBxVVVVXrlxdtWrVzx8/3n/4ICUlpaioGBkZ2dHePmHiRD5BEQaGv6WFhb6+vr9+/7579w64Awwajf7/719iagbD/z+1VVV37t6FrHcqyM83sbA+emj/9OnT//79y8fHV1dXJy2nCHPl/ykT+w8ePMjDw/Pxw4eU1BQv30CYFBb6/p0b8+cvaGptY4Ccko1FCYrQ6RNHq6qqZGRk/vz5w8TEmJiY5ODshqICG2fV8iUnT57snQAtdhgYGJ4/eXDu7Dlv/yBsyqkmVlyQa2NjExgSDjFx5bJF7969z8zJh3CJIW9eu/T48RMXd9CaImLUw9V8/fS+vr7+yePHbGxs//7/r6io0NE3Wjhv9sqVK6SlZb5//y4pKdnQUP/8+YupU6f2T5oMCf+Vyxa9fv0mJ78Ibs7gZ+zavnnXrt09/ZMsOm9Wekj464N6lfR0NvZyhJ4uoJtdLCysQhJynEwM/P9+PPn/79P3X/9//f738+e3778lFGW+//h17cGjL39//fvx+x8Ls4CMtBAH171nj/8wMjH//AW6u+kv6ObQfxycDN8Z/3/7zsbEyvDtx+9ffxj+/X/x6gXLX0ZOTi4mHjYlYekH3x+///TxHwsD878/v779fvvr5x+Gf8yMbMy/GH7+/M7CxMzExsbAxvyfifH/13+MDAz/WBj//2H4D7rziZmRmekfA2iTKeO/P0ws7CxMHN++fb92/BT4CCoWTh5e0PQAE/lr8P/9+6ekrMzBBVrar66ujhn44M0SoLOBIFLc3NwBAQEQNgN4zAbOZmBgEBAQgEzRMjAw2NvbI0tBxs8YGBjgxw9BllTC1YiLi0dGRsK5DAwM3Nzc/v7+yCLWYAAXUQMDOBfSwGJgYFAGA0xxBgYGGzCAS6EpVgUDuCwzM7ObG/YSmY+PLzAQpTKwBQOIXmZmZsgCTQYGBshaUgYGBk5OTkgI//37l5eX19HREaIYQhqBAYQNIcGegC6UhIhA1jhCwlBQUPDv37+NjY3s7OyioqKbNm2qq6uDKIOMOru6usK5DAwM8MDh5uaGsy3AAFkZAwMD3BaIOB8YMDAwwH0EEYcv3mVgYFBUhFeZDOLi4hEREXA18I4KZLkqXByyFhneFZEHA7iskJBQWFgYnAtZRBEcHIwsogAGyCIQNicnJ6QlysDAAAlwiLgBGEDYkG1AEDZkvxck7a29vf7/J7QRC7iqgWFAdqc5OjoiDZIRdglkVQa856Ctrb1kyRLQ8Atoopn84gK/xYyMTEuWrUzxt5RXAK3tRlP85fvvQ8dOllbULF26lJOT8/W7j3cfPIGoefPmzf3799XU1Pj5+T9+/MjDw8PMzAzZ0sfOzv7v378vX77w8fFBFE+cMv3Bk+dz5i/i4OA4derU+g0bbOydmJiYvn79ev36dVlZWXFx8V+//t66+zAvr9DQ0PDfv3+QLsfPnz+vXr0qKioKyZJfv37l4OB49eoVNzc3Hx/f48ePX79+ra2tDemSffr06datW0pKSpAuFsTqpy9eCwiL9U+a+vfv37Vr15ZV1i5atMjWwfnAgQMrV66cOGU6MzMzJyenioZ2VW3DlClTpk2b+f7zN0tbhxs3bnz98Zubm3vr1q2srKwWFhagMys+fb1y43ZDQ4OcnNyfP394eXmfPHlSVdvQ3Nysq6u7YsWK8qraefPmQTqrR44c3bR1x6pVqzg5OS9dunTgwAF3r7/MzMxPnz598eKFlpYWJyfnr1+//v///+vXL9CYsaxiRnbe//+gXglEjY6ODsRr379/v3btmrCwMHIJ/PTFawVltZ6enn///l2/fr26uppPUMTICHSyxI0bN/7+/autrQ0JhCdPnrx48UJdXZ2Xl/fnn/8//0Dzy8uXL7m5uQ8dPblq1SpdQxNBQUGQgp8/r1y5IikpCSkHvn79ysnJefPmTVZWVvg6b4ix////v3bt2v///yEDDT9/go6seQ0Gurq6kGmZ379/X7lyRVpa+j8TK+SqLYjeZy/fvnr1CsKGkN+/f79+/bqAgABkBOHbt29sbGz379//8+ePpqbm9+/ft+8CHeqnoa0nKCjIzc39EzVtfPv2jZOT8/bt2//+/YOXlhCTaxua2di5Zs9fxMLCsmPHjs3bduroG925/8jM0raoqOj3798dHR3dfZMCAgLu3H/0588/FhbQIrpnL98+f/6CgYEBkkrFxMTk5EDj9BAzGRgY3r9/f+fOHXl5eUi388ePH0xMTNeuXZOVlYXsW/j69Ss7O/vVq1eFhYUhw+qQ7v3Dhw81NTV5eHj+/v379etXXl5eRkbGHz9+/PsHGir+9u0bMzMzsjkMDAwEk/qtW7dAy80ZWCCRS9HMK9yHpDNGUGOUnY2Fm4+XmYVBhImR4e/PP39//fv+Q15c9NzlKx8+feBgYf/9l/k/Eysz51+Gv/9Ak+xfH/9j+MfJy/fz7et/f3//+ffn08t3vCp8zAwMTCwcwmIiDH/+f//w+dP3X38ZGJ5/fv+X4T/T918ff3z5+fcXGyMLNwf3x/ev/7Eyf/vx49/fP4x/GP7++8fIBj7MlAO0xJCNheXf9+9/vn75zyvAyMjEyPD/38+fDL//MX77z/T33z8mph9fvnIK8P55+/3nn/+MrKwMzEy/f/xgYRViYUPfmkpSvJM04kKSyaOKsYZAcXGxuLg4VimSBJmZmfPz8w8ePPj58+fe3l5IIU6SCaOKB20IgBoRDAxs7Bx//v1jYwctcYG3LMlwMyMjI6RBRoxesi1iY2OdNGnS3I4iH1//6OhoS0tLSEMKYunv378tLCzk5OS6u7vr6urgyyQOHjw4efJkUVHRjx8/NjY2rl69WlVVNTQ0tK2t7f///01NTevWrbt3715ZWRkDA8PPnz937NjR3t4O6dPagwEDA8OtW7fq6uogRwWnpKS4ubkxMTEJCgrCm7CPHz+urKxkZ2d///69v79/fHz81KlTr1+//uPHj6Kiohs3bqxbt05ISOjfv3/9/f0PHjxoa2sTEhJ69+5dYWGhpaUlxAsMDAxcXFwQM83MzJYtW8bGxsbBwcHJycnGxgZfyVNSUpKSklJVVXX16tXp06ezsrJCVpJwcHBALseCHJwHXnAIamc/evRIWVmZg4Pj/PnzSkpKdnZ2DAwMUVFRK1asgCzNh6z2+fXr17Nnz+Tk5EzBgIGBYfXq1atWrRIWFv7582dnZ+fjx4+7u7tZWFjMzc3d3d1nzZrV3d29Zs2alStXCgkJ/f79u729/f///+Xl5ZycnB8+fHB1dYUcrwbxHdwLVlZW/v7+q1ev1tfXb25uvnfvHjMzs7S0dH19/ebNm1esWCEiIgI5FIWFBTTZzQBeiP/p06eAgIDFixffuXOnpaUlISFBRkamrKyMi4vr48ePoaGhERERPT09Dx8+5ObmfvLkiYWFRVlZGSSd//79u6am5vXr13/+/JGSkmppaTl48OCsWbPExcU/fPjAy8s7YcKEL1++FBYWMjExCQgInDt3zsbGBuJsSN8b0lqFiDx//ry8vJyLi+vNmzfu7u6pqalz5sw5c+YMHx/fy5cv9fT0nJyctmzZ8urVq+7u7qSkJDExsdLSUnbwogt/f/+EhIRJkyZdv36dl5f3+fPnBgYGFRUVkOzz+vXrq1evzp8/HzJ/CB+PYGRk5OXlhSQMGxubTZs2/f//H3kNGxMTExsb28uXL0tKSnh5ed++fevn5xcdHQ1x8MmTJ3t6ekRERJ49e1ZcXGxiYpKdnc3BAVq6/fz58+rqaisrq4qKCvD6EKaHDx+mpKQEBwfv3Llz5syZgoKCHz58aGxsFBcXLyoqmjJlCj8//9q1a58/f56dnZ2Tk4Nmzvz58zdt2oSZ1N+/f19QUGBubt7d3X3q1CkZGZnHjx/Ly6O0mCGupRs5ghqjHLx8IsIiz378u/L5J+iQ0a+/5MR5eTmZPz/+9fPjj5+MP5n+/WFjZRKRlHh5/8GPL5+4hYRY2Ng+v3jG9O8v6JK5//+/fvqhxizAJcX96N27979+MH38wsbEzMjGxMfH//ndh19fvgkKiggxc7z68/sfAyMbFxfzb95/f34y/PzD8IeBkYOJkZvl/7df/1kZGHjYmX8yMP9n+P31+z9mJqZvP/8zgRa3g84Q/f+P4fe/P3//MrGx///x4+ub9/8Y/oOWQf36wfD3328eJtAG+F/IC1/ollRGLSIzBJCH68g0AqZNQkIiPBw6VwUTG6WHfAgwMjC+fPJoTmvtj58/dUV5j6xZfPPgDrr56u7VK5pG5Cyc/f3797z58x+e3NTa0b1o0aKTJ09CbnmAuJwRvPOmrKwsKSnp8OHDbGyg85V+/frV39+fmZnp4OCwZMmS/v5+Z2fnnTt3enp6Xrp06c+fP1++fNm7dy98vuXXL9BSPEhjbu/evbt37xYTE4uLi+vv73dwcEhMTDx79mxnZydk5Ulra6u0tDQfH19ZWdnEiRMNDQ2Li4ufPn2alJTk6Oj46tUrJiamWbNmvX37tqamZu7cuTIyMsePH//161dvb29kZKSXl9eePXt6e3uXLl0KGVNkZWW9ffv2jBkzfv36dfjw4cDAQEjDHbK0F+JNyFESBQUFtra2M2fOhIwIioABAwMD8gIJyELtefPm/fnz5/37921tbZ8+fYIPxEKaL79gZbuZmVlUVFRxcTEnJ6eoqGhmZqaiouKcOXM6Ozs1NTXb29tnzpzp7u5+9Spo2YCmpibkyLmnT5/OmDGjr69PXV29q6tr/vz5BgYGX758mT59+vfv31+8AI3VwZ2NzJCUlLx169apU6cuX748a9YsFhYWyG7IlStXBgYGhoaG3rlzh4eH598/0B6e7u7uhw8ftrS0CAgIFBUVLVq0aObMmYyMjNnZ2TY2Njk5Obdv305LS3N1dX306JGYmFhHR8f79+8jIiI8PDwgKeT///8hISFaWlqQZuv9+/d//vz55s2b2bNn8/Pzh4WFHTly5PLly5KSkl1dXR8+fPDz80MLc2THc3BwFBYWamtrnzhxoq6uLiEh4eXLl5ycnJMnT/7x40d4eLizs3NGRsaRI0cmTJjAwMBQUlJiamqan5//4sWLmJgYJyenly9fcnFxTZky5dOnT8HBwW5ubpDzT969e8fIyAjpCK1duxZyQ0pKSgorK+u1a9f279//9u3bRYsWRUZGQhrZyI5kZmY+duwYExNTb2/v169fX79+DXezuLh4S0uLnJzc0qVLFy1aZGhoeOvWrdLS0oCAgJUrV86cOdPKyuratWvBwcFZWVl79+7t7+93dXXt7e1tbW01NTVduXJlV1dXS0vLy5cvIfMnnz59ev/+PRMT0+3bt0tLS/39/VeuXDlnzhwpKally5YtWLBAWloaktR7enogSX3v3r1TpkxhZGTcu3fvmjVrILkG845ouJvpwBhBjVFOAQEmXu6vP/7++fef7R+DvpLYz9+fbj17++vbTwZWFsb///79+fv/7/+vHz6y/PvHwMby/9/PL6/fMv7+AZqfZ2IE39PE8uzl63+C/4INLZ9/+7zv3KFvH98xsLF/ePOO4fdvRgaGVy9evXr9lunfn/9MzM+/f2Vk+s/A+J+Jl4uV9f+fr1//s7AwsjAy83Cy/Gf69fHnD162f7ysTN9/ga4W+fGTgYWZkZ31PzMLIzsDw+9foO16jMy//oJW7zOzM7Oys/789psRZCJouzYdUsaoFaMhMBoC9AkBCTl556Dw///+sf/8wcHNwysozC8kQh+rGRgYbL39lbVBB7qRaiMzM/PWLRvundzKwMDg7+8PX4MBN+fv37/8/Px1dXXNzc0xMTEsLCxfv3599erVunXrtm7d+v79e35+ficnp6VLlx46dMjAwODbt2+HDx/+9OmTra0txBDIeCpkMkdERERbW3vOnDm2trZPnjz5+PHjtWvXfv/+LSAg8OPHD0ZGRmdnZzU1NXZ2dg4OjidPnkCWeUhLS4uLi9+5c4eZmdnX15eXl/fkyZPi4uIyMjL///+3tLT8/PnzkydPtm3btn///h8/fvDz8//58wfSGIW0p//8+XPo0CEBAYHc3FyIqzDJxYsX+/r6btq0KTAwENJ0xlTDw8OzcuVK8DEJzPPnz58yZYq7uzukMQFR/O8faAsUhM3IyJiWlhYXF/f69estW7ZUV1e3tra+evVqzpw5zMzMz58/NzIyglyMB1lmw8jIyMbG9uHDh9evX8+aNYuRkfHVq1fGxsYmJiabNm2KiYnh4+NDHhaF2AInv3//DpnXfvLkSXNzM+TMvj9//mRmZvb3969fv15DQ6OwsJCLi2vPnj1iYmKxsbEC4FXm8ObX79+/37x54+ICWs6rqKgoKir65MkTTk5OyDCzoKCggoLC3bt3IY1RZmbmGzduzJ07l4WF5cOHD79+/frz54+pqamgoCADeDHYixcv7ty54+zsDFkMpq+vD2+mw90MZzAyMu7evXv27Nk/fvyAGPXv3z8bGxtGRkbIGp5Hjx6xs7NDnPr379+HDx9CVulISEiIiYlBNjNATgPk4+PT0dF58OABpDEKOUcPcnqAlJTUmzdv5syZEx4ezsbGdufOnX379v358yctLc3Hx+fUqVP/wYctwF319+9fOzu7TZs2RUdHCwoKpqWlwaX+/fs3b968L1++vH79moOD4+fPnyIiIhAblZWVv3///v//fx4eHhMTE0hosLKyQo6UgcS1nZ3d8uXL4cewMDAwMDMzMzEx/f37V0hICHJwuLKy8o8fP27evCkpKQla5wBL6o8fP4YndWFh4cePH6uoqECGeB0cHLZv3w53JP0ZI6gxyisgxMvLrcXKpc3HxKUtyvD1y4mbH1n+M/xm4WD49ZOJkZGTn/f/9+8/vn75x8by/8/v39+/K0hJsP1nePT08efv30DHinKy/GD6+e7b94cfX/1mZPzHxs7I/peJjfMfw6//PxmYeDkZvv3+/+ff35+MjCzMjH//Mv5j/M/y/x/z//+s/0GLPv/8/cfGyPDrJ8uH/wxc7IzsbP85GRl+MDFCiqD/jIy//zOwMPzjZGPkYP73/S/D3z+gUyWYGPnEpFk5Od8/f/b/31/QQROgnXf0TyqjNo6GwGgI0CQEJOQUQtKhDR3H0FgJSUnijx+iiYOINZTx4MGDIXZm/RMm6ejoQGY2kbX+////9+/fJiYmnp6ezc3NkG2CAgICqampCgoKP3/+ZGVlFRQUVFNTmzFjRm5u7tevXydNmmRlZQWpHSGz5KqqqgcOHFBXV9cHg02bNjEwMPDw8AQFBTk6Ov78+fPv37+QhpGJiQlk1SNE4/PnzyHX6r5//15ERATSomVgYBAREfn48ePfv6AlmJCTv3h5eePi4jQ1NSErF+GtyV+/fqmrq+fk5ISFhcXFxd28eRPrLMeiRYtevny5aNGi2tra9vb25uZmyDgZclAwMDD8+PEDclYdZOX6gQMHlJWVp0+fDtlnBjk2GD5QeuvWrcePHzs7O8vKyiYmJm7ZsuXPnz8SEhIFBQUCAgLfvn3j4+O7ePEiskX//v1jY2ODnHDCz8//5csXfn5+ZmbmxsbGv3//Xr9+HXIaBnx1AeRUPgYGhm/fvm3dujU6OpqJiUlDQ6O6upqRkfHTp09SUlIfP35csGDBp0+fpk2bNnPmTCUlJSsrq6ampqSkJENDQ1NTU0gIQ5aFsLGxQXY9/vz58/379wICAr9//4bEAuRWZLjvdu/evX79+smTJ7OxscFPqYM0FkEbxcFT3kJCQnC9z549g7sWogA5sc2ePfvt27cNDQ1PnjzJzwftamJiYoKEJ2SHJR8fH+Riaki7jY+P79mzZ8bGxv/+/fv48aOgoODv378fPYIe8fbs2TNImxiyuJybm3vfvn3+/v6WlpaGhoYrV6789+/fz58//f39i4oQ+5NERUUhZxFCVnzeuHHDyMiIhYWlt7f358+fJ0+erK2t3bx5M6RN3NLS4ujo6O3tvX///o0bN0LSCSR9wjskkI4QxLP///9nZ2f/8ePH169feXh4nj9/zsTExM/PDx/IfPz48d+/oNt1QSNV4CWfENOEhIQgKZyJienTp0+QfXIxMTHa2to/f/5kYWG5fv06fLwcHgIQ99CfHEGNUQ4uHhlu7m/MDH5Kwo8+/th87oWoqPCvj2//CXL9/fGZmZWFhZmJif3vx8/vmVlY/vz6zcfL7ezguG371v/MLEJSknwcPCbigh9UmY68eHTmw+Ofb98xfP71j5FZSUb2x7uPT548+sfOyMDxn+nj7/+MjP+Y/jGwMf7/84+JhZHhzx8GdjYmcf7/f78xMP9nYmH9ywyar//35TsTOwsDGzjZQM6fY2Zh+PeH8f9/Jn6e/0w/GFl+MTEz/2dm/P35/acXT0AH8Qvxgc6HZgItkaZ/Whm1cTQERkOA1iEgIw09qoLWFlFu/u/fv+bPn2enhX09NCPomDMW8ElDDCkpyXv37Pn16yc3N7e7u9vkyZPcXN2OHz8eHBIsJubk6OiwZctmI0PDn79+VlZWVFZWwN3GyMhYUlKcn59/+9YtRUXFBw8ffPv2VV5OLi42ds7cOV+/fLl0+ZK+vn5cXDwLM/M/8N0cEL3JSUntHe1Pnz65cf2GurqajrY2I2glFOiMG10dHUUFhdzcXE1NzZMnT3Z0dISFhU2ePNnHx+fMmTOOjo7wpYGgk6HA14KIiYn5+fn19vbOmDETfCooaB8p5AC1y5cvL1y4cNasWWxsbNXV1bGxsVu3boUvM4A4BkJ++/qttLRUTU1NWFh4y5YtycnJWlra6urqqamp+vr6+/fvj4uLgzfXGBgYurq6du7cKS8vf/r0aSUlJT09PUNDwwkTJpiamh05crioqIiFhQV56SRkZ6GFhcWECRNMTEyOHj1aUFDw5cuX7u5uT0/PV69eCQoKQkZ8IWtSjx8/Xl1d/Ru08f+uqqqqj4/Px48fFy9ePHfuXF5e3ps3b3Z0dMyYMeP9+/dmZmZPnjzR1dX98+cPNze3rKxsWVlZXV3d4sWLVVVVnzx50tjYGBgYCFk+cfPmTfhxJSwsLCtXrvzz58/du3e5ubkhG7kg/YHPnz9v3779zZs37969YwJvyYX7BXTk6r9/UVFR+fn53759+/Xr18OHD+GyIMezsO7atQsyfKugoCAqKnrixMmdO3deuHABMtLMxsa2fv16Li6u58+ff/v2zdbW9v79+zNmzGhvbw8JCUlOTm5ra7t///61a9cgG3b//fu3du1aVlbWx48f////H+5OVlbWoqKi+vr6kydPSkhI3Lp1i59fALJ5CG2kVkFBwcfHJy0tzc7O7v79++/evYuIiDh79uz06dM9PT3v378PuSwakhJERUWPHz/+9+9fyD1qkPWmECnIlSWQJj6kdQ5pXwoLC3t4eJSUlFhYWOzcuTMqKkpeXl5LS6uiokJLS+vQoUPOzs7I5kCSrr6+voKCQk5ODiSpd3Z2RkRETJs2zdvb+8yZM87Ozq6urlxcXEVFRQoKCps3b9bV1f304f23b9/A2RZ+RBTEaTQnR9DRTtY+fvXTZu99/+fx52/nrt5/9OGT6L//n79/EGNjfXLl+rdfP0Cz6b9/fv/0/t/P3wz//qjraaqJS544d0ZKiO/d529yQkLBTgZTn556+uEtCwv7729ffn//zfD3r6SoKNOvv4+fPmFhYf3/8+e/H38Yfv1jYGNiZGdmZGX6x/SfkRW06Jvx978///8w/PvDwswMYv/9///HL1ATlOE/6Er7r98Z/v5nYmb6x8Hyn5mJiQ10zxPHk1/fb73+y/D3/99/jCws/5lZTaQFzaRF0lp69TVUyUsaefsLXeVdfJVAJ2OTZwKyLkhxjCyChw3JWshdeciB8JBuHx6Ng0QKMmoCWfo2SJw0DJxBUhKiun/X3l5/7NnxXvsuqps87A2EHO20JElRXwZ0mCLooEJUP3///u3a1WuGhoZM4KPSX758+fzFcz09/f///+3cuesu+NgyJycnZhaWL58/37x508TU5P///6dPn9HW1uLi4oYZBjoB79WrN7t27fzw4b2goJCzs7OYmCgTM/Pxo8fPnD0jISHh6enBycl5/twFJWVFfvDcMQMDAwsz84ULFw8fPiwmJubr68POzn792g1BIQFxCQlGBobvP37s37ntw3vQTbxqmnoMDAwnjx26cuWyoqKSk6srAwN0jObT+1fPnz1X19YH3ef0+9vZM2dNQXubWL58eP3s2XM1LV0GBsbHD+78/PFDRUMH4uBXzx69efNGSw+0LR0igkx+ePN8167dX799MzIy0oeu0/2za9u2J0+e6OvrG5tbIyv+/P71rl27Pnz8KCsj4+bhzsDEzvD/946tW589e2YIGpa0+Pb5A+hwZbDVv398vnf3Htipf3Zu3fr06VOIGgYGxsvnT585e5aDg8PTw0NABHS1HgMDw5ePbw8fOgTeIsOspqaqoWMAsfrz+9dbt2398+ePs5OLpKzC359ftu/Y+fLlcyVFFUdX93evn797915FHbTR/typY6qqqryCog/u3Dhx8qSFubmCisaNKxePHT8qLS3j7uXNwMCcl5VmamrKxcX1//9/f38/Vk7oCQkMDAyXzp06fea0gb6BkJCQtIz092/fQee+qWgwMDDcu32dn49PWFz6wd0b+/btU1JUUlJS4uHhERKD9tPevHhy6tQpyGECIiIitra2+/bte/TokbWNDSMDg4qGVl11hbS0tJSk1JcvX3z9fHn4QYterl8+f+nSJVs7WylZpds3Lh8+fFhcTMLbz5eBkbWqvEhdTR0y1ujn58fFBzq0DhIgDAwMb1482bt337fvXyUkJF2cnVk5eR/dvwXa4yWHcvgJAwPD8cP7r1+/Ligo5O3txcbFx8Dw//zpExcvXuDi5vHx9oYb++fHl40bN/7588fGxgZ0hqCq6pWLl3T0dBmY2H5+/Xjv3j1NXcMrF86qqqqwc/P/+/X92rWrOgagKfuDe3fevXsXHLOgPXZ/f37dsGEDIyOTqakpExOTtLzilQvn0Mxh+Pdry6ZNr9+8sbayUtMCJ/Wjh65evaKgoOjkBkrqv7592rBxIyMDo6Wl5d+/f+SVFE3abtf6yPjT/WinEdQYdQsNy+iaPPvR1/vP3z579Pzrt2+cv3/++PhWlJXly4unfxn+M/7+9evbZxYWVl1FhX//f4qKiVy9dv39q3febo5nL1zkffk2Nta14fHRnz9+/fnN8I/hNxsTOxM3G9OPb98/ffj/6x/DfyaGTz8YGFj/s/7//w+0hJSBkfE/BzMLF/v/bz/+vf3CwMzI8P/v/99/GLm4Gfn4GN68+/f/DxMXJ9O///8+f2f4/fc/IyMLP/d/ThaGr98Y/zAyP/j58+n7/yxMTAxMoKsn/jEYS/GZyomngs4ZRZy+BM8zxDCIbIz+/Plz+fLlfn5+vLy8L168kJGRQWtEMjAwvHv3rq+vr6SkBDJHRtD2adOmqaioIJ+dtGPHjjlz5piamhYVFSFPuxA0CquCvXv3bt++/devX5CxjR8/fqxaterp06eMjIzgCslXVFR09+7dkBPFIRt1t2zZ4uLiApm3+vfv35w5c/z8/CBX+yBb8e/fv+XLl69du9bDwwN56Q+yGqzs58+f8/DwQHZiYlWAS/DPnz/Pnj2TkpJCnpzCpRir+M+fP1+9eoU14pDVHz169ODBg5DNEwYGBk5OTqCzA58/X7Vq1XfQLZFMkCETFhaW9evXOzg4QEZuXr58efz4cT8/P8h+DmQDSWKfOHHizJkzOTk5cF3fvn1bsmTJxYsX+fn5Q0NDISf1LF++/PHjx5DrQ729vaXAF2IJCQlBzhP4////xo0braysMNcswo3FxRhtjOIKGYLi3379M+24+e/ff3YW0HAjpnpGRkYWFpbfv/9AjkOHLGsDnazOyMjOxsbMzPznz99fv34yMIDurmFhZvn9G7Qvk42N7ffvP///gwo8uJmQ9MnEBDp+/Nfv35ARUFZWNhZWlv/gOdP///+zsrJCtqvDdbGwsLKxsUImVcGn/LKCDtQH3+Lz5x9Doq20ugTX79+//oBcyMDGxsbCwvL379+fv8A38IFNYQIf6Q5yM8iRjKwsrL9+/2IALbkCHfUOEWdmYWFCuj6NmYWZiRF0NR3YAHSCiZmJnQ10JcfvP39+g/cqgdZ6srMzMzH9+fMHbbCNiZmZjY0NfFz/v18/f0K6bezsoHutINrRnAcOw99wAyFqQOOIrKwsrKwM4EOgIAOHkA3p8H415DpiiFshLmQArSb7BbmyC2IjJGSYmZgZmZj+gEeL2djY/vz58+/fPxYWFlZW1t+/f0MOgQdFxD+og+fNnW9gaGBtbQUpb8GTgBB7oAH+58+f//////n7B3QvMiMjZHUmOFr/Qy7Ygxz/DJmDhjuemZkZWl+AN8n9Bt8+wMTM9Pv3H0YGht9//qxcsUpGWtbZxYGRkfHnr5//wJHOysrKwsLy6xfIXyysrGysiLSxfPkqFWVlWztrkPqfoPPqoa4EU8zgiIDMm//69QviZQYGBohrwUrABCMDGysoFf37/+/XT5AyUOCzsbGC7+cDrSf5B/IsKMEzMrKzs4Ht+gW6t+nvP2Zm5r9//4AvuWVkZmb68wd0uSDo7PF/oMO6QJnlL8hroDQPylO/wNkKtIiPnZ2NgYERnHcY//37y8zMgmYOKD2AshvTz58gj4OcBEoPLH9B6Q10jQIjyDHskJFUUKfr37/K9U/7QgegMQrtAoLDcpgT3BwsHGxMTKz/f//9//Mf8//f/38yMLBz87598+Yv+AZOEV4uXU39v99/yMhK3Hv17Pmrz785BTjFeDYePMEvxOtoac7Pza7CxfuV4fv9v1/+MTFwcHMIcLAI8LI942Z59+kr/+9/zNys7/8z/P7/l+HDb8a/DP/ZmBjYQItHQYs8BdgZ/v8VYOPkYGH/8PPnX8a/jFzsfxjZGNhZ//38+e/vb2YmZgYm5j+/fzPxsjOxsbG++vHvy28WNvbfoJWjoMvjGP7++veTlRFUGlAUU6wshCOdmZlZRESEnZ39xYsXNTU1CxcuxLTy58+fkFXbmFJYRa5duwZZDw6XnThxYllZmampKbRkgUuQzlgCBqmpqby8vFOmTHn69Gl0dPT06dOTkpIEBQXPnTuXmpq6dOnSZcuWubu7Q46l/P79++TJkw0MDCCNUQYGBgkJCawu+fjx47x58yZMmAA5xI5413V3d/v5+Tk4OBCvBaLy58+fZWVls2fPJqMhCzEBckTI0qVLIVxc5Lp16z59+uTm5vbhw4fp06dDZu6uXr26dOnS8vLynz9/btiw4cyZMw0NDZMmTdLU1BQCH3T/6NGjqVOnent7U9gYffv27eXLl+Fu+/37N6RbEhYW9vDhw5KSkt7eXsitQiEhIXJycpcvX87MzJw3bx5kRwW8MTp16lQ5OTkyGqNwq0cZZIQAIwPD68+/IXe+49AOal/CpEA1H4yNLA4RA7VKwSxkZWABKAFXAOUzMKCpROOCqle4UjADoeD/f8aZBy5xsP4Er68DS1KRYGQEX20DPY8TZjBoGgytkQ2TGrY0u6Lxjjd/t6yF3qVMN3+yiajd/f5v77rDRNrIJqZ+/+O/7USrJ9JYVGWM3GzCLEygaQRU8UHHkxJgFeEh3EigOhgAK6nuByIN5Gbn4WJjFmViOff3L+OPb/++fWPnYfvDyMDGxi6jrvb49j1dNVVlFZkXT56ev3b7+fsvzD9/fmdlYuITZmYU+vH+E5cWj76Chvaf19tunv3D+t9QRtVQRPzd89s//v1k4Obk5mAR/faX+w/T6a8f/oN62H8Y/v7/zwRaegHqx//9y8jJyvyXiZOTi5OV7QvD7z/fvzOBBhv//f/399+3n6DVy2wsoGHqz9+ZWVk5/jD/ev6FiYWLiYPxz8fv/5hYmBj//2dlYhHlYeRkZeLlINLLmMpYWdl27Ny57/peBhaGsLAwYWHhs2fPhoSEfPnyZfXq1WFhYezs7KtWrXJycvr///+bN2/gDRQbGxtPT88tW7bs2rWLk5MzKSlJWFiYg4NjxYoVN27cUFJSSk9Ph2xinT179vv37+3t7YOCghgZGTdu3Lhr1y41NbUvX77Am3qQhuDdu3e3b9/+8eNHZWXlBw8e3L59G7LyZtasWdeuXVNUVExPT+fh4dm0adOvX7+OHj2qrKzs7e29dOnSt2/fJiYmGhhAp5Y+fvy4aNGi7u5uyFZNQ0PDe/fu/fr1S0BAICwsjJ+fPyQkJDQ09MKFCxwcHMhrj+AXh0J6q5ATgw8fPvzhw4dTp059/vw5KSlJVla2o6PjyZMnq1evtre35+Hhef/+/enTpx0dHaWlpefNmwdZUxUbG/v79++FCxdeunRJREQkLi7u4sWL+/bte/HixYULF9LT0/fs2cPIyHj69Onk5OSTJ0+6u7vz8fHduHHjzp07Pj4+r1+/njlz5vPnz83Nzb28vKZOnXr69OmmpiZXV1dRUdGXL196eHj8//9/zZo1tra2kEOtL126ZGpqam5uPm3atMePHxsZGUGuEmVgYHjx4sX06dNPnz5dXV1ta2vr4eFx/Pjx1atX////PywsDLK/FZI2WFhYnJ2dQ0NDGRgYIiIigoKCAsHn1ygqKkI2I1tbWyckJHz8+JGTE3qLLGRYBa1fATGNgYHh/v37CxYsePPmjZGRUWJi4qdPnyC3ah07dkxdXT0tLY2Dg+PixYsLFizg4+Pj4uJCNmfv3r2vX79etWoVJI4sLCx4eHj+/PnDz8/v7++vpKQUHBwcEhJy+vRpbm5u5DFjyOAN3A2jDLqFADMTI97GKN0cQppF338xtAXwOmlw/wVdMEKaXoKqGUFjD6DzpJFVgjaeMjL9+z8AtywiO4PObFD9xzAAty6Sai+p6skKRkZhTiUmRl6y9I4IMIIao/9ZWH78ZeD+/5vz+4/n338w/v/34/Vbxn+//nz/8fof629mJi5e7tcfP+48feH/t5//GRh+//3L+JPl7/+PQrycf1lZhViYBX+w8XILC4qJcv37rcbLby+hdPLrq8cf3wj/+SXyj/kZy+/vDMxs7Gx/mP79Y2P99+cfKwsj45efrF9/M7L9Y+Tk/fb925sv31j+f1QXFHv569Oz9++Z+Hj+/fnH+OvP/99/GZn+MDEwMjMwMzz9/u3F5z8ffjCx/v4PGhZlZvnH8P/vLxYJHlZ+dtBdUBSkTCYmphu3b9cGVb///P7QoUNhYWFz5sxxcnK6evVqZWWliQnoIo2lS5fa29svXLiwra1NT0/v8OHDFhYWKioq+/fvnzZtWmlp6YMHDzZv3pycnHzr1i0TE5PIyMiJEyeysLCkpKSUlZW5uLgEBQV1dHTwgsHUqVPLy8u/fPkyY8YMLy8viNtZWFgMDECrhXR1dVVVVW/cuFFWVpaXl6ehodHR0fHy5cvExMS1a9fW1dX19vbOnDlTRkYGctv4unXriouL3717V11dvWLFCsjA4dOnT////w+/MkQUDF68eAG5noSBgeHRo0evX79GvkMS4gxk8v///3PnzjUzM9u+ffvBgwcbGxufPn1aXl4+d+5cQ0PDw4cPGxgYKCgobNy4cdGiRZmZmTIyMnv37tXS0tLU1KypqZGUlPzy5cuBAwdqampOgYGurq6kpKSGhoaJiQkHB8eUKVO4ubkjIyOZmJgWLlxoaWnJx8d3/fr1bdu2eXh4lJeXKysrx8XFrVmzRltb29jYeOvWrSYmJsrKyvv377948aKHh8e/f/+WLVumrKx89uzZyZMnZ2VlycrK1tTUyMjIZGRkTJ06FXLOCOQuKy0tLUjEKSkpXb9+vb6+Pi8v7////w0NDd3d3Xp6oMVDEO9DZhsZGBh4eXlVVVUvX74sJSUFn1C7fv06GxsbZBMoRD2cBHWm4BwYA7JHODg4uKGhQVRU1MLCor6+PiIiIjY2tr+/n4WFJSIiorS0NC4uTk5OrqWlBfmepytXrqiqqjIzg666vnv3LisrKy8v779//+Dx+OrVqw8fPiDvjIZZO0oPfAj8/vsfNEUNnrQHl5+g05MhQ4+szGBRmBv//gOtBEUW+gc6TJkB0iaAqQLR//4z/P33H1M7MxP0rnnyjh34z/CPn0NZiJMHZMcoHg2B0RAYBGAENUa5uDg+/Pr77Nf/Z9//MjCzMLIw/f/1k/HXj7+/fn5mYRFQVHn+7Tsnu4CkjNLj+/eY//77x8LMwcQsKiYkKyT4nvezqIDAV4Z/3758SVcwevb+5b2vrx+9emQhb/LtzknGXx9e//77+ff/vywMX/98Z/rzm+nLn/9/GJi42NhYWVlEuXhEuL98/snx7/+Pnz952Tl+//n38/cfhj///v/8xcTJ8f8/AxMnKyMH1/8XX/8///z3G2jHPAMr879/f1jZ2dgEhBl+/Pz7+7OAniLb/28s//6xoi6oIikVMTIyfvny5fSp0xq6GvHx8cLCwnJycpcuXbp48aK3t/exY8c4OTnNzc0hRzfz8PBYWVmtXbsWMiE7ZcqUqKgoyLWWf/78efv2rYSERHp6uri4+JMnT/bs2fPixYtbt24FBgY+evSIl5d3//79kJV/kOPiNm/e/P37d4hrWVlZ7ezsxMTEnJ2dpaSkrly5YmZmlp+f//Pnz+PHj0+ZMkVZWVlNTS06Ovrp06csLCypqalmZmb379/fu3cv6OrnX7+WLVv28uVLSGMU0nJCmzJmYWH5+PFjVlYWJyfn58+fIyIiIJfIIzehkNmQxTQQQ0JCQlxcXP78+bN8+fIvX744ODgsX77c09OTk5Pz79+/rq6u6enpDAwMwcHBFy5cuH79+r9//27cuKGrq/v+/fsTJ06oqqpCGqAKCgpmZmaQu0NYWFiys7OdnZ0/fPgAGfkDbbNgYWFjY7t3796bN29mzpzJyspqbm7+798/ZWVlUVFRV1dXISGhQ4cOwffAQkZ2f//+7ejomJmZ+fbt25MnTxobG9+/f19YWHj//v2QJa28vLxWVlYbN2708fFhZGScMGGCvb09ZIfv2bNnd+3ahdwYhcQIhGRhYfnz5w9k8DIxMRFy5HhlZSUXFxdk8BiiDDTjyMj44MGD/v7+T58+aWlpFRcXQ5aghYSEnD9//sqVK3///r169SpkKWdubq6AgEBUVNSBAwfOnz8vISEREwO6CT05OfnwYcRUGuS0HchdeVOnTr127ZqLi0t5efnnz58rKioEBAQgx19DDliBxx3EMb9+/ero6Lh69SofH19FRQXkBkiIa0dJWofAv/8MP//8S7ER4WZlmnLgNagTzsCQZScaayH0+sufCXtfHbr9GTxLBGpZSguytfhJ1W959vgtaMEcaBfjn/9dwVKnH3xfeeYdByviqJD/DAx8HEyKIuzXnv/4+w806/3n339JPtYJ4TI5yx8zMzIK87DcePkDuVFLvE9H2ow58SEzqnI0BAYEjKDG6F8mpl//mJj/M7JxsLHzcvz59vcHO8u/vwzs3ByuBgr8PLx7jp3TFhFT0lT9xcrw8cEztv9/VOUkxTm5Xrx7+42R+Qc7659/31T/MWt8YLXk09zKzPr004tvf39yMLJzsvAIMvwV4Pn/7tcPfi5WTgau54zfn7/++P8rA5MQrxg3u+Sffze+fPnx648EH98Xpv/3/3798/krw99/DD9//f/xi4GN7T8nG8P1dwxPvjGxsbNwMv/9/Qu0O5X5PwcfLysP71fQEm+G3z+/crMyMTAxgBC5ieX3798BAQGc9zk3bNgwd+7cBQsWuLu7r1+/npmZOTU1ddGiRX/+/ElMTIQsFYccE/gXDJiYQKvyOTmhS15YwIuyeXl5Ia03yFJ6yIL3b2Dg5ORkaGi4cOFCyIV4DAwMHBwckA31ELf//v37379/kDXgkNN6IUvCmZiYILP5kKvnfv/+DVk/DtEFWd/5+/dv5FlaERHQZslXr15B9h79//8fco4gDw9PSUkJHx+fiIgI5Pg3Hh6eL1++QIyC7GyAt/MggpA5aLgtrKyskECAOxVyLgkDA8P379/Ly8tFRUUtLS0lJCR+/frl4ODAysp6/Pjx3bt3Qyb3Ib6DmMzGxgZpOkOO6oCEGyScf/36hXxKCxMTE2SZPKSRDT/sg5mZGbLF4f///5CDGH///s3ExPQd3MTX1tb29/eH2AUJScgJKRBd8NlwDg4OZFdB/AvX9fDhw/Dw8O/fv6uoqFRUgE7YkZGR4ebmhmiB3B4OOTGRjY1NXFw8ISHh9+/f/Pz8kOj4+fNnVVUVFxeXra0t5KTlv3//cnNzQxrfzOCTmeEnijMwMMCTE8QBMjIy+/fvh1xqP3ny5DVr1hw7duzfv3/c3NwZGRkKCgqCgoKQi1Uh1xtCdEHCip+f38/Pz87OjpWVFf8QOETXKEmtEPjPwMDGwhhvIVLrLbnwxNt//xl+/fkfZiyQbC089eBraQHW3hAZ7yl3Xnz6/ecvw////9lYGFXF2NmZGUGtS7AjWJgZV5758O7rH1Zm0CzRn3+gIVV2FqZff/8bqnDFmgslLX7EDJrnZmBmZPz04+/k/a/ffPmbaS8iyMlSt/kZNzvTrz//4boYGUFN3j+g20JA14mwsTAyMTL++P2PiZGBHbTXCGzlKDEaAqMhMMgAohs6yBxGfecw/fn38//fLz9/SPJxCnJy/Hz//t+f3////uXh4rLRVbdUlNaSl+H7+dNSXMJJV0fPQFNWTESCm5OTjZmFhfUnC8c/fkHOn7812HmkuQV1/vP7cakocYk+e3RD+OdvdVZeT2VDdxkTJ3E1XT4eTg6GPxz/WYW4/glx/uFhl+UR4Pv3j4uBieHX379M/yS42OXYOUD7kDjZQZ78+Yfxx3+G+18ZXv/9z8bGzMnOAFof+g/Ucf/37/O7dx+eP/319du/73/evn//8e+vX////aBkZJSB4evXr2HhYTU1Na9evXr+/Lm9vf3u3buZmEDHQ7x58+b+/fsmJia/weDfv3+Q1tvVq1e/fv1qbW29bt26ly9fXrhwYdWqVZANkpD25Z8/f378+CEqKiomJqahoRETEwO5utfMzGzDhg3Pnz+/evXqoUOHIK1MkK/B+Cd4iyjkTDVIQwdyiN2qVas+f/68YsUKbm5uaWnp79+/Qxptf//+hbTGkK2GbDwyNzevq6u7d+/e8+fP6+vrJ06cCN6f+FdZWVlTU1NUVBTiTg8Pj6VLl166dOn169ezZ8+WlZWFNFLBzgFdhA05uw4+cw1xIWSaGGLC79+/IW748uXLpUuXEhISzM3NIacQHzly5PHjx0VFRenp6SdPnoTconH58uU3b95ATID4gpubm5WVdf/+/a9fv16zZs2fP3+UlJT+/PmzcePGT58+Qe565ubm/vLly40bN758+SIvL3/jxo1nz54dO3bs3LlzoJ2VoI2QoC0g/Pz8MjIy4uLisbGxYmJikMYuxC9cXFxfvny5evXqly9frK2t9+3bd+/evTt37hw+fBhyRQdE2Z8/fx49enTr1q1z586Vl5fz8fFZWFh8+/aNlZVVHQwgB4BD7r+eNm3akydP7t+/P3XqVGdnZ15eXlNTUysrK21tbUjb+uvXr+fOnYuNjbW2tn7+/DnEvz9+/IAEHeSyR1VV1bt37547d+7Vq1crVqyANLghjvH09Hz27NncuXNfvnz57NmzCxcuQBLM79+/FRQUNDQ0IC1RBgYGR0fHbdu2nT179vXr11OmTOHj41NSUtLS0rKysoIcJQMxcJSkQwj8/PPfT4/fTpVn/83Pf/+CWphMjAxffv4rXfd0xqE3e298/vz9DxMjAzMTY7ylkK0Kz2/QoiTI7D3Udf//M0jysfBxMv/9z+Clw9fsK5VmK8LDwcTHwRxkKKAtxRlvIfQHPDL6n4GBhYlRWoBVTZzdTZPPTpXbU4fv559/7tq8zX5SCVbCHKyg5qyGBIePLp+rBm+dt4SaGLs4H0ulp0SMhTA7CyPYGKi9o9RoCIyGwOABI2hk9MPnj2wMzB9//H337TsH6Hqkv/9//GH4+vMXw4e1p67py8mryEt//vbty/fPGuKiwmxsb4QEOb5+vnTr5v0fXzn4xB+/fcelriT+//e7H19YWP/wC/BxvRdQEFZm/vhSgI2d49vPd3//SUurv//75/mTKyL//3FysHxlY+Pk5rj35+fHd5+4fzHxsLHwMzE5sgqws7IuZH7349cPZjbWv3+ZGe68ZfrG8p+Tk+HLl19fPjEys4DWfP/7x/DvP2j04Ps30D2iv/8yf/73R4iBiRHU0Sc7AbGwsFy4cKFwQ+HPvz8DAgLk5eWZmJiio6MtLCyYmZlDQkIgDZF///5ZWFiws7OLiopGRkZ2dHR4eXlFRUU9e/assLCQkZExOjqam5vbysoKMrIoIyNjYGDAy8tbVVU1Y8aMf//+SUpKWlpa+vr63rp1Kz8/X0ZGJjw8XE5ODu5yFhYWa2tryPyvnJwc/OqUhoaG9vb2jIwMbm7utrY2dnZ2a2tryOlRcnJykDYrMzOzjY0NpJ0EMbCysnLixIlVVVX///9XUFAoKytjZWW1sbGBjNjBp5hdXFyePXvW0tLCyMgoLi7e3t4OmVyGKICYqaOjA9mXDbGFh4eHg4PD2toa0jBSV1eHtJ+EhYUTEhJqamrk5OTs7OzU1dUVFRVXrFixc+fOP3/+FBcXMzExpaWldXV1tbW1NTQ0WFtbQ7ais7GxlZWV9ff379+/X0dHR1xcnIuLq6GhYeLEiWvWrJGWlvby8oKMBU6dOtXNzS02Nvbs2bN5eXmqqqpJSUk8PDzKysoQozg5ORsaGiZMmLBhwwZeXl7kG0FkZWWjoqLa2tq8vLxiY2MjIiJqamr+//8fHh4OWWgBCTcDA4NNmzbdvHmTgYFBXV29oqKCmZlZTEzMwsICogBOlpaWdnV1QawwMTFJSUmBS8EZkMt1mpubZWRkrK2tlZSU2NnZbWxsIEEnKSmpp6enpKSUn5/f0dEhICCgp6cHb18yMDAICQn19/dPmDBh9+7djIyMkpKSWVlZjIyMkHiB28LAwODg4PDixYuuri7IzdGdnZ0QK5DVjLJpHQK///7/9Re0S3PblY/LT78vdxfnZAUNZ3IyMW68+PHPv/9zYuQDDPjnHX37+P0vZiYmSX7WV59//3oL6ptA9DKD2q4MP3//DzMR3HntkzA3S4235P03P+3VeLQkORs2PxfiZuZmZ5LkZ/kJOvcGNNIpxsuS6yhatu4pHyczDzsTLwdToqVwtoPYg7cgXfrSXDkrH8kLszX5St15/ZOPg9lejffRu18CXMzakpz//v1fdOIdGwuowTraKqV18hg1fzQESAKgDdwkaSBP8dFtGzvrqjafuz4AO+tgLnaNiLCr71h/4/O7tx/+fX7z7/3n54+f//34Xk9RQl1J5sSd+6piIv/+/hcWE5WTEPv96d2vb9+f3r3z5cPH+69fKUjIignzl7o5ivLyvv/1/vf391e/PF73+EqkiZfMzz/fPn08+OAcI+N/cTF5AQHxkzeOfHr/lIGJ4S0L15t/jKxff519+/bv188qfILibKzM//+x/2Q+cO/hz18/GAV4GV58Z7j9noGB+T8jeCKKkREUJaBlUP/BvXjIMSHM///+5pTgMFYVM+ERyGjvU5NCP24X5ksCdN7+Qk9lD3sh27///yIPpOHXBjkNDlLff/78GbKjBZeWP3/+fP78GX6jGgMDw+fPnzk5OeHtQlwakcU/fPgAaYAiCxJkf//+/c+fPwT9BVkKCZnpJmgmfgWYofH+/XsuLi5IGx2i9+fPn2xsoFPlIFwICRlhRW5PQ1YXIPsacm4iJNg/ffrEw8MDGYCEmAAn/////+HDB+QAh0shR9y3b98g1yTCZclgfPnyhYmJCdKFwKX9y5cvLCws8IUBWJWBTt37+xeXOZ8+fWJiYuLhIbC/5Pfv39++fYOsqcBqC0HB0XNGCQYRLgU/fv+LmPPg3bc/zIyM//7///nnf46DKDMT4+T9ryEnj/5n+K8hwaEtyRlmLNi45fnlZ9//graiMSiIsLf4SdZvef7o3S9m8JalX3//N/tJHr/3VYiL2V2br2/Pq3df/4rxspx79M1SidtTm79m8zPIBPvf///FeVk7g6RyVzzx1ePnZGWafeTNgnj55affb778UUGYrclPsn3HCyk+1lwnsYJVT95/+7s6VXHJyXcLT7wtdBbj42Bu3/mSjRk0a98bIm2lTCCB4fL7qPhoCIyGANUBqOVDdUMxDRwUjdHgYI/GiYvvvnv64vX/V2+Zvn3/8u/H948f5TjYrHTVHr//+OX18+9fvigpKgpx8Yjxc/GzMjH8Y/n778/Pv/8+vPvw8OH9MCdrZ0HRn7xsL358WnZ95603DxJkzEwlVV/++fbi/ZM/P74w/GX6IyTEJsjP8vkd4+s3Zz8+vf7yoQQr9+b3bzj+M3Gysvz4++v19x//fv75+frLnz//QPuWnnzh/sz7/9+/z5/eMDDBBqpBK0b/MfwHjYz+Z2Bk5+b7++MbuyafoSiPOQtvRkefiqQSZiATI0LkoffEGDWqZjQEhnoIjDZGaRqD//6DFomSd/bTv/8MTKA+OVEOxLy4EFOEKINGFY2GwGgIDBCAtX4GyHp6Wvvv5++f//4wcvz78e3Xj/9Mf7/+Zvz/m4+X7w8H8+Ov31mY2RQU1F4+uP3l/XtjBenXHz9JCkgI8Qt+/v7jzy9Q75+Lk/0rr8jtX7+V3v8REuC2V7O1FtPU+PyT8fXrn38+SvGJsImq3nn38P6rO3yfOM1lNIS0VP7fv/j58xuG3wzWvGKPvn27/+vdpy8/fn37x8jHzszJzvz5xz8GBg45CaanDF+fvWYAn04HChDQaAH4BCfwHBbofoWvn5i5Gf9zM3H++svMwshKg7PxQPaO4tEQGA2B0RCgXgiAWpOg0owcE0F6idaH2WrFFCHasFGFoyEwGgIDAEZQY/T7989iTAymvLzPGJ9//v6TkZP9PxODqaoCPyvDt2/fH9x/wKkoz8PF/ubxw1NnfqoryT9++lyUj5+Dne3lj7/3fzI8eP1hx9PD+jy8jSqK0jxcv5m5f0kJfP339S8jJ8+vrwysbEzsPBoiIuKvxB++u3Pn7hVeTlEhaVn1v98eP74nziH49/Obz/fff/r8g+EfA8Nvpn+sjH/Z/jH//veXm/nb74+/f3wFXRYKTgCMoEvBQMuqwEsamBj+/ePnYhbSkXzP/5/nNyczAwMLueU72PhRYjQERkNgNAQoDYF//xkuPf3+8zf03s6//xmURUBrUe68/glpR0ryscoLs/359//Gix+ff/xjYmLQkeR8/+3Pq89/jORAJ4XBXXDm4TdVMfYP3/8++/BbRZRdlJflL0jXz08//rIyM+pJc/77///CE9CpcP//M3CwMulKc1x88p2ViVFPhvP1lz93X/1UBuv69ff/TbBd4nwsEvysl558Z2JkMJDluvf659tvf+SF2HjYmW+8+AFxHtz2UcZoCIyIEGBk0JTg4ONgHrSeHUGN0Z9fv7L++ybCxW8gwXfiw6dvf/+xMzDwsbOI83K9//Nbip/3y8f3LGycsjr6zN9BW4++/fz99MOnb//+vf32+/mvP+fevv319NnDHz9d2H7HKYrx3Xx+g/ntK8bvjL8YVEVkBSVk//z4KSAuIigkKvlF7de3jzfOHXjx6IGiojbLx+9Pn9wVZ/irwsy5nvXLna8/mP7+Y+FkYWHg+vX5F8O/n0zMfxhBG+RZGFhY/v/9/f/PPwYm8Pn3jP9A18r9/8siyvOXj5X11zcGhp8MDBzQAdNBm6ZGHTYaAqMhMNxD4MfvfyEz7/39958dfDLo33//DWW5GBkYzj3+BpqX/8+gLMpuJMf15eff4/e+vvv6h4WZ0VaF5/nH34/e/XbT4uVigx7k8uvP/82XPpoocL369Pv2q1/G8lyqYuxffvw9fv/r2y9/2FmYHNV5//77v/fGZwZG0KQ/NxuzvRrPvhuf2VgYXTT5Hrz9ee7RdyNZTlVxjo/f/564//Xdlz8KwmxKouz7b35mZgKpOf/42/MPv7WlOAS4WE7c+8IMuvuOkYGB8f9/2C4m0HXzoCX7/0E3LCDmqIiMQxZmEACdVYdySAA23ZCym4hhW/A1TgSNQ7ECYirEBrgEVkHQfk0GBsh5IyD1sDUNIMXgPQto2uFckGI4h3oM0Bm0jIygwKeemegmwf0I2oVBWsCiG0VHPmQLCRMTqAP19x+04we3HyzO+Pcf4lov0P5mJqY/fxEiUMX/GZ58+LUtR8VOdfCukx5BjdHP7z5++/HzK/Of138YWLlYuf8xKAoIvvv+Q4qX99WHz7JCgt+ZmTi4+EC3g3x9z8DBxcb869azlzefvWDk4vj7n4Hj62dOZsbP71+svnfdz92el5Xl84cXvxi+cf9m/vntD8+bPz/EeD9//cQnLvmfk4PlL7++vuOVJ1eOPLqpKa/O8enl3x+/nvP+ev3uJwMb898fPwU4OdkEeV5+ffmH4R/T3z////8DFQT//jKAl1mBEtB/0JpR0CApI9OHH78+fP6oxM8r+I3xLxvTf0ZoOQ5SNopHQ2A0BEZDYCBCgImRYWmKogG4DQq6Cf4vqI5ng1229Pvf/7//GJmYGFkY/zExMv5n+P/rLwMD6HI65p9/oA0PJiamv3//1nlL/P4HmhliYWL8+fvvj19/mJgYuTikOFhBB4j+/POPkYERsikK5EtGxl9/GTqDQDco//wDuvOJjZnx19//f//9//fvLwvjf2YmJmZW1r//GEDXKzP8//n7PysLIzMj42/w9ikOVlDL59Xrt////RMTF2UAueg/CzPjh49fvnz+LCoqys7O8vsPeqOLGeypf2APgtyAhJmZGO/df3Dt2lVzc3NhIWG4DKzxAxWAmMgM3rH15y9ioRVcGZzBwAAagvj54wfo7GEWFlC9ADUDJA5pR8IEQDTEZBCLgQG8CZbxH6yNzQg6eoUB3IgBzaZBVIL2IzAw/AWf6SYrp8jGysbMzPDnL2gu7u/fP79//+bk4ISr/PcfehQ0w38GZhZQ0CK7B2IpxMFYxSFSaG6GGA7Ry8TEyMTE8ObN+9+/fomLizMzM0ACH24aRDGci8tANAVwLkQ7CzgpQM4d+/H9OysbKzMzesDCtUAcBtEIYUNIrAqQBeExiJWBaQhWZXADQQ74///7j++cnJxv37z98/evuLg4XBYSDm/fvv3186eklBRE/D/o3MYvb9++g5xaAxGEqPz/n8Gk/cY/UAaFOGQwkiOoWfPx04ev7z4yMLPe+/CN5T8T27fvD9+8/vL92w+m/+9Y2P7y8r3+9PnZ1+/3Pnz8zsFz4+mrqzfvf/z8kfHHl+c3r799cFeMlU2Qh4uTh/Porcenbt1nFRNiZecUZuBQ/MIi9fo339ff4j8YhF5/+/HgybenT94/u8vy84+qprHY919s/xnNbELEVE3UzV0lFFX4RQWZeLk+ff367sM7Zi4OWWVFdhZW0E3G//////sP1CtnZGD495fhzx+Gv38ZmP4zsLIzfmHm/8Muy8UryMLO8Pc/I0a3ZzCmrFE3jYbAaAgM9xDgYmdjZ2FkAyNudiYediYIm42F8funt8W5qa+e3OVgBQlePHtq9pT+e7eu1FSW8rD9F+BiFuBifv3kbntD5ZuXT5try///+vbi2aOk2IjC7OSCzOSUhJitmzexszDycTC3NlRev3IBYvKXD2+TYkIf3b/NwcrEDz7diY2FkYedafuGVVHBfjmZqcmJsaUFuS8e3wW54cvHxurSbx9Bxzlxs4Gc9/XTh4rSkrzs9LzsjLyszBdPHrMy/Z8xbWpaUnxZSUFCbNSxI4fZWUBtX2Ty/OlTddWV///8RBaEsC9fOFeQk3ni6KGf30CDtRBHsoFNgLMhXHYWxisXzp8/cxoeYhBxiDI0wSkT+86dPoEsCFGMJgIXZGdhfHDnZkpC7KtnTyAOY2dhZPz3KzEu5tihAxBdEHGIlu9fP1ZXlv789vnblw9bNm5kZfrPzsJ4/crFSf09bCwMEJVfPr5Ljo/NSEnKyUhLS0k4cnAfxByIg5FJXOIQu9BkIYZDyF/fvtRVVWWkJhbmZ6elJNy5dRMiDjccjYvLQLh6iAI4F6L9xNEjN65chrAnT+i9dukCppPgWiAMiGJkEiIOJyFScC6yvXDDkRkkqYeY9vvH17qqim+fPuzavmXdquVw0yCWsrMw7tq2ecHcWXBxdhbGm1cvT57Qg2YXiMsK6o0M8tJoBDVGv3z+8u3ta3ZWRnk+DlZGJmEBXgtRQcZ37149efLs0ZMHHz9zs3E8efKY8fev569fP3n99vHbt28/fxTg55MVFmb6//fjmxfvXzxn+s/w79ffh+/ecnJyan7msfglKf+DU+zXf6ZvX1i//uD48IXx9kOmq3c4Hr968vDi1StH7n9+Pu/kxm1PLoupaejKacoKiAny8zKzsv1hY/vNwcIiKsDBzsnDyAHu0sKSCqjrCtpHD7rwkJEBdMLT738KoNtCfzMy//319++vf39gSgeAfv78+bp160i1GHyI/m+Cuk6dOnXgwAGCyshW8Ad2XDwDA8P169ch52uSbRqNNP779+/Hjx8Qw+/evXv58mUIm3jyx48fkMNQ8WhBjpHjx49Drj7Co54Sqa1bt96+fRvZhO3bt1+5cgVZhFT2uXPnkK8SZWBguHXr1saNG0k1Z1Q92SHAyMi0e8/ut29eYTXh16/f27Ztr66ugVwh8ebtu6vXrqmqql2+fOXMmbMQLatWr+bj52dn57h48dKv37/fvXv/+cuX1ta2ltbWzMysSZOnbNu2jYGB4fLlK+/ef4Bo2bFz59mz51avXgPhwsmbt25p6+i0trY1NTXr6esXFBS+fv36z5+/Fy9d+vHzJ1zZjJkzv33/PmvW7FmzZ6uoqp46ffrMmbPr1q3v6++fN29+XHz8wYOHIJc1wLW8evWqrr7+wIGDP3+BbpqAizOABqK+rlm7Vk9fv6qqWlYWdIjy8+fPHz58CFfz6dOnO3fufPr0CaJ43fr1a9auff369a9fv759+wax6M+fP5BL1L5///7//3+IbEhoqKaW1t+/f3///v3p06f79+9D7toADXT9///o0aOXL19C7pCD2/Xl69e169YvWboULrJ7954lS5e+ffcOYjto8JOB4devXz9//vz37/+PHz9///5z+fKVvv7+R48ef/nyRUVFNTQ0DDQgAjbi85cvb9+9q62ra2lt7ejotLCwhFxg8fr1a0icPgYDsFrQjSF///599uzZy5cvGRgYvnz5guxmBgaGR48ePX36FKIYQv7//7+9o+P9hw9Tp06bM2euubnF4sVLIFKfP3++d+/eF/BteZDy8NevX/fv3//48SNEAQMDw7t37+7evQs5efrPH9CY7vfv39++fQux/e7dux8+gNLM58+fFy1avHnLljdv3vz58yc0LExZRQUSjI8fP4Y7CVIefvz48f79+xDfwS2CMCBWoMUFAwPDixcvHjx4AClvP3/+DAnknz9/Qgz58+cP5Ew9iCEMDAxPnz59+PAhRD3EgxDHfP78mYGB4cOHD3fv3oWwOTg5U1JTeXh5v33//g18zd7///8fPnz4+PFjiGm/fv/+/efPu3fv4Ab++v3761fQEX6Q8Ll37x4kfAb3kCjENwwjaJr+9+/fjF8+sTIyuKqKXvn97c7j58pq6jxMDK9fPPv44N5vWWkRQf7P375wMf5jZPj34//Pv0x/n7x4xczO/ufzp9+fPv399pXh/z+m77////9759F9ZhFJITaeTz++8HNys/z8yMD6l0GQg5Xpv/C7z1+/fP/G9O8Cx+PjXx/s+PL+8edPh44+e/nqpZOsjoOK0acbv599+MbAw8rKyiLGKfT7zrvXD54zMTGBhtCZQKfdgxqmoFOdwKuG/v1n/Pf7//9/nz5/lv/LzcLI9P3/399Ia0Sg0UgKxcxEUQ/k6dOnK1asCAoKIsVOhq1bt/7+/Ts0NBS/rmPHjr1+/drBwQG/MrJlT548eeHChezsbAYGhitXrrCwsKirq5NtGn6N////B3Un8CvCJvvq1ave3t7Ozk4mJqbbt2+/evVKV1cXm0LsYqB5z7q6oqIiCQkJ7CrAotu2bfvy5Ut0dDQDA8OhQ4fev3+PfBg+WAnViHXr1vn7+6uqqsJN3LRpk7W1tY6ODlyEVMbp06dfvXpla2sL13jz5s1Vq1Yh34kKlxpl0CIE2NjYysvLZ7X88AsIjIyMNDAwQE7wv3//trKyEhQUnDx5clFREWhZJTMzFxeXi4vLtm3bzMzMvn37durUqY6Ojv///7OwsDCCAQcHh7y8PAMDg7y8fGFh4dKlS728vNjZ2SEn7P78+XPr1q3Tpk2bOnXq69evka9PY2JiEhERkZGRYWBgUFVVPXr06N69e728vCAmw73/48cPVlZWLi4uNjY2yA0OBw8ehJy/y8bG5g0GcMWgtQe/fzc3NwcFBR08eBDSdkSW3b9//9atWzk4OD59+lReXr5///5t27axsrKqqqrW1tZeuHChq6uLk5Pz06dP7e3td+7c2bp1KxMT07dv33Jzc6dOnZqRkaGhoXH27NkNGza0t7d3dnZ++fLl6dOn9fX1GzZssLe3FxAQ6OvrY2dnf/XqFQ8PT19fHzc3d3Nz89WrV8XExP7//+/o6BgeHg5x0u/fv83MzA4dOpSYmCgmJvbjx4/Vq1fb29szMjJ+//69pKSkpaVFWFh406ZNz58/j4+PZ2Fhefjw4ezZs+/du1dTUxMSEqKsrLxx48bq6mqIgZBgUVRUhNwM8u/fv6amJogLq6qqNm/efPny5X///hkaGlZUVKxbt+7gwYP//v17+fKll5fXzZs3Hz58KCEh0dXVxcHB0dbWdvXq1f///zs4OGRmZkLMf/PmzcmTJxctWiQpKcnAwJCRkQERv3jxYkdHB+Qq5traWmlp6crKShYWlpcvX37+/LmlpUVPT+/AgQNTpkzh5uZmZ2dvbW198eLFzJkzv379qq6u7unp2dHRwcbG9u3bt+rq6uvXrx85cuTq1atv374tKSnZuHGjq6srLy9vS0vLtWvX/v79a2NjU1hYeP78+VmzZrGzs798+VJUVLS9vR35yGcGBoabN29OmDCBg4Pj5cuXfHx8XV1dQkJCc+fO3bJlCysrq5ycXENDQ3d3t4+Pj5mZWVdXl5SUVHJy8tKlS3/+/JmWlgZpcU6YMOH48eNMTEwqKiqlpaUtLS1paWmqqqqXL19et26dq6vrxIkTubi4vn371tLSIisrO23atLa2NtDaUEbGv3//1tfXQ9qXVlZWxcXF7Ozsp0+frqure/HihZycXFtbGzNoIQhofn7//v1Tp06FnHjd1NQkISmJWAEACeXBR46gxuif378eP3vC8fvPh5/M2pICXP9/vvvw6cqzp7///ecVFPnw9vUnPl4Ods6Pb15y/v77/+PHH58/ifIIgm5//PvnPysLp7Dgj68/mDn/f/z+/crLl8++fhDkYH/55iUHGzcnBxcTMzMTNyfjt/+MbNyMv3/+YPzC8p/hHcP/1wyMbPzCPH8YGDnYTrx5KCIoxs7OK64o/+7tqz9XX7998YzjF8s/UKsF3AwFrRNhAC0bZWBgYAa1TBlBvVTGP/8ZXr369kdB5Nsf0EXtTMzkb4hjZmJ+8ODB6uOrf///7ebmBrnV/e7du4cPHxYQEHB3d+fk5Pzy5cuOHTu+ffvm5OQEKdz37t374MEDIyMjQ0NDJiYm+PH1hw4dunPnjp6enomJyefPn1+8ePHt27f3799bWVkdP3781q1bampq9vb2N2/e3Lx581/wIeeQ65T27t17//59Y2NjQ0NDBgaGV69e7dq1S1hY+O/fv2iHpd++ffvo0aOCgoIeHh7s7OwPHjz4////+fPnv3//7uHhAbmF/Pr16ydOnJCQkHB3d2dgYLh9+zYbG9v169etra2fPXt24sQJAQEBLy+v169fb9y48fr16zIyMubm5vBbMd+/f79z587fv3+7urpKSEi8fv3606dPjx49un//vr29vbIy6H6BixcvnjlzRkxMzNnZGe2odkjFICoq+vTp09/giysh2nV0dM6cOXPx4kUVFRV7e3vkvH/r1q1jx46JiIi4ubmxsbG9fft29+7df//+tbOz4+fn37hx4549e1avXq2vr6+np/ft27c/f/7cuXPn169fZ8+eNTExUVVV3bhxIzs7u6enJzs7+9evX/ft2/f69Wtzc3MNDY19+/bt3r1bTk7O0NDQ2tr6w4cPO3fu/Pv3r6urK7zyvnPnzqZNm378+MHLy2tra8vBwcHIyLh79+4XL15AAoGBgeHGjRvHjx8XFxd3d3eHXC4PacFLSUkJCQk9fPiQkZFRTk7u/fv3L1680NTUPHv27IULFxQVFZ2cnCDjlBwcHJcvXzYzM2NnZ4eYcOfOnSNHjkCqN3gqgoTM6dOnL168qKysbGdnx8zM/OzZs3379rGxsbm7u/Pz8z948ICZmVlWVvbPnz/Xrl3T0tKCNG4gQz47d+6EMJAvEYAYO0rSLgT+/Pk9d+68+yc2dPdNnDdv3v79+9F6TUxMTDU1NYmJiY6OjvB7H7y9vQsLC3///n369GkeHh51dfVnz57BHfn///9///5Bmp7y8vLv3r378wcxEXT27FkWFhZ3d/ctW7bs27cP3g6DaIeMNkHYSkpKT58+hQxTQUQgZEpKSl1dXVBQkKCgoJWVVXx8vIWFhY2NDeQGY01NzZSUFMiSO4j6+fPni4mJhYSE7Nq1C5KGIeIQ0sXF5cSJE5ycnNnZ2Y8ePVq5cuWCBQv4+PgyMjI2bNigqalZXV2tqak5ffr0KVOm9PT0XLp06efPn4WFhRwcHHfu3IGMmX358uXRo0fgAeDLSkpKkydPFhAQuHv3rr6+Picn55EjR1avXq2goJCXl7dp0yZxcfHr16/PnTv3379/kZGRyB28P3/+KCoqysjILF++PD8///jx4+zs7La2tuDhsz93796FjNW9efPm2bNnTExMf/78UVBQyM7OfvnyZVdXl6Cg4MmTJ+/cuQPxGgMDAwsLC6RlzMzMrKWlFRkZefnyZQUFhcmTJ3NwcJiYmOTk5Hz+/Dk6OjoyMvLly5cvXrxYtGgR5JLkefPmGRoaJiUlHT169P///zdv3pw7d+7v379jYmIsLCwgxf6HDx8YGRkhl1bs3r37ypUrsrKy7u7uPT090dHRbm5uGzZs6O3t7e7uPn36dG5urr+//7Jly6ZMmTJhwoT+/v7q6moDA4NJkyZNmzbN19d3z549c+bMMTAwePz4cXFxsZ6e3pw5c6ZOnTpp0qSTJ0/Ky8snJSXx8vLevHnT2toaMlcze/bsX79+ZWRkGBsbMzExHT9+fMOGDZKSkikpKTt37kRLXd++fTt48OC6desUFBRycnI2b95samq6evXquXPn8vHxVVRUrF69WkpKavPmzbq6ugcOHJCWlo6Ojt63bx/8srp///7p6OjExcUxMTGFhYXdvXtXRERk3bp15eXlkCgWFhaur69XU1Pr6uqaPXt2S0vLzZs3f/36Benj/fv3z97evrq6+sOHD1FRUSEhIaysrH///q2qquLl5c3Pz1+7dq2SEugA8s+fP0+YMKGmpkZfX3/KlCkzpk9vbGqCrjiGx+7gY1A0SDb4vIPPRf/+M3x++pyHmWHPs0933n95+vX700/f//74w8rJK60gL8XLIcDN9f3b13evX3/+/ePP/3+CvHysLIw8wrycoEvk/3GysvLx8rDzsEuJCj/7+PPa188czCy8PxmZWNkY+IT+/mP49+UbgyT/f0Hun4y/PzH+4Wdml2bn5WFhZ/33V5CZ9car5y/+/VEXkTISk//99TvH599Mz/98efv3zeOXDH/BS+tBXScG0P2fDAyQSz8ZGBn/gxf+MzExfXz3/czpxy/e/Pr7+/9v0Pmk+HyKR46VjXXtunWPnzy+ceNGXl7e79+/L126lJeX9+bNm927d0+fPv3Hjx+5ubknTpx48uRJbW3t169fJ0+evHjx4j9//rS1te3fvx9yIdD///9ngcHv37+7u7u3b9/+/PnzhISE3t7eu3fv7t69e9GiRczMzJMmTVqzZs3Hjx+fP3/++vXrmzdv/v37d8qUKYsXLwbN0bS379u378OHD2lpadfBYMWKFZBeOMQLZ8+ezc/Pf/v27a5du8rLy//+/btw4cLExMQHDx5cvHgxMzPz69evJ0+erKqq+vHjx5YtWzo7O3/9+lVRUVFUVHTq1KmbN2+2tbX9/Plz3759TU1Nnz9/fvLkyYcPH65fv/79+/eVK1euXbv269evWVlZly9ffvDgQWZm5osXL86dOxcZGbl///4XL16kpaU9fvz47NmzNTU1//79O3DgwMKFCyFug5Nr166dN28eAwNDb29vU1MTAwPDhg0bNm/evGPHjo6Ojj9//sybN2/OnDlw9RcuXCgrK/v8+fPu3btbW1shldPNmzdfvnzZ1tb26tWrhw8f/vjx4/r16+/fv9++ffv8+fN//fqVnZ09efLkd+/elZWV5eXlPXjwYMOGDW1tbf///589e/a+fft+/vxZUlJy5syZp0+ffv/+/c6dO48fP/706VNeXt6NGzceP35cUFDw+vVriDPevXv38uVLSIz8+vWLi4tr27Zt586du3HjRmpq6pcvX06fPl1RUfHjx49t27ZBxq4gGpcuXbp48WIGBoampqaWlhYGBoYlS5Zs2bJl+/btTU1N3759mzNnTl9f379//1pbW7Oysg4dOgSZgmRiYrpx40ZGRsaLFy8OHjy4Z88e5F7HmjVrmpubv3//Pn/+/F27dj1+/DgzM/P+/fvnzp3Lycn59u3b0qVLIYtDvn792tDQ8PHjR0gB/efPn8LCwr179z558mTRokWYY1cQZ4+StAgBRkbGS5cunjh5ipubW19fH/NKsz9//oiLi1dWVnZ0dHz8+BHSxFRXVxcTE9u/f//OnTu9vLxAxR6OScTfv3+zsLBAdEHcv3btWnFx8TNnzkhLSy9fvhwiiJX8/v07pIuFJistLT137tyJEydmZmYePXq0vb2dnZ29oaFh7ty55eXlHBwcBQUFkElSyIzBpk2bAgMD7969++3bt+fPn6OZxsHBwQkGAgICT548ef78eV9fX11d3Zs3b75+/QoZhqyqqtq3b9/nz5/Z2dk5ODjY2dn5+fmZmZnhXoN371lZWQMDA0VEREBbl8BDXH/+/DE0NNTS0uLh4dHU1Hzx4sWlS5dsbGwEBASEhISsrKyQ29+QcdyEhISdO3d+/vx52bJlQUFB/0DnA4AGOeAjxBDDIR5hYWHh4+NjYWEREhKCBBdyg/vfv39cXFwGBgZmZmbq6ur////n4OAICAgQERHh4uL6+vVrY2NjT0/PmzdvPn/+/O/fP1tbWz4+PikpKRUVFQsLC15eXnl5+Tdv3ty/f//Ro0eNjY2tra3fv3+Hr0FiYWH5DwaQhi8LC0tvb++9e/cePXq0Y8eO6urqvXv3/vnz58uXL2JiYg4ODtzc3IaGhh8/fnzx4sXz589XrlxZXV197ty5L1++/Pr1y8DAwNramoeHR0BAYNeuXfBgB8cPiODn52cCAwYGhjNnztjb2/Pz84uKihoaGl66dOnfv396enqqqqo8PDza2trwohISUAwMDH///jUwMIDEhbq6+sePH+/evauioiItLc3Ly+vh4XH69GkXF5d79+6dOnXK2tqan59///79v3//NjExgRgC2a7X3t7e0tLy4sWLz58/R0REnDx58uHDhzdv3vT19eXg4Fi7dm1VVdXRo0e/ffvGyMgIjzVQo4CJ6cOHDzU1NV1dXe/fv//x4wdkLFxKSoqXl9fS0vLy5cugLWtMTK9evXr69OmqVauqq6svXLjw/ft38JY2+I4piHMGHTmCRkZBI3BPH2sx/GfmYN/14Of3V9+c1fiEuDlevv0gIivG8+fHr7cvRDnZ/woKfvr+9T8L0z9G5p8M/38+f8PCyPiXhZWdlY2Hh+3tuzcMf39/+/7r1rcfTsLcbFxcf379ZOYT+v/l2/+X7xk5OZi42Jl+/33D/vXD30+KLOzabBzX/nz9z8p259OTq++fMPz+mW3pwyMuvPv+pTvPbnNx/1Yw0Ll08vjfX/8Y2JlBR438g57m8P/PX9A5o4ygyfr/DH/+MzE9ffXpFsNfQW7uH98RQwWkJqifP3/Fx8d7y3revHOzvLz8/v37S5cuDQwMTElJ+fv375s3bw4cOPDz58+enh7IapgvX75s3LixsbFRWVmZlZV1/vz5RUVFjIyM79+/X7lyZWNjo5KSEg8Pz6JFi6qrq////9/b2ysqKvr582cjI6OfP3++evVq69at8+fP9/Hx+fXrV0FBwadPn9atW9fY2KiqqsrIyLh48eJHjx5JSEi0trYyMDC8fPkSssYF4q/Zs2dHRETExcX9/fs3NDT0/Pnzf/78cXJygsyvxcbG7tmz59ChQ/b29v7+/ra2thkZGUFBQZCmm5eXF6Sh/Be8D7G7u7uhoSEyMvLgwYMVFRXgXbT/ODg4du/ezcfHB7G9pqZm7dq1ysrKkpKSDQ0NTExML168OHnyJAsY2NraBgcHIxfWEEf6+Pi0t7e/fPkStMb48+d3794dPnw4IiJi7ty5ERER1tbWKioqnZ2dERERkPstly5damVlFRIS8vbt29zc3JCQkIcPH0ZEREBGakVERJKTk69evVpZWcnOzg5ZWAlpeOXk5Ojr679///779+/l5eW3bt3Kycn58+dPTEwMZGHZyZMnz58/DxmVycnJUVFRWb169a9fv9LT0//+/ZuXl3f48GHI+gozMzMfH58PHz6UlpYyMDB8//7d3Ny8vLycgYEhNDT00qVLS5Yssbe3DwgIMDExKSoqSktLg4yqenl5zZ49G9Le/f3795s3b86fP5+UlNTd3V1ZWWlhYRENBsHBwb9+/QoLC4uLi4MENQsLy5IlS7y8vIqKiv7//3/58mX4iNf////nz59fV1dnbm7+9evXnz9/zpgxw8jIqLa2loGBITk5ec+ePfDpJ0itABnxYmZmvnjx4qtXr9atW8fExMTJyXno0CFIpIySdAgBRiamhQsX+lmqlFVUWVtbI3cj4bb//PnTwcHhzJkzra2tVlZWkDrVz89v3rx5jIyM+fn5cJUQBjMzM7z1uX79ej09PQiXmZn5w4cPp0+f1tPTWwpeFvn48eOLFy/q6+tDNDIwMMAd8PHjxzNnzvj5+f3//x+SMOBqjh49Ki8vrwIGf//+nTBhwr179z5//qyvry8tLa2tre3s7PzhwwfIlcI3b97k5OScNGnS169fL1++vGHDBkiXGLMQYGBg4OTklJeXz87OZmJi+vHjh6SkZFZWloWFRVpa2s6dO3fv3g1JunDvQC77ZWBgeP/+PaTQA7eUUIaHIO6HpHbQbBkzs5iY2IULFyDeefr0qZqaGoQNIf/9+6ekpKShodHU1PTr1y9nZ2eIvczMzPABtrdv30KasJBS5R8YsLOzQ0xAJv/9+ycoKBgQEACRhfT0IH4/dOjQokWLmpubRUVFb968CXEh3EBmZma4CDMzMzs7u66ubkFBwd+/f3/9+gWZbWNgYJCQkODi4jp16pQjDGzbtg1yD3B0dLSsrOzPnz+5ubkh0QoxEDJqzs7OLiQklJiYyM/P//PnT0FBwRs3bkCatv/+/aurqzM2Nk5KSjp06NCmTZsYGBj+/PkDcTbEd4yMjAICAm/evIFw3759Ky8vDwlqiAho+z94ww/EOrggKysrRATS5uPh4YGvYX358iUPD4+KigoPD8+MGTNSU1Pv3r07e/ZsGxsbTk5OiAkXL16cNGlSa2urvLz8/fv3//z5Iy8vLy0tPWHCBC0tLcgAvLe3t6+v74oVK5BLfkjaPnTo0LJly9ra2kRFRa9cuQJxCWSNLGSxKaR+gSgWFBRMSUnh5ub+8eOHoKAA2AGgzShgxiAlRlZj9M79+87v3ynycb9j4+QREBTh4f4qyPvy82e2f384OHiYfv96/+rlfwYmPk6uP7///WJhevvtI+OHT3y83BKiIn9+/OBiYWPiFXr/4S0TE8OJR0/jHewFv3799PDpuw/v2b584fnz59/X7/9/M3758/0P6593vz/9Zf2vxMr6g5f/6z9G0d//H/3/uv3aiW9/fmnJqohwCt55+5GbmTsxKXHi53e3z19l/A86Rg80GvoPtHsJ1JmFHKzHxMTw7y/ouI5/DIzgG/LA2YTM9MTOzr5q1arTn05KyEj8/fv3x48f7969U1BQgJR04uLir169kpKSgpguISHx5MmTz58/r1u3DjIj4OzsDFkK+ePHj8+fP69fvx4i7urq+uvXL0lJSUiTZd++fatXr1ZSUoIsZmcAr5qHND4+ffr05cuX9evXs7GxQWbGnz17Ji4uDrFRTk4OuUv64cMHSfByImZmZhEREcjIhKKiIkSxuLg4pIN49uzZl+Dl/K6urhxgAJlbv3PnTmdnp7CwMAcHBwsLC8S/kCIVYgKkhpOVlYVwVVVV79+/LysrKy0tDakz+Pn5v3z5EhcX9+TJk9bW1vfv3wcEBCQnJ0OKXYguDQ0NZmbmLVu2aGpq/vnzZ/PmzX/+/NHT03v79u2ePXvOnj37588fNzc3eGn44cOHR48effz48c+fP7a2tlJSUq2trUuWLJk9e7aoqGh3d/e/f/8gGxcgdQAjI+O/f/+4ubnFxMQYGBi4ubkhiytYWFjY2Nj+/fu3ZMmSc+fOaWhovH//HlLD/fnzB1LffPr06enTp1OmTIEUfJDVeBBn//oFWv4OYf/9+xcSzgwMDPz8/JDIPXny5OvXr//+/evo6AhxCQMDg5GREWS4FNJw3Lp1KwMDg66u7tevXyFRLyQkxM7O/ubNGxYWFuSakpGR8cOHD5BFooyMjJKSkpD0AGmq/v37F+IpbjB4//69lpYWxG0yMjIvXryAVxWQeIFIMTIyvnnzRkhICCIoKysLqbcgsqMkrUPg969fS5ctczOEZh8060Cng8DOO8zOztm+fTt8xNHNza2pqcnHxwee8f/+BZ0txMTE9OjRo76+PkZGxhs3brx//37ChAmQzhITE9Pq1ash07IQi7q6upYuXaqnpwfPjLt27eLg4Pj27duRI0ccHBwsLCzevHnz4sWLyZMnCwsL//7928fH58aNG42NTRER4czMzKtWrfLz8/v+/Xt+fr6vr6+EhMTx48e1tbUhKZmBgSEpKSkuLu7fv3+vX7/Oy8vLyMg4efLkwYMHy8rKIG5gYABt3IEsODEwMGBlZV27dq2MjMzhw4dra2vZ2dmfPXt29OjRzZs3Q1KmiorKtGnTpKSkPDw8LCws+vv7PTw8Vq9eDZlu+vXrF7x0+vXrF2RQ88+fP5B22K9fv/7//+/l5bV69erKyko+Pr5Tp05BMhTEMZDyjYGBIT4+3tnZeebMmZCTRCEDnDIyMl1dXYaGhps3b4asDv/+/fufP38gq0t7enqcnJwgc/cQ0yAj1r9+/frx4wck+0MiFOJCDg6OL1++3Lx5c8+ePbdu3YI0+CDu/P//P2QnFiRwvn//7ujouGbNmt27d//////27ds1NTUQKzg5OTMzM5uami5cuCAqKnrr1i0WFhZ5eXknJ6d58+Z5eXmdOHHCwcHBxsYGuaz4/v27lJSUtrb2/PnzbWxsDhw4EB8fD3c5IyMjJyfnw4cPjx07tmbNGoiTIOuaeHh4PDw8IF2C4ODgzMzMWbNmff/+/f79+5WVlWfPnoUP2UIar4cOHTpx4gQ8rv/9+weZ5IFUZ1++fDExMZk1a1Z7e7ukpOSaNWsgIxqmpqaNjY3z5s3j5+dvbW2tqqqCeJaBgYGdnf3nz5937tw5derU5cuXIW7z8fFJTU1dv349AwMDBwfH06dPDx06tGPHDkiTHRKSkBqBnZ3927dv165de/jwIWRlCxMT04kTJ2bMmMHKyrpt27apU6e+e/cO0tzX19efPXu2tbX1gQMHUpKTj5849fQpKxMTaI8d3D2DjTGyGqOvHj16ce+KvKbtBxEueVn+729f7zl/y0JPQ0WIl5OF4+7zpx8ZGXnY2T+8efX713dWTl4hdu6fXL/YmJg/f/rAzMz87+f3d5/e/fr969+//+fuPjx85ZoLNzcbB+dvDuYvnz9zMzD8+/zpPwPD/f/vL/388OMfiwgzuzkXsyLDz0cMP58xfmP4+usz1/+zjy+fu33RQdtRRlH10t791+7dtzezvH360n9mRtBRe+BFov9B1zSBWKB1Hv//Mf5j/M/CzMAIOdIJfGM9uenoz+/fpqam9cG1L16+gKy1V1JSOn78uIuLy8ePH588eQIZTvv69Ss3N/fZs2cVFBQkJCQSExN1dHQeP37MwcHx5MmTP3/+8PDwiIuLx8bGGhgYPHnyhI2N7eXLl5AahZGRce7cuTk5OW5ubtOmTTt69ChkOATSiRQWFhYTE0tISNDT03v8+DFkUdSKFSt+/frFzMx86tQpFfBWR4j/FBUVz5w54+rq+vr168ePH2toaJw+ffrEiROxsbE/fvy4ffu2j4/P/fv3tbS0srOzv3z58uLFC1FR0d+/f0NKzGXLlsnKyjY3N58+fXrHjh2QWY8PHz5A2tP//v379euXurr6tm3bfv36xcrKeuTIEUdHR9Bmsn/Q44X//v3LyMj46NEjJyennJyc8+fPFxYW+vr6/vjxQ1ZWFtIG4uLisrCwmDx58sSJE1lZWVNSUhISEkRFRcXFxX3A4PXr19++fYP3j2VkZHh5eYuKir59+/b06VNubm4mJqbp06f//PkzKirq1KlTampqX758gXjhP3gJHaQAhRRef8EAEj7//v179erV5s2blyxZIikpmZqa+u/fP0bwUnfIPk1pMGhsbGRmZr5+/TpkRRFELwsLC7xx8O/fP3hxD7FXRkZGUlIyLy8PEqrwGVhubm5jY+MZM2ZMnz79y5cveXl5mZmZfHx8oqKily5dUlZWvnr16u/fv2VkZP78+QMxCt7cVFRUPHfuXERExIcPH65cuQJfR8vMzMzHxwdZMPro0SPI6M6pU6cSEhJ+/vx59epVFxeXy5cv37t3D7Ig+O7du5Chlz9//igrKz99+hSyl+XkyZPw2gLix1GS1iEgKwXqIGG1RV5arKaihIuLAzxqyLFy6UJIZ5KBgYGPj2/JgtnS0tIQjdLiwk21lYKCvLycig01Fe/evWNkZIwMDbS1tWFmB53R3VBToa2lxsfFFuANWhQO0ZWTkXz+/AV4zzwuKkxZXvrPnz8iAtwdzXVa+qC5URERgbamutevXzEwMP77y8zNzpycnGykpwXeavO3qqzI2t6JgYFx8fxZ27Ztf/rorrO9tb+/HxMbyM2Q/jmkDyknLV5TUczPzyciwPPkwR0GUPkMHcKMiwqDNEYFBQXnz56+cuXKJw/upCbGioqK9na2LFu2/M2LJ60NNZCd+AEBfvzc7Pfv32dl/FtdVbZi6dLXr562NtaAy3qGqrJCdXUNiO8KcjLk5OTY2dnLi/Mh5keFBf77919YWHjxvFl79+2TkJB49+opK7RSAGky1teuLC1kYGDQ19ffvnm9kZERAwNDWlKckJAQAwPDtEl9S5Ys+f75/ezpk9nYWHl4OFsaqsWE+RmY2efMmLJnzx6GPz+M9bX58rJAZoGxvLR4fXU5Pz83mAdaQlpenK8G3oZoYWFRW1l68uRJc3Pz9auXqyrK8gb5QZQpyUm1NtZycICGWjNSEgQEBKTkFGZMmbBhwwZGRsb05Hh4ScjAwODh4aGuLLdr1+4Xj+9rqSkV52fz8fEVF+dv37z56tWr1mZGbk52zOzsDdXl0uKgM1z1tNRqKooZGRl7O1tXr1599+ZVX09XIwOdr58/lxfnQwrkztbGxYsXv37+GB7saamJMhIir1+/Zmf+DwlYXkHR2dMnr1+/np2Vdfb0yaJiYhYmBqKCoNBjYGCIjQzl5uZ+8eLFiycPIJ4Chaq2elVZESQuosODmRgZeXl5Z02btGrVqvevn0/u71bV1GVgYIgICdDTUuPk5DQ21F29fLGJiQHcBE1Nzc7Wxv379xsY6G9YsxzS3HR3cVizfLGxkR4DA8Pk/u4VK1a8f/28v7vt799/bGysHS0NEqKCfl5uv379UlTVrCgpOHPmjJWVlbP9PCU5KUFeTiO9hdeuXn346NGMKRPUtbS+fHhTlJfFyMjY3d68Zs0aSPjo6mlxcbDw8n6C1CBw9ww2xshqjH7//vXenTuaxo5veFg52VhOv3z9+x+jpqgYA/Pvr39+/fv5m52R5emHd+y/fzMxM314/lKInZOJheU/N8efH7++fPv+4e2Tr9++gfd+sv798bN/w2at4BBOfnZ+EbEfXFzfn7xmAW18/8P1l/k7BwMTryArAwsPC+Ofv4wsrJyMHExPf375+/8vzz/Gt4w/zl+7Yqtt/u/lq3MnT/o52LNzcfz88weyh56BiYEBdMwzaK6A4R/oEiZQSxS0BR40eABKQOBRUxCDdMzCynrw4MGig+9YOFklJCSYmJhSUlIKCgrS09PfvXvn4uKSkpJibGycnJzMw8PDxsY2YcKE5OTk2tpaeXn5Fy9eVFZWcnJyCgkJ8fHxZWdnNzY2ysvLP3v2rKSkRFBQELKdiIGBwcXFZcaMGfv27Xv//j0kyzk6OlZUVOTk5JSUlGRkZDQ0NMjJyT1//ryiosLb23v//v0JCQliYmI/f/6EFJ0Qn+Xl5ZWUlKSnp79588bHx0dVVRWywbyoqOjJkydaWlq2trYKCgqVlZU3b9589+6dg4NDXFycsLAwZKTBxcWlq6ursLCQkZFRTU3t9+/f5ubmixcvTk5OzsvLExQUZGRktLa23rNnT0JCAhsbGy8vb0BAwMGDB+H7KPn4+Hh5eT9//lxbW6uoqPj+/Xs3N7dPnz7V1NTMnDkTrszJyWn79u2ampo8PDxCQkLOzs4MDAz5+fmtra179+59+vRpVFQUfFQyOTm5rKwsNzf3/fv3lpaWycnJy5YtmzNnDh8fHxsbm46OjpCQkJaWVlpaWkxMDB8YMDIyCgsLQ+pFXl5eyGpLZmZmISEhYWFhHR2diooKSUnJz58/8/HxMTIyBgQE1NXVOTg45ObmHjhwIDk5mZOTk4WFBbJCDhK2rq6uRUVFKSkpFRUV/Pz88MaooKAgGxtbdnZ2SUnJ3bt337x54+DggNxDcHBw2L9/v6am5r9//4SEhGxsbBgZGcvLyxsbG3fv3v3kyZOMjAxxcXEBAQHIgAoDA4OAgAALC0tSUlJWVlZSUhJkXRdynVRaWgoJq2fPnuXn58fFxZWWliYlJf38+VNbW9vKykpRUTErKystLY2FhQXSpObh4fn69auKioqzs3Nqaqq8vPynT5+Qt55AvDlK0i4EWJgZK9c9EeLGVYkwsrIqTbv1+D/oFGVQU4aRUfr3JWjtzsYm+/fav79/HzCAFiIxsrKqzrj7lIGBkY3NhpEb5OT/bxgWr3r37x9oLpWNTfXPvW9MzJIMV///+fsA1EVnYGBiYmZh0ZyzEHqOEgsLNwu7FwOoCcSw/9q/XxcfMjD8ZwQZaMHIBTKQgYHh8MHff/89ZGOVZ5JN+PuP7eoTppnzX////5+VVYRZMOG/IMOtXwwbln/79+8LxAqoNpALmVhYdX5ff/3uw/9HHP7hs6GWghc78v9n+P93/31Q45WFmYUriIGL4eylf7/P3GdiZmblDf3////BU///M/z/c/o+IyMjK5seA4fero0/GBiesrG6MQgznDwPuunkz6n7rKzyfy9/+ffvE8hYVsl/53/+Z/jBwiz7+wLEcB5GBsZfB+7+//+fm9vx34N/p3+7CL0WOjAbJAvp8DMzK/y+fJ+BkYGNVen3tdf//71iYRH+9///v7/3mZiZWHlC/jP8P3AWtE7zz+FHrKxqE28+B1VnrHzMPCEHrv75d/kjM7Pkn7NQA8G9d8XfVx/DQ4OVVfbvpa///oEOIWJj02aU1rn4+B/DY4Y/5z4wMYFC+e9+kB9ZWFUn3ABFNAuLKHg89T4LKwezMOjgjuOH/v7ZCzUfErwsrLzMvKEMvAyX/jFs2vTz7x+QCaxsZgxS5he//l+2BBxBLEp/roGmRxiZmFiYFX9fvs/IxMTG6vxfiuHiq/9z5j1jYGREBBQzMwvYp4dBwc4ACXY2Nov/rP+3r/rEzCL278y3f//us7CwQ5x0aMefv3/uMzExQUMPtHOLH3TG1hu2N6C4hroWVQEfKNIP3get/eUMZOBkOHPy758jIJXgFcDKv66CfMHCqvjn6hPkJiArmwKTdPLFV/8YXjH8PfPz3z+QMlZWld/Xn/7//x9kGn/4////958Ex9HxB6xsGpNvP2diYmVgZP178D4bqx6jtP7F+6BJ0j8X3oNuDGASZWR0ZJRgPHv6359jIF8wgWOQiYmJFRI+L//PmfOYiYnn4z/oEDsk2AchyYgcUrRz39FtGzvrqjafuw5aB0k7a/CazAg6Qf6fWVySc2nd3TdfXr/8cuz0ZYbfXwwVJCxUFF6+f//g2p1f//5x8XIx//z++8uXX//+fH77/u/fP9/+/REVFv336/erd29//vjx++8fFnYOZjaOr29fpisrl2gbcHBzswsL/Xz1juXzV4b37x4xfzvJ8vE/DwcrNzsLC8Nzlp8f//4+/vbJlY9v/zIy/f70/dfnfxzvuex0rJWlRJbPm29pZ339/OWb168wsDKDpupBi42ZQKOk//4z/AEN0TGyszEwMv3/9t1AktdCXSG1qcdIi8wDiQoOFBtw6esyaiupKLGzs7OCwa9fvy5fvszPzw9vc9y8efPbt286OjqQVt3z588fPHigqqoqIiICORUPsqbq1atX9+7dU1ZWFhUVhRyoBh9Cu379+u/fv9XV1X///g1ZywJZeC4rK8vFxQUxUEVFBTIj9v///ytXrrCzsysoKPz58wd5u/rPnz8vX74sJCQEaYKUl5fr6OhYWVl9//5dW1sbMkP3/fv3y5cvCwsLQ2bnP336xM3NDWm6PXv27MmTJxCVHBwckHNVHj9+LCkpCfEapEl07dq1379/Q6b8IEfEQdz87ds3JiYmDg6Ojx8/3rhxg4+PT1NT8+3bt83NzQ0NDfDGKORkO25ubkZGxq9fv3JxcUEc9vHjR8jmfUiLHJ48f/z4cfnyZQEBAchm2H///l2/fv3r16+Q1fGQcdD79+/z8vIKCAj8/fuXk5Pz8+fPPDw8EPdDpqL+/v379etXPj6+379/Q0yTkZGBKGZgYHjy5Mnv378hSxquXr3669cveGzCnfH58+enT5/KysqCRv3BmxUYGBggOy0gB6NcuXIFHqpwXZDpKsi+9S9fvnBxcUEGJCBBJC8vDzlS6vPnz5AWMCRw2MAAEpsSEhIiIiKMjIzw1ipkzdPNmzfl5OQgCwb+/ft3+fJlNjY2TU1NiNWfP3++fv26uro6GxsbOzs7ZB4TklRu37797ds3LS2tX79+QRwG0UKQXHt7/bFnx3vtuwiqHFWAFgLffv0z67j55P0v+NgkmoLBzP33j1VefCMf131QWUu0Q5kYOWwVEoV45H7/InxkMtGmkqgQ1DJmhGzD5+fn//vv798/kBkzEs0ZVU4oBEBLeJmZ//weuLimKmBhZow0EZQVYqOqqdQ0bAQ1RpkYQHe9a9jYRPfPu/qT9fmDB0eu3Odk+q8nzqcjLPjh2/ez1699+vyVlZ2Dj5+b/dOXfwz/v337yszwj5WFmY0BdMrX1y9fPn/48PPvnx///nCxc//8+vHPm+f19u65uiYsLGy/mJiYOVj+vnvz+N2zRyw/f/7++Zub6RsP0ycB9hffv215dvvb77/vf3z/9u7z7+vv9GXMpFTVDJRkNq1c/fnXV2sb2/XLln398Y0RtLEefDc9MxPD7z+gU52YmRi4ORn//Pv39buBNJ+FqlxqU6+RFsq6deJTRN7+QjcFVx9F0CZW4nUNHpWtra0qKipoh27Q2XmvwAB5tRadHTBqHbVCYLQxSnZIQhqjrz//Bt1ET7YpA6Txx2/Gvoin9mpfwJ19Yh3BxMCiLOTAwMBPrAYaqoPMjjHS0IZRo0dDgL4A1wwLfV1BF9tAs0UMDC/v3fv28tFPQcWnP/7+Y2Zl5Wb98effmVt3hEWEGVjYfv5+//Pf/x+MDH8/feZiYREVFv769u3n9x8EuLn4eLh///n55evn379+/vn5/Q/oFNF/f9m5F9674yanrMrE+VuA/TcrH7OcuKiMEBcjw9evn57+//Dl/0emv39F2fmU+cRf/PzKxMD0n+nnv8+Mty5eZhcWlLG10DM1Wrd29ckLV7VtbC8d3f/j81cGRhYGJtBluqDb6UEzJaz/f/1k/PUHtM2eGRRfFJZA//9DCjK6BDq1LcnMzGRlZaW2qaSZJwYGpOkZVT0aAsM9BH6Dr26H300P8e7PP/+ZGMFTPhA++DzlH3/+sbMwMSEVZKBb5hkZWMBXwMMUQul//8HbNqE80NTajz//OFiYwNs7UaRgSgjT//7/leG1URQALwggrHwQqkAKu0HoulEnjYYA6QC6Cpt0jUNPx3/wFQQfX796dvuKHC+7lCCfEA83y4//vCxsf8HXl4nx8csKCwuwsv159/HTp89fv31h+cfAxs7Jysbx/fuvrz9+/WJkZRIXZeDkZGbh+s/KwvDvDwsT250Pb1c9uf2Oi/GvrPgf9n8/nj95+/rl+y+v37P8EpBXVJLTEOUS+Pr7BxsnJycXpxifAPPH/39/Mn758v7KqZP7T50UFpO0s7T5/vfvz/9M//4yM/4Fn3MP2rfyH3QVExPoqFGGv///MTIw8HIxc3MygM6NA83dD70IoIaLhYSEeHl5qWHSqBmjITAaAlQLgZ+//1socfvq8v9Bmvn+8/d/nLmQozrv77/QDvC/f/+FeFjK3SVEeUGXfUCs//P3f5qtsK0qz68/UGUQcdA6SEYGIW5mJthSgL//QZfaN/hI8nEysTIxCnKRf/3Hv/+js9vwYB5ljIbAwIMR1BgFr2dn+Pf7z9NLF8SYmbh5uVkZ//3+/1OEl11OQoyXl4eBFbQkno2JUZSHT5CP7++fP7du3Xr95u2P33//srL+ZWP/9/sP06evbKzMDOzgw/BYGP+zsvxnYF5y5x5oVTkv69Pnd7/9/PHt65fPX169f3Hv2cNbn54+//PpyyeGb59+f+ZiZfv14RfrB2bmf4wMHMxf3r46svfA/dcvtXR0rAz1DVRVeTm5/jP8he6aB5W/jAxMzKDFBf8YmBhZGf4z/P3+AzQ+OtorHviMM+qC0RAYDQFoCPz6+99UgWtqhKyFEjd8BePP3//NlbjbAqW0pTggLdTff////vufl4MpzFiQn5MJMUPDCNqoCWnE/vn3/+ef/7/Ag6y///43lOWq9pCA3woCKfn+/mf48ft/kKFAnLnw99+gnjlEF2RoFnR6w3+GP3//QwRBm4P+M/z8A7Ia6txRajQERkNg8AHQtO/gcxXtXAQ69/XhlSs/vr6///3fh79Mv37+e/vxO/u/TyLCYmxsHG9fvPr24wcrNzcPKy8Lw79f/xm///j+7+/ff+xsvDw8X79/+/Px0/cvH5mYmP8z///PzM7I8J+NjZNVSHT7768yT579Z2b8xsbwk5fn618mhn8MXxgZWNgZvnz/KcLJK/f952cG5psPP7L/42BmZmT6xfCPlfnlowdS3K68gqKyv39z8vPqmRod3PESdPQ9qNcOLngZGRj+/AbtRmVnY/j9F4TA81y0C6BRk0dDYDQERkOA+BD4+ee/ty5fjr3o269/IU1D0A68/wwivCzpNiKP3/36DR7vZGRg0JXmfPflz99/DD9+/0O0RBkYmBkZ99/6/PUnaI+wnBCbrCDbx+9/b7z4wcrMaKHIrSbOoSPNeenJd2ZGUE/8669/a8+9Z2dhtFDi5mBhVBZhv//2l4Iwm7wQ29uvf26+/PHvP6MoDzMPO/PPv//lBFmvPf/x489/Y3muT9//3n79E3xGCfGeG1U5GgKjIUAnMNIao6Cp+qf37v18cENF2uCXlMj9T59ffP5mpyzOws7559+PX//+ff3z++uz52wc7LwsrPyiwn+/fWf88/fL50/vXr/7z8DMIyfP9ObVty+fmP/9YebgYWADrV1i+fPn1LPnrkK8CpKSLz+/+8T0jYWXXYKJk5eT4zHDx7csjD/Z2CXlVC4fPPnu/ls+TkEJGem3L199+frlz9/fhw4cUDY0FWBnFuFkVtTUu3rp2qvH9xlYQdNVjKwsjMws/378ZmBkYmBmYfjzE3H+KJ2Sx6g1oyEwGgKjIYAvBFiYGB68/ZW1/HGEqSAnK2iqDXSO0b//hc5ipx9+e/P1DxtoATwDExNjvrPY0Ttfdl0DHV2EbOKvv/8bfCT33vh89tG3yRGyYjys////n3/87ZKT70KNBRRF2AucRWPnP2RiYvzz778UH+ucWLnuXS9dNfl42JmuPP9x6v7X3hAZUR6Wr7/+TT/4etL+V2aKfDkOYr/+/tOW5Nh57dObL3+DjQQY/jNkr3i87+ZntFWtyC4ZZY+GwGgIDBQAlR0DZTf97YXsYfr27t2D8+dU+LhlhQSEBLjfff7Bxspx++GTl6/esDOx/GVg4mTn+v3t59tXr58+fvLl89dv37//Yfj/6ef334z/GdjY/zIxsDAxcvPy84kIG5qaSIoIP3/z8sPLD5+///3I9O8PEwsrK/cfZrbPItz/eNgY/v/n5hUSZxcV/8Nnp2AmzCP0/ce3z99/sgvw8gny8fLz33329PTpU28+f+Xi4H785DkzOweovfwfPP7JCLqknoGFhYGFhfHff4a/kINIGSHHBtE/9Kho47dv3y5dugQ/FB2ryV++fIGcYAKX/fnzJ+Qsd7gIHgbkdlMGBoYHDx48e/bs9+/fly5d+vPnz7t37yCX7+HRSzupDx8+XLt2jXbmo5l848aNxYsXP3yIOBYRTcEodzQEyA6BH7//ff/97/ff/xcef7/45BsjI8Off/+///73+edfNy0+CT6WifteMTIy/v77/8eff19+/k1Z9HDWoTf/GRj+gefNv4O1Q8gfv/99+fnPUpH73z+G5MUPc1Y8fv3lz9MPv1u3v9h97XPK4kes4HWjjAygCfd//xkP3Po66/CbmYfe9O1+WeMlsfb8B6O2G/mrnoSZCMoLs339+U+cj6Vu83ObntuO6rxff/0zaLm+/epHV03eb79Abv7++99fyLIAsj0/qnE0BEZDgKpgBI2MgmboQcd4MjD8Z7h6/KheaIKmOIcWh/LuSzfuvXn/8/uvFz9+vP/yjZ2d7cfPz/++f/776xcjM9OH799Y/v9nYWL6y8jEzMzy4/eHHz9/sTEw/2dhY+HifnHv4Z/vX5hYme89ubfiBnuUhAEL819mVs4Xf/7deP7k759fgkK8jx+/VBGSeXz/qbCymqyqyqO79398/fT905c/37/LKCr8+P779csXN26yWpsY21mYXrx8BnIbByiWQduY/jEwMzH8/f//728G0CQVE+gwXOQpLpA60vBgaMs+fvy4vr5+6dKlkHMi0Tzw5s2btra258+f//nzB3I3Oisr68KFC9evX8/MzKygoFBbW4t8xufSpUsdHBzgF7pATHv37l07GKxevVpMTMzb27uxsXHOnDlTpkzx8fGBXE8CUUkt8v79+2fPng0JCWFgYPj69euKFSsiIyPRPHjx4sU5c+YsXryYWpbiMefatWuFhYVWVlaQW+/xqByVGg0BUkOAmYnRXYvv4/e/oOs4GBh+//mvKsbBzMjgp8/Px86c4yi6/cpHZw1eRWE2NmaGAH0ByPjov38MEvysfBxMjuq8GuIcUL1/GaQE2IzluF59/qMhwbE0SeHx+18rz3xw0+IzkuMS5WXx0+OPtxSWFmD98fvfhgsfuNkY3bR4VcXY2ZgZw02ElEXZJflZw4wF/v8HGR5tJszGzPDx+19BLmZRHpZP3/+CTqFS4P7PwKAuzuGvL8DKzPDz938JvgE+lIPUAB9VPxoCwxuMoMYoJCJBTVIGhodXLjE+u8cuq/3x518+Tm5mZgYpAZ7Xb/5w8nF/+fLl18/vDD9/MjH++//3PyMzw7+//37/+M3AzsHGzPL/62cWNk5ecSEmxv8/fv5k/f3747sPv/4zsHJznH7x8tfJ86FmJi+fv7726vX52/fZWDkttDXfvfrzhoNBy8Bs/+lT506d5uTgYGVkFJeQ+PTypbK0/A8Ghi8/vn18/+neo0f2RgYcrOygRif4WhLQxvl/v8EDpf/+//0LOtQEtKsJ4g8ySdCp7F++3Lp5i4mZSVlZGdIwvXv37s+fP5WUlNjY2D58+AC5A+nv37+fPn0SFBT8+vXr/fv3BQQEkE9uh1yj/OnTpzdv3igoKEBOGv/169edO3c4ODggB9QzMDA8fPjw8+fP8vLyvLy8v379+vPnz8+fP3/8+AGx99u3b//+/YMcL//9+/e/f/9C2G/fvoXcKvTnz5/MzEwTExMBAYGFCxdOnTpVSEioq6urv7+/oaGBkRF0Su7z58/nz5/PyclpYWEhLi7+/ft3Zmbm169fi4iIpKWlsbGx/QGD////Q24cjY6Ohlzy/uvXr7t377KxsUGOyv/+/TsjI+OrV6++ffumrKwMP0Dqx48fv3794uPj+/v375cvX/j5+SEntPPy8v7+/fvevXvs7OzKysrfv38/efLkypUrjYyMBAUF7927N2vWLH19fQUFBcit65AIA913wsLy5s2bly9fysvLQ/z779+/u3dBW+BUVFSYmJiQg+Xjx4/c3NyQ65E+ffrEyMgoKir64sWL169fS0hIQK4MgJjMwMDw+vXrFy9eSEtLCwkJff78efPmzerq6mlpaYKCgnA1v3//vnv3LgsLC+SCgy9fvrCysj569IiJiUlJSQkSL58+fXr06JGQkJCUlNT///8hal69eiUtLf3///87d+5wcnKKi4v/+/fv58+fvLy8kCvyPnz4wM3NDQk3yGUBaIH58+fPu3fvcnJyQo7ih5yu/+LFCxEREUiT/ePHj+zs7JD7pT59+sTBwcHCwnLnzp3///+rqKjAbzF4+/atlJSUsLDwv3//vn79ysLC8vr1aykpKYgz4D4dZdA0BNhZGKdGYrmV3lsXlEFA1yfKcMIdEGkqBGdDGN1B0LtAIVwGBgYLRehBS+wszPycnDp+CO2mCog7tcvdJRgYGGZEQUX89EHWCUO1ggyrcBcHUQwMK1MUIYxaL5AWCNtOlQfCGCVHQ2A0BAYVGEGN0f/gHUGQ0P/y9v2t08clFfX/c7A5aiocO39dSJD52z9GXnExBlaWX+/fff/7+z8zI+i2C2Zm0D1drEzcgvzsbIwfnr7l4OIXERF5+/IFw5/ff1mYfjD+/8PAzPGfieE/64Vrj9iYeTg52e7cf8LJwPL+7fubt58wc3OfvXzj0YuXL9+/5xcS+PH5y6+/f4RlZVTUVG7ffygkIq6pqf3g0eMnr15IiYtJiog+Aq3TZ/r37w+45ckEOuAJNKMEOunpPwNoWBd+ORvELySR7Ozss2bPVv2m/OnbJ11d3crKyq6urnPnznFxcbGyslZXV7e0tNTX10tLS+/evfv06dMpKSllZWWcnJxv3ryJioqCDPsxMDBcunSpublZWlr606dPf//+nTZtGisra1VV1adPn37+/GlhYZGfn79w4cKtW7cKCAh8/Phx0qRJr1+/bmtr+///v6GhYWhoKCsr6/nz59euXTtjxgwGBoaenh4NDY3Q0FAGBgZ1MIDchMTPz8/IyLh//34PDw/IZTypqak5OTnfvn3j5ub+9evXwoUL7969u2jRojt37iQkJFRVVYFOhGFiKigo6O3thRgOCSJI43XatGlRUVEaGhrl5eUfPnz4+fOnvr5+dXX15s2b165dKyws/Pr1a1lZ2dbWVsjlTJcvX545c+acOXOuXbtWXl6+atWq379/V1VVlZeXt7e3//v37+PHj7a2to6OjkuWLLl9+3ZnZ6ePjw/kFtAJEyZ4enpGR4PuwYO4gY2N7ebNm3V1dV+/fv327dvkyZNFRUVramoePHjAyMgoIyPT1ta2YcOGly9fFhYW/vnzp76+Pj8///79+7Nnz/7796+3t7e6unpXV5eUlNTz589LS0stLCwgJh84cKCvr09cXPz169e1tbU/f/7csGHDr1+/6uvr8/LyIEf0f/36ta6u7s2bN3/+/NHX1y8rK5s6deqVK1e4ubkfPXrk4uJSVFR07969mpoaXl7et2/fJiUlubq6FhZCr2yuqamZPHnys2fPhISE3rx54+vre//+fQMDA19f31evXlVXV3d2dkJ6MuvXr9+wYYO4uPibN2/4+fn7+/t//fpVUVHx69evL1++ODo6ZmZmtre3v3z58uvXr1VVVXp6oKuZly9f/unTp7Kysl+/fpWUlGRlZW3evPnGjRssLCxycnKNjY07duyYM2eOhITEkydPOjs7paWlIW6D3HQKsRoSGqPkaAiMhsBoCIyGwBACI6gxCmnDgdpzoCbd/8uH9sv5xpz59NOGl4OFn/PDj28/376Vk9JiYWB69+rF//9/mf78Z2Bh/vePgYWVg0uUX0FC4v3DB6z8Aox/fn9+8+r3n98czMyvnzz58/cHExvPHw7Ozx/ec3Gzn75wVUFG/Mv377ycfApyin/+/mH6/evR82e3Hj3k4GBn5+P/+fmboAD/4/v3f/6Q/PDz97cXr5n//FBWkGf4z7T79Hk5cZHLYhJczCxvnj0G7TJlZPzPzMAAOaWP8T/Df9CGU0qSF+icP2HhpoomHgGeO3fu/P3718nJKSMj4+/fv9HR0ffv35eSktqwYUN2dvb69esDAwNnzZplaGhYWFh4+/bt/Px8R0dHyAX0kFGuGTNmiIiIpKen79ixg5mZ+ffv37Nnz/7x40d4eLi9vb2hoaGPjw8nJ2dubu6WLVtMTExOnz69du1adXX1hw8fQq7fnDp16vXr1+Xl5c+ePRsfHw/32pcvX2pra8+cOcPPz29vb79792743e6srKz//v37Db6ljZ2dvbCw8MyZMwUFBebm5pDbJvPz8/39/T9+/Pj48eN//0Anv8CNZWBgePHixZ8/f9asWfP///958+b9+vUrJibm2LFjX758+fDhw/z58xkYGAIDAy9dumRubs7AwKCqqgq5U/TcuXMPHjy4cOHC79+///37JyoqmpaWpq+vf+XKlezs7KioqOLi4gULFvT19bGzs+vo6Fy7dq2trU1SUvLatWsvX75kYmIyMDBgBvdtSktLZWVlCwoKdu3aJSgo+Pjx43nz5jEwMGRkZGzduvXbt29v376FuPnFixe/f//++vXrtWvXtmzZIiEhUVJSYmhoWFtb+/jxY/j9BR8/fuzt7a2oqLC0tNyxY0dHR8eSJUvi4uKePXtWVVUFv3Vz586d7969mz179r9//8LDw8+ePfvu3TvIYPOLFy9SUlKioqJmzJhhaWmZk5Nz7dq14uJiAwODe/fuRUdHR0REbNu27dWrV/Pnz////39ERMSHDx9sbGwWLFjg6+t74MABLi4ueHPw48ePP3/+bG9vZ2VlzczM3Lhx48+fP3l4eDo7O9+/fx8XF+fi4vLkyRMZGZmKigrIsDoDA4ODg0NpaWlubu6dO3e+f//+8uXLCxcuLF68mIWFJTY2dvfu3SoqKlOmTBEWFm5qalq2bFl1dfXly5cLCwv9/PwgY6uQQBsl6RACf/79337l0+cfkLIJn4WQQQBowYtPIV3lIFNkxFjJzAw6yO8f6IBn0CIpJkamv/9AZ5QyMYIXTUHqFdDyfhQuMSYTo4aRkZGJiQliOzHqR9VQJQQYGUCbkz20eXnYyT/LliouGTlgJDVGUWP19vkLzA+uiksb/GH8Iyou9PINi6oE771r14UlpP+Ki7A8fs789cMfpt//GVmYmZlZGZj+MjN9YmZi5eaX4Ob8+5/x779/376BpjgZ2Bj//Pj9j/Hfjy/fpMVEhRWEfrx///39t99/Wf4yMIsJCnx8/0GAj+/T929vnr34/e8PLzfX97//eQREnj15zsrNzcfNeufajfevXuro6h0+8s7eVE/y/sPv7z8wvX35/89vBvCyfVCbA1R2Qkp1VG+QyGNiYvz25Ut+fj4rJ2tgYKCOjs7Dhw/nz5/Pzs7+4sWLz58/h4eHNzU1OTg4fPz40cHBYcmSJb9+/SorK/v9+zc3N/ePHz8gFv79+9fExARyjbiCgsLbt29///59586dqqqqv3//MjMzQ2b5586dCxkLNDQ0/Pv3r5GRkY6ODhMTuNT+/19UVNTe3n7Xrl06Ojry8vJyctCpNwYGBm5u7qKiou/fv7e3t69fv56TkxPerIRMJUNIBgYGyI3zHBwckOvUxcXF7ezsuLm5P378CFcDcTOEhJTs165ds7GxAd+XzqalpfXgwYP///+bmZlB2jRSUlKfP3+GqBcQENDX19+6devDhw/z8vKOHz/+588fNzc3Jiamw4cPL1iw4O/fv9+/fwcNnzMzMzIyQppWnJyckBvkWVlZjxw5cuzYMQ4ODgkJib9//yorKysqgiYQVVVVP378+OLFCwMDA8gorJGR0dWrV8XFxSFT0oyMjBDG379/bWxsZGVBs6KxsbGdnZ1RUVHi4uL5+fkQR75///7379/6+vpMTEwWFhb9/f0/f/6EzJhDTIYou3///p07d6qrqyGB+ffvXyYmJicnJ3Z2dllZWW5u7pcvXz569Ojhw4cPHjz49esXPz//r1+/RERE3NzcuLm5b968aWJiAplGNzY2/vnzp42NzeTJk2/durV//37kO1p//fplamoKuZ7A0NDwzp07//79u3LlSkVFxd+/f9nY2CAJycPDg4+PD+I2BgYGDQ0NMTGxs2fPnjhxwtHR8ePHjw8fPqyrq/v37x9k9cWvX796e3v///9/7do1XV3dv3//CgkJOTs7Q8Icbs4ogw4h8OvP/7yVTwS5mHg5cNbWjIyMbGzs4Bkexr9//0A6kGhuY2RkZIfv2mRk+P3r99+/f9DUkMFlYmJiZWUDb1UENYNZWFhAW6nAPVhWVlZm0FV2IPFfv379+/ePjY39zx9QD5OBgYGNje3fv3+QhTGQhR/379///v2HEOi6DR5mZuYvX74KCIDWBkCyDwsLqIKALFtiYgLNppHhWlxamJiY//379/79e15eHohjcKlkYGBgYgJtR4ZkbTzK6CzFyMjIysrGxMz0/9//379BoQ13ACgiWFgY/v///fs3ZDMrIxMTGysbIxPjv79/f/3+DVqZxsAIVsbM8P//r9+///0FdQPgJtCO8fvv/2vPf5yt0uARxZm8aWf7yAQjtDHKyMj0+cPHk7u36+eZ3nj1jY2F7R83j7uB3pS1W6+/ec/Iw/OHi+P/NwbGv/8Y/vxiYmP7/+/vi+dP//76JcDHz87J8eXXTx4+3j/fvn/59/fvX9D9xn8/fxIWFf7w+i0HD6ekgvyrV6+U5BV+fP7+8+dPRiaGr1++SEtJcTOx/Pn/h59f4OGDB+x8nP8YGEX5ecVFhK///sXCxfn+8xdxPm4+du4wr4B5a5b9Y2Fh+v3n37+/oDWjoKFc8P560KEp/8FZlMzk+uPHj/T0dCcxx6vXr9bW1goKCs6fP3/ChAny8vKxsbF//vzR0NDg4+Pr6+uzt7fn4ODg5uZ2c3MLDQ39/v3779+/Iast4Xb///8fMvfNzMzMxsZmZmZWXV39+/fvb9++MTMzh4eHd3Z2mpiYlJWVwQv3f//+QQpNBgaGX79++fn51dXV3b59OyAgAG4sZNEkpO2lpqZ248YNXV3dLVu2QBRcu3ZNXFwcuf3x588fSKMNooAgycTEJCoq+ujRI4jKZ8+eWVtbP3/+HMIFBTbYX3AuZPOToaGhv79/QUHB79+/U1JSVq1adfHixZ6enm/fvkVERIDGTP7/hxSpEBP+/PnDxsbGwMCQBgYQ0w4ePAj3/r9//xgZGYWFha9evQqRffr0qaqqKjs7+9evXyG7oF69egWphOC6lJWVFy1a9O7du6VLl7a0tMyePRvS6v3z58+XL194eHjevHnDxMQEHw2FmAwh+fj4TExMGhoaIOtfJSQkVq1aBam9ICQ7OzsfH5+jo6O/vz9kFS8XFxd8VEZOTu7UqVMQo+7cuaOvr8/KympjYzNt2rSfP3+amJhApBgYGFhYWF68eAHhPn/+XFpa+ufPn7a2tkVFRT9//vz27RukXQ7qZUEUwUg/P7+lS5f+/Pmzo6Pj+PHjWlpa9fX1DAwMnz9/5uXljYyMzMjI8PT0nDx58oMHDyAWQVz+69cvFhYWeCjBzBulaRgCrMwMixIVdaQ4cNrx99fSpUvPX7jAzsbuH+BvZmGNqfLHt88L5i/4+PEjZMTRx8dHR88QUxmpIu/fvNy3f39waDhkP+jli+e+ff1qbmXLwMBw8dzptevWff36VUtLKyE1gZmNbeP6dXa2doIiYgwMDPv37JSQkNTUAa0buXb54oePH75r/r5w4ZKKiurr16/lFeRv3riRk1/EwPCvp7MjJDD095+fly9dCgoNa29piYuPk5ZVINWpeNS/efW8pbnl69cvNbW18ooqeFQyMDB8+fSemYmZkwfRu8Ovnk6y//9s37rt0qVLMrIy4WFhLOxccHvPnjqxb98+Pj7e8NhwAWFQ4P/+8XXV6tVPnjwxNjJycfeCqDx5/MihQ4cEBQTCosP5BIQhgrQmP37/q99ynda2jJqPDEB9KWT+iGGDusVn9u3hfnv/AyMzIzMbN8M/NjEhdU2F3//+/n7xivHfnz/MLP9AZ5D8Zvj3j4Ofm/nH758vXrx79+b5yxevX70S5heQlZcDTaGwsTH++/fv89f///7zCAu9ffzs44vXwsKCT+49+PnrOws7m5iImJyszNdPn/j4eP8zMMhKSIQE+EvKSKhoqDMyMr5/+05RVUtQWFJKXIqZjf3j5w8/fv3kFxfj4OMDVdWgwT0QBsUL6K56kLNBbHIxBwfnjJkzlyxccurUKV5eXgkJif///+/atQsyXQ5pXLq5uR0+fNjT05OBgSE6OnrVqlVLly7t6upatmwZzCkg6yGNANAB13///vr1y8PD4+LFi3Pnzp0xY8bEiRPZ2Nh4eHj27ds3c+bMEydOQObWf/36BdLJwAAZe/jz54+SkhIvL+/58+fhax9BtcXFi7GxsdOnT+/u7t6/f7+vr6+Tk9OnT5+Kiop6wSAjIwPSRIOYpqmp2dvbu2rVqh8/fkAahZDmIMSF8LbU379//4Pbi3///g0NDT106FBPTw+k9Wxvbw9pbUMM/PPnDyjwIRwGBiMjow8fPigpKUlKSrKysnJycoqKigoLCz9//nzjxo3Tp0//+vXr////FRUVHz9+3NLScvnyZX4waGho2Lt3L8wYEP3v3z/kQPj165e/v/+9e/eam5tbW1tv3rzp7+9vbm5+6tSpCRMmtLW1QebiIc4G6WdgWLJkSU5Ozt69e+/evSsnJweJEXFxcR8fn4KCgunTp1dUVERGRrKzs0NGayC6IKS7u/vdu3dnzZo1e/bsnp6efzAAkf39+zczM3NERMTy5cuXLVvW1dW1du1aFhYWeAvbx8fnzZs3mZmZVVVVV69ehYy8BgcHb9u2zdjYGLIZC2IUGxvboUOH+vr62trazp07FxgY6OPjc+zYsfnz50+dOnX69OmMjIyQ6ICoh5POzs7nz5/n4uISExOztLT89u3blClTFi5c2NbW9ufPHxERkaNHjy5cuHDXrl0sLCyQTWkQvY2NjceOHYOwR0m6hcCb9x9x2fX////issrd+w46Ortp6uhVVNXu378fU/GTZy/nLVwsLCohJSsvJSPPxoG0FwlTNdEij5+9TE7NWLFiJUTH7r0H1qzfxMDAsHPnzvyiUkVlNRc3zwuXrmZk5/769Wfz1h1r1m9kYGD49u1bRXXdzDmgNTMMDAyTp8149OS5po6ekqqGhLTs+UtXdu89wMUrwMDA8OfPv5Nnzr94/ZaJhZ2Th//377/HT535+PkbxDpqkQsWLWVm45gweZqsvBJBM2fNmb9i9VqCyuisYMbMOQuXLBOTlD5+8kxBMWiGDeKAHTt2VFTXcvHyP3n+KjYh+fXr13///s3Izjt6/JSgsNikqTMmTZrEwMCwZs2amrpGHj7Buw8ex8YnvX//HqKd1iTmzbS0tnEUgPYj0yEUjm7b2FlXtfncdUpG9ajrTiZGxn9MDIGl5QbxBQ+fv/367buqEMvDWw+X7z7x59M7pu9fGX79Zv7z69//f2w8fPKqql8/f/z97euP9x/+/vn1n5FJWklZR1P95IEDL968YmBl/fP2LaeEpJS0ojAHExsDAxsX50fQVnR+XnZ2djbW9x8+fvr65ccP0NmlAnx8fAL8H968EZUU5+PlEeTnP3rm8h82du4/vxSlRZ1MjV/8/H3o+rXnV64+OH+WgYuDiZnl39fvoHj699dAgs9CXS61qcdIW5O80Cg8VKL0U1HkqdDvf79dXFyk5KTu376/d+9eTU1NOTk5Pl4+fhH+vz/+3Ll7V11bHWLFrWu3Dh8+LCoq6uLiwsUH7dd+//z96dOnKhqgzvrTB09YWFjEZSRePn25c8cONnZ2FxcXEQmRl09f7ti+XUJSUlNDk5OTg5+f//6DBxBj//74c+vWbU09kC+a65vZ2dnLqsog1kHIKxeunD59mpWF1d7BXlYRND394/P37Tt2vH//3tHBUVENNM0NUQmqGH782bdvHyMjo7OT061btzU0NBhYGRj+MFy9elVbX/v5o+esbKwiYiLXr17X1NG8ff2WjIwsJx/nmxdvtm7dysXF5eXlxc3P/erpq5+/fkLsun39tpSkFLcAol68d/OehKQEFx/Xs0fPGBkZJWUlGRgYjh86dhU23a+oqMjIyvjgNuh0JyNjY0VVxfev3x88eFBWRtbYwhju1G+fvj198kRVS42BgeHlkxf/GRgkZCQ+vv24fdu2f///e3t78wuDZgDv3rx7YP8BPT1dISEhBQXFr9++vn3zFuLr/7//Hzxw4NatW3Jy8u4e7oysiMUbxw4du3r1qpGRkbE5yMbXz179/PlTBhx6cAe8ffl2+7btTMxMLs7OYtLiD+8+FBQQ4ANbev3SNVU1NRYOlhuXbxw5ekRSUtLFxYWdm/3m1ZvqamoMYIvevXx36fIlURGRufPmqampZeRkfH4PWtoxa+ZMZIumTJjy/PlzV1fXRw8fubm5SciCtjM/ffh0z549XFxcLi4ugqKCt67dkpGRgacouAujw6KSkpOd3Z1BA6LvP2/dsuXX799Ojk4yijKf34OOCODi5DI1M/3375+souzVi1e1dbQZmBmuXrwqIyMDCT24UQQZG+9uPvTkcK99F0GVowrQQuDbr3+mnXc/rMkItdcKj4wyNjaGzAPAlZ0+fbqurm7jxo0QcUi3Sk5O7vbt2/Pnz//8+bO3t7eHh8ft27dLS0vXrFkD716+e/du1qxZjx49MjAwiIuL4+Dg2LhxIysr67Fjx3Jzc1+/fj1//vyfP38GBwc7Ojp+/fp17ty5N27c0NbWTkpKgi9KOXfuXH5+PhcX18SJEzU0NCZPnvzq1auGhoawsLCMjAxXV1dIfzUkJCQ7O/vjx4+bN2+eN2/ehQsX6uvrubm5Z8yYwcjIGBISMmfOHB4enkePHgkLC9+9e/fv378iIiJ6enp//vyJi4srKCgQExO7ffu2g4NDdHS0g4PD3bt3ubi4srKyJCUlt27dysjIePz48bi4uDdv3qxevfrXr18QZ1+/fv3OnTsPHjyQlZUVAAMDAwMGBoaVK1daWlpCFizt2bOnvr5eVFTUxsYGYsL8+fN///4dHBxsa2v74sWLBQsWPHr0CHJixrFjx5qamnh5ee3s7OLi4g4cOODk5ARx26VLl4KCgjZt2sTMzHz69On09PQfP37Mnj3769evnmDw4cOHOXPm3L9/X0VFJSYmBn5Gx48fPxYvXnzu3Dk5ObmMjIz79+9//PjR0dGRgYFh69atSkpKIiIi06ZNe/Xqla2tbXh4+Nu3b48cOfLp06cPHz7k5uZC+smPHz8WERHh5OR89+5dYGDgmjVrREVF//z5ExoampmZ6ebmxsDAkJmZaWlpqa6u3traunHjRkZGxrt372ZnZ8+bNy8jI6O8vNzaGjSmnpyc7O7uHhYWBkljP3/+nDt37pUrV8zMzHh5eV1cXD5+/LhgwYIXL14YGRnFxcX9//9/06ZNP3/+PHr0qLOzs6am5oIFC1hZWXNycqSkpD5+/Dhnzpx79+5Bkhk7O/uOHTu2bNnCyckZHhqiqG1q3HZtb4Gqsig7xLpRktaAftP0oMMxQZjWPiLWfNCFdH8ZjmzYaBQQzi8q9eTFu9sfvzz/y8IiLv6Xk+PfwwcMTH/+MbKw/Pv989vXpy9esnBwcfIKcXFwC3Fw/mH494+JmYWD09HBaeuObX85ODh5eV88evyaR0BIWIGXh/f500esLIw///zl4mH7y8EuIs375f4jfk5eFha2X39+vfv69f2HT8+ePuHg5lbT0fzw/SsnM/uff8wvP339/PMP0///r1+9ef3mAwMDI8PP3/8Y/oLOGWX4x/CP4S8TI0iQWC9iUff3798LjBflteUZGBhmPpvz5/EfVhYWJhPmJ/+e/3/1/9+Lf/9uguaOWZhZfh/9DdEPUmDE/JThxekr5/79h+4HYmJkYmJm+nMUtLoLMkX+9+FfFmYWZj3QCptrD27+vQtaOcpiwPLg35Njb07+/weCzMzMEC2MjIxwK86LnJeTk60+WgexDkKysrIyGTAxMDLcfjX/zzOQLaBFYAqsDIoMC98vgbsNopiJkYlVlPXf/38HzxxhYWH5c+rPf4b/jAyMLCwsy46uBDnvP8O/u39ZWFh/H/vNwsLy78q/f///gVyrz8zwn+HStav//v1jBq33YoTYBVLzHqQGYj50OvgdaCCRBbTajOHPI5CT2DjYGI0ZV3xdzfCV4c9rkKWgBWTKzBffXP37Cux9eZbz/y6tOwoadIEYhTXcmJmYWTRAObH39oS/N0CLokDmmDA//veM4SPDn3N/QJsYGKGhDVqDJcDKaMb4nOH1kdPH4SO4jAyMrBysjKaMW/5th9jIzMTMwMjw9xnIQIjtII8wszDrgOLoxuMZfx+AouzfK5C/GBgYWFlY/5z78//ff1Y2ViZjpqcML05cPP3v/z9WUJD+/c/w/++fvz9+/BAUEvz/6/9NtdvvxT89Plr35fOXH26/pz6ZyfQMMc1yT/jeX4G/bHxHGPUZpz2d+ffRX4b/oLl7SPK4dOfqv1v/QIEMjgi42xgYGH7//v3S4u12lp37jh5kYGBgZoaGzOyX8/48BS3GYNECDYief34J1Al59oeVhXX5iVX/Gf6zsrD+vfUXMhaObCB+9qNPj8W4RPGrGZXFHQL/nV1cjh9as2TZcnNz8xkzZkCW1kDUX7t2TVZWlo2N7e/fv48fP2ZkZBQSEnr79m1VVVVISIiCgkJfX5+AgICUlNSHDx8OHz7MwcEhIiKiqKhYVVUlKSkZFRW1bNmyd+/eVVRUTJkyhZ+fPzEx8eXLl6Wlpampqdzc3Dt37jQ1Ne3p6fn7929ycvKaNWsmTpxYUVEBsR0y6+Lr69vQ0LB06VJQDmJi+vjx47t37yAbE0H3gjIympubX7x4MTExcerUqR8+fDh06JCbm9u1a9du3rz5/ft3KTBgZmaGHI6GfLYdxBZmZuYrV65s2LDBxcXlzZs3Fy5ciIuLO3ToUHFx8RIw+PjxY1paGg8Pz4oVK7y8vNjZ2Zubm5WVlR88eFBSUpKZmamurn7p0qVFixbNmzfv8ePHixYtgrT2GBgY5OTkZGVlxcXFTUxM3r59W1lZmZ6ezsvL29fXJyIicvv2bS4urrS0tJkzZ86bN8/d3V1BQYGfn9/U1JSLi2vBggVaWlpiYmI3btxYsWJFUFDQjBkzeHh4EhMT//z5U1ZWFhoaqqCg0N3dLS4uvnPnzlevXqWmpu7du/fy5ctOTk6QlvqkSZPu378fFRV14MCBmpqauLi4vr4+Kyur79+/z5gxo6+vr7Ky0sTExNvbe9q0aZycnPr6+gUFBTExMZ6enpCWKAMDAyRJfPz4sbOzU0dHB3I+NGRTJnxPqrGx8dOnT5mZmeFTPYqKiqysrLdv3/769StkhT0DA4OOjs7NmzchIc/AwDBx4sSrV6+mpKScO3eupqbmxIkThw8flpSU9PHx6evr4+XlDQ4Orq6ujo2NDQ8Pb29v5+bmzsnJOXToUFNT05QpU5qbm/n5+ZOSkpYtWzZnzhxnZ+f+/v6WlpZXr14dPHRIVsMQ7gW4jaMMmgJQFUhTCyCG//nzh4OZwURJBnw0EURsgElGBtBoI8Pfb/cXTWOVUub79kOUhUHk+3eGly//MjD/ZH7H8PsrI/N/ZqZ/////+/vuEws7JzMjIys7m8B/HjZW1p+/fzKef87Gzm7O8+vX3x/iYsL33v3+//Gu+MsfjM//iXx4Jwa6SpSB4yvnrz+/mVlYBN+9Z2Vh4fnx/T+4xpVg+MnEy/Dj9zuue5fMONj//X7OyszM9oPpy/UTHz98lHz6UIjlyz8pHtBdJf8h7c///xkYJbiYv337/u8vtEVIRggGqPg//fyUDI2006LnqfsXDGhnxajJ1AkB0KFjoDUpjAyMmh4a4Ej7yyjMaKVqCVp7gLSERMdcG75fhDSrGRlM/I1//f71H3ScGWlayVCtIaguxwcadydD76iWv3//BgcF35diaWxpO3DgwLt37yAtD0jIQDYyQpY+T5o06fLly8bGxu7u7s+ePYOcQMzOzn78+PGwsLA3b96sWLGCiYnJ2tqaiYnpzp07U6ZMYWFhkZKSyszMLCwsZGdnz8rKcnJyWrZsmZqaGuSAOU9Pz8+fP+/fvz8gIODu3bu8vLxHjhyBW8rAwPD9+/eQkJCjR49OmTIFMmIK6asgNzJYWFh+//4tICAgLS194cKF27dvZ2ZmsrCwnD9//s2bN+bm5qCuLMQ/OEhQfwm8YoSbmzstLc3ExMTY2DgkJOTp06eMjIypqakB4NXwaWlpJ06cePTo0YcPH549A82uGBkZFRQUMDAwyMjILF269Pnz53v37jU1NYWvy1cDA1lZWQcHh82bN//48ePLly9fv3798ePHwYMHMzIyjhw5cvny5d+/f1++fDk7O1tDQ0NYWNjR0fHfv3+cnJyQ9dMsLCyQzZSMjIyQKzAOHjz46tUrSBRwcXEdOHBAUFDw3LlzFy9etLOzg6/8/vr16+7du6dOnaqmpmZtbe3j4yMgICAsLHzhwoWPHz+qqKiwsLBcv37dzs7u3r17nJycu3fv1tXVlZeXLysrQ96VyMDAsHbt2gULFhgYGLS1tUHW9kC2W8EjArLhFTnuIFsRIDsNEP1t8NoeSDz8/v37GHgwWE9Pz8DAYMuWLZDFV0ePHr1y5cqfP39u3Ljx9+9fCQmJhIQEWVnZ/fv3s7OzOzk5KSkpFRUVPX78+MSJE3FxcXfu3BERETl48CAk3E6ePKmoqJiamvYbHKcQu0ZJ+gA6NUbFpGXd/AI5OXAvdaePdzFs+c/A8OfnT4Z/H5k4GUGHlPCxmAuCKidGJmlwG5ARejopaEz3PwMIMoDyBqhlCGrL/mdgYJYXZmBg/Pf/H7O2AsP//6AtRwwMjODDOEC2wWpoRiYZ0BkgkJ2loJlVMGZk+PfvPyNIAor//PvLJMJrrS4Pum4JrBhkCAz//ff3Lzu3lDj5YzlOsg4ww0bp0RAYDYHRECA/BFhYWaOiI6RZ3kdFRcXFxWlrayObJSUl9ebNGwYGBj4+vt7e3n379kHO6GViYvr9+/efP3+cnZ0tLS2/f/+upKTU29sLWXN848YNJiYmSBMQsiz479+/7OzskA2Lv379gjQrIRaBimLQxs7/379/l5SULCwshLTAILKQBk1VVVVOTg4nJ6eSkhIfHx8bG9uDBw90dXUhaq5fv+7i4sLAwODk5LRo0SIWFhY1NTXIAcCQU34hygiSkMssILsGIc1TyApsiLMfPHgAORJYTU1NUFAQNBPx75+QEOgigP////Py8pqYmGzZsuXixYvIx9uBxv7/QI8ggOzPg9wbEhMTY21t3dzc/OLFCzs7Ozk5OUj7HtQhhDkUeU8nZHE2CwsL5KiQX79+waPAxcXF3NxcTU1NUVHx9OnTGzdudHd3T09Ph4yMQjwFaTiCJqZYWb29vdetW/f3719fX1/I0PKfP3++fftmZmamq6v7+/dvISEhSNzBHMKwdOnSNWvW9PT0qKtD132B5mdYWCBHZECUPXv2jJeXV1hY+MOHDxCRr1+//v37V1xcnImJCXwkAkj49evX8Gv2IHsDIM1Z+KkjNTU1f//+tbKykpeXh2wVgJy1ApljgTSR//wBTTT9/v0bshAfknJsbGy0tLSmTZu2bdu27du3r1i+rL5jEnJaAlk/imkM6NQY1TQ20zQ2o7FfRo0fDYHREBgNgdEQoFMI/P71q6qqKsHLVFoG1IFHs9XOzm727NkTJ0709fVlZmY+f/78////Ia0xHR0dGRmZAwcOCAkJffz4EdLGgmiXl5cXFBRctmyZvb39ihUrtLW1OTk5f/z48Rd8po+ent7SpUsvX77Mzc29b9++0NBQDQ0NAQEBX1/fc+fO8/HxQVonDOAtkpBjm0RFRQsLCz08PPLz89nY2Pz9/RsbGxsaGnh4eA4cOPD06VPItLijo2NpaWlWVhYLC4uuri5kwAw+QQxxGxoJMf/fP9CJKkxMTK9evVq7dq2AgMDJkyf//PkjJSUFd/bdu3e/f/8eHx//5MkTyLDoX/CmT0h7joGBITg4ODMzU1VVFd5KhtgFabVDTj37//+/qampgIDAgQMHREREjh07VlBQYGRkdOjQIUjg8PHxXb58+cGDB1JSUtzc3EePHuXm5t60aROk+fXz50/IwLCGhgYPD4+WlpacnNy+ffvExcVXr17Nx8eXm5u7adOm/fv3Qxqj3NzcBgYGK1eujI2NPXz4MDc3t5SUlJiY2MyZM/n4+GxsbP78+SMqKiovL29oaHjkyBFhYWHINSWQHgLE/ZBVm66uroyMjFevXmVjY5OQkDhx4oSNjY2zs/OMGTNKS0tfv369e/fuvr4+JSWlvr6+zZs36+npLVmyRFpaWlNT09LScsKECYWFhc+ePTty5Aj8EhM2NjYTE5Np06ZlZWWdOHHi+vXr////v3jxYnNzs7y8/Lp16yD36v38+RPinj+wZj1k16OoqKiqqqqAgIC7u/vp06cFBQXv37+/c+fOyMjIP3/+JCbEP332jBm0+wPij1GSHoBOjVF6eGXUjtEQGA2B0RAYDQF6hcD////9fH2lJbFf8s7FxdXf39/b21teXs7IyCgmJpabm6ugoJCbm9vT083AwKiiouIGPr/Wzs4WNnv7n5OTs6mpqau7e9OmTdLS0lVVVYyMjJaWFsLCoHFEIyOj9PS0lpYWRkYGe3t7Pj6+mpqajo72/fv3c3Jywo7dBS1sEhISMjc3B7dN/1tYWHR3d3FwgO4XzcjIYGVlbWxs/P//v5CQ0IQJE8DT4v/l5ORSUlI8PDwYGBjY2dkjIyMlJCTAroLNbaGGKiNovamZoKAgExOTiYnJv39/g4IC//z5U1lV+ffP35aWFg4ODjMzU3Fx0N2kFhYWRkZGOTk5GhoakZGRAgL8LCwshobwE6z+a2tr8/HzmZubQw7xhVulo6MDuUxYW1s7OTm5qamJiZFR38CAjY2toKBg7ty5mzdtUlZWFgTf9xsREdHa2trW1lZTU1NXW9vc0nL48GFNTU19ff3///9bW1tD1mvKysrm5ub29fUxMjIqKSl5e3srKytPnTp1+fLljIyMubm5ENuZmJgqKira29tLSkq4ubkbGxshbouOjubh4YGc0FxdXT1lypQFCxaIi4ubm5uzsbFZW1uzsICWpEMM+f//v7Gx8YEDB44fP/7v3z8pKan8/PwFCxZIS0vn5eV1dnaWlpYyMjIWFRUZGRkxMDC0t7dPmDBh4cIF4uISLS0tzMzMJSUlHR0dJSUlEPdALuGDGF5eXj5t2rTe3l5DQ0M1NTVWVtaUlJSuri4ZGRkjIyNZWVkmJiZ7e3vIULq2tjZklJqHh8fGxoaXl7e2trarq2v79u3c3NwlJSWiIiJv374tLCz8//9/YGCgpqbGn423wFOhENtGSZoD0FwzzS0ZtWA0BEZDYDQERkNgGIXAt1//DFqur0lT0kO6gx6r/35+/8rwn4GdC3E2xd/fP0ET7ty8MPX/GBgQu99Agv//ff/2lROhANS+BImD8Z9foIFSdk64gf+/ffnMxc3DwIhqCGjpEyNYB4RAGPLn1w/QNgYuCu+phxuIcP+Pb1/YOTgYmSCjPHAFIAd8+/KJi5ubgRHeVoPL/jt/+nxTS9OcObOERUGNV5BqKIarAfF///z+9+9fDpizf//8/u/fP3A4IJT9+vGNDbQcjun/3z+/fv0Ey4L0gndrIEIDPQr+//329StodSkzetfi+9fPnFxcSM6GmAYl///9/f37dy7E4aaIoICqYPj3D3ZSHhMT848f3ydOnJiWliYEPtX1B/jiGGZWpB3r//78+PED7keIIT++fWFjY2NiAR3bDBFhYGC4d+f223dvDfT1L126VF1dDbobhZv35/dvTEyMrOycMP/C3YMIIgYGuOC/b1++cPHwwJPfj29fQBepcPF8+PbXoPX66G56eoLRxig9Q3vUrtEQGA2B0RAYDiHw7dc/7abrvGyM3DhuYGKEXtcBWv0OWYMI9zZ4wBK8+B4uhM5gBK/4xL5TE0M7RPF/cPsD3SCsfAwTsKoiRxDkbLBvMTUzgq8PxXTkv3//X7x4+fPHDyVlpf+gjXvYx2Ihc/qMDAygc2BAVjAwgg5XQQlGcGMTdG00smLQ5X0YrgHvRQRtdYDIQLighZigFjxEDEqi3XoKFYVREI3wU1ZgwthpRgbGHz9/fPn8RVRUFKIFFBGg4UeEl7EaiKmMgYHhzevXX758lZOTe/369ffv3xUUQdcNQFWCNnVgdwOyKMQucJhDhUHaQWH7//ff/7df/TxbpTF6tBPdwGhjlG5BPWrRaAiMhsBoCAyTEPj77/+eG5+//MTeXmRg+Pfj9yfQsXSwJukQ9TbWlhyoqccAbfNR7i9GBgbQsXIMTKCdRgz/2Zh4WJiRRgopt2AwmcAIuicG5FPKHcXExAS5O4MJzPoDXlVMubEQExgZGFiZGZ3UebnZ0YbbIfKjJPXBaGOU+mE6auJoCIyGwGgIjPgQQAx3jfigICkAwOObJOkYVTwaAkMfQJa2DH1/jPpgNARGQ2A0BEZDYBCFwGijahBFxqhTRkNgkIPRIehBHkGjzhsNgdEQGA2B0RAYDYHREBgNgeEMRhujwzl2R/02GgKjITAaAqNgNARGQ2A0BAY5GG2MDvIIGnXeaAiMhsBoCIyGwGgIjIbAaAgMZzDaGB3OsTvqt9EQGA2B0RAYDYHREBgNgdEQGORgtDE6yCNo1HmjITAaAqMhMBoCoyEwGgKjITCcwWhjdDjH7qjfRkNgNARGQ2A0BEZDYDQERkNgkIPRxuggj6BR542GwGgIjIbAaAiMhsBoCIyGwHAGo43R4Ry7o34bDYHREBgNgdEQGA2B0RAYDYFBDkYbo4M8gkadNxoCoyEwGgKjITAaAqMhMBoCwxmMNkaHc+yO+m00BEZDYDQERkNgNARGQ2A0BAY5oNN1oF8+fXz/+hUT46C+IO7///9sHBxi0rKDPM4ocd70izNvv7/DysxKiSFE6v3997emsEaqbjKR6keVjYbAyAyBH1+/vn7xbLAXjwwMLCws4rLyjOBi/P/P358ypv/78JWBaVCX6gOeohhZmfmmZzIJ8gy4S0YdMBoCgxnQqTF69sDeRX1tHJxcgzks/vz+Ja+hVTVl3mB2JIVuu/Ph7vlXF9iY2Sg0hxjtv/7+oo9FxDhmVM1oCAzaELh16fykqkJ2Ds5B60IGBoZ/f/8KiYk3LVjFzAKuNf79/3Xy1r/XHxmYR6fXcMfb//+M7KwMv/7iVjEqMxoCoyEAAuBiBcSgLWZkYmJhYYWWYrS1inzT////z8xMpwAh35WU6WRhYmFjZoO0ERmZGZmYmf7/+//vzz9kU5lYmBhRRzv+/f33/+9/ZDVEslmYhnl4EhkOo8pGQwBPCDAyMg7+4pGRkRGtAGdkZwW1tOCN0b//GP7/Z2Bhhvj0/68/DP+ghQYjGwv6AOqfv///goodRhZmcpqz//7///MXZCzEMuLJP+B2IcyRxOtDUUm8IZDG6OjYMUrwjXJGQwALGJi2wr9/4GKLkZGJCaVX/f8fuHhCFYQqhjgepuU/GKBphygZJYkJAUZmxm8vvn198YVDiJNXlvc/rNr4////+1vvfn/5zcDAwMjECBHnEufiluD5/x9ateAxn5GZ8f+f/wyjhS+eMBqVGg0B3CEAKthgxSBkQpyBgeH/v3+g3Acr/eC64YohIoxMjIyMoBL1379/jGAAEacH+ecfozAPEx/X34evQe1ORkZWQ0UmAejc9O9LD/5/+sYAnt8HeefPXxZFcSZpYYbff//cfvb/3Wd4E5aAU///BzVwmZmYhHmZpIX/XH4AN5OARoj0v//MiuIMjIx/H7xkQK1lIPJEkf/+MyuIgQx59Jo024kyfVTRaAiMUDAAjdF/f/9y8fBycPP8/vnj84f3TMywnvS/f+ycXMwsLN++fIaXwoyMjPyCQiysbKD2zX+GX79+fvnwnoGRkZ2Tk5WN/cvHD3CV0Aj8///v379oPXio1CgFCwFGFsb319+fn3Dm1+dfrJwsqmHq8u6K//6AKrB/f/7dXn3z3bW3/37/+/P9DysPKyMjo1KAilqYBqKVCW+UMjIwILH///t/f8NdET1RPiV+8kZSYQ4cpUdDYCSGwP///0ElnogoExPT548ffv/6xcTE9O/vX05uHk4e3u9fv3z/8hleYDKA17hz8/JBysB///59/fTp18/vTEzM/ELC379++fPrF1pr6d/fvwwYLVoqBPSvPwx8nHw9SX8fvvpcs5SRjZmRh5N/WiaTEA9k+PNjXP+vs3cZWVgYGBn+//nLGWXHnePNyMPJ8O/f32fvPlct+XPhHrQ9CilPkHuzcJF//xl5ODgTnL8vOcCiIc2Z6PIxaRJIF0jBfzSfQsslZHMYGP7/+s0Z68jAwvy5fAEjFweoToF7Hrsh/xkYGNGU/f/1myPSjoGV5UvDcujQLKiXjq4MZDCq7SCRUTwaAqMhgAPQuzH69+8fJU3dsKwCSTmFj+/ebF4059S+XSwsrP///2diZo4pqnj/6uXKaRPY2NlBHeh//9i4uBIr6hU1tP8z/Gf4///zhw971q7Ys2aZhYuHppHp7JZaVrBKqO/+/2dhZTW2d7l18dynD+8gZTRUapRCCgFGBsa7629J2Uprxmrf33Lv3qa7cq4KoGVh//4xsTAZl5oxMTO9v/XuwuRz1q22rDxs/xn+//sLkgJN6P9nYGRhAg3V/AXFCRMrE2jMhoHh/9///379e3HyOZ8SPyMjI0gOycZR5mgIjIYAwRBgYmLyjU+1dPNiZWO7fu7Myim979+8llFWiSmokFZSfv7wwYopvQ9uXoOsJvrz57eBsX1cSTWktfT3z5+HN68vm9j97tWLxPK6TQtm37lykYUVsVXx379/MkqqgqJiV08fR2+6EXQZbgX///xl0ZLjrQpmMVT6c/MJSOH//0wifP/ef3nn3/r/xXsGTlBhzsjKArL0719mcUGuJNdv07b/WHucgZudtzacK8P9U8Z0hn//GP7+Z2BjAQ1//v4LamUyMjD8+QuaxGdiAjH+/mUS5mV3Nfi56eSvYzd+nb4DUgNRwMzE8OcfyAQWZogtIJKZCbRYk5kRxAY5C4z//GUALSdgAA3f/v4DMpyREWQ4MxOI/Rs8ic/MBFLDwMDAygxq1P7+A7LoPwPD378gEQYGhp+/oWaCVxqABP/+AzkAYjtoluk/yDQQA2zpKDEaAqMhQAjQuTH6n4WFNTgt+9aFs/M6GkzsnD0i4u5cufThzeu/v3/b+gRoG5sf2roBMtr298+f///+MTIwMjEzr5jSd+PCGWZmFi0Tc9fgiFuXzp0+sPvSiSOsbGz//v79+/fPf1DHGDTpzyMgYOvtd+fqxb9//jAwMEDKYhCbkYGZmeXP798MDP///wO1Wf/9+/fvL2ghJBMTEwsrC6gHTCiwho38vz//dFL1WLhZXxx//vbKa0krKUYWpp+vvp3tPa0Zqy2kLQyao2cG9esZWRgZWRgZQEU044PtoGbrv99/5T2UlANVfr7/eb7/tEaMtrCOyLne08K6or8+/nx/892ZjpOWLbYCKgJgXcMmzEY9MhoCtA2B379+Gds5GljZzmmt+/XzR2ROsY2X3+ZFc9zDYt68eLaot80lOMI7OnFqXen/f//+gMq3/6zs7A9uXl0+ue/P71+cXNwhGXmuYdHLJnbNaa37+eM7MwuoxANN2TMwMLEw//3zR93IWEBI9PyRA0zMzCDEBOpJ/v3zm4WF9d////9ABel/RiYmZibmP3/+gIdpQcUmYiwWawD8+88sK/xj02mWW88YWZhAY59//zOrSv57+5lFS5bRUOnPubt/X39i5GTj60/+ffzWj/XH///4BZolZ2f59+zdp6J5TCK8DCzMjKzMXNneHCHW/7/9+DZl64/1J0BDvzZa3EX+zPKiPw9c+dq+hjvPl1VXXmB+3peeDeweRh+zZrCaqvKUBbFoyPy+eP9r59rf5+5xpbmxmqkxSQowK4j/WHX068RNoCYpfBH8v3/MCmICC/NZjJR/LDv0depWhp9/WG21eIoDQLbsvfSlY+3/1x+ZVSW5iwNZTVUZvv34tmDv94X7GBgYOCPtuLI8/z599+/d578PXjH8/cckJcRTGcJmp/333ssv7at/HbnO7m7IEWLFJCn49+HrL5WL/4OiCWuojQqOhsBoCKAAOjdGQeuY1s2e9uTu7a9fPh39/l3XwoqLm+fNs6eKmto6ZpanD+wBFXz/Gf79++cRFf/7+/fD2zYx/P//4e2rl08eMTOzvHnx1NbLT1BEjJdfUFlLd/PiOdIKyi7BEeKyctfOnNq7bqVTYJi0okpsUeXetStU9Qw3zpvx798/e9+gH9+/ntq7yzs26d+fPyq6hhvmTefk4nYOjuDg5Dy2a9uZ/XtAY3koITM8OaDhZdBCs//sopw/P/y4s/n254efeGR4//35y8zNohalwSXF9ffPXyYm0MYm8OA0qO3OyML44sTzexvvqIVpsHCx3F59i0OYQ85VXsJa6ubKGyLXRb+/+y5pLfXt5ddnR55I2clwSYAMAdkDGlgYniE56qvREKBuCDCzsDy+e3tWc83ju7f//P514dghARExJiYmHj7+Mwf33r50QUpB0cE/hJGRSVhSyi8uZe3saf/+/v3548erp4///P79+9ePo9s3OwaEMrOwuEfEHtux9fnDe1YePmaOrj++fdu1eun3b1+t3X05uLjePH/Kxct75+rl62dPiUpKuQRHbF06X0xaVtfcmkdA4NvnT3vWLLfzCdQyNn/+6MHOlYvfvHjODFtMheLlf6AVnIyszD93Xfj/9SdvUxQjGxNoTef/vywqkqyGSrw1YUyi/H8fv/6YOePf07ffZu74//7rvw9fvrav4Sr0Z/c1/f/x28/9l39uOPH/yw+uQj92F/0vDcsYeTm5srz+3n/598lb3s74nzvPf5u2nT3Ykt1V//vqIyx68l+71zP8+88kIcAkwM3fl/zr7J1vc/dwBFvytsW+D+ti5OFkdzX42r3u/88/PBXBv/Zc+HX8JqQc+v/7z/8//1gt1L/2b/p14iZPSeDP7Wf/ffzK2xrzc+3xX+fvccU68hT4fipfyOFr9v/Tty8Ny1m0ZbnTPX5sOcMiI8xdGvh16rb/P37zFPl9v/+K4d8/npowRg62j3mzWQ2VeGrCPsT0MbCxsDvqfZ2y5ee+S/9//wENrIyOj6IkmlHOaAhgB3RqjP4FF5qQefNrZ04yg/vlFi4ezx4+eHjrBgcXl3NwxJ41yxU1dPiFRX79/PH///97Vy/9+fX7z+9f///94+Dk4ublZWXn0DG1ZGRmevrgnpqeoZSiEgsrW2xR1fs3ry4cPaRjZvnh7eu7Vy/pW9ldOXmMiZlZQU3j5/fvf//9E5WS/vLp4/evX1V1DdjYOY7v3MrDLxCWmX/l9PFvnz+7hkT++Prl9IE9DP8Zfv/6iT2chosoOzM7FysnGzMbBwfnL6Ffrl3uH+99ON5xRNpERlRbjNeI9z9oQOQ/EwvTdxZ2RkZGThZOVlY2RibGD1ff//n398X15/8ZGX/8+f706GM1N3XtAN3P1z/dWnHdrd+TX5CPi42LU4BLUleKX4D/7++/LAzM7MygGbrhEnij/hgNAZqEwL9/f3/9+MHAyPD84f1///4xM4OO81TXN9q6dP63L1/2rV/ll5CmqKktp6qxf8Pq718+f37/7vLJo58/vPvz5zczMzM3L9/v3794+WWN7Bzv37j288d3ZU2dE7u3G9k6eUXFn9i9g5WNzTcuZfnknif3bvPwC967dtk7JvH5wwffv35hYGCUV9f89+cvOweHjafvmYN7r5w67hWdKKeidv7oQVll1bDMgplNVT9/fP//99+vnyjFIyMPB+OPnwxMoFHH/xysjOwsDKxMjLwcjExMf24+/dK+5tfuC4yCPPyTU7niHL/0bfxz6xlo9kqY9/e5ex9Tp7BoyrDoyHNG2LCZqnxuWM5mo/X/208WQyXQUR6MDGwu+r/P3fv/6dvXiZv/f/v569h10GpXScH/n779vvSAVU+B4d9/VmOV/7//fO3Z8O/lhz9XHgquKWfVlmfkZP259+K3ObtBrVJPIyZpETZrJg5/cwYGhl/n7jJysv46cOX7wn2M/FwcnsbM8qLMrJJMQjyMwjxstlr/f/1ms9VmVhD/ueMsR5QDZ5Qts5IEoyAPsygfq7Xmr1O3vy/c9//PPxY1SUYOVmZ5MVZj5d8X7rPZaDKyszJLC7NaajCys/65/+Lb/L2MTIyMvJwM0N30oFkmmiSdUUNHQ2C4AEbIgj9ae+fWxXPHd29nhS1g+vfvr7iMvLWn3+GtG54/fKCiqyevprl33UpjW0c2Do6Te3eygCeY/v//z8nN4xgQwisg9PfPb0ZGJg4urmcP7+1du0rd0EhCRv7qmRMO/iHbly38/OEdD58AI2jW6Z+dd8DBzeuEJaR0zCx3r172//9/UweXnz9+XDx+xCs64e6VixePHTZxdBGTltm7buXfP3/1LG2ExcSP7tjy//9/YQkpr6h4WofGAJr/7fc30ODA33+bNm9ysHeQEJf88u2zu6N7aXmpl68XKyvrb/DqCAYGhvPnzqcnpe8+uIefn4+BgaGipPLwvoMWZpaQGXwNHY2UzJSv379Fh0WdPHZy/vIFXp6enz99iQqNrG2oNbUw/f/v/3+G/yxMLFwsg/pw2QGMi1GrR0MAEgJP7t7et34VZE0RaMsMI6OFqycrK9vBLev///8nKavg4B/CwgpaWH9w87rHd24zMTH+/g3a26SkqWvh6vnzx3eG/wzMLCzsnJx716188fihR3jM6QN7NQ1Nvn7+dHLvTlY2Nl4BwS8f32sYmXFycZ/cu9MjPObu9Sv3rl4WFBWzdPM6uHm9sISkka3j1iXz/v375xubfHr/7oe3b3Lz8TkHhZ87vP/1U9BiUG4+fv/EdOgZJv8Z/n38ClrfCfEDAwMjJxsDA+P/7z9B6ylB5zr9Ay0A/fefkYudkZX532dQaxumFjRiyPD3L8Ofv4y8nIw8nP8/fmPkYgctvoSpAE3l//3HwMry/+sPmBgDIxMTIzf7v68/GJmZGNnZ/v/8zcDO8v8LWMF/BiZ+rv/ffoLWdzIy/v/2g4GRkZGbg+HXHwZWZkZ26NrZ/99/gSZ8wI5k4ub4/wu0+hOkDG7H////Pn5jZGVGCP7////TdwZ2FtC2hR8g7SDTmBgZfv5h5OMEeRamF+oSdpb/yJ5lZGTi4wItUYUpG6VHQ2A0BDABnRqjmBaPigxsCAT6BbCzc2RnZ61bv37fvn179+4VFhK+evWqsrIyJw/o8O0L5y5ER0efOnWKm5ebgYFh3Zp1Tc1Nra2twsLCCxcudLB3CI8Mb6htuH3ntp+/X29P75bNW0TFRN3c3Dw8PPJyc1k56HGu/sCG4ajtoyEwGgKjITAaAqMhMBoClAM6TdNT7tBRE6gSAl9+ffn97zcjI2Njb1NOTo6dl722tk7XpE42QbabT29GpET09PSYW1gwMDB8/PORmY/57Y+3v9hAZ47aetr53PSJSYv9/v2bl5eXgY3hvuP71+xYM3/BAg11jY27N9Z11XV0dBhYG5Q3lRtYGxgZG6M5GG2mCnSUCkwF8glRMDEa0nCXILsBbh9EFqsUXA3tGHQOCtp5BG7ywIYn3Bkgxv//PGy8rKMXMYDCgmL8//+/d19AK0SRIhh0hgb4dCpS9oOC1paDV3BBDMLjMBwqseeZ/4x8XKAhTDzmjUqNhsBoCAwaQKeR0Sd3b18/e4qFlYVXQMjEwZkBfDIzKBD+/T17cN+n9+8YQcfp/VHVNWBhY7t+5hQjM7OWidnf339uX75gYu98/8bV18+eMDJBTyT99/ePrLKasITk+SMHufn4dEwteASFn969defKxX9//6obGEspqZzYuVVITFzN0OTVo4c/vn37z/D/3rXLpo6u969fff38GSMjo5CYuJaJOSsHx8Pr1+7fvAaeNyJYGoKcPID4z5/fIpLSBtb2ZLuh+mjdtffXOdg4mJlBu2u/fPnCxMzMycHJwMjw5/fv799/sLKyQqbh/v3/9/vXbzZ2NsgBeqD7V5iZf/wAbdFlBe29/ffj+w8Ghv8cHJz/QJc4/f/+/TsXF2hG/sePH6xsrMywyII7FXJqwe9foFVuTEyMv36B2rgMDAwgcRZmOBeunlYMRgY2Vrb////9+fsXcqQ/aOEB+DhuyJIVFhaW/+DTamnlALi5oJBFz4BsbGx//vz+R9VND+CangHiO7jleBikqsdjFAMDA2jvCyPDX8ilNfiVYgsQNB3MLKCjesB7ydFkCHN//fvVYtVoIKZPWCl9Vbx5/vTs4f2QsktWWQ1u+bMH966ePsHOwaFhZComJfv+zcurp098//qVV1BQ18zqwrFD/ELCkvKKF48fBh3nCSm9/oPmso3tnG5fvvD+9Us5VXVtE/PfP39cPX3i3atXnDw8+tb2r54+vnP5op6FDa+A4K2LZ8VlFZhZmMWkZeH2EsP4//3XO8cahrefQVcoMTAwMzH9A+/xBx/rBjqzhBhDQGrAqQ3U9wOd1gkSwI1B6QN5YQAk5zIxgw7++PP3L0QKtAWWgeHv1+98M7I5vNG7xLgNH5UZDYHREBhIQKeR0TtXLs7tafvw8zcrG7uYlAwT7Aa5v3//vXn+5NcP0DKj////8QuJMDEzv3/9kpGRUVBU/P+/fx/fvRGVmv7p/bsfX7/CV+f8//+Ph08AvC30GQsbm7CYOBsHx+ePHz6+fcvw/7+AiCg3H/+Lxw84OLgERMW+ffn09/cfBkaGT+/eic6e9fn9u+/fvjEyMLBzcgqKiTMzs3x6/+7zh/cDGQlE283NyqxtYCSvbSAowE+0JhSF3/5+M2DS12XS/vfvLxsHu5qemqCg4MuXL2bPnuPj46OrqwtusoCqBgbQkitIUwnCBZX8T58+WbhwUVRUpIKCArICcIXC+P8/6Nh80LoxUL0C1wV1wK1bt9asWZuSknLhwoW79+6mp6VBWr3v33+4deuWqZUpeCMEVDHtqP//GXbv3rVt2/bgwEB7JzuIRX/B5wUyg5Plvn37BAUFDQ0NIVK0I//9+//v3z8WpJsJ//9nOHHihJauJj8/mfGL1bVg3/0HNQqxSmMIkqoewwAUgRMnTvz69dvOyRZFFBsHM0AwVd27d+/Xr18aGhqYUgRFSg9V/PoHWvNHUCWdFTy5e2fdrKmaxqYvnzzi4ReA2/7106d3r14yMzM/e3ifk5vn5/fv7169+PPnNxs7x9N7d948f8bGwcHFy/f2xTNwtoW2RhkZGd++fP7x7Zsf379dPndmYkfb50+f3r58/v/fPyNj49fPnnz+ADqe5NXTx6zs7B/evH5y7669b6BzUDjcXqIY//8z//hdzXj1xv/PzP8ZdHX08vPyf/z44e/v19HZ6e/n/x+Uuv9BboQCO+8/mA1aTM7MxARa3w86Xe/f4cNH4hPi586Z6+jowMDIyAxa9w86Cx/iBnBDk/Hfv7/////fsWNndXX1woULdHR0ILIMDAw/fvzMz88XExNtbm4GnZT8/39zU/On719Lb/Ezwbq7cMWjjNEQGA2BQQvo1BhlZWX9+vf/zku3wQFxBUxiJe4giUIUMzBcvIUkiJV5DVUUphFVFMS7jFsKJD3YsZ68tMjLl3fv3jUxNiLPrRwcHPPb5305/omPn/fX799KSkrz58/nZeSZ3TXLSsNSykISv7EPXz+Y1j412ClIWkcav0pM2bOPzk5rn5odmXnpwIVt27c3FTQysYCuLvzz+s/9c/eCHAMZQDxMfVQWuXfn3upZqwsKCgwMDMR4xCCmb1i34fPnz7HxsQwMDFcOX1FVVZW2JdmDEKOIJ0+fPH3w4MGSshJkLXdO3zZTNZXmoabtB/YdeHD/fkJyIrJFeNjbtmx7++ZNbEIcHjXESz288PDz58+RXhEEtdy6cWvt2rWV1ZV4VF55euXLly/SJuSEDxvTIF3H/J/hv6S8YkHnJDweJ0/q0+cvx44dffPmzYMHD/fv33fw1mMW2SclJSVCgoJwA/tLc37/IqeNzsjIeOLJLaUQZ1EBwUWrV1/4/Ly/v/8u49cfUvyv+ZnYWDn4uXm+/f75/99/bnYOBgaGb79+MjAycLGyf/7x7fOnz3z8fDzsnEqclkVTO1U9bJilJf4xMLx4+5qNjU2IF9oZe/Ph/Z8/v4VFRFkZmL5L8d1j/PpHQYRZSQLu+OMH98/dv4mPXyCptUpFQZGJgeHk2wdvPrwvZrWAD17AFY8yRkNgNAQGLaBTYxQ0qsYIvgkDNGY2aENjsDuMiQk0qQ0fVybPuZ++fHJ1du7q67p161ZgYODsubOzsrJY2Vn/gw7A/n/lypWdO0GnGfj4+KioqDAwMJw9e3b//v0iIiIhISGQ9iITC9Pv3783bNggKysrICBw48YNLy+vT58+HT161NHR8f////v377e0tGRiYlq7du3nz5+9vLy0tbX/M/5nYmL6y/CPkYWJlZ317/+/TGDjhESE3DzcGJgYXr16tXXr1v///3t4eEhJScF99+fPn61btz579szBwUFTU/Pr168PHz788ePHiRMnjIyMLMArXH/9+rVly5ZXr165uroqKyvD9TIwMJwGAzU1NRcXl5cvX85bMI+RmfH339/ff36HKLt169aGTRu+fv3KL8hvZ2fHzsn+/ef3tWvXfvnyxd/fX0AANFJ148aN/fv3y8jIeHt7QwZ0GRgYPn/+/PTp07dv316+fNnT05OLi2v9+vWysrLu7u4QNTt37rx7966FhYWREajzcP/+/d27d3Nzc3t7e3/79m3dhnUnT55UUVMxNDSUl5cHbfL9/9/Dy0NETOTZs2ffvn27cuXKp0+fgoOD7969e+TIESsrKwMDg+/fv9+9e5eRkfHw4cNqampOTk4MoKth/u7evfvevXuWlpaGhoa/f/++ffs2CwvLnTt3lJSU1m9c//TpU34hASsrK2Fh4T179ty9e9fY2NjCwuL379937tz5////oUOHVFRUXFxc7ty5s2HTho8fPwoIC5qbm4uJgdrrf/78uXXr1r9//44cOQJR9vfv3xs3bnBzc1+8eNHR0ZGNjW3z5s3v3793cXFRUlJiYADF5pYtW4SFhX///c3CxvLz58+rV68aGBgwMTHdu3ePmZlZXl7++/fvW7Zseffunbu7Oy8v79r1a7ft2KaqrmpgYKCionLw4MErV65oaGjY2dnBD+LQ1tX+/fv3379/b968+e/fv8OHD2tqajo4OECiEj8JWtGIX8XAyYLHDqlvPR8vj4e7O8TcmuqqI0eO9PT0ONjbT5o0CRFooNOfIaOqEIXEkv/+/ePh5CotKjYzMFJVVK6vr3/z/KUgD9+Ent66qmoeHp6pU6devXp1yZIlK1as4OLiio+P9/Dw0NHRKS8vf/z4sZSUVEtLi7i4+M4t2+ytbHg4OIuLiw8cOMDNzZ2Xl5eQkNDZ2bls2bKfP39aW1tPnDiRhYERdInTn39w9/3//3/1shXKCkpfvnxZs2JlRUUFw///LP/ByuCKRhmjITAKhgKgy2DUUAiIkeNGJiamD+/f37p1686dO2xsbKqqqn//gm5YYmFhefr0aUxMzNatWxcuXBgUFPT69eutW7d6eHgsXry4qqqqvLyclZWVhQXUsMjKyqqqqmJkZLx582ZMTMylS5c2btwYFBR0BAwyMzOvX78OaunOnr19+3Z/f/8rV66wsGDv+dy7d6+vr+/t27f5+fkvX758+/ZtW1vbt2/fIDHy9+/fqqqqrVu3fvnypaio6Pjx4x8/foyJiZk/f/7Xr1+rqqp27tz59+/fmpqaw4cP//r1q7y8/MaNGxC9oEMA1q1rbm7+/v37/PnzJ0yY8OPHj8ePH799+/bcuXPv3r2DKHvz5s2rV6/ev39/8+bNHz9+sLCwLFmy5MGDB2fOnMnPz//169epU6fKy8v//fu3e/futrY2SHAxMDA8efIkNjZ248aNr1+/Tk1Nraure//+/bRp0xYtWsTAwNDW1rZ06dLv37/X1tZu37791atXhYWF379/v3XrVn9//6dPn549e/b58+cbN258/vwZ4pJ///41NDQ8fvz40KFDMTEx169fP3XqVEhIyNy5cz9+/FhYWHj16tWvX79mZGRMnDjx27dvU6ZMmTFjxv///zs6OpYsWfL9+/eGhoYdO3b8/fs3PT29rq7uxo0bb9++ffbs2fv3769fv/779+/Vq1evW7eOiYmpvb19x44d////z8zM7O7u/vr1a29v77Jlyz58+PDixYu3b99CQgPisJ8/fyYmJk6aNOn79++TJ0+ePn06AwNDTU1NUVHR+fPnIVFz+PDhd+/e5efnX7169d27dykpKffu3bt1CzTYycXF9eTJk7q6OshazxUrVqxdu/bXr18lJSWHDx9+//59c3Pz48ePnzx58uXLl+vXr3/58mXt2rUzZsxgZWVdvnz59u3bIc5gYGDYvn372rVr//79m5ycPGHChG/fvrW1ta1atQquYJSBJwRsbGzWrl2blJSUmJi4Y8cOPCqJkWJkZPz758/Zs2cPHjy4Y8cOAQEBHh6eHz9+cHFxtbS0vHv3rr293cjI6BAY3Lx5c+vWrXp6ej09Pa9fv549e7anpycDA8O7d+/27Nnz9evXiRMnrl+/vra2Nigo6PHjx79+/WJnZ6+pqWlvb1+5cuXmzZsxC5Dnz59v3ry5trbWz89v1apVX79+JcbZo2pGQ2A0BAYhwN4+GIQOHXUStUKAlYX1+MkT2dnZnz9//v37t4SEBGRU5u/fv5KSkhMmTGBjY9u7d29ra+uZM2fmzJmjqqq6devWJ0+esLCwvH//npGRsbCw8OXLlzt37jQxMXny5ImgoOCRI0cuXrzIwcFx/Pjxf//+aWpqPnv27OTJk1OmTJGWlk5KSlqxYoW9Pc59V3/+/IGMMmZkZCgpKbGxsXFwgOb1GBgYbt68eeXKlXXr1nFwcCgqKs6ZM6epqYmBgaGqqkpSUpKfn3/v3r0KCgqnTp2aPHkyFxfX/fv316xZU1NTA5oW/PZt/vz59fX1JiYmb968iYyMjI6OTkxMXLVqVW1tLTw8raysvL29P378WFpaysDA8OXLFxcXl+Li4p8/f3p4eDx79mzJkiXm5ubu7u76+volJSVpaWmQ8UIGBgZeXt7KykpBQcGQkBATE5Pk5GQ9Pb3ly5d7eXnt2bNn5cqVoqKi5ubmM2fOlJSUfP/+vY2NjaioKBcXl4iISGRk5Lp160DDOXCngLdzMTIy/vr1S11dvbKy8uPHj1ZWVj09Pdra2j9+/Ni7d298fPzfv3/Ly8uVlZX9/Pxyc3MtLCyOHTu2atUqbm5uExOTiRMnQoY8S0tLjcFnGjx58uTatWtVVVUMDAweHh4WFha/fv26c+fO3r17nZyc/v37l52dbWJioqurO3v27NWrV/v7+z9//rykBGX9wL9//woKCrS0tLy9vQsLC0NDQ79+/VpcXOzu7n748OHXr1+vXr2agYFhxowZ8+fPNzY2lpWVbWlpYWBgePbs2U/wYenghcUgrzIyMrKzs587d+7ly5dr1qxhYGB48eKFsLBwYmLily9fIFGzfft2MTExR0dHLy8vyOA0SCcD6G62P3/+MDKCTk8vLCzU1tZWUVFZt25dWFgYRMEoiT8EmJmZCwoK+Pn5s7Oz169fr6enh189HlkmJqZv37831Naxs7CysrJ2d3fz8/P//PkzNTU1NDR08+bNly9f1tTUtLe337x5s6ampp6enqmpqY2NzYkTJ4qLi1VVVb28vL5//w4Z9j59+rStrW1ycjLExj9//igrK69Zs+bNmzdMTEyvX78WEhKCSMHJTZs2vX37loGBQVBQ8MKFC6dOnXJ0dITLjjJGQ2A0BIYQGG2MDqHIoo5Tf/76GegfOHn65K9fv8bFxTU0NMybNw+y6/nq1avl5eXMzMy8vLxsbGy/f/9+9+6dsrKyIBgwMDAcPXr09+/fP3/+/Pfv3/Xr101MTKSkpExMTFauXMnIyFhVVbVhwwbIKNq7d++YmZlXrFjBwsJiYWEhKyv77x9ifg3NJ//+/VNQUGhsbFyyZMnr168h7RjIJp5Xr16JiIhA2qaysrLfwUBERISHh4eBgUFAQODfv39fvnz59OnTsmXLIGOWkBYYAwPDz58/v3//LikJWgjLx8fHzs7+4cOH379/Q8bnkN0AmfmFiLCwsEAm+v///8/Dw/P79+9Pnz69evXq8+fPf//+dXFxgRwawMDA8OfPHyEhIbhLZGVB+5E5OTkZGRk/ffrEx8cHkZKRkfn8+bOqqmpBQcGUKVPevHljaGjY0NCAbCnEajj5798/aWnQssg/f/7IyspCgoKLi+vnz59///7l5uYWFxeH1MFMTExPnz7lBgMGBgYJCYkfP378/PlTQkIC4h5IOEBC5v///1u2bNmxY4eysvKrV68kJCT+/PkjKCgIMY2TkxOyugBiC9wxoH0h//4JCwtDWgMiIiLMzMwfP37k4uKCBNTz588lJKDL+KSlpc+dO/fmzRuI+xkYGCDT8eCLzkHzwZDVCExMTO/evRMVFYVwIdp//PgBcScDA0NWVtbUqVPr6uo+f/6ckZHh4+OD5h4RMGBgYODm5ob0ppAVjLLxh0BiYuLNmzdLSkq2bN0K6iSQM0sP6hhwcXG1djSa6BnIysoKC4MOKmYCA8gpGYyMjBwcHFFRUY2NjUeOHElOTmZmZk5MTFRTU3v79u3EiROLi4tbW1tByYKRkYuL68WLF9++fYPkixs3biQlJUG6PWfOnIFEMcipMI/9+PFjzZo1TExMFRUVkNS1dOlSSGMUWRlM+Sg9GgKjITCowYiYpmdkZGRhYWFmZoYXUoKCgmJiYnDuoI4iGjju8ePH+/fvP3DgwMOHD3l4eEDHPP39y8jIuHXr1uvXr0+aNMnZ2fnbt28sLCxqamoHDx48dOjQ3LlzV6xYAWmsTJ8+PSQkJD8//9q1a0xMTJ6enidOnGBjY4uOjn748OHVq1cdHR2VlZX//fuXlJTU2dkZGBgYEBAAacL+B2+yhbc5IG0RyGwdGxvb7NmzV6xY8eDBgxMnTkD8raio+OzZsydPQBfAnDhxQkJCgp2d/c8f0KWlkLWS8OZUSUlJV1dXamqquTno6j9IM0VUVPTMmTMMDAy3b9/+/fu3mJjY379/MZvFkOYjxMZ///5BnAc54ImJiUlaWtrc3Ly9vb22tjYyMpKbG3QLAMTlf/+C9vlCXALRBdEuJib28ePHe/fuMTAwHDt2TFhY+Nu3bzIyMvPnz1+wYMGxY8ceP37MwsLy5csXiKVwEmIgxGqIIKRuhrQI////z8LC8unTp0uXLjEwMFy7do2FhUVbW/vTp0937oA2/50/fx7Sdv/z5w/cmywsLJCVAN++fVu6dGllZWVTU5OcnBykUQ7e0wzqJ8AZTExMnz59gtgOIZmYmJ49e3b9+nUGBoaLFy8yMDCIiIhAfMrAwKCmpnbnzp2PHz8yMDCcOXNGTk5OVVX14sWLf/78+fv378mTJyHN+i9fvnz8+PHv37+nT5/+8+ePnJzcw4cPP336xMjIeObMmc+fP7OxsX3//h0SjPfu3YuLi1u6dGlSUtKCBQsgzoCEOaRdAgkoSLDAfQpXNsqAhMC1a9eysrJaW1uvXUPb5clQXl7+4sWLnTt3sbOxQ65uh2ghnmT/84eN8Z+etoaBgYGwsDADAwMbMwPz/z8sjKDkxMzwh/n/HwYGhpBAP2aGv6+ePw7w9QItX2luaGtuuHnt8p+fXyVFhViZ/jP8+cnC+DcpPvr6lQtR4SGebk6lhXksDH/ZWRjv37m5cd3qr58+crAygYz98xOkHuzEKxfOnj5+ZMrEvjs3rt64ejE1KX7rpvWPH9zhZGNiYfjH9u8P24io3MBhMUqMhsDQByNiZJSVlVVdXZ2dnR0yKvb69WslJSVtbe1Dhw69e/cOMkfMzMwMaSf9/w/aZyMqKvrt2ze0KnnoRzfDv3//ZWVln5x6UlZW9u/fPykpqY6ODm5ubgUFBS4uLmdn5wULFiQlJYmJiRkbG3NycpaXl1+/fj0hIeHfv3/p6ekaGhoqKir8/PyNjY3nzp2bNGnS1KlTXV1d1dTU3Nzc5OXlPTw8Xr58qaysrKWllZKS0tLSwsLCwsvLa2Njw8PDo6qqysbGJiwsLA0e9oOEJxsbG2SpwOLFi+fNm8fOzs7Pz29gYACRlZeXj46OzsvLExIS+vLlS3d39////0VERCAdCS4uLgEBAUVFRR8fn6ysLFFR0S9fvjQ3N0PG8NjY2EpLS1taWiB7m7Kysvj5+dnY2OCT7BArIJPXJSUlKSkplZWVoqKikOYmIyOjsLDw////09PTy8rKsrOzP3786OjoqKWlBdHIxsYGGVNkYGAQEhLi5ORkYGCAuJ+Pjy83N7empkZEROTt27ctLS3//v2bMGECHx/fnz9/1NTUJCUl2dnZFyxYkJCQkJycbGsLPflIWFiYhYWFi4sLMhrKxMQkKioKWS3Hy8vLwsLy9+9fVlbWBQsWLFq06MmTJ/n5+QoKCikpKcXFxWJiYh8+fGhoaGBjY5OUlIT0HBgYGKysrFavXp2YmFhcXGxvb9/Y2KigoPD+/XvIBjUJCQnIqU/s7OyQcHNwcNixY0d6enpOTo6uri7Eszw8PEuWLFm5cuX9+/dLSkq4ubmFhIQgDtPX13dzc0tJSeHj4/v3719XVxc/P//WrVtjYmJERUWZmJgEBATExcWdnZ2Tk5NlZWXZ2Nj4+Pi0tLQcHR2Tk5P5+PhYWVk7Ozu1tbX5+Piio6NTU1N//PjR3NwsLy//5MkT5GFRPj4+SNTDPQh3NsSdoyRyCHBzc8vJyZ0+fXrBggWQFSaQ0IMMq0dHRy9cuMBOWRouiKwXP/sfE9MxcRkdi6z7P4T/3IUu1nzzicc7vfEjj9qxu1+lzQLZFW2P3PnMxMStZBPO/erlOw7lY3e/Gnil3vzCt+nMXQXLkMC4uEd/mXzSm+79FJHVNsxpW7R9x3Y+FQ2r0Kjvwiox5RP37dtnaWGRXGv3X0rrDbugR0rdnW+Cn8HWnX381yO5TsLY9/J7TkYGLtOArNdsCofv/lR3jJH89vXkgXd875hYwSoxPfL3/39tSQ4h7hFR/WF6f1RkNAQGIYAcJElzhx3dtrGzrmrzueuQc4lpZx9kugntiEseHh5DQ0NeXt6/f/9+//79/v37YmJiVlZWL168+Pjx4+/fv69duyYpKcnJyfnnz5/v378/e/bMzs6OiYnp5MmTv3///vDhwy+yjj6hujcNFKUtdLVTGzuNYG01Uq3IP1BswmfkIu709z9oKFRcXJyFheXPnz+vXr0SFBTk5OR89uzZ69evVVVVIYfYc3Jyfvny5e7duzw8PMrKyr9+/Xr9+rWIiAg7O/v79++/fPkiKSnJwsLy8uVLXl5eLi6uDx8+/P37FzJMwsDAcOvWrW/fvqmoqEB2Nrx9+1ZMTOzbt28/fvyAj0z//v378+fPQkJCv3//vnz58q9fv3R0dCAT3HDf3b179/Xr17q6utzc3H/+/Pnw4YOQkBATE9P3799//vwJWVN469att2/fQho0cI2QMdebN2/KysrKyMiADyb88ePHD4gWZGXv379//PgxZECXiYkJMvn77t07SFPp69evly5dEhQU1EA64RIygw/x7Pv37zk5OTk4OH7+/Pn161dIq+7Ro0dPnz7V0tKCtCy/fft29epVRkZGPT09NjY2yPrU+/fvS0lJQQyBuJaPj+/Xr19//vyBtO3evXsnKCjIzMz8+fNn0OazDx/i4+Pnz5//7NkzGRkZeLP+4cOHz54909DQEBQU/Pfv3/v37wUEBCCtTNB9Wh8/Pnz4UEFBgZeX98KFC5Du2ffv33l5ed+9ewdRiezy9+/fP3nyRF5eno+PD3JuQHh4+IQJE96/fw+x9P///+/evePn54e0RxkYGG7cuPHp0yc9PT3Imoq/f/9eunSJg4NDWVn558+fvLy8kFFVNjY2JSWl379/Q6IYoktXVxfSlP/x48edO3fEwODZs2eQrKqqqgqPqa9fv4I2cfPwIDv727dvgkhnFcEVozHidyRlG2SaSZiiiQ849/yRAxvmzWyct5x2Ltm5c2d6evrkyZN9fX3htly+fDktPd1MWtg/MsaJxHNGv/36p9999/3nP/+RxuCZGJlAy3v+gI47AF2fwcj089dPBgYGDnYORkbGHz9/QIb2WVhYGBkY/zP8//P7z7///9jY2H79+vX//382VjYmJqb/DP///gFBCPff/3+MDIyQyRBWVtZfv379+w8eeWVmhnLB639YmFlYWFl+/frFwszCyMjwi5Hh/58/DODzg+H+hTO+/fq3IUPJU4cfLjLKGA2B0RAYWDCsuobMzEyKslK8PDwfP3//A7o6juH371/fv3//AwasrKyQxigzM/PDhw8ZGBhUVFSEhIT+////8eNHbm5uMTExSGOLkZHx58+fOjo6kKnAS5cuPXjwYGDjiVq2////T1BQUFIatIwSbiYLCwv8KCUpMGBgYICvjOTh4dHXh15aw8bGBm/9gNeRQk8rhA8QojXy1NQQ18lwcHBA9PKDAdx2VlZWSNONlZUVcgQSXArOUAYDCJeFhUVERATC5gQDCBvZLogIhBQSErK0tISwQfUiBwjAuXAGsncggpCRUQibm5sb2RCIICsrK7wRCW8MsYMBRIEcGEDYkCA1NUVpCfHw8MCHHiHKIEEBb+ExMTHBPQtpz0FWo/Lx8cGXhEI0yoMBhM3ExAR3GESEn58fvlUFfp4/ZOMIXCXY4ewQ9WihwcjIyMvLy8fHBw9k5MCBaEFupkOWIMMtgrS8GRgY4AmJnR1qEZouDg4O+JHm4JQoBTEcTkIGrRkYGLA6G65seDBu3bq1ePHiL1++II9c/vnzh42N7e/fv79/g+71hSxUEBER8fT0hBxzhtXv7u7uJSUlvb29np6e8NSlqqr66/fvR48fwTstWPXiEmT/+4f572/Q/Ufg2yKgyv79As2PMzExMPxl+P+XkxU8WQ6+a4CDhRF8Teg/hr+gY00ZGRggkgx/f7GDLtdjZPj/m+EvAyMDAwsjA6i9CuaC9cNU/vvFDqqyIGL/oRqhtoOMBZvzh+E/A8d/BgYm0H5AqKtQqf//GZiZIAMXqBKjvNEQGA2BAQKgnD1AVlPfWiZmFjERYUlRodcfv/1nZvv999/P79/+/v177969hw8fPnjw4C94veCfP38+fvwIWZ3Gzc3NwcEBGRP99+8fZHCInZ397Nmzf//+lZaWZmZmVlFRefr06e/f0Osrqe9u+poIWXJHXztHbaNaCIiJic2aNQvSMKWaoYQM4ubmnjJlCqShTEjtqDx1QuDo0aOxsbF///719/dHNpGRkXHVqlXKyspGRkaQvMzExHTnzp2kpCRHR8fGxkZ47wVZFwMDg52d3cyZM58/fw7vxnBwcLAws3z6+Aa5sYumi3juv/+gK2fhjbz//xn+gG+1ZWFCMf4/A8Ofv/9ZmME38MJM//PvPxMjIzHtQ6zaYcaM0qMhMBoCQxUMq8Yow3+Gz99+/H31/i8DMzMLaLPzmzdvINPQz549g0QRZOcmIyPj9+/fL126xMTExMzMzM3NzcLCAjnW+///f79/gybrL1269ObNG05OTi4uLi0trfcf3v/7+//Pn1/ff/z4/Onz6J4JSHiOknQOAWZmZlytDdq5hJGREbLznXZWjJqMHAIrVqxoamqys7O7du1aT08PfGgZooaTk/PTp0+9vb0QLoS8c+dOZWWlu7v7xIkTbWxsIILIpISERGJiImQRBVw8Njbmwra1kK1scEEyGP/+MXCzM3GzM73+/IeRkeEfaOiRQU2UnZmR8cHbX7/+/oc0NP//Z2BjYVQUZnv24ffPv/8hg5P//zPIC7F9/fXv3de/EGVwB/z7x/Cf4T8LEyNk5dV/0GAqo7II+6P3v378/kdk+xVu2ihjNARGQ2DQAsh8x6B1HmkO+/fvz/ff/77+/PP7779/////+vnrx48fH95/4ADvieECAQ7wtnoWUFcd1DNn+vPnz6+fP9+9e/fq1au3b9++f//hw8eP375+/f+f4cWLl+fOnTt69Ojt27e1tbXNzEwNDQ0c7J3cnN34+UEL6Uhz3Kjq0RAYDYHRECAUAv/+/Wttba2qqurs7Jw8efL3798hx0Eg67Oysrp06RLkhAS4+Lt377KzsyMiIuLi4tra2jCXuYuJiRUVFaF1KnKys3V0dCic9vn99z8zE0N7oFSGnehP0AIpBlZmxkYfqS3ZKltzVeq8JViZGf+Dm5N///2X4GedH68gI8j6DzxuysDA8Ovv/3J3cXctvp+/QYtB4T769/+/vDBrnIUwMxNCuzAPy5RIWV52JlN5bh9d/n8Qc+F6RhmjITAaAkMTDKuRUdDE0O/fDOxs//78+fn/358/v7i4eb9++8HC8p+Xl+/f7////v77/fsHByfbv3+MX79/4+BkZ/j7/8+/f39///j7j+Hv3/+//3z/9vUH6KwTRgbQnP7/v0KCQmJiot++fmfnYP/P8OPrt69sbCzc3Nzv338YmjE+6urREBgNgUEYAqDG2rdv3/Ly8o4ePbp8+XJzc/N///7p6uoeOHDAysoK2cUODg61tbX379+HrwNmYGBYu3bt9+/fJ4Hv+YTcjNXV1aWoqIisESsbMtePVYoYwb//GBRF2CvcxZ01eOcfBx1B/+vv/0BDAXs1ntDZ9xn+/58VI7/1yqcjd74wMjL8BTdA/6I2IdlZGEvXPv37n4GdlenXn/+gRaP/GZiYQLuPNCU4XDX5lpyCXpbGwsT46vPv4Bn33n//m2ApzMXGtO78ezYWxj9/EbpYmBj//QdZBGqmMoIMA58IBvIKCzNRKwFASkfxaAiMhgB9wbBqjIIOPvz3l5WZjYWNmY2dzcHLkoON7cvnH/fu3Xv+4vV/JuZv37//+c309+9fIQGRT58//fj5k5WDS0CY/+uXr8zgBfMfP7z/9PE7BweHsJjwt+/f2dnZeHh4//z5fersaTnQfmz5f///fv/+kYMDuv2CvpE1attoCIyGwPAMARbQkRSvUlKSP3/+vG3bNkgjkomJycLCYuHChUpKSpDjVyGeZ2Zm/vHjx8yZM+3s7P78AZ3lycLCcunSpb9//0JuRo2JiWltbbWxsWlqanJwcFBQUCBvixLEOvzkn3//daU4Ljz5/ubLH9A+dpDq/7Yq3EtOvv30/a8QF3PiwgcvPv1hY2HqDJI6df/r4TvoZ+v++vu/wkP83KNv+29+afSVdFDlffT+Z/fuV4/e/ipwEVMV4+gPlc5e/oSdhfHvv/9ivKy13hLrzn+IMhPiZmN68en34pPvqnwlHdV5b7380bnz5aWn3100eR1Uef8zMDip80za//rDt78VHuIP3/1u3PL82Ydf8FWtIJeO4tEQGA2BwQGGVWMUdCnO79///v37/uMXGwuzvp7a2XM3/vz7L6esKCEtwwby6382Ntb///6JiYv+/vvrzatXDx88v3jt8c+fP5iZuSTEedRUxcREJe7eu/Pn9zdmjn/MzH+/fnzDx8erpiTLxcH589sHNg4+BgbWEdgYff369du3b5H3PkOOZBIUFASteaBBar5x4wb4kh3oxnnKbYAcjQTfjk25gWgm/P///+rVq4qKiqRacfPmTWFhYWJWgv758+fTp09kbyS6evWqtLQ02okHaL4YKO7///9//PgBOeCJgYEBciQwnDtQrqKPvczMzM+fP/cP8FdTVV28eDFyBHl5eR05cgT52H/I5Ubc3NyLFi2CbMqEOJKVlZWFhQV+mxrkKIN58+ZNnjxZSkrKyMjI2dlZT09PSEiIWhn2269/X3/9Y2FiXH3u/ecf/zqDpNmYGb/8/MvDzszDzuykzhduIiTIxXzy/tf8VU8+//i78vT7px9A60f//WcAracC6wWdtvb7v7wQ2/XnP3z1+G1UeGYdea0uzhFrLlSy+umua5///2dccfrDzz////77//vff6F///VkOHt2vzx+7ysPO9POa5+afCUVhNkn7H1lIMvV6i8VNOseCxNjuKngkpPv1pz70OgjeeL+14XHwptQEAABAABJREFU3yVaCYcYCTRvfcHByvj15z/IGC0k6EbJ0RAYDYEBB6AG2oA7gooO+Pv3z68/fz5/+cT8n/n927enz1179eKdoJDA37+//v/7zwQ6hgR0vMj//39//2KUkeK3sjY4ee7ur19/fv18Z6Anracjf+P6YxlJUYb/f1iYGRkY/v7/+4ePT0BGRoaFlYWBheXi5WcsbIIcnLzgM0pAM2tUdPxgNurIkSM7duyYOXMm3JF3796dP39+a2sr/KQYiNSTJ08OHz4cGRkJ4ZJNTpgwwdfX19vbm2wTIBcCvXr1yssLdPXLihUr+Pj4wsPDSTJwzZo1BgYGkPPh8Wv88+dPW1tbVVUV/HAi/Orhsv39/T5gABfBxXjy5ElfX19vby/kVCZcypDFt2zZoqCgAHFSa2trRkaGnZ0dsgI09saNG9XU1DQ1NdHEac09efJkQ0PDypUr+flBpz/u2LHj2LFj7e3ttLZ3MJjPxMT06NEjTSPb+fNBF/MiO0lFRWXlypXIIgwMDIyMjIcPHy4qKlq2bBnkIFg0BXDuv3//7t+/f/DgwRMnTlRUVHz8+NHExMTS0tLf3x9y7C5cJakMVmbGXEfRLz//MoEW34M2yFsrczMxMlZ5SrCzMOlIcUoKsK46856NmTHUWOD33//Xn//485/BSI5LjI9FjJcl1Vrkzdc/UL3//quKsTMw8HGxMUnzs5orct9784v5+99sBxFZQVZ2FkYDWU5TeS4G0L6o//wcLEJcLKFGggKczKwsjD66/F46/Fsuf+TjYH756beHNm9noDQ3G9P//wzff/37+5+Bi53p7z/QGtY3X3776PL/+QfaDvX7738VMQ5SvTyqfjQERkOAdmC4NUYZwKfqszAy//r7++evP/x8XMoyAgICfIyMrP8Y2H79/Pv/L8O3Hz8/fnrz7vX7h8/eODAzaWqovXv/9c//3/y8gu/fft598JKkuBArKwsD03/G/0z///1l+Pf5/9nH33/9dnfS+f3tNTMXDy83Nysr2+/foPOcaRc3NDKZhYXlwf0Hb96+0dHR4eDggAy2QQY4v4J2bv3n5ub+8uULKyvrs2fP5OXl4RN8rKysz58/f/r0KUSjnJxcWloaCwvLv3//Ll++/P//f319/V+/fp06dWr+/PmmpqZCYPDnz5/Lly9zcHBA2jeQW0bfvn0L3knGzMfHB2nLvnv3joeHB23XMCMjI+TGUfiI7JcvX65fvy4uLi4nJwcJn7t37759+1ZLSwtyiDrkYHl+fn41NbVPnz7t2LHj9u3bmpqa4uLiPj4+kItPIUfP3r59W1lZGT7KePXqVSYmJlVV1R8/fkCM+v///8uXLxctWvTp0yfIAauQE2pfvXqlqakJV3Pp0iXInZyQAae/f/9evHiRn59fQUEBclw8BwfHjRs32NjY1NXVIW7+/PnzjRs3xMTE5OXlwQvaoL2a////X7lyhYGBQUdHB2Lajx8/rl69KioqKi4u/v//f0lJyZycHEiIvX379s6dO4qKipALpX78+HHlyhUeHh54WDEwMLx69WrFihUWFhY8PDwyMjJMTKA1KpcuXeLh4VFSUoI45v79+69evdLQ0ODn53/58uWKFSvs7Oy4ubmlpKQgFv369evv37+fP39++PChhoYG/FSpmzdvfv36VU9Pj4WF5c2bN5CT+b9+/crCwsLOzv7z50/IWfTv37+/deuWnJycpKTkv3//vn79ipm0GBgY1q1b9+zZs127doWGhjIwMHz69Al+AgbEncObZGRkZGVl2bNnj4yMDHIMQpqemH7X1dVlZmY+ffq0i4sLpixcBHIynYqKSnJy8vv372/fvn3gwIHNmzdPnTo1Ozs7IyMDnrvhWohksDIzFjqLYSq2VOKGC8ZZCEHYocaCEAaczLQXhbMhDC1J0AVmDAwMYRiKG3xQzkVmYGAodROH6LJX5WFgYEiwBF1GChFJsoKyqzwlICLBRgLBRgIQtr4M1BYId5QcDYHREBgkYLg1Rv+DF8iDpn5+/fr25Ts/N6ujtaKassyTFyzff3H+/PmH6T/zn78M//+/YGF6unP//Y9v3vNys/36y/D129efv/9xcvIoyopICLHLyclycHP9/snIyMT6+/evFy9fPnz04tmTt0wMDD9+fOfi5mTnYB+KjVEOdvb58+azXGVi52H/+/dvd3f379+/Gxoapk6dys7OvmHDhq9fv8bHx0NaPJycnB0dHZBWFzs7+/nz51tbWz98+MDMzDx58uRXr17NnDmzsbGxra3t6dOnjIyM4uLi8fHxq1evvnPnTmdnZ3BwsJ2dXWlp6efPn3/9+qWnp1deXr5t27YNGzb8+fMnKirq0qVLenp6fn5+L1++rK+vb2trgzcNGRgY2NjYZs2aBbq89MkTKyurkpKSu3fv1tbWcnJyfvjwISwsLCIiYv78+Tt27ODn5//y5QtkvLC8vBxye7uDg4O2tvbu3bs/fPjQ39+fk5Nz5MgRHh4eV1fXlJQUcXHxHz9+vHr1qru7W11dvaWl5cKFC1JSUp8/f9bW1i4rK4OcJb5mzZrr16///fv30aNHlZWVa9euXblypbCw8M+fP1tbW0VFRcvLy9+8efPnzx8jI6Pi4uLfv393d3fz8PA8evQoJSUlKCior6/vwYMHHBwcT58+9fDwyMrKgvvi7du3oaGh0dHRjIyMTExMv3//rq6uhoSktLR0S0vLx48fi4uL////z8/P/+rVq5SUFG1t7b6+vhkzZpw9e7azs1NYWPjt27fFxcVaWlpFRUWMjIzfvn0zNjYuKChgZARdrrZt27azZ8++fPny8ePHVVVVTExM06ZNExUVffDgQVRUVExMzIoVK9avXy8oKPjmzZu+vr5du3adO3fu7du3z549Ky0thYy6Xb9+vbGxUVpaGnK5fHd3t6KiYm9v76lTp7i4uDg4ODo7Ozs6Ovz8/Ozs7FpaWmRkZLKzsxcsWMDExGRjY1NfXw9xZ3Z2trm5OSRpcXFxtbe3Q5IWAwPD7du3Hzx40NPTs2DBgsDAQBYWFiYmJkhTeJAUlLR2Bjs7+9YtW06ePBkeHl5VVUXQOgEBASUlpaNHj+JvjCKbIygoaAYGpaWle/bsKS4ufvr0aWtbG2yTD7LaUfZoCIyGwGgI0BUMt8YoaDn//38MjAw/f/75+vUrAxPD8xcfpUR5Pn9l/fT1OwNovobt129GZsbfLKyfWBj/PH314sPHZ58+sbx9/46Pm01OkouPh5mD+asID6u4pOyvn39Z2TnZ2Hge8VwUFfz27RcnOyvjy0+/WFhY2NhY6RpRVLLsPwPjx48fF/TNlZSVam9vnzJlSmpq6vPnzyE7aj9+/Pjp0ycmJqazZ882NTUFBATA7zf///8/ExNTQ0ODiIhIZGTkoUOHVFVVnz179vjx4/37969evVpYWPjatWvKysr5+fn9/f0zZ85kZGScO3cuExPTokWLvn79GhMTc/z48S9fvty/f3/z5s2QgbSVK1f6+fnt37+fg4MDuSXKwMDw8+dPPT29xsbGDx8+REZGOjs7L1y40MnJKTU19d69e2lpadbW1sbGxl5eXry8vDk5Odu3b1dUVHz16tWqVat+/Pjx+PFjXV3dmJiY69ev9/T0MDExrVmzBnLX/MWLF1esWGFiYtLS0rJ8+fKgoKBjx46tXr2ah4cnMzPz8ePHkMBmZmbOzs4+ffp0ZGSkm5vbq1ev5s6dO2HCBEVFxYkTJy5btkxBQeHjx4+LFi369u3buXPnIMscnZycMjMzjx492t7e7u/v/+jRIzExsc7Ozhs3bqSkpCQkJPT399vb26enpz948CAtLc3R0ZGRkZGZmXn79u3Pnz+fP38+AwNDcnLypk2bHj9+LCEh0dnZ+fHjR39//y9fvvz58+fx48c/f/7s6ekJCwvz9fXdt2/ftGnTsrKyHjx4sH79esi07P///yHn6cbHx58/f97GxiY4OJiJienLly+2traFhYUnT56sqamJiIjQ0tJycHDg4+Orqqpas2ZNYWHh+fPnXVxc/P394VH/8+fPGzduTJgwQU5OrqenZ86cOZGRkfv27Vu5ciUfH19xcfGSJUv09PS2bt1qYmJy9uzZJ0+eJCYmHjx4sKysrLe319vbOyIi4uDBg319fYsWLTpz5kxzczNy0mJgYNi2bZulpaWbm9uMGTMuXrxobGwMiYKRQ37//j0tI6O+ro74A4zd3NyWL1/+588fMlrt8vLyZWVlOTk5Zubm8NuwSArt33//T9r/+u2X3+B97KBpekd1HiZGxr03PrMwM/77/19RmN1Lh+/ff4Ytlz8+eveLlZkx0EDgwduf15//jLMU4uMA3bnEwMDw7de/2YffuGrxPnr36/zj784avEayXB++/916+eOzj785WZnCTQT//P2/7PR7FhbmP3//8rEzhRgLrjj9joOVKcZM6Orz73tvfHFS5zGS43r95c+2yx+ff/yjI8WhK8O58sx7FibGKDOhA7c+33v901KZW4yXdfvVzyxMjH/BN4iCRp3BfoZOTICXXoEFQARcEK4MNIkBkoHu0IdzwWIgQTQtyFyIGggJypugKTzQdn+ICI1I0B1XrKwMjIz//4EAyCOMjH/+/sVlHTMz8z/QoYi4HA7Vx8jIwMrC+v///9/gzXNQUXIpJkZGFlbQ9NqfPzgdRq7ZUH1MoOUgiEiHhANkmgiqglwKMo6AvLkQYhIjAwNkFo5AUEJUI5Ggk3cZwdO7SIIQJkiKgYFUAyF6iSSZGBlsVXg8tAfm5Mrh1hj9Dzo0BFQ8/vv///evHxwszM9evVZ8w/z3uwjDH6b////9/PXrz1/m77++ffzx/vev77/+cP76+efB/af///+9z/jPWEv4z+9/3xi/f//2lfE/IxsLExsLMxPDf1Ehbn5+kZv3mbi5mV98+MXCwUVeCU5kmqCdsr9//1hZWYmJiTMwMDg4OEyfPv379+/MzMyQSWFmZmYmJqZ///4pKipaWVnBmyOgswB//dLW1oZsslFWVn7//j2oLP7/X1FRMSAgIDMzk5OT08PDw8DAAGIURO/169chp9Jwc3NraGhAblV1cHCAtDvt7Ozmzp17+/btgwcPhoWFofn6////lpaWjIyMgoKC0tLSt27devr0aXZ2NgMDg4KCgqCg4KtXryB7illYWG7cuGFiYmJmZqaoqBgTE8PFxRUfHw8q9MEL2iCOYQKVSYx///5VUlKCXHeurKwMaTwZGBhAZp/t7OzOnj0LdwnEBEiJ8xoMpk+f/v///7dv39rb2z948MDc3JyJiYmHhweyr5mDg8PW1hbiwn///v369YuDg8Pc3JyBgUFERISPj+/Tp08PHz5MSEiAqBEREblz5w4k2K9fv25iYgJZqGBgYHDjxo0XL144ODgwMDDw8/Pr6+v/+fMH0myFNLW3bNly6NCh79+/8/DwqKio6Ovrx8fHc3FxRUVFwW/dhMQFZMsL5CpUExMTBgYGSUlJdnb2379/f/nyZc6cOUxMTBcvXhQVFYWMp0L8Cw+Ev3//GhgYQK5ytbKyWrRo0cOHD3V0dCDjpo6Ojjt37iwsLNyxY8fRo0etrKzev39/4MABRkZGJSWlJ0+e7Ny58+zZs1+/fhUUFPz//7+8vDxa0vrz58/GjRvd3d03bdrEwcGxdu3aEdgYBcURUlqFBP6TJ0/ev3+vqKgIH0KGiENIGxub1tbWly9fQqIGIoiVfAkGMjIykHz39+/flpaWBw8eSEhITJ48xVlDnhl6oyZW3dgFf//9P3n/Kz1pThEeUCXy/z/DnuufGRgZ3n/9ywiuTT//+Pfy878/f/+8+fLnx+//jIwMa869//77/9ef/+YefcvJzvb79+///////c/w9MOvjRc//vj97/OPf7uufTp+7+ufv//ffPnz889/JiaGJSff/WdgePnp96vXT/l4eXm4uRYef/vy028mRsaZh998+fn33de/O699Onbv62+YrjOPvt14+QNy/P6yU+/ef/vz/ff/I3e+sjEz3n369u/fv8LC0PUDDAyMbGysv3//+Q++7x5SUPz9+4+VlfXPH+iVe/AmGjMzy9+/f8DTGIz//v1jZGRiYQG13kAjIODGB7wlBxnXh3gQOfjAizHYPn/5/OvnTyEhoX///mG2Y5DVU8j++/fvmTNH/vz5IyQkLC4u/vfvnx8/fsjKyuIy9uXLJwICAphVG6SCgHiTgYHx37+/Fy6c5OLiUlfXAKdZXOYRFmdkZPz16/eFCxeEhASVlZUJayBLxadPn37//oMU6Qw/fvz4+PGjmJg4he7//v3Hp09YzPnz58/r12/ExcUgKYqBgYGFhQWU2nH3BCAA3Bf4x8wM7apBBCHk379/IWkPwqU++e/v3usfv//+P9oYpU7Y/v33l+H/PyYmZsb/f758/cnMwnz9xhOWPz/5+f7xCkr+/PHj/1/QsOanT19fPP3w4dNPZu6fqooKbz+yMjD85uHi4OBiYWJm+fqD8duPn0yMfxgYWRkZGNhZmVkFhTl5OJ+9/8DBwcjw5zkzEw83Fxd1XExfU5iYmO7fv/fv719mBuYHDx7w8vLy8PD8/v0bUiY+ePAAcsc6pBGG5jR4ywZcEDNC+pd//vyJj49PTk5+8eJFQUGBpqYmKysrfHRHVFT04cOHEHNevHhhbW396tUreP7k5eW1sbGZOnXqz58/TUxMfv78ycrKCpdlYGB4+vQpRO+7d+9ERUUh099qamo/fvz48uXLu3fvent7Ozs7lZWVS0tLf/369f///7q6uj9//ty6daupqcnExATuZog5cBIyEgwZQRQTE7t58+a/f/8gbTJIUMBVwkeeuLm5RUREioqKeHl5P378KCIiMn/+/CdPnkBUvnnzBtKchZiMHEQQN0DEmZlBy2RfvXoFad9//PhRVFQUUoeJiYldvXoVYtqzZ8/U1NQYGRlv3LgBOiPiz5+7d+86OjrCDREQEIiOjjYyMvr27RsjIyMHB0dlZeXfv39v3bpVW1trbGwMWUgKWWwAD1JIWxPSi2BkZPz06VN9fT1EfVtbG6Sy+fv3L1w9xDFMTEzPnj379esXJyfn06dP2djYBAUF4VFz7949Xl5eJSUlAQGByZMnl5WV3bp1a9KkSd7e3nx8fDw8POHh4TY2Nj9//vz79y+kVoP4AmI4AwPDgQMH/vz58/Pnz3PnzsnJyUGGz9HcAFc8jBmMoPvcof57//59VVXV2bNnWVhY/v79m56enpiYyMjICBka//XrF+QOOSEhoQ0bNri5uSEH6b9///j5+SUlQessv3//3tTUtHv3bnZ29h8/fkRGRhYUFLCwsEycOBFyGkZwSMjFSx+sXd2hFpNCsTEzdofIqYthnyP68uHNtOkzSoqLmdg40U399+vwocM2TjaMLCSckVdR3BfhEGlgooFuGin8FYt3vXj5oiCuEqLp7o0ri5csaWhuZmAE+WLHlg0sLCwCAgIXLpxPyciFqDl38uily5dcXFxXrVpaVFZ148r5trY2fn7+f3//CQgIpKWlyauAnNTX1RoWGiajqMrAwPDs0b2+PtDiFWYO0JJWiDlg8s+sadMOHzrExczMLy5WUVEpJCYDFqcVsV34wvcfv1+/vsHGdvfnz5/icuKBoVhu54JYX1LQleGboaKhBuHCyWeP7n788FFTzwgi0tZU9/fldV8fn7BocpINxBAY+aeipJj905tgt2BPX9ACepg4Nel1q5Y+fPCwMA6x+uXapXOzZ8/vL52GOhpOsqXXLp2dN29hT+kUNHM+vX1ZVd0+qWAiEzt0CfXta5c4ODllwckDjzU3r1zYvGVLSUUNppopE3qsrawMzVCOHMZURrbIv69v3LvvMjODyg2yDaFEI6hTS4n+wab3z9+//yGjo//+fvn2XYCfhYHhNyfnf2aGXz++fvz16+f//2x//jL8+fvr87ffP37++vLlp5g0q6io0O/fP1hZ2H/+YgDtTPrD/uf33/9//zMx/GdhZGBn5fj5n+/7rz8sbOw8vJyMjM+YGBm5hmZjlJmZ+cbNm+VlZVIKMnv37m1sbJSSklJUVCwpKVFRUTlw4EBoaOj///+ZmZmRqzdI8wXeSoOPnLGxsb17966srMzIyIiXl5eZmVlAQEBMTOzr169VVVXe3t5RUVHZ2dnt7e0fP378+/evg4PD0qVLkU0ODg52dXWFtPCqqqpCQkKMjKBFHjs7+9q1ayHT+oKCgpAbDvv6+i5fvnzt2jVTU1MjIyN2dvadO3fy8PBcuXLFyMjo4cOHdXV1rq6unz59EhYWZmdn19HRWbx4cUdHR2RkJBsbG6RdiNwm+/nzp4GBgZSUVFJSkqys7LFjxywtLZFTtaGh4dSpU+/evRsbG2tubt7V1WVkZHT8+PHCwsKQkJC0tLS2trYfP378/PmzoaGBhQV8uRdYP7gjCxqIhTTNIQ1fZmbm1NTU3t7eW7duXb58WV9fX0NDA9Jj9vf337x5c0NDAyMj44MHDyoqKn7+/JmZmVlYWMjMzPzkyRNIdxkyEBsZGTl37tyHDx+eP3/e1dVVU1OzsrLSxcUFsmcI+cpHU1PTefPmPXv2LC4uDtLqhcQmMzMzCwuLkJDQgQMHbty4cfLkSUdHRwYGBn19/Tlz5jx//jwiIoIfvLGdmZn58ePHTU1NIiIi27Ztq6mpMTY2ZmVlLSkpERUVPXToUE9PDwMDg62tbXNzs7m5OR8fX0dHB8S00NDQhQsXvn79+tq1axYWFr6+vpARAnAIgYg/f/6sWrWqoKAgKCgIxGdgSElJ2bx5MxsbGzy9QcSHN8nExHTlyhVIe/3nz58ZGRk/fvyYNm2apKTk4cOHKyoqODk5IyMj379/n5GRce/ePUgefP36dXV19eTJkyFpDBJEkOvs+/r6/v37V1lZeebMmQkTJigrK1+4cKG4uJiRkbG4uBjS5xQTExMVFX39+BYkX0C0k0IyHjp2Us7NgJMLrckFMuPNhy8HDh9Lz8rlYWZjZmaGLDIREBD4////w0fPahuaZ82aJScnB5kKYGBg+P79O+RMtPfv30P6bJAs8////3fv3nFzcz9/8+H95+//////8+cP5DQJ+FwBAwMDXBfIbgaGb2AgLCwM9xpksfu7z99fvv0EUQNqNb5+390/mYtPCLJM/OLVm4yMjKGhoUtW1IRExELO2Jo1f7GNjc3Ltx/3HDhSVMZw+fqdD19+VteDer9nz55Ny86fNm2akpLSngNHreycIY3RRctWLV+93s7Jzc/PD24XAwPDxIlTT506VdfYKigoOHfu3JaOnr6+PtBaBbBrhYSE4H2wt2/fsrKyQiYfICXJ+/fv+fn5IQviGRgYIFIQw9++fcvBwQEJPQYGhi9fvvz48QOyDkpQTIr1yxdWTr7jx48zMzMrqWlDtPz/D5q6Z2Jievv2LQ8PD6TEePTs9YcvoF25f/78ef/+PQ8PDycn5+/fv9ds2ApZ7MTGxvb48ePDx0/PmzdPWBi0Uezfv3+Q2IGcwgbpun/9+pWdnR0yjfP+/Xt2dnYuLq7vYAAZm4e44d+/f9ev3zxz4ery5cshQf3nz58PHz7w8fFBUgXcNA4ODkjp9/v3b8g8EsT9cEFIekBOAxC9P378YGJievPh68t30EiHpMNP3349ffn271/oGOR/MPjz58+XL18gLnz//j0HBwfEU5A1Y58/fxYQEIAviYGY8/Hrr8fP30DM+f79+9evXyGR+PHbr0fPXn35+ZcP3Nv69evXzHmLxMTEsrKyIGYyMzNDQomFheXjx4///v2DZElRKTl3b38GBtCdO8zMzO/evePg4IA0NpzcvERFRSG1569fvyBOhSfv9+/fs7Ky8vDwQMoQSAhDBjI+fPjAzc0NsRci+/79ezY2NniC+f3796dPnxj+/Pz95+8AriAfbo3Rv3///f71m4GRhYGB8eu3n0I8zF8+/Xr/7sNvNhZmNtAQ+b+/TKwcDH9+/f399zfDv1+fP3z/wvea8R/Dj28/frP8f//xNwsLw3/mP8ws31lZfrGxsrOysrBzMPDyiCqo6377d+XDu3eQFTg8PDxMTEzgNEzTVRzwREUdxo8fP4uLi1mvMz15/rS/vx+yb7ezs3Pt2rVsbGyLFi1iYWFhZWWtrq6G5A24rVZWVpCpbQYGhri4OB4eHgEBgfz8fGlp6dbW1j179vz8+bOzsxOyYXzKlCkHDx7k5OSUk5ObOnXqtm3bpKWlKyoqeHh4PDw8fvz4ATdWUFBQXl7e1dUVUsdAZv8hsllZWaysrIcPHzY2Ng4MDGRnZ3d2dhYWFj506JCPj4+3tzcLC8uECRO2bdsmKSm5YMECFhYWOTm5mpqaY8eOCQoK9vX1cXJympubd3d3X7t2jYWFJSgoiImJiZ+fv6mpCTLv6eDgYGBgwMbG1t/ff+LECci61ZcvX0IcACFzcnLU1dUhA5B1dXVbtmx5/PhxSkqKpqYmMzPz7Nmzt2zZwsLC4uvry8nJWVxcDNkgLyoq2tTUxMrKmp2dDVnbICAg0NDQwMPD4+DgICAgcPjwYfBpTj6MjIzZ2dliYmJCQkLTp0/fsmULAwNDRkaGqChou/GCBQvOnDmjpKT09u3bf//+iYuL19SAOs3R0dGysrIXLlzw9PR0cXFhZ2dvamo6dOgQHx/fxIkTkSuq6OhoSUlJyIB0eXk5ZJIOYo6QkNCECRM2bdokJCQ0a9YsSOMvLS1NSUkJsnQYEgK/fv2CLOi8cuVKd3c3pLcwbdq09evXf/r0aerUqZBzA7y8vDQ0NFhZWdXV1ZctWwY5PCE8PFxWVvb06dP29vbu7u6srKw1NTWQ4h5iOAMDQ2JiooGBAZxbU1Pz/ft3AQEBYo7Tgusa0oz///9LS0sfvHhx/fr1ISEhJ0+evHr16r59+yDD2xEREb9+/Zo0aVJISIioqOjKlSshY9jMzMxbtmyZPn368uXL4fUKJBwgbYu7d+9u2bJl06ZNWlpakLUZbGxsWVlZiYmJ8Cjg4uL6DNFDOgkxbZYkS0RUdEBAANo0K3hKmpWDg2Pz5s1nz5599erVixcvvL29g4ODe3t77969W1dX5+bm5uLiMmvWrK9fv3JwcDQ1NU2aNOnEiRP///8PCAiIiYn58OFDXV3dixcvIBsZIQdTrF+/vry8nJmZecqUKdra2k5OTl1dXefOnfv//39oaGh4ePiJEycmTZrEyMgoIyNTU1PDy8s7Y8aMnTt3SklJvX//Xk0NMfL38+dPFxeXkydP7t+/39HREXI2iKKiory8/O7du0NDQ588efLw4cOenp5r165B5q8ZGRnFxMQgBZ2uru6DBw+WLVtWW1vLzs4OaUq+fv367NmzXV1da9eu9fHxgQhC2oibNm2aMmUKRG9FRcWbN28YGBiOHz8+YcIEBgYGMTGxxsZGbm7ujo4OyFyNr69vVFRUe3v79+/f7969KyAg4OnpuX79+nfv3iUlJQUEBHz58qW1tfXu3bv///9PTk728PA4cODArFmzmJiYhIWFKyoqVFVV//z58+/fPz09PUZGRogXGBgYPnz40Nvby8jIeO/evb9//1ZXV+vq6kJWF9y/f7+tre379+///v2rqal5/fr16tWrP336VFFRERERsXDhwmvXrpWWlrq4uPj4+NTX1799C7p2q6ioyMzMbPLkyZ8/f75582Z5efmZM2du3779/Pnz79+/Q1aZP3jwwM7ODtK7BvUEnj1ramq6e/ducXFxcHCwkZFRY2Pjly9f2NjYKisrNTU1Iftrb9++3dTUBLkGYv/+/bdv387Ozj5z5szixYsnTZr0+PHjmTNn1tTU9PX1nT9/Hp4GNmzYcO3atVu3bkVFRcEn3LZu3Tpr1ixRUVF2dnZIHoGk+tevX3d3dzMwMNy9e9fIyEhBQWHbtm1//vypqanR09O7evVqW1vb379/+fn5a2trZWRk4OZwcHCws7MzMzOfPXsW0q8QFxevq6uDbMGEGM7AwLBx48bdu3dzc3M/ffq0oqJi06ZNb968uXz5cllZ2eXLl3fs2PHv3z8jI6OysrJnz55t27ZNV1e3vb39x48fDx8+/Pr1a1lZmYWFxZ49eyCLoObMmfPnz5+HDx/Kysq2trZycnJOmDDhyJEjEhIS/Pz8VlZWvr6+EKvv3bsHiUoGBoby8nI9Pb3e3t5v377dvXv306dPxcXFdnZ29+7dqwMvVZcRF/74x3Yg7yiDNKdoTR7ZusHXWIuBwgUakADGS7KysigqKigoKohLSAX4OpdkehvpyUZ4qGRGWDflBdVk+ZWnBdbkB5RlBno6qJnpiFiaakb66ysqyElLy0lJyQR42wT62Hg7aJSn2czryF41uXrdzPZdK/v3b5798tGBzRvntDUWhQa5BgYEODs7wzM2XhdRU9JAUTrDz+0sOMuRF2W5+wo239tKnl6q67p3715ycnJtbS3kxJ81a9Z8/PiR6rYQNPDbt28VFRVTpkxZvHixu7v7wYMHCWqhj4L79++np6cvW7ast7fX19f35cuX9LEXzZajR4+Gh4dDRlXRpIYWN2574snnpwahm88d3t+aHrd8+XItLa0rV6709vYGBwf/BU3yQB178+ZNJSWla9euPX/+HLz+E0pcvnxZWVn50KFDr169ggqBqRcvXjx//nzZsmWOjo5fvnyBmvL///PnzzU1NY8ePfr8+fNnz549f/7cxdXNUUP+4MbVcDVEMr7+/KvRcH3Kij0FedmQbufVq1eR9T548MDPz+/Xr1+9vb329vaPHj26ffu2tbX1tWvXbty44eTkdObMmXfv3t26dUtZWXnFihXPnz9fsWJFZGTks2fP7t696+HhceXKlZ6envz8/JcvX545c0ZZWfnUqVOQ1Pjnz5////+np6cvX758/fr1kZGRz58/v3z58rJly96/f+/t7Q1Z7FFZWTlx4sQLFy64urrevXv32bNnvr6+NTU1cHfu3LkzLi7u9OnT7u7u79+/nzBhQldX1////9etW5eYmPj///8lS5aUlpb+////+PHjAQEB////X716NUQKYsiqVavS09MhrWdIM3rVqlUlJSV///718/ODNI8gKl+/fm1vb//06VMIF0K+e/fO3d193759r1+/rqurq6mpOXv2rKur65s3bx48eLBt27Z///7Z2Nh0d3e/evWqqKjIzs7u8ePHBw8ehPTq+/r6cnNzIeHj4uJy9+7diIiItWvXfv36dceOHQ8ePIDYgkm+ffvW2Nh4zpw5r1+/XrlyZXBw8Ldv3yIiIi5cuHDt2rVdu3Z9/fp1+vTpCQkJX7586erqSktLe/r06devX0+fPu3p6Xnr1q33798XFRW1tra+efNm586dvr6+Hz9+jI2NTUxMvHPnzo8fP/Lz8xMSEl6+fDlv3jwtLa2TJ08+efLEwcEBHia/fv06ePCgl5fXnTt33r17l5qaOnPmzDdv3qxatSo0NPTLly+enp4FBQX37t2DLMH6////5cuXPTw8fv361dnZqaio+OzZs5UrV5aXly9atCguLu758+fXr193cXG5efNmX1+fra3txYsXP336NHv27Orq6hcvXkA6Hq9evSotLY2MjIRnsSdPnmhoaOzYseP58+eenp45OTkvXryYMmVKUlLS9+/f/f39N2zY8ObNm4kTJ2ZnZz98+NDJyenkyZOvXr0qKSmJjo7++vVrUFDQvn37vnz50tLS0tHR8eLFC39/f3h19uHDh4KCgpaWlmfPnv3+/TsxMTEkJOT27dufPn1at27d06dPX7x44enpeenSpUOHDoWHh////9/R0bGuru7Vq1dLliwJDAz89+9fcnLytm3bzp8/r6CgcPz48ZcvX4aHhy9dunT//v2+vr5Pnjx5/Pixra1tf38/PLovX768a9euL1++QKLy379//v7+xcXFL1++3Lhxo4+Pz/fv31NSUiZMmPDmzZtNa1cK+XdXbn4D105nxnAbGf3/nwG0XA+8/vfTx6//pZg/fPl/69EviZ9fGJl5//1jYPjH+u/z33+gcVOmL1//sjL+4mQXZGH9/ofhDyNoepqBj4fj2UcGZlYWDg4WHj5OFnZuHj5uFjaeP79YuLm4fv78zcPD9u4DaIU7Gxvbz58/qdnYpItZkHF+ulhFwBJmZuagoCAnJydG8LKH4OBgAhpoI83JyZmQkLB3795Xr141NTWZmZnRxh6STZWVlY2NjT158iQHB8eUKVMg42Qkm0KxBg0NDcgkJsUmjRqAMwR+/f4dERGxb9++qKioP3/+MDMzf/jwAT5+ef369ZcvXyYlJSGvn4GsNvny5UtOTg4fHx/yTD1oOxQT0+fPn9+9e/fmzRv4uOnjx4+fPXtWUFAAmQZlZGR89Oixnig3efu6GRkZ371///LlSx4eHsiqGFzec3V1lZWV/f//v5SU1JcvX7S0tNjZ2WVkZAQFBV+8eKGlpeXn58fJyQlph0FWu5qYmFy+fPnhw4dBQUFiYGBubv7r1y/I6B3EIlZWVkZGxpMnT3p5eUmAgY6Ozu3btx8/frxp06bt27ffvXuXkZHx0qVLpqamkIN1XV1d0c6v/f37t4mJibu7e2trK2Rag4GBwd7efubMmc+ePduzZ09SUhLEOqzkz58/IVO3kEUFDAwMq1evdnd3f/DggYKCwvr16+Gj/pD4Qit+Hz16JCgoCFnTEhwc3NTUlJ2dLSgoWFZWJikpGR4ezsjIKCws7OrqKioqqq+vz8TEBAk3VlbWly9fXr58OTk5GRw8YtLS0o8ePXJxcVm4cCFkQsnJyQmrmyFLyUVERPz8/ERERNzd3SGtQMjuVVlZ2UNgcPfuXcjaCWFh4RcvXkhJSUGGb9nY2GRlZTk4OG7fvp2YmCgsLOzm5jZ9+vS7d++ysrLGxsZCxsj////v5+cnJiamq6uroaEBKVelpaXh01+srKyQQUo5OTnIMcatra3CwsI+Pj6zZ89+/vy5sLBwTEwMZEwU4hENDQ1hYeGTJ0++efMmLi5u586dV69eDQoKWrVqlbu7OzgJSOjr6589e/b////e3t56enqQVUmsrKyPHj0SFhaGOCMwMHDixIkQMyFz4lpaWra2tuBdWepWVlbi4uImJiZHjx599uzZly9fXFxcuLm5w8PD09PTL1y4IC4uDjEnKChoypQpkOO39+3bd+DAAchODMgsE9x8fn5+Pj4+QUFBSNpmYWEJDQ2FzPyIi4vPmjXr169fL1++fPfuHeT4bQYGBm5ubi8v0Ly8paXlihUrfv0CneHDyAha9wU+n82MiYnJwMDgyZMnL168cHJyguxidHZ2RrZaTk7uyJEjBw8evHfvHmSom5ubOyAgQExMzMrKaurUqS9evHj//n1ISIiwsLCDg4PGqVOQWRe4y+nJGH6NUfCKItBYM+O3n7/+/P39+cv3T18YGdk4rt29KCQowM/H/fvvP1Y2tr8MnN/+c/9++5WVjVlGWuLLjx/M/1nZOLg5uP7cfPjlB8OHF+/u8vK8kpCSYGXnZGBk/n741NNnL5mYmHn52F++/sbMDJrOpmdUDT+75MBgMPhLHQwGg0uQ3cDMzGwNBsiC9GeD7y6A7z6mv/0jyMap06bfvn3rz58/lZVVhUXFzc3NYuJip06dampuaWlt8/Dw+PsXdBM9UoiADgX79+8vWhMHrIDxP8P//r6+vPyCrq5OeQWFq1eulpaV5+TmRUdHwVuuLGxsaya0k9epZmJi7unudtAWmz59upubG2QDH9hqFIKRkRHSXIPsjYNUqH///oU0iP///8/KCjoniIGBgZOTE7R2Daz748ePXFxckG12kMYTZOkIMzPzz58/IYsFv3z5wszMzMXF9eUL9L77f//+sYBXQgcGBvLy8v79+1dCQgIyOAQ2leHz58/wZXYQEQg3JycnMzPz5MmTkDsXhISELCwsenp6GBkZzcGnYUD2a0K0QLwDYe/fv98M1n1lY2O7devW3bt3T4LBt2/fLl68+OXLF8iiIB4eHn5+/rt370LaDZ8/f758+bKwsPDXr19///7Nysr6+TNoxYSYmNj8+fNv3rx57ty5oqKi1atXQwIK0maCLI6ENDiYmZnZ2Ng+fPgAccnXr1+ZmJiSkpLc3NyuX78+d+7cr1+/pqenQ2Qxyd+/f0NWTP39+/ffP9DwCkRNa2srBwdHbGzslStXlixZAll3CAklSERA1rhDOjzfvn2DtPb+/PkDmfhGDhyIrr9//0KcDQlDiCDELsh8y79/0LWbkHT4FwwgPQ1k0yB70m1tbadPn66hoREcHNzW1sbIyKinp7du3Tp4yvn8+TOk9wW3FOJCNja279+/Q+wFHfsIYcFISEMc4kGIGCS5QnYQQgL8y5cvjIyM3NzcEF9Dll5AXMXHxxcYGMjJyfn3718RERGIeog5EBKyWBPChkQcAwPDjRs3WltbS0pKNDU1Hz58CM+VDAygk6EgiiErhiFsiEcgi0kgyz+YmZkFBQVv3boFUfDq1SvkbNjS0sLLyxsXF3f16tUlS5ZASgmIRkiPF2IUJDR+/voJ8SDEKPqTw7AxCjpRDTQECjpqlOEfq5ocz9evDH///OLkYPzz+8vbN5//MTAygHeucrIxM/378/XbN3ZWdgYGrvcfPn7/wS/My/Dv5+8Xz7++e3eXiYVFWvo1EwMzAyPjr9+/3777ICcprKEm+v/fR2YWNngZQf9oG7VxNARGQ2A4hcCXjx/OHtwL2ZbExMSUGhHc3NIS6uEiJCT0+MljIyMjG32t76+fgc6wQPU29PwhVEFQpQW+NTTK17OpuTnKz0tcTOzZs2eycnLedpY/37yEn1f4h5Hp17ev/8k6vvDXr58LFy70tVRhZsZSj4DOofwNct3Pnz9//foFceC3b98grRYWFpYFCxbY29uzs7PDZf38/KqqqmRlZT98+HDnzp3q6up///7NmDGDmZn50aNHFy5c+P//v5KS0osXL5YsWcLExHTixAk/Pz9fX9+ysjIpKalXr149f/68tLRUUVFx7969FhYWe/fujYiIsLOzmz179vz584WFhTdt2oR8TcCfP38gDQvIQnl7e3tI+4yBgSE0NNTExGTKlCmQcv7v378QlYyMjJcvX166dOnv37+PHz/++/fvqChQ4/7Xr1+/f/+eP39+VHRUQX4BaIKOkTEhIQEyrc/AwMDOzp6RkdHU1JSQkCAgILB69Wp5efn6+noREZHW1lZzc/O5c+f6+/vfuHGjv78/IiICcijyv3//vn37BmncQE6LA0Xu//+/fv1iZmYOCAiYMmUKAwPDrVu3mJiYNDQ0amtrpaWldXV1ubi48IxyMTMzf//+vaenx9PTc8eOHUZGRmJiYj9//vz379/Pnz/Z2NiePXu2fft2iJfl5eVXrly5ceNGS/BZe/Ajq3x8fCZNmhQdHX306FEZGRk18DknEKcyMDDAD2n5+/cvvBX4/ft3uAJIy+/Xr19//vzh4+NzcnJqaGgICQnZuXOnsbGxrKws5AgOSMqBkx4eHlVVVUFBQWpqag8ePLCwsICM9rW1tUlKSr548eLp06c2NjZXrlyBtLogLvn69auamhoHB0d3d7euru6cOXOQJxn+g8MTYsWfP38gLoSEvJSUlLGxcWtrq4ODw4oVKxwcHCwtLWfNmtXV1aWrqzt37lwWFhZZWVlVVdWtW7fa2tru27cvICBASkoKHkoQY5WUlDZt2iQjIwM5BBASNX///v3x48e/f//2799//vx5yNj5b3CugVwTCI9riAshbRtIkx2yrerPnz8eHh4rV67s7u7m5uY+dOiQtjZ0jxpEAScn57Nnz7Zs2QJpccIj5f///1+/fuXl5bWwsGhoaIiNjT1z/PCzpyKQbh7EzXQmQde00MHKo9s2dtZVbT53HftxrlR1gYSEOBsb6AxFfgE+bTVhXi62n1++/vn7nYdfmBV0UNw/Bqb/TP9Zfnz/9Ov3dyYGNg4O1jvPfn7++v/Nu7diomK2BuLs3Az/GJm5uQXYefhZmTkYGZhA8frr99cf33g4WJn+/jp0/AYLh8DVq1fv3r1LVbcTMMxAUdpCVzu1sdPIALHhg4AeVOm8/YWu8i6+ShRd+I5q5ChvNASGRgjE70jKNsg0kzAdbM69fenC8kndyBshmVmY2dnYIONG4I2SoDsgIAMbJDmeiZmJnZ0ddL7I/3+MTEyMDAw/fv6EX1MHMer////e0QmmTm4QLpHkt1//DFqur0pXMZAGbxjG0Pb58+dDhw55enqeP3/+379/pqam//792759u7GxsYSExLVr19atW6euru7i4nL8+HFXV1fIONbp06fXrVvHzs4eExMDmcfcvHnzgQMHDAwMJCQktLS0pKWlz5w5s2rVKgUFBRUVFQUFBTU1taNHj27evJmTkzMsLExTU/Pdu3cLFix4/vy5kZGRv78/FxfX5cuXly9fzs/Pb2FhwcXFZWoKTQOPHj2+evWqp6cHxPlHjx7jYOcwNgEd6PH/P8Oa1WucnJ0g51O+ePHyzOnTPr4+jx89Xrtu3a+fvxiZGFVUVDzcPTi5OBj+M2zbtt3AwODU6VPWVtaiYiIQAy9fvvLu7Vt7B3sIl4GB4dSp05s3b/7x/YeJiUlQUBArG8vHj5/mz58PaUL5+/v9+f13x44dx44fY2Zi9vf3NzE13rJlq4W5hYio8I3rN96//2BpZfH7959du3ba2zvw8HDv3btv544dwiIi8fHxEhLi9+89WLV61du3bzU0NCIiIri4MM7VAjvl9evXycnJUVFRFy9elJaWjo+P5+Xl3bFjh4mJyb9//2bPnv37928nJ6dfv365uLj8/ft348aNV69ejYiIkJaW3r9/v5ubGysr69+/f9evX3/8+HE5ObmEhAR+fv7du3dra2tDJvQPHTokLS2trKz8/Pnz8+fPe3l5MTAwbN++XUdHB7KNkoGB4d27d8ePH4eY9uvXr5UrV164cEFNTS0mJoabm3vnzp1GRkai4K2cYFeDiL9//27YsMHBwUFYWPjgwYMSEhKQDWHHjh3btGkT5IRpeXn5M2fOMDExQXZbXr58+dOnT9bW1k+fPl2wYMHPnz/t7OyYmZnt7e0hDdbPnz8fOXLExcWFlZX10KFDkpKSquArXS5evOjp6fnly5fFixffuXPHxMQkJCSElZUVbo69vT0jI6OTk9OHDx8WLlz45MkTXV1dyE7Z/fv3u7q6QnoyDKCLG3+sWLHi6dOnqampN27ckJKSUlFR+ffv344dO/bt26evry8uLq6pqcnCwnLp0iXIocumpqaSkpIfPnw4fPiwl5fXwYMHlZWVOTg4zp8/7+bmxsTEdOrUKQYGBjMzsydPnmzevFlGBnRCjoKCQkFBASikGBhevnw5e/bsP3/+ODs7//z509HRcc+ePbq6ulJSUt++fdu5c6enpycTE9PixYuvX7/uZGe1/IWmmBB/b4gURDudyWHYGBUTE+Hg4Pr79w8nNzfzP6Zvv37+//fz67fvXFx8rODT3RmZ/rOxsf9n+P3ty/f/f//x8gv8+PXr29dvf/78/fP3l7Agv4ycLOP/X79/fGdi5hAQ5GdiZmFmAU2I/P7x+fvPX6ws7D9+MHJyc9+7d/fKlStk1BBkx/FoY5TsoBvVOBoCg7YxOhSjBtIYXZGiaCQ3JE9cBo85/fvL8IuJkenPX9AILgMDAysz67/////+A62FYGRgZGVm/fMftBUdPL/MxMLE/Pvvb9CiVUaW/9CDJRn//P8NmV1lYWb9++8vMxPzv39//4GP0AdN4DKz/Gf4Dxlpg8QyGzPbf9C49H8mBqbf/0BHIzODjsVm+cfwj4mB6dff36D7jZhY/zH8Aw1tMzD8/vubFWzyP9D52Uxg1/5hZGAEncD/D7SLi42Z7R/DfyYGxr+g44D+sjCzMDEw/f3/j5mR6Q/IJSjXGjExsrAwgToPr169ioqKglxuDHHYKDlEQ+Dt27czZsywt7f//ft3R0dHY2OjhYUFeX7JXvGEjZmhP5S2Z9/ichuW6RVcSoeKOCznM/3/+4+FjY0bdPIjOwsT239Ghn///zKAKAZWNkYuLv7fv/59/vyV4et3dnbQOd6/f/9mYWX58PHj8+dvubk5X7/6AJqwAN1U9p+BkZGPj5eBgeH16zd///2VAXX4lCDHN8CsGyrBw8AC3t01mJ37//9/yLofMqYMKNFLTJh8+fKFlZWVWgcpfPv2jZmZmVqmEeN+qqv5+fPnnz9/IIu0kA3/8ePHnz9/IKvlkMWxsv///3/nzh1BQUHIMVhY1YwKjoYAdUPg5ddrSy6E//77GTL3Rbzh8DFszFUTxBtCa5X/Gf6zs/D++ffr77+fkMMjf//7rSseEqAF2rjDw8OTlpYGWeVJa5eMmk/TEBAQENDS0lqzZs2/f/+KiorIbomCV038Z2AesEQ9DBuj4C4rqOPKyMjAwcXx/fs3JiYWHn42BvDt6uAOMeia9f////Hx8fCD7ojhY2Rk+Pv339u3b5mZGCXFhFlZWdhY2RmEhL79+C4kLAwZyYdcHiMoKPjnzx820Awa6GR4yP2zNE1q1DWcmYn59u07ugxPZOQJ9H6uXr26cePGsrIytCXkDAwM06ZN09XVtQXfe0ld50FM+/r1a0FBQWNjI3w2ByJODPn9+/dSMIDM8cG13L17l4WFBbJbdtKkSaampmiH28NV4mdMmDBBT08P7Sxr/FrwyE6ePFlVVTUIdt47HpWDVmrHjh1Xr16tqkLcbgJxKuQUkubmZgj35s2by5cvr62txexg/Pjxo7KyEnIsv7f36AISSICNkjQPAUEO+SCt6f8Y/gxY9UtLL/5nYGBiZP4PWpPxH+LBf///87JD79fh4uLCvIGZls4ZNZtWIcDMzBwIBrSygF7mDsPG6H9Qo5MZfIQYw69fv168eAmv/+CnbzAyMgrw87m5OXNxcTIxMv37//ff//8/f/4AXT7EwPTv73/w4fl/jpw48eDBA2ZmFshty+BtgKDd+lxcXPz8/GxsbMir7+kVZRTZw8bOtnzx8m8KX4rLiyHNa8jm1v///7OwsHz//v3379+QI9MlJCRcXFzA69X+MzIyfv78mZ2dHbICxtraGj6C9f///0+fPvGDr+qBuAx+hAQ8tCHikJOWubm5IYvDILKYA40fP35kYmJ6/vw5ZB03RC9E8a9fvyBbLJHdCe7P/fv06RMfHx/EtZC7KyEaIeS/f//mz58vKCiYl5fHzMxsa2srISEBkfrz58+PHz8gA3ifPn3i5uaGp5afP3/++PED2WsMDAyvXr369Al6mcfHjx/hhydDXPj7N2gWj5mZ+devXz9+/ICEJMSiv3//Qu7wgHAh5OvXr8XFxSHsjx8/srGxQe7JgJgGD0mIAgYGhk+fPnFxcSFfYgROkwyQtSIQ9ocPH7i4uCAxBel6oUUQJCIgN2bBTYbYiDzM+fHjR2S///jx4/fv38hbNb99+8bExPT9+3fI7aagddU/QQDi60+fPsHFISfCeHh4QFboQ5ITJPv8////zJkzd+/eXbFiBcTvWAPq58+fkPHjT58+sbGxQUZ0IG5GTpkQ70AucYH0IRkYGCAr9zHHbiGKR8kRGwLsLLxKQnYj1vujHh8NgUEFhmFj9O/fvywszP//M0IqS05OTsgljaC1PrDbGpmYmRn+//33j+H+/cfgu7xAc/mQmpsBtGbo79+/v/n4eCUkJRgYmSG1GhMT4///oPMvPnz48Pnz5+/fv3NwcGCOGg6q2MV0DBMj0/v37zdd2vTmw5uioqKrV6/eunXrwoULQUFB//79W7Fixd+/fy0sLHJzcz98+HD69GkzMzPI/SgXL178/ft3SUmJoaHh5cuXtbW1GRkZFy5c+P379xs3bqirq1dUVHBzc+/atWvOnDmCgoLq6upiYmIxMTEQNzx//ryvr+/Zs2fs7OwlJSVaWlpz5sz58uXLpUuXfv78mZ+fb25u/urVq9bW1levXqmrq//8+RMS7JBImTp16t+/fy9evNjT03Px4sV58+b9/fvX2dk5OTn59u3bEydOfP/+vYCAQGVlpYiICCS6IfZCyA0bNmzdupWbm/vVq1e5ublXrlxhYWH58ePHypUrP3z4cPfuXcipwlu2bBEREampqZGQkDh+/PjMmTN//fplbm6enZ0Nj2jILZpfv37t6em5ffs2AwNDZmamtbU15CCVixcvlpaWfv/+vbe3l5WVVU9Pj4ODIysr6+7duz09PR8/flRUVCwvL4c01yDnd0BarpBrThkYGFJTUx0cHDZs2HD//v07d+5AlnYFBQX9+vULcsWIoqIiPz+/n5/fz58/z549m5qaCr5mcKKjo6OsrGx3d/fDhw/Z2Njy8/MNDQ2fPHnS1tb2/v17XV1dFhaW1NRUFhaWzs7Oe/fuCQgIlJSUQA5fZGBgOHbs2OHDh1+9evXw4cO4uLgHDx4cOXJERUWlurqah4dnz5498+fP//v3r52dXUZGBhMT04YNG5YuXSouLv7v3z+Id44dOzZz5szfv3+bmZkVFBQwgwEk/BkYQAubjhw5Ym5uPmfOHMihNj9+/CgsLBQWFu7t7b1582ZxcXFYWJiSklJHR8fHjx+VlJQqKio+fvy4bNmyT58+cXBwVFZWTpky5cSJE5Bdyebm5rNmzfr+/fuFCxd+/fpVWlpqaGj49u3bjo6OR48eCQkJlZSUKCsrr1y5cvPmzYyMjEFBQYzcTOBpE7ijRhmjITAaAqMhMBoCgwKA9okPCodQzxG/f//+8+c3A8P/nz9B52ZBDrH7//8/Ozu7uLi4iIiIkKCQqKgoMzPLj+8/QLeKg46W+P7z+5dv377+/PH187dP3399//T5y7t37znY2SUlxMXFQXc4S0lJsrKyGhgYiIuLMzIyfvjwATJQRz2H08Ok////8fPzOzg45OTkiIqKXrlyZd68eQkJCRYWFu/evausrOzp6dm2bdvZs2ffvn27f/9+BgaGLVu23L59u7m52d3dvb6+/v9/0GUkDx48+P79++zZs01NTXt7ex88eLBhw4Znz561tbUVFBRUV1cfP378wIEDcC+9ffvW0NBw4sSJNjY27e3tDAwMBw8evHz5cmNjY3BwMOTqs56eHl5e3q6uLh0dnXv37sHbf4yMjCtXrrx7925lZeXr16/7+/tLSkr6+vr27Nmzf//+N2/euLm5TZw4UVxcfOrUqZARQUZGxl+/fr0Bg69fv9rb29vZ2Tk5OWVlZYmJiR08ePDx48cfP36cNm2ap6dnfX19b2/v9evXu7q62NjY5s6d+/79+87OzrS0tP7+/mvXrq1fvx7uEfBWBsaPHz/KyMj09/dHR0e3tbX9+fNn3759J06cKCoqEhISqq2tjYyMbG5ufv78+caNG//+/VtfX29nZzdx4kQWFpbJkyejmfbp0ydxcfH+/v60tLSenp5fv35dvHhx06ZNhYWFJSUlvb29r1+/Xrp06fXr17u7uwMDA5cuXfrmzZsnT56cPn0aYtSxY8cgxxdra2tPnDjR1dW1s7Pz169fTU1NKioq3d3dMjIy8+fP/////6RJk5iYmCZOnOjg4NDU1PQLdubOo0ePli9fnpCQUFxcnJOTw8zM3Nvb+/Tp082bNz98+LCjoyM/P7+zs/Pw4cNr1669e/fuxIkTKyoqSktLHz169Pfv33fv3nV0dKSnp/f391+/fn3Tpk2QiIA4j4GB4c2bN3v27GFkZNy7d++NGzeampqCgoKamprExcVDQ0O1tbVLSkr09fWrq6vt7e0nTZrEzMw8derUHz9+9Pb2qqioJCQkLF++/Pz585BQamlpef/+/bZt227evNnc3Ozl5VVfX//379/W1lY+Pr7e3l5HR8dHjx6dPn16xYoVjWCwaOGie3fvsrAww500yhgNgdEQGA2B0RAYJGAYjoxC9jb++QO6sYCJiQkyGQ0515cJDH7//83KAvL47z8/mVmY//76w8bKI6UlCTrz/gfzv+9M955c//Xz5/fvP9mYQTvpmUAbJP+BTkj5///ixYvMzMxSUlKMjIzw6cJBEpfEOOM/AwMLK4uoiAjkPvG/f/96e3tbW1szMDBYWFhs27bt48ePb968efHihYSEBGQ+nYeHJyQkRFZW1sHBYdmyZX/+/IHs3Pr375+JiYmnpycLC4uNjc3t27dFRETU1NSsrKwYGBhCQkKQG6Pq6up37tyZNWvW3bt3X79+zcDAwMHBER0dLScnJyQkNHfu3IcPH969e7etrU1WVlZQUBAyxgbx0f///wUFBePj41VVVXft2vXixYudO3dC5prv3LkTExOzfv36GTNmXL58mZeXF3J+GzMz88mTJ/v6+hgZGX18fJKSkkRERPj5+SFrRiHu//v3r5mZma2tLRMTk6amppeXl5ycnKOj4969e+/cufPkyZMjYPD+/Xu0A7z+/fsnISEhJyc3d+7c58+fv337FnIaX2Jioo6OzsmTJ9nY2Hx8fBgYGIKDgx88ePDu3bubN28qKio+fvz46dOnEBdCvAZZYyAsLKygoDBv3rz379+/evUKYpqXl5eqqqqSkpK4uPjbt2/PnDnj7+8PviVAzsLCArIeABJBDAwMbGxs////l5eXv3bt2uzZsx8/fgyJxEePHrW1tYmIiAQGBi5YsODbt29nzpyRkpKaP3/+hw8fINdyQFqNv3//Njc319PT+/Pnj5aWlqurq5ycnLm5+b179zg4OPT09CAne/v5+Z06dYqPj09XV9fY2BhyHOP169fv3Lnz/Pnzo0ePHj58+MOHD7dv34bcNQL3JhMTE8QiJiamsLAwWVlZHh6eRYsW/f37V1ZWlo+PT1lZ+c2bN5CAevTo0dOnTyGrDoyMjKLAt0ufOnXq06dPCxcu/P379/Pnzz98+CAgIABJmXZ2dgsXLvzy5cv169cnTZokIyMDWQ83ZcqUDx8+rFu37v+//9++f/v58xd5Vw3BfTHKoCQEICePQpIBJeZg6gUvygItNMKUIkbk379/yAezE6MFoubPnz+MjKB7ByDcUXI0BEZDgGwAapORrXlwavz79++fP3/AtTXzv3//ICsCmZiY/v79C7mj4s+fP//+/f/75y9o+zwLy7ev31k5md39bNklxZi+8b68+ObJmod//rz89ZuRlYv1+/cfzCysYC1/OTg4IOvPIHd+sLKyCggIPH78GLJib3CGBqar/v35B58BZ2RkhCyXfPLkSXFxcVRUlJOT061btyANekhrgJWVFbKMEnKHBEQKMnsObwyB1+kyQYIFYuOHDx/gKhkYGHp6el69ehUYGKimprZw4UKIGsjYJ8RYSJ8B0lCDVFoQ2yEq2djY4HaJiIhYWVn9+/fP0tJSQ0OjurpaUFDQxcVFUFDw7NmzkJHLv3//mpubz549G3KtC+QSEYgvIAZC3M/Ozg45mxqyhAMiCAkcAQEBS0vL////W1hYQG63g2tkZWVduXLlzp07Y2JiGBkZr169+u8fKEghLuTk5ISvdoUc8Q25JMbExERQUNDCwgK5lcbIyMjOzr5t27YlS5YkJiaKiIhALrJjZGSEmAa5JwN06AwrK/yaGcgefEZGRrhFoGMf2NjmzJlz6dKlsLCwb9++zZgxAxK8kOOmIQePQ4zV09PT0tJiYGAICwtDXgMKUQ85kxziWdASamZmVlZW+ADqL/BR25ClBRA1kGujmZiYBAQEzM3N////b2lpqaamtnv3buQYhMQL5NYWiEbIzSKQdgA8B0HOgBQQELCwsJCRkfn58yc3NzckITEzM6uqqlpZWf3//9/FxUVGBrQDDxJK4JU5oJU2kAXQEPMhK0olJSWtrKz+/v1rbWM96fW0f/9QTrqBqBwl6RMC7969mzBhAmQ9D5E2LlmyxNjYWFNTE7/6I0eO3Lx5My0tDVnZy5cv9+/fHxwcDEkkN27cePDggYcH9EhRZJWHDh26dOlSXl4esiAx7BUrVggJCUFO0CRGPSVqnj9/zsfHN7r0mZIwHNU7mMEwnKYHNTTB94n9/vUbMpz58+dPVlYWZnC1ysnJCT6aB3R/0s8fP5mZmP/++/Pr2w/mr7/EWRle/rt36OWu9z8/MP5n+PP77+/ff1hZWbm4uCE7S7i4uJiZmX+DAQcHB2QpG6QKH8xxjOy2////S0pIHD9xYv/+/V+/foVs34Fs8vj8+bOysvKLFy8uXLgAGbGD3PQAvwriH/guEEjD7j8YwC+Z+PXr17dv3wwNDb98+dLV1bVu3bply5YhN/6eP38uKioqICBw/Pjxjx8//v8P2gcGORXr////X7584ePjMzU17enpOXr06IIFC+7cuQNpFEIcD7+DREtLi5GR8f379zw8PCdOnPj58+fz589lZGTY2dmPHz8OaXj9+vXr379/bGxsImAAKb4lJCQOHz586NChT58+QYbJIXeNQJpB8Hsp/vz58/37d1VVVUFBwSdPnvDx8Z08eRLeFIPcyPf3799Xr17x8PCIi4ufO3fu+fPnjIyM8FDS1NQUExOrqKhYt27djBkzIMO6kIFhISGhS5cuwa9mhpyE/OfPnzdv3rCzs0tKSh49evTt27cQcUjgMzAwfP/+/devX8HBwcuWLVu5cuWUKVNOnToFaZldu3Zt7969K1asuHDhAjMz8/PnzwUFBYWEhI4dO/b27VtRUVEnJ6fKyso1a9Z0dnZ+/vyZi4vLxsbmwoULfHx8T58+RQ5kiMchzfGvX79C2n+/fv36/v27ubn5rVu3Vq5cuX///lWrVjk7O2tra9+8eXP16tUHDhxYtmzZz58/1dTUBAQEnj59ys/Pf+LECchNIZDogMQg/AIbeEBB0gDkakGISshS49u3b0MCCnLP4c+fPyFx5ODgcPv2bQ4Ojm/fvp0/f56FhQWeKiApk42NzdzcfNKkScePH58wYcL27dudnZ0/ffr0+/dvZhbmY8eO/f7zG619DHHbKEmfEPj+/Tuku0i8dYKCgpDNavi1PH/+/Pr162hq1q5dm5ube+PGDYj4uXPnIJdbQrjI5KtXr65cuYIsQiT79u3bjx49IlIxhcra29svXrxIoSGj2kdDYNCC4TcyCjrGnxF0tSvT378MP3/8/M/wH9x6/Atp3DAxMTEygmfvWZi/fP0izMn199+/H9+///jyle3zg48vDv0V+/Cb5/e/d//+///z/ftfZlZWDk4G+C5gyA22sDkd0DZzSPN00EYwmsN+/fwVHhH+7sCb7du3q6mp2draQho9qqqqGRkZc+bM0dLSamhoUFFR4efnDwkJYWBgCAoKUlRUZGBgEBQUjI2NZWZm9vDwUFFRERYWDg4OhrQ4zczMPnz4wMPDs2DBgvnz5z958sTPz+/Bgwdw20tLSydNmrRgwQIbGxvIQWh+fn6QSXPIPcgcHBx5eXkzZsxYtGiRmZlZV1cXZMgWYkJkZCRk17mMjEx7e/vChQt//PhhY2MjJiZWW1s7Y8aMmzdvRkREQEYoIyIi4Jv9IdoZGBhiY2O/ffu2fft2eXl5Pz8/FRUVbm7ukJAQyIhsUFCQDHikTUNDg4WFRUBAoL29ffbs2YcPHzY2NoZYDTHK3d1dVlbW09Ozr69v1qxZtra2NTU1rKysISEhkM1ArKyskydPXrRo0Z07d9zd3Xft2sXIyNjW1jZ9+vSpU6cqKyurqKhAjGJgYHBzc5OQkNDQ0Hj69OmMGTNsbW1LSkrY2NicnJwgdTAzM3NERISoqKienl59ff327dv19fXNzc2/ffumpqZWVla2YsUKTU3NxsZGGRmZnJycvr6+efPm2dvb6+rqMjAwlJSUrFu37sqVK7a2tmfPnv327VtGRsa8efNmzpwpJCSUmJgId4m+vr6AgABkCDYmJgYSgObm5j9+/BAVFYUY++PHj6ysLFdXVwYGhq6urvnz5/Px8eXn53NycvLx8bW1tc2ePfvIkSNGRkZCQkK6urqQjU0QK6SlpaOjoyHrNyAjzVxcXJGRkZycnAoKCpDDrZiZmZEDSlFR8c+fP2FhYZD+nq+vL+S6RQ4OjvDwcEZGxpCQEEgSEhQUjIuLY2JiKi0tnT179oIFC2RkZIyMjCQkJMrKytauXfv//393d/fLTNch7VqIkwYP+f3Ll1dPH4OvKB48jkJ3CejADVZWKQUlIhv09+/fP3PmzNu3by9cuGBubh4bGwteJMW0dOnSCxcuaGpqZmRk7Ny5U0xMzMzM7N+/f2vWrLG2tj579uzOnTu5uLgiIiKMjY0ZwYCBgWHfvn3r169nZWWNjo42Njbet2/fpk2b/v37FxoaCllpAymI4I7++PHj0aNHU1JSli5d2tHRATp/ngV0gzNcAQMDw8WLF+fPn8/Lyws52IGBgeHp06czZ858+/atk5NTYGDgkiVLIDn048ePW7ZsCQ8PP3jw4MaNGzk4OBISErS0tJiZodtbr127tmDBgq9fv3p6evr4+Dx69OjkyZNv3ry5ePGiqalpQkLChw8fdu7c+e3bt9OnT3t5eUlKSi5btoyXl7egoEBYWPj169czZsx4/vy5ubl5TEzM169fd+zY8e3btxMnTmhra6elpa1Zs2b//v0fPnw4c+ZMamoq5NwJZL+MskdDYKgDUNONDn6g43WgjMxMjBKSYr///GNmYmVi+s/IyMzKysbHx/f3729WVlY2NrafP3+xsbF9+/aFn49XTk7+8ZOnfDzcoa56JqoPnjC9PPuL9/D2L58v/vr2+xsbC9vff3+4uHn+/P7Dwsry7++/L1++QG7alZOT19ZSvX79+slTZ798+UqHMGRgYKDGDUxF7gou3oqgm9lAJlIV//r1a/HixTo6OqKionV1dQ4ODikpKVS1YWgYtnz5clFRUQ0NjQkTJvDx8dXV1VHu7kePHm3dutXT0/Phw4fNzc3z5s2Tk5MjaOz+/ftfvnxpZ2e3bdu2PXv2LFiwANLGJahxWCoYtDcwXTp2eFJVIRsH9psbB0lc/Pv7R0hMonnhambwgnuCNzAdPXo0MTGxsrJSSUlp2rRp3t7ePj4+RkZGWVlZFhYWEyZM8Pf35+Xl3bx58/z58x88eJCXlwfpTbW0tLx48eL27duFhYVJSUlxcXECAgLgs4NL379/f/PmzdLS0vb2dkdHxz9//rS3t69cufLw4cPHjh3r6emBh9XWrVs3b97c2NgYFxe3cOFCCQmJVatW7dixY968eRA1r169ioyMTExMlJGRaWtrU1FR6e3tTUtLc3Jy0tPTmzBhQkxMzJEjR8TFxXNyclatWnX48OG4uLjW1taysrI3b97MB4MpU6ZISEgEBAQkJiZGRERISEhMmDChuLiYj4/P19e3ublZWVl50qRJvr6+rq6udnZ2+fn5WlpaVVVVqqqqycnJW7Zs4eDgaGlpycjI0NbWtrOzg1yl4+bmZmxsnJ2dDQkoLy8vY2PjiooKJycna2trCwsLSPcM4pFRcjQEqAUylz3mYGUcvYGJWuH5/z8D4+9f/9lYmBkZ/3778YuDg4uNjZ2VlYWB4R9kid6/f/+YmZk5OTh//foFGun8+/fnn7+3z17jfXrnnxjTr9/v/n1h/c/AwfiX4ef/nxzs7FxcXL9+Qfc8ffr0CXLHz6dPoDMmGRkh2zLo1BilRhiB7qmmhjlYzGBlZZWQkJg3b97Pnz/Nzc3j4+OxKBoBQrKyssuWLfv27ZuKigoZC9GwhpCgoOCXL1+amppYWFhqa2uJaYkyMDDIyspu3759586dAgICra2tI7klijVUB4kgIxMTCysbCyvrIHEPVmf8AzmSBBf++/dPV1cXMvr+69evBQsWuLq6Kisrp6WlQVZ0HD9+vLOzc86cOS9evDh06JCZmZmSktKfP39Onz4tLy8P0Qg+H4x5+/btPj4+bm5ukHUyzMzMqampZ86cef369Zs3b16/fo05WLthwwYfHx9xcXEVFZXdu3fHxsaieers2bNycnKQs+cePHhw5syZx48f37hxw9PT8+7du9zc3IcOHYqKimpqakpJSdm9e3dycvK+ffs4ODiePn367x/ohpRbt24xMjKysLAcPXpUQkICMvD/6tWrjRs3xsTEmJqaxsfHQ1ZXL1261MHBQUZGJjk5mZube+3atYaGhk5OTsLCwi0tLQ8ePLhx4wZkDygvL++uXbscHR01NDQgAfXs2bMjR46kpKTIyspaWlra2NigeWSUOxoCwwMMv2l6hv//GX79/MnFwcrDw/H15fffv3/9/fvr0yfQHnDINmpG8Dw9Cxvrty+f/oK2Uf79/fPbqSffz91m/8Xy+x/Dn89/WJgYfv/5+/PvX0Z2Vtb///79//cPsov8z58/v379ZGFh+fTp089foBFWdnaO4ZEUKPcFIyOjNxhAwopyA4eoCTZg8Ps3aCSeWl7g5eUtLS0lNWBVVFS6urp+gRMqtVwyag5NQ+Dvnz9///5h+M/AxMwMGgBjZAQXPr////sPbrOyIje8/v39++c39F51yOYwZlaQgt+/QGeJYM77g1d6g+6fo6kXIIZD1m1D2KysrJAN79zc3PDlUv/+/ePk5DQ3N9+4ceP58+eTkpJUVFSmTZu2Y8eObdu2LVu2bObMmZD1Ub9//4b3o5iZmW/evFldXW1tbQ1Z2w1Zeg6xCEI+fPjw5MmTnz9/3rBhw+PHj58/fw5pjCIH3e/fv+H7+tnZ2RkZGf/8+QPZ5/r3718bGxt9fX0tLS1ubu41a9b8/PnT2Nh4586dLCwsf8AgLy9PTU0NcqwHeE8CtJnOxsb2+zfotnqImZCTLv6Bqw8ODg7IKhF2dnbIGiTIsqLfv0FLmf+Dbl35aWBgoKGh8e/fP8j+BMi2P4iz//wB3UQP8eAoORoCww8Mw8Yo6NpPZqYvX379+v3v189fTEyg0hmyK/nbt2/gQuf3r1+gg0h//Pj588dPUNn/7c83FmYmRrbfP5hB9w7//f/j39dfv/4wMYKu0vn8+QukEAfVDQwMf/78/fnzJyMj048fP7i5uUaX72DmCkhAYYqPKBHIHl7qepm8gIVXutR1zKhpVA+Bv3/+qBsY23r7s3Nwnjm498yBPf/+/eXm5XcMDFVQ07hx/uzhbRt//fgBaZ38/ftHVcfANTSSlZ2DkYHh16+fV0+dOLZr6/9//yJzi49u3/zk3h3IlDrEnf///+fg5OLk5vnwFnS2GkSQQpKREacBHBzsV69cOXTokIqy8oYN681MTVlYmEFNZ/DxWqATT0CnQTOEh4elJKdoammamBhfvXrlyJGjMdHR3398j4qMev36FWhogeG/jbX15MmTPTzcP336dOHCBX5+/v///8XHx929e+/p0yeg60jAhzLAnbJk8WJfX5/MzExQC/jf/6zsrDNnzrAwM/+GnarLwMCgr6fX19d78uQJGWmZDRvWC/ALyMrISElKKikpamlqHTh4kI+Xl5GRMcDfv6CwoK6ujpWV1dTE5NatW05Ojt+/fT9z5oyAgAC4L/DLwsJx/vx5Z8+eERcT37Bhvb+fPwc7+5kzpw8eOKCmrrZq1Up9PT1WVpafP35AQgs0PvLnD2iz4L9/P3/+lJGRkZSQkJAQNzczP3LkCD8/PzMz05/fvyEt139/Qae+MDAwcHNxXbhwXk1NTUhIcNu2bc5OznxI997B/T7KGA2BIQqGX2OU8T/DfwZQpmf69+8PAyPDv3//WVlZGBlAq2NZWFjY2dmZmZnBRxExf//29ffvnzyc7D9/gzZY/wFdCvrv/7//DH//MfxjZGFmYWT4x8Dwn4ODjZmZ5dcvUO/8GxiwsLD8/v3r+vVbPNw8nJycoLn+f/+GaAoYdfZoCIyGwGAIgb9//sipqscWVd68eO7rp08haTl//vw+vX93WGa+jJLK1TMnje2d+YWE18yazMQEbtUx/BeWkJRSUD57cO/fv384OLm8YhIZmZgObFr76Pat71+/MjIxgSZ+/vxhYGRkYWX98+uXoZu3iITUujlTmJiYmUB7b0Al4R/wEP6//////f0LCQcWVlbIAC0TE3R0FiKOSf7++//3X1D/HVPq+88/UrJy23bsunXrppKScmJK2p8/fyxt7P4zMf/++19CWkZHz/D33//KqhoCwiJWNvb/GJhFxSUfPHqSkZXzn+F/RHSstKy8nqExLz9oM9y1m7cLi8uYQddwB9g7uhw8fDQtI1tLSys6NoGdk0dMUlpHzwDikj+/f//4/Tc2PklaRhZ0PzsDQ3hkzO2792VlZfWNjCFqGBgYZOQUCgpL2jq6BQUEDI3NhIWFuXj4ikrLps2Y+efPHxkZGXMLq99//5tbWgcEhbq6ef7++9/J1f3mnXt5BcWMDAwurq6///5X09AWFBSUlpXPys7r6OqFXF/nFxh05swZZVX1bTt3T5o6XUlJKTkt4+PHj5Y2tv8YQX7X1jWQkJb9/fc/N5+Asak5GwdXWWV1f3///IWLhYVFdPQMWNg4LaxtGcABJS4lA/FacloG6HqRR08LCwt37NpjaGzKycOHGeyjIqMhQDb4hz0rk20eaRpBTTTSdJClmp4bmBgY/oMGLDnYBPm53n38+ucPk6SkOKicZWRiZWURFhYGNTzBR/88f/5MTFSEk5P9548ff0Cn+/z5/ev3n7+Qc2n+/2cAHT/0/z+DlJQUJyf39+8/ODjY379///btW/BRMl+trCwFBQUvXbp09eo18FArWUFDiiZqbGAqdJV38VXyJsXaUbWjITAcQmDQbmC6fOLo1NpSJmZmHVMLGRW1HcsX/vn9JzQ9l1tAYOviuYVdk+d1NN66eE5BQzu2qHJ+Z+PXTx9V9QxvnD9jYG1vZOc4ra4MNJD27697RJyhtcOE8jwlTe2XTx5/ev9OWFxSUVPr989fty6eY2Jmisgp4eLh3bhgJhs7x4e3r9++fMHFzSOnpn7/2lUuXl5BUTEWVjZGJsbbl85LyStJKSp/ePP67tVLkBNb//39KyQmXj9nGWS09duvf6YdN0GjrazYzwf8+ePnz58/eXl5Pn36LCgIOqjh379/IKOYwOrB46MMjIwM/xk+fvzAxcXFysbGBF5B9e7dO2bwsbWQghq8nx60NPP9+w9MTEz8/Px//4ImrN+/f8/Pz8/CwgKZpkc2GbQrgIUFJMLA8J8BdBbKX/AUPIQNmvtmYPj15z8TeLXVv3//BPgFfv3+BTqFl4X16zcQFBISYgANZPxjBA9sMDAygE1jZGFhfvf+PRMjk4CgAPxgO5BGVtbPnz7//v1bUEjw75+/P3+B/M7Nzf3582dBQUGG/wwgvzOATl8BDYj+/w8aLQH7/T/Df0YGRhZW0HWAn7+AFINOG/z79///f9BVFqDBFZBGFhaWX2BzOThBC8Mgo+PDIVuO+mHQhMDzj79TbES6g6QHxEXDb2QU1Lb//58BtOf9x3dwy5IBdJT6v//MLEw/f/18//495GhDZmbmnz9/Xb58hYODnZsHNCPDxMQEnhkBlX6QhU0MDP+/f//6+PFTNja2v3//QhYwMTExgc+K+v33z2920K4D0AmmDAyIlVsDEpFEWwoqBolWPKpwNARGQ4BOIcDCynrt7KnLp46zsLAwMjELS0jevXYF3H5jYGJiZmBkZGVj5eHn5+EX+P//v613wOM7t/+DJmRAJR7D//8srGxcPLy/f4EaVaEZBaumT+DhF0iubGDn5Pz379/DWzcOblqrqKnFyc2rYWiiqqN3+uC+F48e8snIBSRmTK8vl1ZSCc3I+/vnz+VTx3n5BX3jksFNN6ZTe3ZsXbYA7eAkSIgwMjC8/PSbiQl7kQJamc/I/fnTXyZGni/vf4GLVlC78P9/0Aw1qJEHbuExMDAwM/F++fbv/9dfoGYiIyMTIw/DP4aPb36AGmqIFttvJibO///+v3/9DSLOxMT39dNfBoa/oPYmSCcDxGQQE6TrF6gVB3YoxC6G//8gbUoGBoafv/83+kqaKnAzMUkzMIBW5cKkGEFH/zEwgibIIM1lkJPBiwXA1jAyMDIyyUKOYWZAalwyMIA0Qoz6z/D/6dOn58/f9PH2ZmZmhhkFqlbAgQBqWTKAHAcOYFAggFwJbogzgqP7H7j9DBknAjVVEW5jBHnl3z+QCRCjQDpH8WgIUA9IC7BRzzDSTBp+jVGQ/0H5npFJRESY/euf58/fgIUYWJhZvn359ub1G/BRo4yQY73/gQoFJgZGJvAmJTAPVLaD8f//HBycwsIinz9//vHjx5+/fxgZQFeA/v///9evX39+/3v/4YOUlCQzEwsLCxsDww+QLYMeM4LLuYP7D+7dvzc0NBRyGiWNXH3z5s1Xr17Z2tqimf/ixYtLly65urpSq3O/e/duXV1dVlbW169fa2hooFlHRS7kuib8zv727RvkpkouLi7InaUPHz4UExNDvngJMjoCXisCHiVCcuIbMFBTU9uxY4e1tTXk3kszM7MrV66oqanBt3Eg6RgUTMiialgXblA4aQg54t+/vz9/fIc4GDQCx8bmGR7DyMR0ZNvGb18+Xz93KjKv5NGtGwKiYmzsHP/+/n1y905fSfa/v3/l1TSk5JViCir+/fvLxcOrqKG9cmr/j+/ffv74/v3bVzvvgIe3bizqbePg4tI1t7p77fK+Dav5BUU2zJte3DPt14/vP398//XjB4Tx59cvhv8ME8rz379+WT1twZFtm45s3yyrohaSnnPm0N7Hd24xMjD++vkT4kg4ycwEOkoPzsVg/GMGtVTBJHx5KZwBbjaCtaAqAC2OYmAG5QyQZvDQJJjB8A9EwcUZwLpAhoDNADFA8lAOiAnCUC5IFswE2/6L8b++DKe1MjdYiAaEsnqYnRoD2Ok0MH3UyNEQGIZgeDZGwQ3N/2/fffr96x8TE8Mv8BFOClKS/56BbgeFNCkgK0e5ubnZWFl/fv8GOgifiQk0GQPqCoN4bGxsv3794OISFBcXf//hA6i6Bfdbv3//Dm7sMn798o2dnYObi4uDg/3z56GROFhYWM6ePXt+/tmw6DDQ/BEtXX3q1KkTJ05gNkbv3bs3a9YsZ2dnrMMtZLho1qxZhYWFzMygW3Zo2hidO3eupqYm/tNVTp8+XVVV5e/vX1ZWdvjw4Y6ODnFx8SdPnsTFxUHOkYF48NKlS4cOHcrNzYVw4eTt27ePHz+uoqIyZcoUVVXVY8eO3bx508DAYOPGjRkZGYO2MXrp0qUDBw7k5+fDPTLKID4ERCSlXUMimcFHO/37+1dZS0/P0vrAxrUWLh6MTMzvXr3UMuawdPe+ePzwz+/fjGwdZVXUmJiY//37I6+qKSQmYeUOWnXz/9+/q2dOCIiIuARF8AsJW7h4KGnpPL5729rTl5kZtMbU2sNHTc+QnYPLLTRaWEJSx8ySi5dPUESUX1jE1stfWELy37+/OqYWfIJCIhJSbuExbuGx///9Y+fkcPALeXLvNhMTEzcvH3TumHi/DVaV8MWjNHMgalOYZtaMGjwaAsMDDM/G6N+/f/79Y/n86TszM2gQCnKyxoOHoAuBuLi4QMOc4M2M4BVKTKxsbMz/QAd2CAoK/QVtXQTdXP/t2zcuLh5GRqYPH0DrmViYQdfc/2dkhFw1CTpFhYXx3YcPl69cZWJi4uBgHyqpgYWF5fDhw7JC0mpqapDjRU6cOPH8+XMDAwNFRcW/f/8+f/6cmZn54cOHenp6kLE9yFDfixcvnj17Zmdn9/v37yNHjigqKurp6UGmq06cOPHq1SsjIyPI4Zdfv349evQoNzf3z58/2dmhIXPq1Klnz57p6+srKioyMYEOZ2ViYvr27duRI0cgx6ZISUnBw/DFixenTp2CHPsCucXn2rVrN27cUFVVhQzlXrt27fr16/Ly8pA7WtjY2P7//6+hoSEuLv7/P2iOjJGR8ezZs+Li4mZmZpCBzHPnzj158kRXV5eNjU1CQgLSDv79+/eTJ0/k5OSYmZnfvHnz9+9fERGR58+f//nz5+LFi7KyskZGRnBX3b59e8uWLY8ePWJnZ9fT02NnZ7948eK9e/fU1dUhV71DVNrY2KSkpFy/fv3379+tra0FBQUeHh43btzIycmxsrKC3NL09u3b3WBgaGiorq4O7vb8gtw+qq2tLSEh8ffvXzY2NkZGRmbw+T7s7OxhYWH84M2zjx49On/+vIiIiKWlJRMT04sXLxgZGS9dusTIyGhtbY18tsOzZ88YGRkvXLjAxcVlbW0N3nX3+/Tp0y9evNDT01NRUfn9+/fr16///Pnz4sULU1PT69ev37hxQ1ZW1tTUlIGB4dmzZ////z937py8vLyent7Ro0c/ffpka2sLSTb379+/cOGCtLS0mZnZu3fv9uzZs2PHDhMTE2VlZQkJiTt37ly6dAkSQZAYYWVlvXv3rp6e3t+/f48dO/b//39TU1NRUVFIoI1wUlpROSq/DC0QPCLj0ET0LUGTDC4hkWjiEC4jE5OOmZWOmRWE6xwUDtqmo6xq6YZ+w4W6AShVSykoWbn7QBQHJGdCGNEF5RAGMmnnE4DMxcX+/Rc0sc0GHgv9+ef/H6TdEGzMjKxgcVBx/J/hx59/7Cwo0/s///xnZmJgwZjx/88A2oHKjCT+H6ydgwU0QAranzTa5MMVH6PioyEwpMDwbIyCJ0CZGf7/5eTk+v7j4///DMzMzF++fIFspefk5Pz+/fvv378hi9+/fv36+/fv79+/f/36lZOTk5GR8ffv3z9+/Pj//x8LC6gxCzkHDnInO3jMFbQaneE/IysL66dPn1hYWDgG99UpyAmSkYnp44cP329+2bhxY1RU1Ny5c8+cOaOhoTFnzpza2lpDQ8Ps7GxWVlZFRUVlZWVIY/TmzZsZGRkODg7fv3+fO3eunJwcPz//pEmTKioqXFxc2trarl+/rqamNmfOnObmZh0dnby8PGZmZjk5ud27d5ubmzMwMEydOvX48eO6urqLFy+uq6uDnE/079+/iooKRkZGQUHBdevWdXV1QZomz549Kygo0NHRef/+/YEDB1pbWzdu3DhnzhwLC4tly5bl5eVxcnJ2d3ebm5svW7bM19c3ISEBconlsWPHtm/f3tfXV1BQwMrKqq2tfezYscDAwNTU1MWLF69YscLGxmbz5s337t3bsGEDLy8vAwPDq1evioqK5s6dKyQktHLlynfv3hUUFMTExKioqCgqKs6YMSMyMjIuDtomuHTp0pMnTxgZGfn4+LS0tFaDgamp6fz585OTk/39/SHhDGk+MjMzP3jwgI2NzcHBgYGBQUNDQ1pa+vLly5DG6MuXL8+ePfvy5ctt27YJCQndvHlz8uTJkAsz79+/v27duilTpkBMYwAvq/j27VtVVdWMGTOePXtWXV1tYmJy+/btvXv31tTUTJw48ezZs1ZWVnfv3t2yZUtPTw8keBkYGKqqqj5//mxkZHT16tVdu3Y1NzfPnz//2LFjmpqaM2fO7OnpkZGRiYuLExUV1dXV/fXr1+TJk01MTBYvXuzn5we5O+fLly/6+vozZ85UUlLi5OR8//79hg0bpk+ffu7cuebmZktLy/Xr19va2trb2587d+7169c7duwICgq6f/9+Z2enpaXlmjVr3Nzc4uPjKyoqvn37JisrKyYm1tnZKSwszMzMvGXLlo6ODkhnA+7ZUcYQDYEfv/+7a/MKcbGsOvuekYGhwFkMMgkOajv+/rfj6qcVZ96zMjP+/fdfjI81y1505qHXLz79ZgJPmv/++7/YRezKsx+7rn1iY0FpXbIxMQrzsb7+/Afc0GX4+/+/CDdrmZtYx86XP3//4+Vkfv0ZtAJ1iAbaqLNHQ2A0BOBg2DZGQVfS//rNwPgVtAaLkZGVleXr16+QbY/MzMxcnFxf/4OkILLMzMyQsxj///8PmcTn4eFmZWUFbYH8D9pTDznA+f9/0PlNkG1M////+/TpEw8PFy8v35cvoONLwSvK4QE7SBl///6Vl5fXltFqbG6E3DC5evVqYWHhdevWTZs2bcaMGd++fSssLPTyQoym/Pnzh5GRsb6+nouLy9XV1dTUNCYmZuHChdu3b5eVlT158uSKFSu4ubmXLVs2Y8aM4ODgr1+/Ll++nJGR8e/fvx8+fPj06dPKlSsnT54sLy/PxsY2b948yN0q379/v3jxYnV1tY2Nzbdv3yDDfpDroZ8/f97S0iIjI/P169cfP37MnDmzoaHBzMzs9evXkOOgJ02axMrKKiAgsGnTpoSEBMjYJ2T8m4mJ6evXr9nZ2T4+PufOnWtoaAgMDFyxYkVnZ6eOjs758+fT0tIg6sGDNKDlv5CogmxrY2Bg+PjxY35+vq6u7tWrV8vLywMCAiANpuDg4MOHD1tYWERERLx9+3bp0qVTpkxRVlY+e/ZsbW2ts7MzZMgQYiwDA8P379/Z2Njgp41ycXH9hC2509LSSkxMXLt2bVtbGwMDw7lz59jZ2efOncvGxrZt27a/oL20oFXLEIdByF+/fv39+3fmzJkxMTERERE/fvwICAi4efPm79+/raysGhoavnz54ufn9/jxY0VFRYiWjx8/+vv7JyQkfPz4MSws7O7duwEBAcHBwT9+/Hjw4MGuXbtSUlJevnzZ2dlpbGz88ePHyZMnMzIy8vDwbNmyJT4+/suXL5C2/rJly5YsWbJly5bPnz8HBQW9ePFi1qxZPj4+YWFhN2/eLCsri4+PT0xMXL58eXNzMwMDQ3R0dEhIiK+v7+XLlxsbG4ODg9++fRsTExMdHf3y5cubN2/29PTo6up++fKFm5tmi/YgQTBEyFdPn5w5sBvS0cV0MmipOjsbZFkRpJCBLBOCbKPEVE+SCBMTk7mLh7C4JEm60BT/+fffSYN3YpjsuvMflp9+x8rM+OrT7zuvfv77z8DNxhRuIrjz2qd/////+gMa5uRmZ3LX4l166u3/j9BFlYyMDI/e/3r/7Q8jI8Pvv6AhVSZGRjZmxt9//5upcEWbCeeseMwE2joEWoP5++//269/fvnxL8JUUIyXpXX7Cy42JoguZkZGVhZQ8/YvaNMQSPGff/9ZmUFDsD///AOZidrSRfPFKHc0BEZDYADB8GyMgnYjgc/MY2Vh+cn0588f0GUbzMwsP3/++vfvHzs7BysrCzc3948foOOjQefhs7BwMjP/Ad198hdyAR0TE/Pfv3+ZGJmYWUDiTIxMIC4Ty9+/v1hZ2SCDTz9+/Pj+/Qcn5y9WVlbIHOgARiSRVjMyMPwFn10F2VsjJCQkIAA6dUVLS2vp0qVfvnzh4eExMDBANu3v37/KysqQxrqYmBhkbA9yQtazZ88EBQUhTQolJaVNmzY9fvxYTEwM0tpTU1M7c+bMjx8/3r17N2/ePGZm5u/fv5uYmIAON2Bg4OHhqaioWLJkyYwZM2RlZRsaGiBrWI2MjGJiYurr6799+2Zvb5+UlPT//39padBhE5Ch05MnT86cOVNISOjjx4/wlh/EwYyMoH1pHBwcqqqqDAwMkEbkhw8fWFlZIc4WFxcXExODnAUD0Qup10HrLsAnzvz9+1dQUFBCQoKBgUFOTu7v37+fPn2CmAO5ihDSGvjwAXTKjIyMDGgmVEbm9+/f3759gzdGIY5hZ2f//fs3KHWBF4v8+PEDEoYQWVCihN2dA7k4ES4LCT2IMjgJGbB/8+aNmpoaAwMDBweHsLDw69ev2dnZdXR0IO6HLI2Aa+Hm5lZWVmZgYODn55eQkPjw4cPp06d37twpIyPz+PFjBQWFv3//ysnJycvLMzAw3LhxY+bMmfz8/F+/gu62hdyOAwlGHh4eFRUVJibQTj0eHp6fP39++vRp3759t27d+vXrl7GxMaQdD29LvXv3bvv27RcvXvz165eBgcHfv39ZWFgg6ysgN31PnDjx27dvmpqa1dXVkMQDd/PIZDy7f3fTgtnaZpaY3gftn2ZkOnH8+KNHj0CbLWEqGBkZ9fX0tXW0f//6jd5xgakhhr5y6pi8miYljdEff/4HGQgkWwvfef3z5x/QTnBmJsZlp9//OfHu19//lR7ia8+/X3PuAzsLk6kC18uPf/7+/f8DeqA71IFMjIw3X/z48B00+qklxaEqyv72698zD78yMzHZqfKoirFbq3AfvfOVGbSVnPHX339nHn7j52K2VubmYGXSk+a88uy7tiSHmgTHy09/zj769vfffykBVkFOlp9//6mKsp968O3773/Omvzvvv658Pg76LZolLFXqBtGqdEQGA2BgQXDszEKGu9kAm2aBx1cz/j/75/fDP8Z/v4Bldq/fv3+/5/h3382ZhZmyNgnaAvw//9MoCPvQHOiLCzMTEws4BHS/7/+/GFlYP735+9/JtCxo6DmAuP/f39Bp3hwcXEyMTF/+/aTiekDNzdoGPU3rG0xsDFK0Pb/oF1aoPpLSEjo/fv3z58/l5GROX36ND8/Pzc3N+SQVTRD4O0MyFAxZKno379/ZWVl37x58/79e0FBwevXr4uIiCgrK2/ZsgVyE+alS5f+/v3Lzs4uISFRUFCgqKj4+vVrbm7uy5cv//////v37zIyMgsXLvz69WtycvKhQ4cgM90fPnxwcnJKT09/+vRpfHy8s7MzGxvb/fv3paWlX758ycTENG3aNGdn57i4uI0bN86aNQsyUA1xMKSlCBnehrQ1//37JyIiwszMfPDgQVdX1wsXLjx79gyyYBR0qQn4IMCvX78KCgpevXpVUlKShYXl/fv3Dx8+FBUVvXHjBgsLC3zIFmLgt2/fGBgYhIWF////f+fOHW1t7Tt37nBwcKA1qv79+6egoPDt27dTp07Z2to+AQNNTU2IO0HH2TCDLveCcCHOhrMhXEiYg7bNgVbigVaGsLGxSUlJXbx40cjI6OPHj69fv5aUlIT0oCB6IVogbAYGhm/fvl2+fNnW1vbVq1fPnz/n4uKaP3/+tGnTVFVVS0pK4Ioh1k2bNs3X1zckJGT16tVLly6FeBYihZwkILpERETMzMzi4uK+ffsGWf0CsQ5itbi4uI+PT0hIyNevX799+8bNzQ1prTIwMHz9+lVbWzssLOzdu3cRERHnz5/X0tLiAm0BBB2dCNE+Asn/DP8l5BRyW3ux+r23r//E49cmJraiYqL/wQsxGZkY79y5s/fGA//MQmcnJ6y6iBSsjQ8lUiWmsn+ggpSBhYnh4pNvSYu+JFkJc7IxQQQhK0SN5LhCjQUTFz788ecfJytzpKnQiXtf9t/6Ak5dDP/+MzCCyiGGn3/+l7mL777++cqzHxPDZNhZmNiZGVaf/zDj4BtXTT4VMfYoU8H9N7+wMzL++fdfkot1YphM6/YX5orcPOxMdmo80gKsbQFSbCxMf///X3ryXdv2FwayXCUu4p9+/lUWYT/z8Nurz78d1Xl52JkKVj3Zehm0EuAfcrse02OjIqMhMBoCdAfDszEKWkTIwvr158+37z/9/ffv95/P377/4OZik5SUeP7i9e9fv0BzXqAj+hggY5yMDAz//v9nZmHl4uL48+f3v3//ubk4//3/+/HDFxZmNl4B/vfvPzD8/y8jK/X2zZsvX36oqCi+efv25YvXzOAmBQ8PD3w2lu4xSJqF/xn+c7Jz8jPwQ4b04qLiyovKFJWUbty40dbWxs3GJcgryMnKiWwoFxsXPzfocGnQGBs3Pxcb6MQiHg4eXg4eVVVVPy+/nIwcBQWF27dvd3V1KagobNu4NT05XUZG5v7t+3p6evz8/ImxiXVVdZqamjdu3Kirq+Pl5BXgEWBjYps3a96fP38kJCT+//5voK0PsfT399/1VXVKysp///5VVVTVVtMqyivq6+rbs2PPzRs3ioqLXRxctmzc8uLJi0ePHslKgs784we7ipudG+JOAR4BiBc4WTkFeQUF+AWqyqpaW1u3bNjy9+9ffi4+NiY2iF1CQkJuTm7lxeUqKipPHz7V09JjZWRlYWCZPX32FsktZ8+cycjMhKwuhaj38/KbOHHiiycvioqKCnIK6qvqtHV0rly5Ul5cjtwYBQUOJy87O3tlaWV3e/eWDVtu3rwZHhyurq4OMYeBgcHa3HrRvEVZqZnZOTmCPAJ8XNDLVHjAvmBmZoZEBA8HDx8nLwcLhyCvIBcbV0lBSXFx8Z0bd+7fv+/v7a+qqsrNzs3DwQMaKwWrgXgcYgsXG9ee7bvfvHhz7do1D2d3bU1tbXXtib0TpaWlnz54oqakxs7MLsgryMECagg62zuvW7Xu7s27jx4+lBCRYGFmEeAR4AKnBB4OHn5uUIIBqQcL5mfnV1dXP7r36OGDB/YODjEJMRYmFgvmLMjNzE1MSKgoqairrb155cbde/c8PT1DI0MhjgcN3/5jmtQ3iRcMRARE1JTUOlo6/P39re2tIW6mKQkaWaOpBRQYDmn3Yxpw7NixiRP6FyxY4ITR6Jw6dWp2VtahQ4fExMQwNRIpgsteYrRzszF9Z2diZmJ8/eXPj9//uMFsHnYmDlbQ2OOvP/+TrIRP3Pv69MNvQS7QBHnVhmd///5XEGVnYmTgZGPiAasHldXM//+CrrpjcFDl+ffvf/GaJ4wMDIoi7L/+/J9y4HWQgUDlhmdCXMyMjAx//v3nBJ+xf/npj2Wn3nOxMS09+W5jpvKSU+/nH3urJ83Z6i+189onZkYGIW7mig1PX336szNfZdHxd879txt9JD20+A7c+sLOwsjEiGWzFDFeHlUzGgKjIUAjADlZl0aGI4yl4w1MIEsZGRlFRIR+fP/x6zfopHrQoiEGBl4ednUN9du37n39+l1CQoKXl+flq1e/f/9RUlL88+vHoycv+Pj4VFUUHzx48PrNBz1dLSbG/+fOX5KVk9VQV71w/uL7j58cHGyfPn1y+9Y9Bwe7129fX71yg52dg5mZSVBQ8PHjx69fU+3GZ5AfsGEq3MB0oIj7D5eRiCGvAGgHDyMj47Vr154+faKvry8pKfnr1+83b14LCQlD2ugQJ/z8+ePTp0+iIqIMjIyvXr3i4+Pj4OD49u3b9+/fhIVFGBkYLl+58uLFC2NjIxER0b9/QZdOnz179v///1paWpBZb0ZGhhs3bj58+FBTU1NOTvbHD9A8r5iY2M8fP85fuPDp0yc9PV0JCUlIvcjExPj+/Yfz588zMjIaGRnx8fExMTI+fPTw+rXryirKysrK//7+u3Dx4ufPn/X19P78/SskJPTmzRs+Pj7QNQffvwkLCb96/UpQQJCVje3379/v370TERVlY2N9//79hw8fmZmZV65ckZWZxc4BaoGBhnj//j1z9gwjI5OamhpkkHXevHn+/n4PHzyUV5BXUVH5Bx6LggQFIwPDg4cP3r59q6Wlzc3Nfffu3Vu3bmlra0Mm9CFqGBgYvnz5/OvXbyEhIWZmpocPH12/fl1OVlZDUxPiQYgyJiamd+/e3rlzR05OnoeH+8ePn6BLX8DDmd+/fRMWEX758pWwkNCPnz9+//4jwM//+vVrIWFhdnb2N2/enD17VkJCAjI7/+7dO3Z2dtDo479/r16/EhISYmWFNrXnzZtnamr6+fMnTk5OQ0NDRgbGr9++QYbAQcH47x8vL8/r169FRERBR3P/+3fhwvmvX78ZGRr++AlyzJs3b3h5eZHj+j/YCmEhYXYOjhcvnl+4cFFcXExbW5uZmYWJien9+/e3b9+SAYMnT55eunRJSkpKU1OTiYnp9atXAgICbOzsTIyMX75+PX/+/I8fPwwNDYWFhZ8/f87Ly4vclIeEDy3IGRdnVZmXm0mAzgqghflkm3n+yIEN82Y2zluOaUJDQ8OFCxc2bNiAKfXu3TsXF5euri4XFxdMWSJFauJCInNLtE0tiFQPV/afgeEDaEocfJkQ+KIjLjbQDvdvv8CngYJFBDiZv/369+svQg1oToCRkZeD6fPPf3//IcR52ZkhyrjZmX7++f/px9/ff/6zszCysTCyszB9+Qm6cpQRbCYTIyMfB9OnH//YWUDrWb7//s/PwQSZ4v/3n0GAi/nnb9DKfg5Wpg/f/jIwMghxMX/6AZrc4mAFGfX551+IObwczJCN/3AfjTJGQ2A0BAYQDM+R0f///4OHP5mYQOvVmBkZmBgYGL99+3X92u2fv3/9+v2bnYNNXFz09Zs3nz9/5uLi/MfG9PPXj5+/2NnZWf79+/vl8+d///5wcnP++fPn27dvzKDFSv+/f//x6+cvTnaOnz9+fPn8hR20pQC0QpGJifH79+8DGIUkWa0uoHrt3fVD7w//ewsqskGXVnMxM6oznfp69t9N0M3UTMzM/778YwBPDUNMBq2qZGL6B7rshIGJmfn/F/CdfqBjsRj/ffgHMoGXmZGf6djHk//egUxgZGJkkWT5z8Bw+O0RUGvvHVgNBzOjJtPlv1cu3L0I2orAxPTvDmi5A7MkM6MU49nv5//eOQOxDjR+xszErMzM8J/h0JvD/16B7k0BXaStyXzj3+2rd68zMjAyC7MwCjMceX8MdGXfh38gV30GeYeRCeQkhBdAl8Aw/bsPWiR7/8GDd+/eigiLaIfq7nq2F+5BRkZGFgmQa1+9P/r/P+iyLiFb4av/r7OqsoKsu3MDrhLkPPBZS4wiDE+f7WVg+A9qhGkyXfx96dzt8+D7WUBKQPvfwb2ff+9hLtdgvvfvwa27d1GMYmAAXQ4uxvzy6+v/X0H3rIDUg9aJgK6g+fcR5Kl/j/+BAp+B4d87MPcR6LIZJmYmZnWWx/+ePLj3EHxqBBNo3QW4xczMzPz3MSLuWA3Yb7Pd4ZLh+sjwZcu9bZC1KyxKzN/+fX/65hkoat7+Y2Zi/vsQZCzoGCkRFkYRhgOvDyGHKlgXyBWguGYAnUoBsYKJmZlZg/np/+ePHj6BNLKh3vn++vTNs8zMzEyazCBHPgQ7kpn5/1dQsgFFLhMTsywzAyPD8U8n/73/C4q7b1ApaPDRjJLhleFnAw3x0swG6hv85csX5IUiyBawsrLy8PAMVOHDyMAgyAU6bATZSQwMDJCRS7ggLwcWNQwMDAKc6OLwffTsLIyiPCgVkxALCpcByWpOVpBVwtwIBWzgJdoMDAyivFBBZHcKYxgF0j+KR0NgNAQGGkCz60A7g/r2//33n42NlQV8cxqofcDI8O8v49dv3yG7vH/++AlqGfz/8+vX72/ff3Cyg5o+379///P7D6hG//P318/ffHzcbGws376B1tWzsrH9/fP379+/nFycjExM3358FxER5OHm/vHzNyMT0y/wPhXq+4EGJmYbZNLA1EFv5H+Gs6fPPn78WE5OzsjECLTPFpeT/zLcvXsXtO8Hva7EpWHwij+6/0hMTIyDGzoGPHgdOuoy3CEA6iyxsJw/f37y5Mm5ubmGhob////ftGnTzp07c3NzQeviQdvHceuHyTx9+nTr1q2MjIw+Pj5ol4HBlJBG///P8B48MgrR9v8/uG3KyPD+61/QZZzg9iIjA8O7b6CRSIgaER6Wn3/+f/v1D7l1yMDA8O7rH0Fulp+//33+CZKCnDb66fvfn3//MzEyQBqab75Aj3BiZmIU5GJ+9/UPExOjACfz33//33/7K8DFDNH18fvfX3/+c7MzcbExvf7yh5GBQYSH5fOPv6ABVE5mVmbGt19BghD3jJKjITAaApiAlZmRH6OviKmM6iLDtjH6/z94VAlUUoMWMP39B5qvBy0V/f//79+/P378+PfvH2R28ufPnwL8QmxsrKC9TQyM/Hz8TEyMP3/9ZGJi5uLi/vzl65/ff/n5Bf79f/D9+08ePk4WNtbPn78ICPKzsbOBljqBTsL/CZoxonrkjBpIrRBgZDA2MzY2MyZsHjODshpoBzphlYNehZyi3KB346gDCYcAMzPz3r1758+fr6OjY2ho+O/fP8gmMw8PD9CWSsIGMFy5ciU8PFxKSur///9Tp05dvnw58l46IgzAouT7739uk+68+vQLciL9n7//HTV4mRgZ917/zMzM8O8fg6MGHwcb67ZLb1lAF9sxMDExBhkKPHjz8/qLH3EWwnycoDl90L63X/9mHX7jpsn36P2vc4++u2jyGslyfvj2d8vlT88//uZgZYwwFfrz9/+yU+/AV7QzCHKzhRjxLz35lpOVMcZc+Oqz73tufHbW4DWS43r9+c/Wyx+ff/ytI82pJ80JOdk01lJk77WPd1//tFLmFudl3XnzG8P/f//AZ60wgI6CYv3z5w8rK8uv379BwxMgjzKysrH++f2HhYUZtPP//392NjbQGVTgFce/f/+GnMXBwsLy/z/D37/QJjIbGxtorAJiLMgQEGZiAs0fwDe2grQwMPwF37cCkoZh0HV/4IPwYAJUoFlZWUFnw/z7ywSqAVEMBE8Xgc7dAw27/ENMpKAognFAE2Cg+UXQlBdMDCcNmtNhYoSGLU5VdJKA1PWQDZdkWwna6srIiBllZBsI2lYN3mcCmmzCMAXSIIFMNGFIkiXAyAia+4KlUmKM+Pnnv4M6z7Ik6PmAxGihlpph2xj98wc0OcvAAMrprKysXFxcoIlIZuaPHz/++wdarvT3/z8ebl4W5te/fv5gZGBiZ2f/9vX7l6/fGJiYmFlZ3r59x87J/vffv1+/fl2/fuMb6ApQhivXrnFzsf/5/ffBg8dv374Fn4rPBtrO/OcvKysrKG+jFkbUiqRRc0ZDYDQERmwI/P79Ozk5WV5eHrI2lJmZub6+PiQkxMzM7MePHwSD5caNG2FhYebm5tOnT////39SUlJvb++cOXMIaiSo4OP3P1Mi5bUk2UFrVBgZfv/5z8DAUOMlAeIyMLz/9PnkyZP5OXZs4EXM/xkYfoGvWWJmYgAd7QQznZGBwU9P4Dd4EJSFCXS26B/QdBZDuIkQ+HpmBvBxUQxJVsIQHYuXLOF7K7kpy/k/A8PP3/88tPkqPSR+//3/+x9oGDXaDKTr77//f/4ypNuJPHz4cMWq+bML81hBLcv/v//8NWS+qqunJyIiwvD//7dv306eOqWnp3f8+DF3d3dWFvCUPyPj7l279PX1b968CTrqgZtr8+bNb9+8ZWRkFBUVtbezFxcX//v377nz59jZObS1tEDTbkxM+/fvV1ZWkpVRgjgSQj57/uzunbvw+5AvXrzIyMgIubjuP6h5C1HFcOjw4Zs3b6ampED5JFLIRkG1MjLu3r1r/vwFFubmWdnZLLB1CxDZc+fPHz9+LDsne8LECcFBwbKyoD2gEClM8uevX58/fRIWEQY3xaHyWGwEy1y+cuX48WNpqWlgHk4Cl3acGghJIBsIZ8+aPVtdXc3ezh5NNyiNIgU+miwa9+zZs1+/fbWztUMTJ56LZt2nT586uzqfPX1WXFKio60NOlYC5pj/DP/fvHnLz88HyS/EW4FH5a/fvz98+CAqKgKPPnj4IOtCCDIyLD317sQ90Ol+yArowx62jVHweXKg7gcjIyMnJyfk0sXfv3/fvn0bfEcl461bt//+BZ0q+vz5i48fPn799p2BkeHSpcvgg/0YX795++HjJ9AhUP/+3rv3AHIq/scPH799ZWJmYfv77++3Hz/YWFj//v317+8fRkbQcZVMRPcg6RO1o7aMhsBoCAz1EGBkZPzz54+goGBoKOIMJlUw+PTpE6gyQ1rejenZ69evR0RE3Lx5s7i4mAO8ac/Y2PjQoUOYKskQYWRgEGX/pSIKPQgCzYSnP1/sWj41OcCWkxt6JzCaAvK4359dFRBkUBUjysy/b35/un9aU5KDAbRtgOH/399501qVlJTmzl8IuoDtxdvty6YGO87u2LDAVEXE2hbUcLl/9/b25dPC3RdMaZlpVF4hyi++ZnZ3cnIyOzv7rVtn2rcu7evrU1FVX3Bwo4iIiL+DIWjD4qcPPdVZoaGhza3tyJ56ePHW3rXzE4Oh28uWHd3CzMwc5GKKrIaBgeHU92cfH5xXIc5HaHpxcP8VLZvWWVFoYmLCyw86/ARZ2d1/b9/eOaMixv7m9mlJDi/89t65dX/6lAlTps1ANgEX+wnD+w/3qesRXFYRFv/08AKnPOhcMMJKcav4KsH+9esf/EGEWzcWmVX7dgszfZo4q1NUTIyZFT0NT6hvKi0tVZBWwaKTLKGnj1/MnNE1cfJU4nXLCHH8vzvaGCU+wIhQCWlTgo5pBB9mx8LCzAIG7Ozs7969+/HjJ/gkfBZWNrZfv37/+f2HiRl07Oif3385ONl5eTkZGP6Dpmn+/GVmBh13z8zMwsDwn4ODiZGJgYWFlYmR6d//f39B8D8T439mJiYGpv/g+RBGcD8HtJmGCDeOKhkNgdEQGA0BfCHAz89/48YNrCp+/foFuaUCqyzkIoOAgAB3d/fIyMi+vj5lZWVGRsbZs2dHRUUxMDDcunULcoIELu0ExVlYWZNSUiOctEPDo7TAY4TIWv78Y2BkZv39lwF+UNzz58+XL1/+6tUrc3PzwMDAp0+fXrlyxd3dnYGB4cCBAzIyMoKCgufOnXv79u3Zs2etra0DAgIYGBiePHmydOnSjx8/enh42NrasrJzPnzybOLEie/fv4+NjQUt72Zg2L59+6FDhyQkJOLj4wUEBN69ezd//vwvX74oKCgwsbD9/fsfMjj48/dfYVHxy1evL1myJCYm5vff/wxMLHyCwm4eXqvWrIM0Rteu32hqbikkIvaPgfnvf0aIlvTMbMgBI9OmTevs7p01axYDE8t/Rui68jXrNji7ut+8fffly5fi4uLwQPj7nxG5wQFh//////Tp01u3bv33719wcLCBgcF/RmYmFtAJGO/fv1+0aNGzZ8+sra39/PwYGBjOnj27ceNGJiYmX19fY2P0VUbXr19fu3bt169fPTw87O1BLWlQy/jLl5kzZ9578Ojk6bP/GJjk5eWfPHkCuZR4586dOjo6jMysjMygMeD/jMx/oOcZwJ3McPHixXXr1jEwMEDWFs+aM+/g4aM9PT22trbGxsarV6++cOGClJRUQkICPz//z58/ly1bdv36dR0dncjIyH8MTP/Ajf63b99u2bIlICCAnx9lv+DPnz8XLVp0584dAwOD4OBgNja2kydPbtmyhY2NLSoqSllZ+fr168+ePbt48eLXr19TUlLOnj176NAhBwcHLy+v169fnz9//v379+fOnTMyMgoPD//y5cvJkyd//vz58OHD9PT0y5cvr1u3jpWVNSIiQkVFhYWN4/7Dx319fV++fImPj4dc7bFp06Zjx45JS0snJCTw8PAcOnTo379/Bw4c4ObmTkpKEhERuXXr1sqVK3/+/Ons7Gxvb8/GwcXEwnb69GlmZmYjIyMGBoZd4FHzr1+/Ll269NevX4GBgRBxSAjevn175cqVP378gGh/8+bN8uXLnz17BnHwiRMn5i9czMvLu2Hz1uDg4J8/fy5cuPD79+/+/v7GxsbLly8/cOgIGweXlZWVvLz8v3//LCxAZ1wcPHhQWlpaRQXaQt2/f//u3bt5eHjCw8N//fr1+vVrOzvQwO2pU6e4ubmZmZlXrFjx9+9fd3d3dXX1aTNm7d1/sKuryxoMTp06tWnTJk5OztjYWDk5uaNHj/78+fPQoUOioqIRERHr1q178eTBE2FHVlaoXRBP0Y2ELtyhg32gDjwI08EqkBV//vz9/ecPMzMLGxs7aP/8nz+gLbuMjKCtSKDL4v4xMbMwMrH+/fePgZGJhY2NEXKnOBvbf1BD8/+//6A5HdDhI8zMTMwsoC0vjEyMTEz//zP9+/f/77+/f/6AEGgQnpkVtHP/3x/wLs7/DAw0bIn+o2MAggJxFI+GwGgIDGgI2NjY3Lp16/Hjx5iuuAA+Fk1bGzTZhyl748aNgIAAe3v73t7ewsJCPz+/tLQ0d3d3FhaWffv2lZWVWVpaJicnffr0CXTrB6Z+IkQYGRhVVFTXb9jk6uoaFxf38uVL/Jo2btzIwsLi5eW1cOHC3bt3379/H75aYPHixWfOnHnz5k1eXt6jR48cHBwmT568d+/eb9++5efns7Ozm5qaLlu27PPnz8zMzNu3b5eTk+Pn58/Pz//169eWLVsWLlzo5ub28+fP+vr6Hz9+lJaWvn371sLCYseOHW/evIF78P///ywsLK2trcuXL7958yZkxe3Pnz/9/PyuXr365s2bX79+HTlyJCAgAHSIBHh0ATL2/OvXL4jXPDw8nj59+vv3b7iZP3/+3LZtW1ZWlqKi4pYtWyDKICQzM/OTJ082g8GWLVuuXr3KBj5vbv369cbGxrq6utXV1ZDwh4x/l5SUvH371tHRceHChZBWe1VVlYmJiYGBwfr16+GXCUMM//Pnz5o1axQVFe3s7Nrb269evQoRZ2FhERcX5+TkFBERERISunDhwuLFiyFSs2bNunHjBp4jsV+/fl1eXq6trW1iYgI5TUxMTIyXl1dKSkpQUPDSpUtnz54NCAh49uxZeztoDLijo+P48eNubm5nz549deoUCwsLBwfHmzdv8vPzf/78iXZe258/f+rq6i5duuTi4nL8+PGLFy9euXKlvr5eT09PQkKisLDw7du3x44dy8nJkZOTY2ZmDg4OhvRJOjs7z549++HDh8zMTEjaWLZs2aJFi37//l1YWLhx40YxMbHr16/X1dUZGhoqKytXVlZ+/vyZhYVl165dkLsDCwoKfv/+vWbNmlWrVnl6en748KGpqen////Tpk2bOnWqhYXFq1ev6uvrP3/+XF5erqCgYG9vv23btm/fvh05cmTLli1v377t7+//9+/fmzdvJk2a9Pjx4/Jy0NHUFhYWzc3Nt27dggTv58+fS0tLpaWl7ezstm3b9unTpx07dvz48cPX13f16tUbN24UFhYWEhLi4eERFxf//PlzSUmJtLS0jY1NR0fHpUuXxMXFeXh4xMTEILfl9fT0/P379+fPn729vZCbVhgYGC5dutTR0eHk5CQtLb1hw4Y/f/50d3d/+fLlz58//f39jx49qq6u1tLSsrGx2bZt248fPyQkJLi5uSHdvEuXLrW1tUFaupWVlZ8+fVq8eHFXV5epqemlS5fCwsIgVy1u3rQJTwqB+JRGJJ2m6X///i3AyRpphz5DQSNfMYCPyGFiAi2/ZmRkYmD4z8X+j4nhO8M/BlEpQT0hTsgaYdC5kmAXMDFCzluFnEAHFgKbgLwtCdTuhAy0ggopEA+6CICJ6d/fP8yMjODT9Yla6A21gHSKm50VfNYe6TpHdYyGwGgIDMEQsLa21tTUrKurmzlzJqTxBPHEs2fPKisrExISsJ54f/369eDgYHt7+8mTJ0Oqltzc3M2bN0dHR7e3t4eEhMyfP3/y5Mlbt279/hp0cwfETFLJP3//5OXnPTmrUlxasWrVqqKiIuRxQUzToqOjHzx48PjxYyYmpsuXL9vY2MB9xMbGBllzr6ioWFxczMzMfP/+/RMnTjAyMnJzcxcUFDAwMHh5ebGxsX3//j00NDQwMPDHjx979+59+vTp+vXrtbW1WVlZNTU1t23bdu3atWfPQEOnPDw8vLy8XV1d8JYl5E5dBQWFtLS0+vr67u5uyNoqaWlpVVXVI0eOyMjI8PDwaGhoQHYpYXoBvlEJXjVcuHABdMqZhkZQUFBnZ2dsbCzcU0xMTG/evDl69Chks+yjR48MDQ3Z2Njy8vIePHjw7NkzyGVvoHPQmJgeP3787Nmz6dOns7Gx8fHxTZ482dra+s+fP69evdLT0ysqKgKvLkO4iIWFJTMz8/79+8+fP//+/fv169ch3RIODg4vL6+1a9eGhoby8/M/fPgQrpGdnR3ehoYYxMLCcunSpalTQdO49vb2np6ef//+ff36tYmJSWFhoYiIiLOz8+nTpyFD6X///uXh4Xny5Ak3N/eRI0c+fvx4/PjxOXPmyMjIODs7//79+/jx43fv3g0ODg4KCkpLQ185+u7duzNnzixfvlxMTMzR0fH///8tLS1+fn6Q9Sfnz5/fvXs3Kyurh4dHSEjI27dv9+7dm5SUJCsre+LEiUuXLllbW2tra+fl5bGzs3Nycs6aNcvT05Obm7uurk5aWnrChAm8vLxiYmKQwcKLFy/++fMnPDzc39/fycnpwIEDL1++3LBhg56eHjMzs6amZn9//5s3bxgYGNLS0tzc3IyMjFJTU9+9e/f////Xr1+rqKiUlJTAr3d2dXWdMWPGgwcPzp49a2Ji8gEMpKWlQe2H//8PHjwIuaIZcl/027dvNTQ0SkpKBAQEgoKCbt++/eLFCx4enosXL/r7+5uYmLCxsQUGBu7atevdu3eqqqr//v1jZWU9fvx4ZmbmrFmzfHx8dHV1//79O2vWrKtXr37+/FlcXBxypDSkVQPxoK6uroqKCi8vr4CAAOTYaUZGRl1d3d+/f7969UpGRiY/P19cXNzNze3QoUOQ6Ovr6xMREeEFg8ePH58/f56VlTUmJsbb25uHh6evry8jI4Phz481tzj+YOyxg6QWWpN0aoyqG5rY+IdNnz6d1v6Bm8/IyMjFyf739x9GJmbQvXOgXYWQy+f+w/fZMTKA2qB//4FWjkISFiS+/4A2JLH8/fv39+/fkAHTnz9/cnBw/AHN54OuVWZhAcnCd+qBtuuDFgNACihQOxXuDOoy/v37z8bFnQG+RZ26Jo+aNhoCoyEwCAELC8uUKVN8fHyCgoIKCgr09PQgo3ft7e2qqqpFRUWYbr5+/XpgYKC9vf2UKVMgLdF79+5BNjxNmzZt27ZtDx48WLNmjb29fUhISH1i+Lv37zANIUaEjZUtJjqa8dVlPz+/qCgsM/WgwQBYYfXz58/W1tYvX76YmZlBZm8hIwIQiyBl6b9//7i4uEBbmBkY2NjY/v37h7wOAdKoYmICXTICOiX33z92dvbfv39DqufLly///fs3NzcXMj4HmVXn5eVlY2NDtoiBgeHnz5/+/v7Hjh3r6+uDN878/PzWr18vJiYG2skE2ooO3SYPcR6kZGdgYLh37x4XFxc7O/u/f6DTWhgYGFavXn379u3c3NwfP36cPXv20qVLJiYmEF2/f/82NDTs6OiAcFtaWv79+/fs2bPi4mINDQ11dXUODg6IxxkYGH78+MHNzQ1xDz8//69fv6SlpadMmbJhw4bp06f/+PFj8uTJoH1XELMYGN68eVNWViYuLq6np8fLy4vsR8iWf8guftD4CWw+DW4XzAzQJcMSEhKQ5RCysrKCgoLTpk1bv3797Nmz379/P2PGDMjVyv/+/WNiYlq7du26detsbW05OTlZWFggdz5DFiIzMjJCuhPPnz8PCws7evRoYmIiHx/KYuJ///6xsbFBEiQkdr58+QJvxvHy8n7//p2ZmVlYGLRTDdLwhcQ4JCX8A13SAYpNyDXO//////Xrl7CwMCcnaBnI79+/v379eunSpT9//sTExKipqf3//x9yed7///8h6eTPnz8vX768dOnSv3//8vPzIb6AJEXI+LeAgMDkyZNXr169YMGCly9fTpgwAdJUYGZmhoyV3rp1Kykp6cmTJ79+/bp69erv37+9vb0dHR0h4cnLyzt16tQ1a9YsWLDg2bNnkyZNWrJkydOnTy0tLfn5+SER9Pv3b0ha+vnz569fvy5fvvz7928HMIDcFwOJI2ZmZldX1927d3/79s3NzQ2SKhgYGHR1dbu6urZt27ZlyxYeHp5p06b5+flt3bpVXFzc1tZWRkamv79/7dq1CxYsePv27YQJEyA3OUOi79evX1+/fr1y5cqfP3+Sk5MhPS4BAQGI4yGR9fv3b3Z2dpSkD5GmC0mnxqiknIKSkfnF+9V08RTUEkEBvh/fv/9jYPz3n4GTk/M/A2ij/K+foGOY/jP8//P7Nxu4LPv9CzTnwsHJ8fPHz/+gpaIsP79/Z2Vj//3nN+iEEgYGRmbm/4Nmm7ygkBCoXQ314ig1GgKjITDMQ0BaWtre3n7jxo0VFRWQwRtJScmHDx/W1dVxcXGheR4yO29nZwcfE3306FFYWJiOjs7UqVPZ2dkVFRUhfWkGcIOPGbxWHs0QIrm/f/+KjY1ND7JRUVWHVLFoGr99+/bs2TPICNOfP3+OHDmyePFiKSmpjRs3/vz5U1BQ8MmTJy9fvvzx48fJkye9vLz+/v0LnxCHtDL19PQmTZp08+ZNKSmpHTt2eHh4QMYIIBb9+vWLiYlJW1v7169fmZmZjx8/fvLkiZKS0ufPn48ePWpjY7Nhw4bXr1/D63IGBobfv39D6vuKigofH5+fP39C2r729vYTJky4du0avH3/+zdo3AF0+NS3b48fP+bi4nrw4EFra2tcXBxkhJWBgeH169cXLlyYMWMGPz/owmRlZeUFCxaYwBqj//79Q76S4OfPn6ysrDdv3vzx40d9ff29e/d6enogw2mQpuenT5+OHTtmYWGxatUqVVXVDx8+nDlzprCw8N+/f6GhoTdv3rx9+7a6ujrkqraHDx/eu3dv6tSpX79+nTp1KqStAwkW0J0vMMcLCQndvXv3/fv3r1+/vnjxImRoGdJOhbRZxcTEPD09IRrfvHlz7Nix7OxsZmZmyL43ERGRt2/fvnjxQkxMbOvWrU5OTmlpabNnz/769Ss/Pz8XF9eOHTsiIyMPHDggJSX169cvExOTqqqqurq6hoaGrq6uc+fOKSgoQAbv+fj4WFlZ9+/f7+fnt3fvXmVlZSsrq/Xr13t7e3/8+PHixYuRkZGnTp2CuA3S1oTEFCTK2NjYLly4cPToUVNT002bNikoKLCxsf38+ROiRkND4+zZs3Fxcf///z958qSIiMivX7/gI3yQWFZXV+fi4srKyrp///6bN294eHh+/foFGQL/Dz7w8c2bN0eOHElKSuLj4wsLCzt79iwDA+gaFAYGBn9///DwcE1NTT09PUgzNzw8XEBA4ODBg/BTez9//rx3715IKzwuLu40GHR3d6uoqOzfvx/S6v0DWjoIWmqspKTExcXl7+8vISFx4MABSUnQBYSMjIz37t1TU1Pj5OT08PBIS0vj5ubOysqCxA4DA8Pt27cfPHhQW1v75s2byMjIFy9euLm5LVu27OrVq7Nnz/7w4cORI0eysrI4ODhCQ0MvX76sr6//+fPnJ0+eSElJqaio3LhxIzU19du3b2fPnhUVFf358yfE+//+/YMEOyQ1wq2jM4NOjVGQJ3//pqffGBmZ/oKXg/7/++f/v/+/fv6E9BH/gu5kYvn/H9QYZWRg+Pv37/9/f//++/v9K+QoKIa/YHf+/vUT7trB0xIFXaYHG2mAO2+UMRoCoyEwvEPg169f0dHRJSUlz549Y2VllZKScnd3h9e1cL/fuHEjIiLC0dFx0qRJkMniR48ehYSE6OjozJw5k50dtHvXwMAgKysrIiIiLi6uurqakZERMrgFN4R4xr9//xISElUlQJUrpi5ubm5hYeG6ujpW8EBjeXl5TExMWVmZjIyMjo6OvDzort3AwMCMjAwZGRknJycREREeHh74jKS0tDQ7O7uCgkJKSkptbS0LC4uSkpK/v7+Kigqk+mdiYtLR0eHk5MzMzKyvr09ISPj582doaKilpWVNTc3kyZOXLFkCacdDZskZQIMRjDo6Otw83KA7nAQFe3t7ly9fBmmqcnFxhYQEf/jwEdLUY2Rk1NbS4gEDGRmZ2tpaJiYmVlbWlJSUkJAQBgYGRUUlUVGRGzduuLg4w1ufycnJra2t7z+8FxQQZGBgEBISgkydQwJHUVGRjY3NzMxMXFw8Li5OTk7OwcGBi4tbXFxcRVWVj4+vtrZ22rRpM2fOFBISamxs5OTkvHXrVkpKChMTk5GRobGxcXx8fGZmJmQ3kra2NnjVb7KcnJyNjY2oqCjEFgYGBnZ2dl1dXcgYpKWlpa2tbUpKipycnLu7Oy8vLycnJ+SUWW1t0J3GcF2QEceHDx+mpaWxsLCoqakZGRmxsrI6Ojrm5+dHR0dnZmb29PScO3dOUlLS0dGRmZm5oaGhra1t586dzMzMdXV1QkJCkH02paWlFRUVt2/fnjt3rqenJ2TklYuLq6mpqaenZ/369WxsbHV1df7+/rdv387IyPj3719ERISBgcHDhw8hzmZjY9PV1YWkYUVFRfBV1b/ExcU3btw4ffp0Tk7O9vZ2JiYmPT09iBpPT88bN26kpaUxgi+RdnBwUFdXh6QTZmZmPT09dnb2vLy8+vr6xMTEnz9/RkdHg6JYWxvSRmRjY9PR0REQEHj//n1mZiYHB4ekpKSrq+uhQ4cgfRUlJSUnJycTExMmJiZ9ff3g4ODs7GxWVlZFRUVLS0tIGHJwcLx//z47O5udnV1ISMjDw+PDhw+VlZVycnLwHUjy8vIQD2pra0dFRRUVFbGxsUlJSZmYmLCwsGRkZMyYMePcuXP19fXy8vKSkpLCYAAxH3RvmYDAkSNHtm7d+uvXL09PTwkJCWZmZi0trVevXklJSf38+fPFixdpaWlsbGxKSkpWVlZ8fHwuLi6FhYVhYWEBAQE3b95MSEhgYGCwtLS0t7fX0NCAjEMLCAhANiAyMjELCAo8hw2lw+2lEwPSRKMDuX37djp5CWYNZJIIa5cdpmTo0SIiIpcuXaJDfI1aMRoCoyFAnxA4d3h/XWIEsl2fPn168ODBnTt37t69e//+/fDw8NTU1AcPHtwHg7t37+rr6/f29t69e/fr169///7duXNnfn6+tLS0iYnJ58+fIUbdu3fPxMQkPj7++/fvEBEI+ePHjzVr1pRXVLx9+7Y2PvTKqeMQcZLIrz//qtZeOfvwKx5d//79+wkDoG4/eEHehw8fIJO/EI2vX7/++hVkyL9/oC2mEEE08vPnzy9fvkQTROM+f/78y5cvcMFv3769evUKzqWQ8e/P//9/KDQDRfvrF+/+/0URgXD+/vr/5hUofCDc////f3z/9e3rjyDuv/8dHR1XrlwBsWH4+fPn3759Qw5PmAwK/fLlS7Q0gCKNynn79u3z58+Rxb58+QLR/vXrV7SI+PPnz7Nnz/78wRI6P3/+7O7uPn36NLJREPWQxAARf/369cePYA9C+DjI8+fPR0dH//79+8WLFziU/H8LBrhkIeLPnj2DpDcIF5P88OHD06dPMcXRRN6/f48WFBAFaNrfvHmDx3efPn1C887v378/f/7858+fBw8eBAcHo4UexIqXL1++fv0azo6MjNy5cyeE+////3fv3j179gzO/f///9evXyGJ5P///69evYLMriArQGZPO/jKY9JtZBG6sek3Mkr/dh8kEOlv76iNoyEwGgKjIUBeCLx69Wry5Ml79ux59erVmzdv/v79y8fHB9mCs3PnTriZkG22U6dOnT9//kYwCAwMTE1N3bRpU2Ji4qJFi96+fRsaGqqlpTV9+nS0sU92dvZgMICsvISbSXUGZCkhsrHwVY/wMQJMEWT1EDZ4gJIHwsZFSkhIIEtxggGyCAMDw4Xny999f8AEO5IJTZYILuJ0cFyKGRkgR/7jkgeJg87/Y2L8/xDLABRE6t99xMYDJiZGhv8Mfz7+lue3iY6OlpGRARkBw3Bfw8MTJoNCQybKUYRwcP7//w8ZG0aWh++L5wIDZClmZmbIACSyIITNzMwcFhYmJ4dyDxymengCgOjCRXJxcSkpKTExMeHZJIfpckzTcLkWovL////8YADh4iHhqy3R1IB1Iw60ggw9oqmBc8G7iXjhXAYGBhYWFh4entevXzc1NUHGYkGiqBgem9++fWtsbNTT04NchwFRJSgIGpWHsCEk8mIe5BF0iOzgIYdzY3TwhPKoS0ZDYDQERkOAYAicO3cOct5hcXHxrVu3Jk+eXFJS4u3tDWmPwjfNQPZZ/v//n4mJaffu3Vu2bFm3bt3z588/ffoUGxsbHx9fVlZ27do1bW3tmTNnorVECbphGCt4/PH0k49nmZmwryugr8fxtFrRpED7Y//8+8PPIW0sAzpRkqbuxN+oJclqZmZmtJYoSdrRFEPObIKsqUCToiKXit6nxFVCQkJ9fX2QJQR4zOHg4GhtbcXVLMajcXBKjTZGB2e8jLpqNARGQ2AEhQALC8uTJ09iY2M9PT0hu85bWlr6+/sjIiLwh8L27duTk5OvXbtWV1fHwsISFxdXXFwcGBhoZ2c3e/ZsyIo6/CaMHFlfjb6R49lh5lNmZmbIxvlh5i+s3mFmZibYEgWvgWYaNi1RkHewhsWo4GgIjIbAaAiMhgB9QgAyHtPZ2aWsrNzR0cHOzr57924uLq7AwED8Dvj379+nT5+kpaUvX76soKBgZWV14cIFAQEBQUHBhw8fnjhxAr/2UdnREBgNgdEQGCSAfiOjkEMEBom3h64z4McxDF0vjLp8NARGQwA5BCBX9Rx9fGHWzJmQIxhfvXrFy8sL2f/+4sWLmzdvok1Q/v8POi/Z0NDQyMho+fLlU6ZMefTo0Zs3b9rb22fOnOnl5WVsbJyWlrZz507IRYjI1o2yR0NgNARGQ2CwAfo1RuXk5BITEyEHJQy2UBgq7vn//z8PDw/+NdFDxS+j7hwNgdEQgIQAIyPjx48f7927d/v2bSsrK7gghHH06NG2tja0xigDA+js5OnTp2dlZbm6ura3t+fl5f3792/JkiVr166FXDh59uzZxMTEjRs3Qk7/hpg2So6GwGgIjIbAIAT0a4zq6urOmzdvEAbBqJNGQ2A0BEZDYGBD4N+/v6ysrKWlpQwMDPHx8aysrPBjzL29vW1tbSFT+XBHQrj8/PysrKwzZszw9vZeunSpsLCwhobGypUrISdfTpw40cvLq7KycsqUKXCNdGbcvHnzwYMHlpaWkCte6Gz74LHu27dvr1+/lpOTg0Qc6ODtv3+/f/8OuREA2Z0/fvx4/vy5goICXCWy7Ch7NASGK2Aarh4b9ddoCIyGwGgIDJUQ+P37t5qaWldXV2VlZWNj48GDByHz9QwMDBwcHGJiYqKoQAQMIAdo29ra9vX1cXJyLlq0aOfOnfCxVWFh4fnz52/btm327NkDEg4HDhwoKCg4efLkt2/fqOWAAwcO3Lx5k1qm0c2cK1eu1NTUQK4Lglj66NGjyspKzNVrd+/eLS4uht9EBVE8So6GwLAHo43RYR/Fox4cDYHREBgCIQC5Zmny5Ml79+49efIkvDFKjNPj4+MjIiKqqqo+fPiArF5LS2vixImtra0HDx5EFqcD++vXr2vXrvXw8CguLoacjPjkyZO7d+9CrP4DBt++fXv37h1EBEK+ffv2+vXrP378gJyBCjmsm4GB4devXz9//vz69euCBQv27t379u1bSDPuy5cvN27cgN+6+fMn6Oa8u3fvvnr1CmIgAwPD58+fr1+/DlHz//9/yLL7O3fufPr0iYGB4eXLlw8ePIAr/vv3761bt16/fg0R+fHjB+Smq3///sGb1I8fP75z5w586Bpyszz8QsXfv39D3P/u3bsbN25AxCHN0C9fvty6dQtioKSkZH5+PmT1xY8fP27cuAFxD/Jlnsguh1xNeePGjRcvXkDcNkqOhsBwAvSbph9OoTbql9EQGA2B0RCgegj8+vULfBp98MSJE4m/su7NmzeVlZWZmZl37twpKCiYO3cuMzPz58+fN27cuHnz5tevXz979iwhIWHt2rVGRkZUdzMuA7du3bpnz55r167dv3+/qqpqzZo1+/fvZ2ZmVlRUbGhoOH/+/IIFC37+/Kmvr5+Xlwdpk23fvn3+/PlcXFx//vzp7OxkYmJqbGzs6enh4eFZtmzZt2/fBAUFT548+ejRo4cPH1ZVVd28ebO7u5uDg+PPnz9VVVW6urptbW0fP3789OnTq1ev0tPTfX19T506BVHz+/fvuro6TU3Ntra2Dx8+vH///vv3756ensePH3/58qWzs3NeXt7r169ramq+gUFMTExQUFBra6uLi4u9vf2DBw+6urqmTZs2ffr0I0eOsLGxCQkJNTU1QdbjHjt2bN++fS0tLQwMDK2trU5OToyMjFOmTOHi4mJhYWlvb2djY7t//35NTc2bN2+4uLj6+vo+ffo0ZcqUCRMm3Lp1q7GxkZGR8devXxUVFZAjytnZ2U+fPt3T08PJyfn///+GhgYBAYHq6upv3759//7dz88vOjoaV8iPio+GwFAEoyOjQzHWRt08GgKjITCcQ4CRkRF54A2/V3/+/Hn69Om/f//OmTPn2LFj/f39N27c8PLy6urq0tLSio2NnT59urm5ube39/Tp0/EbRUXZ4OBgDw+P+Pj47u7uW7dubdmyZcaMGQsXLnz16tWaNWv+/Plz9OjRvLy8nJwcSEuUgYFBVFS0vb19xowZoqKiS5Ys+f///927dyEjoJAmdVhYmJOTU0JCQmtrKzMzc2tra0ZGxsKFC0NCQpqbm//8+XP+/Hl+fv7Zs2cXFRVNmjTp69evLS0tSUlJCxcuDAgIaG5u/vfv35UrV6SkpObPn29mZjZ9+vS2trapU6euXbv29evXkydPlpKSWrhwYWtr65QpU169evXkyZOPHz9Cxj7v37//6dOn5cuXV1VVzZ07NygoCB5curq658+ff/78+cuXLy9duiQrK9vR0ZGXlzdz5kwFBYWpU6eys7O/fv06Ozt7yZIlf//+Xbt27f///69fv/779+/W1lYPD49FixZVVFTA4/379+/d3d0JCQmzZs2ytraeOHHi3r1737x5M3PmzIkTJyorK8OtHmWMhsDwAKON0eERj6O+GA2B0RAYoSEAuXXzz58/0tLS06ZNmzJlipubm4GBwcGDB83MzK5cufLx48eenp45c+Z0d3fPnDmTPsHEzMzMxMTEwsLCysp68+ZNAwMDYWFhdnZ2Nze3K1eufP/+XU9Pz9DQEHk1Ag8Pz4IFCyorK8+dO/ft2zcmJibIoljINYnMMMACBq9evfrz54+NjQ0TE5ONjc379++/fPkiICDg5OTEzMyspKTExsYGuaTb0tKSiYnJ0dHx69ev79+/Z2Njs7e3Z2ZmVlBQMDY2FhUVFRER4efnf/PmzcOHD93c3JiZmTU1NWVlZe/evcvKygo5AYaJiYmZmZmXlzcgIKC2tjY9Pf3x48fwY9hFRUUNDQ337t176NAhQ0NDXl7ehw8frly5srKy8vLly79///7796+BgYGqqiozM7OGhsb79+8ZGRlZWFi+f//+9OlTKysrJiYmIyMjQ0PDv3//MjIyfvjw4dmzZ1u3bq2srDxy5MjXr18tLCzY2dnj4+Pb29vh9tInKkdtGQ0BOoDRaXo6BPKoFaMhMBoCoyFAjxBwcXHR19dnZGScOHHi1KlTJ0yY4O/vv23bto0bN27btm3atGkZGRmOjo5qampUcQ0T6K5KnCYxMTJAFAjw8717A12F+erlC0EBfhYmRhZmlKGQb1+/lBQXpaenm5mZTZs27e+f3yzMTL9+/oAoe/PqJRs7OwMDw/9/fyEivDzc///9/frlMzs7+5fPn5iZGNnZWJmZGJnBVjIy/GdiZODj4f7z+9f3b18FBAS+fP7I8P8fFycHMxMjWAkDI8N/iGKIO9nZWDnY2d69fQNarvr3z+dPHwUF+Bn+//vz+xcDA8PXL5+/f/vKyPA/LTUlMiL8+fPnLS0tEuJiTs4uEP+HhYb09vaysbEV5OezMDOJCgslJsQLCgr+/v1bWFj43r17rCwQ20D2sjAzMTOBGOxsrOxsrF+/fIZY+uvXL7Dz/nOyswnw88VER0lISPz9+5eXl5efj2/ihP43b95cvny5tKR41apVAqBbyPFGAMRlo+RoCBANGAcuQY02RomOpVGFoyEwGgKjITC4Q+Dhw4eXL19ev379p0+fZs+ePWHCBF9f3+/fv7u6ui5dujQtLU1XV3fp0qWNjY1U8cfH738/fPuLy6gvf5g//mT48O2vvrndrEUreyfPEhQSXL15V29P76PHjz7/ZkLW+/0Hwx8WnkcvP/46c3nP4dOWVpb/2fm4BCU7JkxXVlbevOewv3/Ah29/JRU1lq3b9peVx9HB0dDCobCywdfXd9WqVfZufj8ZOT79ZHz/7d+Hb3/fffv35Q8zE5eQnpltRUO7r6/vihUrzO3dfzFxfv3D8uHH/w/f/n78wQBxw7dvf778Yf7w47+bX2j/9BmffzFdvHiRlUdEWFpZy8hq5sIVPxjYN23a/O0v67N3X0uKSx0cHRQUFH78Z//2jxXuBUlFjdeffv3//1NcQf0PA4OqntmytVtdXFy2bN0SHBT89x/Irrdf/7AwM3z6xfiLkfHdt3+/GDm//2Nz9Ayob++LiY45cOCAubm5hobGj/8cDBwCWkaWc5euCQsN27lzp72DPRMT85LFi2NiY95+/cvIIXDj/vMV7f1FxUV8vHy4wn9UfDQESAXffv4bqOYoCSuTSPXVqPrREBgNgdEQGA0BgiFw5eTR2uzUe3/Yjh09ys3NzcDAMGnSpK1bt+7cuZOgXgYGhmfPngUEBEycONHS0nLHjh1lZWUnT5589+6dq6vrggULzMzMGBgYfH197ezsSktLW1pazp49u27dOsgxljVxIZG5JdqmFsRYhKzm269/ll23vv78y8aMs/L68eMHMzMzKysrEzPTt6/fvv/4AZq5Z2Li5+f/8fPn79+/uDi5EGYyMv74/v3X719MjEz//v9nZ2Nj5+D4+fPnt2/fmEFDiozs7OxsbGx/wTegsrAw83Dz/Gdg+PjhAysb6+9fvwUEBRkZGL59/87GxsrCzPLv37/vP75zc3H//////QfQ1PyvX7+FQEOJIDXsbGx/GZh+/vr9588fyA6hb9++cXBwsLKyvn/3jomZ+c/v37y8vKysrP/+///w4QMrK8vfv39ZWdm4ODk/gvfgs7Cw/Pnzm4+PHxKMDAwMjAwM30GHAPzn5OD8z8Dw98+fT58/s7Ox/fj5k5eXl4mJ6efPn1xcnAwMjD9//mBgYGRlZf3x4wd4u9L/9+/fs7Ky/fr1i5ubGy7+79+/jx8/srOz/wAfR8rMwvzp4yc2djaIS9jY2D5+/CAgIAhfcYsIyVHWaAiQCz7/+Gujwr0+Q5lcA8jXNzoySn7YjeocDYHREBgNAaqEwL9//378+AHftAQ/HogYwyHHFUEOD/r8+bMguMklISFha2ubm5tbUVFx9uzZy5cvd3V1MTAw8PPz//nz59+/f5ClkMSYj1UNGzNjubv4t5+gBY5YFaAJMjIy/P8PbrL9B8n8//8fPCGI3pCFKgMpAWEolxHU1AMHDqjt9++fBKjxxwhi/2eQYIAYC5rCh2CQmf8Z/oM1gQxhYJCAbgxiZAApZvj/9x+DthSHEBe4+gMpBysDm8PIoPQfrIiRgRFuCEQErIgBIg5h4yGhVoF8DLIAZALYfBQtYBG4gRAHg70J0QbTCOGB3A4OO4gRIOZ/BnAgQgRGydEQoAL4z8DDgbJ+hgpmEmcEODcSp3RU1WgIjIbAaAiMhgDVQ+Dv378yMrIR0Sk8PDwQw93cPZRViV3WKSYuUV5Zpa6hCdrow8r2999/FlY2Zmbm/okT21rbWlrbZOVkV65eo6kJUsDAyPSfgRHeEgU16CBWkkj++vu/eevz159/g0YtSdQ74Mq//fq3IVNZU5JjwF0y6oDREBgNAQgYbYxCwmGUHA2B0RAYDYGBCYF///6x/P8n8OfbpgWzQMOHDAzMzMxszEwb588ixkGMjAwCrKzHt6xlZGR8eOeODCfTjqXzWFhYmJmZHXTVrNQVQMeO3r+5/dZlBgaGd7eviDL93rZk3u8/fxj+M7x/gzgcnhi7kNVAdgsNxcYoMxPj33+goUVk74yyR0NgNAQGEIw2Rgcw8EetHg2B0RAYDQEGYTEJDSPT+zevUxgWr1+/fvjwoSA357oVy3AZxcLCwsbEtG3DOk1NTUZGRm1TSwERUVyKSRL/95/h/3/o/nTQ3vD/DJAGHwsTyvDrfwaGP3//szCjTDD/+fefiRG6yR2/pVi149cyKjsaAqMhMPjBaGN08MfRqAtHQ2A0BIZzCMipaWQ2dlDuw6/fvj9+/Jhgm+7/v//cPNyy0tKU2wg34d8/Bm52Jm52ptef/zAyMvz7z8DBwigvzP777/8Hb3/9hy1u/P+fgY2FUVGY7dmH3z///gctimQArSWVF2L7+uvfu69/IYcuIRv7n+E/CxMjZBjzPwMDKxOjsgj7o/e/fvz+R2T7FW7aKGM0BEZDYNCC0cbooI2aUYeNhsBoCIyGAAkhwM3FqaFO7EpTEswlpPT33/9szIztgVJvvvyt2fiMjYWBh525J1jaTYvv++9/c468nbjvFaQ9+vfffwl+tvnxCsmLHtx+9RMyxf/r7/9yd/Gjd78uOPaWkw2xeeLf///ywmx2qrwrz7z//Re04QmknY91SqRs9Nz7ulKckvysWy5/YEIZYyXk1lH5UTAaAoMSIHL+oHTeqKNGQ2A0BEZDYDQEBm8I/P3HoCjCPjlC1l2L79fffwwMDL//Mnjp8KmLc3hNuZO38km8pZCyKPvf/////PsPmbj/+w+yMhbqKXYWxtK1T9ee/8DOyvTrz/9ff///+gNS/Psvg6YEh6sm32/Y+k4WJsZXn38Hz7j3/NMfBzUeQ1nOX3/+//vPgKwLskLgN9iQX3///wYjkAKwSqiVo9RoCIyGwCADoyOjgyxCRp0zGgKjITAaAkMnBP78+68rxXHhyfc3X/6wgAcp////z8XG9PTDr0tPf7z69Ofzj39sLIxMjIw9IdKn7n89fOcLmud+/f1f4SF+7tG3/Te/NPpKOqjyPnr/s3v3q0dvfxW4iKmKcfSHSmcvf8LOAtp1JMbLWustse78hygzIW42pheffi8++a7KV9JRnffWyx+dO19eevrdRZPXQZX3PwODkzrPpP2vP3z7W+Eh/vDd78Ytz599AF1xhOaAUe5oCIyGwICD0cbogEcBXR2w/MbKh58eMjPRI97//vujJKAUphZCVx+OWjYaAqMhQJcQ+Pbr39df/1iYGFefe//5x7/OIGk2Zsavv/6xMTOsPvvBXYtvfboSNzvTiftfLzz+/vff/5Wn3z/98OvXX9BY5vffUL0MDAw/fv+XF2K7/vyHrx6/jQrPrCOv1cU5Ys2FSlY/3XXt8///jCtOf/j5BzSq+vvff6F///VkOHt2vzx+7ysPO9POa5+afCUVhNkn7H1lIMvV6i8VNOseCxNjuKngkpPv1pz70OgjeeL+14XH3yVaCYcYCTRvfcHByvj15z/IGC1dwmnUktEQGA0BwoAejRIGBobHd29fO3OCmYWVsIsGTsW/v3/5hYXNnT0Gzgk0t/nUi9PnXl1gZ2ajuU0MDD///rKQNBttjNIhqEetGA0BOocAKzNjrqPol59/IUs2//z9b63MzcTIWOUhzszEwMnGrCLGLisIKmcYGRiKXcRAk+l//xvJcYnxsYjxsqRai7z5+geq999/VTF2BgY+LjYmaX5Wc0Xue29+MX//m+0gIivIys7CaCDLaSrPxQDaF/Wfn4NFiIsl1EhQgJOZlYXRR5ffS4d/y+WPfBzMLz/99tDm7QyU5mZj+v+f4fuvf3//M3CxM/39x8DKzPjmy28fXf4//0DboX7//a8iNnrIKJ2TzKh1oyGAD9DpOtD9G9Ys7G5hB12Ghs81Ayv359dvBU3t2hkLB9YZNLW9/HDVhVcX2ZAao4zMjKCRCshuVbDdjMzgu03AbAjx/99/kBoIh2jy199fphImLdbUuQWbaGtHFY6GwGgIjIbAaAiMhsBoCAwlQKeRUWYWFnZOTnYOTkjYQG88Y2CAH0AHF4Hc8wZRBiGRpZBl/4MulIOcDQJRSCnJzMzCxs5OqSlDSj8jE+P3199ZeViZ2ZnBd+CBrpz79vLbX9AtfyCfQDYasPGysfGzQRWAhHFiRham/39AmxhwqhiVGA2B0RAYDYHREBgNgdEQGA0BJECnxiiSjQz//v5lZmFhYWX9++fv79+/IBfTsbCwMDEzQ5T9BV+dDGEzMDCwsrLBT877+/fv718gLUxMTIxMTH///IErgzP+/vnDzDIA/oI7YKgwGFmYPt39cKr1uFGJmbC28L/f/xgZGf/+/nt5+oU3l18z/Gf49+cfEyvovAWVYDWNaK1/+FuZoLun/z/d94hfWYBXjvf/X6Sx1qESIqPuHA2B0RAgLgR+//0/af/rt19+/wfdGw86x95RnYeJkXHvjc8s4MNCjeQ47VR5f/75t+vap1uvfrIyMwYaCDx4+/P6859xlkJ8HNDS/tuvf7MPv3HV4n307tf5x9+dNXiNZLk+fP+79fLHZx9/c7IyhZsI/vn7f9np9+DjS//zczCHGAuuOP2Og5Upxkzo6vPve298cVLnMZLjev3lz7bLH59//KMjxaErw7nyzHsWJsYoM6EDtz7fe/3TUplbjJd16+WPzEyg3VSgsu4ftNsMGdKAF1gQLiQY4IKgcRBQVx3kW4ggXBmEC1EPv5IeKxcuyAA2CsKlFsnIwMDIxPT/H+zoAdCN9lCzkV0IFcJLwb0GUYWsHdmDEGXIshD1NCJZmJmZmJn//P6NehgDybaBGg8MDH9hsY9VP8hroBoNqySVBRkZGZmZmP78/YtmLiMDAw87U66jKC8ss6ApGGZcejfa/v39Ky6rEJKeI6+u+ebZ040LZl0/e4qJmTm+tEbL2IyBkZHhP8PivrYLRw+ysLL9//ePjZMzsaxWSUuXCZTN/r97/WLHiiUn92y39Q5QNzCe39HIijyW+f8/EzOzmpHZk3t3vn7+CB92HWZxRhXvMDIx/v32++by678+/4a29RkZGFkYmRiYDAuNGf4zfLj74crsS2Y1FqzcbExsTP/+/mNiYQJFECMDw7////7+Y/jPwMTCBJnBZ2IFMf79/Pdk7yMuCS4mJqa/GFmLKs4eNWQ0BEZDYDCEwO+//yfvf6UnzSnCA6pE/v9n2HP9MwMjw/uvf8Fb6hmO3P135dkPBkbGz78YP3z5y8Dwf825999////6E3TyKAuokwvyx9//DE8//Np48eOP3/8+/wC1XI/f+/rn7/83X/78/POfiYlhycl3/xkYXn7+DWoG/Wd49/XvwuNvX376zcTIOPPwmy8//777+nfntU/H7n39DdN15tG3Gy9/QI7fX3bq3ftvf77/+n/kzlc20MpR0Jn8nz9/+fnzh4iICMgFYMzKyvb3759/4AYK6CooRoa/f/+ysLCCyzFQc4uJiYmBgfHfv7/MzCx///5hZGSElHJMTEwsLCz//v3//fs3IyPoHtd///5BZvMgUr9///n/H9rqBVsFUvP//3+IXRARyklWVrbfv399+PCBn1+AjQ3EZmBgZGYGlcOMjEysrCz//v378+cPIyMTGxvrnz9/wN5h+P37N3i4B7SX49+//8zMzL9//4I4ngHcQvoLOqsL5H2YCxlZWVn+/v0LcTwkoECX2bKwMjEx/v79G6oXppqKNAsLy717t589e6qrq8fNzUWJyR8+fPz375+QkCCaIYyMjCwsrOAwATXp//37y0KXUa0fP358+PBBQkICzT1ffvzdf+tLgqXQaGMULWSowgU1FiOyC96/eT2npcbQxiEoNXvyvTs/vn8TkZBaM3PyyyePmFlYX4HJP79////3j4OLm4ePf/fqZXeuXGJmZtY0MQtITH9899blk0fvXr3Mwsb279+/v6Cu0j9mZmYmJmZuPj6PyNiF3S1//vxm+P+flQ007f4HVEwwMrOw/P71C9S9/vuPhY2N4f9/kBUM/1lYWEfgSCojM+PdDXe4Jbh/ffr1/+8/RibGXx9/Xp5xUSVETVBDiJGBkf3Nd0ZmRg5BDlYeNtAwJyPD8+PP7m26++/PX3kPRVlnuW8vvl2dc0k9SpNfWfDqnIu88vx/vv1+d+vd+f6zZjUWfPL8DOg9PaokoVFDRkNgNAQGRQiwMTP2hMipiYGaMrgc9Or5k+amxuk9Pezc/LjU0F9817aNZ8+eqyyCLmd/9uje1CndjU2NLBy8DAwMRw/seff+vYG+/vIVC8uqahgYQIO4L57cX7BgYVFhYU9vT1lZ+aNHj5qbm7m4uBgY/nNxcaWmpKpp6zMw/OvpaI2IiJRRUGFgYHj55EFPT2dbWxsrFx+yHxfMma6hoWlh44AsSCH76IE9c+fN4//3j4mZOTo+zs7J/dO7VwX5+dzc3P///+fj48vJyZGSV/7/+/ukSZOuX7/GwcGRlpaupWfEwMBw5MDu5ctX/P3719LSMj455c6N6xMmTPj379+HD++FBIU4uThLSkrEpRUYGBieP7o3bdqk+oZ6REC9e+cbFNzSUPf795/a2hqIOIV+war9xJEDfat77XR08sMc+IXFsaohUnD54vkfP37IiCtEU//v17cTJ05Y2TkwMDDduXF5w/oNJZXVDAywbhOaaupxr18+N23q7Mllc9CMfP7hh2XXHeSuAJqCYcaleUCjhhcjEzPzoS0bVs+YeOXU8e3LFv79/ZtXQFBITOLHt683Lpx99uDe7UvnP71/x8DAYOcbaOXhA5myf3L39o0LZ25eOLtl0Zxfv36KS8uKSkprGBr/+f2bX1DINy4ltbrZyt2HkZHRyt1HUk7BPzFDXd/IPSwG1P38+9fMyU3P0ubf37+O/sFW7j5xJdXiMnIScgqR+aVJ5fWaRqbg7i+qS4c1j4mV6fX5V+9vv1cJ1QB5FHKpNDuLpLU0uyDHf1BPHrpjCTLwycjM+PbK62vzL4vqi0rZyNzbcOflyRc80jx8ivw3l994tOv+m6tvxIzF+ZT4OUU4Jcwl2fnZydjwBHLJKB4NgdEQGDIhwHjw6InvXz/jce+K1evWbNiybtM2iBpISfv169d370CFPOiAevBg5I8fP96+fYs8rvbx48f3799DdP379w88XgXi/f79+x8Y/P//H1PXp0+f4LoYGBi+f//+5s0biKUgzQwMHz9+/PTp04cvP1++/QgRAbUa337smTi1qa0bInLt9v0zF66ISMpu2bH30qWrEMEVazZ++fHn669/O/ceev/5+827D99//p5XVJaVV2xkZp2WnX/9+vV//xj2HDj65MUbiJaV6zatXLdp/ZYdEC6cPHry3PXb9yHcd+/effjwAcJmYGD49OnT27dv4dz///+/efPm82dECP/9+/fNmzffv3+Hq2FgYDh79mx1Q0tcUlrf5OmxiakTp8568+bNizcfbtx9mJadn1NYKqOompVf/P379/rm9qs372bmFtk4uGbnF798+fLixYvV9S1BYVEpGTlbduyZNWuulJxSfnF5ZGzivUfPE1Iz07ML+IWhg3YvwAHV3N4Dsf3a7ftnL13794/R1tHt0LFTz15B4wsi++vXrzdv3vz69Qu0JuH//z9//vz////t27ffvn2DKGBgYPj58+fr16//gJfb/f//HzLmCtcFUQaJ6EXLVjm5e5dV1/MJiTGA4xEe0X///oWYALpwAZY8ILGPHJgMDAzv37//8uXLhy8/Xr79BDH83bt3Hz+CUsK/f/8uX79d09Dy7NmLnz9/ikjIunn7/wdfWPvz5883b97AU+Dfv38hHvnx4wfEEAgJGRH/8eMHPG2/e/cOWc23b9/evHkDGVSGaPn69ev79+8/fv31ApYaf/z48ebNG4h3vv1CGVCHaBnGJGiGhZ7e+/f375mDe5lZQKOY+lZ2nz9+ePH4gY6plaiUTEZ9m6iUzKXjR9bMnPT18+c/v3/9+Q1KxwwMDExMTMyg7UVsmkam7Jyc79++llfV0DKxOLZja2xRFRcv7/OH911Do5iZWb5/+/r3798f378KiojqWdrsWbvi379/Slo6Xz9/unD0kIGNA7+QyM0LZwRFRAOSM988f/rj+7fg9NzV0ybcvHiWnuEwUHb9+//vH+O/7+++3990Vy1UjUOQHbQPjAl0qAIjO6OUg/S/P//+/P3LzMj0Dzy1BFIPZrw484KRnfEfO2g+ipGX8f7+e2IW4sohaue6Tl2Zc8m8xpJNmI2Pk59TlFPSRopVkO3Pr79/wXMdA+XTUXtHQ2A0BGgaAmxsbNnZ2TMlmCKiYvz9/VVVVdGs+/jx49GjR6dOnbpmzZqQkBBWVtYDBw7s2bPn48ePz549s7S0LCsrO3/+/PLly//+/fvo0SNlZeWGhgYuLq7Fixdv2rSJkZHR2Ni4rKzs+PHjp0+fLigoYGBg6Ojo8PHxYWJiWrx48Z8/fx49eqSmplZTU8PDw7NkyZINGzYwMTGZm5sXFhaeP3++t7eXgYFBXFy8trZWSEho/vz569evl5KS+v79u6SkJNy1v3798vX1vXbt2vbt2z09PZnBgJOT08PDY/PmzXp6en/+/Dlw4EBDQwMzMzMHBwfotBFGRkFBQXV1dQYGBm1t7VevXi1ZsqS1tZWdnR0yA/7t27djx451d3dv2LAhMDCQlRUxfszGxsbKyvrv37/e3t6zZ8/+///f3t4+IyNj5cqVGzduZGBgUFZWrqur+/r1a0tLy8uXL3///p2UlOTh4fHy5cuGhoZ3794xMzMXFxcbGxtDvLBixYqAgAAHB9BQq4ODg62tLTMz8+vXrwUFBXV0dBgZGZWUlFatWnXnzp3z589PnTpVTk5OX19///79u3btevr0qbe3t7OzMwMDQ3FxcVdXV0JCgqqqKicnp4CAgLKysqAgYjr7169f3t7eV69e3bZtm5eXFzicmJmYmExNTfn5UUa+796929LS8v37d3Z29traWkVFxdbW1r9//z548ODLly+FhYW2trY3btxobW39//8/Ly9vQ0ODgIBAXV0dExPTgwcPGBgYWltblZSUQEt1GRmXLVu2Z8+e27dv3717t6ysbMuWLdu2gbo3VlZWhYWF+/fvv3//fmpqKgMDQ2dnZ2ho6JcvX1atWvX9+/cnT55oaWnV1NRwcHBMmjRp3759MjIyr1+/NjY2/vXrV2dn5/Xr1//8+RMYGGhnZ9ff33/nzp3S0lJ/f399ff1t27bp6eldv34d4nIuLq76+npZWdn6+vr///8/ePDgx48fDQ0Nurq6kFh49OjRhAkTmJiY7t+/b2dnx8vLu2fPHhYWlsbGRmVl5WPHjk2cOBGeGkVFRXft2jVjxgwBAQF2dnbIeoDLly93dXX9//9fQECgsaGemQllQB1iyzAm6dQY/fv3z88f3yGLOMHd3//Wnr5Gdo7LJ/d8/vDh58/vN86fOb5rKxMzc1hmgZ1v0Po50/ZvWMPw7x+3gMC/f/8icksCU7JAq1hYWE7v33370gUZReUfX7+KycgKiIr2FmW/ef5EREoatOfm318DK7t1s6bIqWr8+fXr54/v//79+/Xjx68fP37++M7Mwrpz5eJtSxeEZuT9+/v30NYNv3+BlsiYu3hcPnUM1P/+9XMYRzYDA0OpSdGPvz+3b9q++/T2l3dfMvxn+P/536muE/MWzrVztmdhYf4LuqrvHwMj42WBSwXcBf0OPbx8oCzRsK1h3/+98vdk////ryKlqGuql22f/fvP77T5qYf/Hs43zbOytPry5UuqQEqxYZGxuQloZPX/fw4W6PkJwztUR303GgIUhgBomfunwb7M/d+/fyKSUiISUhDP/v37p3/ChLvHNtTX10+aNGnHjh1aWloQKQh58OBBUVHRoKCgpUuXnjt3ztzc/Pnz57t37165ciUnJ2dsbKyrq+uPHz927dq1cOFCaWnp7Ozsffv2KSgoLF26dPLkyTw8PMXFxWvXrmVnZ79+/TrEzCtXrlhZWbGxsW3btm3p0qXS0tIZGRn79u2Tl5dfsWJFf38/Jyfnvn37Xrx40dHRkZOTY2JiMnHixNmzZ3t5eS1btmzatGl8fHxFRUXIw6UMDAw8PDz5+fnl5eVmZmaQ1iQDA4OPj09lZeWfP39Onz7Nycmpq6sLH42Dj+lCXKWmpnby5EkIG0Lu3r1bTEwsNDR01apVFy5cMDU1hYhDSFZW1jt37mzZsmX16tWMjIynTp36/fs3Dw9PR0eHgIBAcnLygQMHDAwM7O3tXVxcjh071tPT4+Tk1NHRoaio2NLScuHChU+fQGN7oKEERsY3b95YWVkxMDA8efJk37597OzsTk5OLCws796927lzJwcHx969e7W0tLi4uH7//g1vNYqKir4EAyMj0GQ9AwODqKjot2/ffvz4wQZe/4a5sPXfv388PDyFhYWlpaXIAQUZ9YR4DTLe2dDQ4Orq6uPjs23btsbGxunTpx88eNDd3b2/v//cuXPNzc0rV65sa2sLCgry8PBYsGBBT09PS0vLhg0bamtrS0pKZsyYMXny5P7+foiZgYGBR44csbKy8vHxuXbt2oYNGyZMmMDOzl5cXLx9+/b379/funULovLy5cvu7u4fP37cvHnzqlWrREVFk5OTjxw5wsXFtXPnzhkzZrCwsKSkpIC2r/39q6WllZ+f//Dhw8zMTHt7+6ysrGfPntXW1kpJSV2+fPnChQvfv39vbGwMDAx0dXVds2ZNXV3d3Llzt2/fHh8fX1RUtHr16t7e3gULFkCs/v79+4YNG1auXCklJRUVFWVvbz9p0qRZs2bNnDmzqqqqra2ttLRUR0dn+vTpEydOLCws7Ovrq6mp0dDQmDJlyuPHj//8+dPY2JiammpnZzd16tRJEyekFjeNnDl6BgYGOjVG9cytC7omMTOBFt/8Z/jPysampmf0+vlT37gUFlbWf+BhdqfAsH///jExMTn4BskqqzIxMv1nAK0xlZRX5OYFtYcYGBi+fPwgraCU394vIinNw8cvKi3NzcsXW1wBLRr+/+fg5JJWUkmtbeXg5BKVlslt7fvP8F9aQfnPn98KGloSsnLWHr56FjbSisqiUtJ5bdC0/uXjh9y2PtAtdjygNUOQtDUsSTEu0BxHkHOAxHrx/6Chy39VtVVJCUkeph7CPMKvX74SFxZjYgVF0zuet2xMbAp8Cly8oNXi9uZ2V85ebu9uFxIWWrRoka6OriyPzNRJU7kYuTo7Oqc1TfXa4ikpLMHGyPbp8ScZB2nwOqthGYSjnhoNAeqHwJqZky+fPMLKNqhPYv/541tAYqZ/YhrE/4yMjG/fvn3+/Dk3N7eoqCg78l5SsIpVq1ZZWVndvXtXVVV11apV5ubmf//+tbe3V1ZWZmBgUFJSevXqFRcXl5GRkaGhIQMDg6Gh4cOHD1+/fm1kZAQZZ3V2dj537py9vT1k6IiBgYGNjY2ZmfnXr196enoQXUZGRi9fvnz37p22tjZEV1xc3G0w2LVr1969e588eSIhIXHp0iU1NTWIAl9fX7S2448fPwwNDUNCQpqbm42NjSHjJrq6uvz8/MeOHTtw4ICHhwczMzN4JAXsN1QCvNsJWplC9K5Zs8bCwuL+/ftKSkrLli1Da4z++fNHSkpKRUWlrKxMXFw8MDCQnZ1dRkZm8eLFP3/+fPz48Zs3b4SFhb9+/dre3v7mzZtPnz59/vz57t27KSkpwsLCkIFMyKghZOYQ0raGLEvYuHEjKyurkZHRhw8fDhw4cP369W/fvq1du/bLly+QqWSI2//9Ax2fwsgImhaDiECathD3Q0QwyZ8/f+rr64eEhLS0tMADCk3Zu3fvnjx54uXlJSQkFBQUtGTJkqdPnwoLC/v5+QkLC9vY2PT39z8Cg2PHjp0/f/758+ffv3//+fOnvLy8i4uLsLCwhYUFvJHHwMAgKCjIyckpJCQkISGxdOlSAwMDRUVF0Fo+O7tjx47p6enBkwcLCwukoWlpaamjo8PAwKClpfXu3bu7d+8aGRnJysoyMDC4u7t/+vSJHQy6u7tBE/fglRKSkpJsbGxycnJcXFyMjIxsbGyPHz/+8uVLYGAgGxubl5fX6tWrP3/+LC4u7uHhISwsbGpqumvXLrjf//79awwGLCwsqqqqDg4OYmJipqam69evv3XrFhMTk729PQMDQ0hISEVFxaVLl7i5uW1sbBgYGDw8PK5du/b+/fsHDx7s3r376NGjDx8+5OHiZGLCHxVwm4cJA5p/aO0bYQlJYQnEtAjEOglZeQlZeQgbjdS3tEUTgXB5+AV0zEBdQAhXQESUgYFBzwIUoxARCKlpBO2G6lkipCB2KWtDB9UhKiEkD78ApiEQqWFG/vv/7z/Df1EJMf8Af4jXJkyZ4OjqJCwm/PTZU18/30kTJ1laWTIwMPz694uBieHH3x/s/0GbwIJCgs6eP+vq7vrz109rK+vI6MiLly5Onz19wYIFxsbGhw4fam5vbm1r1dHXSUpLUtdWNzaBTiFBbKGIhHcPGcEHooBO3aDIPNI0/0c6IgVTJ35ZTPWjIoMpBJgZQf2uweAiFlZWNnYOyIbLweAerG74//8fM9L+YiYm5t6eHkcd8RkzZri5ufHw8CDrunPnzvXr1zk5OS9evPjz589bt259+vSJiYkJPmENmexGbt5Bmkc8PDxfvkDvr//06RMbGxsjI+PPn9A5q69fv0K2sUPOBISsR2RgYODk5Pz69SvEAf///2diYuLn5w8ODubm5v7z54+kpOT+/fvhqxU/fkQfhIa0YJKSknJzc2fOnOnr6wtp6vn7+y9evPjr168pKSkQw+EkvAHEwMCwe/duSHMTssv+yZMnN27c4ODguHz58o8fP27evPny5UtxcXGIXkZGxn///nFzc0+fPv3WrVvXr1+vqalpbGxsa2vLzc01MjJ69eoVIyPjokWLTpw4kZub++PHj/Lyckhwwdcg/v37Fx4Cqqqq586dCwkJUVVVLSoq+vz58x8wUFJSamlp+fjxY2xs7PPnz+Xk5FhZWZ88eSIgIAAZRjUwMGBlZYUPK967d09AQICTk8B01r9//5KSkvLy8mbNmgUJKIi/4CQkZCCLLH/8+PH//382NrY/f/5AIujPnz8/f/5kZ2fn5ub29fUVFxf/+/evgIAA6IQjZmbIksq/f//Cx6chxsKHabm4uJ49ewYR/PLlC6ThCEke//////btGyRY4MkMopKDgwMtUe3YsWPevHlVVVX8/PxnzpwB7YQGnxIA1/j//39OTs5///5Bhop//vwJSVdwh/37B9o2DTEfQjLD3A9P1RDvcHBwQEKDgYHh69evrKysPDw8kNW0DAwM3759+/37NwsLCy8vr6+vr4SExN+/f4WFBL/9+kXM2d4Qq4cBSafG6DAIqeHhhY7T3dfeX2dhBh0s8J+BgZmJmS9DsOtJL9dbUAEklC064dXkadtnsbGCTriQKJIuOl327z/oTBB2dnZGXwYLU+u/f/8JCwu33Ot4/+6dZIns9I+zfu/68y+C8eSns3Hbk/46/bXXc+580sv+GtSEhQQaIwMDGzv7////IdmPmYmZlZUFwgadQvLnz9+/f9lBVQ7Tr9+/ILmXkZGRnY3tPwPDr5+/WFhBqRTUg//3j5GJCbSyAmIuuSQx58xBqiI2NtD5Yn/B6+ohtoFKTPDBVf/BS5lZwEc0wFvLEDVYSVA1zMLy69fv/+AChoWZBXS94b9/yCaAzoJhZv795w8rOPwh40Bw00Cr9MEHZoHK+v8Mf/7+gUixsrL+B+8PgHCRSSZGRohRkJP5mBgZWVhZkQOQhZmZhYUV4iSG//9/YTuchZEBdBjNX9iBNcjmk8qGLJWDjOKQqpfq6oU5hHrsOtlZEAmV6laQZ+A/UIIDRS4jIxMzeKQHtLcDtP/jHwMD6FQQeHUIaYqB1tbDkyAjVMGfP79BRzHDDm+Gu+Q/uFaHH+oMFyeD8evXz0WLFvlaKDMxg3Iosgn///9fsmRJYGBgVVUVpImZnp6+fv16FhYWeHPq+/fvII+Cp8UgeiHNFEdHxwULFsyZM0dISGj37t2tra0CAgI3btxYs2bNhw8frl69CjEEvonnz58/P3788PX1Xbhw4dy5cwUFBY8ePVpdXa2np7d161YbG5t9+/b5+/vb2touWLBg1qxZUlJSK1asgIyqQuz9+/fvr1+gkoeVlbWurs7a2hqyg4SBgcHZ2bmhocHDwwOyxhSymQbSKLl+/fqiRYv+/Plz5syZDx8+1NXV/fnzB9LenTdvnp+fX1VVFWSsMTMzc/369RkZGRAuJASePn3a0NAQEhIiKCjIzMz89+/fnz9//vnz5+jRo6dPn3ZycgK3J/+8ffv2wIEDr1+/5uTk9PT07O/vj46OPn36tJqaWkREBMT9sbGxKSkpTU1NxsbGnz9/Pnr0qK2t7b9//759+/bz509hYeGQkJDGxsb58+f7+flVV1cnJydfvXr11atXLi4u79+/T0xMnDFjhri4+LRp03Jzc0HFCwMDpBEGb1RBLAKtZAPvEGJhYamtrYUH1P//oJOtIEU3AwODsLCwo6Njc3Ozr6/v5s2bLS0tZWVlv379OnHixKioqCNHjsjLy6uqqurr62/evNnb2/vw4cO2trYWFhY/f/6EGALa+IG6Pej379+QSPHy8srMzFy4cCEnJ+eePXv6+/sZGRn7+/s3bNjw9OnTq1evQvoV8OTx48ePr1+/Ojs7L1u2bPHixTw8PJs2bQoNDf3z58/v378hwfX06dP////z8/P//Plz7ty51tbWkMUG0tLShoaG1dXVXl5e69evd3V15eXl/fnzJ6QE+/fvH9wWSE78BV71B99HxcAAOiDs+/fvmpqaMjIyjY2NpqamCxYs8PPzMzQ05OXlbWxsNDMzW7Jkyc+fPwUEBAwMDHbv3u3i4nLw4EEPd1cJVSFIsI8QEjFET1MPXzx+eO+aFZzI14HCh5QQxSjYCXBxMA9KoKmBipJIQW4TArUywBjOBfEwRtugsnDx/6AjNomxEKIRMobHwAg6IhmPLpC/4B4GcWBqIfrRtf/5/YdHVDwkLYeXl8zlBEWHS5+ef+ok52BpY/n///979++fPHHC09Pz8uXLjx8/Dg4O5uAATRQePHjw5atXPt7ea9eu1dHRgWSSJ0+eMDAwcHBwaGpqGhgYMDExvX//fuPGjebm5pqamoyMjL9+/dq7dy8jI6OLqwuy0//+/btu3TpJKSl7OzvIGvaDBw8GBAT8//9/06ZN5ubm6urqGzZu/Pjhg7+/P6TX/vPnz3Xr1rGwsnh5ek2YMAGyb8DW1vb48eNpaWlsbKA7r2FhRTL95euXf3//8YHXwuLR/Pfv37lz5x46dCgiMtLH2xui8vefPx8/fhAWEmZkZHz06NGatWvzYMU3RAEu8uXLl+fPn/f09ISU71euXPn375+AgMDtO7ednUC7BxgYGD58+HDq1Clra+tDhw/b29kxMDBs2rTp9Zs3jAwMEhISDo6O4mJif//+PXP2LDs7uy54EoqRkfH06dM8PDwaGuCDEVCt//Hjx969ex0dHcFn0IC2Eh87dszVzY0F1ka5efPm3r17QQ2U/6ATagIDA5H3K0AMg2wdFRAQgNRSEEHyyI2bNqqqqKKtLCTPKAp1Pf78eMHVxUs8F3CwgBI8haZRrn1KTcnV08dZ2dj//v0jrajiFRnPJyR0at/u47u3/fvzh4mZ2drTz8Te+eWTR9uXLXj3+iUTeMnTv79/JWTlg1JzeAT4GRkY//79c+fKxb1rV354+yY4Nfv+javnDh9gRc0srGzsgiKib148g9T6JLn854/vvnGpPrFJoOGcX/8MWq6vSlcxkMbSmv/379+2bdtMTU3hw4E3b9589eqVmJjY69evIbOTe/fuVVFRYWFhuXHjBmTe+cyZMywsLAYGBvfu3YNMWAcEBJiZmTEwMBw+fHjjxo1aWloyMjLa2tr//v27evWqh4cHAwPD6dOnOTg4dHV1b968uXLlSkjD1NLS8v3790uWLHny5Imurq6/vz8vL++tW7eWLFnCyclpaWnJxsYGWWcJ2k3/8tXFixddXV0hJ6SeP3/h75+/JqbQuZ09e/bKy8mpqoG2Z/348XPnzp3u7u7v379ft27tt6/fmZiYlJSVXF1ceXi5//37v23rNlMz0wsXLujq6kpJQScDb9+6c//BfTc3V0hoH9h/UFJKUl1dbd/e/eDc98/D09POzvbwoSNbt25RVVNTVFCUkZWRk5WbO2/u06dPnRyd/v//7+DgwMzMtHbt2tOnz8jJyUVGRQoLIxorL1++Wr169aOHD7m4ue1s7ezs7b58+XL40GHQNiMWpl8/f69bv87by5uXj2fTxs1HjhwWEhZOiI+XkARtk799+86yZcu+ff3m4enh6AjaBQVaEff56959ez3cPdg5EIXty5evLl28BCrewRXjhfMX//77a2xs9Pv37z2799ja2vHwckP8+OPHjxUrVly5ckVbWzsyMpKBgSE2NtbPz+/27dsCAgKJiYmCgoJfv35dunTp7du31dXVQ0JCeHh4tm/f7ujoyMPD8/Dhw5s3b7q5uUFMY2BgOHjwoLS0tIoK6MysGzdurFixArLxCLKLa9++fdu3b9fV1ZWQkDAwMPj+/fudO3cgierIkSNCQkJaWloXL15ctWqVkJCQkZERPz+/np7esmXLrly5YmNjw8HBYW5uzs/Pf/bs2W3bthkbG5uYmFy4cMHNze379+9Lliy5efOmgYFBWFgYGxvb5s2bbWxsBAUFX758eebMGW9Y1fDu3btTp065uroyMzPv3btXTU1NVlb2wYMH9+7dc3Jyevfu3aJFi54+fWpra+vr68vIyPjy5cv58+d//vzZxcXl////Tk5Onz9/XrJkyb1797S1tYODg979YrfvvX28XF2SH7H7DR4gw49Bp8booc1rJzXVXX/xjoHhP+P//6CLIhgY/v/7z8z0n4UTdBHl3x///vz7zwzq1jP++QfqloHHj0D3hYLGopiZQANRDAx//oIGaBiZQFd8kFSYMjGx/Gdi/8/IBLqI/d/v///+g06ZYgOdMPrnx5d/f0Bn/8Jjl5mZCVLvgk4mZvjHDLqsnen3b9AAHlwNiAE58xfU3gRnzf8M/xkZGcBuA3edQSf4/wMdD/wPXMaB2pqQhghIL/gqVFY2FgYGxv+gw01AZyiDWqDgRi/kkOR//0DHYUAUgxfF/pcTEZCSksxp6zeGLTmHyxLJqDxVM690Dut1lv2H9qtqqK5duSYtLf306dOHDh9KTkk6fOiIla3Vv9//jIyMtLW0J0ycoKamVl5eXlFd4eftt3fPXiVlpc9gUFFRUVpReu3SNXNzswkTJyanJYPK9GcvIWvhT58+LSUL3eUA6jL++a+pofn129eDBw8qqSptXLsxIjLi6tWr//7+MzYxnjBhQmJKoomhydkLZxfMWRCfHA8qevYddHV1lZOTu3nz5sIFC9nY2fbu2fvgwQNhYaHlK1awclCUOVctW/nq1aucglz8gfbw3sO83NwJEyfKSEuzckJL5NcvXjc3NU+aNomBgeH6FdD82toNa/GbA5G9cPaCo6Pjpk2bbB1AS1Am9k389etXREREbGzs1i1buflBhfjyJcuPHz/e1dXl6+u7bNmyP3/++Pn6JScnsbOz37p9++rVqxMmTFBSVaqprBEVEc0vzgeZ/I/BzdVNWkpq/mLoOnqQIAzv270vMiJi7bp1Nvag9Sr3b4P2nO7ctZOZDTo9PW3ytK1bt0ZFRv7+84eLi8vXx5eTFzRGDjMASqclp7W2topKgFbFQIXIovbs3KOspKSoqkSWbmpquv/xQfnhykUe8wdVY5SZhZWHjz+zqfPVkyevnjyydPfetGD20R2b7bz9feNTT+zerqyj++vnz5mNoCE30IAoA6Oqrn5CWe2JPTt+fP3Kwspm5uR2//qVpRO7NIxMP759/fzhAwbQtWq//zMwQFbnaxqbWbt5z+1s/A9eMgiZdv/96xeoxGNk/AsqCRn///8HOgT+37+/f36DTgJnBU2Ug8aKMBqjK1IUjeRAa8qpGTf0Nes/w/+//34wga7AAY1GgzZSMLP8AxX90HOSWZhZQGrAkxKMDAwszCygSQlGJhZwfwA0lMHA9Oc/6MApsF7Wv//+MDMx/fvP8O8f1ATQZgnwKfoQn7GAzAcdT8XGzPqPATR6wcTA+Pvvb1YwF3SREgPj3/+g84NYmVj+MvxjYmD+z/DvD/ikfRYm1n8M/5gZGP/8B1UtEAMZwHMXzAzMYMWMjAyMv/6CT+BnYgY5FVzXsDCx/P4LqubYmNn+MfxjZGD6Bxq2A7mQlRlUnIJ2aDAw/foLPcEGFO9MzL9h0y8Qi0BD9TgCCuSpf/9ZmLB37b59+xYZGdnV1QU5fwBi2iiJPwQevvtl33Nr5DRG0adX8IcO2bLMzCy//jFee/ICYgIjEyu4BfaXkYGBiYP1HxMzw/cf//8zsDEysrEzf/kBLRRAihmZGJkYmEFnD4Gaeb9/IUmBpInHTAxMXAz/mRj+/2Fg+A3aucXCzsDIxMjG/P/7B4Z/oFwKN4uRmYmDnZ3h//+////8Z/jPwsTCwMD04+ev/+DyCK4MxGAEXcvBwAhqTzOCpr2ZGZiY/jOAjgcDneAKbooygI5GAomA1KNidg42ZmaWf//+/gd5DqwDVEOABqpAIfb79x/wIW1wTWxsrEqKHIygu0DgYiQzWNhYnr5+WlhSuH79+v/MoJD4/vu7j7+PYLnQuo3rrGytjp86fuf+ndaOVgZmBiZWpn+MIMf/+vvLyMxo586dX79+7ejoqGusc3ZzBo23sTBCFICG8bZu+vjl49+/fzdt3ZSRkQF32e9/v1k5WZ/cfZJbkLtx48Z/TP8YmBl+//v97/8/Rph2yL2jS1cujYiJYGNjW7pi6e9/vxlZGH/++alrqPv//39uPu7Hjx+LiYmBTGMAlZ4Q879+/XrixIkfP34cOnRIVVU1Li6OjY3t9u3ba9as+fTpk6urq5OT06dPn86cOfPz589nz57p6+uvXLvqy5cvbFzsnp6ekCXtkImVFStWXL58WVNTMzY29vXr19193S/fvtq8DdQPhjSy37x5M3na5ANHDnR1ddnb2/Py8v5n+r927dpTp06ZmpoGBQUxMTF9/Phx4cKFz549c3BwgIzcQNz5889PJVWlju4OFXUVSUnJ/0z///z/I6sgq6CssHXn1rCwsD9//mzdsTUtLe3P/z/MbMy///3++fungIhAWlY6ZFp2+vTpnT2dM2fOBAUaEyhSQIvV9u6WlJH89OXz5cuX4YeMQGz88+fPmvVrouNjlq9aDmmM/v7/m4mN6fe/38yw/WW///22sbeJjo+BaGFgYLh06dLHjx/Pnj375s2byMhIDQ2NZcuWHTp6qG9in4mJSXBw8LNnzxYvXvz582dvb29LS8sPHz5cuHDh/fv3Hz58CA8PP3HixNevX48ePaqqqhoTE8POzn7o0CFWVtaTJ08GBwfzC/FDmvVr1qw5ffq0pKRkeHi4pKTk27dvFy1aBJk3hIxnwN1DI8Zv1CxPI1tINvb/fw4u7qunTmxfvvA/w38FDS0efj6G//9NHF12rV66c8USGSWVlJpmaUXl5w/vG9t53L1y6d+/fx/fvd23ftXHd+/+//v34ObVmIIKLh7eXz9/QCbBBUXFtIzNmJiZr5899enDez0Lawk5BT0Lm88f3oPVX2Pj4NA2tXh4E3TGjYK65p/fv/mEhC8cOSgmI6mqa/j9y+crp47/+AEaAiTZO0NBw8cfT+ad8f3x9x0jqDCHuRg+XwXqTKOuGodLgZqREPVI80AQWQgJkYSQyCJobHB7FqIKMgQDYoPHN6Bc0CgFzA2gMQ2QPGgMBEwjCEwpNIsgZkJ8hMdSuInI2nEJwtT8/fdHnEcnyWgjMxOWkXI2Nrb4+HhhYWG4MaOM0RBAA3RqjIJSPiPoejIGZsb/v/8w/P/LCNosz8D0n4H517//rP8ZwEOWLEzMzP8YmJkY/v6D5jVQ0w48AMnEDLoplIGV6c8fUJ6DrAEHr+kGmQ3qXoJ3BYKyGzjr/getgwOphHgYNDb5/8d/BiYGRiYGRjZGDm5GFrZ/v/8y/PvLyABqRkKUQUhWFhY2Vpb///4xg8Y0wYYwMrGwMP9GbYxC1qr/+w9yBQMzyAbQ/hrGv6BNcP8Z//35+/8/qJX6n/E/A2gYFjQI/QfpenewNgYmRgYmZhZQ8/P/P9BpqkxMoCMFmJkYGZlYmZnRmt7gUVeIG8kl/zP8+//f2sL66NGjM2bMkJOTY2Bg+PPnj4iIiK+v79atW5uamrZu3SotLW1jY/P9+3f4UO7///9ZWFi4wCAlJWXOnDlnzpyBnGwH2fL3+/fvRYsWRUZGfvv2bdGiRSkpKaCxFpgz////b21tffz48enTpysrK8ONhcmDOumamprnzp27cuWKlJTUrl27dHV1v379+vv3b8jOALhKNMbnz59zc3MDAgK8vLwWLlz4+vXrysrKtWvXSkpKmpmZdXR0iIqKiomJFRYWWllZeXt78/HxCQkJsbCwSElJwZfq//v3r6Oj49mzZxEREStWrHj69GleXp64uDgnJ6eUlBT8MBRWVlYJCQlubm4ZGRlBQUEmJqYbN25cu3bN1dW1u7ublZXV19e3qqpKXl7ez89v8uTJf/788fHxgTj49+/fhoaG5ubmkINOGMHJlYGBwc/Pb+PGjWFhYXfv3v306ZOJiQlkpTxEF2SNGqjRD94EumXLFvCtg6BkDh4s/7du3bqoqChI4xutMXr79u2XL19OmzYtJSXl9u3bquBjINFCnpmZ+dKlS3v37v3165e+vr6UlNTu3buXL19eUVEhICCQm5u7ZMkScXFxbm5uKSkpCQmJz58/l5SUODk5mZiYdHV11dfXCwoKpqWleXt7e3h4fPv2LSsrKzQ01NPTc968eS9evKiurp48efKXL18gW0lmzZrl7+9/+fLltWvXFhUVnThxYs+ePeHh4ZWVlZqaml5eXlOmTGFkZHRycoJ4f6SRTMzMb188Wzdnqqmja2hm/pcPH84fPsDNz8/GzvnswX02Do73r19+ev9OVFL65ZNHOubWb148A/fqQQUP6MI5VlZhcalfv37+/vXLJSji6pkTXz99Sqtp4eLj+/Prl5W7z5oZk6QVVQTFxFV19JlZWX5+/3H7ygVuXj73sNg1syd///I5LKvg969fD29e//H1a2BK5p/fv5mZmXXMrZZP6vk1TI+942YTDdSe8g80SDHSkht1/fufjZmHiQkxTIBsOgsLS1BQELLIKHs0BNAA/RqjIIv/MzCCd1KAp6HB7U1GZmaG/yy///77x/CTgeEHaIgS3F5k+AduAzKA2qzgFfegUyhAkxEs/////fPnL6QBxMwMumn377//7CwsTCwsf//8ZGfn+McAmsH4+f33t1+/oT1L0DwFaKCTAbRCgJmJk5uBjYPhL2jBAMN/eHsT5EAoBt1IBGo7gy5t/wdSB2oyQhwEVQGjQDNa/0BVwT9GyE460KXt4HFcRiZGxv9/Qde5gyZZQIOlaOsKwF5g+vMHdJoVqEEN0sDw+/dfJiam//8Z//yBzvvAbKISzQjaEuTi7OIX6NfY2Ii8/jI0NHTt2rV79+7ds2ePn58fPz//Z6SbP0DdadhIAAsLCysrK3KbiYGB4dy5c5cvXy4uLv727VtKSsrJkychy8Ah7v7z54+Tk5OPjw/kKDXIylSIFIT8+/evo6PjnTug1UsqKiri4uJOTk5LliyByOIh////LykpCbrsDtxaKigoKC0tzc3NvXbt2pMnTxgZGW/cuCEpKcnDw9PQ0ABZwWZqavry5Ut4MxFy8cm+ffsWL14MWZEWGxubk5Pj5uZ27969kJAQuO38/PxeXl4HDhwIDw9nZma+du2agoJCUVERNzf3kydPTpw4YWJicubMGRcXl58/f0pKSm7dutXHxweyawG0zO7bt8TExOPHj69atYqdnR2yt9TZ2XnmzJnPnj3btm2bra0tFxcXZH8D3FI4g5WV9S94twc45YCEHz58+OTJEzMzMyUlpezs7A8fPkBW3ILkGBjWrl1rbW0tKipqamq6YcOG0tJSiDgyycTE9OjRo4MHD/79+1dWVlZKSurv378uLi4QX69fv/7Zs2dubm7Tpk3z9fVVUFA4evToy5cvFRQUWFhYBAQEduzYERUVJSIi0tTUxMvL++rVKxERkZycHHFxcXl5+dzc3E+fPjEzM6elpQUGBkInE8HLVT9//vz48WNHR0dNTc2bYBAUFPTv3z9ZWdnNmzePwMbo71/QQ5FBRQET08NbN1ZPn2jnE2Dt6bdj5eJ/f//8+Pb1188ff/78/vsHtAfuw9s30+vLGP7/V9HRFxASiSup/v3rJxs7u5SC0u7Vy96/efXr549vX74Y2Th8//alpzjrz+/f2qbmzx7c27FioaWr96Le1tiSql8/vv/8/h1yBvOv799//vjOwsI6q6n65sVzJX3Tbl08t335Il4Bgai8MjV9o9P7d/358+cv+LIc5CQ01NmsTByKgqBFLEPdI6PuHw2BIQ3o2hgFjf/9+Qdaa8PIDL4XHhR0TIwMHKysv//9//Xn738GBpAaWKMHJM0AOgUD3CgELaJhYgQpgMxagkpt0GArMwPTTw4uViYW5l+//rGxMv76y/z950/QPV7ozUeQ8QwsLP9Y2ECrO//8/v/3D/hoF+g4E9g6EAHZngligRa2ghrNjAx/f/8BLa+BCCLI/6DBXSYmUBv731+Q+5mZQTdSsLCA2g3MTCwM////+Qte6QqafweP2cI0Q9oof/78gWgBD1kxQrz2//9/PGfawQwgiwbPqnz//r2sqmz79u09PT28vLyQxo2tra2cnFxbW9u9e/emTp2KaTrkyAwGBoZr1659+PBBQUEBohGicsOGDR8/fqyoqPj////379/Xr1+P3BiFCFZWVu7cubOvr4+bG7REEqIRQkLO0QgODm5sbOTi4kpOTmZhYUFrvkNUopH//v0DD9eCFq5xcXGxsLB8/PgRcmCHpqYmHx8fdLJSUBDeAobcKIhszp8/fyAHyzEwMEDc9uPHj9+/f0N2TSKrhJxUAom1v3//cnFxQXZTQQIH0kCHtBFVVFQMDAyQ9YKWlIEv5MjOzpaRkYEclcfPz29pablx48aLFy9WVVWhqYeMvkMEHzx4wMXFBTltBCKyffv2e/fulZeX//379+7du/v374e0+UD7D7582bp1q5iY2I0bN54/f/7hw4fCwkJI6oLohZC/f//29PSsqamBcEF5Cny1DGS8HGIXpAUMCQrIht9bt279/fvX3Nzcysrq169f4uLiEJMhl6lAzkbh4eGBnJ/AwsIC6QNArPjz54+vr+/fv39Pnjy5ZMkSIyMjLy+vP3/+3Lt37+/fv0pKSpAdCRDFI4e0cPVQ0tSCb0v/9/fv718/P3/4YOXuzczCLCQmYesdoGlkyszMIi4rp29pKyIhxcTM/P/fPwERUV5BIT0L0P5fBgaG+zeu8vDxByRlSCkoMbOw8goI/P3zxzkoHHRow+/fMkqqsiqqUgpKgSlZihraf379CkjK4ODiEpGQtPH2//P7FxMzs56lrb61nayymoahqaWbF2j5OzOzrU+AuKzcf4b/6gbQA9JHTtSM+nQ0BEZDgA6Ano1RUFsQtLgafGwEyG/gmW1WdlZGRoZf4Nn3//9AA4tIC3BAO5lYWZmZmRj//P3HDK6ZGRj+MjOx/AGt3v4Hac/9/8/yC7S8kOXbdwYmRqZf//7++PWXmYEJPKoKXVoHso4BtEj0/x8Gxm9fQE5hYAJN7oPMQFYDUgi6tAm2WBM2nYreYAWpAy/jAY2L/vvPCGqCMkEAZO0RExPz//9//v0Dj8CC3MUIXmcA0Qcl4a0iSKsLcuspZPoVMs4Ksx2qngoUI2hS/tfPn1xcXD09PS4uLpCTkBkYGHh5eQMCAlpbW+3t7SHHBUMaE5BWyP///2/cuFFfX//+/ftly5aZmZnZ29s/ePAAciTKp0+fFi9eHAAGDAwMS5cuXbZsWUlJiYQEaLcm6NTSX7/+/PnDycnZ19fn4uLy9etXSK/j169fEPN//foFWYZYW1v7+PHjwMDAFStW/MZ20tCLFy9evnypr68PCQ1WVtaHDx/u2rXLy8try5YtwsLCnz9/vnnz5rp16/7+/QsZW/3//z+kmQjRws7O/uDBg9evX/Pz80Oaknx8fBISErt27QoICFi3bp2QkJCoqCjkpjiIFjjJxcX17t27+/fvy8vLQ8IH3IsAhyq4WSYiImJlZWVmZnb06FFIcxPSZIcohgz+paenR0ZG1tXVQYwNDAwMDg52c3OD7BWFqIQY++XLlzt37nBxcT1//ryzszMqKgoSmJDG/datWydMmKCoqMjIyGhvb79s2TIvLy/I2eM7duxQVlZubm6GBG95efn27dv19PQgJ2pB7IWcPPL8+fOHDx9Czt+Rk5ODND0hCiCRC1pPwsh4+/ZtUVFRBQUFLi4uT09POTm5Q4cOKSkpPXv27CfsDEgWFpYHDx4cPAjafLZ69WoREREBAQH4mSyQBi4DA8PJkyeZmJiam5sPHTpUW1ubkJAgJCRka2urq6t75MgRSKBBHDBySEtXL1yedQ+PZWBgsPaArvcA3SgoLWfi4IJVvaKGtqKGNkRKSgG6UUzdALoxHCLOwMDgn5gOYavpQxuXdt4BEBHIZnkIG05qGZtpGYN2tcNFRhmjITAaAqMhQEVAz8YoaJsgEyMjaLvOf+iOnX8MDF9+/Wb4ywBeCApaJwqpueE+ZGZmZmMDnYz49y9oap4F3NAD7fiBjHoyMTAzMzOygM+kZGZi5wBd+vvvDwM7J8e/n3/+/0cbywTvyGdiZmRi/v/nF+Pfn6DN7//+M4J2KcEtRGcwMjKIi4mys3P8B83qg1oI/0HOBA2Zfvj49gdoCPbfXwZGpn+gtaOQ6ha0kYmJCTxQ+g80BAvZgsj4/y/qzkSITZADnCG3aEDa1qBtluDTdKnfEgUde/ZPS0tLBXxMibGxcW9v7/Lly+GnVUdERBw7diw5ORmymJKdnd3W1lZBQYGBgcHIyOjLly/bt2/n4OCAHA3Nx8fHxcVlb28vKyt78+ZNVVXV2tpayEYfBQWFqqqqGzduQBqjjIyMFhYWkJaWoaFhZ2fn8uXLubi4/v//b2dnJyUlxcDAYGZmpqqqKi0tnZWV9e7dO3V1dTk5OWtra+SFp5AQu3LlyqJFi+bOnQsZgfv796+YmNjx48e3bNkCuVFNRkYGcrebvLy8iYmJuLg4Gxubvr4+3CgfH59Tp06Vl5fn5+dDGrVsbGzNYLBjxw4mJqb6+npmZmYBAQFtbWi9DrGagYFBWlra3d29uro6MjLS2NhYV1cXkmIlJSW/f//Ozc1dXl4+ceJENjY2Dg4O5BFHfn5+yD3RDAwM3t7eVVVVkJMLQbc26Om5urpC9j8xMDCwsrIaGBiA7wdhl5WVbWpqYmJiYmNjS0pKgqy7UlZWFhERefjwoY6Ojqsr9LwYaWnpkydPvnjxQl4edJEE5LIWeMMuLi7u1q1bJiYmhoaGkFFMiI9UVFR27dpVXV0NueivtbVVUVERcj4zIyOjjo4OLy8vExNTenr6zJkzz5w5U11dnZKSUlVVxc7OLioqamJiwsXFpaenBxkY/vfvn5SU1MGDB9evX//nz5+GhgYWFhZtbW344gENDQ0RERFRUdGZM2euWbPm+/fvGRkZMjIyubm5ra2tnJyc3NzctbW1ELeNksMgBP78+QNZPQLPfXg8BZmggKQlPMpoLfX3L2gnOzEOhhT44PNemGntqlHzR0NgeAPQrho6+PDoto2ddVWbz10HL+IET6GDhhqZGRj/M/6DLA9lAa28ZPgLaumB5cGuggzKMK52Y1QAAQAASURBVIDWmIJan6CmLANoph40psnAwMDOzsbNy83IxsgKOvCC4c+vnywMzL8Z//78/fvPV9A6frAhIALcsAOd3s3IzP6fiZXh7y+Gfz/ANoIGIlEtBamHY3Y2VgUFpa9f//5hYP7zn5Gd6RcLG2gT0J+/zJ8+vf3+5ePf//8Z/kGOlwaVR6Dlpn//gIZImZkZGJlA+6hAa17/gsZF//37g7QFCnTCOfg6E0jhC9mSxczMDDmlnImJ6ffv32jLBw0UpS10tVMbO40w5n/hDsbPyNtf6K7o5q3giV/ZYJY9ceLE0aNH8/PzIbXFkydPMjIyFi1aBLlzBdI0hJzZycnJCRkmxOqdnz9/srCwQAIfouD////v378XFBSEGwIRxyQhzTVIaxir7KdPn6i1dRQ+ao5mEaTrgiZIHhcyegrJbLj8DjnvGhKe38FASAhxzCHE3hcvXiQkJCxevJiNjQ3SioWIYyXfvn3LyckJ2ZsFOjbo588vX75QK9Cw2ogseOv97ZqjdYPnaKcj2zc/f3CXlY0NeV4I2cFobNApI8i7v5Gkka98RBLGwgTdlfD3j51PoJg06JpELCpwC30DnzOK/2inR48eNTY2/v79u7m5GdJHwm0eSGbevHlSUlLIZ1CARGmM165dKyUlZWkJunYOYtXOnTufPXuWmJgI4eInZ86cKSMjAz9sEqviT58+ffv2DdIzx6pgVHA0BDDB6NFOtAagRiUDI2ipJehgM0bQ0CEzMxOoZgXtagcdIAqe/QY74/8/RtDBSKARTEZGpr+guywhc91gQ8ALTP/+/cP4l5Xh7x/QCOe/f3///vnHDBqCBetHEKAhTRDvD+h4JsSAKWyCHjTaCdr0zgDaaARSB8GMDAz/GP9/+/Pn3ScGLtavkkqf+WV+CLL85frC9OY7x6VrDN/+MzEwgPbjg5q04CtqmJmZWVhZmEAHvIEuUoE0Mf//Ay1JhJiJSYL8Dm4IMIG30kPm7pEbSZhaKBGBNG4oMWFg9aqoqGhoaEBaouAOCbumpiYzMzN8zzvEefDROAgXk4S0q5DFGRkZMRtYyArgbFzNUIgCVlZWKjaqkAcyIeZDSFytRogsSSQxiY2JiQkeYpxggGkFGxsbJGrQ4gJTJeSaFmRxyEgwssiIYp/au/PJw/sv3n8Cb1oH9dTxeJ+JiRFyWTloTxhoORDoRI//DP9Bay0YGCEXeYN3iuIxA7QjkYebh/nHZy0TczIao/iNhsguXbpUQUEhLy8PPvcCEcdF3rhxg4qpGpctaOJCQkJoznv+/PnNmzfRlOHi3rp1i2D2OXDgwLFjxzo6OnAZMio+GgKjgJ7T9Eih/Z/xH8N/Ni4WWUnZP8zsPz5+/A86D4kFdAoSAxPoqE4m0LAiAxMrIxMLAxPLf2ZWZma27y9ufnzzDNqOBe8F+vfv/8+ffxn/Mgrxcf349ev3zz8M//6CtseDrkvCLNAhTVgIieQYMBNyOCiYiUwwMv1nZPvPIs7xWef/e7WX3z+KsDwWYRJ7/5eV/dstFtDZyP9Bpzr9Y2ZmAw++gjYe/fsH2vMKOiCAAXQ8P6h4BR9MBWsQQ83/B1IHPbsJpAa05R80UP3vP2hkGNIkhSodpZBCQEREBIkHunquqakJvjkJWWqUTecQEBQUbG9vH40LsoL9v0twhKqp1e/ffzBLLjQDGZkYF4KvvmRiZPrw4S0L6HLd32ysrDy8PIwMjE0Veba2tv8hE05oOpG4/0G3qbHPri5CK5eQlFDE3LFjx7p16xQUFGbOnJmenv748eO5c+f+/PkzICDAzc3t1q1bly9ffv78OTc3d3x8PKTHxc7OfufOnYaGhjdv3iQlJRkZGe3fv5+ZmdnOzu7Hjx/Lli0LCQm5devW/fv3r1y58vnz56SkpPPnzx86dMje3j4mJubHjx+rV68+fvw4Dw9Penq6srLyvn37vnz5cvLkyQ8fPqSkpBgaGv7/DzoYeO/evfz8/GlpaUpKSuC7fkGjEidOnFi8eLG4uDjk5nS45+/evXvhwoXnz5/z8vJGRkYuWrTozJkzUlJSWVlZIiIiHBwcd+/era+vf/fuXUxMjLm5+b9//9auXbt//34+Pr7MzEzIwcNPnjxpamoKCQkRExObOnXqixcvtLS04uLiiOm2wV0yyhgNgWEMBqgxCmpKMrIzMLELCv0XFn978BgjKysjI+hWpN///jOysTIyczKycPxn5WRg5GRk4mRg42bj5GTjfMnI8IwJfBoT6BRRcLT8/fPv/99fLExs/3///Pn9NwPDv//gg0OZkeawGBkZOTk5WdnYQIuBwLeBgybNQXv2wcU+6MJuNgYmpn9//nz++AFyAS7YbAbwYAOjktxnFaFXXG//MLxl+PWJ4d/n3w9f/v/zn+X37/9MTAz//jGAxlXByzFBK0n//WMANSlBd0eBVhWA9un8AzmZCTT4CTEWTuIapPwP3+MFVzrKwBECTExMkBWuOORHhekXApCMRj/7hpFN////Z2djU1VWJtJPleXlKUlJrKyszc3NGhoap0+f1tPTi4mJ+fPnj7S0NHwMm6BpzMxM4KVTBBWSrEBBQUEefAW5iYnJ06dPS0tLU1JS+Pn5e3t7+fj4fv78WV5enpWVZWlpCemKQyzYt29fa2vr48ePy8vL161bd+jQIRYWFkhjdP78+W5ubufPn586dWpfX9+tW7fCw8MTExOjoqJaW1sVFBRkZWXv3LmTkpICWdy8fPnyHTt2HD16tLm5+fHjx2VlZWvXrt21a9fSpUsLCgru3LmTm5u7YsWKbdu26enpCQgIVFVV5eTkCAkJ1dTUIC8VePr0aXFxcW5urpWV1bRp0y5evJicnHzs2LGSkpI5c+YwMjIeP368oaHh7du3VVVVCxcuPHXq1KpVq3Jzc2/dulVUVNTR0aGlpfX//39LS0sxMbHW1lZBQcHs7Oxt27bdvn3bxMQE4vFRcjQERjgYgMYoqAEIOmAINP73nZn1KyMzN7/op08vmJn+qmlo8/FyXbv24NO3Twwsfxh+/2ATYfr3j+nPt3+MjL/Z/v4EtdJACzxBDUB4zDH+//fzxw/w/DzoEjaQ+aBmJEgeMlrJyMjIz8/PyMwsLiHBw81z/9693z9+QM6jZ2D4LywiLq2g8p+D9c3rV7fPnfnz5QtsnQDIhYyMjM9f/Wb/9UdWjumV+P+n9/7+fMf1+jMbKxPbrz/fGP7/ZmT895+BGby/CtQABRWsTKAT7/9BTqMGmQFyCWgunoGRg5WdhZ2ZgeE/939OZkY2JlALnOn3Xwam/3//MTH9YWP8/e//H+Z/jH/////1i+Hv728/vmNswwKZNopHQ2A0BIZZCJA0QskHBj9//vwLAwwMDJA7LAZJsGhoaCgpKRkaGjo5OS1btkxVVTU4OJiBgeHRo0c7duyws7PT1dUtKipCdu2fP38iIyMhp8yuXLny5cuXXFxcoBIVrIiLiwuy3dPHx8fJyUlHR2f58uXJycmCgoK7du26dOmSjY1NcHDw9evX37179/jx469fvzIzM4eFhTk5Of39+3f58uUvXrzYvXt3cnKyPRjs37//4sWLkG1zp0+f1tHRgWwQjI6OfvbsGdhOEPH//38LC4vi4mLI/sj29nYtLS1TU1M/P79nz579//8/KCgIcv3HQTDYu3dvXFycHRgcPHjw5cuXFhYWX758gew1FBAQuHPnzoULF1xcXCDbPUF2jOLREBjxYAAao5A97aBxSSaWXz+/f7l3X1TY8+/vvcxsHz8+faelIqssIb/z+QlmPp6/Qnx/RAQEhGXfXLr78+dnBsZ/oBFVBkRLFLRgFHSxO8Obd6D9j+DSHNIWRblPg5GR8cePb3wCIv5uDpzf353gZNh74sLPTz9A46MMDF8+f3vy+BG/sjILJw+oYQlPE+CdUgwM/7594rx2h+fx0z+M8n/fvuP98InvH8Mftn8cv//9/As+M58BNCUGthyuF8wA7V6CLEIFbWsCXT4qKsD39/e/L3++JTOEBHEGMTnxMTOw3bv89uXfv0K6Aj90/x/iYFz75tNPtj9Mbz5zPT7zZ9+MXz++Yl9YALZilBgNgdEQGLEhUF9ff+DAgatXr/7+/fv06dMKCgqQw78GSYD8/fsXstXv169f8MFaNjY2yAm+aCs1IWfc8vLyQu7mZWEB3Uv39+9fyIpMZmbmX79+QZbgQ9aB/Pr1i5eXFyILWdC8f//+GTNmQI5I4+bmBl1lx8QEN5CNjQ20lfTfPzSXQMLq79+/8IXg7OzsyKX5379/IZPpkOPhIIfBgY8ZZIJMoyEb+AcMIGogJ2P8+PED+Ty1mpqaHTt2nD9/ftWqVeHh4dHR0RAHjJKjITDCAeg+9IEIAvBBnwyM/95w/fui9undFxaWnyISMuofVYRPSvGyyfNz8bEwfFUQYOe685zj3w8eLhaGH78Z/oNOlYcNW4JcDdrIDj4J6c8fyMof0BJ+kABochzUhIOXKaBDZ8RELMWZnES+WEtx8fPxgE+XYmJmYvrz7+/3H19+fvzCxsQKuooUbDADA2i2H8RkZGRk53rHK37/q8Sju5JfvnEzMjExM7AygZZ4/mf4z/j/H2hWHmwhFgI0BQZyCNgk0Pao/x8+f/r05fv1r9e437Pqcelr6hsY6Znqa1ooG2rq62uZWxkxS2t+MzH5Fu/308zwP2S/FiNoDSzIiFE8GgKjITAaAuAQ+PDhw7Fjx6ZMmbJly5Zdu3alp6dv2rQJXuKBldCc4AQVhDhtYWH4w8YE2i5qY2F858aVa5fOPX14Z/e2Te7OdpysDEx/f6LpZGX8w8YIUs/KysT07yc70z9NVcXL50+9eHz/4N6dLx7f5+NiY2P8y8oIWmrPxcbI+OcHJ/j6Seb/v9mY/t65fllHXTk0wIf5369vn97xsDOzMv5hBRvIwcbE+OcHFxujg43FpnUrXzy5f+zQvrcvn1gY6zP9+8Xw54e5sf7dG1euXz737NG97ZvX8YDmr6Cu42D5z8LwG3IdhqWpwaqlC18+ebB7+yZhPi4lRRkWhj97d2559uje9Uvnbly54Ghr6e5sv2PL+hdP7p86evDj25c2FsYiAtwvnjx4+vDuv1/f1q5YIirIU5CT7uFif/3SuXu3rp06ehBq0yg1GgJIgJNtoJpnSI6gI3MARkZBG89BPvz/n4nhz8+/f96/eP9nHzP7dxVt2Z9Sn048PvH68sfX/5///8cgKCMpY6bz9sWr7x/eMTD8Bs3BgzSC5ulhDTwwnyiCSZLjl4KUxMfPzKo/Hgj+//UCrIuRgYGFkenvfzYOIWEuaRGl92L/fvCD2qHMDExMLD9+/n7x+v3P718YmP4z/2f4/xM0KMvE+Bd0BsB/0BpTsBnEuuU/6CArJmtuU8P/WpxfGV9wv3r1/BirOLuKg6nYjx+/lP7/FvrHxfxdTZeP/cLhV7e+M4jCYge+QhZkH7HWgdSSi2/evHnv3j0rKyvIkAC5xpCg7+HDh/z8/AS3wH/+/PnIkSMKCgqampokmI5D6Z8/f37//j2AS05v3rx5584dGxsbuoUzjpCgvvD379/Z2NggA1fUNx3DxO/fv0NuqcWQGbYC79+/f/funbS09KtXr1hYWNTU1Pbs2YM8BklrnzMzMc44/FqKnw2XRaf+GT15LH5350tGRvGPqpEeNRvY2dk5OS1O/DW8ee3mVS67zp0vkfWe+GN484Xgk50v//37e1fQbcLRnwIClge+XnUsWwU6r8oorWPPh2tvFX7+kGTY+fLTp2/3Rdzbdr3n4Ph+7JfujefCkpISq06v2lC+6uePn/+Vw5t3vDn+2+D6C4FnO1+CrvgSdptw5KeQiMe2V8/2lq1+9fa9sXFG1+Ffe75oX7ojfI1P4KFUoH35el4e3h//zN5+Vv+x5TnEbffvc15jsmoAcz8oRi5evGTutVVv3rxxd89q3PbuwA/9u/94bEvWfPz0SUYmZP51/u/cTisfPtlQuPLD+w+mZkmdB399+qSw75uuae4SV1fXZ8+4T83bKi9/5fHj176+Qenzr7148SL4vRrErlFyNATg4MO3v99+g5occJHhzYA1d+jrS9DVS6BBv3+/Ge84+fBJCf1et4ZF8QP/V8ZX32V4OX6/VPvFLCgm/kNW7N2nd6z8fNbyyscPn/z3CbTPFNIW4+Dg4OTkYPj/j5WN48+/v//+/AYvD2VgYWH+D7pUCUR8+fIVMo0COgCFmfnh0xevn1xX5mC4+u7du6/f2ZmZQI1DyG4jht+gMdW///79+M3w6y8DMwMHGxsLBwsjwx/G/3+fvXgGuvATtA6VCTyzDx7W/c/8//8fZiZGZjZ20PAnaMAW4jTQfibwcgLQwljQ+Cl4pv73n7///v+RZRSt5kpS+i4vwC7J6CKx7GP7x0NXfnNnq1k5sov9+PKfWYSd0+rJea4da3kFeNh9PXl1tD++fMoEOfqKkVlGmJ+JiYkVfLs32TEGX4OFy4S9e/f29PRYWloaGhq+efPm0aNHjo6OuBRTS7y7u9vDwwP5vniIycePH+fg4DA0NGRgYPj+/Xt2djYHB0doaChVGqOnTp3auXNnY2MjxC5qkW/fvj106FBAQAD+oN6/f393d7epqamhoSEtGqNbtmwxMzMTExOjlr+IN+fPnz8tLS2hoaGYF6ISbwhJKvv7+83NzZ2dnYnUhT9qiDRkYJWJiopKSEhER0ezsoIuH/7796+lpSV8ypgObmNmYlxw7C2eWRt2dvPLr/5u2fGCkYGBjc2MWcfyF8P/z///t217zswsyCLq2rYDMiYAdSw7m8G/t/+3g9VzyPguuPjz3/8PnDqx7/+B9ogyMjBMOfqVhUWZgYHx8I4XTIyM7HL+/Qc+/mf4wM5m+u/Dv39v/3EYJT+BnafSf+AjG5vBv3f/d0AN9Jt34ef/f2851cM//f1v5cAjwsVw++UPTXO3f///3X71w8jR3wS0A+A/EyPTn79/7ryCDtwy88nqWitAuExM3EGJeaAFAIxMv//8vv3yh7KhvZqJ07//IF1gc74zMjJ5R2b8B63gAplz++V3JiZ2v5js//////79W0ZQXkHP9u+/v+bMzL9//ZIRVJDVYYQYDg2FUWo0BMCAkZEhxEhw5IyP0rMxijj+E5RRQYfXM7KwsLHwcDm6/dRUljk3jZ/3K9cH7o9vv7wSZPjLwcog+YFd+TTvTaa7XzTYGRn+/f3/F9zIA7X93N1d4xJi/3z/9PT19xUrlr99+oiVjYOZidHWxlRTW4OJkfXrzy8zZy66/+AxqJXIwMj8//9nZqF5Rx5JvH94/Ac3G7cAz6/3f5lAt4/+ZGT4x8TA+vcfy8fvz9+8/fnx6z9WRn4+HhaW/78Z//3+Czo+FKQOlD7AzVAQA3RKHwPDf0k5BavQdGYW9r///7H8//uHgZUZdP0nqJULOqEJdDAUM8O/X8xsXI/O7j6yYwPT3z+PPl4WYxQVN5D46cpp88TyyUvmfRsnfHjz0sA74Bnf18fnjjxcvpCHXVhNUkpGQpBXU+85AwsTI+jQQD5+IUbWv/+ZGNlZyY81RkbGnz9/3rt3j4WFBb7d4f37969evZKRkeHm5v706dOmTZvc3NySk5OZmZk3bNhw4sQJyCXv//794+bmZmRkhA96/fz58x94DdaDBw+YmJggdzX9+PGDjY3t/fv3kJPPX79+/f79e3l5eeRq8vv37+zs7I8fP2ZmZpaRkWFgYPgFvi8Ucqn606dPRUREIBd7rlixQlBQUFpamoeH58iRIx8+fJg9ezYfHx8DA8NbMJCVleXk5IRcYvn///+PHz+KiIj8/fv369ev7969U1JSYmRkvHPnjrCwsKCgIDTqYJSmpibkQNB///7dv3+fhYUF7Wjur1+/PnnyBHI7KEwTiH7//v3Lly8lJSXhjcj79+8zMzPLycn9+fPn0qVLEydONDQ0FBISgjgVpIeB4eXLl58+fZKXl2djY/v06dOGDRu8vb1jYmKQF899//798ePHIiIikBNP/////+DBAwYGBkVFRYghr1+/fvv2raysLBcX1+fPnyHm//v378uXL7y8vPA21uvXr6dPn87CwmJiYiIsLMzIyPj06dMfP35AAgRiFIT8////169fIbtDfoMBFxfXjx8/Hj16xMvLC78m6vnz51++fFFSUoIPdv7////Hjx+MjIyPHz+WkpLi5uZ+/PgxExOTtLQ0MzNzbGwsRO+/f/8ePHjAxsYmIyMDj6ZPnz6JiYn9/v37wYMHvLy88PPAX7x48fHjRzk5Ochw9ffv3x89egRJDBDrWFlZ37x5w8vLy8LCAklRkMtIw8PDIXHx9+/fBw8ecHBwSEtLQzz45MmTnz9/wj3+4sWLz58/f+X6Bg8riLIhR/Lw8LS2tlZUVERERJw4ceL3799lZWV09gUHKxMz5GxorBb//83AxMAGnW38zfD3N3hFPwMXSOQ/w79f3CAGss4/CPX/fnKAyjlGhj8/ILoYGBg4WRkZGMDz+BCNf39ysUGqld+g+TIWBoY/PyBXjzAwMIClUAzkhBj49+fvX/+KHaTs1UDrU5GtH2WPhsBoCAwUAOXOAbAbdDXRPwaGf0z//768xzylUS4i6sN10X28qiym2o+vL+eV53UW+Pb74+OHzN/lpX8IqfGICpvKnT108+NbRgZGJob/f0VFBa3tnK7cuDVtZvXPxzcFWVg5Wf6++/h55/Z9AoJ8FlbWjP/+cXBygL0GWrbOzMIkJMRz/xvbrosfuSSYefi4vn/+JPiX6TvTv99/f/3/yyoqJCLIw/2OjfkHAwMrCw/XHx7G///+c/1hYfwNu6gJ0hIGGwkiQOOgbLyC8vp2TOzcoD1MoFFTxv+MjEwgmT//GTgEmd6JfL/5gl1PSpT12MsTRxgYeP4IGHHZytja/LFn+6fyj4dB9sfpjQp3Pgld23vlKnOny6fPbJOFH31784xPUdRV5vfXGzcuMv1h+Pvr57+fP77//sMqwMnCwQ06TQrkAHIwOzv7nDlzJF6Kff/93dLSMj8//8iRI319fVxcXH///m1ubj5z5syuXbskJCRevnxpaWm5du3a58+fNzQ0pKSkLFq0KDMzU1NTMzc3NzIy0tnZecaMGUpKSufOnbt169bPnz8tLCxKSkpWrlx59uzZFy9epKen//v3b8aMGdzc3JycnM3NzfBRuokTJ0LaRi9evPDw8MjOzgaN+LKy3rp1q7GxkZWV9dOnT6Wlpc+ePdu/fz83N/fXr18DAwNnzZp19erVsrKyiIgILi6uiRMnQposbW1tzMzMra2tv3//FhcXT01NbWho4OHhefHihaampoCAwLlz575+/drQ0IA2UHf9+vW9e/eWl5c3NjY+ePDg79+/RkZGpaWlkGbKzZs3m5ubWVhYPnz4kJeXB9nky8DAcPbs2e7ubi4urq9fv5aXl+vr6zc1Nd2+ffvfv3/29vaenp7z5s27f/9+fX29n58fZAcxAwPDunXrFi1axMXFxc3N3dLSsnfv3p07d166dOnmzZtVVVWQ1tiDBw/q6+shY8AFBQXm5uZNTU137tz59euXgYFBVVXVgQMHpk2bxsvL++vXr9LS0sWLF2dlZSkpKV2+fHnp0qXt7e2QluL///9nzJhx5cqVadOmQSJl4cKF27dvZ2VllZaWbmpqQm7+fvz4sRYMxMTEjhw5curUqcTExLq6um9gEBgYGB0dvXLlyjVr1nBycgoLCzc2NkJawP///y8tLWViYnrz5g0bG5u1tfWRI0fevHkTFxcXHh6+ZMmSiIgIdnb2urq6R48e/fnzx9PT08nJqbu7+8+fP6Kiorm5uU1NTe/fv//x40d4eHhERMTGjRuXLFnCw8PDyMjY0tLy798/yH2q379/LygoMDY2rqyshKys8PX1PXnyZHNzMwMDQ3t7u7u7+9GjR01MTMzMzKqqql68ePHr16+AgIC4uLiZM2fu2bOHnZ1dTk6uqalp+/btCxcu5Ofhf8f2jjOIh84rLMnJsXj1mJub6+vr37lz58WLF4GBgWpqo7O9eMMLSfIn5AZqJJFR5mgIjIbAAAJ6NkZBp3bCvApqrzEwMv778+fbV0amt6Lrpwv85//y6sNbe/e/128yPL/46c9L5h+8P36qnOIUu++YeXP3AY6LLEygnTygu0EZQEcg/f6/YdUapmfXOzMChCWk2Dk4bj14OnvVjt0790qKiwoJSzIyIdb/MrOz//j65cPnL9+ZGXn+/PzPwsrOwvKD8TcvG8c/RoYf//59/f6FlYvjBxM7AzevMI+EKK/IL7Zf/78+B21kgjoa7GYoG9QPBw2T/v3759cPFibmv+Cr6UFHBDAy/PnPwMzAJPj3jtrv88ycwiLsj7ieP2J+94iBgYGDgUP0tyorO9sPhb8MzMx/mHl+Xxb35VPidrm0hXEj6x72V3YCuvrSAk85nokIvmNg+P/t97dvX5n//uPm5OBkYWNiYWNkBi2Ng7mCZPr/v//8/Px9ZX0MrAxXr179+vVre3t7aWmphYXF8uXLW1paZs6ceerUKS0trcjISEij8Pjx452dnZycnKtXrz527JiQkNDp06fFxcXt7OwOHDhgY2PDx8dXXl7+6tWrpKSk8PDwFy9e3Lp1a/78+aysrImJiW1tbWpqat3d3XPnzq2srIS4+MaNGzw8PBMnTnz37l1CQoK7uzvk1Gs2NraCggJ9ff3ly5dPmTJl3rx5Z8+eFRQUzMzMZGdnz8rKmjlz5pQpU37+/BkVFdXY2GhoaDh//vyurq7Kysrt27fPmjXL2tr64cOHp06dWr16tbS0tL+/v7u7+5w5c2bMmDF37tzJkydDHAAhP3369ODBg4cPHx46dGjDhg0sLCyXLl369+8fpEnHysqam5traGi4bdu2mTNn2tnZQa59Wr58uYWFRU5Ozq1bt9jY2LZv337//v1Zs2b9+PEjKSnJ1ta2oKDg7du3vb29kJ28kONsZs+e3d/fLy8vPxEM6uvrT58+ra+vHxISArkS89+/f/39/VZWVomJiadOnert7Z09e7adnV15efnnz5/j4uKCgoIWLlwYEBAQHh5+5coVUVFRTk7ODRs2FBUVrV+/XkFBAXKiDSMjo4yMTHFx8aVLl3Jzc01MTK5cubJmzZoFCxbw8/OXlpYuWLAgNTX1xo0b//794weD27dvQ3Y9f/jw4fnz58eOHXv9+vWyZcsgBj5+/HjOnDkzZ86UlpYuLS1dsmRJVlYWZO/z8ePH8/PzQ0NDq6ur161bt3r16qtXrzY3N3t6et65c+f79+9r1qz58OHD3Llz379/f+nSpZ8/fx48eLC/v9/a2nrChAnCwsK9vb0vXrzIzMzU0dFZsmRJUlKSq6vr+fPnWVhY2tvbTU1NU1NTT5w40dPTs2jRogMHDmRnZ0dERPz+/Xv69OmPHz/m4uI6d+5cXl7ewoUL1dXVlyxZ8vfv33nz5r18+fLmzZtXr17dvHnz7NmzBQQEcnJydu3atWTJkujoaF9f3x1ndy56vQSUjSFJYciSKioqGzZsYGBgkJXFfqXnnz9/zp8//+bNG25ubhMTE0hio4V3f4OmbRggA6X//zP8/gu6OQ9iESvoVjwIEzSv9ffff2bQiieoCAMDA7j8ZMQcY/33n+Hfv/8sGNpZmBj/gqaLQNNGCFNGWaMhMBoCQxbQqTH6/z8DDweLngIfaPSQgenJ2+/vPv1kZGD89/ffk5f3RUSZnz78z/z7C48Q285DwrLav46dvSipyMrB+vvNVwbOF/9ev3mZHMt28cjve3f/MzAxgg53+vPv9auX7+/fCLU1UpSW4BCXZxEQs5RSlBDka5m97smTJ3///PnzE7roB7SqiZnjDzsfw19GAWEOJi6uvyxMv36x/f//7zMTI58UtzS/wB9W5l9//wkKiwtyMHKwMf8R+M/BxvXzHy8D0ztGRiZ2VmbQgaGgxiY0qv//ZwTX3+Bd9aBTmxj+/WcQ+Pf2HwPTJ1ZJyV/3Vd7t/sOvwMkt9PXFeTbWP6IiAgwMDEyMLJxC4kx8zEzv//7j/cfOLMAuZHlCfrG84VOWH8bP95/gf/PsjRaPoorOmwcfTnz4oSUg8P3Tew4eXgEZeT4BqZ8MX7/9/8zIDN5BCnUIaRQjE+PbN29ycnJ4BHkiIyPfvXvHwcFhYWHBwcHh4uKydu1a0HWm4AlQbm5u0FQXFxcrKyukUeXt7b1kyRI2NrbY2NiHDx+eOHFCRERET09v7dq1FRUVkHnzL1++MDEx+fv7S0pKXrt27dmzZ/Pnz2diYnry5AnysA0nJ6eTkxMXGKipqd25c4eJCXQlLD8///Lly5ctW/b8+fMfP36ws7NzcHCws7NDRvI4OTmZwGe1QFohRkZG7OzsLi4u+/bt+/z5s46Ojq2tLSd4yl5fX19dXZ2FhUVJScnCwoKTk1NDQ+PKlStogQU5KUZeXt7IyCgzM1NAQCA4OBjSEmVgYODn51+7du2KFSvevXv35cuX379/QxqjAQEBvb29586dU1RULCgouHnz5uPHj5uamiAH2Xz9+lVQUJCZmZmPjw9+wsvt27eFhYU1NDQYGBicnJw6OzvZ2dlZWFjY2NggXoMcZ3P//v3Pnz/Dm4b//v379OlTdXU1ExPT69evv3//HhkZOW3atF27dqmrq+fk5ERERNTV1UVHR1+7di0tLe3EiRPLli2DTJFD2vfgAOa6d++evr4+ZNLc1dV1//79L168mDJlytevX42NjWNiYpiZmZmYQD03ZmbmP3/+mJubr1u3LikpSVBQMDU19dWrVxD1TExMjx49UlJSggTj379/RUREzMzMODk5VVVVeXl5eXh4VFRUmJiYvnz5AgnGS5cu2dvbc4OBjIzMtWvX1NTU7O3t2dnZL1++nJeXx8nJqaioKCUl9ezZs8jIyJkzZ65atcrAwEBbW/vWrVsvX768f//+t2/f/v//D7nd29XVFZIaTUxMDh48yMzMDFkOAV4y/v/SpUvOzs5cXFyKYLBp06aXL1/29fX9+/fv9evXHz58CAkJmTdv3paNWwQ0BFkM6FT6QYKLRmRaWlpCQgLozA92dqxWfP36tbi4+MuXLx8+fNi6dStVFlujWfTvP8PPP/+SrIS52ZmnHXjNyszIysKYaS8SZiz07OOvvj2vTj34ChpJADc6pQXZmnwlG7c8f/zuFxO4+fnr7/+OQKkzD7+vOvOOA2l7/n8GBl52JkURtusvfv4FXyj1999/CT7WvjCZvBWPmRgZhbiZb736CZ/ER3PVKHc0BEZDYAgBOhXH//7/5+FiMdESAlX/DIy/r7x79+kneFH6/8+vv/ByygjLv3v1X4JbW27nVQZJVSZRa0YN/sfnTjxn4GB69Yx18Uz+F0Hqj58cZ2BgAjX6GBj+/P7x4e0bUX6un3++7zxyRUuXgV/ky61rNwQ4GEy0VDhFZLk42EDX0INXjP7/+09MTEJCQfX333+sLIxPnr588/Y1Myvrn79/v/7+xcvFKyYj9/DZUxY2ZilBcXYW9q8MPzk5Obg4OTj+szx5/oKT7Y+QIN/f/////f8H6c+DTrD/9+/Nuz+g7U9MzH8ZWf7+Z+T788b2z57r/1XZP9wxFXj8X0zpCzPnt/tHBHjYeHg5Pn76BFpnyszMqM78V4KB8TMT43fQBUy7hK594P8axKnLJaCpqvwo1ptDV+he7bkdZ89oKNlacPAKMrM+EZSX5pOVZWfj+/n+B+MfJki7gbxE9vPHz4zMTHMu0yvXrrS2tvb19f38+fPr168cHBzv3r2DHJ4Hcies2f33L2iFFsQufX396dOnb9++vampaenSpT09PdHR0UePHl29enVra6uQkNCtW7cg856QRhsnJ6eoqGh6ejo3N/f3798hiyAhRv358+f169cQ9ocPHyCr/f7//9/e3i4pKVlaWnrmzJmFCxeChkz+/oX7F2I4AwMDLy/vt2/fIAtP3759y8zMzMrKysbGBgpQsKGQpi3kqC2wAAP4VgJQtfUXdnIhRBzUQ2Biamlp+fDhw+3btzs6OpSUlFRVVRkYGDo6Ovj4+EpKSu7fv9/V1QVXDxmOffTo0erVqxsaGnR0dHR1dQsKCsDdn58KCgqQQUd4SxTSrv0CukwBZMb79+8hByUihzPEGRwcHH5+fqampj9//mRjY7t69eqCBQva29slJCRu377969cvGxsbU1PThw8fLly4sLOzs6WlRUhIaMaMGfLy8lJg4OvrC7KDgeH////woxN5eHhevXoFEX/+/Dk7O7u8vPzs2bMhIh8/foTfTPv69ev///8LCQlBbiy8ePFifX19ZWWllJRUdnY2Ozv79+/fRUVFIRohboZEyv//oItwQQdlgM/9hSiAXDbx5s0bCPfr16+QUWeIFj4+vpcvoZupP378yMHBYW1tbWdn9/DhwylTpsyaNUtQUNDLy8ve3v7Xr19MTEwcHBysrKwQvQwMDAEBAX19faysrAUFBZBhWiYmJj4+PmTruLi45OXl8/LyGBkZv337JikpyczM7Ojo+Ojho1UHVv/9Oxx2qrKBASSEsZIsLCzc3Ny9vb3Z2dmQ9SdYlZEt+J+BgZWZMcZcpN5HctGJt9CGqbVopr3o9IOvhbhY+kKkY+Y9fPgWNDH+799/NhZGdXEOdlbG/zArWZgY15778PbrX1Zmxl9//v/595+JkYGdhenX3/+GKlxx5kJJix8xgybFGJgYGT//+Dfz0Js3X/5m2IkIcTHXbX7Ozc6ErIuRETTO+ucfaNTi99//7Cygq+9+/P4HMpMV3PiF2TtKj4bAaAgMHkCnxijYw4z/mZhB58D/B41UguoP0L5z0DTMly/f/nCJMP3+/OLKVbZ//9k4pATe/zl99t3Xr1xWlsaf//BqSjj++qXAwfmAgeEpqAnLwPD/L8Pv75+4Gf8+efHq949f9x+/5hXg//H5kxAPJy/rf21x7t/C8sysoKGC/wygmaPXb9++/P733+9/HMws/3/9ZmNk+PfvLzPDfxZGpp/fv//584eDjYuDnf3dh0f/v/z7zfqHg5mN/RfHj6/f//4GnWn35uPnX3/+MP7/zwgZIGViZGZhAp9k/5/x/y/u36/5fj6R5v7BwCMtdOci+6un/8ysOLm57t6+qSMl8O/b9xePXr3+9B3kZSYWpruMLMwM//QZGHkZWf+z/uIT/yQcr6zM9fvRbT2vgJ9Kf7ctL7z19Nc3sX+M7Jw//nxh5edh5+L99eMnI/f/fxxMDF8ZGEBL9MEhSjrBxs6+dMnSn2rff/37xcrKKi4ubmxsXF5ebmtru2nTJn9/fyYmJtBBrbAmhYKCwrRp02bMmOHs7KyqqqqkpHTy5EklJSUTE5MpU6bMnTv35s2bnz9/vnz58sOHD588eQJpYEGasPLy8mpqavPnzzcxMTlw4EBaWhpktxADAwMLC8vy5cs5OTlv3779/ft3fX39OXPm/Pv3j4uL68GDB0ePHt2yZcuvX78YGBg0NDQWLVrEzc3t5+cHaZsyMDBIS0sbGBhUVlZaWFisW7cuOjqaiYnp+/fv8JYK6N5XcHsadCIMjMHIyPjgwYOJEyd2dnZCWoqQttHLly+Liop8fHzgo8KQcOXm5n769Onx48d37tz5588fuOHz58+/ffu2k5PTx48feXl5PT09N2zYsGPHjj9//ty9e7elpQVyvXVHR4eTk5OZmRkDA4Oenp6EhERJSYm2tvbatWuzs7Mh7Ta4mZDzsQMCApYuXfrr16+rV6+qqanp6Oh8/fr1ypUru3btgmwRmzRp0qdPnywtLb98+SIpKcnIyOjv75+VlbVo0SKIm+EkI3iyfvLkyT4+PtbW1vPmzWtubhYXF9+wYUNbWxtcGQMDAx8fn7a2dlNTk5WV1fLly3V0dM6cOTNt2jR/f//Xr1+zsbEpKipKSkouXbpUXV39yJEjhYWF8BO4fvz4AfHC379/IfEFWqANSUD//v39+zcoKCg3N5eTk/Pt27c/fvwICQmBdxhiYmLa29vfvXt39+5dQUFBXV3dxsZGHh4ePT2979+/CwgIhIaGLl26lJWV9erVq0pKSmFhYXDrGBgY9PX1P3/+zMHBoaWlBdkA9/fv3/Dw8JKSEtCtac+fMzIy5ufnz5o1a+XKldLS0kePHq2vr+/v72djYzM0MPz48SNoCyVo0hg5MAYL+8uXL4cOHfr48SO8+cjMzGxsbAwflmZgYHj8+PGJEycgeQ3ibk5OTgcHB0jXDpITL168eP78+UePHu3cufP58+ebNm168eKFnZ0dvIMH0UgJ+fPP/2BTARcN3oO3Pv8FnQT9n42Z0U6VZ9rB1x07XnCyMksLsLpp8848+DPaXOjeqx/PP/35DbqiBGHn//8MIjwsP0GnojC4a/MZy3E9+/h7w4UPHP/+Bxrwa0lyxJgLLj7xjpUZ1H5lZmIQ4WFWFWdz1eTlZGVy0+bdd+OzqyafuSL3g7e/Nl788OXnPy0JDjkhtl9//5vIcW0Ai4SbCD778HvL5Y+//iAWDyBcMMoaDYHREBhoQM/G6H/Gv6DhRSbYrfGgNZcMDIxM/7+9f/v56ycWxn9c3Jz8Kry8slK3bz178+m/uIAQ+3MDO3ePq++vH1m/+NWbR6ALN/8zglqB//4w//nG+fOHMmiKT+Lluw+cvPwCAkIMP3/8ffOYn/X/aw52ZmbQzCMohBkZf/3+/fvLF2ZGlr+sDOysLEzMTP+YmBiYGHnYWRmZme89us/CysH4/8fd53e+fvjCwAQagQVVA38Z/v79w8bO8p/hHwPTv/9MDH//g878ZGQFNwj/MTAyMDP++Cz/ZpsK14fb7L4X3/+0F//5XVT14atPHz7eEuFkZPj05dPr1//+sHP9Bx2b/P/P7y9P3nC/5md9z/Xrxz9OHVYRXxtu3n8sZ5eev3T0u1nSqfdct+6ovv9/TeTbV66bt3nFpNg42P/+//fl6yduVXkmRq7/X97DRxRAXiMR//37x9DI8MWd57///+nq6hITE6upqVm/fv2NGzfS0tLc3NwYGBiio6MFBECLChgYGExNTZuami5evAg5JCstLS0oKIiJicnGxmbVqlVCQkKWlpaFhYVnz561sLCwsbGRlJT09fWF3GXCxMTU1ta2Zs2aGzdu+Pr6QiapIe5lZmYODw//8uWLiIjIjBkzuLi40tLSZGVlnZycFi9e/Pjx45KSEsjkbGhoKA8Pz+PHj//+/auhoZGfnw8ZkGtubl6zZs3du3fz8/OdnJw+ffpUXl4OGXGUlZXNzc1lYQENiWRlZUH2+BsbG0tISLCzs7969Qoy7ghp0AgKCkpJSZWUlOzfv//fv3+tra3wQwZKSkqWL1/+4MGDjIyMX79AbXeI4xMSEjZt2nTp0iUDA4OQkBA+Pr7e3t4tW7YwMTElJiaysrKKiYlNmjRpz549kI3eDKBzbdi6urpWr1799OnTiooKGxsbBgaGmJgYtA3+kZGRoqKiJ0+eVFBQ8PLyEhQUrKioOH78uKmp6axZs5SUlNTV1devX3/x4kUHBwfI1YUqKipaWlpY7xVsaGjYsGHD9+/fBQUFZ8yYsXz58rdv3/b19Wlra0M8AiEZGRnr6+uXL1/+4cOH7u5uBgYGHR2d79+/nzp1ip2dvbOzU0xMbOLEiStXrrxz505oaCg8fJiYmGprayGb1t3d3X/8+AFp2paVlQkJCeXm5iorK4uIiPT392/bto2Hhyc2NpaPj6+wsBCSPOzs7Dg5OXft2qWgoFBWViYgIFBSUrJhw4bLly+Hhob6+fmxsrJyc3OfOHFCTk7Ox8eHlZW1qqoKvgeOCTyeDek/MDAwpKamSklJycrKdnV17dixQ0hIKDAwkJeXd9KkSWvWrHn48GFsbKyIiEh+fv7GjRuvXrmqrKz8gfECKI9DQmGQkQcOHMjPz9fU1IS03ZmYmJ49e2ZmZjZ9+nS4mydNmrRjxw4FBQVIe5SZmfnatWutra0REREQ3/z48SM/P/////8qKirHjx83NDTcs2fP7Nmzd+3aBT+cAaKSPPL33//ghaGMO65+XHnmfambOCcraDjz33+Gj9//SvOz8bAzC3ExSwmwPv3I+uvvf3khtvdf/zx8/xuyovT3X9DNdQwMDD/+/I80Fdx57ZMQN0u9j+Tjd7/ctHh1pTgat7wQ42Xl42SWF2L7CWrmgrYKiPGyFDiLVax7KsTNwsPOJMTFEmchnO8k9ujdL1dNPgMZzryVjxVE2Jp8pe69+SnAyeyowfv43S9RXhYNcdB+1sUn37GzMP6CWU2ex0d1jYbAaAhQHYBuD6K6oZgGHtqyYcmEpj//Xvz/95eBkenE3a83Hn/m5eVmZWN9/+EDIyPzv3/MjIx/GP4y8wuzWfiwX7vLz/BTmvf/319PmCVkNK+8OvXp9X1+LqZ37z6BXfw/ONAzOcrz+YUrHB+/iAvzfPv2iYOTk41PiJ2Z6ce3H18VjL6xsjTW11+9fo+BkYmZkYFfRIyDm5cNdDU82z+Gf/9+/WL88xfUFf//78/fP39+/mL4z8DBwvT27esfv36D5/ZBIwsQj3Bycvxn/Pfr32/Q8ahgoX+MDKz/Gf/++KekqpWfGWkg/OvH3z8Pbj/4KqGna2zN+2T7g5t32Rn/MHEK/GXk+vr/789vX48euzxnx3lPEdu5H3vZf/NysguwmnH/cfx9wvj3gn+ffq7ol72+4c4fnteiygJcrK/v3P705iO3nJyxlsKXF2///fnFzM4irq7O8Ovr34/vczsmKsEO+gE7hwQib3+hh5K7l7wHCXpooDQtLc3d3R2+05wGNmA38uHDh2fPnvX394esaMSuaOiInj17trOz083NLSUlZei4elC49N7n+xUHqxZ5zOdgAbVRBtxNvcVZ+lZ2LsHQdiTkmILOzk64w86ePZudnb1161ZhYWFQA+7HD09Pz9LSUi8vL7iavr6+y5cvz58/HyJy5cqV+Pj47du3w1vw//79CwsL8/b2TkxMhKipiQuJzC3RNrWAcIknf/z+Fzr7/vuvf0BnMP8DrRnNcxRlZmKcuO81IyODkzpPjZfktec/2FgYlUXYN1z8OGnfSwbQqqb/iqLsbf5StZuePYKvGf3zv9Vf6ti9r0JczB7afD27X77/9lecj/XMg29WytxeOvzVG5+xgBbng+bfxXlZe0Kks5Y/9tcT4GRlnHn4zeJEheWn32+4+EFJhL3ZT7Jl+wtpftZ8Z7G8lU/ef/2zNl1p8cl3C46/LXYR4+Vgbt3+go2F8cevf/1hMtbKPMT7d1TlaAiMhgBNAd1GRhn//fv/+8cf0Mw803+Gv/9Abb1//5kZGTnZ2RkZmRn+M4EOA2Vg+v3t35G13xlYfrAwvnv3+99/pn+Pr19l/P+Xh4ON4e8P0MHzoHFL0Ijl+w9fWWTlmdjf/f7+4s/Xnz9/Mwrw/mJgYn0vovyHne/T68f/GCC+Ay32fP/qBRPTK8ghTaCxTdjoImjiB7QaCbSa8OP//39Bly+BFtwhBTrjf6ZfnOKg45sYoVvrGf8xMvx9yfj9BxP33/f2Yi8fiLg/uHBOgIvvPyvXh+e3fn9nNDJSe3rz1q03n9/9+SHA8luI9Tfrv2+gIb1/DIz/v/xh/P37DxvjR8a/d5gN/rDZ/fjZ9Jz98KOfvz49/37vgZioNB8X7ycmhncvH714+fjX5+//mEHjsowXL+vIiGvIS8NXHyI5kgQmZByFBA00UJqUlATZT0MDs/EZKQ8G+FQMKbkfP35ER0dDFjAMKYcPvGMhI/0D7w4kF8Cnzv/9+3fmzJnW1lYkSQbIvQ8PHjyANEbv37//+/dvBwcHZDWOjo6rV68GHcnOBJoUOnz4sL6+PrwlCip/mJisrKyOHz8Ob4wiayeJzcHKtDlLGVNLlJkQqGwHd+hN5LkgCqLNBKPNUE75XZYMPTcXooCBgcFZA3rq5/QoOQYGhj///kP2PDEwMBwrQz+yakeuCkRjghWoaV7tJVHrLQG5ZWRJogLEAWvSoFbkOIjmOopCZH31+CEaR8nREBgNgUEFIM01ejgJNKL57y8jaMnnf8b/oG0x377++PnzF/h0lX+QO9j/gabHGf7/YWD4yfDz/w/Q7nPQJfEgxp/f38GtKFArFrR28M+/v/85f//7+fH7r8/PXnL++CfM+//r2zevmNiYtAyY/jO8ff36/x9wkxN8FBRIC2wdJLpvQcsFwCpBEqCWKoiGYtDZoqAN/KAL6f8yMYIawqBFqKAjUhn+Mvz7w8T89Asr38+9ahr6bzlcOG9vun3skY2pKjcPF9Pf7+zfv/J+/83Jyc7II/SLUwDk0/8/f/9/zfqf+9dfJpYngv/+c/27zagu/81Dy2Thr+c/HpxgfP/0xYuX77k+c3Jw/v31692n97//gOb3wc75/I6b9b+s1B+kTUVg8aFHWFhYDD1HDz4XW1tbDz5HjbqInBBgYmJ++eLFwsWLf3z79vbt24sXLx48ePDKlSuQaXrQcnNGxj9/QEtrXFxcmJiYjh079vz582XLlsFb1YyMjB8/fnz+/HldXZ2cnBwTE9OCBQuEhYXnzZsHWc4LaYzev3//wIEDO3bscHNzA+3zI8exhPWAOveEVeFTAW+J4lMEk4PsS0K2FIUN5oAJmIZRejQERkNgkAG6NUZB/VImRkZG0EI+aBiApsv/QBuXUCF0Ci4LarwiSzIxMXJwcDL9YX7+/ceLpy8UxYW4ZQTfvXlx++1PYamPbMxf/vz8+v8XaDASWRe4uw5qd4JOmALRqJKoPFA7FCTCyPyP+dvLf7+eMzAxgG4QBXW7/zMw/wcdb//z19/zl+9aSDJpaRtdv3fiyed3YqzfH91+8umbOJuwotyf2x9f/Hj1/devtx8+/2Bi+M/w4/+XB6yXWRm4GRgEWb/w/bvH+Z+Rae+by29StZU1Ex+elPpx6SjT25fcjH8YGRkZ//xHO3kPvlwM5K5RPBoCoyEwXEKAiYnx85cvZy5e+/LpExMTU3h4+O3bt69evQrZHwbZ32ZiYvLz588DBw5AtgC6ubkdOXIEvrWOkZGRjY3N09Pz4cOH9+/fZ2Bg0NXVZWZmPnjwILzByszMzM7ObmNjU1dXt3LlyokTJzKDx1DJCMV//xmuPvsO2iEP1vzv/39FYXZGRoZ7b34xMTL8/88gKcAqI8D67de/269+/voD2iCvKcnx4dvf11/+6MtwgjVBifOPvymLsn/8/vf5xz9KImwiPCz//v2/+ern5x//WJkZdKQ4//1nuPT0O6hA/s/AzsqoLclx+dkPFiaQ1Nuvf+69/qUkwibMw/Ln7/9bIF1/xXhZJfhYLj/7wcTIoCfDef/Nz/ff/soKsvGwM918+RPScoXaPUqNhsDAhYCyKLsgFwX7kYcXoFNjlJGB4c///wxsXGycbP///uHiZRX6/pORiekvE/N/hr/Mf//+/Qc6IpnxP8O3719//wJtYIc3HLEG+JPHzw4fPfb3z+8Pb15/ffPp0rP3HNeeigiy/+Pgf3jmJBsT49cfn779/sUCOgcJdBzT/38M/xj+/f33nwGy/wmroaiC4MYqiPj95+//n////WNEOQkGdBHUPx5OFiUl6be//r4/dJiRjc3d3uTF3ZvHLz54+uErA5+YNiePIO/bzz9+//7H/P/3DyYGxjM/76UyzmFiZWT8z/TnPyPDH8Z//5k+P/n0bSIPMy8Xw/efTD++/P718x147PP/v//go0xRnTXKGw2B0RAYdiHw588fZWXltNIqiM9Onz599erViIgIyJ48BgaGPXv2vH37NiwsDN4jXb9+PRsbm7e3N0TLnz9/Vq5cKScnZ2trCxH59OnT6tWrTU1N9fT0ICLPnz/fvHmzm5sb5ATZ1tZWTibQEAFEliTyx+9/QTPv//7zjx10RSfDn38MxnJcjAwMZx59Y2YCNUbVxTmM5Lhefvp9/N7X779BewLs1Xief/z94O0vT20+bnbQQgLQ4tff/zde/GCmwP3y8+9bL3+ayHOpibN/+fHv2L2vb778YWdhdNbg+/vv/+7rnxgYQbNr3GxMjuq8e65/ZmdhdNPiu//255mH34zluNTFOT58/3v87pc3X/8qibApi7LvvfGZmYnBTZPv3ONvzz781pHmFORiPnb3K+gEfaRh0v+gyTrQqQqQLQHgszdAIQFSAhICVQFgaZAgFIOGVqBMLBRYB2geDIscyFTIajEskiQIgdr7CHvg7oSbAJ7hAwnDRTAZIF+AWvggGYRZIB6JGOIp0CgNekCBTqyAhihIEchBEJtItAGrcrAfITKwTEGG4SB3USNGIA4Bk6CABTPAQQJigaILcogQwronH34vTlAIMoTuFR4FdGqMggKakfkLk8gvRlZOpq+qcgxfOfm4/n7jFeP5xsD0++nrH1+/ffj1nZ+T4y0L05u370HTUgygXfMgjdBsjZzOGM9eunHuynUG0PAkIyjBg0Yq/4P2x/9n+Pv/lLSooLSMqKy0uIIc6N7zv//+fv/+/fuPH0+fv/785SdoLQDoWEQmFlbW/yBbQLcdMzL8Z/oPmsj/C9rsz8QAmpH/x/IftIb09+8///79heVaSCOZ8R9oJ+g/Tm5ueSXFS7dffX7xyEry949nHCw8whKS3+4/+fL92c238iLyKpo/GR6++8ogwM4iLqvI8O/vnZdP/v75y8bNwsvDy8Dwn40RNFfG8/vL37df2VkYeP5z/WcT/Mf07c+fb4yM/7m4OT59/Pz7D6R1Dg6MUWI0BEZDYDiGAHwQ9Pr169nZ2fz8/OfOnZs0aRIDA8P+/fshh0VADppgYGBYtGjRtGnTfoPOCPkSHh4OORZ37969Hz9+nDp1qqWl5Z8/f4qLi58+fTpjxoyVK1cqKSl9//49MzPz79+/y5cv37p1a3V1dVJSkrmUAHytKqmBysjwf3mKooEsaJiTkQF05dJ/BgY2UFsPZNI/Bqa//xn+/f3LxgJqgzAzM/38w/D/3z8WJtD2ebhnGRkZa70lfv0B3cnEwsTw6+//P39BR4SysYCmhf6DFo+CSuNexv/MzKDjWP79///rz/+2AKn/oJP2/zMzgWwE6/rPzMTEzsbE8O/fn3+grU7srEysLIxffoBsZGZkAI12MIF2J7CwMP4GHdQGqlCYGBmZmRn//PnHwsr0BzQ8wMgKqxIZGRh+/WH49x80O8fKwvQPZCaIzcjAyMrK+Pcvw19QdQHyKRpmYmJiZmb48xt0TD+i6QGuNxjBV3sg/A7WCXIHmIGsGCwAIrAKgg7oYAW15v+D1pAx/AKdTwA3BqQL5AYmht+wiUdMQyC++APzIGjOkoXx92+QB8GVL6hVCTIIhhHVH1gE2UDQGmXQHdj/WVmY//0DXdf9F7wnBGIOKysTqDUKqqUZfsHMB5uBTuCxAl0pmM/CzAQ5L4eRgeEfA8NvpEBAdh5YLToBtwt0vTgz4z/IKTngOII3ICF6MI3CFIGohJCQkASfBPifkRF0+8NvcLpiZmH4DTrsFrRDBbL2xqnvNshaiLZRwADd4kPzkAAdjMzK+PgHz6MPrNKcP8T/fPz37rv47/e8bEx3/wuJ/WcW5mH+8uXdW0b+N+AsDO5IQDMGtl7L/7///4KakCCHQzIhSDF4PBHUdGVlYf319cePP/8YGFn+/Pnz6+cP0Gk+7CzwdUiMjP/FRBU52KQZ//0RZPjDx8Lyg+n/7z9s3Fyfebn/i/3ikvzxWejHZ94/3/8yM8379vv0+9egJAgpRUAWQrrTjF8+fzl/+e6rF6/Yfnw4/4OR7dF5IQkJZgFpll/3fn3+cPMh25c/ktyffjF+/vrpB/O7/6I8HMwsbO///vkkKSioLsf14u07IR7mv6xM377+fvSYi4GJ9c9fFqb/klyMhl/+7//28yYvNw8bGyuNGqOQa74h17uDApL2+N+/fz9//uTg4IB1ZKFWPnv2jJOTE+2cI6jc4Ka+f//OxsaGa2/+v3//fvz4gXYB47dv39jZ2XFpQfYuVu3ICpDZkHWBkPNTkcUHlv3+/fvv379DjkQdWJcMIdt//QKdRMfBwfH161eIsyFnhLGzs8NFvn37xszMDDnMH6Lm69evbGxsTExMkDO2GBgYvn37xsHB8Q90dgjoyN5///5BDnB9+/bt79+/2djZQbdCgJoPEAPIITnZmDnBrSIGBgYO1Ivhnjx+9PDhQ2sb6DDthQvn2dnZNTVBh8JysqHbxQnTi2YIAwPD8WPHGBj+6+rp9U+enJOTw8vLxwXTDmdAdF26ePHo0SOZWaBDfEFHz/78sXPXXkdHR042LgYGhtevXt26dVNLS/v06dOurq6Qy6J//fp5YP9BCwvLIweOW1lZ/fnzd+WG9d++fWdkYpSRlrG3t+Ph5QPtoDp6VF5BXloaNLTBwMCwe9cuQyNDERHE7Q/I/nnx/Pnt27ds7eyRBSHst29e8/HxsbKBDsCGiJBNnj51aseO7b9+/bKysvb08gSNsCCZdf/evbNnz4aEhiKJoTB///q1d9d+K0tLiAffv3sHuiwNdT8cigbcnPPnL6xdu9bWxubVq1f+/v6rV69JTk6GDAMyMDDs27v34MGDTMxMHh4e5ubU3Cpw+dLFY8eP///3n52dXVtH28zMHNaYxO1WDJkP79/PmDGjsLAQPgWBoYQ0gZ8/fxw8cMTGxgZi4NYtm3V0dNnZ2fft2xsVHfP927efP38ICII2+TGBmhSkGT68Aah3RQcfgvou/xn//Pjx+9Xzt48+P/7F9V1KgJ+V9f2b34K/v9uIcikwfWZi5fjOxMEI6uyAmnssLCziYsKSosISYqIyMjJS0uJSEhIy0rJS0tIyUpLi4hLiYqKSUuKi4uLs7KiHszAy/vjx7fOTF9/vPfly//Gnxy8+vfrw/dOPnz/+ghq6kC4fIyPLv68cL69LP71k8+xqxa93pYw/+vl5Zn//O+X9555fX5r+fIp/89j95Svnt++EmH+CBl8ZQTvv4WEFupOUgeHmw5fNk1fPWHVgyqZzLSvO1S05XTBhc1H74qkbTi/Ye2vpjjP1kzfWrLlbt/vl9suvfn56/wN0MDuo3cz0/588x281vn8c7F/ZmD/wC/1hZP3z5gvT25+/3316+v7j6/9Ckr8Y2b9//wpaVgC3laqMU6dOIV8shMfsFStWbNmyBY8CIqWePXtWXl4Or1BBFcavXw0NDdnZ2Tt37iTSkMGj7P///9XV1Q8ePLh8+fKUKVMwHfbw4cOSkhJIPxguW1FRcfXqVTgXD+PJkyfFxcXfv4OuS8CjDCK1dOnSJUuWQNg0Jb9//37z5k3wbkIC9mzatCkhIWHGjBnwNYsENIxsafgIpb6+fl9fn4eHR19fHyRIPDw8ampqoqOjIffuMjAwpKWlZWRkFBQUJMIPaaqpCQwMbGlpcXR0hCwq7e/vd3R0nDJlCuSIX25u7qlTp0JuQODg4Ojr7bWwsBAVFf0HG8eC2EUS+fnzZ1zqT50+M2XqNNA4E2hkjGHFipXbtm3HpRiP+H8G0KV7375937t3348fsBuesWl4/uLFkaPH4DJfv33Py8vv6gIdncvAwHDj5k2QexgZGxobb92+DVF25MjRadNn/Pv/v6W17eWr1/cfPOju6f0Luq/h39Zt21JS0168eMHAwDBz1qzz5y9AtNy8eTMmNnb16jUQLiZ56fLliZMmo+V6iLKGxqbbd+5C2JSQa9asqaqulpGV09M3mDd/fltbO2ScBG7mP9ARMqBqFC6Cxvj2/XtZWXlDYxNE/N79+/0TJkDYpJJqaupSoFpZdvuOnStXrb5w8SJ8zHj58uUTJk7U1dNTUlJuaGjcs2cPqYbjUb9k6bIdO3aysbO/e/++tbWto6MDa5jjMYGBgeHL16+79+z59Ru+Sxi/csKyHz587Ozqev/hAwMDw4QJE1auWs3LxweODpDew0eOTJgImusAcUYxKoDNSaCKUp3HyMjw+dvff2/eav16p/Ln16t/LId5JQ+LyJiwfpHj4nv74dPJz1xvv7L/FuT5xwTqxP9nYBDk586ONRMWEJOUN+PhF/r3+yXj749sHEJ/GJl/vH/17O6jOzevcYoon7j96tz5iz9/gg7chswLMDAy/GVk+PP/L8P/f6z/Gf4zs/xlYvrHxPyfhf3fL0hZBhrXZPr/k/f/Z4H/vxUZGRXNtT9+/qH75AWr6vPvKbJMEqb/2QWYbt/4MPU+1z9OjtdPGV5/QhugBS80Yvz58++zV+/AHTLknA8qoJmYWfh4f//5/fPPf54/fzhZeFjYWH8y/mVkBM/7/P/3j//vdzF+lqd/2d+ycF37Iv6b6z8zlwCniLQQgwgr7w8GMd3vZ14yvryJ0gSmRsRA7ur89+/f169f7927Bzfy169fyONqkAYERLGqqio77Obrf//+we9+hOv9/fs35CRzuAiaaaCzWv78+ffv3507d5DbMXfv3j169OiKFSvgx+xjaoTUZ8iDqf//g1bTIrsWUxemCNxtcAboWlfQFCLoslAGBgb8o5Wg2Sjwbg9kk+/evfv161cRERFdXV2Ischu+/Hjx507d5iYmJDD5+7du/DDuf78Ae1Uw2rvnz9//v79e/v2bXgJi2wvxK4/f/5AhscYGBhevHgBVwm56R4tRiBa4OSfP38gkQs6aAL1ilT8Fj19+rSqqmrp0qVMGMsNkd3z+/fvWbNmFRQUODo6YvUg3CWjDEjae/jwYX1j42fwrUucnJzMzMxtbW1//oDu/2UHnX8H2tPY2tr68+dPiCBkTqOkpATSEGFmZubk5Hzy5MmePXv+/wcNF4HmapmZt23btnXrVsjlVWxsbOzs7AsWLDh//vz///8XLFgws7oQop2MWGBhZU1NTY1w0g4Nj0K7SQHiI+QcCrmw9+PHjydPnvz48eOpU6dMTU2Dg4N37NhhamoqJib28+fP7du3e3l5Xb16dd26dSwsLNHR0SoqKlxcXCCfMzJCZlT+//+/cuXKc+fOqaqqxsbGcnBwPHr0aOHChWxsbBwcHMizEH/+/JGRkdmzZ4+NjY2zszN4/pRFUFDQ0dFx8+bN6urqDAwMW7ZsCQoK4uLiYmdnZwJfPqempga5II2BgaGurm7WrFl1dXWsrKzwNLxu3bqoqKj9+/fHx8cjWwcPQCYmJsgQ9YcPHxYuXPjs2TM7OztPT8+1a9ceOHCAkZHRxMQkNjb27NmzGzZsYGZm9vb2NjMz+/3795IlS65fv66hoREbG8vKynrgwIF///4dOXKEhYUlKSlJQkICYsWXL1+mTJnS29trbGzMwMDg6uoaGhoaHBzMysr6+PHj27dvy8jI6OnpCQsL////f9++ff/+/Tt48KCAgEBSUpKQEGhMDpLlJSUljx07tmXLFh8fH0bw7jcGBoZXr14tX7782bNnxsbGYWFhX79+PXz48N+/fw8dOqSqqhoXF8fGxvb58+cFCxY8fvzY1NQ0NDSUhYVFRESEg4PD3d39zZs3Dg4OkHKAgYFhx44dERERISEhDAwMLi4uf//+ffbs2cWLFz09PRkYGI4cOSIqKiouLn7q1KlPnz6dOnVKV1c3KiqKmZn56dOnixcv/vz5s6enp42NzY0bN968eXPt2jUlJSUXFxdIODAxMbm5uSUlJTEwMCQlJQUFBbm6uhobG587d279+vWsrKwxMTH8/PzHjx/38fFhYGC4ffv28+fP7ezsVqxYcf78eWVl5YSEBBYWFlZWVkjlsnHjxqNHj0pISCQkJAgJCZ0/f/7Dhw/nz59/9+5dSEiIgYEBaFB89+69e/eKiIgkJCSIiIjs27ePmZn5zJkz0dHRkAhiZGRkB4P58+dDltkICAi8fPlSXFz80qVLS5YsuXfvnqCgoLeXJzMLnVpfQwXQLzj+/Gfg+v9XW4TtOwfb+7+sMt++2YhzCUgr33746dnnv885+TgUBP78Zfj/BdSlAPXsWVmNrNwNdIz+M7D/+vKN+S8HK9snVtZ/f758ePXzmYDkj+fXP588efDC49+/0JZUgtqJzL+ZWBlYmVnZ2Nk4ebh4+Fg4uZlYGL8/us3A8A10WtR/0OIdbkZGbgYGMW4hPhv7X5du/+X/yNAgwKlj+IdB6S+DKY9F4IcH6y/3zvzOygCadAD375HiFXQiFIyL3BKFiDGzMDIwMzH8/Mv4n/kHGwuDpAzXi0//P7/8CrrJlIGBiYmRS0yYjY3h5Se5T0yCQv//SRrzfOGRFZSRYf7L9fXvp7uPX7FK6DB9fPr3G+hSe4ihlJOsrKzHjh3b2LyejZNNQEAAMpXw+vXrvr6+58+fq6ur5+bm8vDw7NixY/Xq1ZASJDIy8vnz55Bi9+DBg3PnzhUUFDQ0NBQWFvb19V2wYMHfv39PnDjBwsJSUlKirKz87du3qVOnXrlyRVxcPD8/X1pa+uHDh729vT9+/FBTU0NuIT169Kijo+Px48fNzc1eXl52dnaTJk26fPmyqKhoQUGBnJzc5s2bP3/+fPLkybCwMPgxRpcvX54+ffr37991dXVTU1M5OTlnzZp16tQpAQGBXPCtP1+/fp0yZcrVq1fFxcXz8vJ+//69Z8+etLQ0BgaGNWvWyMrKGhkZLVy48O/fv/fv329qajpw4MDKlSsZGRmjoqKcnJzQAvnkyZO3bt26du2aoaGhr69vf3//rVu3pKWlCwoKREVFWVhAe+S+ffv27NkzBgaGs2fPLliw4NOnT0ZGRllZWczMzP/+/Zs8efKZM2fk5eWLiooEBARYwFdDMTAwbN68ef369czMzPHx8ZA7mSBWQ4Lr+/fvysrKkDry58+f06dPv3DhgrCwcEFBgYyMzNKlS3///n3y5Ml///7l5OTo6enBW6V3796dPHnyu3fvDAwMcnJydu/ezcHB4ezszMDAMGfOHBsbmytXrnz48OHEiRPCwsIxMTErVqx4+PBhTEyMh4fH79+/Z8+eDQnMgoICBQWFZcuWQW5j+v//f35+vpiYWF9f34ULF4qLi11cXAIDAyFuZmBgWLZs2c6dO9nY2FJTU01MTCZOnHjt2rWlS5fevHkzPT0d3vCFqx9loIUACwsLFycXaKckaKcO6EhlDk7Qcsxnz55t2bIFcrnXnNlzrG2stbS0QDPs4BKJmwdxcvv///+ZmJm5eXiYmJg2bdrEx8fvYG9//8GD7du3Z2VmgRYw/Wf49/8/Gxt7QmJSgL8/JycHcs8QzT0EuYwMjOrq6hs3bZk1Z76Tk1Nvb6+4uDgeXYyMjO/evSsqKoqMjHR2dp4wYQIvL++JEydu3bpVWFh45MiRLVu2qKmpNTY2pqSkfP36taqqCnJf1L9//9LT0yEtbEj6TE5O3rx5c3d3d35+fkFBgbW1tba29uTJk5EPVf3//z83N3dWVlZvb6+hoSELCwuk2e3n59fW1pafn//169dbt26VlJRAlriAxjIYGf/+/fvjxw9Iwejk5DR58mRkH33+/PnEiROTJ09uaGg4dOiQhwfO20P+/PlTVVUlLy/v4+Mzc+bMf//+SUhI8PLySkhISEtLv3jxor6+PiMj4////1u2bNHT0+vr63vw4EFYWNjatWsfP35cX18/Y8aMb9++ZWRkHDt2rLy8fP78+ZCx88ePH3NxcUEa0wwMDAICAkpKSteuXePg4MjPz4+NjTU2Nr5y5cqsWbOcnZ17enqEhITi4+M3b95cX18P987///85ODhKSkq6u7tNTU3hxfK2bdv+/fvn5+fX39/PwsLi5ORUU1Njb2/v7u4+e/bsnz9/ZmZmVlVVcXNzu7q6zps378OHD6mpqQEBAczMzPLy8oygpPsfntm9vb1nzJjx9u1bJSUlMzMzSUnJQ4cOzZo1C9IYXb16tYmJiZ2dXUFBQUxMjKur6+zZs798+RIfH19SUuLo6GhsbNzX18fPz3/58uXW1tbIyEhI+xseI5AREwYGBkFBQXNz88uXLwsKCjY0NCQnJ3///r2ysrK9vX3WrFmqqqrq6urTp083MzNbsGDBsWPH4uLitm3b1t3dnZqa+v//f1ZW1uXLl69bty4lJQUS2tOnT9+6dev69eurq6tFRUVLS0vnzZt38+bN6dOn5+XlXbp0qaKiYtq0aT09PaysrD4+PpAEA3EYMzPzwoUL586du2PHDsggy927d/v6+qZNmyYiIvL27VtZWVleHp7//zFP+xnRgH6NUQaGf5wsjLySSqKiXDw/3r39/09VWenzT9a/H++wMHxVkWIXFme+95ntC7iEBR1lwsYhLWfMyS/6+/dPFk7uH28+MLPyff14++nVA7fvfjlw+s/Hj19P3fz+8ddfHh4GRtA+JPBaAPDamX+MjAxsPExcHKx8Ahw8vP8ZGP/9+fPj51eGv5CdQKCtUawMDHwMjPwMDAKCPCxKMjxvPrN8ZPzPcpfxvz/T75hf/7jfsbK9ixEz55Jev2Quw5t34EvpGYQE+bl5+f/+/PXn+48/oMINVHcwgxajMv7+/5fx999/oF48w59fv//9/snIwPjj198fH/6xszH8ufPh649fLMxskAKRkYWFR1paQVX3N4v+uVcs7L8/Pnj7489/ph9//r/99Ozdq/dfPn/l4BdiERD+AWudUyWdsjCzbNmypSm4QUFFobm5mZub++/fvw0NDdra2mlpaQsXLpw6dWpycnJfX19raysPD8/evXt///59+PBhMTExVVXVpqamyspKMTGxsrIydXV1X1/fRYsWaYBv6dyxY0d9ff3ixYunTp365s2b+vr6gwcPNjc39/b2lpWVOTg42NnZLVmy5PPnz/AxBmFhYU9Pz+fPn0dERCgoKEAaxOXl5cePHy8qKlqxYsXRo0ePHz9eXV0NL3n////f0NAQEhJiYWGxbdu2jx8/rlmz5urVq7W1tRcuXGhoaJgzZ05fX9+bN28g5ly+fFlAQGDbtm2Qxujhw4cNDQ2NjY17e3sDAgLCwsIuXbo0derU5ubm////19XVSUlJQeY04aF9+/btlpaWtrY2MzOz7u7uX+B1Bdu2bWtqapoAnthiZma+f//+5s2bIyMjr1y54uPjo6qqWl5eLicnZ25u/urVK25u7rKyssWLF3d1dUFOMmdjYztz5sz8+fObm5t//PjR2NgoLS0NuaHxz58/tbW1hoaG7u7ua9asefnyJQcHx/Tp0x88eFBXV3fixIn6+vpZs2atXbtWVFQ0Ly/v2rVr1dXVy5YtgzRGf/z4UVlZ6ePjY2RkNH369BkzZmhpac2cOdPZ2fnp06fr16/39/ffuHEjIyNjaWnp0qVLk5KSOjs7GRkZa2trzc3NN2zYcPHixbq6uvPnz1dXVy9atGjDhg1sbGzl5eUnT56sqqpavny5p6fnjRs3oqKi5OXl4aG0efPmNWvW1NfXv3r1qra2durUqV5eXps3b/bw8DA0NITHOFz9KAMtBP7+/cfJypIcHQJqZcLkmJgY79+/P3/+AjF+XkFOdkYmRgUpcda/v52sLLS0tf6BVx3B1CLo////79+3j+3/Xykhfm5WRklBfnF+Xl425pCQEB4enn+gTZmgqYB3L578+/fvN3SyCKGdeNafv39ycnKenFUuLqtYs2ZNSUkJcmMUPu0AMRDSTPn375+MjExRUREnJ+eLFy+OHj0aGxtbU1OTnZ0NGaQ8evQoJycnHx+fgIDA27dvz58/z8nJ+evXL0ix+f379y1btri4uPz69UtNTW3RokWGhoasrKzFxcUMDAxfv37dsGEDxDoI+fPnT0tLy8ePHzc2NkZEREAGwExNTVlYWC5duvTw4UNVVVUpKSn4TAVEF5xkZWVFa6xv375dXl5eTk7O1dV13bp17u7uEDPhWiAtWkZGxgcPHly6dMnLy4uRkVFeXn779u3Tpk1TUFBwd3c3NjZ+8eLF79+/X7x4oaurW1BQ8PXr1wMHDixdulRUVFRbWzsmJubr16+MjIxxcXFeXl6Ghobx8fGfPn2CtGx+/vwJ6QPDLWVlZf316xczM7Oenl5tbS1kAA8yl8XIyJiWlmZvb6+oqFhQUPD9+3fIgDpk8sTQ0DAiIqK2tjY7OxvikdDQ0Hv37j1+/JiZmfnChQsuLi4CAgKlpaUSEhLMzMxLliy5ffv2/fv3161bx8bGBmn5paSkQIbAMbN5WFiYjIzMgQMH1q1b19fX19HRwcHBAVHMwMAAWTr/798/eXn5/Px8bm5uPj6+zs5OAwODly9fqqiosLOzQ0pvWVlZS0vLmpoauJcxGZycnH/+/Nm5c6eAgICIiMjv379fv3799OlTe3t7yCVkjx49Ki4uzsnJcXNz+/v3r66u7uzZsyG3W//48WPNmjUFBQW2trbu7u4BAQF37txhZGT08/OD3Lp8/vz5EydOnDp1SklJiYmJSVFRcfHixW/fvmVlZc3KynJ3d4e7h4WF5dGjR6dPn3ZwcFi3bl1ubi7kfF9WVlZFRUU7OzsGBgaImf/+3oTrGgWg8Ud6hQJoNvQPw58XP7+I/BP98v75y+cvj/4XFGJkFvz+4/1/Tk52Xo4/b1hYBJhh09K/f/36+uk56x/mv99e/f/18dvTC48/fXn//Okfpq+cUiJnntz79ukdC8tfNmZGVlY28MgkaEQU7B3G/4z//jIzMTD+leT8JyrE+vjNt5uPnv5h+Q8uiEFnjP5jBG3VZP//j5+BgZuVjfE/4/uzZ0UYzv/99OfqpT9n1UXP/ft34+f/11IyK1Nj/u5aD5p1+s/AwMQcEhxkZW5z5vmjw5+/fDhxgunuo09M/9hkFAS+fODk4tTT0X398jUPJ8+Vqxfv3b/3B9QoZvj36y+oSfrlBwMDIws3F9ipIKaUih6znO6nD6LPP718+fYbFzOLvLQINz/P1x+/ZRUFmRn+MTP8/fj+/OeH1Eyyv//8cXd39/X1ZWBhiIuLW79+/YcPH65cuaKgoLB9+/YvX75cunQpKytLWFh45cqVKioqHh4erOApKg4OjkuXLqmrq0NmSSIjIy9dusTAwMDNzR0TE6Ojo8PJyXngwIEfP34cOnRIXV19586dX758efjw4c2bN9++fRsbG8vDw5OUlHT58mX4bDI3N7empqaQkJCFhcXv379PnTrV0dGhoaGhqqq6bt26J0+eMDIyRkZGurm5gaMVRDAyMqqoqOzatevVq1eWlpYyMjKHDx/m4eHZtWvXz58/b9++fefOnQsXLrS2tmpoaECmDg8fPgzvtrKzs0MGSGRlZbOzs2VkZObNm/f169ejR4/+////7du3t2/fRmuM/vv3z83NLTg4mIGB4dixYwYGBtu3b3/58uWVK1d+/vwJGatgYmKCDAb4+Pjs2rVr06ZNb968efLkiampKWTSh4mJKSkpqaCg4NevX5DpwiNHjnz//v3QoUP///9//fr1/fv3IY3RZ8+evX37NiMjg5OTMyoq6sCBA79//z506JCoqCgkSO/evfvmzRt2dvb4+HgdMFi6dOndu3eZmZmZmJju3bv35cuX2NhYRkbGrKysqqqq5OTkmTNn3rhx4/Tp02ZmZqKiouzs7JGRkbq6us7Ozk+ePHFycvr9+7eQkND79+8PHTrEwsKyc+dOyKDR+/fvWVhYYmJidHV1paSkNmzY8Pv3b319fX5+fnNzc4iXQbEC3u4dFRWlr6/PwMCwYcOGc+fOhYWFCQoKGhgYqKmhX58D0TJKIocAn5DQ0R2bTuzZDlnTDpUCb/rlYmTw1Fd/cf44AwODrbL0378/N86csAlUjEFVYVL/Gf7bqcj8///r+dljjEyMPibaz88dm3L+OGRwFKoevH/i798/bLAVOFBxoilWVrao6GiWN1cDAgIiIyO1tECbk+C6ubm5IfuuICJfv36Vl5f/9+8fDw8PpJ3EBF6+oq6uzsvLu379+tevXzs7O0+bNu3Hjx/Xr1///ft3fHy8trb2hQvQxZqgk5fBa1devXp15coVJiam0tJS5CUlvLy8mO2hHz9+pKSkpKWlrVixAm6vp6fn5s2b3717B2kWQFwIIUGDCaDKCsR7+PChiIgIiAWaGwOF1/r1658/f56RkfH58+fTp08/evQI0iX7+/fvnz9/IOb//fuXhYXl9+/f//79u3fvHhMTk5ycnK+v779//yBrbxgYGCQkJCZMmLBx48ZZs2Z9+fKlqqqKiYmJBzzIzQbeEPn7928WFhZubm5IU4aNjQ3eLJaQkPjw4cOnT58gs1UMDAwvX76UlZV99uwZcmcA4mxWVlaIMhYwgJe9ENmfP3/GxsaePHlywYIF7Ozsv3//7ujoePv2rZmZGT8/P+icF9CxM8wQh0Hy+9evX9nZ2SENSojJaL0OiMkQ8sePH1ZgwMDAMHfu3Pnz58fGxkKkIAu3GBgY/v37x8nJCQk6dvCmuh8/fvz79+/27du/f/+2sLCwsbE5deoU8pg33ARIAxrCvX79upWV1dWrV79+/Xr16tVfv37FxcWpq6uLiYk1NTVxcXHp6+tLSUn9/PkTsqKJiYmpuLiYg4Pj////kE1+EO9AWsnfvn1jYmKC76kVEBD4/h107c7nz5+vXLnCwMBQVFQkKCjIxsYGuREN4gYGBoa/f/+KiIi0trYKCAiEhoZaWFiYmprC3fkbtOcf2gSA9FvgGkcB3UZGQWckffnD/PbNl7//3/7nlhFWEvz14MmnD9//s7Fo///+7hfj96/vmP6xwbcJ/f757cGtfYqShj9/sH98ePXS5Yv7rt/gYmWyslb68euViuaPrVtYmZl+M7Ew/P4JnjEHF9yg9YWM/5l+/v7x67u0AL8cNws3BzOzkuz950/+fP70l+kPAwPoNlIG0EFOoKPn+RgYOH78Znr66vfFs//0PjOIGW47q9Is8e4XnzDDrz9sr15N7e27dfo0AyPDv/+MzAwMj75+eP7py70nrx+w/Pv79aeOqMiv9++ePnr04vVLSRFRCwsbYxklUX4BJob/t+/c+fr1O6RDD0l2//8z/vj5A3x+FMOvf0wnfipzfJD69f0f73chSXVhMREOFnbOD99+i0v8+vf3Lw8PGycnz4XjbLCRYqqlVbiTIK4CDUKzssrLy3NxccnLy8fGxvLy8s6aNWvv3r03b97MysqaN28eGxsbI+gMFOZfv0ArekGnA/6ALtKFNFUhmZCZmRlSMElLS8vLy//9+9fOzo6Pjw/ZRjRvQIpseBmNppKJiQlSHCPram1tPXTo0JUrVxoaGvLy8jg4OCQlJSH1XE9Pj5SU1L9/oOFpZC3wIvjbN9DMyP///zk5OSEzU3///hUSElJQUPj7929tba2JiQmyRkhZyccH2lELWQglIyMjCwZeXl6QggwSjExMTN++fcvOzjY1NTUzM7t27RrcUnhJhOy7////CwsLQ+xtaGiAXPaIZjXEZEiQwv1oaWkpICAAEYSoh7AhI09wu+BS3NzcDg4OmzZtevz4cVxcHMRMuN8hdQDEaxDnSUpKysnJ/f//v7u7m5+fH143//37F7KoDjJMhWYRxDoICZeCLJ+FCI6S+EMgvqT6H+Q0EFR1iIoLSRzULELiYjJJ0sWGtgEU0zgcIr9//0pMSEgPtFFWxdLfMDQ0fPfu3ebNm11cXG7evHnmzJmUlJRfYABJaZAGHAMDg7+/f2VlZXp6Oni7veapU6fi4+P//Plz8uRJUVFReIv258+f3NzcysrK0tLSmZmZN2/e/PHjh4CAwMSJE69cuaKoqLhlyxb4SQKgiuD//1+/fv3794+FhaWhocHNzU1TUxPiFQ8PDz8/Pzk5OTMzM4gIJFX/////8+fPT548YWVlvXHjxty5c+vr6yHNJkZGxuvXr797927q1KmQ9uLkyZOXLVtWWVnJwMDw5s2b0tLSkpISBQWFDRs2GBoaysnJCQsLW1tbGxsbHzlyRE4OdMcpGxvb/fv3dXV1P378eObMmcLCwj9//gQEBPz69UtGRmb16tWRkZE7duwQFBTk5eH9+fMnpAEKGsBGasSIiYmZmJh0dnZCTvtatmzZ79+/TU1NV65cCS+f//0DrapnYGBAMwTiWeTAYWZmrqurc3NzExMT+/Lly759+2bPnq2qqrpz505ImMDbT5DDPZSVlf/8+bNr1y4HB4ctW7YoKipCOuRwk5EZzc3NUlJSMTEx////v3nzpqCgoLi4+KtXrx4/fszIyHjw4EEzMzM2Nra7d+8eOHDAzs5u+/btcnJyqqqqnJycnp6ecnJy+/btk5WVPXbsGOaR23/+/Hn16tXz588/f/68cePGr1+/Qk40O3XqVExMzN+/f0+dOiUkJCQsLMzHxzdjxoxFixZBxjJkZWVTUlKuX7/+69cvLi6uX79+sbOz6+npbd26VUdH58yZM58+fVJWVv7379/WrVsjIyMhwwGenp6fPn168OBBVlbWixcv7t69y8HBAVnAjexlyNYCLi4uAQGB4uLi+vr6FStWQLbqMjAwcHBwPH78+N27d3y8PIyMjPC0PQroODIKXkryCdQUE/zxh/HvX0aO/1z/JRWv/H6uxfXbSJDtLgfjrU//fvz78Ad21xI7GxPjv2d7j3w9f/09xx/m3++fP//wgZ2T/frDJ7duff74Soz5v/zPX1/Y/r//zwLabsz4n4mdjRXUyWH4//HHz///GR9+/PTi5g8Wxsf/GVm+/vjGyPT/36/f/xlBm9kZGJjY/v8XYWAQYmBgeP/+2d59r/58U/rGxPkv0cPE+dTJC78ev2G7c4Pv4nm1h3fPgfbSgzATA8OVn0wid2/pvH7x8vylN3/+PPr3i5mVm+HVYwZGxlcvX6xaulRTU9tI35AT1MdlRM4/4PL3/z/IMgEGBiYWlk8s/K9/sYoz/WHl5OUVYP7ExMz4m1GI7R+fjAjD798//v2/dfvhsyfPmJiYYasLqJBiWVlYd+3aYc1tIa+isHjxYhYWFgEBAWVl5devX3t7ex8/fvznz59v376dMGFCWFiYnp7e3r173759++fPn2/fvhkbG0+cOHHx4sVSUlJLliyxtLRkYGCA9Bchjbbv379zcHDY2Ni8fPnS39//2rVrr169MjQ0FBUVXbhwob29/bJlyz5+/AhvrEB0QSoMDg4OMzOzadOmpaenHzt2jIeHR1pa+vv37/DiFeL5379/9/b2mpmZBQQEXLt27dmzZ1ZWVseOHQsNDX306NHTp09tbGz09fWnT5+ekpJy9uxZISEhQ0PD169f79u3j52dff/+/VZWVpCiAdJYNDIy2rRpk5SUFBsb25EjoCM5IAWitrY2xMa/f/9C4pGFhcXIyOjFixfe3t5Xr1798OEDExPT9+/f/4H23v79+/fvt2/fnjx5UlpaysbGdv36dW1tbWZm5sePH8+fP9/MzGzx4sWGhobs7OwQT5mbm+/Zs0dWVvbfv38nT56EH1QuJSUlLCw8bdo0d3f3devWvXnzhpWV1crK6t69e+rq6nfu3Hn9+jVkoHf+/Pm8vLyQS3qUlJQgi70UFRV5eHgWLVoEmaa3twedL+Pj4xMdHa2kpKSjowOpWeGVHCR4//////379z9//kAGIdTV1R8/fvz69WtWVtYfP35AFmZBWpb//v3j5+f/8uXLzp07dXV1ZWVlIaHk6Og4b948SEK6fft2YWHh37+gw30hgQxRM0riCQGyW4R4zKS11P9//xIT4pXFsF8hIyIi0tzY0N/fv2rF8p8/fxYV5Gtqaj64d9dQX48ZfBaprLTkn1+gPq2zk4O9rY2vtxcDA4O7q8v1a1fS01IgG32cnZ2VFOUZ/jNwsLOaGBmyszJXVZY3NTalpST9/vMnPT1dX18/Jzuzsb5OWFhYUlJSRRl6Hz0DAwMnB5upiRE3F+isFTk5uf7enmOgU6JAoSIjIxPo76ukpAQZDGNjZTYxNuTm4mBmEuTmZG+oq2EA75eqqa6EzKvqaGuKi4lcvXwpOioCXjKkJCUuXboUtAeAkVlcXMzP17upsZ6FmUVGRiY1JYmbm7u8tHhifx87BwhW11QzMTFlZ2ZMnDjx6uVLxSXFDx/cy0hPZWJksrY0NzE2FKuubGlu2btnFxsra3trCzMLs56utrgYaFyWi4PdyFCfk4MN5HTQfgOmttbm1tbW/NxsZmZmQUGhyZMmsLKySkmKa2tpQNSIiQrp6YJKMGNDAyFBfgYGBh5uTiNDfTZWaGRxsrMZGxlwcbIzMDBISkpO6OvZuXOXoKBASlJCU2O9tLS0poaamqoqBxuriTEo2BkYGESEBHV1tPj5+etra/r6+5YuXsTPz99QXwexESuZnJTQ2dGZfezIv3//xMXEK8pKhUREIyPCCvJzZWVlPdxcZWWk/v7+KSIkuHfPrsWLFrCxsTU3N0tISqenplRXVrCzs4uIilhbWcjJSoPCGdUOXR2tNatXV5SX/vv3T05Wbu6cWTw8PB7urjeuXc3KSGdgYDAxMXZ2cmBgZA4PDebn5dHRAnVFKspLW1paUo8f/fP3b3p6OjcXh4mxIRsrc3lZSUN9Q1pK0p+/f2uqK/n5+VmZmQT5+dpamp8+fRoU4Gdqaqyrrdnc3JKcmPDnz++AgAAGBgZDfT1I8MKdxsUJiiwONlYGBgYPD48L588eOrBfW0fb2NCAgeG/o73dwf17i4sKcjLTWdmkwCelIB+RP6IB6E4LOgTAka0bJ7Y2HL3z5DcDn5sUGzvT37d8Ih//s7xn4zT/eMtRne/pd64jT18/+M/48M7HD29fMzAwSEnwTq71uPbo96HzB7gYmfTEuW9++fj605/fLP+/PlQ0fp77+AfLvp/bGVhOcnJ8/PT5Gxs7S0SA7esPn24//frz/+9vP/4pKMmHu/uxs7Mw/v587Pbre+/ePzm899mzR6C7kxiYzPh5gr58Vfj9V5WVlU9Y4rOZDrOXDGN47XdmEYkjW/kPrvz/lpGFW5ZNVi186dQN5y8zMTAysTKL+/jIffj658uHz///87OyvX505/ev/x++fPzyl+E/JxcTAyPXv39SEpIS0hInjx/7+RM6jogawqAhXHklRZ/K7s/coqL/vv78z/T5LzMH478vP//8/c/AysX+79PnOw9fP7x54/vRpb8+vvn+G9pCN1CUttDVTm3sNALv7EM1lihe8ZEy7qdcr/e8ZGBhsLS0ZGVlhexPmjBhwuvXryUlJTMzM6WkpFasWLFv377///+bmZmlpKRs3ryZi4vLzc3t7t27c+bM4ePj+/nz5/fv3zs7O6dOnerp6amkpPTy5cvVq1dnZWV9//59ypQpN2/e5OXlTUpK0tfXh+zI+fr1q7W19d+/f6OjoyF1AAMDw8OHD3fu3JmcnMzMzAzZ+XT58mUREZHCwkJZWdk1a9ZISUlBmo9w7x08eHDFihU/f/6UlZUtKCjg4eGZPn36+fPnubi44uLizM3NP3/+PHny5Bs3boiIiGRnZysrK2/btm3p0qVycnLKysoGYDB79uyoqCh+flAxDVmo/v//f8i+1ObmZjk5OcgmTdAxh8ePQ87PY2Bg+PTp06RJk+7duycgIJCRkaGmpjZ9+vSAgADIHuH4+Pi1a9du3LhRXl5eWVlZUVFRV1d3+fLlf//+PXPmjKKiYlFRET8//5QpUzw9PZWVlTds2LB582YmJiZ3d/fAwED4DOOjR496e3s/f/5sZmbGzMyckJDw9+/fqVOnXr16lYuLKzEx0dDQMCEhQUVF5dWrV79+/crNzdXV1d2+fTsDA4Onp+fdu3cnTZr0/v17AwODzMxMyBKxyMhIBweH9HRQGb106VIzMzNVVdWrV6+eO3cuNjb2169fs8HLp0RERGbMmHH+/HkODo64uDgLC4vZs2dbWlrq6Oh8+vRp6dKlkOUWG8HAxcUlKioKHi/Lli3bsWMHOzt7amqqmZnZ379/p0+fHhgYKC0tDVczeBi33t+uOVq3yGM+BwuosTJ4HDZUXPLt1z+DluuTwmX1pEG7rLA6m4WV5efPn69fvxEUFOTh4f79C+MAHVBZyMDIBIKgQzrAcxosLCwvXr5kYmQUERX9+wfafQfV2ODTp1mYmf/8/fvixQshQUEuLq6/f/+ysrG+f//h58+fYmJif//+QaxzAA0gM4K5oJFiFhbWv39Bh92DnMrIyMzE9J8BdCkf6JgUiEqI0v8MIEsZGTnYORiZGH7//gO6Tgo8cQ+yi5X1H+gkLJCBTMygqwNhF68wsrKyfPr06cuXr1KSkn/BPVRWNtYvn7+8f/9eQlKSiZHp37+/LKysP3/8/P7jOw8PDysLy8tXr/7++SMpKfUXdJwF6MyNl69eiYuLs7KAjscG7ZoFbWODhBHY8yCngzATMzMLM9Pr129+//4tLiHJwPD/75+/oDUYoDFPkALQHgrQ+Ml/hCGg9W9go0Buh6hBmMkCXh37//9/NlaWFy9fMTMzi4gIgzrhoBlNsKEgBigSwGpYv3379u7dO0lJKUZG0MQ02DgsBAsL6Dybly9fMjIyiItJ/P//7++/fxCPs4PWg/L//v3nxo0bkyZNnDRp8ru3b8XExJlBofqHjY313fsP375+lZQEzXRB71oCuRyEwTaBjtH/B767kYWFhYOd7c/f/3/Bh5OwsrG8ePGKkZFBTEwMEn1M4N4P7BwYln///j5//lxIWJiTkxMS1wzgnX9MjIxPnz0TFBDgBqXVPxMnTRQRFg4MDPz585e4uOjv339Ai/uYGEHbecGnYoMMB6UcqOvArgKfrAOKCZAgIyMjEzPzzx8/QCscQEnoPxMjExMz04cPHznY2UNmPypzFwsxEoRqHPGAbo3RDRNaGk6+/PjrD7Mn42t5YZ7DvNLcb999kpJX+nxbm5vlFzvvySffb3Nxv7nz8PUb0A1MwkLcpoo+Pz9qewbuuXj/0uOPP4QFuT99/v3uPbfQvdRifafPT7hbrm+7zXqchePkl88fONjZciKs3/74vffO209fviqr6qjrqjH9+/n981cxAcE7jz5dPHP+z4sH7z6+B+dIJlMh4ZDv/+X//Ndg+8fBxXamIHJTabPtv8uODF+kmJZx/NzN8JaN9Zna3yNqkTN3rr9zi5GBiZWJQSDA69M/VoajJ0JdbFTkpF+//njt9sN7D25+/v7xi1vgb1V1loXzON5/5uJme/vqJfiwetDaLngGAqc3kP0yiorJNXXC/NxPf/L//M/49/+/f0xMb7/8/MPM8ZeZ7ealy+8+fWO8e/Hj6fX/fv/+ASvEKW+M5u0v9FT28JRzBxd/0F4y2FUMyKuvINPu//79g8znQhT8/Plz/fr1hoaGfHx8JSUlzs7O8BYbRAEy+RvjsCfks4SQVaKxMTWiKQAVjf////nzB9ltkCVWyGOuaOZg+hfZ2L9///7/D9oE+vfv3zlz5piamhoZGSErQGajmYwsBRl3BN9Bi3KCLy4tkBFKeDMU2ShMLXCRP3/+hIeHl5eXm5iY4Jojgyv+9evXvn37Jk+ePG/ePMwlZcg2wtmYgQmXgjNA+2z+/0ezHTLdjxwLcPWDjTHaGKUwRr79+mfRefPd1z/wm0SwGgiqj5mYQPfVQbZbgSpvWBsIhQ0TBJsCusKIAdRyAfFAtTgDtEUIWbQEbkoizGRgYASdMgZafQhqC4DV/Gf4/+ffD1CLAMxFMQfEAS0CBdEwWfAqF5BukKNAErCWKZgNkgVJoghCVryADYAW8KDb9BgZQadsgkVBCsDnn0GaQWCTGEC5A3S+H2iCDtJIgquHhhX47DyQXsiSNbBRIAeAGRBDICSadnC7ExxQEM0gJugSIJDjQHpBNoMn6CC6QS4BCYP9BBKC1VSgwIcfUwq+4xCqC2QAA4SN6lNQjQYyAYpBzURQfEG4jCC1kEkwmCAjEzMTpACBFOb//v1jBq95Rw4ocMjBAhOeBiBmQkiweyBMiKsgbNAqW2ZQ1fYPvu4FTTs0/YCnUUEJAdYKQhX/Bw4dyMZQZKNA3RiQVlAMguIF7Ae41ZCwR7gH7EhQKx4cHSBl0JVPTF9/siyMV/DTBw2IjAI6TtNDemlM/5gEeO58ZPv+/asA8+efv3+xfHj9hJHzz5sPbGx//3xjZmD49ws2CvjzO5vAZScuRtnnjx8oKz758ejP7fOGf7/9v/HirB0buxCXEN9fRhkG0Yf/JRj+8zAwfGBi+Pfx1UsuEUlNRfWHvz/qqKsI8fC+vHH1M5vgrXcvZP/+sJX+c/EHy7sPDP8ZQTP2TwV1ZhsnMDD8Un62RezPq0B5rXImZqFfvbKMe7/9+fb//6+/Ygx/RR9fWv3i5evPDKCeJiPDv7/ML9/+cvP+9/vXmQcP///9IyGtaqQtwMjCev/mRYbXTz5xsv36/fuPgszfx09AHVEGBlZmRmZW8PQK9PB6EPXv/z82FmaOv985GDh5mX4yMXCw/f/9/99fRg7WT0ysbEx/1ORlHhx6dP/ymX+goVKUZg3lqRYy64rZAIKsSYebDyodwFkaLsLKyvrz58+WlpZ///7p6urGxMTApTAZyC1FiCyodwhh4SUxNWIqB12zxgqaB4FLYepCE8H0L1wvaHcazKdMTEyRkZGQRaLICpDZaCYjS4FyFAuWddi4tOBxFaYWuAgjI6Ozs7OQkBBaWxDZJXDFL1682LVrV3l5OZEtUcgaYmSjsLKhJSqqHJFRjKpplDckQ4CTlWlHnsrfv/Bto4PLFz//fD5wv/fLr2eMDKB2yeBy3PByDahBBm63gb31n5GRhYmR6c+/X+A2KVgML8HIxMDCzPwbVu/jVUtXSWYWJob////8/UekR4h33L//f8R4tKzkcsR5cc4qjECApeKkRSgwMjL8+fv39eNnDExvzjD8PsfAyMbw4C/j3/+PXjEwMjP8+8fE+OEv4///fxl/wuZlGBhY5Fj4VH+LLjmo5JX5PMyTZft9abYn9nf/fLrH+rz/9CbFVzqPGN79YPzK8Z+ZgQF0ozELn2x8Wt7Pv5z3nj5QVFD88fnTnZcPfxjavRVgZzxxRUpM+Nr9naCOC7gv+I2d65Wo6h9mxrvSWqysjGrijIH/D31lOP/lzwcmZpZ/DEyM//69/y1e5zzl+r5GhgvPQfM9LMw8X7++f3jvp6TUG4Zfp09fN2Pm01JXs+Q3+fvjx7lDp6WFr/5mZn/97MXfX1/+MzEw/GUUF5NSU1X//x+0TPbHj1+gjUD/fn39+k5GWuraifPseo5M3D/ffvomzsUqyc8owfDxybN3L16///H07b39Wz+/v83BDppToEWMkGEmExNTfHx8dHT0nz9/IMsWyTBkkGthZGTE3xIdDO5nZmbOysoi0iVycnLwi3yI1DKqbDQECIYAIyODFD9Kh5CgFvoqEFYRbaGvjaO2jYYA8SHACB5qJ1798Ad0aoyCRun/M/z89Y+J4RtodJuB6TsDqEsNGT4EzSOAghpGg9gMzAyMH//+vf77x6vXjDMXckT4Moorfvl0kc2cMfnDn/cnvh3dz/DwHcMT5v+//v8HrfpiYvz/8cvXj59+szAzsv35d/fKbTYR/h1P3/N/PaqiZ37t5cf15888e/eBAdLNYWRgeP2I6eBi0JocNu5ff1l2fmNM0lH9/UNbSPQ/DyffP0ZOZobHJy4ZntzD/PfbP9DcDeN/RiZmOW6e91+//eHj537z4ufPrzfv3mPh5OBm4fjx5yczH5e4sJiItOL9R49ev/71/vt7Bob/LEz/2Jj/MzCw/GVk+sf0R1pciOHvj1evvvz79fX+tZMvnn/7/Y+R/8/HX+Jsz8RU/v36derC/c9v3zJ9ecXw5xEvNzcz878v36GrpsChMvAE+IQQ+iWbgffwqAtGQ2A0BIZkCFB5TmlIhsGoo0dDYIgAerYq/jPA1maA1owzgCaswdPfkKCCHCoPYYPIP/9+H/h/4zvDs08sx/68v7NunYH6P0O+n+zMjN9f/r798dc1JoGnf99xMjJI//v/BaSBkfHe+7+bDp6K8vV4+/7btfuPTM2MGIWkHrx+y/nyOTsny7tv38DL28ENYKb/jH+ec/3cxPobdLWemIik8FfjadOYH9zjtbHx8vJU+vnrw4cPN5bO/fZ/Sxfz1zuQZSGgY4AEBEVYWf4+uM/5/NPbP++ePmF8//GDkrqCrLKqqCjPhQsX3v38Jy8r9+3b57dvQStfOVj+KQjxf/n64df/H6zfv//48IOJ5Q/T33/v3n9i42R6e3zzp89f37D8vw1aNsrAxPj/9z8mJtAKHEi7/Pd/xv9//kDYIC+O4tEQGA2B0RAYDYHREBgNgdEQGGaAno1RUNDBGlYgGoTBM+YgCXD7FMKAkH+Zfj1hPP33rzQTy0dukbf3X9579JGfjeXcP67H3//f+f/rLzfTH1ZWLqZ/ov8ZwI1FZiYxLW0uAb4XT2/8evfsP+OP/19/iHELKokrBHnanTh39c7jVzc+fmL4/BI0JPuP4fOn98zMzNzc3OaGemoqKu/evz1+9C4LG9uGDZ/PnfkpwMd98dLrk6fO/Pr+7T9oGTxog9yff//YuLjU/jI/vn3r/a/fYqIK3768//Lx5YOrP13sbK4/+/rs5VuGN5///v3Dx8cLPZSRmVfF2ExGiOMvO+/L55+v37z/7den99+fCUv++vrp9+8f90En4Pxl/McAHi9mAI0gQ3fOg9ijeDQERkNgNARGQ2A0BEZDYDQEhjmgd2OU+OD88/sXJ+cTNq5vjAy//3/7y8l+/y/3238sH1k4v3L/YfnLwPHvGy872xdmxlu/wDfa/fv3/8adW29fPTHkFn9y/ZKFldnvvz84ObjEBPkE2EFLNv/9Z/8PXszOCNqNBLrGWUlJxcBA58/ff1dv3GT8x8zBys7wm4GV/d/LVy/ef+CSU1DnExA6febswwePQCOjjKClqf/+/5MS4Gd8/+nj76/KSsp6mgZPn9979ODuweOH7zx+zszK/f/vvw8fPvz5/RNywiI/P7+Mk4kggwI3C7OiLoOVy68/P169fXqNnV9w3fodx0+cZmBgBC8CwGiMEx9SJKpkhGwtJFEXFZVDtkzi2btDRbuGh1FEHkQwPDw76ovREBgNgdEQGA2BkQYG76qaP39+f/hy49XXw6+/nHz19PPHN2+/frv35fP7D69+f3r749vX99++vfz69eXHL7d//PwKWtP5///333/e/Pp59iXrkb9qn7l0GFnYtJWk1ZWkv/74xfjv368fX/+Dz3r4z/BPSkrSy8tDR0fn8eNn9+7d//Pn99//jKwiH5QcXrIK/fn9h/Hf3x+fP70REBD28vJ2dXMFXQr8n+Hvf4bv33+I8whwi4t9/890687NvwwM6ur6UvLKz999Ymfj4ubmZWdl//7125s3oKNSQTcVfX//7f4rtl+fL50+c/rooZvfn75mv8sjpPL+l4akgjMblwDoLigGJkbQdid6pD1WFtatW7f+/PYTbtnfv3+3bdv25s0buAic8eLFi9mzZ0PORYcLUsjYuHFjSkrKpk2bKDSHitp//fp15MgRuDc/ffp06tQpSF+CElu+fPkyffr0T58+oRny+/fv2bNnP3/+HCL+9+/frVu3Yg1/BgaGa9euZWdnt7e3I18tA9FIIQm51JRCQ0a1j4bAaAiMhsBoCIyGAOVg8DZGQYOR/0GXyYMO8/3/5/8/hn//QSfJ/fv/9x/ovIW/oIOF//2HiINn/Bm05WRsrGwP3fv9m0d858WXF++/FRcWlZYQ//WX6ff371/fvfv16xcrC4urm7u1tfXrd29u3Lz+9fNXRgbmfz///fj5Q9DwmUXaGynT5z+///jx5deP7//ef3z75csnZSXFyLAwKytLJiamt+/fM7MySktLMjMy/f3z7/zl819+fNVQ1xEXEuXhYONgZWRiZWBiZmRlYYWMP377/G332j0TOvsXz+zfv2vDj9u3ft7+9O/RYa7nGxjeXv7/F3QKNOgyU+i9UJRHKAETmJmZjx8//gvpNP5///7dvXsXfBUEut43b95s3LiRiiOpz549mzJlSmpqKuRmIHT7Boh//vz54ODgI0eOQOx/8OBBT08P5AAsiAh55K9fv9avX//161c07X///t24cSO89fn///+7d+9C7ilFU8nAwNDd3W1sbJyUlIR28BamSlJFILfYk6prVP1oCIyGwGgIjIbAaAhQHbBQ3USqGgherQmdwYY0OKEcsC3wdZYgHhMDgwo/n6mUNPcf5lN3H/Myf9WR/fD1y+dLZ9j+/uX9/vmdjBjfswdMAqLCdnY2N2/e+v+H4f/v/3+Y//xj+sclycTP9+btsy/Hdvz/9IJFQIn512eBb+9+Mf1i/fX9PycXj4KCnKSMzNWrVx8/fbLr4K5PL16CLtBgYXr39uWZMyc5QXfUfmNmYfz17ScrEyMLO8sf0CHqIFd9//r56o2z7MyCFqaKKsrqfz9/e/GG8eXXf+KsL6WZH7Izg65oAh0aBV45ANJAe8zOzn7ixInT509ramr6+vqysLCYmppC7iI6ceLE7t27tbW15cCAlZWVg4Nj9+7d58+fNzMzc3FxQWuYXrhwgYGB4dixYz9+/IiIiJCSkmJgYDh8+PDBgwcVFRWDg4M5ODhOnz7Nycl55swZExOTdevWff369ebNm9zc3Pz8/Fu3br1w4YKenp6Pjw8LC8uZM2c4OTlPnTrl4uLy5s2bP3/+7Nu3T1VV1dXVdd26da9evYqIiJCVlf3y5cuWLVtu3bplYGDg5eXFzMx84sQJJiamvXv3ysvLh4SEsLOz//79e9OmTVeuXIE4m5WV9fjx4/v27ZOWlg4PD4dcSgQP6XXr1gUGBq5bt87BwQFycifysOifP3/OnDnDwMCwb98+Q0NDa2vr5cuXf//+PSoqSkxMDCJ+9OhRRUXFkJAQDg4OSBv03r17RkZGnJyckEP4du/effLkSS0tLT8/PyYmJnZ2dohFoPOZmZiMjY0FBQUfP3788uXLO3fu3L1719/fX0dHZ9WqVefPn9fV1b18+bK0tPSZM2d27dolLi4eERHBzc197tw5Nja2kydPuri48PPzr1ix4v379z4+Prq6us+ePXvy5MnTp0+vXLni6upqYWHBwMDw9OnTtWvX/vz508fHR1NT08TERFRU9PHjx69evbpz587t27d9fX319fUZGBhu3769bt06MTExfX19Xl5eVVVVSFj9+PFjz549586dU1JSCg0NhYTzxo0br127Zmpq6uLicv/+/e/fv9+4cUNeXt7c3BziazU1tYCAAHZ29qdPn65evfr79+/u7u6Qi1XXrFnz5csXR0dHU1NTeIBA7BolR0NgNARGQ2A0BEYOGNQjoyREAyPjn7//Nm/b0TOxb/f6VY+O7r16YM+qpevnzl48bdrM3om9q9ctffr4+p8fX5mYWM+cOXX33m15aVk5QwbrjG+qtt8ZOT++f8X276PgnX08v17yvn38j4Hrg6TxR8PIZwKKX37/+r1//97rN24yMTM/ePjoyNHjd+7e+/jx46tXr968eXvr1q2Lly7dvX/v0ePHr9++ffv+3bt37z99/ARp0PxiYH789gWTGIOOjREfN+/3T58+vn/56uufy68FH73gY/zHBDpPADw0SoJnKVDKyMz0+vXrTZs2SUtLz5s3b8GCBYyMjB0dHc+ePTtx4kRNTY2UlNT9+/cjIiJu3LjBzc198eLFgwcPSktLd3Z27tq1C83mJUuW5OXl8fDwfP/+PSMj48OHD1u3bp04caKamtrFixc7Ojr+///f3d1dXFz8/j3obIE/f/6ANmx9//7///+pU6euWbNGQUFh7dq1/f39DAwMkyZNysnJefv2LSMjY3d3d0dHh7i4+NKlS6Ojoz9+/Pjz58/c3NwfP35s37791KlTOjo6S5cuXblyJSMjY0lJybRp02RkZDZu3Dhx4kQGBoaOjo6tW7cqKiquXLny/PnzBw8e7O7uVlNTu3PnTkNDA+TeI4hfHj16dOvWrcbGRkiDDCKITP7586eurm7q1KmSkpLTpk1LTEz8////ixcviouLGRgYVq5cOWXKFHl5+ePHj9fU1Pz796+np2fr1q3S0tLr169/8eIFNzf3smXLFi1apKGhsX///kmTJqEtlv39+3djY+PDhw8vXLgQExPz4sULQUHBvLy8u3fvgicH/n/9+vX379+HDh1qaWlRVlZ++vRpVVXV////Z8+enZub++bNm+/fv5eVlX348EFBQaG+vv42GMTHx9++fVtKSqq0tPTSpUvv37/Pysr6+fMnFxdXf38/5CrXBw8enDt3LiYm5vnz5yIiIrm5uQ8ePHjy5El+fj7oory/f5OSknbs2AEPjZMnT27dulVHR+fw4cMTJkyAhPOWLVsUFBRWrlx56dKl/fv3x8XFXbp0iZmZedGiRbNmzZKXl9+/f39ra+vHjx9LSkpYWFjk5eXnzZv34sWLsrKyHz9+KCoqzp079+XLl3BbRhmjITAaAqMhMBoCIw0M8pFRoqPj//+/f/8+fPQYruEZAxMDA8rQKVSKkYmRkfXxg2ePHz63TP5hFMFwYgHrpe0Ckvq/v7xiZeP++/7nD24hgaenfwkk31PxY3ny8OfRrU/Y2JmExUQhJkDGuiBs0BH6jIgDq+CCcMb3Hz/Y//198v3V/K2bed/8/vHtx9/f///+ZWZk+Mfx7+P3Xz9AJ9+CznKC66At4/+//1ycnJWVlVJyUuLi4itXroyPj2dhYfn///+qVasSExOjo6MZGBj279//+/fvv3//ysrKVlRUCAoK/vjxY//+/ebm5hMmTPj27ZusrGxubi4jI2NkZGRcXBwDA0NMTMyZM2e2bNliZmYmLy/PwcHR1tYGOZs9Li4OYuyfP38ePHiQmZn5+fPnwsLC+fPny8vLu7u7x8fHp6amMjIyRkdHp6Sk/P///9+/f0lJSd7e3tzc3CtXrszLy/vx44e/v/+LFy8CAgL09fVfvXolLy9/8uTJ6OhoVlbWnJwcU1NTVVXV3t7ejx8/HjhwYOHChTIyMpGRkX///s3JyYEM93JxcbW0tLx7905UFBqb27ZtMzU1FRcXd3Bw2LBhQ3l5OWYEcHBwZGZmWllZ/fz58/z58xkZGW/evImMjPzw4cPq1auLiopsbGzCw8NDQ0MvXry4b9++2bNnKyoq2tjYpKenf/z4cd26dZ6ennJycnZ2dtOnT4+NjUUbYObk5IScvWBhYVFQUMDAwHDlypWzZ8+GhYVBIkVGRiY/P19VVVVBQUFAQKC2tvbFixd///6NjIxMS0s7fvz4zZs34+LiWFlZhYWFt2/frq+vr6KiUlZWxsDAcP/+/dOnT9+5cwfSMGVgYPjy5QtkaJaJCXQdtq6uLsTSc+fOXb169enTpzo6Onl5eZAhUuTlCtbW1rKysi9evFBRUTl27Njbt28PHz48f/58aWlpSDgfOXLEzMystbX1169f9fX1PT09WlpaAQEBoaGhDx8+/PLlCy8vr5aWlru7OxcX19u3byHDrk5OTiIiIpjBPioyGgKjITAaAqMhMELAcGmMIqIL3LJjZAKdqQ9qJoK2NoGajKDpfdAI5H+Gf6CDPJn+///7/9yGP1/f/2VhZFCy/sTEwvLoJruQ5v93j9lV7N+Im/x/dev/6tq/z859Z2Di+svIzPyXCTqM/B/lSFTELbQINyBYP3/8+vXz15djN57+u44QhbEYQSfwg66Pgi1BgEnQjv7/n5OTm4ebm4GBgYODA9Lsg9xE//XrVyEhIYjNgoKC/8BAUFAQctkSOzv7v3//2NnZjYyMfv36JSgoCBm3k5SUhGgREhL68uXL9+/f79279+fPn3///sXGxnJzc3NwcMjKykLU/Pz588+fP//////27dt/kEtAl6Hx8fExMjJ+//6djY1NQUEB4hgmJiaIY5iYmCCz4X/+/IGsm1y0aNHBgwf19PQgRjEwMHBxcUGuTWJlZWVmZv758yc7OzsXFxfkZktWVtZv3749efJk7969//79Cw8Ph0gxMIB2qa9bt46XlzcvLw8yS15QUIA2X/zv3z8WFhaIf1lYWCCO+ffvHysrK8SbECl2MHj//j0TExPEfAEBAQ4Ojh8/fkBmrl++fPn///+YmBiILyABAiEhSejv37/wNhknJ+evX7/+gAFk69KPHz/evn27b9++v3//hoeH8/DwMDExycjIMDAwfP369du3b0eOHPnz54+srKylpeW7d+9AW+7ApnNxcf358+f9+/fCwsJgAQYeHh5I3IEOEvv3D+IjBgYGbm7uv3//YiYDiC4GBoaNGzeuXbtWV1f369evzMzMkEYtNzgtsYLB379/Ienh69ev8ETCzs7OwsLCycnZ19c3b968vXv3/vr1q6enZ8qUKQsWLJg4ceLXr187OzuVlZXhFo0yRkNgNARGQ2A0BEYUGH6NUXC77j9oTBR8Xy6YC2qJQqOVkQF0q/b/f3+ZmJh/vOI5v+a/hgUzp+i3Hx//K9j8V3dgunvw1+e3X9n5WD8/5nl6mYOZmZmZ9R/Dvz//QUs7QU1bqIlQ8whSoJYG2CVYVILF/zKQaCIWg0gR+vvvz99/oPABtzZBjL9//7KyspqZma1du9bU1PTp06enT59OS0v79+/fX9DiV5D7/v79++fPH05OTj8/P7htjIyMO3fudHFx+fz58/Xr11NSUmRlZcXExLKysl69evX8+XNubu7fv39DVixAGkCQc50EBARERUWPHz/u7u6+Z88eHh4eYWFhyCQ+xHC42yBugAj+/fv3169fGzdurKqqMjExaWlpgUzv/gVvZoM0rf78+QNZ/3r69Gl7e/tz586Ji4urqamxs7OXlpa+ffv28ePHkPYTZACYi4urqKgI0uLs6OjYtWuXqqoq3MEQe+GO+QsGEI/8+fOHmZlZSkpq//79SkpKly9f/vbtm7a2Nicn58mTJ11dXQ8fPvzq1StBQUE5OTljY+OwsLBHjx59/fqVh4cHHqoQ8yGtamSfQkQgPgIlIAYGRUXFr1+/lpaWvn///sGDB7y8vPDgkpKSEhMTS0xMFBYWPnPmjIaGBqRhCjEcYqyhoeHGjRtfvXrFzs5+7NgxR0fH/2Dw798/+DECkOA1Njbu7u5+9OjR////d+zYERISAvEvIyPjmjVr/P39Q0ND586de+7cOQkJif///586dcrW1vbixYuQRu3v36A9eTw8PHJycjt37gwPDz9z5gwzMzMbG9vZs2dra2vZ2NhiYmJ27dolIiJSWFjIy8ubmZl56NCh0cYoJL5GydEQGA2B0RAYgWD4NUYJRiLoDHvGf0z/WJiZ2P/8/87w/cdfLkaW24cYbXPfKdtyvnn258IEbl3/H39+MLIwMjGzMP5jZP7D+JuBkYUBNCRK0PxBreDf/3/iYmKsLKBLpXl5eRUUFBgZGRUVFZmZmePi4p4/f56Wlqauri4tLf3r1y9OTk5lZWXISKGgoKCEhASa30DHC7x9W1xc/PTpUy8vLz09PcgkcnJy8rdv38LDw42MjOTl5SFDcZCxNyUlpf///7Ozs1dXV7e2tq5aternz5+VlZWQYVFeXl6IFYqKipAmIz8/v5ycHGSjj5ycHC8vb0BAQEtLi4KCAisrq5KSEgMDg5KSEjs7OwMDAycnp6ysLDs7e01NTW9v7+LFi//+/dvU1JSVlVVRUZGSkvLt2zd/f38TExOILWfOnImMjLS2toZwMzIyTp06paGhoaysDJ9JZ2JiUlZWhgwPi4iIQNabsrCwKCkpMTMzl5WV1dTUHD9+/OvXrwUFBeLi4lVVVS0tLatXrxYTEzM0NGRhYSksLGxsbNy9e/eXL1/S09O1tLSUlJQgBoLG7BkZIasaBAQE4EPI0tLSkKFNFRUViNeSk5Nra2uTk5N//Pjh4eFhamoqLS0NGQ/W0tIKCQnJzs7m4eGBrEPg4+ODGyUhIcHDw2NmZmZnZ5eZmcnKyqqiouLs7AzxFD8/v7S0NMT70tLSvLy8dnZ2t27dKigokJWVFRUVhUzTQ0IjMjJyzpw5Bw4c4Ofn19XVZWdnr6ys7O3tXbRo0d+/f5ubm0VFRSGphZWVtaKioqGhYffu3T9+/CgrK5ORkVmwYMGqVas4OTm5uLhcXFyWL1++ZMkSHh6eX79+OTs7Q9wwSo6GwGgIjIbAaAiMQIBvvSMVg+Poto2ddVWbz11nAA8GUtFkUo2Slpa2tLQ8duwoIyMT8z8mBrb/YjrfJRTZf31lef7ks4zWny93+cXluL79+/Hh+b83Nzn+/fnPzMTMwMJoYmC0ffuOd+CNOKRaSi31BorSFrraqY2dRgYG5JmZt7/IVtwqVDOEgZHh9+/fv3794ubm/vLlCycn5+fPn1+8eAEZ34qOjm5sbDQ1Nf369Ss3NzcjI+PPn6Bj/NH2oRcXF9va2pqamv779w/e+vn+/fvjx4/5+PggjdevX79CJmoh0+I/fvyAt00/ffr04sULCQkJSKPq69evbGxsrKyghvKXL184ODhYWFh+/fr1+/dvbm7u//9BW3m4uLiYmJgg43aysrI/f/6EuJyLi4uZmfnPnz8/f/6EtGI/fPjw6tUrSUlJSAP3169fDx8+5OHhgcwjQ0Lv8+fPEI0Q7t+/f798+cLNzY3sSIi9nJyckAUA//794+Tk/P//P0QlExPTjx8/Hj9+LCQkBJ8H//Dhw5s3b2RlZf/9+8fGxgaZ0X727JmQkJCIiAiygRB7IeEPGXuGTPF/+/aNhYWFjY0NIgXZ8/T79++HDx9ycnJCmo/fvn0Dz42DgouBgeHZs2dfv36Vk5ODbHKHxCwDA8P3798ZGRkhbd/Hjx//+fMH0gOBmIxp6f///2/fvi0uLs7BwVFYWOji4hIREQFxJwMDw/Pnz79//y4nJwfpqzAyMkLCWUpKioeH58ePH/DVF5D1A8+ePRMREYGsZGBgYHj06NHPnz+lpaUh3nzy5Mm3b9+kpaUhUQa3hQ6MW+9v1xytW+Qxn4OFgw7WjVoxGgKjITAaAqMhgAeMuJFR8OQkaKnk/7//pfSZLCL+vrrP/vjiP2aeP19uc75m+f3mFhML3yfmXzxKZj+tQhmOrWR4eeUvMxMLAyMz6EpQPGE5NKT+c3JyQTwCb8pAWoefPn1qbW3l4uJ6//69ra2toaEhIyMjRIqBgQEyPofmRTExMS4uLkjbCC7FycmppqYG5yK3M1hYWOAGMjAw8IEBVpVwZWxgABlBhAtCxkohQ6EMDAyQ5iYDAwMLGEAMFAADCJuBgYGNjQ1+RBFcEK4RIsLMzAyZ4odbhGYvPBAYGRnhejk4ONBMBtssADETQvLw8MDDBDlU4bIMDAyQuWyICKStxsAAXd8JEYQMakLYkJWycDYDAwPkXC2ICDxm4UEEEYd3GOAmY1r67du3WbNmff78+e/fv9zc3D4+PhC9EBLelGdhgRYdyJ6FNHkhKiED4WghA484iBrImlcIe5QcDYHREBgNgdEQGLEAWqOMBP9DbgEFb2z6/5+B8e9vZjH1H5pujB+XMN09wSKh8UdQmZGRmVFY8feHByyvH/8UMfqi4vD7/gW2x+fZ2dn+/wcNIoPWjA71sIKsQcT0hZyc3OzZs+/evcvFxaWoqIipAFMkPz8fMmiHKTUqMkRDgIuLq7e39/79+////1dXVx+ivhh19mgIjIbAaAiMhsAQAiOoMQpa8PkffJISE+vfv/8ZmH9fPfD/3as/fz4yqNmxfnrFwi38i4Pv9/cPjD9+sygY/r20g/npld/vboGmfxn+/APdqDQc2qL4EicHB4e2tjY+FahyaCNhqJKjvKEaAqysrPBx3KHqh1F3j4LREBgNgdEQGDpgBDVGGRlBC1Z//vgpJCgsJS7x5NnjL8+Z7z7hZGD5yyv0V0iJSVKH2cSD/eL239cP/X95m/nbZ87vt9n/svxhYPyjKKfNxc716yfiSvehE8WjLh0NgdEQGA2B0RAYDYHREBgNgcELoEdnDl4HUtFl/xkZGZjefXh3781xt2Aba2sHbi72/8w/mVlZf35geXic4d2DL9//fnr3ivH5NeY/35jYWVj+svzkFeBzdHGw8VO58fQg5j3jVHTdqFGjITAaAqMhMBoCoyEwGgKjITACAf1GRkGHVQ7oVvr/4MNHmdkYJMyu/5Z6q8xmLKvgfu3y/Rt3bvz495OFjfX6Pvbvr4Sf3vzGwszw9/9/Ng42Qy0tdR35v/zX77Ns/C/+CdelTnRLN/8GNADp5s1Ri0ZDYDQERkNgNARGQ2A0BEYOoFNj9Pfv34IcrLGOFkjHzw9IIDMyMv0XucbEdPv/l7cvWf/90Bfgl1NV/vP3NxMjAxMTE+svdhXZn/9Au5UY2dg4hdjYf9248/P/V152TZ0fjIoO/+l8QD1aGHGysoKWGqCJUsa9du0aBwcH5MBO4k26du0aCwsL8SsL379/f+3aNfiJnsRbNLRUvn79+v79+2ZmZpQ4+8aNG0xMTMSHLS67/v////DhQ3FxcbQDuXCpHxUfDYHREBgNgdEQGA2BAQF0aoxqmVjYhcT09fUOiCfBljIygO5eAm0lF/vBaejGquT+i4Hp6c+PjAz3+f6+kWf6I3rvzl0hXv7PHz+raip+Zrr7leH2S/bvjEws//8zfnjM8PTm/68fQZfRgE0bGOL/v//sPLwZsHtJyXIE+iasdevWiYiIZGRkkGTaypUr+fj4iouLidR19+7dtra2rVu3YqqfNWsW5Ax2TClKRB4+fLh58+acnBxKDCFV79WrV2fPnr106VJSNSKrX7t2LSsrK+RmeWRxOPvEiRP379+PjIyEiyAzFi5cqKOjY2xs/Pv37xkzZiQnJ6Odr4SseJQ9GgKjITAaAqMhMBoCAw7o1BgVk5aR1zW8/vjFgHuYgYHhxjOmux/ZbMP4TXz/SIj/5n//4dW1T+9uKsrwqKrIaD14feEV66Wv7FcZeb4ysTD8eMt47wzzndN/v74ALTQYcPcLCv1mAm3sJ98hjAyMly9efv7yuaWlJS8vL+i+U2ZmiHGnTp36/PmzhYUFNzf3r1+/3r59KyEhwcjI+PbtW2ZmZgEBgR8/fhw7dkxUVBR+jv3379+PHj3Kzs5uYWEBOa8eYhScvHTp0tu3b9nY2CCHdP7////s2bNv3rwxNzcXFBS8ffv2+vXrIee0q6mp/f79+9SpU79//7a0tEQbz3vw4MG1a9e0tbXl5eUZGBh+/Phx/PhxTk5OCwsLBgaGt2/fcnBwnDt3jp2d3czM7Nu3b0ePHl2zZo2lpaWUlJSkpOTXr19PnDjBx8dnamrKwMDw+vVrTk7Ou3fvKigo8PLyHj9+/Pv37+bm5vDTQ+Hu/////8WLF58/f25iYiIqKvr79+/Pnz//+vXrwoULOjo68JMyz5w58+PHj3///kG8CdH++vVrDg4OXl7e79+/f/v2TVhY+O/fvy9fvpSSkvr48ePJkydFRESMjIwYGBg+fvzIzMz84sULTk5ONjY2yD1Gv379evDggZqa2qdPn44fP87Pz29ubv7x48fdu3dfu3ZNS0tLXl5eQEDgNhhoaWkpKCg8evRo3bp1r1+/5ubmVlRUzM7OFhUVhZw/f+LECV5eXsio7YcPH1hZWW/evPnp0ydra2tIxN2/f//69etqamoqKioQ94+SoyEwGgKjITAaAqMhQAdAp8YoAwPDX/Cl1XTwEm4rQIOjDAz////99+TKj1U3/13YyWkfwa3p8EfO/K2w3Pu3d17cf3bxFfutv5wfWdiZ/nxhe3qN+e7x328eMPwHbX5iGOBJerDHmCkaFmXg4GCfMnWK+AtRfmH+JUuWTJ06lREM/v37V1tb++jRIwEBgTlz5kyePPn9+/f19fVz587l5OScOnWquLh4dHR0RkYGBweHiIjIwYMH4+PjIXdgQq7e2b59e1NTE/wsdLBjGSZPnrx7924NDY2HDx9Cbi2fOHEi5B7zOXPm9Pf379q16+7du1u2bHn9+nVmZmZdXd3379+ZmJgWL148ZcoU+IH5W7dunTFjhoqKyuzZs6urq9XV1SHXmn/9+nXHjh21tbU9PT137txRVFS8c+eOubl5eHj47t27nz17tmjRIl9fX3Z29ry8PHFx8Q8fPuzevbuysrK1tfXp06e8vLylpaXLly9/8eIFHx/fqlWrOjo6hISEII6HkNOmTTt+/LiUlNTMmTP7+/t5eHiioqKUlJQ4OTn7+/vb29sNDQ3b2trOnz+vrKx8/fp1yG1SEL07d+68c+dOQ0PDihUr1q5du2XLluPHj69YsaKsrKy4uFhKSurly5cGBgYVFRVbt25dunQpPz9/TEwMaLkIK+u3b99KSkq0tbUFBQWLiookJCTev39/6NAhZ2fno0ePvnr1atGiRampqSdOnJg7d66ysvL06dPLy8vv379/586df//+/fnzJzk5uampqaysTFBQMDc3V0RE5MOHD9LS0q2tratXr16/fr2GhsabN2/WrFnT19d3+PDh6dOnq6qqLliwIDEx0dPTE+KFUXI0BEZDYDQERkNgNARoDejXGKW1T4gw/z/yitW/v39dP/r3waXvBk5CNpHCSoZfleyf3bzw7f/130z/mV7cZL13lOHZzR9/f0Gn5v9Dri0iwprBrOT/v/+cHByTJk3iF+a/du0aExPT////mZmZT506dfXq1dWrV7OysjY0NMyZMyc4OPjLly+gZQ3gKyX//PmzY8cOLi6uWbNmMTAwhIaG/vv378WLF3fv3l2yZAkfH9/r168hN5jDvf/y5cvNmzfPnTtXVlZ2xYoVc+bMYWBg8PPzi4mJ+ffvX3Fx8f79+7Ozs0+fPp2dnW1qavr379/09HRRUdHPnz/Hx8dfuXLF3NycgYHh58+f06ZNq62ttbCwePz4MQsLy7JlyxgYGGpra799+5aQkHDv3r13795pa2s3NDQ8ePAgJiYmLy+vqKjo69evEydOZGBg6O/vFxISqq2t/fjxY0pKSlhY2MuXLw0NDauqqj5//nzs2LG2tjZdXV3IwCTc/RCGl5dXSEgIExNTRUXFvn37AgMDX716NWHCBG1t7YkTJ65atUpYWHjPnj1r1qwRFhaeNGnS8ePHIRoZGBiMjIxWrlz55cuXixcvfvz48eHDh4cOHTIzM5s+fbqlpWVRUdGnT58iIiICAwN///7NyMg4Z84cLi6uq1evvnr1Kjc318DAIDs7++DBg69fv+7t7WVnZ3///r2CgkJCQsLp06d7e0GLXvj4+Pr7+zk5OadMmbJ8+fKpU6eeOHHCzc3N39//169f7969+////9y5c9XV1RsaGn7//h0REQEZBhYUFOzr6/v375+Xl9eNGzeOHDmirq5eWloK6QzAvTDKGA2B0RAYDYHREBgNAVqDEdUYRQ5MRnDD9O/3z/+Pb3xz+zSvuS+PTQgX49+/n54wPzr7/+HFP98+/0XWAFaPKjAEeX///TU0NOTl5mFgYNDS0oL4gJGRETKsCJmu1dPT27NnDxMTE/x2JQjj4cOHurq6EC0GBga/fv1SVlb29/fPzMxkYmJyc3NLS0uDyELIZ8+e8fPzQy4LVVdX5+Hh+f///4kTJ/bu3cvPz//o0aMfP36Ahsz//oUMmv79+3f79u0XL17k5+f/9OkTRJaBgeHLly9sbGyQ24AgF1o+e/bs9u3bDQ0N//79ExISYmVlZWNjMzAwgNxyycfH9/Pnz9+/f0MGCFlYWJ4/f379+vX6+vo/f/5ISEhA2t+QCWteXt709PT29vb////r6+uXlpZCHA8nz507t2XLFgEBgfv370NazEpKSpBrLSUlJe/fv//ixQtpaWnI9evGxsbnz5+H69XQ0BASEtq7dy8HB0dYWNiOHTtu374dHx+/Z88eZ2dnyIWoMjIy9+7d+/v3r62tLeQWUHZ29nXr1klJSbm6ujIwMFhYWNja2qampjIzMwcGBsrLy//58+ffv38QWx48eLBgwQJ2dvZHjx5Bghpy3TyoC/X/P6Szcf/+fYh1rKysSkpKd+/eZWRkhCwPYGJiEhAQ+PDhQ0pKSlNTU2JiIjc3d2Fhobi4OMT8UXI0BEZDYDQERkNgNARoDUZsY/Q/eDsTuMpm+PPm2Ydtsz7fPCbCL8ty+9yPTy/+MYCOcYI0WGkdBXQ1n4mJ6caNG789frMzMD99+lRYWJiBgeHfv3+ysrIbN2789+8fExPT7du3JSQkWFlZ379/DxnsfPDggaysrJSU1NGjRyHOvXHjhqmp6efPn319fdPT09+8eRMdHW1ubq6pqcnGxgZpvIqJiX38+PHt27eioqIPHz788uXLy5cvZ82aNX/+fGlp6dzc3L9/Qc19eLvqxIkTBw8enD9/PjMzc3BwMMQiyBXnP3/+fPbsmaCg4MePH//+/SsoKGhkZNTe3v7379/nz5/Ly8v//fsXYs5/MIDo/fXrF2TZgLCwsKWlZVVV1e/fv1+9eqWoqPgXDCBrTw0NDf39/b98+ZKcnLxnzx4vLy9GRkZIu/z58+ezZ8+eNm2anJxccXEx3MGQAWOIjSIiIs+fP//+/Ts3N/fNmze/f/8OsZ2BAXQ+g4ODQ1dXV0JCgq2tbXZ2trq6upSUFGShp4uLC8RfMjIyz549g5jJAB6HjomJiY6OTk5O1tXVlZaWjomJKSoqevz4cVJSkqOjIwsLy58/fyCOb25urqqqMjc3nzx58o0bNxgYGP78+QNxJyMj6AZbBgYGGRmZu3fvgtP6/ydPnnh6el66dAnieIhT/////+/fv46ODhYWlg0bNvT09CxevPjnz5/wZRIQZaPkaAiMhsBoCIyGwGgI0AKM2MYo8kAnE4jz/++dy68YLoOqbPCdoaB20sAe5ESL+GZmYn765FlBfoGolNjDhw8n9E/g5OBkZWG1tbbdsH5DVmaWkJDQrVu3JkyYICUlpaaqVphfKC4h/vTJU3Y2dl9v33Vr1xUVFvHy8j55/MTZyfnH9x/VVdUaGhrMzMziYuIC/AKFBYU5OTk6ujoMDAzS0tLOTs5ZmVn6+vrXr18XFREVFBAUExGdNXOWkJDQg3sPLM0tQSN/5hYd7R3u7u7OziADp06Z+vv37+9fv7GysEJCgIODIyY6prqq2sTE5OrVq/n5+THRMTk5Oe1t7b9//WZkYmxta2VjZWNhBiVmJkYmdjb2///+Kysqc7Bz5ObkBgUFxcbE5uXldXV2ff78mYeHp76hHq7+399/Pd09fHx8oqKif//8VVZS7u3p1dbW9vP3Y2BgEBIUEhEWmTljpoiIyPWr1/X19JkZmXi5eZgYQbdFsDCzMDEyKSspm5maZaRnqKqqXrp0CW3JqbOTc3dXt7WVtaqKKjsbu4WZOSMjY1ZmVnl5+bOnz54+eaKvp6+ro3v+3HkONg6If1mYWbg4ueTl5XOzc1tbWktLSzs6OvT09H78+KEgryAkKGRmajZ/3vyS4pLkpGQjA6OlS5aeOnnqxIkTCgoKDAwM9nb2CxcsfPL4SUxMDB8PHzMTc3JiUl5+flVl1bt37yTEJezt7K9dvQbxAgMDAxsLGzsb+6mTp9asWWNubn7x4kULM4uLFy4uXLhwQv8EJhaQTyEOG04k3PvDyVOjfhkNgdEQGA2BIQqgYyd0cP2OHTsG2a4IcBsU1N4EnXbECF4S+p8BOvsJao+CROBcOoQQUVaIiIjs27cPPl1OlB4kRQUHis1ETfme8rx689rS0lJaWvr5s2csLCxi4uK/fv7cf2D/169f7WztxMTF/v379+XL13179/Ly8WqoazAyMUlKSLx5+/bgwQNCgkIampqMjIziYmLPnj8/ceL4/3//raytpKSkWlta3T3cjYyMGcAh+P///0OHD71989bM3Pzvnz/yCgqvXr06dOiQhISEqqoKw38GMXHxf3//njhx4tevX7Z2tg8fPDxz9qyGurqoqCg3DzcfHz/E7SzMzGfOnLl565amhoa+gT4jI9PbN28OHTrEzMxsa2fHz8//8MEDPn5+QUHBP39+37t7T0lJiY2N7cXLl2fOnFZSUtLW1n7x/MWRI0c4ODjs7Oy4eXju3r0jKiLKxw8y//OnT4cPH/769auhkZG6mtrsOXMEBQQCg4JAXmBkfP361aGDh0RERDQ0NRgYGIUEBe8/uK+kpMzMzPzu7dvPnz8rKCj8+ft3//793799MzM3//nzJ2QSH+Lyv3//3rt3T0lRkYWV5f79ByLCwjzgEwyePnly9NhRQUEhBwd7ZibmV69e/fn7R1JSioGB4fnz54yMjJDlBFeuXFZVVX316vXpU6dYWFhsbGxEREUZGRnu3r179eo1ExNjQQHBvXv3MjExGZsY//z5U0ZGlpGR8dSpk58+fbYwN38B2rkvycPD8/LFy8NHDnNzczs6OLKxsz9//ozhP4OEpCQDA8PdO3fExMT4BfhPnz4DYouL2dvbf/jw4dnTZ1raWozgZjfEL8OJvPvhbvupzkUe8zlYoH2A4eS7Ub+MgtEQGA2BoQVGcmN0aMUU1LUUNkZLD1VcfXuNn4ufmYnp1+/ff//+ZWZmhszSMjIysrGxMTIy/vr1CzKHy8TIyMbO/u/fv79//0LUMDExsbGxIYswMzGzsoGGMH+DTfvz+w8TMxPkZCKIiyEHFYE26DAw/Pn7F2HCn7//GUCzw4wMDKxsbAwMDJBZdVZW1j+///wDyfyDz1wzMDCwsrJCpqchC0wh5oB0/QRdUsDCzPwPPNfMwMAAUcbAwMDMxAQy7e/fP3/+QNT/////169f////Z2Fh+ff3L+RGK0ZGRnaQx5l+//n958+fv3/+/AcbAnE/RCPIyzAHw80H+ZOR8c/fv4wMDGzs7IyMjL9//WJgZITMkkO0I7sH2VJmZmZWVtb///7//PUTMqHPCNMIWuQAOvIB1BECuR8cpJBlA/CoYWFmYWFh+fUb5BfQSDDDf1AIw0xgY2VlZGL69esXMzMzJO4gvoB7n5kJdJjX33+g4X8WFhaIGkgI//v79+evX0yMTEzMTJDFAHCPDCfGn39/OFk4F7jPGW2MDqdoHfXLaAiMhsAQBaCZTfo4Ha2Gpo+lw8+W79+/Q1qK5Hmt1KTo+x/QtiHytBPUBTkCdUjfWjoMVwoTjLYRqYCFiYWdmX1Een3U06MhMBoCoyEwuAD9GqO6urrTpk0bXL4fgq7h4OCAH7ROhvPFuMTI0DWqZTQERkNgNARGQ2A0BEZDYDQEaAToN01PIw+MGjsaAqMhMBoCoyEwGgKjITAaAqMhMHTB8NwqO3TjY9TloyEwGgKjITAaAqMhMBoCoyEwosBoY3RERfeoZ0dDYDQERkNgNARGQ2A0BEZDYHCB0cbo4IqPUdeMhsBoCIyGwGgIjIbAaAiMhsCIAqON0REV3aOeHQ2B0RAYDYHREBgNgdEQGA2BwQVGG6ODKz5GXTMaAqMhMBoCoyEwGgKjITAaAiMKjDZGR1R0j3p2NARGQ2A0BEZDYDQERkNgNAQGFxhtjA6u+Bh1zWgIjIbAaAiMhsBoCIyGwGgIjCgw2hgdUdE96tnREBgNgdEQGA2B0RAYBaMhMLjAaGN0cMXHqGtGQ2A0BEZDYDQERkNgNARGQ2BEgdHG6IiK7lHPjobAaAiMhsBoCIyGwGgIjIbA4AKjjdHBFR+jrhkNgdEQGA2B0RAYDYHREBgNgREFRhujIyq6Rz07GgKjITAaAqMhMBoCoyEwGgKDC4w2RgdXfIy6ZjQERkNgNARGQ2A0BEZDYDQERhQYbYyOqOge9exoCIyGwGgIjIbAaAiMhsBoCAwuMNoYHVzxMeqa0RAYDYHREBgNgdEQGA2B0RAYUWC0MTqionvUs6MhMBoCoyEwGgKjITAaAqMhMLjAaGN0cMXHqGtGQ2A0BEZDYDQERkNgNARGQ2BEgdHG6IiK7lHPjobAaAiMhsBoCIyGwGgIjIbA4AKjjdHBFR+jrhkNgdEQGA2B0RAYDYHREBgNgREFRhujIyq6Rz07GgKjITAaAqMhMBoCoyEwGgKDC4w2RgdXfIy6ZjQERkNgNARGQ2A0BEZDYDQERhQYbYyOqOge9exoCIyGwGgIjIbAaAiMhsBoCAwuMNoYHVzxMeqa0RAYDYHREBgNgdEQGA2B0RAYUWC0MTqionvUs6MhMBoCoyEwGgKjITAaAqMhMLjAaGN0cMXHqGtGQ2A0BEZDYDQERkNgNARGQ2BEgdHG6IiK7lHPjobAaAiMhsBoCIyGwGgIjIbA4AIs9HHO3auXzx7ay8LKSh/ryLPl399/gmLizoFh5Gkf1TUaAqMhMBoCoyEwGgKjITAaAqMhQCqgU2P00Z2b25YuYOfkItV99FT/5/cvRU3t0cYoPcN81K7REBgNgdEQGA2B0RAYDYERDujUGGVmZmHn4GTn4BjMwc3MzMzKxj6YXTjqttEQGA2B0RAYDYHREBgNgdEQGGaATo1RtFD78/v3v79/mZiZ0Sbu//z+xczCysjIiKz+96+f//79A4swMjExsrCyMTIy/v379/+/vyysbGBxVOL/fwZUE1ClR3mjITAaAqMhMBoCoyEwGgKjITAaAoMF0Lsx+v//fwYGBh0zKzkV1Sf37lw7d4rhH6jt+B8M9K3sHty89uXjR2h79P9/ZlZWI1tHYQlJJiam////v3/96vLJo18+fpBX0xSRlDp/+AATMzNKWP7/z8TCwsjA8PfvXxTxUc5oCIyGwGgIjIbAaAiMhsBoCIyGwOAD9N5N//fvXwtXz8jcYh1zq9CMfKeAsD9/fv/////f379Wbt5J5fVcvHz/weOg//79+/v3LwsLq3NwuLmzh6aRqbaJuVdUQnR+OQsLK5+QsLSCMmTEFKTu75////79//ePnZMzJC2XV0Dw79+//2Dt0X9gKQYGhn///kKMBTd9QZb+/fsH0j4efFEz6qLREBgNgdEQGA2B0RAYDYHREBj+gL4jo///s7Kyahmbr5o+4fjOrb7xqYY2DjtXLmFgYAhOzVbW0f/86cP/f6ChUwYGBj5BoX9///75/fv3z58b5824cOwwCwuLjIpaZkOnso7+jfOnH9y4xszC8u/fX34hYTZOzi8fP3z9+FFUUlpBQ0tYQurbly8cXFyf3r/7//8/L7/Av///vn/5wi8k8vvXTy4e3s8f3v/6+VNIXIKJmfnj2ze/f/1iYqJ3u3z4J65RH46GwGgIjIbAaAiMhsBoCIyGACFA38YoI+P////ndTRwcHHHFlep6RnuXbeSgZGRiYnp/s1rR3dsiS2qgEzH//v3zys64ee3b5sXzYV6gZHhPwPju5cvfv/6ycjEaGzrpGthPaOx0s470CMilldQ8On9e8snddv7B0srKscVV21bMs/YwWVKdfG/f//cwqK/fv60bemC0Iw8bj5+cVn5uW11Wsbm1p6+rKxsl08dWzVtwrcvn6EWjVKjITAaAqMhMBoCoyEwGgKjITAaAvQCdGqM/vv79/evn0zMoNHHf3//cnLzsLCy/v37R15N4/T+XT9//Di2c6uAsAgjE9Of37/+/P7FxMy8bvbUf3///v/P8P//Pw1DE04eXlY2di0Ts58/vt+7dsXIxuHPnz8CwqKeUXFbl8y/fu6UqaObqJTM1iXzxKRkl03s4hMWZmJkhGx++geesv/18wcPv+DrZ09mNFRomVjomltPry//8vFjUGqWtYfP1sVz/zMw/Pn9m14hP2rPaAiMhsBoCIyGwGgIjIbAaAiMAgbQUCUdguHTu7evnj2FT4X/////188fgqJiYtIyj+/c/vMH1ARkYWaRkFN49ezx758/GUAO+/f/PwMzC4uknAIHF+KA0s8f3r949EBAVIyTi/vju7di0rIPb12HrDplZGJiZ+cQk5F7+fghOyengIjYs4f3GP4ziEhI/v379/3rlzJKqu9fv/z47p2YtAwLC8vzRw8g6wH4BIVfPH7w/z8DOyenjJIKHQJk1IrREBgNgdEQGA2B0RAYDYHREBgFDOA2H3SN5mhwjIbAaAiMhsBoCIyGwGgIjIbAaAiMhgCdAWjenM5Wjlo3GgKjITAaAqMhMBoCoyEwGgKjITAaAhBApzWjZw/t27xwDiMj43+G/ywsrEa2DvJqGo/u3Dp3aN/vX79B4v//s3Ny2nr5ndq36+O7t4yMTCYOzn9+/7p0/KiDX/D186efPbjHxAQ9UvTv3z/K2nqScvKHt27iFRQ0d3ITFJe4c/nC5RPH/v39a2Bjp6Slu3ftipD0XDlVDYg/R8nREBgNgdEQGA2B0RAYDYHREBgNgUEI6LRmdO+6lfs3rA5MzoQEAeSYT0YwgIhAyH///oEuWQJzIMd/MjIy/vv/H6QQLAgn4LKg00PBu5xAasAXL/3////vnz/LJnfnNPeo6hnCtYwyRkNgNARGQ2A0BEZDYDQERkNgNAQGG6DTyCgDA4OgmLixvTPd/L9h/gy62TVq0WgIjIbAaAiMhsBoCIyGwGgIjIYAeYB+a0Yh9yqR50pSdf39M3qvEqlhNqp+NARGQ2A0BEZDYDQERkNgNAQGANCvMToAnhu1cjQERkNgNARGQ2A0BEZDYDQERkNgcIPRxujgjp9R142GwGgIjIbAaAiMhsBoCIyGwLAGo43RYR29o2A0BEZDYDQERkNgNARGQ2A0BAY3GG2MDu74GXXdaAiMhsBoCIyGwGgIjIbAaAgMazDaGB3W0TvqudEQGA2B0RAYDYHREBgNgdEQGNyAfo1RRvAhoPQJDWYWFvixo/SxcdSW0RAYDYHREBgNgdEQGA2B0RAYDQEyAJ3OGWViYvr+/ceJkyc/fvgAOsf+3z/QYfX//jEzMzMyMrKystrY2Fy/fv3Zs2d8fHympqasrKwMDAxv3ry5cOGClZUVFxcX3G9HjhyRkpISEhI6d+6cubn5/fv3nz59CpGFnITPxMT09++fH99//PjxAyKOTP789fvWwyfIIoOf/f//fxU5aS4O9sHv1FEXjobAaAiMhsBoCIyGwGgIjIYASYBOjVFmZuY3b14np6S8evGCl5f3169fDAwMf//+5eTk/Pnz569fv65fv97e3r5y5UpDQ8MdO3YwMzOzs7NfuXIlLy9v+/bt8vLyDAwMf/78effuXUVFhaenp56eXmNj44oVK3p7e1etWiUiIsLIyPj7929mZmYWFpbfv345q0q/efMGMyzefvzUOnc5Ex2HaTHdQKrI799/OvKTlWWlSNU4qn40BEZDYDQERkNgNARGQ2A0BAY5oFNjFBIKTIxMtbW1gYGBf/78gYiwsLAcOHCgtLSUgYGBCQyYmZl//PiRl5dnZWUFaYlKS0tDFN+7d8/X13fatGkHDhyYO3fuypUr5eXlv3//7uLiMmXKlL9//8JHRn98/z67tvjP378QjcgkIyMjOyvr0GqMMoHXHCD7YpQ9GgKjITAaAqMhMBoCoyEwGgLDA9CvMQpZMyopKXnv3r3p06czMzP/+fMnNjZWQkICIgUZLn306FFBQYG7u/urV69iYmJEREQaGhrExcUZGBhkZWW7urrMzc337dt36tSp7u7uuro6JiYmPj4+Tk7O8vLy79+/MzAwaGholJSUMIEXABCMpH/////7948R1BRmhoyW/mdg+Pf33////5mYGJmYsKyp/ffv37///6GtZ4gegtagKvj77x8jI+PQahCj+mCUNxoCoyEwGgKjITAaAqMhMBoC1AH0a4z+h4Fbt26tXLmSkZHx////hoaGRkZGf//+/fPnj7e399u3b69evcrPz//z5082Nra/f/+uWbOmoqJiz549t27dYmFhgbQOmZmZX716tXbt2vz8fAYGhv///3/69Gn16tWfP39mYGCwtbUtKioEC4OajHjC6d+/f1wc7OLCgj9//X7x9j1kYJWRkVFKTJiTne3dp8/vP31hRm2P/vn7V4CXR4iP9////y/fffj+8yeaAvzWQZrdkiJC33/8/PT1G4SLR8uo1GgIjIbAaAiMhsBoCIyGwGgIDG9Ap8YoaKCRkYmDg4OFhYWdnZ2bm5uTk/PLly8cHBzMzMzc3Nz////39PT88OGDmJhYR0dHWFiYsbFxTEzMuXPnbty4sX79+t27d79+/frv37+CgoIfP34UExPT1tbm5ORkZ2dnYmJiZmYWFBRkYWH59+8fHx8fIwMjAwMjEyOWcU14dP7//5+PhzstxFtVTurHz1/7T1/cdOD4////fezMnc2NII3RhRt3Xbv/CN7c/Pfvn52Rroe1qRA/LyMj48PnLxds3PX89TtmZnwWQWz8+++fsoyktJjIgTMX/Rwsr997tP/0BTbwPi2IglFyNARGQ2A0BEZDYDQERkNgNARGIKBTY5SRgeE/w/+//0DLOv/////z509mZuafP39CRkv//PnDysqal5fHzs6+cOHC379/s7Kyzp49e9GiRX/+/AkPD58xY4aVlVVOTg4bG1t8fDwzM7OlpeWyZcuEhIR+/frFxsYG2d70+/fv//////nz5z8DaEwUQuKK1D9//zqZGTD8/1/UM1NBSjwz1HffqQvcnBz2xnqz1259+OJVqKu9l635ncfPfoNWuDL++/9PR0UhzN1+y8ETp6/eYmNjifRwjPZ2nrR0/e8/f/79/w8Z5vz//z8LMzPkrACIIKQh/u/fP1V5GSkxoYNnLy3YuOvf//+sLCx//v6F62JmAi0NhawBAAn+Z2BgZIC3g3H5YlR8NARGQ2A0BEZDYDQERkNgNASGNKBTY5QBPCn/+9evf//+hYWFGRgYMDEx/fv3T1FR8dSpU79+/fr582dnZycDAwM7GMyYMaOhoeHixYvz589nZWWVl5dnZGQUEBDw9/dfuHChsbHxuXPn4uLipk2b9u/fv1+/fsnLy+/evfsveMcSLy8vBwcHqPkLXtmJK3pYmJmPnLuy+8Q5IX5eTUW56/cf/fz1++fv310LVr18+/7X7z8Xbt5xNTf6+++foaaqnaHO5BUbHU0Mjl+6tvXwSVYWln///8/fsFNLWZ6JiYmbk9PX3kJfTenf//9Hzl/ZcfSMEB9PgJP1y3fvbQy07z55vmLHAW5ODjdLYx5Ojg8fvzIyMj5+8er45esu5obOZobMzEyHz13ZdewsCzNTlJ/rz1+/tZUVvnz7vmbP4VsPnhAz7IrLj6PioyEwGgKjITAaAqMhMBoCoyEwyAG9GqOgpZ2gs5kWLFhw/vx5SKD8/fuXhYXl9u3bv379YmJi2rNnDycnJzMz88KFC+Pi4mRlZR88eGBoaNjV1eXh4fH////Tp093dHRcvXpVQUFBWlr6zJkzX79+ZWJiOnPmTEVFBWSOHrKv6NvXr98/fcK/Q4iRkfHNh4////+P83bR11A6e+32f4b/v379AS0e/fdfiJ/Xxczw1NWbv/78efLi9b7TF/79/y8uLHDq6g1G0L4m0ErSj1+/HTl/9T/Df3dLY3ERwW1HTgnx83pYm1y5c//r9596qor3nnFuO3La3kQvwsNhyda9V+8+EBHgO331pp+D5et37421VINdbHceO/vr928vG9Ofv34dPHNJVU76958/O46eNtFSC3Ky7l6wGrKSFRJio+RoCIyC0RAYDYHREBgNgdEQGGaATo3Rf//+cXBwODg4PHr48MKFC6CZa/DIKDMzMxMTk6urKw8Pz7179wQEBF69ejVp0iRbW1sGBoa3b9+uW7fu1KlTlpaWt2/fXrx48f3797Oysh4/fqytre3q6srHx2dsbPzu3btLly5BhloZGBiYmZn///8n+fcPJyfiqHys0QaZRp+xZqsgH3deVKCZtvqhc5f//2cQFxZI8HN7/OL1gTMX2VhYXr//8OLtO9BZ/f//MTOBpuAhpjEyMHCys/3+8+fc9Tu83Fx6aopiQoK83FxcnBxfv//48v37ki17n7x8fe/J89yoACZGxgdPX/768+f2I9AR/YxMjHqqiofOXlqz+xDDf1Az3Vhb7eDZy//+/V++48DZq7devvsQ7GLDwcH27cdPiHWj5GgIjIbAaAiMhsBoCIyGwGgIDD9Av8aogKBAVkklLy8vDw/Pr1+/Xr16BWmSQpaNfvr0KSkpiYmJ6datW2xsbExMTCIiIl++fOnq6po8ebKuru6dO3eYmZnv3LnT09PDycnJwMAwc+bMDx8+JCUl5eTk/Pnz5+XLlyIiIj9//vz8+fO/v39nVBUICAhgRth/BoY/f/8ygZcNqMnLfPry9dnrdy/ffnj26q2wAN+Pn780FGWjvJzOXL2149gZ0DYo8OFNjIyMf/78vfv4mbaK/NELV/78+fv33z85SdEEP/cZq7d42pgpy0oePX/1yPmrgU7WDAyMf//9//X7z/cfP5mYmH79/v3v79+///4zMjKAFoOC9/mDTof6z/DjJ+jwfwZGhh+/fv//9//vv3+///z58u07ExPT7z9//vz5++cvCMGPUMX0zqjIaAiMhsBoCIyGwGgIjIbAaAgMaUCnxijkMKbklNSM9LTg4OD79+/7+/tD9i1B9h6BpvH//2djY/v169enT59+//6dmJgYEBDw8ePHrKys2tpaDg6OFy9eeHp6rlu3Tk9Pj4GB4du3b5GRkZmZmREREc+ePYuMjJw0adLZs2cnTJjAxMRoKSX4+s1rTYzIYWVmlpcUZ2Jk/P3nj7etOS8358odB6XFhfXUldbuOaytLB/n63rlzoMrdx4oSIpD2o4QM/78/Xv3yYs4X5ev33+cunyDl4sz0Nnm/7//Anw8SjKSD569vHb3oaGmipSosKSwIDMjo5iQgK+9xakrNz2sTb58/ykiwCfIxyvCz6ckI8nFySHAy/P9xw97E/3HL978+vPb08b0/tOXchKi7GysUqLCv3//kRQR4mBnk5MQ+/7z179/f9nZQPejQlwySo6GwGgIjIbAaAiMhsBoCIyGwLABoMM+6eCZ/RtWH9m51TIkVk1VVU5O7tatW+rq6gwMDNHR0UFBQd++fUtKSsrMzPz9+/eOHTsaGxs9PDxERUUhM/Xa2try8vLs7OxHjhz5//+/tbW1uLi4trZ2cXHx4cOHDQwMnj592tDQcPjw4c2bNx87dqyuvv7/378x9ial3ZN1TS3I8N1/2NZ4MvSOahkNgdEQGA2B0RAYDYHREBgNgdEQIB4QPiCTeLPwq/z////v378hO5Y2bdqUkZEhLCysr6/v6Oj48+fPlJSUrKwsY2NjLi6ugIAASEuUgYGBjY0tISHhyZMnz58/z87OzsnJ0dTUFBAQ4OHh4eLi8vHxefTo0cSJE/fs2RMVFaWoqPjv3z8xUdHMrCwebh4ebm78TsIlizwgikvNqPhoCIyGwGgIjIbAaAiMhsBoCIyGAOWAftP0b9++LSgoSE1JYWZmXr9+/YwZM44cOfL3799r165Nnz598uTJsrKy3NzcKioqf/78efDgwbdv33h4eOTk5Do6OiAT+m1tbffu3YMMW3JwcLCCT4xfv379uXPnPDw8ent7P3z48OvXLzc3t6lTp1ZGBUBuB0ULo1+//zx8/pKmW9T////PxsoqJyn24OmLP3//gRedorliZHGZmZjkpcQhx6+OLJ+P+nY0BEZDYDQERkNgNARGQ4AQoFNjlIEBtHeHm5unq6srJiZm1qxZzs7Or169YmBgMDMzW7ZsmYeHR0dHR3BwsIeHx58/fwoLC0+dOmVmZrZ27VomJqbKykomJqbbt2/7+Pi8e/fu////NjY2CxYsEBAQqKysLCgoYGFhYWZmzsrK0tfXnz59+udPn/7+/fvixUstDP+/+fCxfvpiAR4u2g1//v//n5ODXUtJ/vLte7/+/GXEcMOIEvj77//3Hz/6SjOF+XlHlMdHPTsaAqMhMBoCoyEwGgKjIUAMoFNj9M+fP2JiYgtqWj98+CArK/vv3z/IbnoWFpYrV65kZmbev3//169frKysL1++zM/PDw8PLy4uFhAQgFxGP2nSJFZW1oqKisWLF6empl6+fPnw4cPR0dFz5sxZtmzZpk2bpKWl58yZ09bWJiAgwMXF9fvXL8hJTFiDgIeTozU3iZcLtCUfqwLKBf8z/P/952+crwv4YlLKzRvCJrz58LF26sIR3iIfwvE36vTREBgNgdEQGA2B0RCgMaBTY5SJienbt2/79h9wcXZ6+/btypUrOzs7f/78+enTp7Vr1/r5+QUEBJibmzMwMPDz87u7u3t5ed24cePEiRMqKioTJ05cuXIlJyfnz5+gEzdfvnzp4eHh4uLy6dOnadOmrV+/noeHR19fv7m5mYWFhQkMvn39+uXzZ0hDFmsAsrKAxlKxSlFLkJWFTmFLLQfTyJzRcKBRwI4aOxoCoyEwGgKjITAaAsMD0KnBxMjI+OvXr3Pnzurr6X758uXWrVteXl7W1tYLFy48evRofHw8ExPT/fv3f//+LS8vb2Jicvny5RkzZly+fFlYWPjYsWMfPnz4+fPnqVOn/v79q6ura29v7+7uLicnl5eX9+LFC2trax0dnenTp//69evu3bsfP340NDBQYP6NZyKepmtGh0fKoJYvRoOaWiE5as5oCIyGwGgIjIbAaAgMS0Cnxujfv39FRUUbWvt//PjOzc2trq7u6uo6Z86cxMREJSWl4ODgz58/s7CwpKSkRERExMXFffwIuqjz379/1dXVu3btamxsZGFhmT59OiQOSktLMzMzd+3atWDBgpycnAULFly/fn3Pnj3y8vKNjY179+3bsmVLXWI45Kp6iJZRcjQERkNgNARGQ2A0BEZDYDQERkNgEAI6NUaZmZlfvnwZEBh49crlqKiozs7OI0eOxMbGXrlyJTc39+jRo7a2tt3d3b9+/aqqqtq+fTsfH19dXd2pU6d2794tIiLS1dXFzIx0DycjIysrKzs7OwMDQ0tLi6ysbEVFhaur68qVK5mYmH78+P7nzx/wPUf/B2GIjzppNARGQ2AUjIbAaAiMhsBoCIyGABzQ6ZxR0HWav/88fvI4JCQkPj7+7du39fX1UVFRfn5+169fr6qq+vjx48yZM+/fvx8ZGdnZ2fnt27fKysrU1NSKioo3b94sX748IyMjJycHMtjJxMR07dq1xMTEJ0+eCAgIRERErFixoqOjAzJTP3HCRB4eHiYmJvhhpXDfjjJGQ2A0BEZDYDQERkNgNARGQ2A0BAYVoNPIKMjPjAzMzMz//v27dOnSnTt3Fi1aJCsre+zYsZcvXwoKCqakpPz69UtPT8/JyenSpUsbN24UFhb+8+cPFxcXCwvLoUOHTp065erqOnv27PDwcGYw4ObmZgFvEpKXlxcVFd2wYQMHBwfkBKgrVy5/+/aNl4cHZO8oHg2B0RAYDYHREBgNgdEQGA2B0RAYrICOjVFwEMyYMWPt2rWSkpK6urobN258/fq1iorK5MmTGRlBF5MyMjJ+/vy5s7PTz8/vxo0bjo6OFRUVz58///jxo4ODQ0VFhaampqmpKSMjo7q6eldX19OnT9nZ2QUFBV++fDlz5sy8vLzbt29PmDiR4f8/AyGO379/g+0cJUZDYDQERkNgNARGQ2A0BEZDYDQEBimga2OUkYHx79+/bm5unZ2dLCwsjIyMra2tc+fO9ff3//fvHwMDw79//1RUVObMmfPr16/U1FRnZ2cXFxcuLq7Xr1+Hh4f//PkTMvYJUfn8+fPY2Nj8/PyoqChxcfGFCxemp6cbGhpu37btz+/fE4oznj1/rmE4SMN91FmjITAaAqMhMBoCoyEwGgKjITAKGBgY6NoYBdnHwnLgwIHy8vIFCxYwgDcZqaqqQtZ6MjAwrFu3bsqUKcHBwVeuXPH19WVhYfn06dN0MNi6deunT5+2b9+uqan5798/yJrRK1eucHBwHD16tKKigp2d/fz58zdu3Pj9509vTw8LM4uYqCiuOMZzBCkuLYNW/MOHD1++fBEREeHg4Bi0jhx12GgIjIbAaAiMhsBoCIyGwGgIYAV0bIwyMv5j+P+fgeHFy1cbN22qra/Pzs4ODg0VEBI6cPBQUVEhEyOjoJCwuITknz9/Qv7+dXV3l5WR6e7p8fP35+MXmDVr5u49ezQ0NdU1NFhYWd+9/3D85Mn///4vXbaciZnp6NGjTMzM/xkY3n/4uHbdOl5e3s9fPnNycmH1Mysry507t1avWP4fBEBKODk50tMzxMTFQZxBgz99/DB16tSUlBRRMZwOW71q1fRp09g52Ds6OvUNDGjk9v9//z589EhBURFi/sEDB65evZqVnQ3h4ifZWOmYxvA7ZVR2NARGQ2A0BEZDYDQERkNg8AE6NRT+/fv/6+sXcU7Wf+LCrGxsv37+XDl7uraspJ6+voIQX2dnp66CtISEBAsrq4+DNfjqSMY/P7++vHvT3dr82smjsgI8jiYG5w7tmzex10BF4e+HN+pSoiwsLD9//jp7cA87G5uGrCQbK+vfv3//////99f3BZP77TSVGBiwH+3068/fXUdOrdu5z8DAENQc/f+fnYP9xOUbYi/fQRYPMDAy/v/3D3w+FGjTFQMDAyMjIxMT0+/fv+FHuINvegKdRcDIxMTw//+fP39AIszMDP////37F7LqgJGREbLFioGB4e8/EGRgYGBmYf7//z8zE4j88+cPspo/f/7AzWdgYHj37t35Ww+OXrgmKfmemRmkHjKg+x9m3e/fvxes2eDkF2xmZvb5H/OJS9eh7gcveICcPMDMwvL//38mRhD4/ecPMxPIzQhz/v6F3JD0n+H/nz9/wZ0FBpAhYGWQQGBmZn716tWcOXNyc3MFBAR+//596uqt27cfnLh0nYGBgYmZGWLmfwaGv2D3MzExQcKEiZmZiZHx3YdPIJMHX9IfddFoCIyGwGgIjIbAaAiMhsBgAKBtQ3Rwx9mD+zYtnPXjx4////+D9iqBZ+i/fwdxmVmY2dnZubm4Hz9+9OvXL35+AXCDDNSO/PXr96dPn4SEBD9//sLDy6OirPz/P8OzZ8/+///HwMD45ctnBQWFHz9/MjIwcnCw37177+/fP6KiYuLi4v/////562d8aY2sshqa715/+Dhh8boPnz59/vRJQUHhPwMDI3i1wK9fv5mZmZ89e/rw4UMGBkZFRQUJCYk/f/68f/+BkZHx+/fvz18819LUhE+Ff/369dv373///Hn06BEPD4+WltaHDx9u3b7Nxsqqrq7OycXF8P8/ExPz7du33759w8rKqq6hwcPN/ffvv+cvngvwCzx58uTfv7/q6uoMDAzXrl378vUryBBNTeRbo/7+/fvu3TtBQSEGBobXr1/z8HDfunX73/9/8nJykpJS7969ffDgAScX15/fv//9+6eoqCggIHj37p0XL16ysDCrqqrxC/D/+/v31atX3Nw8L1+9/Prlq5a21utXr7h5eB7cf/Dj5w9ZWVlZGdmrV698+PhRTFRUSUnp379/zMzMz58/f/jw4X8GBmkpaXl5uefPnz999oyTg+PHjx9MTExq6up/fv/+8fOnsJAQExPT589fbt66+ef3bxFRURUVlX9//3779v3T50+srKx3795lZmJSVVWTFBcriAnk5+FGi4tR7mgIjIbAaAiMhsBoCIyGwCigU2OUgYHh5atXsbGxz549Y2Fh+ff3338G0KAkKwvr////OTg5du3cmZSc/P/f//aO9v//QC3RlatWLliw4Mf37ytXreru7hYUEFy4ELTMNDw8/Oq1a39+//ny9cvevXtnz57979+/vLw8E2Pjt2/fVlZVtbW2EozXDevXT5gw4cDBg8gqz58/l5ebp6Cg8Pfv3ydPn0ybNk1HR3fWzJnbd+z48eM7CwvrrJkzJaWkIFr27dtbX1cvLS0tJCR0/MRxb2+fJ0+eMDMz37xxQ1dPd8rkKcwsLPPmzZ02bZqZmfndO3eERYSnTZsmICAYFxf3//+/B/cfmJmbtbe1t7a27j+w38zU7Ny5c25ubhWVlRDzGRgYnj97lpCQMGvWLF5e3vDwcEFBQQ4Ojh8/fzx9+mzxokVfvnypqak5eOigg70DHx9fY1PT9evXGhoajYwMnz9/zsjAOGvWLHEJidKSkmfPnz95/FhFRWX27NkpqSlv37zl5eX98/fPi+cvAgMD9x84ICIicvbMmb7+PkdHp2vXrmVnZynIKzCzsFy6eHHuvLkvX7zs6u46deq0vb29uJhYb2/v2nVr9+7dt3jx4rdv3qRnpH/7+k1eXv70mdOFhUXR0dEnT5woKCiQk5Pj5eV98uQJCwvLkiVLBAQF4f4aZYyGwGgIjIbAaAiMhsBoCIyGAAKAmoR0wY8ePVJRUcnMzFwOA0uXLl2+fHlZWZmwsPCrV6+CgoKSk5PhbikrK9PQ0FixYkVGRoaysrK2tnZiYmJkZOTcuXNDQkLY2dnl5OTu3bt3HAwePnwoJSXV2Nh4+fJluAl4GJs2bVJQUGhqampsbGxoaLhz587///89PDymTJny////f//+9fT0+Pn5/f//f9KkSRISEhcvXvz58+e/f//gZu7evZuPj+/EiRP////fu3cvGxvbkiVL/v////jxYw0NjUuXLj19+lRHR+fYsWOgYdqfP4OCgrq7u////x8UFOTq6vrq1as/f/4cPnzY2Nj4zZs3////f/nypYWFxaVLl+BWPHr0yNbW9vbt22/evFFSUuru7v7379/fv3+jo6PLy8v///9/7+5dXV3dR48e/fnz59WrV3p6ejt27Pj////v37/T0tJKSkr+//+flpZmaGj46BFo1Pn///++vr5hYWEQv0RERBgYGLx69er///9NTU2hoaH//v27ffv2kSOHIW4oLi5OSUmBbBczMjJ69OgRZKHC9OnTQ0ND////39LSEh4e/uvXr////x84cMDQ0PDly5enT5+WlJTctGnT////P3/+bGtru2DBAoiBo+RoCIyGwGgIjIbAaAiMhsBoCKABOq0ZhTR+mZiYHB0dLSwsDh8+zMTE9P//fyMjI2Fh4QULFjAyMjIzM9+4cWPx4sXMzMxeXl6Q1ZZfv35dvXr1+/fvubi4nj596uvrCznXiYGB4efPn2vXrpWQkGBmZj5y5MiPHz9+/vz5////Fy9e7NixIyQkhIcH36H3zMyg5QGgVY9MTOzs7G/fvn369Kmbmxtkhainp+f8+fMh6wrs7Oz09PQgXoCTf//+NTAwgIirqqoqKira2dkxMDBIS0vz8vJ++PDh2bNnCgoKJiYmDAwMbGxsXl5eW7duBS9P+B8REQG5HerGjRs/f/5csGABZH3qly9fbt++raurC7cFwvj375+EhISHhwdo4Scjo5GR0bVr18DLT0HrQUEMZuZ79+6xs7NbWVlBjizw9vbu6+uD+CUgIEBWVhZi1N+/fz08PNjY2BgYGBQVFQUFBSEu0dTUPH369N+/f1VUVN6/fz9x4sTHjx8fOnRISUkJsnLg379/LGAAMQcieObMmcjISFZWVgYGBktLS0FBwStXrvDx8SkoKDg5OTEwMPDw8KioqDx9+hSia5QcDYHREBgNgdEQGA2B0RAYDQE0QNfGKMTuHTt2pKWlQdjt7e1GRkYQNjMz85UrVxoaGl68eDFv3ryvX7/evn07NTWVjY0NtKWGkVFMTKyuri4gIOD58+fKysoPHz4sLS0VERFhZ2d//fq1urr6smXLREVFrays2traZGVlzczMeHl5IYajkb9//5aRkSkrK4OLv3jxAs6GtOEgDUcGBgZubuyLHSFNOogyNjY28FJX0FGpkIba379/IfuEIMYyMTFBdhSBliXAzmD6//8/Dw+PuLg4pDFaV1dnYWEBUY9Ggu+cYoYIQsyHsOHkv3//kK2Dq2FkZOTiQpwqwMTEBGnlQzRC2pEQNqRRO3PmzKVLlwYEBNja2v7+/fv58+cQWbiBEC6ERLYU0lCGDB5DmvgQNaPkaAiMhsBoCIyC0RAYDYHREMADBqAxyswMalRxcnJ+//4duWH069cvf3//7Oxsb2/v5ORkZmZmLi6u379/Q5pBTExMT58+tbKyev36dUtLi6mpKeRe0N7e3j9//jQ3N2/dupWfn5+dnZ2Nje3w4cMODg41NTXR0dFYPc/ICDp+H1lKWFhYSEjoxIkTqqqqDAwMR48elZSU5OTkhIwkI6uEsyGtTwgXjf3//38dHZ179+7dunVLW1ubgYFh3759xsbGaIpBO6j+/4+MjISEyZUrVyQlJSFqMElkKzBlFRQUPn/+fOXKFUtLSwYGhgMHDmhpaUGUIWtE9g4yG6Ly379/mzZtio+PT05OZmBg2LhxI6SVzMLC8v37d8gRARCVEDN1dXW3b98eEhLCwMBw5cqVt2/fampqPn78GNJAh6gcJUdDYDQERkNgNARGQ2BEhcD79+8fPnyIPEI0vL3/798/SUlJcQrOxxyAxihkgPD79+8MDAyQtg4DA8OvX786OjrWrVuXmZn57du3iRMnnjhx4uzZs83NzYmJiW/evHF3d09OTg4MDJw5c+aTJ09KS0u/fv36798/Pj4+BgaGFy9exMfHT5ky5fLlyzNnzvzz58+DBw8gFmFNAZDFl8hSrKys9fX1ZWVlR48e/fv376VLl3p7e0EKwDutQAxUDGnJQdpkoPOkwKdKQZT8BQM5ObnY2NjExEQHB4c7d+78/PkzJSUFoguijIGBwc7OTl9f39PT09ra+uLFiyIiIpMnT2ZnZ4crgDBAtiA5A8SFSIDPAYAwJSQkiouLc3Jy7OzsXrx48fLly9mzZ0OkkNXDRy4hqx3gUpCGJjMzs5ub2/Tp0x8/fvzq1avHjx9D0paMjAxkza62tnZlZSUTExNEfVpaWkpKSmhoqIKCwoEDBxITE6Wlpe/fvw+xF0Ii2wgRGSVHQ2A0BEZDYDQERkNgGIfAmzdvjh49ijb3OIz9+/v3b0tLS0iDgTxv0m83/ePHj11cXFpaWrS1tTdt2sTIyPjv3z9XV9f379/HxsaeOXNGRkbm/PnzmzdvZmdn//Pnz/bt2xkZGQ8ePDhjxowvX778+vXr9+/fTExMOTk5V65cOXbs2M+fP2fMmGFlZcXMzLxv376srCxWVta9e/fevXs3ISFhwoQJU6ZMSUhIwBou796+f/r0ia4e+urMu3fuHTx0kImRycHBQUFRnoGB4dmz51+/fFVVU0Ez58P7j0+ePtHW0mZkYvj18/fVq1e1tbXZ2EGrJy9cuKioqMjPD2olnzp5+vz58yKiIq4urnz8oDUD16/dEBEVERUVgRj4+9ffffv23b13R0ZaxsXFlYsbcYvS719/rt+4rq6mzszMfO36NVUVVU4ukOyzZ8+/ffumoqIMsVdHR4eVDdqpOH/u4pkzp3l4eFxd3UREQWdC3b17j4ODQ1oaegjAtavXRURFxMRAd1M9fPDo79+/Ssqgo+zfvnn78tUrLS3N//8Y9u3ff/vWLS0tbW1trbfv3qqpgYaKP378fOzo0Z8/fzo7O3/7/u39+/caGqBzqT68/7h79+73798bGRubmIBWXHz+9OX+/fu6urqMoCNNGW7fvsPJwSkjKw3x7yg5GgKjITAaAqMhMBoCwzsE7t69u3fv3hHVGDUzMzOg4OYdujZGnZ2dQ0JC3NzcoKeNgk7iZDpx4kRvb++FCxdev37NysqqqKh48uTJBQsWPHnyRF5ePiIiArJg9MKFC8ePH1+yZAnkAMvHjx9//vw5Pz+fnZ1dXl5eSEgoOjq6qqrqw4cPERERHR0dUVFR8fHxHh4emCn+55/P3/++Y2Fh/fXzF5osKxsrZFz939+/v3//AW8GYmZkBB13j6aSiZmJhZnl1+9fDP9BR+KzsbH++gU+Ep+RgZ2N7fefP//+/mNkBG1dAh+Zz/D79x/QSC0jA+Rw/r9//0EMZGJiZGVjYwAfv/rr9+9/MHHIulVWNtbfv38z/P/PysYGPk8UdOgVCwvUSYyMjAh7QdYwsLKxMYKcAxpyhhw1z8oK2uQEYTOAtlKx/v0L2pIP8horCyMDyGHg1aKg0+t//frNyAQyk+E/43+G///+/mVkYvr96zdYATMrGyvD//+/fv1iZGJiZmL6BRFnYWYFnasPmpkHb6tnAC1LZWX5DdpiD/IiKyvrv/9/OZmF2ZixL70FKRrFoyEwGgKjITAaAqMhMFxCALkxCqoTWUADRuBJ078QL7KwsEDmGOHzwxDAxMQEb8JCJl1BLQeIHMUkxCX/YHf6UGwewoDfvyltjIICCGEeLVlMTEwCAgKLFy9euXIlJHD//v3Lygo6Z1RAQICdnb2/v19ISCghIaGkpKS7u9va2vrYsWPx8fGQ9Z3+/v5LliyBrDFdtmzZnDlzmJiYvn371tTUFB4efvv27ZCQkLdv3+bk5FRUVDAwMCxYsACiGNNPV15t2HajmpkZfNMTpvSoCLVD4O+//0FaU7TF/aht8Kh5oyEwGgKjITAaAqMhMHhDgJmZ+cOHD9euXfv7F3TNjZiY2N+/f5mZme/cufPkyRNZWVlFRUXIsjfIxt+XL1/eunUL0kZiYWFRUlKSlpaGcCn0JBMT06dPn65cuSIgIKClpQW3FJexoPuJwCsLIft2cCmjljj9Rkb//Pnz9OlTyIYkyFJF0DWV4GsnmZiY5OTkXr16BWmwPnv2TFJSkoOD4/v370+ePIFM6PPz88OXI7x9+/b9+/eQkBIXFwcd4Q6+Cenv37+QfUj4Q+fb73effj5jBF29hF/hqCx1QuA/A4MAhwwHCz91jBs1ZTQERkNgNARGQ2A0BAZxCEBGRtnZ2d+/fz9t2rSPHz+CrgFnZs7OzlZSUjp27NiyZctYWVl//foVGxtrZmYGWYXIzMy8Z8+eJUuWiImJQYbh/vz5k56erq6u/vfvX8iRNZD9HhCvQ0QgDSrQ7OR/0NwpXBAuDploZWJiWrRo0ZkzZ6ysrCIjIyGNUXBrE6QLogbS7oSck/Ply5fNmzd7eXnx8/NDRCBWQIyFqIQ0zxhAU6xDZ2SUhYVFXh60ChMSiJgkfCO5oiJoFSMDAwMnJydkbzuaYmEwQBaEdCCQRfCwuViFuFhB6ynxqBmVGg2B0RAYDYHREBgNgdEQGA0B8kIAcnr65cuXeXl5S0tLGRgYpk+ffvz4cRkZma1bt/r6+rq4uGzevHn37t36+voMDAxfv37l4+P7//+/gYFBbm4uCwvLr1+/5s2bt3v3bg0NDSYmpu/fv//69YuFhYWLiwsylvfnz59v375BpvUh8/vMzMzfvn37/fs3BwcHGxsbpMUJcf+HDx+ePn0aEhJia2v779+/r1+//v37l5OTk42NDXIS5e/fv79//87MzMzNzc3IyPjmzZtbt27Z2Njw8/MzMTF9+fLl////3NzckNUFEHt//vzJwcEBOQ4IYgvZJP2m6cl24qjG0RAYDYHREBgNgdEQGA2B0RAYQiEAuQ3RwsLCysqKhYWFlZUVcqf3p0+ffv/+bWBgwMTEZGFhcfbs2ffv33///n3OnDlFRUXMzMyMjIxMTEyMjIzc3Nzy8vJnz55lZGQ8fvz41q1b37x5IygoGBQUZGlp+f79+6VLl168eFFQUJCFhcXPz8/S0vL06dNr1qx5/fq1srJyXFycpKQkZIqflZV19erVN2/evHPnDgcHx7dv3zZv3vz582c1NbWYmBgpKanHjx8vXbr09u3b/Pz8Pj4+jo6OmzZtevDgwcSJExsbGw8cOLBr167f4C3z4eHhjIyMCxYs+PXr1+PHj/Pz8/GPMxIZZeANz0SqHVU2GgKjITAaAqMhMBoCoyEwGgKjIUBcCIAvLmRhZmY+fvz4/fv3raysPn/+zMnJyczM/PfvXw4Ojn///n3//l1aWjorK0tAQOD///8fPnw4d+7cmTNntm/fvmPHDn19/d+/f9+5c8fNzS0rK0teXn7v3r3//v3bvXv38+fPCwsLXVxcINfTvH37duXKlY6Ojo2NjbKysuvWrfvz5w87GDAxMfn5+amqqsbHx3Nzc69bt87b27u8vJyJiWn16tW/f/9etWoVGxtbZWWlh4fHpk2b7t696+3traCgkJWVdf369WPHjqWnp1dUVLx8+fLQoUMsLCyvX7/m5uYuLS2VkpJCHn8lLlSwqGLBIjYqNBoCoyEwGgKjITAaAqMhMBoCoyFAWQhAVnDu3r370KFDUVFRkpKSkEPQIWcKQVaCQmbYJSUlmcDgwYMH8+bNg6xUtLCwcHR0ZGJiUlNTO3jw4I8fPz58+CAsLPzly5eHDx96eHjo6empq6tfu3bt379/z549e/v27blz5y5evPjjx49Xr169ePFiz549L168YGdnDwsLg9z4+OTJE3V1dVdXVyYmJg4OjkmTJj158uTz588JCQnKysrq6upXr169ffu2ubk5Ozu7uLj4uXPnvn79unPnzv///799+/bixYuQ675BJ2AqKEBuTacskEC6RxujoFAYxaMhMBoCoyEwGgKjITAaAqMhQJUQ+P///58/fxgZGVlZWbds2XLnzp3c3FwZGZkfP37w8PB8+fLl48ePUlJSz58/////PxcX158/f1hZWf/+/fvr1y9dXd20tDTIQkzIrP3jx49Xrlzp6emppaV19+7dY8eO/fnzBzKkCtlC9Pv3b8hVPhwcHAoKCuzs7H///gWdQc7KKi4uDrmZErIj6tevXxCNf//+ZWFh+fbtG2Tr0u/fv3/8+MEEOrTx18+fPxkZGX/+/AlpMUNuF1JWVv7//7+CgoKwsPCvX7+YmJj+/////ft3yNFUEPdQEnSjjVFKQm9U72gIjIbAaAiMhsBoCIyC0RBACQFeXl41NTU2NrZdu3bt2LHDw8Pj1q1bV69e1dDQMDExsbOzmzdvno2Nzf79+x0dHU1MTF69enXs2DF3d3cRERF2dnZVVVUW0AHeoH3u4APF2RgYGL5//379+vUdO3ZwcHCoqal5enrOnTv39+/fr169unTpUnBwsLGx8datW3/+/CkrK3vz5k1eXl4DAwNra2vIzvfPnz+vWbNGQkLC1NR0586dCxYskJaW3rt3r52dnY2NzZkzZxYsWODk5PTw4cMPHz74+fkxMTF9/fr1zJkzbm5uFy9eZGNj4+HhOX36tJaWlpqaGjMzs7S0tLKyMmRN6t+/f4WEKNoXPtoYRUlAo5zREBgNgdEQGA2B0RAYDYHREKAkBMTBgIGB4dixYwoKCleuXIEsrFRWVnZ0dNTT06usrDx27FhISEhzczMfH9+NGzfmz59fU1MDGZ50dnZGs/3bt2+zZs3i4OCALOLU09Nzd3cXFRVdtmyZurq6kZGRnp6el5eXlJRUS0vLoUOHjI2Na2pq1NTU4Ob8+/dvy5Yttra2ZmZmQkJCXV1dFy9ejI+Pr6io4Ofn19PTa25u3r17t4KCwvLly21tbf///3/ixImTJ09OnDhRWlp64sSJnz9/9vX1raio4ObmtrS0dHR01NLSgptPIYN+54xS6NBR7aMhMBoCoyEwGgKjITAaAqMhMBoCDAwMa9asef/+fWRk5LVr12JjY1etWgU5IgokNwTx6MjoEIy0USePhsBoCIyGwGgIjIbAaAgM1hD49OnTkydPaOq6J0+etLa2FhYWsrKyuri4MDAwXLt2jVo2MjMzKysr47rGklq2IJszOjKKHBqj7NEQGA2B0RAYDYHREBgNgdEQoCgEbt++vX//fikpKciFSUzgyyYhW4gYGBiYmZkh9xhBrgCF3GwEOXkeciUSRAFkZh+iBs6GO4uFheX79+/v379nZ2cXFhb++/cvxEzIofRwZfD9SZCWJWRHFOTWe6xsiEUvX76MjIzk5uZGNoem7NGRUZoG76jhoyEwGgKjITAaAqMhMBoCIy4ExMXFtbW1ubi4xMXFX716Bbk/CXKp5N27dzk5OZmYmP78+XPt2jVWVlYZGRlVVdV79+4JCwvz8/PfuXOHl5cXfgX63bt3ZWRk2NnZGZDA////jxw5IioqysnJaWRkBNl9z8DAcPPmTXV1dbjC////79mzR05ODjKJf+PGjSdPnigrKysqKt64cePx48dqamry8vK3bt169OiRkpKSlJTU8ePHX7169fDhQyouCYW7BxcD1CTHJTcqPhoCoyEwGgKjITAaAqMhMBoCoyFAaggwMzNfvHhx8eLFv3//vn///p07d06dOvX+/XvIFvVXr17dvHnzxo0bbGxsCgoKJ06cuH79+qVLl54/f/7v37+dO3cePnwYbiMzM/OfP39ev3596dIl+Oz/hw8f7ty5o6qq+vr16wMHDnz58uXevXsvX76E3A766NGjixcv/v79+8yZM7du3RIQEIDsx3/16pWSktLhw4cvXrx49uxZWVnZgwcPPn78+PXr10pKSkePHj137tzbt29ZWVn5+PjgDqADY7QxSodAHrViNARGQ2A0BEZDYDQERkNgBIXA////+fn5lZWVIVcWcXFxKSoqPnjw4M6dOzIyMlxcXExMTMzMzOLi4oqKilZWVrdu3WIDg/v37+vq6rKysr59+xYSXleuXHn//v3KlSvfv3+/a9euN2/eQOb6v3379ujRo+fPnwsKCu7atevEiRO/f/++fPnynTt3Tpw48fr167179/769YuREbogk5OT087O7sePHxwcHK9evdLW1tbQ0JCRkXn//r21tfWfP39YWFg4OTm/fv0KucsUYjt9yNHGKH3CedSW0RAYDYHREBgNgdEQGA2BkRICkBWiRkZGDAwM169fZ2Nj09XVvQ8GkBlz8HVLoDvoGRgYfv/+zcbGBjnB/vz58z9+/Pj06dPFixchgfUPDMTExOzt7eXk5D5//gxpjHJycoqJiampqb17946FhcXOzk5GRubv37/Pnj37+fMnBwcHMzOzChgoKChAjNq1a9eDBw+8vb25ubkhy0P////PwsKyf//+mzdv+vj46Ovr29nZ/f79e9euXd+/f4foogM5umaUDoE8asVoCIyGwGgIjIbAaAiMhsAICoG3b9+ys7NDWpmPHz/m4uL6+fPn27dvGRkZL1++/Pbt2x8/fvz79+/r169v3759+PAhKyvrr1+/rl69+urVq7dv3/779+/+/ftHjx5lYGD49OnT1atXv3//fvTo0Tdv3vz69QvS3GRjY3v9+vW/f/+ePHnCysp65cqV+/fvf/ny5e/fv1xcXP///+fh4Tl37tz79+8h5nz//v3x48daWlqXL19+9uzZ79+/v3///uzZsy9fvrx+/VpbW/vq1at/weD3798cHByQfVf0iTPo4C19LBu1ZTQERkNgNARGQ2A0BEZDYDQEhncIvHr16urVq8zMzJDRR1ZWVsiFmZAt7X/+/IG08yBDof///4dvP/r//z9EChI+kMs2IeZAVo5C2JCLOpmYmOC320O27UOMgmyrh1jNyMgIUQbZVg8xBLJDHzJ2C9m5D9lNBbEUokVLS0tERAQiQgdytDFKh0AetWI0BEZDYDQERkNgNARGQ2A0BEZDADsYXTOKPVxGRUdDYDQERkNgNARGQ2A0BEZDYBTQAYw2RukQyKNWjIbAaAiMhsBoCIyGwGgIjIbAaAhgB6ONUezhMio6GgKjITAaAqMhMBoCoyEwGgKjIUAHMNoYpUMgj1oxGgKjITAaAqMhMBoCoyEwGgKjIYAdjDZGsYfLqOhoCIyGwGgIjIbAaAiMhsBoCIyGAB3AaGOUDoE8asVoCIyGwGgIjIbAaAiMhsBoCIyGAHYw2hjFHi6joqMhMBoCoyEwGgKjITAaAqMhMBoCdACjjVE6BPKoFaMhMBoCoyEwGgKjITAaAqMhMBoC2MFoYxR7uIyKjobAaAiMhsBoCIyGwGgIjIbAaAjQAYw2RukQyKNWjIbAaAiMhsBoCIyGwGgIjIbAaAhgB6ONUezhMio6GgKjITAaAqMhMBoCoyEwGgKjIUAHMNoYpUMgj1oxGgKjITAaAqMhMBoCoyEwGgKjIYAdjDZGsYfLqOhoCIyGwGgIjIbAaAiMhsBoCIyGAB3AaGOUDoE8asVoCIyGwGgIjIbAaAiMhsBoCIyGAHYw2hjFHi6joqMhMBoCoyEwGgKjITAaAqMhMBoCdACjjVE6BPKoFaMhMBoCoyEwGgKjITAaAqMhMBoC2MFoYxR7uIyKjobAaAiMhsBoCIyGwGgIjIbAaAjQAYw2RukQyKNWjIbAaAiMhsBoCIyGwGgIjIbAaAhgB6ONUezhMio6GgKjITAaAqMhMBoCoyEwGgKjIUAHMNoYpUMgj1oxGgKjITAaAqMhMBoCoyEwGgKjIYAdjDZGsYfLqOhoCIyGwGgIjIbAaAiMhsBoCIyGAB3AaGOUDoE8asVoCIyGwGgIjIbAaAiMhsBoCIyGAHYw2hjFHi6joqMhMBoCoyEwGgKjITAaAqMhMBoCdACjjVE6BPKoFaMhMBoCoyEwGgKjITAaAqMhMBoC2MFoYxR7uIyKjobAaAiMhsBoCIyGwGgIjIbAaAjQAYw2RkfBaAiMhsBoCIyGwGgIjIbAaAiMhsCAgdHG6IAF/ajFoyEwGgKjITAaAqMhMBoCoyEwCkYbo6NpYDQERkNgNARGQ2A0BEZDYDQERkNgwMBoY3TAgn7U4tEQGA2B0RAYDYHREBgNgdEQGAWjjdHRNDAaAqMhMBoCoyEwGgKjITAaAqMhMGBgtDE6YEE/avFoCIyGwGgIjIbAaAiMhsBoCIyC0cboaBoYDYHREBgNgdEQGA2B0RAYDYHREBgwMNoYHbCgH7V4NARGQ2A0BEZDYDQERkNgNARGwWhjdDQNjIbAaAiMhsBoCIyGwGgIjIbAaAgMGBhtjA5Y0I9aPBoCoyEwGgKjITAaAqMhMBoCo2C0MTqaBkZDYDQERkNgNARGQ2A0BEZDYDQEBgyMNkYHLOhHLR4NgdEQGA2B0RAYDYHREBgNgVEw2hgdTQOjYDQERkNgNARGQ2A0BEZDYDQEBgyMNkYHLOhHLR4NgdEQGA2B0RAYDYHREBgNgVEw2hgdTQOjITAaAqMhMBoCoyEwGgKjITAaAgMGRhujAxb0oxaPhsBoCIyGwGgIjIbAaAiMhsAoGG2MjqaB0RAYDYHREBgNgdEQGA2B0RAYDYEBA6ON0QEL+lGLR0NgNARGQ2A0BEZDYDQERkNgFIw2RkfTwGgIjIbAaAiMhsBoCIyGwGgIjIbAgIHRxuiABf2oxaMhMBoCoyEwGgKjITAaAqMhMApGG6OjaWA0BEZDYDQERkNgNARGQ2A0BEZDYMDAaGN0wIJ+1OLREBgNgdEQGA2B0RAYDYHREBgFo43R0TQwGgKjITAaAqMhMBoCoyEwGgKjITBgYLQxOmBBP2rxaAiMhsBoCIyGwGgIjIbAaAiMgtHG6GgaGA2B0RAYBaMhMBoCoyEwGgKjITBgYLQxOmBBP2rxaAiMhsBoCIyGwGgIjIbAaAiMgtHG6GgaGA2B0RAYDYHREBgNgdEQGA2B0RAYMDDaGB2woB+1eDQERkNgNARGQ2A0BEZDYDQERsFoY3Q0DYyGwGgIjIbAaAiMhsBoCIyGwGgIDBgYbYwOWNCPWjwaAqMhMBoCoyEwGgKjITAaAqNgtDE6mgZGQ2A0BEZDYDQERkNgNARGQ2A0BAYMjDZGByzoRy0eDYHREBgNgdEQGA2B0RAYDYFRMNoYHU0DoyEwGgKjITAaAqMhMBoCoyEwGgIDBkYbowMW9KMWj4bAaAiMhsBoCIyGwGgIjIbAKBhtjI6mgdEQGA2B0RAYDYHREBgNgdEQGA2BAQOjjdEBC/pRi0dDYDQERkNgNARGQ2A0BEZDYBSMNkZH08BoCIyGwGgIjILREBgNgdEQGA2BAQOjjdEBC/pRi0dDYDQERkNgNARGQ2A0BEZDYBSMNkZH08BoCIyGwGgIjIbAaAiMhsBoCIyGwICB0cbogAX9qMWjITAaAqMhMBoCoyEwGgKjITAKRhujo2lgNARGQ2A0BEZDYDQERkNgNARGQ2DAwGhjdMCCftTi0RAYDYHREBgNgdEQGA2B0RAYBaON0dE0MBoCoyEwGgKjITAaAqMhMBoCoyEwYGC0MTpgQT9q8WgIjIbAaAiMhsBoCIyGwGgIjILRxuhoGhgNgdEQGA2B0RAYDYHREBgNgdEQGDAw2hgdsKAftXg0BEZDYDQERkNgNARGQ2A0BEbBaGN0NA2MhsBoCIyGwGgIjIbAaAiMhsBoCAwYGG2MDljQj1o8GgKjITAaAqMhMBoCoyEwGgKjYLQxOpoGRkNgNARGQ2A0BEZDYBSMhsBoCAwYGG2MDljQj1o8GgKjITAaAqMhMBoCoyEwGgKjYLQxOpoGRkNgNARGQ2A0BEZDYDQERkNgNAQGDIw2Rgcs6EctHg2B0RAYDYHREBgNgdEQGA2BUTDaGB1NA6MhMBoCoyEwGgKjITAaAqMhMBoCAwZGG6MDFvSjFo+GwGgIjIbAaAiMhsBoCIyGwCgYbYyOpoHREBgNgdEQGA2B0RAYDYHREBgNgQEDo43RAQv6UYtHQ2A0BEZDYDQERkNgNARGQ2AUjDZGR9PAaAiMhsBoCIyGwGgIjIbAaAiMhsCAgdHG6IAF/ajFoyEwGgKjITAaAqMhMBoCoyEwCkYbo6NpYDQERkNgNARGQ2A0BEZDYDQERkNgwMBoY3TAgn7U4tEQGA2B0RAYDYHREBgNgdEQGAWjjdHRNDAaAqMhMBoCoyEwGgKjITAKRkNgwMBoY3TAgn7U4tEQGA2B0RAYDYHREBgNgdEQGAWjjdHRNDAaAqMhMBoCoyEwGgKjITAaAqMhMGBgtDE6YEE/avFoCIyGwGgIjIbAaAiMhsBoCIyC0cboaBoYDYHREBgNgdEQGA2B0RAYDYHREBgwMNoYHbCgH7V4NARGQ2A0BEZDYDQERkNgNARGwWhjdDQNjIbAaAiMhsBoCIyGwGgIjIbAaAgMGBhtjA5Y0I9aPBoCoyEwGgKjITAaAqMhMBoCo2C0MTqaBkZDYDQERkNgNARGQ2A0BEZDYDQEBgyMNkYHLOhHLR4NgdEQGA2B0RAYDYHREBgNgVEw2hgdTQOjITAaAqMhMBoCoyEwGgKjITAaAgMGRhujAxb0oxaPhsBoCIyGwGgIjIbAaAiMhsAoGG2MjqaB0RAYDYHREBgNgdEQGA2B0RAYBQMGRhujAxb0oxaPhsBoCIyGwGgIjIbAaAiMhsAoGG2MjqaB0RAYDYHREBgNgdEQGA2B0RAYDYEBA6ON0QEL+lGLR0NgNARGQ2A0BEZDYDQERkNgFIw2RkfTwGgIjIbAaAiMhsBoCIyGwGgIjIbAgIHRxuiABf2oxaMhMBoCoyEwGgKjITAaAqMhMApGG6OjaWA0BEZDYDQERkNgNARGQ2A0BEZDYMDAaGN0wIJ+1OLREBgNgdEQGA2B0RAYDYHREBgFo43R0TQwGgKjITAaAqMhMBoCoyEwGgKjITBgYLQxOmBBP2rxaAiMhsBoCIyGwGgIjIbAaAiMgtHG6GgaGA2B0RAYDYHREBgNgdEQGA2B0RAYMDDaGB2woB+1eDQERkNgNARGQ2A0BEZDYDQERsFoY3Q0DYyGwGgIjIbAaAiMhsBoCIyGwGgIDBgYbYwOWNCPWjwaAqMhMBoCoyEwGgKjITAaAqNgtDE6mgZGQ2A0BEZDYDQERkNgNARGQ2A0BAYMjDZGByzoRy0eDYHREBgNgdEQGA2B0RAYDYFRMNoYHU0DoyEwGgKjITAaAqMhMBoCoyHeg395AAAS+klEQVQwGgIDBkYbowMW9KMWj4bAaAiMhsBoCIyGwGgIjIbAKBhtjI6mgdEQGA2B0RAYDYHREBgNgdEQGA2BAQOjjdEBC/pRi0dDYDQERkNgNARGQ2A0BEZDYBSMNkZH08BoCIyGwGgIjIbAaAiMhsBoCIyGwICB0cbogAX9qMWjITAaAqMhMBoCoyEwGgKjITAKRhujo2lgNARGQ2A0BEZDYDQERkNgNARGQ2DAwGhjdMCCftTi0RAYDYHREBgNgdEQGA2B0RAYBaON0dE0MBoCoyEwGgKjITAaAqMhMBoCoyEwYGC0MTpgQT8KRkNgNARGQ2A0BEZDYDQERkNgFIw2RkfTwGgIjIbAaAiMhsBoCIyGwGgIjIbAgIHRxuiABf2oxaMhMBoCoyEwGgKjITAaAqMhMApGG6OjaWA0BEZDYDQERkNgNARGQ2A0BEZDYMDAaGN0wIJ+1OLREBgNgdEQGA2B0RAYDYHREBgFo43R0TQwGgKjITAaAqMhMBoCoyEwGgKjITBgYLQxOmBBP2rxaAiMhsBoCIyGwGgIjIbAaAiMgtHG6Gg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- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Saved reconstructed page to: parse_outputs/page_1_reconstructed.html\n"
- ]
- },
- {
- "data": {
- "text/html": [
- ""
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "--- \n",
- "### Page 2 — Reconstructed HTML"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Saved reconstructed page to: parse_outputs/page_2_reconstructed.html\n"
- ]
- },
- {
- "data": {
- "text/html": [
- ""
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "for i, (img, blocks) in enumerate(page_images_and_blocks):\n",
- " page_num = i + 1\n",
- " display(Markdown(f\"--- \\n### Page {page_num} — Reconstructed HTML\"))\n",
- "\n",
- " md_text = blocks_to_markdown_page(blocks)\n",
- " html_content = render_markdown_to_html(md_text)\n",
- "\n",
- " # Save for downstream use\n",
- " html_path = os.path.join(OUTPUT_DIR, f\"page_{page_num}_reconstructed.html\")\n",
- " with open(html_path, \"w\", encoding=\"utf-8\") as f:\n",
- " f.write(html_content)\n",
- " print(f\"Saved reconstructed page to: {html_path}\")\n",
- "\n",
- " # Display in the notebook\n",
- " display(HTML(f\"
• New matrix‑multiply instructions that fuse attention kernels. | Delivers dramatically higher AI‑compute throughput and efficiency. |\n", - "| **AI Multi‑Frame Generation (DLSS 4)** | Generates **2 ×** frames per render pass; supports up to **8K** resolution with < 5 ms latency.
Works with RTX‑ON ray tracing. | Doubles frame rates in games/VR while preserving or improving image quality. |\n", - "| **AI Management Processor (AMP)** | Small RISC‑V core integrated on‑die; handles work‑graph scheduling, context switching, and VRAM orchestration. | Reduces CPU‑GPU overhead, improves effective VRAM bandwidth by ~30 %. |\n", - "| **Enhanced Transformer Engine** | • Optimized attention‑fusion kernels.
• Larger on‑chip scratchpad for KV‑cache reuse. | Cuts training/inference time for transformer‑based models (e.g., 30 B‑parameter LLMs). |\n", - "| **Performance‑Scale Metrics** | • **Base Blackwell GPU:** 10 petaFLOPS (NVFP4).
• **Blackwell Ultra:** up to 15 petaFLOPS (≈1.5× base, ~7.5× Hopper H100).
• **Training:** up to **3×** faster vs. H100.
• **Inference:** up to **15×** faster vs. H100.
• **GPU density:** up to **4×** Blackwell GPUs in a single workstation (e.g., 4× RTX PRO 6000) without thermal overload. | Quantifies real‑world speedups and scalability. |\n", - "| **Memory Subsystem** | • **Memory bandwidth:** up to **3.2 TB/s** (GDDR7).
• **Capacity:** 48 GB or 96 GB HBM3e per GPU.
• **L2 cache:** 120 MB shared. | Enables larger batch sizes and higher model parallelism. |\n", - "| **Power & Thermal Design** | • **TDP:** 350 W (standard), 450 W (Ultra).
• **Cooling:** Dual‑fan + vapor‑chamber; designed for up to **4‑GPU** packing in a 2U chassis. | Provides a balance of performance and power‑efficiency for data‑center and workstation use. |\n", - "| **Software Stack** | • Supports **CUDA 12.5**, **TensorRT 9**, ** cuDNN 9**.
• New APIs for **work‑graph scheduling** and **dynamic kernelRegistration**. | Ensures compatibility with existing AI frameworks (PyTorch, TensorFlow) and optimizations. |\n", - "\n", - "*Sources: Search results 1‑4 (NVIDIA blog, architecture briefings, performance tables).*\n", - "\n", - "---\n", - "\n", - "## 3. Key Benefits (with concrete numbers)\n", - "\n", - "| Benefit | Concrete Figure / Example | Impact |\n", - "|---------|---------------------------|--------|\n", - "| **Accelerated AI training** | 3× faster training of a 70 B LLM compared with H100 (≈ 2 days → 16 hours on a 8‑GPU Blackwell cluster). | Reduces time‑to‑market for new models. |\n", - "| **High‑speed inference** | 15× lower latency for a 175 B parameter model (≈ 30 ms → 2 ms per token). | Cuts **cost‑per‑token** by ~85 % for large‑scale serving. |\n", - "| **Graphics performance** | DLSS 4 can push **120 fps** in 4K gaming titles (e.g., *Cyberpunk 2077* RTX‑ON) vs. 60 fps with DLSS 3. | Enables smoother VR/AR experiences. |\n", - "| **VRAM efficiency** | Effective VRAM bandwidth ↑ 30 % thanks to AMP; allows **2×** larger batch sizes on the same memory footprint. | Larger models can be deployed on a single GPU. |\n", - "| **Scalable multi‑GPU configurations** | 4‑GPU workstation (e.g., 4× RTX PRO 6000) shows **2.9×** scaling efficiency on mixed‑precision training (MLPerf Training v2.0). | Facilitates dense AI clusters without extra rack space. |\n", - "| **Cost‑per‑token reduction** | Estimated **$0.00012 per token** for a 70 B model on Blackwell vs. **$0.0010** on H100 (≈ 88 % savings). | Makes large‑scale AI services economically viable. |\n", - "| **Future‑ready ecosystem** | Full CUDA 12.5 support; TensorRT 9 optimizations; planned roadmap through **2027** with “Blackwell‑X” prototype slated for 2026. | Protects developer investments and ensures longevity. |\n", - "\n", - "---\n", - "\n", - "## 4. Technical Specification Snapshot \n", - "\n", - "| Spec | Blackwell (Base) | Blackwell Ultra |\n", - "|------|------------------|-----------------|\n", - "| **FP8/TF32 Compute** | 1,000 TFLOPs | 1,500 TFLOPs |\n", - "| **CUDA Cores** | 18,432 | 23,040 |\n", - "| **Tensor Cores (5th‑Gen)** | 576 | 720 |\n", - "| **RT Cores (3rd‑Gen)** | 72 | 96 |\n", - "| **Memory** | 48 GB/96 GB HBM3e | 96 GB HBM3e |\n", - "| **Memory Bandwidth** | 3.2 TB/s | 3.5 TB/s |\n", - "| **L2 Cache** | 120 MB | 150 MB |\n", - "| **TDP** | 350 W | 450 W |\n", - "| **Process Node** | 5 nm (TSMC) | 5 nm (TSMC) |\n", - "| **Launch** | Q3 2024 | Q1 2025 |\n", - "\n", - "---\n", - "\n", - "## 5. Competitive Landscape (quick comparison)\n", - "\n", - "| Metric | NVIDIA Blackwell | AMD Instinct MI300X | Intel Xe‑HPC (3rd Gen) |\n", - "|--------|-------------------|--------------------|------------------------|\n", - "| **Peak TFLOPs (FP16)** | 1,000 | 900 | 800 |\n", - "| **Inference Speed (LLM token/sec)** | 15× vs. H100 | ~12× vs. MI250X | ~10× vs. Habana Gaudi 2 |\n", - "| **Memory Bandwidth** | 3.2 TB/s | 2.5 TB/s | 2.2 TB/s |\n", - "| **Power Efficiency (TFLOP/W)** | 2.9 TFLOP/W | 2.5 TFLOP/W | 2.2 TFLOP/W |\n", - "| **Software Ecosystem** | CUDA, TensorRT, cuDNN (mature) | ROCm (growing) | oneAPI (early adoption) |\n", - "\n", - "*Blackwell leads on raw performance, inference speed, and software maturity.*\n", - "\n", - "---\n", - "\n", - "## 6. Future Outlook\n", - "- **Blackwell‑X (prototype)** slated for 2026 with **doubling of Tensor Core count** and **support for FP4** precision.\n", - "- NVIDIA plans **continuous driver and CUDA updates** through **2027**, ensuring backward compatibility.\n", - "- Expect **more RTX 50‑series GPUs** (mobile, laptop, and data‑center form factors) to leverage Blackwell’s architecture.\n", - "\n", - "---\n", - "\n", - "## 7. Bottom Line\n", - "The **NVIDIA Blackwell GPU architecture** combines:\n", - "\n", - "- **5th‑generation Tensor Cores** for unmatched AI compute.\n", - "- **DLSS 4 multi‑frame generation** for double‑speed graphics.\n", - "- An **AI Management Processor** that optimizes VRAM scheduling.\n", - "- **Performance metrics** that translate to **3× faster training** and **15× faster inference** versus the previous H100 generation.\n", - "- **Scalable, power‑efficient designs** that enable dense multi‑GPU workstations and data‑center deployments.\n", - "\n", - "These capabilities make Blackwell the **go‑to platform for next‑generation AI development and high‑fidelity, low‑latency graphics**, delivering tangible speed, cost, and efficiency benefits across industries.\n", - "\n", - "---\n", - "\n", - "### References (source of the original research)\n", - "\n", - "1. *NVIDIA Blog – “Blackwell: 3× faster training, 15× faster inference, DLSS 4 frame‑rate gains”*【Search result 1】 \n", - "2. *Architecture Brief – “5th‑Gen Tensor Cores & RTX 50‑series support”*【Search result 2】 \n", - "3. *Performance Whitepaper – “10 → 15 petaFLOPS, Hopper comparison, MLPerf results”*【Search result 3】 \n", - "4. *Technical Deep‑Dive – “AI Management Processor, VRAM efficiency, memory specs”*【Search result 4】\n", - "\n", - "--- \n", - "\n", - "Feel free to let me know if you’d like any additional details (e.g., price estimates, specific benchmark numbers, or a deeper dive into the software stack).\n" - ] - } - ], - "source": [ - "# Helper function for multi-agent research\n", - "def multi_agent_research(topic: str, thread_id: str = \"multi-agent-default\") -> str:\n", - " \"\"\"Run a research query through the multi-agent system.\n", - " \n", - " The coordinator will orchestrate:\n", - " 1. Search Agent → gather information\n", - " 2. Report Writer → create report\n", - " 3. Quality Reviewer → verify helpfulness\n", - " \"\"\"\n", - " config = {\"configurable\": {\"thread_id\": thread_id}}\n", - " \n", - " print(f\"🚀 Starting multi-agent research on: {topic}\")\n", - " print(\"-\" * 50)\n", - " \n", - " result = coordinator.invoke(\n", - " {\"messages\": [{\"role\": \"user\", \"content\": topic}]},\n", - " config=config\n", - " )\n", - " \n", - " # Get the final response\n", - " final_response = result[\"messages\"][-1].content\n", - " return final_response\n", - "\n", - "# Demo: Run a multi-agent research query\n", - "MULTI_AGENT_THREAD = \"research-demo\"\n", - "\n", - "research_topic = \"What are the key features and benefits of NVIDIA's Blackwell GPU architecture?\"\n", - "\n", - "print(\"=\" * 60)\n", - "print(\"MULTI-AGENT RESEARCH DEMO\")\n", - "print(\"=\" * 60)\n", - "\n", - "final_report = multi_agent_research(research_topic, MULTI_AGENT_THREAD)\n", - "\n", - "print(\"\\n\" + \"=\" * 60)\n", - "print(\"FINAL REPORT\")\n", - "print(\"=\" * 60)\n", - "print(final_report)\n" - ] - }, - { - "cell_type": "markdown", - "id": "fd2a7162", - "metadata": {}, - "source": [ - "### View Multi-Agent Collaboration\n", - "\n", - "Let's see how the agents collaborated - which tools were called and in what order:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "007c6567", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Multi-Agent Collaboration Log:\n", - "==================================================\n", - "\n", - "[1] 👤 USER: What are the key features and benefits of NVIDIA's Blackwell GPU architecture?...\n", - "\n", - "[2] 🔧 TOOL CALL: search_specialist\n", - " Args: {'research_topic': 'NVIDIA Blackwell GPU architecture key features and benefits'}\n", - "\n", - "[3] 📥 TOOL RESULT (search_specialist):\n", - " \n", - "**NVIDIA Blackwell GPU Architecture – Key Features & Benefits** \n", - "\n", - "---\n", - "\n", - "### 1. Overview \n", - "Blackwell is NVIDIA’s latest graphics‑processing‑unit micro...\n", - "\n", - "[4] 🔧 TOOL CALL: quality_reviewer\n", - " Args: {'report_to_review': 'NVIDIA Blackwell GPU Architecture – Key Features & Benefits\\n\\n---\\n\\n### 1. O...\n", - "\n", - "[5] 📥 TOOL RESULT (quality_reviewer):\n", - " \n", - "**Overall helpfulness score: 8 / 10**\n", - "\n", - "---\n", - "\n", - "### ✅ What the report does well \n", - "\n", - "| Criterion | Evaluation | Why it earns points |\n", - "|-----------|--------...\n", - "\n", - "[6] 🤖 COORDINATOR: \n", - "**NVIDIA Blackwell GPU Architecture – Key Features, Benefits, and Expanded Details**\n", - "\n", - "---\n", - "\n", - "## 1. Overview\n", - "Blackwell is NVIDIA’s newest **GPU micro‑ar...\n" - ] - } - ], - "source": [ - "# View the multi-agent collaboration history\n", - "config = {\"configurable\": {\"thread_id\": MULTI_AGENT_THREAD}}\n", - "state = coordinator.get_state(config)\n", - "\n", - "print(\"Multi-Agent Collaboration Log:\")\n", - "print(\"=\" * 50)\n", - "\n", - "if state and state.values:\n", - " messages = state.values.get(\"messages\", [])\n", - " for i, msg in enumerate(messages):\n", - " msg_type = msg.type.upper() if hasattr(msg, 'type') else \"UNKNOWN\"\n", - " \n", - " # Check if it's a tool call\n", - " if msg_type == \"AI\" and hasattr(msg, 'tool_calls') and msg.tool_calls:\n", - " for tc in msg.tool_calls:\n", - " print(f\"\\n[{i+1}] 🔧 TOOL CALL: {tc.get('name', 'unknown')}\")\n", - " args = tc.get('args', {})\n", - " arg_preview = str(args)[:100] + \"...\" if len(str(args)) > 100 else str(args)\n", - " print(f\" Args: {arg_preview}\")\n", - " elif msg_type == \"TOOL\":\n", - " tool_name = msg.name if hasattr(msg, 'name') else \"unknown\"\n", - " content_preview = msg.content[:150] + \"...\" if len(msg.content) > 150 else msg.content\n", - " print(f\"\\n[{i+1}] 📥 TOOL RESULT ({tool_name}):\")\n", - " print(f\" {content_preview}\")\n", - " elif msg_type == \"HUMAN\":\n", - " print(f\"\\n[{i+1}] 👤 USER: {msg.content[:100]}...\")\n", - " elif msg_type == \"AI\":\n", - " content_preview = msg.content[:150] + \"...\" if len(msg.content) > 150 else msg.content\n", - " print(f\"\\n[{i+1}] 🤖 COORDINATOR: {content_preview}\")\n", - "else:\n", - " print(\"No collaboration history found.\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "cc99fddb", - "metadata": {}, - "source": [ - "## Try Your Own Multi-Agent Research\n", - "\n", - "Run any research topic through the multi-agent system:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "aa452c34", - "metadata": {}, - "outputs": [], - "source": [ - "# Try your own multi-agent research!\n", - "# Change the topic and thread_id as desired\n", - "\n", - "my_topic = \"What are the latest advancements in quantum computing in 2024?\"\n", - "my_thread = \"my-multi-agent-research\"\n", - "\n", - "report = multi_agent_research(my_topic, my_thread)\n", - "print(\"\\n\" + \"=\" * 60)\n", - "print(\"YOUR RESEARCH REPORT\")\n", - "print(\"=\" * 60)\n", - "print(report)\n" - ] - }, - { - "cell_type": "markdown", - "id": "b51e3823", - "metadata": {}, - "source": [ - "---\n", - "\n", - "# Summary\n", - "\n", - "In this notebook, we covered:\n", - "\n", - "### Part 1: Basic Usage\n", - "- Setting up the OpenAI-compatible client via **OpenRouter**\n", - "- **Reasoning ON**: Chain-of-thought reasoning (best for complex problems)\n", - "- **Reasoning OFF**: Direct answers (best for simple queries)\n", - "\n", - "### Part 2: LangChain v1.0 Single Agent\n", - "- Used the new **`create_agent`** API\n", - "- Integrated NVIDIA Nemotron via **OpenRouter** using `ChatOpenAI` with custom `base_url`\n", - "- Added **web search** capability with `@tool` decorator\n", - "- Implemented **conversation memory** using `InMemorySaver` checkpointer\n", - "\n", - "### Part 3: Multi-Agent System\n", - "- Created **specialized sub-agents** (Search, Report Writer, Quality Reviewer)\n", - "- Wrapped sub-agents as **tools** following [LangChain's multi-agent pattern](https://docs.langchain.com/oss/python/langchain/multi-agent)\n", - "- Built a **Coordinator (Supervisor)** agent to orchestrate the workflow\n", - "- Demonstrated the full **research pipeline**: Search → Write → Review\n", - "\n", - "## Multi-Agent Architecture\n", - "\n", - "```\n", - "┌─────────────────────────────────────────────────────┐\n", - "│ COORDINATOR │\n", - "│ (Decides which specialist to call and when) │\n", - "└─────────────┬─────────────┬─────────────┬───────────┘\n", - " │ │ │\n", - " ┌────────▼────┐ ┌─────▼──────┐ ┌───▼────────┐\n", - " │ SEARCH │ │ REPORT │ │ QUALITY │\n", - " │ AGENT │ │ WRITER │ │ REVIEWER |\n", - " │ (web_search)│ │ (writing) │ │(evaluation)│\n", - " └─────────────┘ └────────────┘ └────────────┘\n", - "```\n", - "\n", - "## Key LangChain v1.0 Patterns Used\n", - "\n", - "| Pattern | Description |\n", - "|---------|-------------|\n", - "| `create_agent` | New simplified agent creation API |\n", - "| `@tool` decorator | Define tools from functions |\n", - "| `InMemorySaver` | Thread-based conversation memory |\n", - "| **Tool Calling** | Sub-agents wrapped as tools for orchestration |\n", - "\n", - "## Resources\n", - "\n", - "- [OpenRouter Documentation](https://openrouter.ai/docs)\n", - "- [LangChain Chat Completions API (OpenRouter)](https://docs.langchain.com/oss/python/integrations/chat/index)\n", - "- [LangChain v1.0 Release Notes](https://docs.langchain.com/oss/python/releases/langchain-v1)\n", - "- [Multi-Agent Patterns](https://docs.langchain.com/oss/python/langchain/multi-agent)\n", - "- [Supervisor Agent Tutorial](https://docs.langchain.com/oss/python/langchain/supervisor)\n", - "- [LangGraph for Advanced Agents](https://langchain-ai.github.io/langgraph/)\n" - ] - }, - { - "cell_type": "markdown", - "id": "af0a8b75", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..edb4618 --- /dev/null +++ b/uv.lock @@ -0,0 +1,4916 @@ +version = 1 +revision = 3 +requires-python = ">=3.10" +resolution-markers = [ + "python_full_version >= '3.14' and sys_platform == 'linux'", + "python_full_version == '3.13.*' and sys_platform == 'linux'", 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\n",
- "\n",
- "# Nemotron Parse: From Complex Documents to Agent-Ready Data\n",
- "\n",
- "NVIDIA Nemotron Parse (also known as NeMo Retriever Parse) is a transformer-based VLM specialized for document parsing. Think of it as the ingestion backbone for your AI agent. Its job is to:\n",
- "- Accurately extract text and formulas in correct reading order.\n",
- "- Robustly parse complex tables into a structured format (like LaTeX).\n",
- "- Perform semantic segmentation (labeling Title, Text, List-item, Figure, etc.).\n",
- "- Provide spatial grounding (a bbox for every element).\n",
- "\n",
- "It's built to preserve meaning and structure at scale, so your agent can act on the right information with confidence.\n",
- "\n",
- "\n",
- "---\n",
- "\n",
- "### What you’ll learn in this notebook:\n",
- "* How to call Nemotron Parse on a PDF page image and get structured JSON.\n",
- "* How to visualize the model's layout understanding.\n",
- "* How to convert the model's output (including complex tables) into clean HTML and Markdown for RAG or agentic workflows.\n",
- "* How this model complements a separate visual reasoning model in a full document AI agent.\n",
- "\n",
- "Note: This notebook focuses only on Nemotron Parse. Its job is to find what elements are on the page and where they are. If your workflow also needs to understand the visual content inside a Picture or Figure block (e.g., analyze a chart, describe a photo), you would pair Parse with a visual reasoning model in a second stage.\n",
- "\n",
- "Parse tells you where the image is; a reasoning model tells you what the image means."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f087b4bc",
- "metadata": {},
- "source": [
- "## 0. Setup\n",
- "\n",
- "Install minimal dependencies for PDF rendering (PyMuPDF), API calls (requests), and visualization (Pillow)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7921625c",
- "metadata": {
- "id": "install-deps"
- },
- "outputs": [],
- "source": [
- "%pip install --quiet requests pillow pymupdf pandas markdown bleach"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1f2ebcb2",
- "metadata": {},
- "source": [
- "### Configure API Endpoint\n",
- "\n",
- "This notebook uses the call_nemotron_parse function to call the NVIDIA API-compatible endpoint via the Chat Completions API.\n",
- "\n",
- "Requirements:\n",
- "1) Set your NVIDIA_API_KEY as an environment variable.\n",
- "2) Optionally set NVAI_URL to your NVIDIA API Gateway or local NIM gateway.\n",
- "\n",
- "The model returns a list of layout blocks with:\n",
- "- type: e.g., \"Title\", \"Text\", \"List-item\", \"Table\", etc.\n",
- "- text: the block's textual content (for tables, a LaTeX tabular string)\n",
- "- bbox: normalized coordinates {xmin, ymin, xmax, ymax} in [0,1]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "id": "9881a584",
- "metadata": {
- "id": "imports-config"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Using API URL: https://integrate.api.nvidia.com/v1\n",
- "Output directory: ./parse_outputs\n"
- ]
- }
- ],
- "source": [
- "import os\n",
- "import re\n",
- "import json\n",
- "import base64\n",
- "import requests\n",
- "import fitz # PyMuPDF\n",
- "from typing import List, Dict, Any, Tuple\n",
- "from PIL import Image, ImageDraw, ImageFont\n",
- "from io import BytesIO\n",
- "import pandas as pd\n",
- "from IPython.display import display, Markdown, HTML\n",
- "\n",
- "# API configuration\n",
- "API_KEY = os.environ.get(\"NVIDIA_API_KEY\", \"YOUR_API_KEY_HERE\")\n",
- "NVAI_URL = os.environ.get(\"NVAI_URL\", \"https://integrate.api.nvidia.com/v1\")\n",
- "\n",
- "if API_KEY == \"YOUR_API_KEY_HERE\":\n",
- " print(\"WARNING: NVIDIA_API_KEY not set. Please set it to run the notebook.\")\n",
- "\n",
- "# Output directory for artifacts\n",
- "OUTPUT_DIR = \"parse_outputs\"\n",
- "os.makedirs(OUTPUT_DIR, exist_ok=True)\n",
- "print(f\"Using API URL: {NVAI_URL}\")\n",
- "print(f\"Output directory: {os.path.abspath(OUTPUT_DIR)}\")\n",
- "\n",
- "# Class color mapping for visualization\n",
- "CLASS_COLORS = {\n",
- " 'Title': '#D32F2F',\n",
- " 'Section-header': '#E91E63',\n",
- " 'Text': '#4CAF50',\n",
- " 'List-item': '#1976D2',\n",
- " 'Caption': '#607D8B',\n",
- " 'Table': '#03A9F4',\n",
- " 'Figure': '#6D4C41',\n",
- " 'Picture': '#6D4C41',\n",
- " 'Formula': '#FF9800',\n",
- " 'Page-header': '#9E9E9E',\n",
- " 'Page-footer': '#9E9E9E',\n",
- " 'Footnote': '#00BCD4',\n",
- " 'Bibliography': '#512DA8',\n",
- " 'TOC': '#FFC107',\n",
- " 'DEFAULT': '#9E9E9E'\n",
- "}\n",
- "\n",
- "def get_text_color(hex_color: str) -> str:\n",
- " \"\"\"Return black or white for best contrast over a hex background color.\"\"\"\n",
- " hex_color = hex_color.lstrip('#')\n",
- " r, g, b = tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4))\n",
- " luminance = (0.2126 * r + 0.7152 * g + 0.0722 * b)\n",
- " return \"#000000\" if luminance > 140 else \"#FFFFFF\""
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e1d247be",
- "metadata": {},
- "source": [
- "## 1. Helper Functions\n",
- "\n",
- "Helpers for:\n",
- "1) PDF-to-Image rendering.\n",
- "2) API call via Chat Completions.\n",
- "3) Visualization.\n",
- "4) LaTeX tables to HTML and page-to-Markdown assembly."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "id": "4046f1fe",
- "metadata": {
- "id": "helpers"
- },
- "outputs": [],
- "source": [
- "from markdown import markdown\n",
- "import bleach\n",
- "\n",
- "# PDF page rendering\n",
- "def pdf_page_to_image(pdf_path: str, page_index: int, dpi: int = 300,\n",
- " target_size: Tuple[int, int] = (1536, 2048)) -> Image.Image:\n",
- " \"\"\"Render a PDF page to an RGB image and center it on a fixed-size canvas.\"\"\"\n",
- " doc = fitz.open(pdf_path)\n",
- " page = doc.load_page(page_index)\n",
- " zoom = dpi / 72.0\n",
- " pix = page.get_pixmap(matrix=fitz.Matrix(zoom, zoom))\n",
- " src = Image.frombytes(\"RGB\", [pix.width, pix.height], pix.samples)\n",
- " doc.close()\n",
- "\n",
- " canvas = Image.new(\"RGB\", target_size, (255, 255, 255))\n",
- " src.thumbnail(target_size, Image.Resampling.LANCZOS)\n",
- " x = (target_size[0] - src.width) // 2\n",
- " y = (target_size[1] - src.height) // 2\n",
- " canvas.paste(src, (x, y))\n",
- " return canvas\n",
- "\n",
- "# Image encoding\n",
- "def encode_image_to_base64(image: Image.Image, fmt: str = \"PNG\") -> str:\n",
- " \"\"\"Encode a PIL image to base64 for API submission.\"\"\"\n",
- " buf = BytesIO()\n",
- " image.save(buf, format=fmt)\n",
- " return base64.b64encode(buf.getvalue()).decode('utf-8')\n",
- "\n",
- "# Nemotron Parse API call\n",
- "def call_nemotron_parse(base64_image: str) -> List[Dict[str, Any]]:\n",
- " \"\"\"Call the nemotron-parse model and return a list of blocks with type/text/bbox.\"\"\"\n",
- " headers = {\"Authorization\": f\"Bearer {API_KEY}\", \"Accept\": \"application/json\"}\n",
- " messages = [{\n",
- " \"role\": \"user\",\n",
- " \"content\": [{\n",
- " \"type\": \"image_url\",\n",
- " \"image_url\": {\"url\": f\"data:image/png;base64,{base64_image}\"}\n",
- " }]\n",
- " }]\n",
- " tools = [{\"type\": \"function\", \"function\": {\"name\": \"markdown_bbox\"}}]\n",
- " payload = {\n",
- " \"model\": \"nvidia/nemotron-parse\",\n",
- " \"messages\": messages,\n",
- " \"max_tokens\": 3500,\n",
- " \"temperature\": 0,\n",
- " \"tools\": tools\n",
- " }\n",
- " try:\n",
- " resp = requests.post(f\"{NVAI_URL}/chat/completions\", headers=headers, json=payload, timeout=180)\n",
- " resp.raise_for_status()\n",
- " response_json = resp.json()\n",
- " except requests.exceptions.HTTPError as e:\n",
- " print(f\"API Error for nvidia/nemotron-parse: {e.response.status_code} - {e.response.text}\")\n",
- " return []\n",
- " except Exception as e:\n",
- " print(f\"Unexpected error calling nemotron-parse: {e}\")\n",
- " return []\n",
- "\n",
- " tool_call = response_json.get(\"choices\", [{}])[0].get(\"message\", {}).get(\"tool_calls\", [{}])[0]\n",
- " if not tool_call:\n",
- " return []\n",
- " arguments_str = tool_call.get(\"function\", {}).get(\"arguments\", \"[]\")\n",
- " try:\n",
- " parsed_args = json.loads(arguments_str)\n",
- " if isinstance(parsed_args, list) and len(parsed_args) > 0 and isinstance(parsed_args[0], list):\n",
- " return parsed_args[0]\n",
- " return parsed_args if isinstance(parsed_args, list) else []\n",
- " except json.JSONDecodeError:\n",
- " print(f\"Error decoding JSON from arguments: {arguments_str}\")\n",
- " return []\n",
- "\n",
- "# Visualization\n",
- "def draw_layout(image: Image.Image, blocks: List[Dict[str, Any]]) -> Image.Image:\n",
- " \"\"\"Draw bounding boxes and labels for each parsed block.\"\"\"\n",
- " out = image.copy()\n",
- " draw = ImageDraw.Draw(out)\n",
- " W, H = out.size\n",
- " box_thickness = max(2, int(W / 600))\n",
- " font_size = max(14, int(W / 80))\n",
- " try:\n",
- " font = ImageFont.truetype(\"Arial.ttf\", font_size)\n",
- " except Exception:\n",
- " font = ImageFont.load_default()\n",
- "\n",
- " for i, b in enumerate(blocks):\n",
- " bbox = b.get(\"bbox\") or {}\n",
- " xmin, ymin = bbox.get(\"xmin\", 0.0), bbox.get(\"ymin\", 0.0)\n",
- " xmax, ymax = bbox.get(\"xmax\", 0.0), bbox.get(\"ymax\", 0.0)\n",
- " if xmax <= xmin or ymax <= ymin:\n",
- " continue\n",
- " x0, y0, x1, y1 = xmin * W, ymin * H, xmax * W, ymax * H\n",
- " cat = b.get(\"type\", \"DEFAULT\")\n",
- " color = CLASS_COLORS.get(cat, CLASS_COLORS[\"DEFAULT\"])\n",
- " text_color = get_text_color(color)\n",
- " draw.rectangle([x0, y0, x1, y1], outline=color, width=box_thickness)\n",
- " label = f\"{i}:{cat}\"\n",
- " try:\n",
- " tb = draw.textbbox((0, 0), label, font=font)\n",
- " tw, th = tb[2] - tb[0], tb[3] - tb[1]\n",
- " except Exception:\n",
- " tw, th = draw.textsize(label, font=font)\n",
- " label_bg = (x0, max(0, y0 - th - 6), x0 + tw + 8, max(0, y0 - 6))\n",
- " draw.rectangle(label_bg, fill=color)\n",
- " draw.text((label_bg[0] + 4, label_bg[1] + 2), label, fill=text_color, font=font)\n",
- " return out\n",
- "\n",
- "# Records and DataFrame\n",
- "def blocks_to_dataframe(blocks: List[Dict[str, Any]]) -> pd.DataFrame:\n",
- " rows = []\n",
- " for i, b in enumerate(blocks):\n",
- " txt = (b.get(\"text\") or \"\").replace(\"\\n\", \" \")\n",
- " rows.append({\n",
- " \"id\": i,\n",
- " \"type\": b.get(\"type\"),\n",
- " \"text\": txt[:200],\n",
- " \"bbox\": b.get(\"bbox\")\n",
- " })\n",
- " return pd.DataFrame(rows)\n",
- "\n",
- "def blocks_to_records(blocks: List[Dict[str, Any]], page_number: int, page_size_px: Tuple[int, int]) -> List[Dict[str, Any]]:\n",
- " W, H = page_size_px\n",
- " records = []\n",
- " for i, b in enumerate(blocks):\n",
- " bbox = b.get(\"bbox\") or {\"xmin\": 0, \"ymin\": 0, \"xmax\": 0, \"ymax\": 0}\n",
- " x0, y0 = int(bbox.get(\"xmin\", 0) * W), int(bbox.get(\"ymin\", 0) * H)\n",
- " x1, y1 = int(bbox.get(\"xmax\", 0) * W), int(bbox.get(\"ymax\", 0) * H)\n",
- " records.append({\n",
- " \"page\": page_number,\n",
- " \"id\": i,\n",
- " \"type\": b.get(\"type\"),\n",
- " \"text\": b.get(\"text\"),\n",
- " \"bbox_norm\": bbox,\n",
- " \"bbox_px\": [x0, y0, x1, y1]\n",
- " })\n",
- " return records\n",
- "\n",
- "# LaTeX table parsing\n",
- "def _clean_latex_text(s: str) -> str:\n",
- " s = s.strip()\n",
- " s = re.sub(r\"\\\\textbf\\{(.*?)\\}\", r\"\\1\", s)\n",
- " s = re.sub(r\"\\\\textit\\{(.*?)\\}\", r\"\\1\", s)\n",
- " replacements = {\n",
- " r\"\\$\": \"$\",\n",
- " r\"\\%\": \"%\",\n",
- " r\"\\\\&\": \"&\",\n",
- " r\"\\\\_\": \"_\",\n",
- " r\"\\\\#\": \"#\",\n",
- " r\"\\\\textless\": \"<\",\n",
- " r\"\\\\textgreater\": \">\",\n",
- " r\"\\\\newline\": \" \"\n",
- " }\n",
- " for k, v in replacements.items():\n",
- " s = re.sub(k, v, s)\n",
- " return s\n",
- "\n",
- "def _parse_cell_spans(cell: str) -> Tuple[str, int, int]:\n",
- " \"\"\"Parse a cell for \\multicolumn and \\multirow; return (content, colspan, rowspan).\"\"\"\n",
- " content = cell.strip()\n",
- " colspan, rowspan = 1, 1\n",
- " changed = True\n",
- " while changed:\n",
- " changed = False\n",
- " m = re.match(r\"\\\\multicolumn\\{(\\d+)\\}\\{[^}]*\\}\\{(.*)\\}\\s*$\", content, flags=re.DOTALL)\n",
- " if m:\n",
- " colspan = int(m.group(1))\n",
- " content = m.group(2).strip()\n",
- " changed = True\n",
- " continue\n",
- " m = re.match(r\"\\\\multirow\\{(\\d+)\\}\\{[^}]*\\}\\{(.*)\\}\\s*$\", content, flags=re.DOTALL)\n",
- " if m:\n",
- " rowspan = int(m.group(1))\n",
- " content = m.group(2).strip()\n",
- " changed = True\n",
- " continue\n",
- " content = _clean_latex_text(content)\n",
- " return content, colspan, rowspan\n",
- "\n",
- "def latex_table_to_html(latex_str: str) -> str:\n",
- " \"\"\"Convert a LaTeX tabular into HTML. Fallbacks to raw pre if parsing fails.\"\"\"\n",
- " try:\n",
- " if not latex_str:\n",
- " return \"\"\n",
- " m = re.search(r\"\\\\begin\\{tabular\\}\\{[^}]*\\}(.*?)\\\\end\\{tabular\\}\", latex_str, flags=re.DOTALL)\n",
- " content = m.group(1) if m else latex_str\n",
- " content = re.sub(r\"\\\\hline\", \"\", content)\n",
- " rows_raw = [r.strip() for r in re.split(r\"(?{latex_str}\"\n",
- "\n",
- " occupied = set()\n",
- " table_rows = []\n",
- " for r_idx, row_str in enumerate(rows_raw):\n",
- " parts = [p.strip() for p in re.split(r\"(?\"]\n",
- " for r_idx, cells in enumerate(table_rows):\n",
- " html.append(\" {latex_str}$$ {text} $$[Footnote] {t}
\")\n", - " md_lines.append(\"\")\n", - " continue\n", - "\n", - " md_lines.append(t)\n", - " md_lines.append(\"\")\n", - "\n", - " if in_list:\n", - " md_lines.append(\"\")\n", - " return \"\\n\".join(md_lines).strip()\n", - "\n", - "def render_markdown_to_html(md_text: str) -> str:\n", - " \"\"\"Render Markdown to HTML, then sanitize while allowing table-related tags.\"\"\"\n", - " html = markdown(md_text, extensions=[\"extra\", \"sane_lists\"])\n", - " allowed_tags = [\n", - " \"h1\",\"h2\",\"h3\",\"h4\",\"h5\",\"h6\",\"p\",\"em\",\"strong\",\"code\",\"pre\",\"blockquote\",\"br\",\n", - " \"ul\",\"ol\",\"li\",\n", - " \"table\",\"thead\",\"tbody\",\"tr\",\"th\",\"td\"\n", - " ]\n", - " allowed_attrs = {\"table\": [\"border\", \"class\"], \"td\": [\"colspan\",\"rowspan\"], \"th\": [\"colspan\",\"rowspan\"]}\n", - " return bleach.clean(html, tags=allowed_tags, attributes=allowed_attrs, strip=True)" - ] - }, - { - "cell_type": "markdown", - "id": "adc2c212", - "metadata": {}, - "source": [ - "## 2. Quickstart: Parse a Sample PDF\n", - "\n", - "We’ll fetch a PDF that mixes text, a table, headers, lists, and a footnote. For each page, we will:\n", - "1) Render the page as an image.\n", - "2) Call Nemotron Parse.\n", - "3) Visualize the detected layout blocks.\n", - "4) Inspect the extracted data in a DataFrame.\n", - "5) Export the full data to a JSONL file for downstream use." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "11e843a0", - "metadata": { - "id": "quickstart" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using cached PDF: parse_outputs/corporate-nvidia-in-brief.pdf\n" - ] - }, - { - "data": { - "text/markdown": [ - "--- \n", - "### Processing Page 1..." - ], - "text/plain": [ - "\n",
- "\n",
- "\n",
- " \n",
- "
\n",
- "
"
- ],
- "text/plain": [
- " id type text \\\n",
- "0 0 Title # NVIDIA in Brief \n",
- "1 1 Text NVIDIA pioneered accelerated computing to tack... \n",
- "2 2 Section-header ## Company History \n",
- "3 3 Text Founded in 1993, NVIDIA is the world leader in... \n",
- "4 4 Section-header ## Key Stats \n",
- "5 5 List-item \\>\\> Founded in 1993 \n",
- "6 6 List-item \\>\\> Founder and CEO: **Jensen Huang** \n",
- "7 7 List-item \\>\\> 36,000+ employees in 38 countries \n",
- "8 8 List-item \\>\\> Record full-year revenue of $130.5 billio... \n",
- "9 9 List-item \\>\\> 8,700+ granted and pending patent applica... \n",
- "\n",
- " bbox \n",
- "0 {'xmin': 0.033710416666666666, 'ymin': 0.2602,... \n",
- "1 {'xmin': 0.033710416666666666, 'ymin': 0.3484,... \n",
- "2 {'xmin': 0.033710416666666666, 'ymin': 0.4078,... \n",
- "3 {'xmin': 0.033710416666666666, 'ymin': 0.432, ... \n",
- "4 {'xmin': 0.033710416666666666, 'ymin': 0.5273,... \n",
- "5 {'xmin': 0.033710416666666666, 'ymin': 0.5547,... \n",
- "6 {'xmin': 0.033710416666666666, 'ymin': 0.5734,... \n",
- "7 {'xmin': 0.033710416666666666, 'ymin': 0.5914,... \n",
- "8 {'xmin': 0.033710416666666666, 'ymin': 0.6102,... \n",
- "9 {'xmin': 0.033710416666666666, 'ymin': 0.6289,... "
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "--- \n",
- "### Processing Page 2..."
- ],
- "text/plain": [
- "| \n", - " | id | \n", - "type | \n", - "text | \n", - "bbox | \n", - "
|---|---|---|---|---|
| 0 | \n", - "0 | \n", - "Title | \n", - "# NVIDIA in Brief | \n", - "{'xmin': 0.033710416666666666, 'ymin': 0.2602,... | \n", - "
| 1 | \n", - "1 | \n", - "Text | \n", - "NVIDIA pioneered accelerated computing to tack... | \n", - "{'xmin': 0.033710416666666666, 'ymin': 0.3484,... | \n", - "
| 2 | \n", - "2 | \n", - "Section-header | \n", - "## Company History | \n", - "{'xmin': 0.033710416666666666, 'ymin': 0.4078,... | \n", - "
| 3 | \n", - "3 | \n", - "Text | \n", - "Founded in 1993, NVIDIA is the world leader in... | \n", - "{'xmin': 0.033710416666666666, 'ymin': 0.432, ... | \n", - "
| 4 | \n", - "4 | \n", - "Section-header | \n", - "## Key Stats | \n", - "{'xmin': 0.033710416666666666, 'ymin': 0.5273,... | \n", - "
| 5 | \n", - "5 | \n", - "List-item | \n", - "\\>\\> Founded in 1993 | \n", - "{'xmin': 0.033710416666666666, 'ymin': 0.5547,... | \n", - "
| 6 | \n", - "6 | \n", - "List-item | \n", - "\\>\\> Founder and CEO: **Jensen Huang** | \n", - "{'xmin': 0.033710416666666666, 'ymin': 0.5734,... | \n", - "
| 7 | \n", - "7 | \n", - "List-item | \n", - "\\>\\> 36,000+ employees in 38 countries | \n", - "{'xmin': 0.033710416666666666, 'ymin': 0.5914,... | \n", - "
| 8 | \n", - "8 | \n", - "List-item | \n", - "\\>\\> Record full-year revenue of $130.5 billio... | \n", - "{'xmin': 0.033710416666666666, 'ymin': 0.6102,... | \n", - "
| 9 | \n", - "9 | \n", - "List-item | \n", - "\\>\\> 8,700+ granted and pending patent applica... | \n", - "{'xmin': 0.033710416666666666, 'ymin': 0.6289,... | \n", - "
\n",
- "\n",
- "\n",
- " \n",
- "
\n",
- "
"
- ],
- "text/plain": [
- " id type text \\\n",
- "0 0 Text **NVIDIA is the engine of the world’s AI infra... \n",
- "1 1 Text **CUDA**®, our parallel computing m... \n",
- "2 2 Text **Accelerated computing is sustainable computi... \n",
- "3 3 Text **Blackwell** is one of the most important pro... \n",
- "4 4 Text **NVIDIA NIM**TM, part of the NVIDI... \n",
- "5 5 Section-header # Latest NVIDIA News \n",
- "6 6 Section-header **AI/Data Center** \n",
- "7 7 List-item \\>\\> NVIDIA DGX personal AI supercomputers bri... \n",
- "8 8 List-item \\>\\> NVIDIA Blackwell Ultra AI factory platfor... \n",
- "9 9 List-item \\>\\> NVIDIA Dynamo open-source inference softw... \n",
- "\n",
- " bbox \n",
- "0 {'xmin': 0.183596875, 'ymin': 0.2555, 'xmax': ... \n",
- "1 {'xmin': 0.183596875, 'ymin': 0.3508, 'xmax': ... \n",
- "2 {'xmin': 0.183596875, 'ymin': 0.4375, 'xmax': ... \n",
- "3 {'xmin': 0.183596875, 'ymin': 0.5227, 'xmax': ... \n",
- "4 {'xmin': 0.183596875, 'ymin': 0.6086, 'xmax': ... \n",
- "5 {'xmin': 0.5230677083333333, 'ymin': 0.2562, '... \n",
- "6 {'xmin': 0.5911979166666667, 'ymin': 0.2844, '... \n",
- "7 {'xmin': 0.5911979166666667, 'ymin': 0.3016, '... \n",
- "8 {'xmin': 0.5911979166666667, 'ymin': 0.3328, '... \n",
- "9 {'xmin': 0.5911979166666667, 'ymin': 0.3641, '... "
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Download a sample PDF (replace with your own if desired)\n",
- "pdf_url = \"https://www.nvidia.com/content/dam/en-zz/Solutions/about-nvidia/corporate-nvidia-in-brief.pdf\"\n",
- "pdf_path = os.path.join(OUTPUT_DIR, pdf_url.split(\"/\")[-1].split(\"?\")[0])\n",
- "if not os.path.exists(pdf_path):\n",
- " print(f\"Downloading {pdf_url}...\")\n",
- " r = requests.get(pdf_url, timeout=60)\n",
- " r.raise_for_status()\n",
- " with open(pdf_path, \"wb\") as f:\n",
- " f.write(r.content)\n",
- " print(f\"PDF saved to: {pdf_path}\")\n",
- "else:\n",
- " print(f\"Using cached PDF: {pdf_path}\")\n",
- "\n",
- "all_records: List[Dict[str, Any]] = []\n",
- "page_images_and_blocks: List[Tuple[Image.Image, List[Dict[str, Any]]]] = []\n",
- "\n",
- "# Process up to 4 pages\n",
- "try:\n",
- " with fitz.open(pdf_path) as doc:\n",
- " num_pages = min(4, len(doc))\n",
- " for page_idx in range(num_pages):\n",
- " display(Markdown(f\"--- \\n### Processing Page {page_idx+1}...\"))\n",
- "\n",
- " # 1) Render\n",
- " img = pdf_page_to_image(pdf_path, page_idx, dpi=300, target_size=(1536, 2048))\n",
- "\n",
- " # 2) Parse\n",
- " b64 = encode_image_to_base64(img)\n",
- " blocks = call_nemotron_parse(b64)\n",
- " if not blocks:\n",
- " print(f\"No blocks found for page {page_idx+1}. Skipping.\")\n",
- " continue\n",
- " page_images_and_blocks.append((img, blocks))\n",
- "\n",
- " # 3) Visualize\n",
- " annotated = draw_layout(img, blocks)\n",
- " display(Markdown(f\"#### Page {page_idx+1} — Annotated Layout\"))\n",
- " display(annotated.resize((900, int(900 * annotated.height / annotated.width))))\n",
- "\n",
- " # 4) Inspect\n",
- " df = blocks_to_dataframe(blocks)\n",
- " display(Markdown(\"Top blocks (id, type, text preview):\"))\n",
- " display(df.head(10))\n",
- "\n",
- " # 5) Collect records\n",
- " all_records.extend(blocks_to_records(blocks, page_idx+1, img.size))\n",
- "except Exception as e:\n",
- " print(f\"An error occurred while processing the PDF: {e}\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "77d2b8ad",
- "metadata": {},
- "source": [
- "### Export Agent-Ready Records\n",
- "\n",
- "This JSONL file is a structured input for downstream processes. Each record includes text, semantic type, and location on the page."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "id": "2a96f9fe",
- "metadata": {
- "id": "export-jsonl"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Exported 71 records to: parse_outputs/parsed_records.jsonl\n",
- "\n",
- "--- Sample Record --- \n",
- "{\n",
- " \"page\": 1,\n",
- " \"id\": 0,\n",
- " \"type\": \"Title\",\n",
- " \"text\": \"# NVIDIA in Brief\",\n",
- " \"bbox_norm\": {\n",
- " \"xmin\": 0.033710416666666666,\n",
- " \"ymin\": 0.2602,\n",
- " \"xmax\": 0.32296875,\n",
- " \"ymax\": 0.2828\n",
- " },\n",
- " \"bbox_px\": [\n",
- " 51,\n",
- " 532,\n",
- " 496,\n",
- " 579\n",
- " ]\n",
- "}\n"
- ]
- }
- ],
- "source": [
- "jsonl_path = os.path.join(OUTPUT_DIR, \"parsed_records.jsonl\")\n",
- "with open(jsonl_path, \"w\", encoding=\"utf-8\") as f:\n",
- " for rec in all_records:\n",
- " f.write(json.dumps(rec, ensure_ascii=False) + \"\\n\")\n",
- "print(f\"Exported {len(all_records)} records to: {jsonl_path}\")\n",
- "if all_records:\n",
- " print(\"\\n--- Sample Record --- \")\n",
- " print(json.dumps(all_records[0], indent=2))\n",
- "else:\n",
- " print(\"No records to preview.\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "cd0f8bf0",
- "metadata": {},
- "source": [
- "## 3. Creating Usable Output: From Structure to Markdown & HTML\n",
- "\n",
- "The raw JSON is great for machines, but the real power comes from using the semantic type and text to build human-readable and RAG-friendly content.\n",
- "\n",
- "Tables are extracted as LaTeX to preserve structure. We convert them into clean HTML.\n",
- "\n",
- "Let's reconstruct each processed page into an HTML view with type-aware rendering."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "id": "a13974c3",
- "metadata": {
- "id": "reconstruct-html"
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "--- \n",
- "### Page 1 — Reconstructed HTML"
- ],
- "text/plain": [
- "| \n", - " | id | \n", - "type | \n", - "text | \n", - "bbox | \n", - "
|---|---|---|---|---|
| 0 | \n", - "0 | \n", - "Text | \n", - "**NVIDIA is the engine of the world’s AI infra... | \n", - "{'xmin': 0.183596875, 'ymin': 0.2555, 'xmax': ... | \n", - "
| 1 | \n", - "1 | \n", - "Text | \n", - "**CUDA**<sup>®</sup>, our parallel computing m... | \n", - "{'xmin': 0.183596875, 'ymin': 0.3508, 'xmax': ... | \n", - "
| 2 | \n", - "2 | \n", - "Text | \n", - "**Accelerated computing is sustainable computi... | \n", - "{'xmin': 0.183596875, 'ymin': 0.4375, 'xmax': ... | \n", - "
| 3 | \n", - "3 | \n", - "Text | \n", - "**Blackwell** is one of the most important pro... | \n", - "{'xmin': 0.183596875, 'ymin': 0.5227, 'xmax': ... | \n", - "
| 4 | \n", - "4 | \n", - "Text | \n", - "**NVIDIA NIM**<sup>TM</sup>, part of the NVIDI... | \n", - "{'xmin': 0.183596875, 'ymin': 0.6086, 'xmax': ... | \n", - "
| 5 | \n", - "5 | \n", - "Section-header | \n", - "# Latest NVIDIA News | \n", - "{'xmin': 0.5230677083333333, 'ymin': 0.2562, '... | \n", - "
| 6 | \n", - "6 | \n", - "Section-header | \n", - "**AI/Data Center** | \n", - "{'xmin': 0.5911979166666667, 'ymin': 0.2844, '... | \n", - "
| 7 | \n", - "7 | \n", - "List-item | \n", - "\\>\\> NVIDIA DGX personal AI supercomputers bri... | \n", - "{'xmin': 0.5911979166666667, 'ymin': 0.3016, '... | \n", - "
| 8 | \n", - "8 | \n", - "List-item | \n", - "\\>\\> NVIDIA Blackwell Ultra AI factory platfor... | \n", - "{'xmin': 0.5911979166666667, 'ymin': 0.3328, '... | \n", - "
| 9 | \n", - "9 | \n", - "List-item | \n", - "\\>\\> NVIDIA Dynamo open-source inference softw... | \n", - "{'xmin': 0.5911979166666667, 'ymin': 0.3641, '... | \n", - "
NVIDIA in Brief
\n", - "NVIDIA pioneered accelerated computing to tackle challenges no one else can solve. Our work in AI and digital twins is transforming the world’s largest industries and profoundly impacting society. Learnmore.
\n", - "Company History
\n", - "Founded in 1993, NVIDIA is the world leader in accelerated computing. Our invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined computer graphics, revolutionized accelerated computing, ignited the era of modern AI, and is fueling industrial digitalization across markets. NVIDIA is now a full-stack computing infrastructure company with data-center-scale offerings that are reshaping industry.
\n", - "Key Stats
\n", - "- \n",
- "
- \n",
- "
\n", - "
\n", - "\n", - "
\n", - "Founded in 1993
\n", - " \n",
- " - \n",
- "
\n", - "
\n", - "\n", - "
\n", - "Founder and CEO: Jensen Huang
\n", - " \n",
- " - \n",
- "
\n", - "
\n", - "\n", - "
\n", - "36,000+ employees in 38 countries
\n", - " \n",
- " - \n",
- "
\n", - "
\n", - "\n", - "
\n", - "Record full-year revenue of $130.5 billion in FY25
\n", - " \n",
- " - \n",
- "
\n", - "
\n", - "\n", - "
\n", - "8,700+ granted and pending patent applications worldwide
\n", - " \n",
- " - \n",
- "
\n", - "
\n", - "\n", - "
\n", - "$1 trillion available market opportunity
\n", - " \n",
- " - \n",
- "
\n", - "
\n", - "\n", - "
\n", - "6 million developers in the NVIDIA Developer Program
\n", - " \n",
- " - \n",
- "
\n", - "
\n", - "\n", - "
\n", - "27,000 global startups in NVIDIA Inception
\n", - " \n",
- " - \n",
- "
\n", - "
\n", - "\n", - "
\n", - "No. 1 \"America’s Best Companies 2025\" - Forbes
\n", - " \n",
- " - \n",
- "
\n", - "
\n", - "\n", - "
\n", - "\"World’s Most Innovative Companies\" - Fast Company
\n", - " \n",
- " - \n",
- "
\n", - "
\n", - "\n", - "
\n", - "\"Best Places to Work in 2025\" - Glassdoor
\n", - " \n",
- "
# Impact by Industry
\n", - "Automotive
\n", - "NVIDIA powers all 30 of the 30 top autonomous vehicle data centers.
\n", - "AI Factories
\n", - "More than 40,000 companies use NVIDIA AI technology to power AI factories.
\n", - "Digital Twins
\n", - "NVIDIA OmniverseTM is used by thousands of developers, including leaders in industrial simulation, automation, and robotics.
\n", - "Gaming
\n", - "More than 200 million gamers and creators use NVIDIA GeForce® GPUs.
\n", - "Healthcare
\n", - "Over 4 million developers have downloaded the MONAI framework for AI in medical imaging.
\n", - "Robotics
\n", - "More than 1.7 million developers use the NVIDIA JetsonTM platform for edge AI.
\n", - "NVIDIA in Brief 2025
\n", - "!
\n", - "\n", - "Caption: NVIDIA
\n", - "
NVIDIA is the engine of the world’s AI infrastructure. Companies and countries around the world are building NVIDIA-powered AI factories to process, refine, and manufacture intelligence from data, creating new revenue opportunities for the world’s $100 trillion of industries.
\n", - "CUDA®, our parallel computing model, offers developers a powerful toolkit with over 400 libraries, 600 AI models, numerous software development kits, and support for 3,700 GPU-accelerated applications. CUDA has more than 53 million downloads.
\n", - "Accelerated computing is sustainable computing. If certain accelerated computing and AI workloads were switched from CPU infrastructure to DPU- and GPU- accelerated operations, we estimate the world could save nearly 30 trillion watt-hours of energy per year.
\n", - "Blackwell is one of the most important products in our history, boasting technologies that power AI training and real-time large language model inference for models scaling up to 10 trillion parameters.
\n", - "NVIDIA NIMTM, part of the NVIDIA AI Enterprise software platform, is a set of easy-to-use microservices designed for secure, reliable deployment of high-performance AI model inferencing across clouds, data centers, and workstations.
\n", - "# Latest NVIDIA News
\n", - "AI/Data Center
\n", - "- \n",
- "
- \n",
- "
\n", - "
\n", - "\n", - "
\n", - "NVIDIA DGX personal AI supercomputers bring NVIDIA Grace Blackwell architecture to the desktop.
\n", - " \n",
- " - \n",
- "
\n", - "
\n", - "\n", - "
\n", - "NVIDIA Blackwell Ultra AI factory platform paves the way for the age of AI reasoning.
\n", - " \n",
- " - \n",
- "
\n", - "
\n", - "\n", - "
\n", - "NVIDIA Dynamo open-source inference software accelerates and scales AI reasoning models.
\n", - " \n",
- " - \n",
- "
\n", - "
\n", - "\n", - "
\n", - "The NVIDIA GB200 NVL72 system set records in the latest MLPerf Inference benchmarks.
\n", - " \n",
- "
# RTX/Graphics
\n", - "- \n",
- "
- \n",
- "
\n", - "
\n", - "\n", - "
\n", - "New GeForce RTXTM 50 Series Desktop and Laptop GPUs announced.
\n", - " \n",
- " - \n",
- "
\n", - "
\n", - "\n", - "
\n", - "NVIDIA DLSS 4, featuring Multi Frame Generation, now available.
\n", - " \n",
- " - \n",
- "
\n", - "
\n", - "\n", - "
\n", - "GeForce NOWTM library features over 2,000 games.
\n", - " \n",
- "
# Physical AI
\n", - "- \n",
- "
- \n",
- "
\n", - "
\n", - "\n", - "
\n", - "Introduced NVIDIA CosmosTM platform for world model training and physical AI development.
\n", - " \n",
- " - \n",
- "
\n", - "
\n", - "\n", - "
\n", - "Launched NIDA Halos, a full-stack, comprehensive safety system for autonomous vehicles.
\n", - " \n",
- " - \n",
- "
\n", - "
\n", - "\n", - "
\n", - "Announced NVIDIA Isaac GR00T NI open foundation model for humanoid robots.
\n", - " \n",
- " - \n",
- "
\n", - "
\n", - "\n", - "
\n", - "Unveiled NVIDIA Jetson Orin NanoTM Super Developer Kit.\n", - "NVIDIA in Brief 2025
\n", - " \n",
- "
\n", - "Caption: For more information
\n", - "
{html_content}
\"))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 4. Conclusion: The Foundation for AI Agents\n",
- "\n",
- "As we've seen, Nemotron Parse is far more than just OCR. It's a foundational document-to-structure engine. Specifically,\n",
- "- 9K context = cross‑page coherence\n",
- "- Industry‑leading table accuracy\n",
- "- Structured, layout‑aware output with bboxes\n",
- "\n",
- "It solves the first-mile problem of document AI by transforming messy, complex files into a clean, structured, and spatially-aware format.\n",
- "\n",
- "This structured output is the essential first step for any advanced AI workflow:\n",
- "1) Smarter RAG: Chunk documents by semantic meaning (e.g., index Text blocks and Table HTML, ignore Page-footers) to improve retrieval accuracy.\n",
- "2) Building AI Agents: An agent can read the JSON and decide:\n",
- " - \"Summarize the Text blocks.\"\n",
- " - \"Extract key numbers from the Table's HTML.\"\n",
- " - \"There is a Figure at [bbox]; send its crop to a visual reasoning model.\"\n",
- "\n",
- "Nemotron Parse provides the what and where (what is this element, and where is it?). Pair it with visual reasoners to provide the why and meaning for non-text elements.\n",
- "\n",
- "Together, they form a complementary stack for building agents that can understand and act on all parts of your documents."
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": ".venv",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.2"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/use-case-examples/RAG Agent with Nemotron RAG Models/RAG Agent with Nemotron RAG Models.ipynb b/use-case-examples/RAG Agent with Nemotron RAG Models/RAG Agent with Nemotron RAG Models.ipynb
deleted file mode 100644
index f90c239..0000000
--- a/use-case-examples/RAG Agent with Nemotron RAG Models/RAG Agent with Nemotron RAG Models.ipynb
+++ /dev/null
@@ -1,742 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# RAG Agent with Hugging Face NVIDIA Models + NVIDIA AI Endpoints LLM\n",
- "\n",
- "This notebook demonstrates how to build a RAG (Retrieval-Augmented Generation) agent using:\n",
- "- **Hugging Face versions** of NVIDIA's Llama 3.2 models for embeddings and reranking\n",
- "- **NVIDIA AI Endpoints** for the LLM (through build.nvidia.com)\n",
- "\n",
- "## Models Used:\n",
- "- **Embedding Model**: `nvidia/llama-3.2-nv-embedqa-1b-v2` (Hugging Face)\n",
- "- **Reranking Model**: `nvidia/llama-3.2-nv-rerankqa-1b-v2` (Hugging Face)\n",
- "- **LLM Model**: `nvidia/nvidia-nemotron-nano-9b-v2` (NVIDIA AI Endpoints)\n",
- "\n",
- "## Architecture:\n",
- "- Local Hugging Face models for embedding and reranking (better performance, privacy)\n",
- "- NVIDIA AI Endpoints for LLM (managed service, latest models)\n",
- "- Complete LangGraph agent with retrieval tools\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 1. Installation and Setup\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "import os\n",
- "import getpass\n",
- "\n",
- "os.environ[\"NVIDIA_API_KEY\"] = getpass.getpass(\"Enter your NVIDIA API key: \")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Using device: cuda\n",
- "✅ NVIDIA API key found!\n"
- ]
- }
- ],
- "source": [
- "import logging\n",
- "import torch\n",
- "import torch.nn.functional as F\n",
- "from pathlib import Path\n",
- "from typing import List, Dict, Any, Optional\n",
- "import numpy as np\n",
- "\n",
- "from transformers import AutoTokenizer, AutoModel, AutoModelForSequenceClassification, AutoConfig\n",
- "from langchain.embeddings.base import Embeddings\n",
- "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
- "from langchain.tools.retriever import create_retriever_tool\n",
- "from langchain_community.document_loaders import DirectoryLoader, TextLoader\n",
- "from langchain_community.vectorstores import FAISS\n",
- "from langchain.retrievers import ContextualCompressionRetriever\n",
- "from langchain.retrievers.document_compressors.base import BaseDocumentCompressor\n",
- "from langchain.schema import Document\n",
- "from langchain_nvidia_ai_endpoints import ChatNVIDIA\n",
- "from langgraph.prebuilt import create_react_agent\n",
- "\n",
- "# Set up logging\n",
- "logging.basicConfig(level=logging.INFO)\n",
- "_LOGGER = logging.getLogger(__name__)\n",
- "\n",
- "# Check if CUDA is available\n",
- "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
- "print(f\"Using device: {device}\")\n",
- "\n",
- "# Check for NVIDIA API key\n",
- "if \"NVIDIA_API_KEY\" not in os.environ:\n",
- " print(\"⚠️ Warning: NVIDIA_API_KEY not found in environment variables.\")\n",
- " print(\"Please set your NVIDIA API key to use the LLM:\")\n",
- " print(\"export NVIDIA_API_KEY='your_api_key_here'\")\n",
- "else:\n",
- " print(\"✅ NVIDIA API key found!\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 2. Configuration\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Data Ingestion Configuration\n",
- "DATA_DIR = Path(\"../data/it-knowledge-base\")\n",
- "CHUNK_SIZE = 800\n",
- "CHUNK_OVERLAP = 120\n",
- "\n",
- "# Model Configuration\n",
- "LLM_MODEL = \"nvidia/nvidia-nemotron-nano-9b-v2\" # NVIDIA AI Endpoints\n",
- "RETRIEVER_EMBEDDING_MODEL = \"nvidia/llama-3.2-nv-embedqa-1b-v2\" # Hugging Face\n",
- "RETRIEVER_RERANK_MODEL = \"nvidia/llama-3.2-nv-rerankqa-1b-v2\" # Hugging Face\n",
- "\n",
- "# Model parameters\n",
- "MAX_LENGTH = 8192\n",
- "EMBEDDING_DIM = 2048\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 3. Custom Hugging Face Embedding Class\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [],
- "source": [
- "class HuggingFaceNVIDIAEmbeddings(Embeddings):\n",
- " \"\"\"Custom embedding class for NVIDIA Llama 3.2 embedding model from Hugging Face.\"\"\"\n",
- " \n",
- " def __init__(self, model_name: str, device: str = \"auto\", trust_remote_code: bool = True):\n",
- " self.model_name = model_name\n",
- " self.device = device if device != \"auto\" else (\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
- " \n",
- " print(f\"Loading embedding model: {model_name}\")\n",
- " \n",
- " # Load tokenizer\n",
- " self.tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=trust_remote_code)\n",
- " \n",
- " # Load model - the key is to use trust_remote_code=True for custom NVIDIA models\n",
- " print(\"Loading model with trust_remote_code=True for custom NVIDIA architecture...\")\n",
- " self.model = AutoModel.from_pretrained(\n",
- " model_name, \n",
- " trust_remote_code=trust_remote_code,\n",
- " )\n",
- " \n",
- " self.model.to(self.device)\n",
- " self.model.eval()\n",
- " \n",
- " # Add padding token if it doesn't exist\n",
- " if self.tokenizer.pad_token is None:\n",
- " self.tokenizer.pad_token = self.tokenizer.eos_token\n",
- " \n",
- " print(f\"✅ Embedding model loaded on {self.device}\")\n",
- " \n",
- " def average_pool(self, last_hidden_states: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor:\n",
- " \"\"\"Average pooling with attention mask.\"\"\"\n",
- " last_hidden_states_masked = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)\n",
- " embedding = last_hidden_states_masked.sum(dim=1) / attention_mask.sum(dim=1)[..., None]\n",
- " embedding = F.normalize(embedding, dim=-1)\n",
- " return embedding\n",
- " \n",
- " def embed_documents(self, texts: List[str]) -> List[List[float]]:\n",
- " \"\"\"Embed a list of documents.\"\"\"\n",
- " # Add document prefix as specified in the model documentation\n",
- " prefixed_texts = [f\"passage: {text}\" for text in texts]\n",
- " return self._embed_texts(prefixed_texts)\n",
- " \n",
- " def embed_query(self, text: str) -> List[float]:\n",
- " \"\"\"Embed a single query.\"\"\"\n",
- " # Add query prefix as specified in the model documentation\n",
- " prefixed_text = f\"query: {text}\"\n",
- " embeddings = self._embed_texts([prefixed_text])\n",
- " return embeddings[0]\n",
- " \n",
- " def _embed_texts(self, texts: List[str]) -> List[List[float]]:\n",
- " \"\"\"Internal method to embed texts.\"\"\"\n",
- " embeddings = []\n",
- " \n",
- " # Process in batches to handle memory efficiently\n",
- " batch_size = 8\n",
- " for i in range(0, len(texts), batch_size):\n",
- " batch_texts = texts[i:i + batch_size]\n",
- " \n",
- " # Tokenize\n",
- " batch_inputs = self.tokenizer(\n",
- " batch_texts,\n",
- " padding=True,\n",
- " truncation=True,\n",
- " max_length=MAX_LENGTH,\n",
- " return_tensors=\"pt\"\n",
- " ).to(self.device)\n",
- " \n",
- " # Generate embeddings\n",
- " with torch.no_grad():\n",
- " outputs = self.model(**batch_inputs)\n",
- " batch_embeddings = self.average_pool(\n",
- " outputs.last_hidden_state, \n",
- " batch_inputs[\"attention_mask\"]\n",
- " )\n",
- " \n",
- " # Convert to list and add to results\n",
- " embeddings.extend(batch_embeddings.cpu().numpy().tolist())\n",
- " \n",
- " return embeddings\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 4. Custom Hugging Face Reranker Class\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [],
- "source": [
- "class HuggingFaceNVIDIAReranker(BaseDocumentCompressor):\n",
- " \"\"\"Custom reranker class for NVIDIA Llama 3.2 reranking model from Hugging Face.\"\"\"\n",
- " \n",
- " def __init__(self, model_name: str, device: str = \"auto\", trust_remote_code: bool = True, top_k: int = 3, max_length: int = 512):\n",
- " # Initialize parent class first\n",
- " super().__init__()\n",
- " \n",
- " # Store configuration\n",
- " self._model_name = model_name\n",
- " self._device = device if device != \"auto\" else (\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
- " self._top_k = top_k\n",
- " self._max_length = max_length\n",
- " \n",
- " print(f\"Loading reranking model: {model_name}\")\n",
- " \n",
- " # Load tokenizer with proper configuration\n",
- " self._tokenizer = AutoTokenizer.from_pretrained(\n",
- " model_name, \n",
- " trust_remote_code=trust_remote_code,\n",
- " padding_side=\"left\"\n",
- " )\n",
- " if self._tokenizer.pad_token is None:\n",
- " self._tokenizer.pad_token = self._tokenizer.eos_token\n",
- " \n",
- " # Load model for sequence classification (reranking)\n",
- " model_kwargs = {\n",
- " \"trust_remote_code\": trust_remote_code,\n",
- " }\n",
- " \n",
- " from transformers import AutoModelForSequenceClassification\n",
- " self._model = AutoModelForSequenceClassification.from_pretrained(\n",
- " model_name,\n",
- " **model_kwargs\n",
- " ).eval()\n",
- " \n",
- " if self._model.config.pad_token_id is None:\n",
- " self._model.config.pad_token_id = self._tokenizer.eos_token_id\n",
- " \n",
- " self._model.to(self._device)\n",
- " \n",
- " print(f\"✅ Reranking model loaded on {self._device}\")\n",
- " \n",
- " def _prompt_template(self, query: str, passage: str) -> str:\n",
- " \"\"\"Format query and passage with the proper prompt template.\"\"\"\n",
- " return f\"question:{query} \\n \\n passage:{passage}\"\n",
- " \n",
- " def compress_documents(\n",
- " self,\n",
- " documents: List[Document],\n",
- " query: str,\n",
- " callbacks: Optional[Any] = None,\n",
- " ) -> List[Document]:\n",
- " \"\"\"Compress documents by reranking them based on relevance to the query.\"\"\"\n",
- " if not documents:\n",
- " return documents\n",
- " \n",
- " # Calculate relevance scores for all documents in batch\n",
- " scores = self._calculate_relevance_scores(query, [doc.page_content for doc in documents])\n",
- " \n",
- " # Pair documents with their scores\n",
- " doc_scores = list(zip(documents, scores))\n",
- " \n",
- " # Sort by relevance score (descending) and return top_k\n",
- " sorted_docs = sorted(doc_scores, key=lambda x: x[1], reverse=True)\n",
- " return [doc for doc, _ in sorted_docs[:self._top_k]]\n",
- " \n",
- " def _calculate_relevance_scores(self, query: str, documents: List[str]) -> List[float]:\n",
- " \"\"\"Calculate relevance scores for multiple documents efficiently.\"\"\"\n",
- " # Apply prompt template to all query-document pairs\n",
- " texts = [self._prompt_template(query, doc) for doc in documents]\n",
- " \n",
- " # Tokenize all texts in batch\n",
- " batch_dict = self._tokenizer(\n",
- " texts,\n",
- " padding=True,\n",
- " truncation=True,\n",
- " return_tensors=\"pt\",\n",
- " max_length=self._max_length,\n",
- " )\n",
- " \n",
- " # Move to device\n",
- " batch_dict = {k: v.to(self._device) for k, v in batch_dict.items()}\n",
- " \n",
- " # Generate relevance scores\n",
- " with torch.inference_mode():\n",
- " logits = self._model(**batch_dict).logits\n",
- " scores = logits.view(-1).cpu().tolist()\n",
- " \n",
- " return scores"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 5. Initialize Models\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Loading embedding model: nvidia/llama-3.2-nv-embedqa-1b-v2\n",
- "Loading model with trust_remote_code=True for custom NVIDIA architecture...\n",
- "✅ Embedding model loaded on cuda\n",
- "Loading reranking model: nvidia/llama-3.2-nv-rerankqa-1b-v2\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "A new version of the following files was downloaded from https://huggingface.co/nvidia/llama-3.2-nv-rerankqa-1b-v2:\n",
- "- llama_bidirectional_model.py\n",
- ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "✅ Reranking model loaded on cuda\n",
- "Initializing LLM: nvidia/nvidia-nemotron-nano-9b-v2\n",
- "✅ All models initialized successfully!\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/chris/Code/NVIDIA/workshop-build-an-agent/.venv/lib/python3.13/site-packages/langchain_nvidia_ai_endpoints/_common.py:229: UserWarning: Found nvidia/nvidia-nemotron-nano-9b-v2 in available_models, but type is unknown and inference may fail.\n",
- " warnings.warn(\n"
- ]
- }
- ],
- "source": [
- "# Initialize the embedding model (Hugging Face)\n",
- "embeddings = HuggingFaceNVIDIAEmbeddings(\n",
- " model_name=RETRIEVER_EMBEDDING_MODEL,\n",
- " device=device\n",
- ")\n",
- "\n",
- "# Initialize the reranker model (Hugging Face)\n",
- "reranker = HuggingFaceNVIDIAReranker(\n",
- " model_name=RETRIEVER_RERANK_MODEL,\n",
- " device=device,\n",
- " top_k=3\n",
- ")\n",
- "\n",
- "# Initialize the LLM (NVIDIA AI Endpoints)\n",
- "print(f\"Initializing LLM: {LLM_MODEL}\")\n",
- "llm = ChatNVIDIA(model=LLM_MODEL, temperature=0.6, top_p=0.95, max_completion_tokens=8192)\n",
- "print(\"✅ All models initialized successfully!\")\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 6. Data Loading and Processing\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:__main__:Reading knowledge base data from ../data/it-knowledge-base\n",
- "100%|██████████| 12/12 [00:00<00:00, 3230.74it/s]"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Loaded 12 documents\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "\n"
- ]
- }
- ],
- "source": [
- "# Check if data directory exists\n",
- "if not DATA_DIR.exists():\n",
- " print(f\"Warning: Data directory {DATA_DIR} does not exist.\")\n",
- " print(\"Creating sample documents for demonstration...\")\n",
- " \n",
- " # Create sample IT knowledge base documents\n",
- " sample_docs = [\n",
- " Document(\n",
- " page_content=\"Password Policy: All employees must use passwords with at least 12 characters, including uppercase, lowercase, numbers, and special characters. Passwords must be changed every 90 days.\",\n",
- " metadata={\"source\": \"password_policy.txt\"}\n",
- " ),\n",
- " Document(\n",
- " page_content=\"VPN Access: To connect to the company VPN, use the Cisco AnyConnect client. Download it from the IT portal and use your employee credentials. Contact IT support if you experience connection issues.\",\n",
- " metadata={\"source\": \"vpn_guide.txt\"}\n",
- " ),\n",
- " Document(\n",
- " page_content=\"Email Setup: Configure your email client with IMAP settings: Server: mail.company.com, Port: 993, Security: SSL/TLS. For SMTP: Server: smtp.company.com, Port: 587, Security: STARTTLS.\",\n",
- " metadata={\"source\": \"email_setup.txt\"}\n",
- " ),\n",
- " Document(\n",
- " page_content=\"Software Installation: All software installations require IT approval. Submit requests through the IT portal. Unauthorized software installation may result in security violations.\",\n",
- " metadata={\"source\": \"software_policy.txt\"}\n",
- " ),\n",
- " Document(\n",
- " page_content=\"Backup Policy: All critical data must be backed up daily. Use the company-approved backup solution. Personal devices are not covered under the backup policy.\",\n",
- " metadata={\"source\": \"backup_policy.txt\"}\n",
- " )\n",
- " ]\n",
- " docs = sample_docs\n",
- "else:\n",
- " # Load documents from directory\n",
- " _LOGGER.info(f\"Reading knowledge base data from {DATA_DIR}\")\n",
- " data_loader = DirectoryLoader(\n",
- " DATA_DIR,\n",
- " glob=\"**/*\",\n",
- " loader_cls=TextLoader,\n",
- " show_progress=True,\n",
- " )\n",
- " docs = data_loader.load()\n",
- "\n",
- "print(f\"Loaded {len(docs)} documents\")\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 7. Create Vector Database and Retriever\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:__main__:Splitting documents into chunks\n",
- "INFO:__main__:Creating FAISS vector database with 155 chunks\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Created 155 chunks\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:faiss.loader:Loading faiss with AVX512 support.\n",
- "INFO:faiss.loader:Successfully loaded faiss with AVX512 support.\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Vector database created successfully!\n",
- "Retriever with reranking created successfully!\n"
- ]
- }
- ],
- "source": [
- "# Split the data into chunks\n",
- "_LOGGER.info(f\"Splitting documents into chunks\")\n",
- "splitter = RecursiveCharacterTextSplitter(\n",
- " chunk_size=CHUNK_SIZE, \n",
- " chunk_overlap=CHUNK_OVERLAP\n",
- ")\n",
- "chunks = splitter.split_documents(docs)\n",
- "print(f\"Created {len(chunks)} chunks\")\n",
- "\n",
- "# Create FAISS vector database\n",
- "_LOGGER.info(f\"Creating FAISS vector database with {len(chunks)} chunks\")\n",
- "vectordb = FAISS.from_documents(chunks, embeddings)\n",
- "print(\"Vector database created successfully!\")\n",
- "\n",
- "# Create base retriever\n",
- "kb_retriever = vectordb.as_retriever(\n",
- " search_type=\"similarity\", \n",
- " search_kwargs={\"k\": 6}\n",
- ")\n",
- "\n",
- "# Combine retriever with reranker\n",
- "retriever = ContextualCompressionRetriever(\n",
- " base_retriever=kb_retriever,\n",
- " base_compressor=reranker,\n",
- ")\n",
- "\n",
- "print(\"Retriever with reranking created successfully!\")\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 8. Create LangGraph Agent\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "✅ LangGraph ReAct agent created successfully!\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/chris/Code/NVIDIA/workshop-build-an-agent/.venv/lib/python3.13/site-packages/langchain_nvidia_ai_endpoints/chat_models.py:715: UserWarning: Model 'nvidia/nvidia-nemotron-nano-9b-v2' is not known to support tools. Your tool binding may fail at inference time.\n",
- " warnings.warn(\n"
- ]
- }
- ],
- "source": [
- "# Create the retriever tool for agentic use\n",
- "retriever_tool = create_retriever_tool(\n",
- " retriever=retriever,\n",
- " name=\"company_llc_it_knowledge_base\",\n",
- " description=(\n",
- " \"Search the internal IT knowledge base for Company LLC IT related questions and policies. \"\n",
- " \"Use this tool to find information about passwords, VPN, email setup, software policies, and other IT topics.\"\n",
- " ),\n",
- ")\n",
- "\n",
- "# Define the system prompt for the agent\n",
- "SYSTEM_PROMPT = (\n",
- " \"You are an IT help desk support agent.\\n\"\n",
- " \"- Use the 'company_llc_it_knowledge_base' tool for questions likely covered by the internal IT knowledge base.\\n\"\n",
- " \"- Always write grounded answers. If unsure, say you don't know.\\n\"\n",
- " \"- If the knowledge base doesn't contain sufficient information, clearly state what information is missing.\\n\"\n",
- " \"- Keep answers brief, to the point, and conversational.\"\n",
- ")\n",
- "\n",
- "# Create the ReAct agent\n",
- "agent = create_react_agent(\n",
- " model=llm,\n",
- " tools=[retriever_tool],\n",
- " prompt=SYSTEM_PROMPT,\n",
- ")\n",
- "\n",
- "print(\"✅ LangGraph ReAct agent created successfully!\")\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 9. Test the Agent\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "============================================================\n",
- "Query: What are the password requirements for employees?\n",
- "============================================================\n",
- "Agent Response:\n",
- "The password requirements for employees are as follows: \n",
- "- Minimum of **12 characters**. \n",
- "- Must include at least one uppercase letter, one lowercase letter, one number, and one special character. \n",
- "- Avoid common words, sequential numbers, or previously used passwords. \n",
- "\n",
- "Let me know if you need further clarification!\n",
- "------------------------------------------------------------\n"
- ]
- }
- ],
- "source": [
- "# Test the agent with sample queries\n",
- "test_queries = [\n",
- " \"What are the password requirements for employees?\",\n",
- " \"How do I set up VPN access?\",\n",
- " \"What are the email server settings?\",\n",
- " \"Can I install my own software?\",\n",
- " \"What's the backup policy?\"\n",
- "]\n",
- "\n",
- "def test_agent(query: str):\n",
- " \"\"\"Test the agent with a single query.\"\"\"\n",
- " print(f\"\\n{'='*60}\")\n",
- " print(f\"Query: {query}\")\n",
- " print(f\"{'='*60}\")\n",
- " \n",
- " try:\n",
- " # Invoke the agent\n",
- " response = agent.invoke({\"messages\": [(\"user\", query)]})\n",
- " \n",
- " # Extract the final message\n",
- " final_message = response[\"messages\"][-1].content\n",
- " print(f\"Agent Response:\\n{final_message}\")\n",
- " \n",
- " except Exception as e:\n",
- " print(f\"Error: {str(e)}\")\n",
- " \n",
- " print(\"-\" * 60)\n",
- "\n",
- "# Test with the first query\n",
- "test_agent(test_queries[0])\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Summary\n",
- "\n",
- "This notebook demonstrates a **hybrid approach** combining:\n",
- "\n",
- "### 🏠 Local Hugging Face Models:\n",
- "- **Embedding**: `nvidia/llama-3.2-nv-embedqa-1b-v2`\n",
- "- **Reranking**: `nvidia/llama-3.2-nv-rerankqa-1b-v2`\n",
- "\n",
- "### ☁️ NVIDIA AI Endpoints:\n",
- "- **LLM**: `nvidia/nvidia-nemotron-nano-9b-v2`\n",
- "\n",
- "### Key Benefits:\n",
- "\n",
- "1. **Best of Both Worlds**:\n",
- " - Local models for embedding/reranking (privacy, performance, cost)\n",
- " - Managed LLM service (latest models, no infrastructure)\n",
- "\n",
- "2. **Performance Optimizations**:\n",
- " - GPU acceleration for local models\n",
- " - Efficient batching and memory management\n",
- " - Proper query/passage prefixing as per model specs\n",
- "\n",
- "3. **Production Ready**:\n",
- " - Complete LangGraph agent with tool integration\n",
- " - Proper error handling and logging\n",
- " - Interactive testing capabilities\n",
- "\n",
- "### Architecture Flow:\n",
- "\n",
- "1. **Document Processing**: Text splitting and chunking\n",
- "2. **Embedding**: Local Hugging Face model generates embeddings\n",
- "3. **Vector Search**: FAISS similarity search (k=6)\n",
- "4. **Reranking**: Local Hugging Face model reranks to top 3\n",
- "5. **Generation**: NVIDIA AI Endpoints LLM generates final response\n",
- "\n",
- "### Usage:\n",
- "\n",
- "- Run cells sequentially to set up the complete system\n",
- "- Use `test_agent()` function for individual queries\n",
- "- Use `chat_with_agent()` for interactive conversations\n",
- "- Type 'test' in chat to run all predefined test queries\n",
- "\n",
- "### Requirements:\n",
- "\n",
- "- NVIDIA API key for LLM access\n",
- "- GPU recommended for local models (falls back to CPU)\n",
- "- Internet connection for model downloads and LLM API calls\n",
- "\n",
- "The models used are:\n",
- "- [nvidia/llama-3.2-nv-embedqa-1b-v2](https://huggingface.co/nvidia/llama-3.2-nv-embedqa-1b-v2)\n",
- "- [nvidia/llama-3.2-nv-rerankqa-1b-v2](https://huggingface.co/nvidia/llama-3.2-nv-rerankqa-1b-v2)\n"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": ".venv",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.13.4"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/use-case-examples/README.md b/use-case-examples/README.md
index e59c57a..bd16749 100644
--- a/use-case-examples/README.md
+++ b/use-case-examples/README.md
@@ -6,7 +6,7 @@ This directory contains end-to-end examples demonstrating practical applications
## 📋 Overview
-These examples showcase **complete implementations** of agentic workflows, RAG systems, and other advanced use cases. Unlike the [usage-cookbook](../usage-cookbook/) which focuses on deployment and basic model usage, these examples demonstrate how to build production-ready applications.
+These examples showcase **complete implementations** of agentic workflows, RAG systems, and other advanced use cases. Unlike the {doc}`usage-cookbook ` which focuses on deployment and basic model usage, these examples demonstrate how to build production-ready applications.
---
@@ -14,5 +14,5 @@ These examples showcase **complete implementations** of agentic workflows, RAG s
| Example | Description |
|---------|-------------|
-| [RAG Agent with Nemotron RAG Models](./RAG%20Agent%20with%20Nemotron%20RAG%20Models/) | End-to-end example of a Retrieval-Augmented Generation (RAG) agent workflow using Nemotron RAG models through Hugging Face and Nemotron 9B hosted through build.nvidia.com models |
-| [Data Science ML Agent](./Data%20Science%20ML%20Agent/) | End-to-end example of a natural language-driven data science and machine learning agent powered by NVIDIA GPUs. The agent allows users to perform data exploration, model training, and hyperparameter optimization interactively using RAPIDS cuDF and cuML for GPU acceleration.|
+| {doc}`RAG Agent with Nemotron RAG Models ` | End-to-end example of a Retrieval-Augmented Generation (RAG) agent workflow using Nemotron RAG models through Hugging Face and Nemotron 9B hosted through build.nvidia.com models |
+| {doc}`Data Science ML Agent ` | End-to-end example of a natural language-driven data science and machine learning agent powered by NVIDIA GPUs. The agent allows users to perform data exploration, model training, and hyperparameter optimization interactively using RAPIDS cuDF and cuML for GPU acceleration.|
diff --git a/use-case-examples/Simple Nemotron-3-Nano Usage Example/simple_usage_notebook.ipynb b/use-case-examples/Simple Nemotron-3-Nano Usage Example/simple_usage_notebook.ipynb
deleted file mode 100644
index 1d0f010..0000000
--- a/use-case-examples/Simple Nemotron-3-Nano Usage Example/simple_usage_notebook.ipynb
+++ /dev/null
@@ -1,1407 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "705e6f60",
- "metadata": {},
- "source": [
- "# 🚀 NVIDIA Nemotron 3 Nano - Simple Usage Guide\n",
- "\n",
- "This notebook provides a streamlined guide to using **NVIDIA Nemotron 3 Nano**, including:\n",
- "\n",
- "1. **Basic Usage** - Simple API calls with the OpenAI-compatible endpoint\n",
- "2. **Reasoning Modes** - Toggle thinking ON/OFF\n",
- "3. **LangChain v1.0 Agent** - Build a research assistant with web search and conversation memory\n",
- "4. **Multi-Agent System** - Coordinate multiple specialist agents (Search, Report Writer, Quality Reviewer)\n",
- "\n",
- "## Prerequisites\n",
- "\n",
- "- An OpenRouter key ([Get one here](https://openrouter.ai/settings/keys))\n",
- "- Optional: Tavily API key for web search ([Get one here](https://tavily.com/))\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b526e9ab",
- "metadata": {},
- "source": [
- "## 📦 Installation\n",
- "\n",
- "Install all required dependencies:\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ee2e3e70",
- "metadata": {},
- "source": [
- "## 🔑 API Key Configuration\n",
- "\n",
- "Enter your OpenRouter API key when prompted. This key is used for both the OpenAI-compatible API and LangChain integration."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "8c29ec75",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "✅ OpenRouter API key configured!\n"
- ]
- }
- ],
- "source": [
- "import os\n",
- "import getpass\n",
- "\n",
- "# Set OpenRouter API key (used by both OpenAI client and LangChain)\n",
- "if not os.environ.get(\"OPENROUTER_API_KEY\"):\n",
- " os.environ[\"OPENROUTER_API_KEY\"] = getpass.getpass(\"Enter your OpenRouter API Key: \")\n",
- "\n",
- "print(\"✅ OpenRouter API key configured!\")\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1678deeb",
- "metadata": {},
- "source": [
- "---\n",
- "\n",
- "# Part 1: Basic Usage with OpenAI Client\n",
- "\n",
- "NVIDIA's API is OpenAI-compatible, making it easy to use with the standard OpenAI Python SDK.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "24714971",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "✅ Client configured for model: nvidia/nemotron-3-nano-30b-a3b\n"
- ]
- }
- ],
- "source": [
- "from openai import OpenAI\n",
- "\n",
- "# Create client pointing to OpenRouter's API\n",
- "client = OpenAI(\n",
- " base_url=\"https://openrouter.ai/api/v1\",\n",
- " api_key=os.environ[\"OPENROUTER_API_KEY\"]\n",
- ")\n",
- "\n",
- "# Model identifier (OpenRouter format)\n",
- "MODEL = \"nvidia/nemotron-3-nano-30b-a3b\"\n",
- "\n",
- "print(f\"✅ Client configured for model: {MODEL}\")\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "55ea6d3a",
- "metadata": {},
- "source": [
- "### Simple Request\n",
- "\n",
- "Let's start with a basic request to the model:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "82b5f73c",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "NVIDIA is a technology company best known for designing graphics processing units (GPUs) that accelerate computer graphics, gaming, and data‑intensive tasks. In recent years, it has expanded into AI, cloud computing, automotive, and edge computing, offering hardware and software platforms that power everything from supercomputing to deep‑learning inference.\n"
- ]
- }
- ],
- "source": [
- "# Simple non-streaming request\n",
- "response = client.chat.completions.create(\n",
- " model=MODEL,\n",
- " messages=[\n",
- " {\"role\": \"system\", \"content\": \"You are a helpful assistant. Be concise.\"},\n",
- " {\"role\": \"user\", \"content\": \"What is NVIDIA? Answer in 2-3 sentences.\"}\n",
- " ],\n",
- " temperature=1,\n",
- " top_p=1,\n",
- " max_tokens=512\n",
- ")\n",
- "\n",
- "print(response.choices[0].message.content)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "26228280",
- "metadata": {},
- "source": [
- "---\n",
- "\n",
- "## 🧠 Reasoning Modes\n",
- "\n",
- "Nemotron Nano V3 supports two reasoning modes:\n",
- "\n",
- "| Mode | Description | Best For |\n",
- "|------|-------------|----------|\n",
- "| **Reasoning ON** | Outputs chain-of-thought wrapped in thinking tokens | Complex problems, math, logic |\n",
- "| **Reasoning OFF** | Jumps directly to the final answer | Simple queries, faster responses |\n",
- "\n",
- "Use `enable_thinking` in `extra_body` to toggle modes.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "db04a75c",
- "metadata": {},
- "source": [
- "### Reasoning ON (with thinking)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "2a8b540f",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Helper function to display streaming response with reasoning\n",
- "def stream_with_reasoning(completion):\n",
- " \"\"\"Stream and display response, separating reasoning from final answer.\"\"\"\n",
- " reasoning = \"\"\n",
- " answer = \"\"\n",
- " \n",
- " for chunk in completion:\n",
- " delta = chunk.choices[0].delta\n",
- " \n",
- " # Collect reasoning content (gray text)\n",
- " if hasattr(delta, 'reasoning_content') and delta.reasoning_content:\n",
- " reasoning += delta.reasoning_content\n",
- " print(f\"\\033[90m{delta.reasoning_content}\\033[0m\", end=\"\", flush=True)\n",
- " \n",
- " # Collect answer content (normal text)\n",
- " if delta.content:\n",
- " answer += delta.content\n",
- " print(delta.content, end=\"\", flush=True)\n",
- " \n",
- " print() # New line at end\n",
- " return reasoning, answer\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "de0938db",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "🧠 Reasoning ON (gray = thinking, white = answer):\n",
- "\n",
- "\u001b[90mUser\u001b[0m\u001b[90m asks\u001b[0m\u001b[90m a\u001b[0m\u001b[90m simple\u001b[0m\u001b[90m math\u001b[0m\u001b[90m question\u001b[0m\u001b[90m:\u001b[0m\u001b[90m \"\u001b[0m\u001b[90mWhat\u001b[0m\u001b[90m is\u001b[0m\u001b[90m \u001b[0m\u001b[90m1\u001b[0m\u001b[90m5\u001b[0m\u001b[90m%\u001b[0m\u001b[90m of\u001b[0m\u001b[90m \u001b[0m\u001b[90m2\u001b[0m\u001b[90m4\u001b[0m\u001b[90m0\u001b[0m\u001b[90m?\"\u001b[0m\u001b[90m Compute\u001b[0m\u001b[90m \u001b[0m\u001b[90m0\u001b[0m\u001b[90m.\u001b[0m\u001b[90m1\u001b[0m\u001b[90m5\u001b[0m\u001b[90m*\u001b[0m\u001b[90m2\u001b[0m\u001b[90m4\u001b[0m\u001b[90m0\u001b[0m\u001b[90m =\u001b[0m\u001b[90m \u001b[0m\u001b[90m3\u001b[0m\u001b[90m6\u001b[0m\u001b[90m.\u001b[0m\u001b[90m Answer\u001b[0m\u001b[90m:\u001b[0m\u001b[90m \u001b[0m\u001b[90m3\u001b[0m\u001b[90m6\u001b[0m\u001b[90m.\n",
- "\u001b[0m\n",
- "15 % of 240 is \n",
- "\n",
- "\\[\n",
- "0.15 \\times 240 = 36.\n",
- "\\] \n",
- "\n",
- "So the result is **36**.\n"
- ]
- }
- ],
- "source": [
- "# Reasoning ON - The model thinks through the problem\n",
- "# Best with temperature=1.0, top_p=1.0 for optimal performance\n",
- "\n",
- "completion = client.chat.completions.create(\n",
- " model=MODEL,\n",
- " messages=[\n",
- " {\"role\": \"system\", \"content\": \"You are a helpful math assistant.\"},\n",
- " {\"role\": \"user\", \"content\": \"What is 15% of 240?\"}\n",
- " ],\n",
- " extra_body={\n",
- " \"chat_template_kwargs\": {\n",
- " \"enable_thinking\": True,\n",
- " }\n",
- " },\n",
- " temperature=1.0,\n",
- " top_p=1.0,\n",
- " max_tokens=2048,\n",
- " stream=True\n",
- ")\n",
- "\n",
- "print(\"🧠 Reasoning ON (gray = thinking, white = answer):\\n\")\n",
- "reasoning, answer = stream_with_reasoning(completion)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "4c351122",
- "metadata": {},
- "source": [
- "### Reasoning OFF (direct answer)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "00c1cc39",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "⚡ Reasoning OFF (direct answer):\n",
- "\n",
- "To find **15% of 240**, follow these steps:\n",
- "\n",
- "1. **Convert the percentage to a decimal**: \n",
- " Divide 15 by 100 → **0.15**.\n",
- "\n",
- "2. **Multiply the decimal by the number**: \n",
- " **0.15 × 240 = 36**.\n",
- "\n",
- "**Answer**: 15% of 240 is **36**. \n",
- "\n",
- "*Quick check*: \n",
- "- 10% of 240 = 24 \n",
- "- 5% of 240 = 12 \n",
- "- 15% = 10% + 5% → 24 + 12 = **36**. ✅\n"
- ]
- }
- ],
- "source": [
- "# Reasoning OFF - Direct answer without thinking\n",
- "# Best with temperature=0 for deterministic output\n",
- "\n",
- "completion = client.chat.completions.create(\n",
- " model=MODEL,\n",
- " messages=[\n",
- " {\"role\": \"system\", \"content\": \"You are a helpful math assistant.\"},\n",
- " {\"role\": \"user\", \"content\": \"What is 15% of 240?\"}\n",
- " ],\n",
- " extra_body={\n",
- " \"chat_template_kwargs\": {\n",
- " \"enable_thinking\": False,\n",
- " }\n",
- " },\n",
- " temperature=0,\n",
- " max_tokens=500,\n",
- " stream=True\n",
- ")\n",
- "\n",
- "print(\"⚡ Reasoning OFF (direct answer):\\n\")\n",
- "_, answer = stream_with_reasoning(completion)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e58420f8",
- "metadata": {},
- "source": [
- "---\n",
- "\n",
- "# Part 2: LangChain Research Agent 🔍\n",
- "\n",
- "Now let's build something more powerful - a **research assistant** that can:\n",
- "- 🌐 Search the web for current information\n",
- "- 💬 Remember conversation history (memory)\n",
- "- 🤖 Use NVIDIA Nemotron as its brain\n",
- "\n",
- "We'll use LangChain's agent framework with the NVIDIA integration.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "3fc91ce8",
- "metadata": {},
- "source": [
- "## Setup LangChain with OpenRouter\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "fb0142f9",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/chris/Code/NVIDIA/Nemotron 3 Nano - Simple Usage Guide/.venv/lib/python3.13/site-packages/langchain_nvidia_ai_endpoints/_common.py:241: UserWarning: Model private/nvidia/nemotron-nano-3-30b-a3b is unknown, check `available_models`. Inference may fail.\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "✅ LangChain v1.0 + NVIDIA connected!\n",
- "\n",
- "Hello from NVIDIA!\n"
- ]
- }
- ],
- "source": [
- "from langchain_openai import ChatOpenAI\n",
- "\n",
- "# Initialize the model via LangChain using OpenRouter endpoint\n",
- "# Using ChatOpenAI with OpenRouter's base_url for OpenAI-compatible access\n",
- "llm = ChatOpenAI(\n",
- " model=\"nvidia/nemotron-3-nano-30b-a3b\",\n",
- " api_key=os.environ[\"OPENROUTER_API_KEY\"],\n",
- " base_url=\"https://openrouter.ai/api/v1\",\n",
- " temperature=1,\n",
- " top_p=1,\n",
- " max_tokens=4096,\n",
- ")\n",
- "\n",
- "# Test the connection\n",
- "test_response = llm.invoke(\"Say 'Hello from NVIDIA!' in exactly those words.\")\n",
- "print(f\"✅ LangChain + OpenRouter connected!\\n{test_response.content}\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "deac514c",
- "metadata": {},
- "source": [
- "## 🌐 Web Search Tool\n",
- "\n",
- "We'll use **DuckDuckGo** for web search (free, no API key required). \n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "89ab7195",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "🔍 Search test result (truncated):\n",
- "NVIDIA Nemotron ™ is a family of open models with open weights, training data, and recipes, delivering leading efficiency and accuracy for building specialized AI agents. So, what is Nemotron ? In short, Nemotron -4 340B is a decent-sized Large Language Model (LLM) that excels at crucial tasks in today’s world. A Predictable Architecture. What is Nemotron ? NVIDIA Nemotron ™ is a family of open, high-efficiency models with fully transparent training data, weights, and recipes. What Is NeMotron 3...\n"
- ]
- }
- ],
- "source": [
- "from langchain.tools import tool\n",
- "from langchain_community.tools import DuckDuckGoSearchRun\n",
- "\n",
- "# Create a DuckDuckGo search instance\n",
- "_ddg_search = DuckDuckGoSearchRun()\n",
- "\n",
- "# Wrap it with the @tool decorator for LangChain v1.0\n",
- "@tool\n",
- "def web_search(query: str) -> str:\n",
- " \"\"\"Search the web for current information on any topic.\n",
- " \n",
- " Use this tool when you need up-to-date information about\n",
- " recent events, news, technology, or any topic that may\n",
- " have changed since your training data.\n",
- " \n",
- " Args:\n",
- " query: The search query to look up on the web\n",
- " \"\"\"\n",
- " return _ddg_search.invoke(query)\n",
- "\n",
- "# Test the search tool\n",
- "test_search = web_search.invoke(\"What is Nemotron?\")\n",
- "print(\"🔍 Search test result (truncated):\")\n",
- "print(test_search[:500] + \"...\" if len(test_search) > 500 else test_search)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9d7b8ef3",
- "metadata": {},
- "source": [
- "## 🤖 Create the Research Agent with Memory\n",
- "\n",
- "Now we'll create an agent that:\n",
- "1. Uses NVIDIA Nemotron as its LLM\n",
- "2. Can search the web for information\n",
- "3. Remembers conversation history within a session\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "id": "47943c69",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "✅ LangChain v1.0 Research Agent with memory created!\n"
- ]
- }
- ],
- "source": [
- "from langchain.agents import create_agent\n",
- "from langgraph.checkpoint.memory import InMemorySaver\n",
- "\n",
- "# System prompt for our research assistant (LangChain v1.0 style)\n",
- "SYSTEM_PROMPT = \"\"\"You are a helpful research assistant powered by NVIDIA Nemotron.\n",
- "\n",
- "Your capabilities:\n",
- "- Search the web for current, up-to-date information\n",
- "- Provide well-researched, accurate answers\n",
- "- Remember our conversation context\n",
- "\n",
- "Guidelines:\n",
- "- Always search the web when asked about recent events, news, or current information\n",
- "- Cite your sources when providing factual information\n",
- "- Be concise but thorough\n",
- "- If you're unsure, say so and search for more information\"\"\"\n",
- "\n",
- "# Create memory checkpointer (LangChain v1.0 uses LangGraph's InMemorySaver)\n",
- "checkpointer = InMemorySaver()\n",
- "\n",
- "# Create the agent using LangChain v1.0's create_agent\n",
- "# This is the new, simplified API that replaces create_tool_calling_agent + AgentExecutor\n",
- "agent = create_agent(\n",
- " model=llm, # The NVIDIA model\n",
- " tools=[web_search], # List of tools (using @tool decorated functions)\n",
- " system_prompt=SYSTEM_PROMPT, # System instructions\n",
- " checkpointer=checkpointer, # Memory/persistence\n",
- ")\n",
- "\n",
- "print(\"✅ LangChain v1.0 Research Agent with memory created!\")\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a5fc7d27",
- "metadata": {},
- "source": [
- "## 🔬 Research Session Demo\n",
- "\n",
- "Let's test our agent with a multi-turn research conversation. The agent will remember what we've discussed!\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "id": "f659875a",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Helper function for clean research queries (LangChain v1.0 style)\n",
- "def research(query: str, thread_id: str = \"default\") -> str:\n",
- " \"\"\"Send a query to the research agent and return the response.\n",
- " \n",
- " In LangChain v1.0, we use thread_id for conversation persistence\n",
- " instead of session_id. The agent automatically manages message history.\n",
- " \"\"\"\n",
- " # LangChain v1.0 uses this config format for memory\n",
- " config = {\"configurable\": {\"thread_id\": thread_id}}\n",
- " \n",
- " # Invoke with messages format\n",
- " result = agent.invoke(\n",
- " {\"messages\": [{\"role\": \"user\", \"content\": query}]},\n",
- " config=config\n",
- " )\n",
- " \n",
- " # Extract the final response from messages\n",
- " # The last message contains the assistant's response\n",
- " messages = result.get(\"messages\", [])\n",
- " if messages:\n",
- " return messages[-1].content\n",
- " return \"No response generated.\"\n",
- "\n",
- "def print_response(query: str, response: str):\n",
- " \"\"\"Pretty print a research query and response.\"\"\"\n",
- " print(f\"\\n{'='*60}\")\n",
- " print(f\"📝 Query: {query}\")\n",
- " print(f\"{'='*60}\")\n",
- " print(f\"\\n{response}\\n\")\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "id": "de7399c0",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/chris/Code/NVIDIA/Nemotron 3 Nano - Simple Usage Guide/.venv/lib/python3.13/site-packages/langchain_nvidia_ai_endpoints/chat_models.py:842: UserWarning: Model 'private/nvidia/nemotron-nano-3-30b-a3b' is not known to support tools. Your tool binding may fail at inference time.\n",
- " warnings.warn(\n",
- "/home/chris/Code/NVIDIA/Nemotron 3 Nano - Simple Usage Guide/.venv/lib/python3.13/site-packages/langchain_nvidia_ai_endpoints/chat_models.py:842: UserWarning: Model 'private/nvidia/nemotron-nano-3-30b-a3b' is not known to support tools. Your tool binding may fail at inference time.\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "============================================================\n",
- "📝 Query: What is Nemotron Nano 9B V2?\n",
- "============================================================\n",
- "\n",
- "\n",
- "**Nemotron‑Nano‑9B‑V2** is the second‑generation 9‑billion‑parameter language model in NVIDIA’s Nemotron‑Nano family. It is an open‑weight, general‑purpose LLM designed for both **reasoning** and **non‑reasoning** tasks, and it can be used in English as well as in several non‑English languages (German, French, Italian, Spanish, Japanese).\n",
- "\n",
- "### Key characteristics\n",
- "| Aspect | Details |\n",
- "|--------|---------|\n",
- "| **Model size** | 9 billion parameters |\n",
- "| **Purpose** | Unified model for reasoning‑oriented chat and standard language tasks |\n",
- "| **Training** | Trained **from scratch** by NVIDIA Research; the complete training pipeline (pre‑training data, recipes, checkpoint) is publicly released |\n",
- "| **Reasoning style** | Generates an internal “reasoning trace” before producing the final answer, improving accuracy on complex queries |\n",
- "| **Deployment** | Available as an open‑weight checkpoint that can run on a single GPU; compatible with NVIDIA NIM™ microservices for easy integration |\n",
- "| **Languages** | English (primary) plus German, French, Italian, Spanish, Japanese (and potentially others) |\n",
- "| **Access** | Hosted on Hugging Face and documented on NVIDIA’s model hub; a playground UI is provided for quick experimentation |\n",
- "\n",
- "### Why it matters\n",
- "- **Open ecosystem** – NVIDIA publishes the model weights, training data, and the recipes used to build and fine‑tune the model, allowing researchers and developers to inspect, reproduce, or adapt the model for their own use cases. \n",
- "- **Efficiency** – At ~9 B parameters it fits comfortably on a single modern GPU, making it suitable for edge‑device or server‑side deployments where larger models (e.g., 15‑B or 30‑B) would be impractical. \n",
- "- **Unified capability** – By handling both “reasoning” (step‑by‑step) and “non‑reasoning” (direct) tasks within a single architecture, the model simplifies the design of downstream AI agents that need a single, reliable language backbone.\n",
- "\n",
- "### Sources\n",
- "- NVIDIA research documentation for **nemotron‑nano‑9b‑v2** (released 2025) – describes the model as a “general purpose reasoning and chat model” with the reasoning‑trace workflow [web search result]. \n",
- "- Announcement and model card on Hugging Face / NVIDIA model hub, confirming open weights, training data, and multilingual support [web search result].\n",
- "\n",
- "In short, **Nemotron‑Nano‑9B‑V2** is an openly released, 9‑billion‑parameter LLM from NVIDIA that balances strong reasoning capabilities with efficient deployment, targeting both English and selected non‑English languages.\n",
- "\n"
- ]
- }
- ],
- "source": [
- "# Research Query 1: Initial question\n",
- "# In LangChain v1.0, we use thread_id to maintain conversation state\n",
- "THREAD_ID = \"nvidia-research\"\n",
- "\n",
- "query1 = \"What is Nemotron Nano 9B V2?\"\n",
- "response1 = research(query1, THREAD_ID)\n",
- "print_response(query1, response1)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "35797f31",
- "metadata": {},
- "source": [
- "### View Conversation History\n",
- "\n",
- "The agent stores the full conversation in memory. Let's peek at what's stored:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3d5a30bb",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Conversation History (LangChain v1.0):\n",
- "----------------------------------------\n",
- "\n",
- "[1] Human:\n",
- " What is Nemotron?\n",
- "\n",
- "[2] Ai:\n",
- " \n",
- "\n",
- "[3] Tool:\n",
- " The Nemotron model family, available as optimized NVIDIA NIM™ microservices , offers peak inference performance and flexible deployment options, ensuring superior security, privacy, and portability. 4...\n",
- "\n",
- "[4] Ai:\n",
- " \n",
- "**Nemotron** is NVIDIA’s family of open‑weight language models that are purpose‑built for high‑performance, flexible deployment — from data‑center GPUs to edge devices. \n",
- "\n",
- "- **Core idea:** A suite of...\n",
- "\n",
- "[5] Human:\n",
- " What is Nemotron Nano 9B V2?\n",
- "\n",
- "[6] Ai:\n",
- " \n",
- "\n",
- "[7] Tool:\n",
- " NVIDIA- Nemotron - Nano - 9 B - v 2 is a general purpose reasoning and chat model intended to be used in English and coding languages. Other non-English languages (German, French, Italian, Spanish and...\n",
- "\n",
- "[8] Ai:\n",
- " \n",
- "**Nemotron‑Nano‑9B‑V2** is the second‑generation 9‑billion‑parameter language model in NVIDIA’s Nemotron‑Nano family. It is an open‑weight, general‑purpose LLM designed for both **reasoning** and **n...\n"
- ]
- }
- ],
- "source": [
- "config = {\"configurable\": {\"thread_id\": THREAD_ID}}\n",
- "state = agent.get_state(config)\n",
- "\n",
- "print(\"Conversation History (LangChain v1.0):\")\n",
- "print(\"-\" * 40)\n",
- "\n",
- "if state and state.values:\n",
- " messages = state.values.get(\"messages\", [])\n",
- " for i, msg in enumerate(messages):\n",
- " role = msg.type.capitalize() if hasattr(msg, 'type') else \"Unknown\"\n",
- " content = msg.content if hasattr(msg, 'content') else str(msg)\n",
- " content = content[:200] + \"...\" if len(content) > 200 else content\n",
- " print(f\"\\n[{i+1}] {role}:\")\n",
- " print(f\" {content}\")\n",
- "else:\n",
- " print(\"No conversation history found.\")\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "67ab40be",
- "metadata": {},
- "source": [
- "---\n",
- "\n",
- "## Try Your Own Research!\n",
- "\n",
- "Use the cell below to try your own research queries. Change the `SESSION_ID` to start a fresh conversation.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a47b15bb",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Try your own research query!\n",
- "# Change the thread_id to start a fresh conversation\n",
- "\n",
- "my_query = \"What are the latest breakthroughs in renewable energy?\"\n",
- "my_thread = \"my-research\" # Use a different thread_id for a new conversation\n",
- "\n",
- "response = research(my_query, my_thread)\n",
- "print_response(my_query, response)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "4c73a065",
- "metadata": {},
- "source": [
- "---\n",
- "\n",
- "# Part 3: Multi-Agent Research System 🤝\n",
- "\n",
- "Now let's build a more sophisticated **multi-agent system** where specialized agents collaborate to produce high-quality research reports.\n",
- "\n",
- "Based on [LangChain's multi-agent patterns](https://docs.langchain.com/oss/python/langchain/multi-agent), we'll implement the **Tool Calling (Supervisor) pattern** where:\n",
- "\n",
- "| Agent | Role |\n",
- "|-------|------|\n",
- "| **Coordinator** | Orchestrates the workflow, decides which specialist to call |\n",
- "| **Search Agent** | Specializes in finding relevant information from the web |\n",
- "| **Report Writer** | Transforms research into well-structured reports |\n",
- "| **Quality Reviewer** | Verifies helpfulness and suggests improvements |\n",
- "\n",
- "```\n",
- "┌─────────────────┐\n",
- "│ Coordinator │ ◄── User Query\n",
- "│ (Supervisor) │\n",
- "└────────┬────────┘\n",
- " │\n",
- " ┌────┴────┬──────────┐\n",
- " ▼ ▼ ▼\n",
- "┌───────┐ ┌────────┐ ┌──────────┐\n",
- "│Search │ │Report │ │Quality │\n",
- "│Agent │ │Writer │ │Reviewer │\n",
- "└───────┘ └────────┘ └──────────┘\n",
- "```\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d5b10bef",
- "metadata": {},
- "source": [
- "## Step 1: Create Specialized Sub-Agents\n",
- "\n",
- "Each sub-agent is a focused specialist with its own system prompt and capabilities.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "id": "0055100a",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "✅ Three specialized sub-agents created:\n",
- " - Search Agent (with web_search tool)\n",
- " - Report Writer Agent (writing specialist)\n",
- " - Quality Reviewer Agent (evaluation specialist)\n"
- ]
- }
- ],
- "source": [
- "# Create specialized sub-agents using LangChain v1.0's create_agent\n",
- "# Each agent has a focused role and specialized prompt\n",
- "\n",
- "# 1. SEARCH AGENT - Expert at finding information\n",
- "search_agent = create_agent(\n",
- " model=llm,\n",
- " tools=[web_search], # Has access to web search\n",
- " system_prompt=\"\"\"You are a Search Specialist Agent.\n",
- "\n",
- "Your ONLY job is to search the web and gather relevant information.\n",
- "\n",
- "When given a research topic:\n",
- "1. Formulate effective search queries\n",
- "2. Search for multiple aspects of the topic\n",
- "3. Compile the key findings\n",
- "4. Return a structured summary of what you found\n",
- "\n",
- "Always include the sources of your information.\n",
- "Be thorough but focused - gather facts, not opinions.\"\"\"\n",
- ")\n",
- "\n",
- "# 2. REPORT WRITER AGENT - Expert at writing reports\n",
- "report_writer_agent = create_agent(\n",
- " model=llm,\n",
- " tools=[], # No tools - pure writing capability\n",
- " system_prompt=\"\"\"You are a Report Writer Specialist Agent.\n",
- "\n",
- "Your ONLY job is to transform raw research into well-written reports.\n",
- "\n",
- "When given research findings:\n",
- "1. Organize information logically\n",
- "2. Write clear, engaging prose\n",
- "3. Include relevant sections (Overview, Key Points, Details, Conclusion)\n",
- "4. Use markdown formatting for structure\n",
- "5. Cite sources where appropriate\n",
- "\n",
- "Focus on clarity and readability. Make complex topics accessible.\"\"\"\n",
- ")\n",
- "\n",
- "# 3. QUALITY REVIEWER AGENT - Expert at evaluating helpfulness\n",
- "quality_reviewer_agent = create_agent(\n",
- " model=llm,\n",
- " tools=[], # No tools - pure evaluation capability\n",
- " system_prompt=\"\"\"You are a Quality Reviewer Specialist Agent.\n",
- "\n",
- "Your ONLY job is to evaluate if a report is helpful and complete.\n",
- "\n",
- "When reviewing a report:\n",
- "1. Check if it answers the original question\n",
- "2. Verify key information is included\n",
- "3. Assess clarity and readability\n",
- "4. Identify any gaps or missing information\n",
- "5. Provide a helpfulness score (1-10) with justification\n",
- "\n",
- "Be constructive - if improvements are needed, explain what's missing.\n",
- "If the report is good, confirm it's ready for the user.\"\"\"\n",
- ")\n",
- "\n",
- "print(\"✅ Three specialized sub-agents created:\")\n",
- "print(\" - Search Agent (with web_search tool)\")\n",
- "print(\" - Report Writer Agent (writing specialist)\")\n",
- "print(\" - Quality Reviewer Agent (evaluation specialist)\")\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "89494712",
- "metadata": {},
- "source": [
- "## Step 2: Wrap Sub-Agents as Tools\n",
- "\n",
- "Following [LangChain's multi-agent pattern](https://docs.langchain.com/oss/python/langchain/multi-agent), we wrap each sub-agent as a tool that the coordinator can invoke.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "id": "c3a8ae63",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "✅ Sub-agents wrapped as tools:\n",
- " - call_search_agent: For gathering information\n",
- " - call_report_writer: For creating reports\n",
- " - call_quality_reviewer: For evaluating quality\n"
- ]
- }
- ],
- "source": [
- "# Wrap each sub-agent as a tool for the coordinator\n",
- "# This is the \"Tool Calling\" multi-agent pattern from LangChain v1.0\n",
- "\n",
- "@tool(\"search_specialist\")\n",
- "def call_search_agent(research_topic: str) -> str:\n",
- " \"\"\"Call the Search Specialist to gather information from the web.\n",
- " \n",
- " Use this when you need to find current information, facts, or data\n",
- " about a topic. The search specialist will search the web and return\n",
- " a summary of findings.\n",
- " \n",
- " Args:\n",
- " research_topic: The topic or question to research\n",
- " \"\"\"\n",
- " result = search_agent.invoke({\n",
- " \"messages\": [{\"role\": \"user\", \"content\": f\"Research this topic: {research_topic}\"}]\n",
- " })\n",
- " return result[\"messages\"][-1].content\n",
- "\n",
- "\n",
- "@tool(\"report_writer\")\n",
- "def call_report_writer(research_findings: str) -> str:\n",
- " \"\"\"Call the Report Writer to create a well-structured report.\n",
- " \n",
- " Use this after gathering research to transform raw findings into\n",
- " a polished, readable report with proper structure and formatting.\n",
- " \n",
- " Args:\n",
- " research_findings: The raw research data and findings to write about\n",
- " \"\"\"\n",
- " result = report_writer_agent.invoke({\n",
- " \"messages\": [{\"role\": \"user\", \"content\": f\"Write a report based on these findings:\\n\\n{research_findings}\"}]\n",
- " })\n",
- " return result[\"messages\"][-1].content\n",
- "\n",
- "\n",
- "@tool(\"quality_reviewer\")\n",
- "def call_quality_reviewer(report_to_review: str) -> str:\n",
- " \"\"\"Call the Quality Reviewer to evaluate a report's helpfulness.\n",
- " \n",
- " Use this to verify that a report is complete, helpful, and ready\n",
- " for the user. The reviewer will score it and suggest improvements.\n",
- " \n",
- " Args:\n",
- " report_to_review: The report content to evaluate\n",
- " \"\"\"\n",
- " result = quality_reviewer_agent.invoke({\n",
- " \"messages\": [{\"role\": \"user\", \"content\": f\"Review this report for helpfulness and completeness:\\n\\n{report_to_review}\"}]\n",
- " })\n",
- " return result[\"messages\"][-1].content\n",
- "\n",
- "\n",
- "print(\"✅ Sub-agents wrapped as tools:\")\n",
- "print(\" - call_search_agent: For gathering information\")\n",
- "print(\" - call_report_writer: For creating reports\")\n",
- "print(\" - call_quality_reviewer: For evaluating quality\")\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "3eef7e5d",
- "metadata": {},
- "source": [
- "## Step 3: Create the Coordinator (Supervisor) Agent\n",
- "\n",
- "The coordinator orchestrates the workflow by deciding which specialist to call and when.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "id": "fe36faf7",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "✅ Multi-Agent Research System ready!\n",
- "\n",
- "Workflow: User → Coordinator → Search → Writer → Reviewer → User\n"
- ]
- }
- ],
- "source": [
- "# Create the Coordinator (Supervisor) Agent\n",
- "# This agent orchestrates the multi-agent workflow\n",
- "\n",
- "COORDINATOR_PROMPT = \"\"\"You are a Research Coordinator managing a team of specialist agents.\n",
- "\n",
- "Your team consists of:\n",
- "1. **Search Specialist** - Finds information from the web\n",
- "2. **Report Writer** - Creates well-structured reports\n",
- "3. **Quality Reviewer** - Evaluates helpfulness and completeness\n",
- "\n",
- "WORKFLOW for research requests:\n",
- "1. First, call the Search Specialist to gather information\n",
- "2. Then, call the Report Writer to create a report from the findings\n",
- "3. Finally, call the Quality Reviewer to verify the report is helpful\n",
- "4. If the reviewer suggests improvements, you may iterate\n",
- "5. Present the final report to the user\n",
- "\n",
- "Always follow this workflow for research questions. Coordinate the specialists\n",
- "efficiently and ensure the final output is high-quality and helpful.\n",
- "\n",
- "Remember: You are the coordinator. Delegate tasks to specialists - don't try\n",
- "to search or write reports yourself.\"\"\"\n",
- "\n",
- "# Create coordinator with access to all specialist tools\n",
- "multi_agent_checkpointer = InMemorySaver()\n",
- "\n",
- "coordinator = create_agent(\n",
- " model=llm,\n",
- " tools=[call_search_agent, call_report_writer, call_quality_reviewer],\n",
- " system_prompt=COORDINATOR_PROMPT,\n",
- " checkpointer=multi_agent_checkpointer,\n",
- ")\n",
- "\n",
- "print(\"✅ Multi-Agent Research System ready!\")\n",
- "print(\"\\nWorkflow: User → Coordinator → Search → Writer → Reviewer → User\")\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "034d88fa",
- "metadata": {},
- "source": [
- "## Step 4: Run the Multi-Agent System\n",
- "\n",
- "Let's test the full multi-agent workflow with a research question.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "id": "915388d0",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "============================================================\n",
- "MULTI-AGENT RESEARCH DEMO\n",
- "============================================================\n",
- "🚀 Starting multi-agent research on: What are the key features and benefits of NVIDIA's Blackwell GPU architecture?\n",
- "--------------------------------------------------\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/chris/Code/NVIDIA/Nemotron 3 Nano - Simple Usage Guide/.venv/lib/python3.13/site-packages/langchain_nvidia_ai_endpoints/chat_models.py:842: UserWarning: Model 'private/nvidia/nemotron-nano-3-30b-a3b' is not known to support tools. Your tool binding may fail at inference time.\n",
- " warnings.warn(\n",
- "/home/chris/Code/NVIDIA/Nemotron 3 Nano - Simple Usage Guide/.venv/lib/python3.13/site-packages/langchain_nvidia_ai_endpoints/chat_models.py:842: UserWarning: Model 'private/nvidia/nemotron-nano-3-30b-a3b' is not known to support tools. Your tool binding may fail at inference time.\n",
- " warnings.warn(\n",
- "/home/chris/Code/NVIDIA/Nemotron 3 Nano - Simple Usage Guide/.venv/lib/python3.13/site-packages/langchain_nvidia_ai_endpoints/chat_models.py:842: UserWarning: Model 'private/nvidia/nemotron-nano-3-30b-a3b' is not known to support tools. Your tool binding may fail at inference time.\n",
- " warnings.warn(\n",
- "/home/chris/Code/NVIDIA/Nemotron 3 Nano - Simple Usage Guide/.venv/lib/python3.13/site-packages/langchain_nvidia_ai_endpoints/chat_models.py:842: UserWarning: Model 'private/nvidia/nemotron-nano-3-30b-a3b' is not known to support tools. Your tool binding may fail at inference time.\n",
- " warnings.warn(\n",
- "/home/chris/Code/NVIDIA/Nemotron 3 Nano - Simple Usage Guide/.venv/lib/python3.13/site-packages/langchain_nvidia_ai_endpoints/chat_models.py:842: UserWarning: Model 'private/nvidia/nemotron-nano-3-30b-a3b' is not known to support tools. Your tool binding may fail at inference time.\n",
- " warnings.warn(\n",
- "/home/chris/Code/NVIDIA/Nemotron 3 Nano - Simple Usage Guide/.venv/lib/python3.13/site-packages/langchain_nvidia_ai_endpoints/chat_models.py:842: UserWarning: Model 'private/nvidia/nemotron-nano-3-30b-a3b' is not known to support tools. Your tool binding may fail at inference time.\n",
- " warnings.warn(\n",
- "/home/chris/Code/NVIDIA/Nemotron 3 Nano - Simple Usage Guide/.venv/lib/python3.13/site-packages/langchain_nvidia_ai_endpoints/chat_models.py:842: UserWarning: Model 'private/nvidia/nemotron-nano-3-30b-a3b' is not known to support tools. Your tool binding may fail at inference time.\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "============================================================\n",
- "FINAL REPORT\n",
- "============================================================\n",
- "\n",
- "**NVIDIA Blackwell GPU Architecture – Key Features, Benefits, and Expanded Details**\n",
- "\n",
- "---\n",
- "\n",
- "## 1. Overview\n",
- "Blackwell is NVIDIA’s newest **GPU micro‑architecture**, succeeding Hopper and Ada Lovelace. It is purpose‑built for two primary workloads:\n",
- "\n",
- "| Workload | What Blackwell does |\n",
- "|----------|----------------------|\n",
- "| **AI training & inference** | Accelerates large‑scale model training (LLMs, diffusion, recommendation) and high‑throughput inference. |\n",
- "| **High‑performance graphics** | Powers the upcoming **RTX 50‑series** graphics cards and the **RTX PRO 6000 Blackwell** workstation GPUs. |\n",
- "\n",
- "---\n",
- "\n",
- "## 2. Core Features (with technical specifics)\n",
- "\n",
- "| Feature | Technical Detail | Why It Matters |\n",
- "|---------|-----------------|----------------|\n",
- "| **5th‑Generation Tensor Cores** | • Up to **1,000 TFLOPs** of FP8/FP16/TF32 compute per GPU.• New matrix‑multiply instructions that fuse attention kernels. | Delivers dramatically higher AI‑compute throughput and efficiency. |\n", - "| **AI Multi‑Frame Generation (DLSS 4)** | Generates **2 ×** frames per render pass; supports up to **8K** resolution with < 5 ms latency.
Works with RTX‑ON ray tracing. | Doubles frame rates in games/VR while preserving or improving image quality. |\n", - "| **AI Management Processor (AMP)** | Small RISC‑V core integrated on‑die; handles work‑graph scheduling, context switching, and VRAM orchestration. | Reduces CPU‑GPU overhead, improves effective VRAM bandwidth by ~30 %. |\n", - "| **Enhanced Transformer Engine** | • Optimized attention‑fusion kernels.
• Larger on‑chip scratchpad for KV‑cache reuse. | Cuts training/inference time for transformer‑based models (e.g., 30 B‑parameter LLMs). |\n", - "| **Performance‑Scale Metrics** | • **Base Blackwell GPU:** 10 petaFLOPS (NVFP4).
• **Blackwell Ultra:** up to 15 petaFLOPS (≈1.5× base, ~7.5× Hopper H100).
• **Training:** up to **3×** faster vs. H100.
• **Inference:** up to **15×** faster vs. H100.
• **GPU density:** up to **4×** Blackwell GPUs in a single workstation (e.g., 4× RTX PRO 6000) without thermal overload. | Quantifies real‑world speedups and scalability. |\n", - "| **Memory Subsystem** | • **Memory bandwidth:** up to **3.2 TB/s** (GDDR7).
• **Capacity:** 48 GB or 96 GB HBM3e per GPU.
• **L2 cache:** 120 MB shared. | Enables larger batch sizes and higher model parallelism. |\n", - "| **Power & Thermal Design** | • **TDP:** 350 W (standard), 450 W (Ultra).
• **Cooling:** Dual‑fan + vapor‑chamber; designed for up to **4‑GPU** packing in a 2U chassis. | Provides a balance of performance and power‑efficiency for data‑center and workstation use. |\n", - "| **Software Stack** | • Supports **CUDA 12.5**, **TensorRT 9**, ** cuDNN 9**.
• New APIs for **work‑graph scheduling** and **dynamic kernelRegistration**. | Ensures compatibility with existing AI frameworks (PyTorch, TensorFlow) and optimizations. |\n", - "\n", - "*Sources: Search results 1‑4 (NVIDIA blog, architecture briefings, performance tables).*\n", - "\n", - "---\n", - "\n", - "## 3. Key Benefits (with concrete numbers)\n", - "\n", - "| Benefit | Concrete Figure / Example | Impact |\n", - "|---------|---------------------------|--------|\n", - "| **Accelerated AI training** | 3× faster training of a 70 B LLM compared with H100 (≈ 2 days → 16 hours on a 8‑GPU Blackwell cluster). | Reduces time‑to‑market for new models. |\n", - "| **High‑speed inference** | 15× lower latency for a 175 B parameter model (≈ 30 ms → 2 ms per token). | Cuts **cost‑per‑token** by ~85 % for large‑scale serving. |\n", - "| **Graphics performance** | DLSS 4 can push **120 fps** in 4K gaming titles (e.g., *Cyberpunk 2077* RTX‑ON) vs. 60 fps with DLSS 3. | Enables smoother VR/AR experiences. |\n", - "| **VRAM efficiency** | Effective VRAM bandwidth ↑ 30 % thanks to AMP; allows **2×** larger batch sizes on the same memory footprint. | Larger models can be deployed on a single GPU. |\n", - "| **Scalable multi‑GPU configurations** | 4‑GPU workstation (e.g., 4× RTX PRO 6000) shows **2.9×** scaling efficiency on mixed‑precision training (MLPerf Training v2.0). | Facilitates dense AI clusters without extra rack space. |\n", - "| **Cost‑per‑token reduction** | Estimated **$0.00012 per token** for a 70 B model on Blackwell vs. **$0.0010** on H100 (≈ 88 % savings). | Makes large‑scale AI services economically viable. |\n", - "| **Future‑ready ecosystem** | Full CUDA 12.5 support; TensorRT 9 optimizations; planned roadmap through **2027** with “Blackwell‑X” prototype slated for 2026. | Protects developer investments and ensures longevity. |\n", - "\n", - "---\n", - "\n", - "## 4. Technical Specification Snapshot \n", - "\n", - "| Spec | Blackwell (Base) | Blackwell Ultra |\n", - "|------|------------------|-----------------|\n", - "| **FP8/TF32 Compute** | 1,000 TFLOPs | 1,500 TFLOPs |\n", - "| **CUDA Cores** | 18,432 | 23,040 |\n", - "| **Tensor Cores (5th‑Gen)** | 576 | 720 |\n", - "| **RT Cores (3rd‑Gen)** | 72 | 96 |\n", - "| **Memory** | 48 GB/96 GB HBM3e | 96 GB HBM3e |\n", - "| **Memory Bandwidth** | 3.2 TB/s | 3.5 TB/s |\n", - "| **L2 Cache** | 120 MB | 150 MB |\n", - "| **TDP** | 350 W | 450 W |\n", - "| **Process Node** | 5 nm (TSMC) | 5 nm (TSMC) |\n", - "| **Launch** | Q3 2024 | Q1 2025 |\n", - "\n", - "---\n", - "\n", - "## 5. Competitive Landscape (quick comparison)\n", - "\n", - "| Metric | NVIDIA Blackwell | AMD Instinct MI300X | Intel Xe‑HPC (3rd Gen) |\n", - "|--------|-------------------|--------------------|------------------------|\n", - "| **Peak TFLOPs (FP16)** | 1,000 | 900 | 800 |\n", - "| **Inference Speed (LLM token/sec)** | 15× vs. H100 | ~12× vs. MI250X | ~10× vs. Habana Gaudi 2 |\n", - "| **Memory Bandwidth** | 3.2 TB/s | 2.5 TB/s | 2.2 TB/s |\n", - "| **Power Efficiency (TFLOP/W)** | 2.9 TFLOP/W | 2.5 TFLOP/W | 2.2 TFLOP/W |\n", - "| **Software Ecosystem** | CUDA, TensorRT, cuDNN (mature) | ROCm (growing) | oneAPI (early adoption) |\n", - "\n", - "*Blackwell leads on raw performance, inference speed, and software maturity.*\n", - "\n", - "---\n", - "\n", - "## 6. Future Outlook\n", - "- **Blackwell‑X (prototype)** slated for 2026 with **doubling of Tensor Core count** and **support for FP4** precision.\n", - "- NVIDIA plans **continuous driver and CUDA updates** through **2027**, ensuring backward compatibility.\n", - "- Expect **more RTX 50‑series GPUs** (mobile, laptop, and data‑center form factors) to leverage Blackwell’s architecture.\n", - "\n", - "---\n", - "\n", - "## 7. Bottom Line\n", - "The **NVIDIA Blackwell GPU architecture** combines:\n", - "\n", - "- **5th‑generation Tensor Cores** for unmatched AI compute.\n", - "- **DLSS 4 multi‑frame generation** for double‑speed graphics.\n", - "- An **AI Management Processor** that optimizes VRAM scheduling.\n", - "- **Performance metrics** that translate to **3× faster training** and **15× faster inference** versus the previous H100 generation.\n", - "- **Scalable, power‑efficient designs** that enable dense multi‑GPU workstations and data‑center deployments.\n", - "\n", - "These capabilities make Blackwell the **go‑to platform for next‑generation AI development and high‑fidelity, low‑latency graphics**, delivering tangible speed, cost, and efficiency benefits across industries.\n", - "\n", - "---\n", - "\n", - "### References (source of the original research)\n", - "\n", - "1. *NVIDIA Blog – “Blackwell: 3× faster training, 15× faster inference, DLSS 4 frame‑rate gains”*【Search result 1】 \n", - "2. *Architecture Brief – “5th‑Gen Tensor Cores & RTX 50‑series support”*【Search result 2】 \n", - "3. *Performance Whitepaper – “10 → 15 petaFLOPS, Hopper comparison, MLPerf results”*【Search result 3】 \n", - "4. *Technical Deep‑Dive – “AI Management Processor, VRAM efficiency, memory specs”*【Search result 4】\n", - "\n", - "--- \n", - "\n", - "Feel free to let me know if you’d like any additional details (e.g., price estimates, specific benchmark numbers, or a deeper dive into the software stack).\n" - ] - } - ], - "source": [ - "# Helper function for multi-agent research\n", - "def multi_agent_research(topic: str, thread_id: str = \"multi-agent-default\") -> str:\n", - " \"\"\"Run a research query through the multi-agent system.\n", - " \n", - " The coordinator will orchestrate:\n", - " 1. Search Agent → gather information\n", - " 2. Report Writer → create report\n", - " 3. Quality Reviewer → verify helpfulness\n", - " \"\"\"\n", - " config = {\"configurable\": {\"thread_id\": thread_id}}\n", - " \n", - " print(f\"🚀 Starting multi-agent research on: {topic}\")\n", - " print(\"-\" * 50)\n", - " \n", - " result = coordinator.invoke(\n", - " {\"messages\": [{\"role\": \"user\", \"content\": topic}]},\n", - " config=config\n", - " )\n", - " \n", - " # Get the final response\n", - " final_response = result[\"messages\"][-1].content\n", - " return final_response\n", - "\n", - "# Demo: Run a multi-agent research query\n", - "MULTI_AGENT_THREAD = \"research-demo\"\n", - "\n", - "research_topic = \"What are the key features and benefits of NVIDIA's Blackwell GPU architecture?\"\n", - "\n", - "print(\"=\" * 60)\n", - "print(\"MULTI-AGENT RESEARCH DEMO\")\n", - "print(\"=\" * 60)\n", - "\n", - "final_report = multi_agent_research(research_topic, MULTI_AGENT_THREAD)\n", - "\n", - "print(\"\\n\" + \"=\" * 60)\n", - "print(\"FINAL REPORT\")\n", - "print(\"=\" * 60)\n", - "print(final_report)\n" - ] - }, - { - "cell_type": "markdown", - "id": "fd2a7162", - "metadata": {}, - "source": [ - "### View Multi-Agent Collaboration\n", - "\n", - "Let's see how the agents collaborated - which tools were called and in what order:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "007c6567", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Multi-Agent Collaboration Log:\n", - "==================================================\n", - "\n", - "[1] 👤 USER: What are the key features and benefits of NVIDIA's Blackwell GPU architecture?...\n", - "\n", - "[2] 🔧 TOOL CALL: search_specialist\n", - " Args: {'research_topic': 'NVIDIA Blackwell GPU architecture key features and benefits'}\n", - "\n", - "[3] 📥 TOOL RESULT (search_specialist):\n", - " \n", - "**NVIDIA Blackwell GPU Architecture – Key Features & Benefits** \n", - "\n", - "---\n", - "\n", - "### 1. Overview \n", - "Blackwell is NVIDIA’s latest graphics‑processing‑unit micro...\n", - "\n", - "[4] 🔧 TOOL CALL: quality_reviewer\n", - " Args: {'report_to_review': 'NVIDIA Blackwell GPU Architecture – Key Features & Benefits\\n\\n---\\n\\n### 1. O...\n", - "\n", - "[5] 📥 TOOL RESULT (quality_reviewer):\n", - " \n", - "**Overall helpfulness score: 8 / 10**\n", - "\n", - "---\n", - "\n", - "### ✅ What the report does well \n", - "\n", - "| Criterion | Evaluation | Why it earns points |\n", - "|-----------|--------...\n", - "\n", - "[6] 🤖 COORDINATOR: \n", - "**NVIDIA Blackwell GPU Architecture – Key Features, Benefits, and Expanded Details**\n", - "\n", - "---\n", - "\n", - "## 1. Overview\n", - "Blackwell is NVIDIA’s newest **GPU micro‑ar...\n" - ] - } - ], - "source": [ - "# View the multi-agent collaboration history\n", - "config = {\"configurable\": {\"thread_id\": MULTI_AGENT_THREAD}}\n", - "state = coordinator.get_state(config)\n", - "\n", - "print(\"Multi-Agent Collaboration Log:\")\n", - "print(\"=\" * 50)\n", - "\n", - "if state and state.values:\n", - " messages = state.values.get(\"messages\", [])\n", - " for i, msg in enumerate(messages):\n", - " msg_type = msg.type.upper() if hasattr(msg, 'type') else \"UNKNOWN\"\n", - " \n", - " # Check if it's a tool call\n", - " if msg_type == \"AI\" and hasattr(msg, 'tool_calls') and msg.tool_calls:\n", - " for tc in msg.tool_calls:\n", - " print(f\"\\n[{i+1}] 🔧 TOOL CALL: {tc.get('name', 'unknown')}\")\n", - " args = tc.get('args', {})\n", - " arg_preview = str(args)[:100] + \"...\" if len(str(args)) > 100 else str(args)\n", - " print(f\" Args: {arg_preview}\")\n", - " elif msg_type == \"TOOL\":\n", - " tool_name = msg.name if hasattr(msg, 'name') else \"unknown\"\n", - " content_preview = msg.content[:150] + \"...\" if len(msg.content) > 150 else msg.content\n", - " print(f\"\\n[{i+1}] 📥 TOOL RESULT ({tool_name}):\")\n", - " print(f\" {content_preview}\")\n", - " elif msg_type == \"HUMAN\":\n", - " print(f\"\\n[{i+1}] 👤 USER: {msg.content[:100]}...\")\n", - " elif msg_type == \"AI\":\n", - " content_preview = msg.content[:150] + \"...\" if len(msg.content) > 150 else msg.content\n", - " print(f\"\\n[{i+1}] 🤖 COORDINATOR: {content_preview}\")\n", - "else:\n", - " print(\"No collaboration history found.\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "cc99fddb", - "metadata": {}, - "source": [ - "## Try Your Own Multi-Agent Research\n", - "\n", - "Run any research topic through the multi-agent system:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "aa452c34", - "metadata": {}, - "outputs": [], - "source": [ - "# Try your own multi-agent research!\n", - "# Change the topic and thread_id as desired\n", - "\n", - "my_topic = \"What are the latest advancements in quantum computing in 2024?\"\n", - "my_thread = \"my-multi-agent-research\"\n", - "\n", - "report = multi_agent_research(my_topic, my_thread)\n", - "print(\"\\n\" + \"=\" * 60)\n", - "print(\"YOUR RESEARCH REPORT\")\n", - "print(\"=\" * 60)\n", - "print(report)\n" - ] - }, - { - "cell_type": "markdown", - "id": "b51e3823", - "metadata": {}, - "source": [ - "---\n", - "\n", - "# Summary\n", - "\n", - "In this notebook, we covered:\n", - "\n", - "### Part 1: Basic Usage\n", - "- Setting up the OpenAI-compatible client via **OpenRouter**\n", - "- **Reasoning ON**: Chain-of-thought reasoning (best for complex problems)\n", - "- **Reasoning OFF**: Direct answers (best for simple queries)\n", - "\n", - "### Part 2: LangChain v1.0 Single Agent\n", - "- Used the new **`create_agent`** API\n", - "- Integrated NVIDIA Nemotron via **OpenRouter** using `ChatOpenAI` with custom `base_url`\n", - "- Added **web search** capability with `@tool` decorator\n", - "- Implemented **conversation memory** using `InMemorySaver` checkpointer\n", - "\n", - "### Part 3: Multi-Agent System\n", - "- Created **specialized sub-agents** (Search, Report Writer, Quality Reviewer)\n", - "- Wrapped sub-agents as **tools** following [LangChain's multi-agent pattern](https://docs.langchain.com/oss/python/langchain/multi-agent)\n", - "- Built a **Coordinator (Supervisor)** agent to orchestrate the workflow\n", - "- Demonstrated the full **research pipeline**: Search → Write → Review\n", - "\n", - "## Multi-Agent Architecture\n", - "\n", - "```\n", - "┌─────────────────────────────────────────────────────┐\n", - "│ COORDINATOR │\n", - "│ (Decides which specialist to call and when) │\n", - "└─────────────┬─────────────┬─────────────┬───────────┘\n", - " │ │ │\n", - " ┌────────▼────┐ ┌─────▼──────┐ ┌───▼────────┐\n", - " │ SEARCH │ │ REPORT │ │ QUALITY │\n", - " │ AGENT │ │ WRITER │ │ REVIEWER |\n", - " │ (web_search)│ │ (writing) │ │(evaluation)│\n", - " └─────────────┘ └────────────┘ └────────────┘\n", - "```\n", - "\n", - "## Key LangChain v1.0 Patterns Used\n", - "\n", - "| Pattern | Description |\n", - "|---------|-------------|\n", - "| `create_agent` | New simplified agent creation API |\n", - "| `@tool` decorator | Define tools from functions |\n", - "| `InMemorySaver` | Thread-based conversation memory |\n", - "| **Tool Calling** | Sub-agents wrapped as tools for orchestration |\n", - "\n", - "## Resources\n", - "\n", - "- [OpenRouter Documentation](https://openrouter.ai/docs)\n", - "- [LangChain Chat Completions API (OpenRouter)](https://docs.langchain.com/oss/python/integrations/chat/index)\n", - "- [LangChain v1.0 Release Notes](https://docs.langchain.com/oss/python/releases/langchain-v1)\n", - "- [Multi-Agent Patterns](https://docs.langchain.com/oss/python/langchain/multi-agent)\n", - "- [Supervisor Agent Tutorial](https://docs.langchain.com/oss/python/langchain/supervisor)\n", - "- [LangGraph for Advanced Agents](https://langchain-ai.github.io/langgraph/)\n" - ] - }, - { - "cell_type": "markdown", - "id": "af0a8b75", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..edb4618 --- /dev/null +++ b/uv.lock @@ -0,0 +1,4916 @@ +version = 1 +revision = 3 +requires-python = ">=3.10" +resolution-markers = [ + "python_full_version >= '3.14' and sys_platform == 'linux'", + "python_full_version == '3.13.*' and sys_platform == 'linux'", 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