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3621 lines (3057 loc) · 112 KB
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from __future__ import annotations
import os
import re
from collections import defaultdict
from pathlib import Path
import libcst as cst
from codeflash.discovery.functions_to_optimize import FunctionToOptimize
from codeflash.languages.python.function_optimizer import PythonFunctionOptimizer
from codeflash.languages.python.static_analysis.code_extractor import (
delete___future___aliased_imports,
find_preexisting_objects,
)
from codeflash.languages.python.static_analysis.code_replacer import (
AddRequestArgument,
AutouseFixtureModifier,
PytestMarkAdder,
is_zero_diff,
replace_functions_and_add_imports,
replace_functions_in_file,
)
from codeflash.models.models import CodeOptimizationContext, CodeStringsMarkdown, FunctionParent, FunctionSource
from codeflash.verification.verification_utils import TestConfig
os.environ["CODEFLASH_API_KEY"] = "cf-test-key"
class Args:
disable_imports_sorting = True
formatter_cmds = ["disabled"]
def test_code_replacement_global_statements():
project_root = Path(__file__).parent.parent.resolve()
code_path = (project_root / "code_to_optimize/bubble_sort_optimized.py").resolve()
optimized_code = f"""```python:{code_path.relative_to(project_root)}
import numpy as np
inconsequential_var = '123'
def sorter(arr):
return arr.sort()
```
"""
original_code_str = (Path(__file__).parent.resolve() / "../code_to_optimize/bubble_sort.py").read_text(
encoding="utf-8"
)
code_path.write_text(original_code_str, encoding="utf-8")
tests_root = Path("/Users/codeflash/Downloads/codeflash-dev/codeflash/code_to_optimize/tests/pytest/")
project_root_path = (Path(__file__).parent / "..").resolve()
func = FunctionToOptimize(function_name="sorter", parents=[], file_path=code_path)
test_config = TestConfig(
tests_root=tests_root,
tests_project_rootdir=project_root_path,
project_root_path=project_root_path,
test_framework="pytest",
pytest_cmd="pytest",
)
func_optimizer = PythonFunctionOptimizer(function_to_optimize=func, test_cfg=test_config)
code_context: CodeOptimizationContext = func_optimizer.get_code_optimization_context().unwrap()
original_helper_code: dict[Path, str] = {}
helper_function_paths = {hf.file_path for hf in code_context.helper_functions}
for helper_function_path in helper_function_paths:
with helper_function_path.open(encoding="utf8") as f:
helper_code = f.read()
original_helper_code[helper_function_path] = helper_code
func_optimizer.args = Args()
func_optimizer.replace_function_and_helpers_with_optimized_code(
code_context=code_context,
optimized_code=CodeStringsMarkdown.parse_markdown_code(optimized_code),
original_helper_code=original_helper_code,
)
final_output = code_path.read_text(encoding="utf-8")
assert "inconsequential_var = '123'" in final_output
code_path.unlink(missing_ok=True)
def test_test_libcst_code_replacement() -> None:
optim_code = """import libcst as cst
from typing import Optional
def totally_new_function(value):
return value
class NewClass:
def __init__(self, name):
self.name = name
def new_function(self, value):
return self.name
def new_function2(value):
return value
"""
original_code = """class NewClass:
def __init__(self, name):
self.name = name
@staticmethod
def new_function(self, value):
return "I am still old"
print("Hello world")
"""
expected = """class NewClass:
def __init__(self, name):
self.name = name
def new_function(self, value):
return self.name
def new_function2(value):
return value
def totally_new_function(value):
return value
print("Hello world")
"""
function_name: str = "NewClass.new_function"
preexisting_objects: set[tuple[str, tuple[FunctionParent, ...]]] = find_preexisting_objects(original_code)
print(f"Preexisting objects: {preexisting_objects}")
new_code: str = replace_functions_and_add_imports(
source_code=original_code,
function_names=[function_name],
optimized_code=optim_code,
module_abspath=Path(__file__).resolve(),
preexisting_objects=preexisting_objects,
project_root_path=Path(__file__).resolve().parent.resolve(),
)
assert new_code == expected
def test_test_libcst_code_replacement2() -> None:
optim_code = """import libcst as cst
from typing import Optional
def totally_new_function(value):
return value
def other_function(st):
return(st * 2)
class NewClass:
def __init__(self, name):
self.name = name
def new_function(self, value):
return other_function(self.name)
def new_function2(value):
return value
"""
original_code = """from OtherModule import other_function
class NewClass:
def __init__(self, name):
self.name = name
def new_function(self, value):
return other_function("I am still old")
print("Hello world")
"""
expected = """from OtherModule import other_function
class NewClass:
def __init__(self, name):
self.name = name
def new_function(self, value):
return other_function(self.name)
def new_function2(value):
return value
def totally_new_function(value):
return value
def other_function(st):
return(st * 2)
print("Hello world")
"""
function_name: str = "NewClass.new_function"
preexisting_objects: set[tuple[str, tuple[FunctionParent, ...]]] = find_preexisting_objects(original_code)
new_code: str = replace_functions_and_add_imports(
source_code=original_code,
function_names=[function_name],
optimized_code=optim_code,
module_abspath=Path(__file__).resolve(),
preexisting_objects=preexisting_objects,
project_root_path=Path(__file__).resolve().parent.resolve(),
)
assert new_code == expected
def test_test_libcst_code_replacement3() -> None:
optim_code = """import libcst as cst
from typing import Optional
def totally_new_function(value):
return value
def other_function(st):
return(st * 2)
class NewClass:
def __init__(self, name):
self.name = name
def new_function(self, value: cst.Name):
return other_function(self.name)
def new_function2(value):
return value
"""
original_code = """import libcst as cst
from typing import Mandatory
print("Au revoir")
def yet_another_function(values):
return len(values)
def other_function(st):
return(st + st)
print("Salut monde")
"""
expected = """import libcst as cst
from typing import Mandatory
class NewClass:
def __init__(self, name):
self.name = name
def new_function(self, value: cst.Name):
return other_function(self.name)
def new_function2(value):
return value
print("Au revoir")
def yet_another_function(values):
return len(values)
def totally_new_function(value):
return value
def other_function(st):
return(st * 2)
print("Salut monde")
"""
function_names: list[str] = ["other_function"]
preexisting_objects: set[tuple[str, tuple[FunctionParent, ...]]] = find_preexisting_objects(original_code)
new_code: str = replace_functions_and_add_imports(
source_code=original_code,
function_names=function_names,
optimized_code=optim_code,
module_abspath=Path(__file__).resolve(),
preexisting_objects=preexisting_objects,
project_root_path=Path(__file__).resolve().parent.resolve(),
)
assert new_code == expected
def test_test_libcst_code_replacement4() -> None:
optim_code = """import libcst as cst
from typing import Optional
def totally_new_function(value):
return value
def yet_another_function(values: Optional[str]):
return len(values) + 2
def other_function(st):
return(st * 2)
class NewClass:
def __init__(self, name):
self.name = name
def new_function(self, value):
return other_function(self.name)
def new_function2(value):
return value
"""
original_code = """import libcst as cst
from typing import Mandatory
print("Au revoir")
def yet_another_function(values):
return len(values)
def other_function(st):
return(st + st)
print("Salut monde")
"""
expected = """from typing import Mandatory
class NewClass:
def __init__(self, name):
self.name = name
def new_function(self, value):
return other_function(self.name)
def new_function2(value):
return value
print("Au revoir")
def yet_another_function(values):
return len(values) + 2
def totally_new_function(value):
return value
def other_function(st):
return(st * 2)
print("Salut monde")
"""
function_names: list[str] = ["yet_another_function", "other_function"]
preexisting_objects: set[tuple[str, tuple[FunctionParent, ...]]] = find_preexisting_objects(original_code)
new_code: str = replace_functions_and_add_imports(
source_code=original_code,
function_names=function_names,
optimized_code=optim_code,
module_abspath=Path(__file__).resolve(),
preexisting_objects=preexisting_objects,
project_root_path=Path(__file__).resolve().parent.resolve(),
)
assert new_code == expected
def test_test_libcst_code_replacement5() -> None:
optim_code = """@lru_cache(17)
def sorter_deps(arr: list[int]) -> list[int]:
supersort(badsort(arr))
return arr
def badsort(ploc):
donothing(ploc)
def supersort(doink):
for i in range(len(doink)):
fix(doink, i)
"""
original_code = """from code_to_optimize.bubble_sort_dep1_helper import dep1_comparer
from code_to_optimize.bubble_sort_dep2_swap import dep2_swap
def sorter_deps(arr):
for i in range(len(arr)):
for j in range(len(arr) - 1):
if dep1_comparer(arr, j):
dep2_swap(arr, j)
return arr
"""
expected = """from code_to_optimize.bubble_sort_dep1_helper import dep1_comparer
from code_to_optimize.bubble_sort_dep2_swap import dep2_swap
@lru_cache(17)
def sorter_deps(arr):
supersort(badsort(arr))
return arr
def badsort(ploc):
donothing(ploc)
def supersort(doink):
for i in range(len(doink)):
fix(doink, i)
"""
function_names: list[str] = ["sorter_deps"]
preexisting_objects: set[tuple[str, tuple[FunctionParent, ...]]] = find_preexisting_objects(original_code)
new_code: str = replace_functions_and_add_imports(
source_code=original_code,
function_names=function_names,
optimized_code=optim_code,
module_abspath=Path(__file__).resolve(),
preexisting_objects=preexisting_objects,
project_root_path=Path(__file__).resolve().parent.resolve(),
)
assert new_code == expected
def test_test_libcst_code_replacement6() -> None:
optim_code = """import libcst as cst
from typing import Optional
def other_function(st):
return(st * blob(st))
def blob(st):
return(st * 2)
"""
original_code_main = """import libcst as cst
from typing import Mandatory
from helper import blob
print("Au revoir")
def yet_another_function(values):
return len(values)
def other_function(st):
return(st + blob(st))
print("Salut monde")
"""
original_code_helper = """import numpy as np
print("Cool")
def blob(values):
return len(values)
def blab(st):
return(st + st)
print("Not cool")
"""
expected_main = """from typing import Mandatory
from helper import blob
print("Au revoir")
def yet_another_function(values):
return len(values)
def other_function(st):
return(st * blob(st))
print("Salut monde")
"""
expected_helper = """import numpy as np
print("Cool")
def blob(values):
return(st * 2)
def blab(st):
return(st + st)
print("Not cool")
"""
preexisting_objects = find_preexisting_objects(original_code_main) | find_preexisting_objects(original_code_helper)
new_main_code: str = replace_functions_and_add_imports(
source_code=original_code_main,
function_names=["other_function"],
optimized_code=optim_code,
module_abspath=Path(__file__).resolve(),
preexisting_objects=preexisting_objects,
project_root_path=Path(__file__).resolve().parent.resolve(),
)
assert new_main_code == expected_main
new_helper_code: str = replace_functions_and_add_imports(
source_code=original_code_helper,
function_names=["blob"],
optimized_code=optim_code,
module_abspath=Path(__file__).resolve(),
preexisting_objects=preexisting_objects,
project_root_path=Path(__file__).resolve().parent.resolve(),
)
assert new_helper_code == expected_helper
def test_test_libcst_code_replacement7() -> None:
optim_code = """@register_deserializable
class CacheSimilarityEvalConfig(BaseConfig):
def __init__(
self,
strategy: Optional[str] = "distance",
max_distance: Optional[float] = 1.0,
positive: Optional[bool] = False,
):
self.strategy = strategy
self.max_distance = max_distance
self.positive = positive
@staticmethod
def from_config(config: Optional[dict[str, Any]]):
if config is None:
return CacheSimilarityEvalConfig()
strategy = config.get("strategy", "distance")
max_distance = config.get("max_distance", 1.0)
positive = config.get("positive", False)
return CacheSimilarityEvalConfig(strategy, max_distance, positive)
"""
original_code = """from typing import Any, Optional
from embedchain.config.base_config import BaseConfig
from embedchain.helpers.json_serializable import register_deserializable
@register_deserializable
class CacheSimilarityEvalConfig(BaseConfig):
def __init__(
self,
strategy: Optional[str] = "distance",
max_distance: Optional[float] = 1.0,
positive: Optional[bool] = False,
):
self.strategy = strategy
self.max_distance = max_distance
self.positive = positive
@staticmethod
def from_config(config: Optional[dict[str, Any]]):
if config is None:
return CacheSimilarityEvalConfig()
else:
return CacheSimilarityEvalConfig(
strategy=config.get("strategy", "distance"),
max_distance=config.get("max_distance", 1.0),
positive=config.get("positive", False),
)
@register_deserializable
class CacheInitConfig(BaseConfig):
def __init__(
self,
similarity_threshold: Optional[float] = 0.8,
auto_flush: Optional[int] = 20,
):
if similarity_threshold < 0 or similarity_threshold > 1:
raise ValueError(f"similarity_threshold {similarity_threshold} should be between 0 and 1")
self.similarity_threshold = similarity_threshold
self.auto_flush = auto_flush
@staticmethod
def from_config(config: Optional[dict[str, Any]]):
if config is None:
return CacheInitConfig()
else:
return CacheInitConfig(
similarity_threshold=config.get("similarity_threshold", 0.8),
auto_flush=config.get("auto_flush", 20),
)
@register_deserializable
class CacheConfig(BaseConfig):
def __init__(
self,
similarity_eval_config: Optional[CacheSimilarityEvalConfig] = CacheSimilarityEvalConfig(),
init_config: Optional[CacheInitConfig] = CacheInitConfig(),
):
self.similarity_eval_config = similarity_eval_config
self.init_config = init_config
@staticmethod
def from_config(config: Optional[dict[str, Any]]):
if config is None:
return CacheConfig()
else:
return CacheConfig(
similarity_eval_config=CacheSimilarityEvalConfig.from_config(config.get("similarity_evaluation", {})),
init_config=CacheInitConfig.from_config(config.get("init_config", {})),
)
"""
expected = """from typing import Any, Optional
from embedchain.config.base_config import BaseConfig
from embedchain.helpers.json_serializable import register_deserializable
@register_deserializable
class CacheSimilarityEvalConfig(BaseConfig):
def __init__(
self,
strategy: Optional[str] = "distance",
max_distance: Optional[float] = 1.0,
positive: Optional[bool] = False,
):
self.strategy = strategy
self.max_distance = max_distance
self.positive = positive
@staticmethod
def from_config(config: Optional[dict[str, Any]]):
if config is None:
return CacheSimilarityEvalConfig()
strategy = config.get("strategy", "distance")
max_distance = config.get("max_distance", 1.0)
positive = config.get("positive", False)
return CacheSimilarityEvalConfig(strategy, max_distance, positive)
@register_deserializable
class CacheInitConfig(BaseConfig):
def __init__(
self,
similarity_threshold: Optional[float] = 0.8,
auto_flush: Optional[int] = 20,
):
if similarity_threshold < 0 or similarity_threshold > 1:
raise ValueError(f"similarity_threshold {similarity_threshold} should be between 0 and 1")
self.similarity_threshold = similarity_threshold
self.auto_flush = auto_flush
@staticmethod
def from_config(config: Optional[dict[str, Any]]):
if config is None:
return CacheInitConfig()
else:
return CacheInitConfig(
similarity_threshold=config.get("similarity_threshold", 0.8),
auto_flush=config.get("auto_flush", 20),
)
@register_deserializable
class CacheConfig(BaseConfig):
def __init__(
self,
similarity_eval_config: Optional[CacheSimilarityEvalConfig] = CacheSimilarityEvalConfig(),
init_config: Optional[CacheInitConfig] = CacheInitConfig(),
):
self.similarity_eval_config = similarity_eval_config
self.init_config = init_config
@staticmethod
def from_config(config: Optional[dict[str, Any]]):
if config is None:
return CacheConfig()
else:
return CacheConfig(
similarity_eval_config=CacheSimilarityEvalConfig.from_config(config.get("similarity_evaluation", {})),
init_config=CacheInitConfig.from_config(config.get("init_config", {})),
)
"""
function_names: list[str] = ["CacheSimilarityEvalConfig.from_config"]
preexisting_objects: set[tuple[str, tuple[FunctionParent, ...]]] = find_preexisting_objects(original_code)
new_code: str = replace_functions_and_add_imports(
source_code=original_code,
function_names=function_names,
optimized_code=optim_code,
module_abspath=Path(__file__).resolve(),
preexisting_objects=preexisting_objects,
project_root_path=Path(__file__).resolve().parent.resolve(),
)
assert new_code == expected
def test_test_libcst_code_replacement8() -> None:
optim_code = '''class _EmbeddingDistanceChainMixin(Chain):
@staticmethod
def _hamming_distance(a: np.ndarray, b: np.ndarray) -> np.floating:
"""Compute the Hamming distance between two vectors.
Args:
a (np.ndarray): The first vector.
b (np.ndarray): The second vector.
Returns:
np.floating: The Hamming distance.
"""
return np.sum(a != b) / a.size
'''
original_code = '''class _EmbeddingDistanceChainMixin(Chain):
class Config:
"""Permit embeddings to go unvalidated."""
arbitrary_types_allowed: bool = True
def _hamming_distance(a: np.ndarray, b: np.ndarray) -> np.floating:
"""Compute the Hamming distance between two vectors.
Args:
a (np.ndarray): The first vector.
b (np.ndarray): The second vector.
Returns:
np.floating: The Hamming distance.
"""
return np.mean(a != b)
'''
expected = '''class _EmbeddingDistanceChainMixin(Chain):
class Config:
"""Permit embeddings to go unvalidated."""
arbitrary_types_allowed: bool = True
@staticmethod
def _hamming_distance(a: np.ndarray, b: np.ndarray) -> np.floating:
"""Compute the Hamming distance between two vectors.
Args:
a (np.ndarray): The first vector.
b (np.ndarray): The second vector.
Returns:
np.floating: The Hamming distance.
"""
return np.sum(a != b) / a.size
'''
function_names: list[str] = ["_EmbeddingDistanceChainMixin._hamming_distance"]
preexisting_objects: set[tuple[str, tuple[FunctionParent, ...]]] = find_preexisting_objects(original_code)
new_code: str = replace_functions_and_add_imports(
source_code=original_code,
function_names=function_names,
optimized_code=optim_code,
module_abspath=Path(__file__).resolve(),
preexisting_objects=preexisting_objects,
project_root_path=Path(__file__).resolve().parent.resolve(),
)
assert new_code == expected
def test_test_libcst_code_replacement9() -> None:
optim_code = """import libcst as cst
from typing import Optional
def totally_new_function(value: Optional[str]):
return value
class NewClass:
def __init__(self, name):
self.name = str(name)
def __call__(self, value):
return self.name
def new_function2(value):
return cst.ensure_type(value, str)
"""
original_code = """class NewClass:
def __init__(self, name):
self.name = name
def __call__(self, value):
return "I am still old"
print("Hello world")
"""
expected = """import libcst as cst
from typing import Optional
class NewClass:
def __init__(self, name):
self.name = str(name)
def __call__(self, value):
return "I am still old"
def new_function2(value):
return cst.ensure_type(value, str)
def totally_new_function(value: Optional[str]):
return value
print("Hello world")
"""
function_name: str = "NewClass.__init__"
preexisting_objects: set[tuple[str, tuple[FunctionParent, ...]]] = find_preexisting_objects(original_code)
new_code: str = replace_functions_and_add_imports(
source_code=original_code,
function_names=[function_name],
optimized_code=optim_code,
module_abspath=Path(__file__).resolve(),
preexisting_objects=preexisting_objects,
project_root_path=Path(__file__).resolve().parent.resolve(),
)
assert new_code == expected
class HelperClass:
def __init__(self, name):
self.name = name
def innocent_bystander(self):
pass
def helper_method(self):
return self.name
class MainClass:
def __init__(self, name):
self.name = name
def main_method(self):
return HelperClass(self.name).helper_method()
def test_code_replacement10() -> None:
get_code_output = """# file: test_code_replacement.py
from __future__ import annotations
class HelperClass:
def __init__(self, name):
self.name = name
def helper_method(self):
return self.name
class MainClass:
def __init__(self, name):
self.name = name
def main_method(self):
return HelperClass(self.name).helper_method()
"""
file_path = Path(__file__).resolve()
func_top_optimize = FunctionToOptimize(
function_name="main_method", file_path=file_path, parents=[FunctionParent("MainClass", "ClassDef")]
)
test_config = TestConfig(
tests_root=file_path.parent,
tests_project_rootdir=file_path.parent,
project_root_path=file_path.parent,
test_framework="pytest",
pytest_cmd="pytest",
)
func_optimizer = PythonFunctionOptimizer(function_to_optimize=func_top_optimize, test_cfg=test_config)
code_context = func_optimizer.get_code_optimization_context().unwrap()
assert code_context.testgen_context.flat.rstrip() == get_code_output.rstrip()
def test_code_replacement11() -> None:
optim_code = '''class Fu():
def foo(self) -> dict[str, str]:
payload: dict[str, str] = {"bar": self.bar(), "real_bar": str(self.real_bar() + 1)}
return payload
def real_bar(self) -> int:
"""No abstract nonsense"""
pass
'''
original_code = '''class Fu():
def foo(self) -> dict[str, str]:
payload: dict[str, str] = {"bar": self.bar(), "real_bar": str(self.real_bar())}
return payload
def real_bar(self) -> int:
"""No abstract nonsense"""
return 0
'''
expected_code = '''class Fu():
def foo(self) -> dict[str, str]:
payload: dict[str, str] = {"bar": self.bar(), "real_bar": str(self.real_bar() + 1)}
return payload
def real_bar(self) -> int:
"""No abstract nonsense"""
return 0
'''
function_name: str = "Fu.foo"
parents = (FunctionParent("Fu", "ClassDef"),)
preexisting_objects: set[tuple[str, tuple[FunctionParent, ...]]] = {("foo", parents), ("real_bar", parents)}
new_code: str = replace_functions_in_file(
source_code=original_code,
original_function_names=[function_name],
optimized_code=optim_code,
preexisting_objects=preexisting_objects,
)
assert new_code == expected_code
def test_code_replacement12() -> None:
optim_code = '''class Fu():
def foo(self) -> dict[str, str]:
payload: dict[str, str] = {"bar": self.bar(), "real_bar": str(self.real_bar() + 1)}
return payload
def real_bar(self) -> int:
"""No abstract nonsense"""
pass
'''
original_code = '''class Fu():
def foo(self) -> dict[str, str]:
payload: dict[str, str] = {"bar": self.bar(), "real_bar": str(self.real_bar())}
return payload
def real_bar(self) -> int:
"""No abstract nonsense"""
return 0
'''
expected_code = '''class Fu():
def foo(self) -> dict[str, str]:
payload: dict[str, str] = {"bar": self.bar(), "real_bar": str(self.real_bar())}
return payload
def real_bar(self) -> int:
"""No abstract nonsense"""
pass
'''
preexisting_objects: set[tuple[str, tuple[FunctionParent, ...]]] = []
new_code: str = replace_functions_in_file(
source_code=original_code,
original_function_names=["Fu.real_bar"],
optimized_code=optim_code,
preexisting_objects=preexisting_objects,
)
assert new_code == expected_code
def test_test_libcst_code_replacement13() -> None:
# Test if the dunder method is not modified
optim_code = """class NewClass:
def __init__(self, name):
self.name = name
self.new_attribute = "Sorry i modified a dunder method"
def new_function(self, value):
return other_function(self.name)
def new_function2(value):
return value
def __call__(self, value):
return self.new_attribute
"""
original_code = """class NewClass:
def __init__(self, name):
self.name = name
self.new_attribute = "Sorry i modified a dunder method"
def new_function(self, value):
return other_function(self.name)
def new_function2(value):
return value
def __call__(self, value):
return self.name
"""
function_names: list[str] = ["yet_another_function", "other_function"]
preexisting_objects: set[tuple[str, tuple[FunctionParent, ...]]] = []
new_code: str = replace_functions_and_add_imports(
source_code=original_code,
function_names=function_names,
optimized_code=optim_code,
module_abspath=Path(__file__).resolve(),
preexisting_objects=preexisting_objects,
project_root_path=Path(__file__).resolve().parent.resolve(),
)
assert new_code == original_code
def test_different_class_code_replacement():
original_code = """from __future__ import annotations
import sys
from codeflash.verification.comparator import comparator
from enum import Enum
from pydantic import BaseModel
from typing import Iterator
class TestType(Enum):
EXISTING_UNIT_TEST = 1
INSPIRED_REGRESSION = 2
GENERATED_REGRESSION = 3
REPLAY_TEST = 4
def to_name(self) -> str:
names = {
TestType.EXISTING_UNIT_TEST: "⚙️ Existing Unit Tests",
TestType.INSPIRED_REGRESSION: "🎨 Inspired Regression Tests",
TestType.GENERATED_REGRESSION: "🌀 Generated Regression Tests",
TestType.REPLAY_TEST: "⏪ Replay Tests",
}
return names[self]
class TestResults(BaseModel):
def __iter__(self) -> Iterator[FunctionTestInvocation]:
return iter(self.test_results)
def __len__(self) -> int:
return len(self.test_results)
def __getitem__(self, index: int) -> FunctionTestInvocation:
return self.test_results[index]
def __setitem__(self, index: int, value: FunctionTestInvocation) -> None:
self.test_results[index] = value
def __delitem__(self, index: int) -> None:
del self.test_results[index]
def __contains__(self, value: FunctionTestInvocation) -> bool:
return value in self.test_results
def __bool__(self) -> bool:
You can’t perform that action at this time.