⚡️ Speed up function _match_cell_ids_by_similarity by 33%

[codeflash-ai]

📄 33% (0.33x) speedup for _match_cell_ids_by_similarity in marimo/_utils/cell_matching.py

⏱️ Runtime : 570 milliseconds → 428 milliseconds (best of 21 runs)

📝 Explanation and details

This optimization achieves a 33% speedup through several targeted micro-optimizations that reduce overhead in computationally intensive functions:

Key Optimizations:

1. similarity_score (74.6% of original runtime): Eliminated expensive string operations by replacing s1[::-1] and s2[::-1] string reversals with direct index-based suffix scanning. This avoids creating new string objects and uses tight while-loops instead of slower zip() iterations.

2. pop_local function: Replaced min() with lambda function (which had high per-call overhead) with a direct for-loop that manually tracks the best match. This is significantly faster for the typical small list sizes encountered.

3. _hungarian_algorithm: Added local variable caching (score_matrix_i = score_matrix[i]) to avoid repeated list lookups in nested loops, and optimized the uncovered cell detection by pre-computing masks rather than checking conditions repeatedly.

4. group_lookup and extract_order: Minor optimizations including caching setdefault as a local variable and pre-allocating lists with correct sizes.

Why This Matters:

The function is called from match_cell_ids_by_similarity(), which appears to be used for matching cells in notebook operations - likely during cell reordering, copying, or merging operations. The test results show consistent 30-35% speedups across all scenarios, particularly benefiting:

• Large-scale operations (500+ cells): 31-34% faster, crucial for large notebooks
• Code similarity matching: 33-34% faster when cells have similar but modified code
• Duplicate code handling: 30-32% faster, important for notebooks with repeated patterns

The optimizations are most effective for workloads involving many cells or frequent cell matching operations, where the cumulative effect of these micro-optimizations provides substantial performance gains.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 42 Passed
🌀 Generated Regression Tests	✅ 54 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

⚙️ Existing Unit Tests and Runtime

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
_ast/test_cell_manager.py::TestCellMatching.test_completely_different_codes	33.5μs	32.3μs	3.73%✅
_ast/test_cell_manager.py::TestCellMatching.test_empty_lists	4.61μs	4.29μs	7.34%✅
_ast/test_cell_manager.py::TestCellMatching.test_empty_strings	9.65μs	8.37μs	15.4%✅
_ast/test_cell_manager.py::TestCellMatching.test_exact_matches	12.5μs	11.3μs	10.6%✅
_ast/test_cell_manager.py::TestCellMatching.test_fewer_next_cells	12.3μs	10.7μs	15.9%✅
_ast/test_cell_manager.py::TestCellMatching.test_left_inexact_matches_with_dupes	43.9μs	39.7μs	10.3%✅
_ast/test_cell_manager.py::TestCellMatching.test_more_next_cells	13.1μs	12.2μs	7.45%✅
_ast/test_cell_manager.py::TestCellMatching.test_outer_inexact_matches	42.9μs	39.4μs	8.98%✅
_ast/test_cell_manager.py::TestCellMatching.test_outer_inexact_matches_with_dupes	57.8μs	51.5μs	12.3%✅
_ast/test_cell_manager.py::TestCellMatching.test_reordered_codes	12.9μs	11.4μs	13.4%✅
_ast/test_cell_manager.py::TestCellMatching.test_right_inexact_matches_with_dupes	45.3μs	40.6μs	11.7%✅
_ast/test_cell_manager.py::TestCellMatching.test_similar_but_not_exact_matches	40.6μs	36.4μs	11.3%✅
_ast/test_cell_manager.py::TestCellMatching.test_similar_but_not_exact_matches_with_dupes	46.4μs	42.5μs	9.03%✅
_ast/test_cell_manager.py::TestCellMatchingEdgeCases.test_all_codes_being_substrings	13.3μs	11.6μs	14.0%✅
_ast/test_cell_manager.py::TestCellMatchingEdgeCases.test_completely_different_codes_edge_case	32.0μs	28.5μs	12.6%✅
_ast/test_cell_manager.py::TestCellMatchingEdgeCases.test_empty_strings_edge_case	10.9μs	10.6μs	2.58%✅
_ast/test_cell_manager.py::TestCellMatchingEdgeCases.test_identical_codes	13.8μs	12.4μs	11.7%✅
_ast/test_cell_manager.py::TestCellMatchingEdgeCases.test_maximum_length_differences	20.1μs	18.9μs	6.30%✅
_ast/test_cell_manager.py::TestCellMatchingEdgeCases.test_mixed_case_sensitivity	38.1μs	33.2μs	14.8%✅
_ast/test_cell_manager.py::TestCellMatchingEdgeCases.test_multiple_identical_codes_in_next	13.3μs	11.5μs	15.5%✅
_ast/test_cell_manager.py::TestCellMatchingEdgeCases.test_multiple_identical_codes_in_prev	12.5μs	11.4μs	9.51%✅
_ast/test_cell_manager.py::TestCellMatchingEdgeCases.test_similar_reduction	32.4μs	31.2μs	3.76%✅
_ast/test_cell_manager.py::TestCellMatchingEdgeCases.test_special_python_syntax	12.1μs	10.3μs	17.6%✅
_ast/test_cell_manager.py::TestCellMatchingEdgeCases.test_unicode_and_special_characters	13.4μs	11.9μs	12.2%✅
_ast/test_cell_manager.py::TestCellMatchingEdgeCases.test_very_long_common_prefixes_suffixes	14.9μs	12.9μs	16.0%✅
_ast/test_cell_manager.py::TestCellMatchingEdgeCases.test_whitespace_variations	36.2μs	33.7μs	7.38%✅

🌀 Generated Regression Tests and Runtime

import random
import string

# imports
import pytest
from marimo._utils.cell_matching import _match_cell_ids_by_similarity

# function to test
# (The function _match_cell_ids_by_similarity is assumed to be defined above)

# ------------------------
# Basic Test Cases
# ------------------------

def test_exact_match_single_cell():
    # One cell, codes and ids are identical
    prev_ids = ["a"]
    prev_codes = ["print('hello')"]
    next_ids = ["a"]
    next_codes = ["print('hello')"]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 9.60μs -> 8.68μs (10.6% faster)

def test_exact_match_multiple_cells():
    # Multiple cells, all codes and ids are identical
    prev_ids = ["a", "b", "c"]
    prev_codes = ["code1", "code2", "code3"]
    next_ids = ["a", "b", "c"]
    next_codes = ["code1", "code2", "code3"]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 13.6μs -> 12.1μs (12.3% faster)

def test_permutation_of_cells():
    # Same codes and ids, but order of cells is changed
    prev_ids = ["a", "b", "c"]
    prev_codes = ["code1", "code2", "code3"]
    next_ids = ["b", "c", "a"]
    next_codes = ["code2", "code3", "code1"]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 13.1μs -> 11.6μs (13.1% faster)

def test_new_cell_added():
    # New cell code added at end
    prev_ids = ["a", "b"]
    prev_codes = ["code1", "code2"]
    next_ids = ["a", "b", "c"]
    next_codes = ["code1", "code2", "code3"]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 12.7μs -> 11.1μs (14.3% faster)

def test_cell_deleted():
    # One cell is deleted
    prev_ids = ["a", "b", "c"]
    prev_codes = ["code1", "code2", "code3"]
    next_ids = ["a", "c"]
    next_codes = ["code1", "code3"]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 11.7μs -> 10.5μs (11.8% faster)

def test_cell_code_changed():
    # One cell code is changed, should assign best matching id by similarity
    prev_ids = ["a", "b"]
    prev_codes = ["code1", "code2"]
    next_ids = ["a", "b"]
    next_codes = ["code1", "code2_modified"]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 27.6μs -> 26.4μs (4.42% faster)

def test_duplicate_codes():
    # Duplicate codes in both prev and next
    prev_ids = ["a", "b", "c"]
    prev_codes = ["code1", "code1", "code2"]
    next_ids = ["a", "b", "c"]
    next_codes = ["code1", "code1", "code2"]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 13.7μs -> 11.9μs (15.1% faster)

def test_duplicate_codes_with_permutation():
    # Duplicate codes, but order is permuted
    prev_ids = ["a", "b", "c"]
    prev_codes = ["code1", "code1", "code2"]
    next_ids = ["c", "a", "b"]
    next_codes = ["code2", "code1", "code1"]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 13.6μs -> 11.7μs (16.6% faster)

# ------------------------
# Edge Test Cases
# ------------------------

def test_empty_lists():
    # Both prev and next are empty
    prev_ids = []
    prev_codes = []
    next_ids = []
    next_codes = []
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 4.54μs -> 4.65μs (2.35% slower)

def test_all_new_cells():
    # All next cells are new, none match previous
    prev_ids = ["a", "b"]
    prev_codes = ["code1", "code2"]
    next_ids = ["c", "d"]
    next_codes = ["code3", "code4"]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 35.8μs -> 32.6μs (9.61% faster)

def test_all_cells_deleted():
    # All cells deleted, next is empty
    prev_ids = ["a", "b", "c"]
    prev_codes = ["code1", "code2", "code3"]
    next_ids = []
    next_codes = []
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 5.93μs -> 5.90μs (0.424% faster)

def test_cell_code_changed_completely():
    # All next codes are completely different from prev
    prev_ids = ["a", "b"]
    prev_codes = ["foo", "bar"]
    next_ids = ["a", "b"]
    next_codes = ["baz", "qux"]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 37.6μs -> 34.0μs (10.8% faster)

def test_duplicate_next_codes_more_than_prev():
    # More duplicates in next than prev
    prev_ids = ["a", "b"]
    prev_codes = ["code1", "code2"]
    next_ids = ["a", "b", "c"]
    next_codes = ["code1", "code1", "code2"]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 13.4μs -> 11.8μs (13.5% faster)

def test_duplicate_prev_codes_more_than_next():
    # More duplicates in prev than next
    prev_ids = ["a", "b", "c"]
    prev_codes = ["code1", "code1", "code2"]
    next_ids = ["a", "b"]
    next_codes = ["code1", "code2"]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 12.2μs -> 10.5μs (16.3% faster)

def test_non_string_cell_ids():
    # Non-string cell ids (e.g., integers)
    prev_ids = [1, 2]
    prev_codes = ["foo", "bar"]
    next_ids = [1, 2]
    next_codes = ["foo", "bar"]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 11.2μs -> 9.84μs (13.8% faster)

def test_non_ascii_codes():
    # Non-ASCII code strings
    prev_ids = ["a", "b"]
    prev_codes = ["привет", "你好"]
    next_ids = ["a", "b"]
    next_codes = ["привет", "你好"]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 10.9μs -> 9.61μs (13.4% faster)

def test_long_codes():
    # Very long code strings
    code1 = "a" * 100
    code2 = "b" * 100
    prev_ids = ["a", "b"]
    prev_codes = [code1, code2]
    next_ids = ["a", "b"]
    next_codes = [code1, code2]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 10.6μs -> 9.56μs (11.3% faster)

def test_ids_are_not_unique():
    # IDs are not unique (should not happen, but test for robustness)
    prev_ids = ["a", "a"]
    prev_codes = ["foo", "bar"]
    next_ids = ["a", "a"]
    next_codes = ["foo", "bar"]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 11.2μs -> 9.27μs (20.5% faster)

def test_codes_are_empty_strings():
    # Codes are empty strings
    prev_ids = ["a", "b"]
    prev_codes = ["", ""]
    next_ids = ["a", "b"]
    next_codes = ["", ""]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 11.1μs -> 9.67μs (15.3% faster)

def test_mismatched_lengths_raises():
    # Mismatched lengths should raise an assertion error
    prev_ids = ["a", "b"]
    prev_codes = ["foo"]
    next_ids = ["a", "b"]
    next_codes = ["foo", "bar"]
    with pytest.raises(AssertionError):
        _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes) # 1.24μs -> 1.23μs (1.06% faster)

# ------------------------
# Large Scale Test Cases
# ------------------------

def test_large_scale_exact_match():
    # Large number of cells, all codes and ids match
    n = 500
    prev_ids = [f"id_{i}" for i in range(n)]
    prev_codes = [f"code_{i}" for i in range(n)]
    next_ids = prev_ids.copy()
    next_codes = prev_codes.copy()
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 740μs -> 564μs (31.2% faster)

def test_large_scale_permutation():
    # Large number of cells, permuted order
    n = 500
    prev_ids = [f"id_{i}" for i in range(n)]
    prev_codes = [f"code_{i}" for i in range(n)]
    perm = list(range(n))
    random.shuffle(perm)
    next_ids = [prev_ids[i] for i in perm]
    next_codes = [prev_codes[i] for i in perm]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 756μs -> 571μs (32.5% faster)

def test_large_scale_new_cells_added():
    # Large number of cells, some new cells added
    n = 500
    prev_ids = [f"id_{i}" for i in range(n)]
    prev_codes = [f"code_{i}" for i in range(n)]
    next_ids = prev_ids + [f"id_{n + i}" for i in range(10)]
    next_codes = prev_codes + [f"new_code_{i}" for i in range(10)]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 748μs -> 558μs (34.1% faster)

def test_large_scale_cells_deleted():
    # Large number of cells, some deleted
    n = 500
    prev_ids = [f"id_{i}" for i in range(n)]
    prev_codes = [f"code_{i}" for i in range(n)]
    next_ids = prev_ids[:n-10]
    next_codes = prev_codes[:n-10]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 718μs -> 544μs (32.0% faster)

def test_large_scale_code_changes():
    # Large number of cells, all codes changed slightly
    n = 500
    prev_ids = [f"id_{i}" for i in range(n)]
    prev_codes = [f"code_{i}" for i in range(n)]
    next_ids = prev_ids.copy()
    next_codes = [f"code_{i}_changed" for i in range(n)]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 269ms -> 200ms (34.1% faster)

def test_large_scale_duplicates():
    # Large number of duplicate codes
    n = 250
    prev_ids = [f"id_{i}" for i in range(n)] + [f"id_{n + i}" for i in range(n)]
    prev_codes = ["dup_code"] * n + ["unique_code_" + str(i) for i in range(n)]
    next_ids = prev_ids.copy()
    next_codes = ["dup_code"] * n + ["unique_code_" + str(i) for i in range(n)]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 4.08ms -> 3.10ms (31.9% faster)

def test_large_scale_non_ascii():
    # Large number of non-ASCII codes
    n = 500
    prev_ids = [f"id_{i}" for i in range(n)]
    prev_codes = [chr(0x0400 + (i % 32)) * 10 for i in range(n)]  # Cyrillic chars
    next_ids = prev_ids.copy()
    next_codes = prev_codes.copy()
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 1.16ms -> 856μs (35.3% faster)

def test_large_scale_empty_codes():
    # Large number of empty string codes
    n = 500
    prev_ids = [f"id_{i}" for i in range(n)]
    prev_codes = [""] * n
    next_ids = prev_ids.copy()
    next_codes = [""] * n
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 14.8ms -> 11.4ms (30.2% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

from typing import List

# imports
import pytest  # used for our unit tests
from marimo._utils.cell_matching import _match_cell_ids_by_similarity

# function to test
# (see above for full implementation of _match_cell_ids_by_similarity)

# --------------------- UNIT TESTS ---------------------

# Basic Test Cases

def test_exact_match_single_cell():
    # One cell, codes and IDs match exactly
    prev_ids = ["A"]
    prev_codes = ["print('hello')"]
    next_ids = ["A"]
    next_codes = ["print('hello')"]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 9.14μs -> 8.21μs (11.3% faster)

def test_exact_match_multiple_cells():
    # Multiple cells, all codes and IDs match exactly
    prev_ids = ["A", "B", "C"]
    prev_codes = ["foo", "bar", "baz"]
    next_ids = ["A", "B", "C"]
    next_codes = ["foo", "bar", "baz"]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 13.0μs -> 11.5μs (13.5% faster)

def test_permutation_of_cells():
    # Same codes, but order changed; IDs should follow codes
    prev_ids = ["A", "B", "C"]
    prev_codes = ["foo", "bar", "baz"]
    next_ids = ["C", "A", "B"]
    next_codes = ["baz", "foo", "bar"]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 13.5μs -> 12.0μs (12.2% faster)
    # Should assign IDs based on code match, not order
    expected = ["C", "A", "B"]

def test_new_cell_added():
    # New cell added at end
    prev_ids = ["A", "B"]
    prev_codes = ["foo", "bar"]
    next_ids = ["A", "B", "C"]
    next_codes = ["foo", "bar", "baz"]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 12.1μs -> 11.0μs (9.40% faster)

def test_cell_deleted():
    # Cell deleted from middle
    prev_ids = ["A", "B", "C"]
    prev_codes = ["foo", "bar", "baz"]
    next_ids = ["A", "C"]
    next_codes = ["foo", "baz"]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 11.6μs -> 10.0μs (16.0% faster)

def test_modified_code_similarity():
    # Cell code modified slightly (should match most similar ID)
    prev_ids = ["A", "B"]
    prev_codes = ["foo", "bar"]
    next_ids = ["C", "D"]
    next_codes = ["foo", "baz"]  # 'baz' is more similar to 'bar'
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 28.3μs -> 26.5μs (7.10% faster)
    # Ensure code similarity is used for assignment
    foo_idx = next_codes.index("foo")
    baz_idx = next_codes.index("baz")

def test_duplicate_codes():
    # Duplicate codes in both prev and next
    prev_ids = ["A", "B"]
    prev_codes = ["foo", "foo"]
    next_ids = ["C", "D"]
    next_codes = ["foo", "foo"]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 11.4μs -> 9.90μs (14.9% faster)

# Edge Test Cases

def test_empty_inputs():
    # Both lists empty
    prev_ids = []
    prev_codes = []
    next_ids = []
    next_codes = []
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 4.59μs -> 4.66μs (1.55% slower)

def test_next_empty_prev_nonempty():
    # Next is empty, prev has cells
    prev_ids = ["A", "B"]
    prev_codes = ["foo", "bar"]
    next_ids = []
    next_codes = []
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 5.71μs -> 5.83μs (1.97% slower)

def test_prev_empty_next_nonempty():
    # Prev is empty, next has cells
    prev_ids = []
    prev_codes = []
    next_ids = ["A", "B"]
    next_codes = ["foo", "bar"]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 7.09μs -> 6.82μs (3.91% faster)

def test_all_codes_changed():
    # All codes changed, no similarity
    prev_ids = ["A", "B", "C"]
    prev_codes = ["foo", "bar", "baz"]
    next_ids = ["D", "E", "F"]
    next_codes = ["qux", "quux", "corge"]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 44.1μs -> 39.4μs (11.9% faster)

def test_ids_overlap_but_codes_different():
    # IDs overlap but codes are different
    prev_ids = ["A", "B"]
    prev_codes = ["foo", "bar"]
    next_ids = ["A", "C"]
    next_codes = ["baz", "qux"]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 35.9μs -> 33.7μs (6.42% faster)

def test_duplicate_codes_with_different_ids():
    # Duplicate codes, but IDs are not repeated
    prev_ids = ["A", "B", "C"]
    prev_codes = ["foo", "foo", "bar"]
    next_ids = ["D", "E", "F"]
    next_codes = ["foo", "foo", "bar"]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 13.5μs -> 11.8μs (14.9% faster)

def test_non_string_ids():
    # IDs are integers
    prev_ids = [1, 2]
    prev_codes = ["foo", "bar"]
    next_ids = [3, 4]
    next_codes = ["foo", "baz"]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 27.1μs -> 25.3μs (7.13% faster)

def test_codes_with_empty_strings():
    # Codes include empty strings
    prev_ids = ["A", "B"]
    prev_codes = ["", "bar"]
    next_ids = ["C", "D"]
    next_codes = ["", "baz"]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 25.1μs -> 23.4μs (7.25% faster)

def test_long_codes_similarity():
    # Long codes, only small difference
    prev_ids = ["A", "B"]
    prev_codes = ["print('hello world')", "print('goodbye world')"]
    next_ids = ["C", "D"]
    next_codes = ["print('hello world!')", "print('goodbye world!')"]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 35.6μs -> 33.8μs (5.16% faster)

def test_same_code_multiple_times():
    # Same code repeated multiple times
    prev_ids = ["A", "B", "C"]
    prev_codes = ["foo", "foo", "foo"]
    next_ids = ["D", "E", "F"]
    next_codes = ["foo", "foo", "foo"]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 13.4μs -> 11.3μs (19.1% faster)

def test_non_ascii_codes():
    # Codes with non-ascii characters
    prev_ids = ["A"]
    prev_codes = ["π = 3.14"]
    next_ids = ["B"]
    next_codes = ["π = 3.1415"]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 23.6μs -> 22.7μs (3.76% faster)

def test_ids_are_tuples():
    # IDs are tuples
    prev_ids = [(1, "A"), (2, "B")]
    prev_codes = ["foo", "bar"]
    next_ids = [(3, "C"), (4, "D")]
    next_codes = ["foo", "baz"]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 26.2μs -> 25.3μs (3.68% faster)

# Large Scale Test Cases

def test_large_number_of_cells_exact_match():
    # 500 cells, all codes and IDs match
    n = 500
    prev_ids = [f"id_{i}" for i in range(n)]
    prev_codes = [f"code_{i}" for i in range(n)]
    next_ids = prev_ids[:]
    next_codes = prev_codes[:]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 746μs -> 569μs (31.1% faster)

def test_large_permutation():
    # 500 cells, codes permuted
    n = 500
    prev_ids = [f"id_{i}" for i in range(n)]
    prev_codes = [f"code_{i}" for i in range(n)]
    permutation = list(reversed(range(n)))
    next_ids = [f"id_{i}" for i in permutation]
    next_codes = [f"code_{i}" for i in permutation]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 769μs -> 587μs (31.0% faster)

def test_large_number_of_cells_with_additions_and_deletions():
    # 500 prev, 500 next, with 50 new and 50 deleted codes
    n = 500
    prev_ids = [f"id_{i}" for i in range(n)]
    prev_codes = [f"code_{i}" for i in range(n)]
    # Remove 50 codes, add 50 new codes
    next_ids = [f"id_{i}" for i in range(n - 50)] + [f"id_new_{i}" for i in range(50)]
    next_codes = [f"code_{i}" for i in range(n - 50)] + [f"code_new_{i}" for i in range(50)]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 3.48ms -> 2.66ms (31.0% faster)

def test_large_number_of_duplicate_codes():
    # 250 duplicate codes
    n = 250
    prev_ids = [f"id_{i}" for i in range(n)]
    prev_codes = ["foo"] * n
    next_ids = [f"id_new_{i}" for i in range(n)]
    next_codes = ["foo"] * n
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 3.72ms -> 2.82ms (32.0% faster)

def test_large_number_of_cells_all_new():
    # 500 new cells, none in prev
    n = 500
    prev_ids = []
    prev_codes = []
    next_ids = [f"id_{i}" for i in range(n)]
    next_codes = [f"code_{i}" for i in range(n)]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 181μs -> 178μs (1.20% faster)

def test_large_number_of_cells_all_deleted():
    # 500 prev cells, next is empty
    n = 500
    prev_ids = [f"id_{i}" for i in range(n)]
    prev_codes = [f"code_{i}" for i in range(n)]
    next_ids = []
    next_codes = []
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 92.0μs -> 92.2μs (0.220% slower)

def test_large_number_of_cells_with_similar_codes():
    # 500 cells, codes slightly modified
    n = 500
    prev_ids = [f"id_{i}" for i in range(n)]
    prev_codes = [f"code_{i}" for i in range(n)]
    next_ids = [f"id_new_{i}" for i in range(n)]
    # Next codes are prev_codes with an extra character
    next_codes = [f"code_{i}!" for i in range(n)]
    codeflash_output = _match_cell_ids_by_similarity(prev_ids, prev_codes, next_ids, next_codes); result = codeflash_output # 267ms -> 201ms (33.0% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

To edit these changes git checkout codeflash/optimize-_match_cell_ids_by_similarity-mhwr4v88 and push.