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codeflash/tests/test_code_context_extractor.py
Kevin Turcios 9f929c2151 fix: handle annotated assignments in GlobalAssignmentCollector 
GlobalAssignmentCollector only handled cst.Assign but not cst.AnnAssign
(annotated assignments like `X: int = 1`). When the LLM generated
optimizations with annotated module-level variables, these weren't
copied to the target file, causing NameError at runtime.

- Add visit_AnnAssign to GlobalAssignmentCollector
- Add leave_AnnAssign to GlobalAssignmentTransformer
- Update type hints to include cst.AnnAssign
- Add test for annotated assignment handling
2026-01-23 18:46:49 -05:00

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from __future__ import annotations
import sys
import tempfile
from argparse import Namespace
from collections import defaultdict
from pathlib import Path
import pytest
from codeflash.code_utils.code_extractor import GlobalAssignmentCollector, add_global_assignments
from codeflash.code_utils.code_replacer import replace_functions_and_add_imports
from codeflash.context.code_context_extractor import (
collect_names_from_annotation,
extract_imports_for_class,
get_code_optimization_context,
get_imported_class_definitions,
)
from codeflash.discovery.functions_to_optimize import FunctionToOptimize
from codeflash.models.models import CodeString, CodeStringsMarkdown, FunctionParent
from codeflash.optimization.optimizer import Optimizer
class HelperClass:
def __init__(self, name):
self.name = name
def innocent_bystander(self):
pass
def helper_method(self):
return self.name
class NestedClass:
def __init__(self, name):
self.name = name
def nested_method(self):
return self.name
def main_method():
return "hello"
class MainClass:
def __init__(self, name):
self.name = name
def main_method(self):
self.name = HelperClass.NestedClass("test").nested_method()
return HelperClass(self.name).helper_method()
class Graph:
def __init__(self, vertices):
self.graph = defaultdict(list)
self.V = vertices # No. of vertices
def addEdge(self, u, v):
self.graph[u].append(v)
def topologicalSortUtil(self, v, visited, stack):
visited[v] = True
for i in self.graph[v]:
if visited[i] == False:
self.topologicalSortUtil(i, visited, stack)
stack.insert(0, v)
def topologicalSort(self):
visited = [False] * self.V
stack = []
for i in range(self.V):
if visited[i] == False:
self.topologicalSortUtil(i, visited, stack)
# Print contents of stack
return stack
def test_code_replacement10() -> None:
file_path = Path(__file__).resolve()
func_top_optimize = FunctionToOptimize(
function_name="main_method", file_path=file_path, parents=[FunctionParent("MainClass", "ClassDef")]
)
code_ctx = get_code_optimization_context(function_to_optimize=func_top_optimize, project_root_path=file_path.parent)
qualified_names = {func.qualified_name for func in code_ctx.helper_functions}
# HelperClass.__init__ is now tracked because HelperClass(self.name) instantiates the class
assert qualified_names == {
"HelperClass.helper_method",
"HelperClass.__init__",
} # Nested method should not be in here
read_write_context, read_only_context = code_ctx.read_writable_code, code_ctx.read_only_context_code
hashing_context = code_ctx.hashing_code_context
expected_read_write_context = f"""
```python:{file_path.relative_to(file_path.parent)}
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):
self.name = HelperClass.NestedClass("test").nested_method()
return HelperClass(self.name).helper_method()
```
"""
expected_read_only_context = """
"""
expected_hashing_context = f"""
```python:{file_path.relative_to(file_path.parent)}
class HelperClass:
def helper_method(self):
return self.name
class MainClass:
def main_method(self):
self.name = HelperClass.NestedClass('test').nested_method()
return HelperClass(self.name).helper_method()
```
"""
assert read_write_context.markdown.strip() == expected_read_write_context.strip()
assert read_only_context.strip() == expected_read_only_context.strip()
assert hashing_context.strip() == expected_hashing_context.strip()
def test_class_method_dependencies() -> None:
file_path = Path(__file__).resolve()
function_to_optimize = FunctionToOptimize(
function_name="topologicalSort",
file_path=str(file_path),
parents=[FunctionParent(name="Graph", type="ClassDef")],
starting_line=None,
ending_line=None,
)
code_ctx = get_code_optimization_context(function_to_optimize, file_path.parent.resolve())
read_write_context, read_only_context = code_ctx.read_writable_code, code_ctx.read_only_context_code
hashing_context = code_ctx.hashing_code_context
expected_read_write_context = f"""
```python:{file_path.relative_to(file_path.parent)}
from __future__ import annotations
from collections import defaultdict
class Graph:
def __init__(self, vertices):
self.graph = defaultdict(list)
self.V = vertices # No. of vertices
def topologicalSortUtil(self, v, visited, stack):
visited[v] = True
for i in self.graph[v]:
if visited[i] == False:
self.topologicalSortUtil(i, visited, stack)
stack.insert(0, v)
def topologicalSort(self):
visited = [False] * self.V
stack = []
for i in range(self.V):
if visited[i] == False:
self.topologicalSortUtil(i, visited, stack)
# Print contents of stack
return stack
```
"""
expected_read_only_context = ""
expected_hashing_context = f"""
```python:{file_path.relative_to(file_path.parent.resolve())}
class Graph:
def topologicalSortUtil(self, v, visited, stack):
visited[v] = True
for i in self.graph[v]:
if visited[i] == False:
self.topologicalSortUtil(i, visited, stack)
stack.insert(0, v)
def topologicalSort(self):
visited = [False] * self.V
stack = []
for i in range(self.V):
if visited[i] == False:
self.topologicalSortUtil(i, visited, stack)
return stack
```
"""
assert read_write_context.markdown.strip() == expected_read_write_context.strip()
assert read_only_context.strip() == expected_read_only_context.strip()
assert hashing_context.strip() == expected_hashing_context.strip()
def test_bubble_sort_helper() -> None:
path_to_fto = (
Path(__file__).resolve().parent.parent
/ "code_to_optimize"
/ "code_directories"
/ "retriever"
/ "bubble_sort_imported.py"
)
function_to_optimize = FunctionToOptimize(
function_name="sort_from_another_file",
file_path=str(path_to_fto),
parents=[],
starting_line=None,
ending_line=None,
)
code_ctx = get_code_optimization_context(function_to_optimize, Path(__file__).resolve().parent.parent)
read_write_context, read_only_context = code_ctx.read_writable_code, code_ctx.read_only_context_code
hashing_context = code_ctx.hashing_code_context
expected_read_write_context = """
```python:code_to_optimize/code_directories/retriever/bubble_sort_with_math.py
import math
def sorter(arr):
arr.sort()
x = math.sqrt(2)
print(x)
return arr
```
```python:code_to_optimize/code_directories/retriever/bubble_sort_imported.py
from bubble_sort_with_math import sorter
def sort_from_another_file(arr):
sorted_arr = sorter(arr)
return sorted_arr
```
"""
expected_read_only_context = ""
expected_hashing_context = """
```python:code_to_optimize/code_directories/retriever/bubble_sort_with_math.py
def sorter(arr):
arr.sort()
x = math.sqrt(2)
print(x)
return arr
```
```python:code_to_optimize/code_directories/retriever/bubble_sort_imported.py
def sort_from_another_file(arr):
sorted_arr = sorter(arr)
return sorted_arr
```
"""
assert read_write_context.markdown.strip() == expected_read_write_context.strip()
assert read_only_context.strip() == expected_read_only_context.strip()
assert hashing_context.strip() == expected_hashing_context.strip()
def test_flavio_typed_code_helper(tmp_path: Path) -> None:
code = '''
_P = ParamSpec("_P")
_KEY_T = TypeVar("_KEY_T")
_STORE_T = TypeVar("_STORE_T")
class AbstractCacheBackend(CacheBackend, Protocol[_KEY_T, _STORE_T]):
"""Interface for cache backends used by the persistent cache decorator."""
def __init__(self) -> None: ...
def hash_key(
self,
*,
func: Callable[_P, Any],
args: tuple[Any, ...],
kwargs: dict[str, Any],
) -> tuple[str, _KEY_T]: ...
def encode(self, *, data: Any) -> _STORE_T: # noqa: ANN401
...
def decode(self, *, data: _STORE_T) -> Any: # noqa: ANN401
...
def get(self, *, key: tuple[str, _KEY_T]) -> tuple[datetime.datetime, _STORE_T] | None: ...
def delete(self, *, key: tuple[str, _KEY_T]) -> None: ...
def put(self, *, key: tuple[str, _KEY_T], data: _STORE_T) -> None: ...
def get_cache_or_call(
self,
*,
func: Callable[_P, Any],
args: tuple[Any, ...],
kwargs: dict[str, Any],
lifespan: datetime.timedelta,
) -> Any: # noqa: ANN401
"""
Retrieve the cached results for a function call.
Args:
----
func (Callable[..., _R]): The function to retrieve cached results for.
args (tuple[Any, ...]): The positional arguments passed to the function.
kwargs (dict[str, Any]): The keyword arguments passed to the function.
lifespan (datetime.timedelta): The maximum age of the cached results.
Returns:
-------
_R: The cached results, if available.
"""
if os.environ.get("NO_CACHE"):
return func(*args, **kwargs)
try:
key = self.hash_key(func=func, args=args, kwargs=kwargs)
except: # noqa: E722
# If we can't create a cache key, we should just call the function.
logging.warning("Failed to hash cache key for function: %s", func)
return func(*args, **kwargs)
result_pair = self.get(key=key)
if result_pair is not None:
cached_time, result = result_pair
if not os.environ.get("RE_CACHE") and (
datetime.datetime.now() < (cached_time + lifespan) # noqa: DTZ005
):
try:
return self.decode(data=result)
except CacheBackendDecodeError as e:
logging.warning("Failed to decode cache data: %s", e)
# If decoding fails we will treat this as a cache miss.
# This might happens if underlying class definition of the data changes.
self.delete(key=key)
result = func(*args, **kwargs)
try:
self.put(key=key, data=self.encode(data=result))
except CacheBackendEncodeError as e:
logging.warning("Failed to encode cache data: %s", e)
# If encoding fails, we should still return the result.
return result
_P = ParamSpec("_P")
_R = TypeVar("_R")
_CacheBackendT = TypeVar("_CacheBackendT", bound=CacheBackend)
class _PersistentCache(Generic[_P, _R, _CacheBackendT]):
"""
A decorator class that provides persistent caching functionality for a function.
Args:
----
func (Callable[_P, _R]): The function to be decorated.
duration (datetime.timedelta): The duration for which the cached results should be considered valid.
backend (_backend): The backend storage for the cached results.
Attributes:
----------
__wrapped__ (Callable[_P, _R]): The wrapped function.
__duration__ (datetime.timedelta): The duration for which the cached results should be considered valid.
__backend__ (_backend): The backend storage for the cached results.
""" # noqa: E501
__wrapped__: Callable[_P, _R]
__duration__: datetime.timedelta
__backend__: _CacheBackendT
def __init__(
self,
func: Callable[_P, _R],
duration: datetime.timedelta,
) -> None:
self.__wrapped__ = func
self.__duration__ = duration
self.__backend__ = AbstractCacheBackend()
functools.update_wrapper(self, func)
def cache_clear(self) -> None:
"""Clears the cache for the wrapped function."""
self.__backend__.del_func_cache(func=self.__wrapped__)
def no_cache_call(self, *args: _P.args, **kwargs: _P.kwargs) -> _R:
"""
Calls the wrapped function without using the cache.
Args:
----
*args (_P.args): Positional arguments for the wrapped function.
**kwargs (_P.kwargs): Keyword arguments for the wrapped function.
Returns:
-------
_R: The result of the wrapped function.
"""
return self.__wrapped__(*args, **kwargs)
def __call__(self, *args: _P.args, **kwargs: _P.kwargs) -> _R:
"""
Calls the wrapped function, either using the cache or bypassing it based on environment variables.
Args:
----
*args (_P.args): Positional arguments for the wrapped function.
**kwargs (_P.kwargs): Keyword arguments for the wrapped function.
Returns:
-------
_R: The result of the wrapped function.
""" # noqa: E501
if "NO_CACHE" in os.environ:
return self.__wrapped__(*args, **kwargs)
os.makedirs(DEFAULT_CACHE_LOCATION, exist_ok=True)
return self.__backend__.get_cache_or_call(
func=self.__wrapped__,
args=args,
kwargs=kwargs,
lifespan=self.__duration__,
)
'''
# Create a temporary Python file using pytest's tmp_path fixture
file_path = tmp_path / "test_code.py"
file_path.write_text(code, encoding="utf-8")
opt = Optimizer(
Namespace(
project_root=file_path.parent.resolve(),
disable_telemetry=True,
tests_root="tests",
test_framework="pytest",
pytest_cmd="pytest",
experiment_id=None,
test_project_root=Path().resolve(),
)
)
function_to_optimize = FunctionToOptimize(
function_name="__call__",
file_path=file_path,
parents=[FunctionParent(name="_PersistentCache", type="ClassDef")],
starting_line=None,
ending_line=None,
)
code_ctx = get_code_optimization_context(function_to_optimize, opt.args.project_root)
read_write_context, read_only_context = code_ctx.read_writable_code, code_ctx.read_only_context_code
hashing_context = code_ctx.hashing_code_context
expected_read_write_context = f"""
```python:{file_path.relative_to(opt.args.project_root)}
_P = ParamSpec("_P")
_KEY_T = TypeVar("_KEY_T")
_STORE_T = TypeVar("_STORE_T")
class AbstractCacheBackend(CacheBackend, Protocol[_KEY_T, _STORE_T]):
def __init__(self) -> None: ...
def get_cache_or_call(
self,
*,
func: Callable[_P, Any],
args: tuple[Any, ...],
kwargs: dict[str, Any],
lifespan: datetime.timedelta,
) -> Any: # noqa: ANN401
\"\"\"
Retrieve the cached results for a function call.
Args:
----
func (Callable[..., _R]): The function to retrieve cached results for.
args (tuple[Any, ...]): The positional arguments passed to the function.
kwargs (dict[str, Any]): The keyword arguments passed to the function.
lifespan (datetime.timedelta): The maximum age of the cached results.
Returns:
-------
_R: The cached results, if available.
\"\"\"
if os.environ.get("NO_CACHE"):
return func(*args, **kwargs)
try:
key = self.hash_key(func=func, args=args, kwargs=kwargs)
except: # noqa: E722
# If we can't create a cache key, we should just call the function.
logging.warning("Failed to hash cache key for function: %s", func)
return func(*args, **kwargs)
result_pair = self.get(key=key)
if result_pair is not None:
cached_time, result = result_pair
if not os.environ.get("RE_CACHE") and (
datetime.datetime.now() < (cached_time + lifespan) # noqa: DTZ005
):
try:
return self.decode(data=result)
except CacheBackendDecodeError as e:
logging.warning("Failed to decode cache data: %s", e)
# If decoding fails we will treat this as a cache miss.
# This might happens if underlying class definition of the data changes.
self.delete(key=key)
result = func(*args, **kwargs)
try:
self.put(key=key, data=self.encode(data=result))
except CacheBackendEncodeError as e:
logging.warning("Failed to encode cache data: %s", e)
# If encoding fails, we should still return the result.
return result
_P = ParamSpec("_P")
_R = TypeVar("_R")
_CacheBackendT = TypeVar("_CacheBackendT", bound=CacheBackend)
class _PersistentCache(Generic[_P, _R, _CacheBackendT]):
def __init__(
self,
func: Callable[_P, _R],
duration: datetime.timedelta,
) -> None:
self.__wrapped__ = func
self.__duration__ = duration
self.__backend__ = AbstractCacheBackend()
functools.update_wrapper(self, func)
def __call__(self, *args: _P.args, **kwargs: _P.kwargs) -> _R:
\"\"\"
Calls the wrapped function, either using the cache or bypassing it based on environment variables.
Args:
----
*args (_P.args): Positional arguments for the wrapped function.
**kwargs (_P.kwargs): Keyword arguments for the wrapped function.
Returns:
-------
_R: The result of the wrapped function.
\"\"\" # noqa: E501
if "NO_CACHE" in os.environ:
return self.__wrapped__(*args, **kwargs)
os.makedirs(DEFAULT_CACHE_LOCATION, exist_ok=True)
return self.__backend__.get_cache_or_call(
func=self.__wrapped__,
args=args,
kwargs=kwargs,
lifespan=self.__duration__,
)
```
"""
expected_read_only_context = f'''
```python:{file_path.relative_to(opt.args.project_root)}
_P = ParamSpec("_P")
_KEY_T = TypeVar("_KEY_T")
_STORE_T = TypeVar("_STORE_T")
class AbstractCacheBackend(CacheBackend, Protocol[_KEY_T, _STORE_T]):
"""Interface for cache backends used by the persistent cache decorator."""
def __init__(self) -> None: ...
def hash_key(
self,
*,
func: Callable[_P, Any],
args: tuple[Any, ...],
kwargs: dict[str, Any],
) -> tuple[str, _KEY_T]: ...
def encode(self, *, data: Any) -> _STORE_T: # noqa: ANN401
...
def decode(self, *, data: _STORE_T) -> Any: # noqa: ANN401
...
def get(self, *, key: tuple[str, _KEY_T]) -> tuple[datetime.datetime, _STORE_T] | None: ...
def delete(self, *, key: tuple[str, _KEY_T]) -> None: ...
def put(self, *, key: tuple[str, _KEY_T], data: _STORE_T) -> None: ...
_P = ParamSpec("_P")
_R = TypeVar("_R")
_CacheBackendT = TypeVar("_CacheBackendT", bound=CacheBackend)
class _PersistentCache(Generic[_P, _R, _CacheBackendT]):
"""
A decorator class that provides persistent caching functionality for a function.
Args:
----
func (Callable[_P, _R]): The function to be decorated.
duration (datetime.timedelta): The duration for which the cached results should be considered valid.
backend (_backend): The backend storage for the cached results.
Attributes:
----------
__wrapped__ (Callable[_P, _R]): The wrapped function.
__duration__ (datetime.timedelta): The duration for which the cached results should be considered valid.
__backend__ (_backend): The backend storage for the cached results.
""" # noqa: E501
__wrapped__: Callable[_P, _R]
__duration__: datetime.timedelta
__backend__: _CacheBackendT
```
'''
expected_hashing_context = f"""
```python:{file_path.relative_to(opt.args.project_root)}
class AbstractCacheBackend(CacheBackend, Protocol[_KEY_T, _STORE_T]):
def get_cache_or_call(self, *, func: Callable[_P, Any], args: tuple[Any, ...], kwargs: dict[str, Any], lifespan: datetime.timedelta) -> Any:
if os.environ.get('NO_CACHE'):
return func(*args, **kwargs)
try:
key = self.hash_key(func=func, args=args, kwargs=kwargs)
except:
logging.warning('Failed to hash cache key for function: %s', func)
return func(*args, **kwargs)
result_pair = self.get(key=key)
if result_pair is not None:
{"cached_time, result = result_pair" if sys.version_info >= (3, 11) else "(cached_time, result) = result_pair"}
if not os.environ.get('RE_CACHE') and datetime.datetime.now() < cached_time + lifespan:
try:
return self.decode(data=result)
except CacheBackendDecodeError as e:
logging.warning('Failed to decode cache data: %s', e)
self.delete(key=key)
result = func(*args, **kwargs)
try:
self.put(key=key, data=self.encode(data=result))
except CacheBackendEncodeError as e:
logging.warning('Failed to encode cache data: %s', e)
return result
class _PersistentCache(Generic[_P, _R, _CacheBackendT]):
def __call__(self, *args: _P.args, **kwargs: _P.kwargs) -> _R:
if 'NO_CACHE' in os.environ:
return self.__wrapped__(*args, **kwargs)
os.makedirs(DEFAULT_CACHE_LOCATION, exist_ok=True)
return self.__backend__.get_cache_or_call(func=self.__wrapped__, args=args, kwargs=kwargs, lifespan=self.__duration__)
```
"""
assert read_write_context.markdown.strip() == expected_read_write_context.strip()
assert read_only_context.strip() == expected_read_only_context.strip()
assert hashing_context.strip() == expected_hashing_context.strip()
def test_example_class(tmp_path: Path) -> None:
code = """
class MyClass:
\"\"\"A class with a helper method.\"\"\"
def __init__(self):
self.x = 1
def target_method(self):
y = HelperClass().helper_method()
class HelperClass:
\"\"\"A helper class for MyClass.\"\"\"
def __init__(self):
\"\"\"Initialize the HelperClass.\"\"\"
self.x = 1
def __repr__(self):
\"\"\"Return a string representation of the HelperClass.\"\"\"
return "HelperClass" + str(self.x)
def helper_method(self):
return self.x
"""
# Create a temporary Python file using pytest's tmp_path fixture
file_path = tmp_path / "test_code.py"
file_path.write_text(code, encoding="utf-8")
opt = Optimizer(
Namespace(
project_root=file_path.parent.resolve(),
disable_telemetry=True,
tests_root="tests",
test_framework="pytest",
pytest_cmd="pytest",
experiment_id=None,
test_project_root=Path().resolve(),
)
)
function_to_optimize = FunctionToOptimize(
function_name="target_method",
file_path=file_path,
parents=[FunctionParent(name="MyClass", type="ClassDef")],
starting_line=None,
ending_line=None,
)
code_ctx = get_code_optimization_context(function_to_optimize, opt.args.project_root)
read_write_context, read_only_context = code_ctx.read_writable_code, code_ctx.read_only_context_code
hashing_context = code_ctx.hashing_code_context
expected_read_write_context = f"""
```python:{file_path.relative_to(opt.args.project_root)}
class MyClass:
def __init__(self):
self.x = 1
def target_method(self):
y = HelperClass().helper_method()
class HelperClass:
def __init__(self):
\"\"\"Initialize the HelperClass.\"\"\"
self.x = 1
def helper_method(self):
return self.x
```
"""
expected_read_only_context = f"""
```python:{file_path.relative_to(opt.args.project_root)}
class MyClass:
\"\"\"A class with a helper method.\"\"\"
class HelperClass:
\"\"\"A helper class for MyClass.\"\"\"
def __repr__(self):
\"\"\"Return a string representation of the HelperClass.\"\"\"
return "HelperClass" + str(self.x)
```
"""
expected_hashing_context = f"""
```python:{file_path.relative_to(opt.args.project_root)}
class MyClass:
def target_method(self):
y = HelperClass().helper_method()
class HelperClass:
def helper_method(self):
return self.x
```
"""
assert read_write_context.markdown.strip() == expected_read_write_context.strip()
assert read_only_context.strip() == expected_read_only_context.strip()
assert hashing_context.strip() == expected_hashing_context.strip()
def test_example_class_token_limit_1(tmp_path: Path) -> None:
docstring_filler = " ".join(
["This is a long docstring that will be used to fill up the token limit." for _ in range(1000)]
)
code = f"""
class MyClass:
\"\"\"A class with a helper method.
{docstring_filler}\"\"\"
def __init__(self):
self.x = 1
def target_method(self):
\"\"\"Docstring for target method\"\"\"
y = HelperClass().helper_method()
class HelperClass:
\"\"\"A helper class for MyClass.\"\"\"
def __init__(self):
\"\"\"Initialize the HelperClass.\"\"\"
self.x = 1
def __repr__(self):
\"\"\"Return a string representation of the HelperClass.\"\"\"
return "HelperClass" + str(self.x)
def helper_method(self):
return self.x
"""
# Create a temporary Python file using pytest's tmp_path fixture
file_path = tmp_path / "test_code.py"
file_path.write_text(code, encoding="utf-8")
opt = Optimizer(
Namespace(
project_root=file_path.parent.resolve(),
disable_telemetry=True,
tests_root="tests",
test_framework="pytest",
pytest_cmd="pytest",
experiment_id=None,
test_project_root=Path().resolve(),
)
)
function_to_optimize = FunctionToOptimize(
function_name="target_method",
file_path=file_path,
parents=[FunctionParent(name="MyClass", type="ClassDef")],
starting_line=None,
ending_line=None,
)
code_ctx = get_code_optimization_context(function_to_optimize, opt.args.project_root)
read_write_context, read_only_context = code_ctx.read_writable_code, code_ctx.read_only_context_code
hashing_context = code_ctx.hashing_code_context
# In this scenario, the read-only code context is too long, so the read-only docstrings are removed.
expected_read_write_context = f"""
```python:{file_path.relative_to(opt.args.project_root)}
class MyClass:
def __init__(self):
self.x = 1
def target_method(self):
\"\"\"Docstring for target method\"\"\"
y = HelperClass().helper_method()
class HelperClass:
def __init__(self):
\"\"\"Initialize the HelperClass.\"\"\"
self.x = 1
def helper_method(self):
return self.x
```
"""
expected_read_only_context = f"""
```python:{file_path.relative_to(opt.args.project_root)}
class MyClass:
pass
class HelperClass:
def __repr__(self):
return "HelperClass" + str(self.x)
```
"""
expected_hashing_context = f"""
```python:{file_path.relative_to(opt.args.project_root)}
class MyClass:
def target_method(self):
y = HelperClass().helper_method()
class HelperClass:
def helper_method(self):
return self.x
```
"""
assert read_write_context.markdown.strip() == expected_read_write_context.strip()
assert read_only_context.strip() == expected_read_only_context.strip()
assert hashing_context.strip() == expected_hashing_context.strip()
def test_example_class_token_limit_2(tmp_path: Path) -> None:
string_filler = " ".join(
["This is a long string that will be used to fill up the token limit." for _ in range(1000)]
)
code = f"""
class MyClass:
\"\"\"A class with a helper method. \"\"\"
def __init__(self):
self.x = 1
def target_method(self):
\"\"\"Docstring for target method\"\"\"
y = HelperClass().helper_method()
x = '{string_filler}'
class HelperClass:
\"\"\"A helper class for MyClass.\"\"\"
def __init__(self):
\"\"\"Initialize the HelperClass.\"\"\"
self.x = 1
def __repr__(self):
\"\"\"Return a string representation of the HelperClass.\"\"\"
return "HelperClass" + str(self.x)
def helper_method(self):
return self.x
"""
# Create a temporary Python file using pytest's tmp_path fixture
file_path = tmp_path / "test_code.py"
file_path.write_text(code, encoding="utf-8")
opt = Optimizer(
Namespace(
project_root=file_path.parent.resolve(),
disable_telemetry=True,
tests_root="tests",
test_framework="pytest",
pytest_cmd="pytest",
experiment_id=None,
test_project_root=Path().resolve(),
)
)
function_to_optimize = FunctionToOptimize(
function_name="target_method",
file_path=file_path,
parents=[FunctionParent(name="MyClass", type="ClassDef")],
starting_line=None,
ending_line=None,
)
code_ctx = get_code_optimization_context(function_to_optimize, opt.args.project_root, 8000, 100000)
read_write_context, read_only_context = code_ctx.read_writable_code, code_ctx.read_only_context_code
hashing_context = code_ctx.hashing_code_context
# In this scenario, the read-only code context is too long even after removing docstrings, hence we remove it completely.
expected_read_write_context = f"""
```python:{file_path.relative_to(opt.args.project_root)}
class MyClass:
def __init__(self):
self.x = 1
def target_method(self):
\"\"\"Docstring for target method\"\"\"
y = HelperClass().helper_method()
class HelperClass:
def __init__(self):
\"\"\"Initialize the HelperClass.\"\"\"
self.x = 1
def helper_method(self):
return self.x
```
"""
expected_read_only_context = f'''```python:{file_path.relative_to(opt.args.project_root)}
class MyClass:
"""A class with a helper method. """
class HelperClass:
"""A helper class for MyClass."""
def __repr__(self):
"""Return a string representation of the HelperClass."""
return "HelperClass" + str(self.x)
```
'''
expected_hashing_context = f"""
```python:{file_path.relative_to(opt.args.project_root)}
class MyClass:
def target_method(self):
y = HelperClass().helper_method()
class HelperClass:
def helper_method(self):
return self.x
```
"""
assert read_write_context.markdown.strip() == expected_read_write_context.strip()
assert read_only_context.strip() == expected_read_only_context.strip()
assert hashing_context.strip() == expected_hashing_context.strip()
def test_example_class_token_limit_3(tmp_path: Path) -> None:
string_filler = " ".join(
["This is a long string that will be used to fill up the token limit." for _ in range(1000)]
)
code = f"""
class MyClass:
\"\"\"A class with a helper method. \"\"\"
def __init__(self):
self.x = 1
def target_method(self):
\"\"\"{string_filler}\"\"\"
y = HelperClass().helper_method()
class HelperClass:
\"\"\"A helper class for MyClass.\"\"\"
def __init__(self):
\"\"\"Initialize the HelperClass.\"\"\"
self.x = 1
def __repr__(self):
\"\"\"Return a string representation of the HelperClass.\"\"\"
return "HelperClass" + str(self.x)
def helper_method(self):
return self.x
"""
# Create a temporary Python file using pytest's tmp_path fixture
file_path = tmp_path / "test_code.py"
file_path.write_text(code, encoding="utf-8")
opt = Optimizer(
Namespace(
project_root=file_path.parent.resolve(),
disable_telemetry=True,
tests_root="tests",
test_framework="pytest",
pytest_cmd="pytest",
experiment_id=None,
test_project_root=Path().resolve(),
)
)
function_to_optimize = FunctionToOptimize(
function_name="target_method",
file_path=file_path,
parents=[FunctionParent(name="MyClass", type="ClassDef")],
starting_line=None,
ending_line=None,
)
# In this scenario, the read-writable code is too long, so we abort.
with pytest.raises(ValueError, match="Read-writable code has exceeded token limit, cannot proceed"):
code_ctx = get_code_optimization_context(function_to_optimize, opt.args.project_root)
def test_example_class_token_limit_4(tmp_path: Path) -> None:
string_filler = " ".join(
["This is a long string that will be used to fill up the token limit." for _ in range(1000)]
)
code = f"""
class MyClass:
\"\"\"A class with a helper method. \"\"\"
def __init__(self):
global x
x = 1
def target_method(self):
\"\"\"Docstring for target method\"\"\"
y = HelperClass().helper_method()
x = '{string_filler}'
class HelperClass:
\"\"\"A helper class for MyClass.\"\"\"
def __init__(self):
\"\"\"Initialize the HelperClass.\"\"\"
self.x = 1
def __repr__(self):
\"\"\"Return a string representation of the HelperClass.\"\"\"
return "HelperClass" + str(self.x)
def helper_method(self):
return self.x
"""
# Create a temporary Python file using pytest's tmp_path fixture
file_path = tmp_path / "test_code.py"
file_path.write_text(code, encoding="utf-8")
opt = Optimizer(
Namespace(
project_root=file_path.parent.resolve(),
disable_telemetry=True,
tests_root="tests",
test_framework="pytest",
pytest_cmd="pytest",
experiment_id=None,
test_project_root=Path().resolve(),
)
)
function_to_optimize = FunctionToOptimize(
function_name="target_method",
file_path=file_path,
parents=[FunctionParent(name="MyClass", type="ClassDef")],
starting_line=None,
ending_line=None,
)
# In this scenario, the read-writable code context becomes too large because the __init__ function is referencing the global x variable instead of the class attribute self.x, so we abort.
with pytest.raises(ValueError, match="Read-writable code has exceeded token limit, cannot proceed"):
code_ctx = get_code_optimization_context(function_to_optimize, opt.args.project_root)
def test_example_class_token_limit_5(tmp_path: Path) -> None:
string_filler = " ".join(
["This is a long string that will be used to fill up the token limit." for _ in range(1000)]
)
code = f"""
class MyClass:
\"\"\"A class with a helper method. \"\"\"
def __init__(self):
self.x = 1
def target_method(self):
\"\"\"Docstring for target method\"\"\"
y = HelperClass().helper_method()
x = '{string_filler}'
class HelperClass:
\"\"\"A helper class for MyClass.\"\"\"
def __init__(self):
\"\"\"Initialize the HelperClass.\"\"\"
self.x = 1
def __repr__(self):
\"\"\"Return a string representation of the HelperClass.\"\"\"
return "HelperClass" + str(self.x)
def helper_method(self):
return self.x
"""
# Create a temporary Python file using pytest's tmp_path fixture
file_path = tmp_path / "test_code.py"
file_path.write_text(code, encoding="utf-8")
opt = Optimizer(
Namespace(
project_root=file_path.parent.resolve(),
disable_telemetry=True,
tests_root="tests",
test_framework="pytest",
pytest_cmd="pytest",
experiment_id=None,
test_project_root=Path().resolve(),
)
)
function_to_optimize = FunctionToOptimize(
function_name="target_method",
file_path=file_path,
parents=[FunctionParent(name="MyClass", type="ClassDef")],
starting_line=None,
ending_line=None,
)
code_ctx = get_code_optimization_context(function_to_optimize, opt.args.project_root)
# the global x variable shouldn't be included in any context type
assert (
code_ctx.read_writable_code.flat
== '''# file: test_code.py
class MyClass:
def __init__(self):
self.x = 1
def target_method(self):
"""Docstring for target method"""
y = HelperClass().helper_method()
class HelperClass:
def __init__(self):
"""Initialize the HelperClass."""
self.x = 1
def helper_method(self):
return self.x
'''
)
assert (
code_ctx.testgen_context.flat
== '''# file: test_code.py
class MyClass:
"""A class with a helper method. """
def __init__(self):
self.x = 1
def target_method(self):
"""Docstring for target method"""
y = HelperClass().helper_method()
class HelperClass:
"""A helper class for MyClass."""
def __init__(self):
"""Initialize the HelperClass."""
self.x = 1
def __repr__(self):
"""Return a string representation of the HelperClass."""
return "HelperClass" + str(self.x)
def helper_method(self):
return self.x
'''
)
def test_repo_helper() -> None:
project_root = Path(__file__).resolve().parent.parent / "code_to_optimize" / "code_directories" / "retriever"
path_to_file = project_root / "main.py"
path_to_utils = project_root / "utils.py"
function_to_optimize = FunctionToOptimize(
function_name="fetch_and_process_data",
file_path=str(path_to_file),
parents=[],
starting_line=None,
ending_line=None,
)
code_ctx = get_code_optimization_context(function_to_optimize, project_root)
read_write_context, read_only_context = code_ctx.read_writable_code, code_ctx.read_only_context_code
hashing_context = code_ctx.hashing_code_context
expected_read_write_context = f"""
```python:{path_to_utils.relative_to(project_root)}
import math
class DataProcessor:
def __init__(self, default_prefix: str = "PREFIX_"):
\"\"\"Initialize the DataProcessor with a default prefix.\"\"\"
self.default_prefix = default_prefix
self.number += math.log(self.number)
def process_data(self, raw_data: str) -> str:
\"\"\"Process raw data by converting it to uppercase.\"\"\"
return raw_data.upper()
def add_prefix(self, data: str, prefix: str = "PREFIX_") -> str:
\"\"\"Add a prefix to the processed data.\"\"\"
return prefix + data
```
```python:{path_to_file.relative_to(project_root)}
import requests
from globals import API_URL
from utils import DataProcessor
def fetch_and_process_data():
# Use the global variable for the request
response = requests.get(API_URL)
response.raise_for_status()
raw_data = response.text
# Use code from another file (utils.py)
processor = DataProcessor()
processed = processor.process_data(raw_data)
processed = processor.add_prefix(processed)
return processed
```
"""
expected_read_only_context = f"""
```python:{path_to_utils.relative_to(project_root)}
class DataProcessor:
\"\"\"A class for processing data.\"\"\"
number = 1
def __repr__(self) -> str:
\"\"\"Return a string representation of the DataProcessor.\"\"\"
return f"DataProcessor(default_prefix={{self.default_prefix!r}})"
```
"""
expected_hashing_context = f"""
```python:{path_to_utils.relative_to(project_root)}
class DataProcessor:
def process_data(self, raw_data: str) -> str:
return raw_data.upper()
def add_prefix(self, data: str, prefix: str='PREFIX_') -> str:
return prefix + data
```
```python:{path_to_file.relative_to(project_root)}
def fetch_and_process_data():
response = requests.get(API_URL)
response.raise_for_status()
raw_data = response.text
processor = DataProcessor()
processed = processor.process_data(raw_data)
processed = processor.add_prefix(processed)
return processed
```
"""
assert read_write_context.markdown.strip() == expected_read_write_context.strip()
assert read_only_context.strip() == expected_read_only_context.strip()
assert hashing_context.strip() == expected_hashing_context.strip()
def test_repo_helper_of_helper() -> None:
project_root = Path(__file__).resolve().parent.parent / "code_to_optimize" / "code_directories" / "retriever"
path_to_file = project_root / "main.py"
path_to_utils = project_root / "utils.py"
path_to_transform_utils = project_root / "transform_utils.py"
function_to_optimize = FunctionToOptimize(
function_name="fetch_and_transform_data",
file_path=str(path_to_file),
parents=[],
starting_line=None,
ending_line=None,
)
code_ctx = get_code_optimization_context(function_to_optimize, project_root)
read_write_context, read_only_context = code_ctx.read_writable_code, code_ctx.read_only_context_code
hashing_context = code_ctx.hashing_code_context
expected_read_write_context = f"""
```python:{path_to_utils.relative_to(project_root)}
import math
from transform_utils import DataTransformer
class DataProcessor:
def __init__(self, default_prefix: str = "PREFIX_"):
\"\"\"Initialize the DataProcessor with a default prefix.\"\"\"
self.default_prefix = default_prefix
self.number += math.log(self.number)
def process_data(self, raw_data: str) -> str:
\"\"\"Process raw data by converting it to uppercase.\"\"\"
return raw_data.upper()
def transform_data(self, data: str) -> str:
\"\"\"Transform the processed data\"\"\"
return DataTransformer().transform(data)
```
```python:{path_to_file.relative_to(project_root)}
import requests
from globals import API_URL
from utils import DataProcessor
def fetch_and_transform_data():
# Use the global variable for the request
response = requests.get(API_URL)
raw_data = response.text
# Use code from another file (utils.py)
processor = DataProcessor()
processed = processor.process_data(raw_data)
transformed = processor.transform_data(processed)
return transformed
```
"""
expected_read_only_context = f"""
```python:{path_to_utils.relative_to(project_root)}
class DataProcessor:
\"\"\"A class for processing data.\"\"\"
number = 1
def __repr__(self) -> str:
\"\"\"Return a string representation of the DataProcessor.\"\"\"
return f"DataProcessor(default_prefix={{self.default_prefix!r}})"
```
```python:{path_to_transform_utils.relative_to(project_root)}
class DataTransformer:
def __init__(self):
self.data = None
def transform(self, data):
self.data = data
return self.data
```
"""
expected_hashing_context = f"""
```python:{path_to_utils.relative_to(project_root)}
class DataProcessor:
def process_data(self, raw_data: str) -> str:
return raw_data.upper()
def transform_data(self, data: str) -> str:
return DataTransformer().transform(data)
```
```python:{path_to_file.relative_to(project_root)}
def fetch_and_transform_data():
response = requests.get(API_URL)
raw_data = response.text
processor = DataProcessor()
processed = processor.process_data(raw_data)
transformed = processor.transform_data(processed)
return transformed
```
"""
assert read_write_context.markdown.strip() == expected_read_write_context.strip()
assert read_only_context.strip() == expected_read_only_context.strip()
assert hashing_context.strip() == expected_hashing_context.strip()
def test_repo_helper_of_helper_same_class() -> None:
project_root = Path(__file__).resolve().parent.parent / "code_to_optimize" / "code_directories" / "retriever"
path_to_utils = project_root / "utils.py"
path_to_transform_utils = project_root / "transform_utils.py"
function_to_optimize = FunctionToOptimize(
function_name="transform_data_own_method",
file_path=str(path_to_utils),
parents=[FunctionParent(name="DataProcessor", type="ClassDef")],
starting_line=None,
ending_line=None,
)
code_ctx = get_code_optimization_context(function_to_optimize, project_root)
read_write_context, read_only_context = code_ctx.read_writable_code, code_ctx.read_only_context_code
hashing_context = code_ctx.hashing_code_context
expected_read_write_context = f"""
```python:{path_to_transform_utils.relative_to(project_root)}
class DataTransformer:
def __init__(self):
self.data = None
def transform_using_own_method(self, data):
return self.transform(data)
```
```python:{path_to_utils.relative_to(project_root)}
import math
from transform_utils import DataTransformer
class DataProcessor:
def __init__(self, default_prefix: str = "PREFIX_"):
\"\"\"Initialize the DataProcessor with a default prefix.\"\"\"
self.default_prefix = default_prefix
self.number += math.log(self.number)
def transform_data_own_method(self, data: str) -> str:
\"\"\"Transform the processed data using own method\"\"\"
return DataTransformer().transform_using_own_method(data)
```
"""
expected_read_only_context = f"""
```python:{path_to_transform_utils.relative_to(project_root)}
class DataTransformer:
def transform(self, data):
self.data = data
return self.data
```
```python:{path_to_utils.relative_to(project_root)}
class DataProcessor:
\"\"\"A class for processing data.\"\"\"
number = 1
def __repr__(self) -> str:
\"\"\"Return a string representation of the DataProcessor.\"\"\"
return f"DataProcessor(default_prefix={{self.default_prefix!r}})"
```
"""
expected_hashing_context = f"""
```python:transform_utils.py
class DataTransformer:
def transform_using_own_method(self, data):
return self.transform(data)
```
```python:{path_to_utils.relative_to(project_root)}
class DataProcessor:
def transform_data_own_method(self, data: str) -> str:
return DataTransformer().transform_using_own_method(data)
```
"""
assert read_write_context.markdown.strip() == expected_read_write_context.strip()
assert read_only_context.strip() == expected_read_only_context.strip()
assert hashing_context.strip() == expected_hashing_context.strip()
def test_repo_helper_of_helper_same_file() -> None:
project_root = Path(__file__).resolve().parent.parent / "code_to_optimize" / "code_directories" / "retriever"
path_to_utils = project_root / "utils.py"
path_to_transform_utils = project_root / "transform_utils.py"
function_to_optimize = FunctionToOptimize(
function_name="transform_data_same_file_function",
file_path=str(path_to_utils),
parents=[FunctionParent(name="DataProcessor", type="ClassDef")],
starting_line=None,
ending_line=None,
)
code_ctx = get_code_optimization_context(function_to_optimize, project_root)
read_write_context, read_only_context = code_ctx.read_writable_code, code_ctx.read_only_context_code
hashing_context = code_ctx.hashing_code_context
expected_read_write_context = f"""
```python:{path_to_transform_utils.relative_to(project_root)}
class DataTransformer:
def __init__(self):
self.data = None
def transform_using_same_file_function(self, data):
return update_data(data)
```
```python:{path_to_utils.relative_to(project_root)}
import math
from transform_utils import DataTransformer
class DataProcessor:
def __init__(self, default_prefix: str = "PREFIX_"):
\"\"\"Initialize the DataProcessor with a default prefix.\"\"\"
self.default_prefix = default_prefix
self.number += math.log(self.number)
def transform_data_same_file_function(self, data: str) -> str:
\"\"\"Transform the processed data using a function from the same file\"\"\"
return DataTransformer().transform_using_same_file_function(data)
```
"""
expected_read_only_context = f"""
```python:{path_to_transform_utils.relative_to(project_root)}
def update_data(data):
return data + " updated"
```
```python:{path_to_utils.relative_to(project_root)}
class DataProcessor:
\"\"\"A class for processing data.\"\"\"
number = 1
def __repr__(self) -> str:
\"\"\"Return a string representation of the DataProcessor.\"\"\"
return f"DataProcessor(default_prefix={{self.default_prefix!r}})"
```
"""
expected_hashing_context = f"""
```python:transform_utils.py
class DataTransformer:
def transform_using_same_file_function(self, data):
return update_data(data)
```
```python:{path_to_utils.relative_to(project_root)}
class DataProcessor:
def transform_data_same_file_function(self, data: str) -> str:
return DataTransformer().transform_using_same_file_function(data)
```
"""
assert read_write_context.markdown.strip() == expected_read_write_context.strip()
assert read_only_context.strip() == expected_read_only_context.strip()
assert hashing_context.strip() == expected_hashing_context.strip()
def test_repo_helper_all_same_file() -> None:
project_root = Path(__file__).resolve().parent.parent / "code_to_optimize" / "code_directories" / "retriever"
path_to_transform_utils = project_root / "transform_utils.py"
function_to_optimize = FunctionToOptimize(
function_name="transform_data_all_same_file",
file_path=str(path_to_transform_utils),
parents=[FunctionParent(name="DataTransformer", type="ClassDef")],
starting_line=None,
ending_line=None,
)
code_ctx = get_code_optimization_context(function_to_optimize, project_root)
read_write_context, read_only_context = code_ctx.read_writable_code, code_ctx.read_only_context_code
hashing_context = code_ctx.hashing_code_context
expected_read_write_context = f"""
```python:{path_to_transform_utils.relative_to(project_root)}
class DataTransformer:
def __init__(self):
self.data = None
def transform_using_own_method(self, data):
return self.transform(data)
def transform_data_all_same_file(self, data):
new_data = update_data(data)
return self.transform_using_own_method(new_data)
def update_data(data):
return data + " updated"
```
"""
expected_read_only_context = f"""
```python:{path_to_transform_utils.relative_to(project_root)}
class DataTransformer:
def transform(self, data):
self.data = data
return self.data
```
"""
expected_hashing_context = f"""
```python:{path_to_transform_utils.relative_to(project_root)}
class DataTransformer:
def transform_using_own_method(self, data):
return self.transform(data)
def transform_data_all_same_file(self, data):
new_data = update_data(data)
return self.transform_using_own_method(new_data)
def update_data(data):
return data + ' updated'
```
"""
assert read_write_context.markdown.strip() == expected_read_write_context.strip()
assert read_only_context.strip() == expected_read_only_context.strip()
assert hashing_context.strip() == expected_hashing_context.strip()
def test_repo_helper_circular_dependency() -> None:
project_root = Path(__file__).resolve().parent.parent / "code_to_optimize" / "code_directories" / "retriever"
path_to_utils = project_root / "utils.py"
path_to_transform_utils = project_root / "transform_utils.py"
function_to_optimize = FunctionToOptimize(
function_name="circular_dependency",
file_path=str(path_to_transform_utils),
parents=[FunctionParent(name="DataTransformer", type="ClassDef")],
starting_line=None,
ending_line=None,
)
code_ctx = get_code_optimization_context(function_to_optimize, project_root)
read_write_context, read_only_context = code_ctx.read_writable_code, code_ctx.read_only_context_code
hashing_context = code_ctx.hashing_code_context
expected_read_write_context = f"""
```python:{path_to_utils.relative_to(project_root)}
import math
from transform_utils import DataTransformer
class DataProcessor:
def __init__(self, default_prefix: str = "PREFIX_"):
\"\"\"Initialize the DataProcessor with a default prefix.\"\"\"
self.default_prefix = default_prefix
self.number += math.log(self.number)
def circular_dependency(self, data: str) -> str:
\"\"\"Test circular dependency\"\"\"
return DataTransformer().circular_dependency(data)
```
```python:{path_to_transform_utils.relative_to(project_root)}
from code_to_optimize.code_directories.retriever.utils import DataProcessor
class DataTransformer:
def __init__(self):
self.data = None
def circular_dependency(self, data):
return DataProcessor().circular_dependency(data)
```
"""
expected_read_only_context = f"""
```python:{path_to_utils.relative_to(project_root)}
class DataProcessor:
\"\"\"A class for processing data.\"\"\"
number = 1
def __repr__(self) -> str:
\"\"\"Return a string representation of the DataProcessor.\"\"\"
return f"DataProcessor(default_prefix={{self.default_prefix!r}})"
```
```python:{path_to_transform_utils.relative_to(project_root)}
class DataTransformer:
def __init__(self):
self.data = None
```
"""
expected_hashing_context = f"""
```python:utils.py
class DataProcessor:
def circular_dependency(self, data: str) -> str:
return DataTransformer().circular_dependency(data)
```
```python:{path_to_transform_utils.relative_to(project_root)}
class DataTransformer:
def circular_dependency(self, data):
return DataProcessor().circular_dependency(data)
```
"""
assert read_write_context.markdown.strip() == expected_read_write_context.strip()
assert read_only_context.strip() == expected_read_only_context.strip()
assert hashing_context.strip() == expected_hashing_context.strip()
def test_indirect_init_helper(tmp_path: Path) -> None:
code = """
class MyClass:
def __init__(self):
self.x = 1
self.y = outside_method()
def target_method(self):
return self.x + self.y
def outside_method():
return 1
"""
# Create a temporary Python file using pytest's tmp_path fixture
file_path = tmp_path / "test_code.py"
file_path.write_text(code, encoding="utf-8")
opt = Optimizer(
Namespace(
project_root=file_path.parent.resolve(),
disable_telemetry=True,
tests_root="tests",
test_framework="pytest",
pytest_cmd="pytest",
experiment_id=None,
test_project_root=Path().resolve(),
)
)
function_to_optimize = FunctionToOptimize(
function_name="target_method",
file_path=file_path,
parents=[FunctionParent(name="MyClass", type="ClassDef")],
starting_line=None,
ending_line=None,
)
code_ctx = get_code_optimization_context(function_to_optimize, opt.args.project_root)
read_write_context, read_only_context = code_ctx.read_writable_code, code_ctx.read_only_context_code
hashing_context = code_ctx.hashing_code_context
expected_read_write_context = f"""
```python:{file_path.relative_to(opt.args.project_root)}
class MyClass:
def __init__(self):
self.x = 1
self.y = outside_method()
def target_method(self):
return self.x + self.y
```
"""
expected_read_only_context = f"""
```python:{file_path.relative_to(opt.args.project_root)}
def outside_method():
return 1
```
"""
expected_hashing_context = f"""
```python:{file_path.relative_to(opt.args.project_root)}
class MyClass:
def target_method(self):
return self.x + self.y
```
"""
assert read_write_context.markdown.strip() == expected_read_write_context.strip()
assert read_only_context.strip() == expected_read_only_context.strip()
assert hashing_context.strip() == expected_hashing_context.strip()
def test_direct_module_import() -> None:
project_root = Path(__file__).resolve().parent.parent / "code_to_optimize" / "code_directories" / "retriever"
path_to_main = project_root / "main.py"
path_to_fto = project_root / "import_test.py"
function_to_optimize = FunctionToOptimize(
function_name="function_to_optimize",
file_path=str(path_to_fto),
parents=[],
starting_line=None,
ending_line=None,
)
code_ctx = get_code_optimization_context(function_to_optimize, project_root)
read_write_context, read_only_context = code_ctx.read_writable_code, code_ctx.read_only_context_code
hashing_context = code_ctx.hashing_code_context
expected_read_only_context = """
```python:utils.py
import math
from transform_utils import DataTransformer
class DataProcessor:
\"\"\"A class for processing data.\"\"\"
number = 1
def __init__(self, default_prefix: str = "PREFIX_"):
\"\"\"Initialize the DataProcessor with a default prefix.\"\"\"
self.default_prefix = default_prefix
self.number += math.log(self.number)
def __repr__(self) -> str:
\"\"\"Return a string representation of the DataProcessor.\"\"\"
return f"DataProcessor(default_prefix={self.default_prefix!r})"
def process_data(self, raw_data: str) -> str:
\"\"\"Process raw data by converting it to uppercase.\"\"\"
return raw_data.upper()
def transform_data(self, data: str) -> str:
\"\"\"Transform the processed data\"\"\"
return DataTransformer().transform(data)
```"""
expected_hashing_context = """
```python:main.py
def fetch_and_transform_data():
response = requests.get(API_URL)
raw_data = response.text
processor = DataProcessor()
processed = processor.process_data(raw_data)
transformed = processor.transform_data(processed)
return transformed
```
```python:import_test.py
def function_to_optimize():
return code_to_optimize.code_directories.retriever.main.fetch_and_transform_data()
```
"""
expected_read_write_context = f"""
```python:{path_to_main.relative_to(project_root)}
import requests
from globals import API_URL
from utils import DataProcessor
def fetch_and_transform_data():
# Use the global variable for the request
response = requests.get(API_URL)
raw_data = response.text
# Use code from another file (utils.py)
processor = DataProcessor()
processed = processor.process_data(raw_data)
transformed = processor.transform_data(processed)
return transformed
```
```python:{path_to_fto.relative_to(project_root)}
import code_to_optimize.code_directories.retriever.main
def function_to_optimize():
return code_to_optimize.code_directories.retriever.main.fetch_and_transform_data()
```
"""
assert read_write_context.markdown.strip() == expected_read_write_context.strip()
assert read_only_context.strip() == expected_read_only_context.strip()
assert hashing_context.strip() == expected_hashing_context.strip()
def test_module_import_optimization() -> None:
main_code = """
import utility_module
class Calculator:
def __init__(self, precision="high", fallback_precision=None, mode="standard"):
# This is where we use the imported module
self.precision = utility_module.select_precision(precision, fallback_precision)
self.mode = mode
# Using variables from the utility module
self.backend = utility_module.CALCULATION_BACKEND
self.system = utility_module.SYSTEM_TYPE
self.default_precision = utility_module.DEFAULT_PRECISION
def add(self, a, b):
return a + b
def subtract(self, a, b):
return a - b
def calculate(self, operation, x, y):
if operation == "add":
return self.add(x, y)
elif operation == "subtract":
return self.subtract(x, y)
else:
return None
"""
utility_module_code = """
import sys
import platform
import logging
DEFAULT_PRECISION = "medium"
DEFAULT_MODE = "standard"
# Try-except block with variable definitions
try:
import numpy as np
# Used variable in try block
CALCULATION_BACKEND = "numpy"
# Unused variable in try block
VECTOR_DIMENSIONS = 3
except ImportError:
# Used variable in except block
CALCULATION_BACKEND = "python"
# Unused variable in except block
FALLBACK_WARNING = "NumPy not available, using slower Python implementation"
# Nested if-else with variable definitions
if sys.platform.startswith('win'):
# Used variable in outer if
SYSTEM_TYPE = "windows"
if platform.architecture()[0] == '64bit':
# Unused variable in nested if
MEMORY_MODEL = "x64"
else:
# Unused variable in nested else
MEMORY_MODEL = "x86"
elif sys.platform.startswith('linux'):
# Used variable in outer elif
SYSTEM_TYPE = "linux"
# Unused variable in outer elif
KERNEL_VERSION = platform.release()
else:
# Used variable in outer else
SYSTEM_TYPE = "other"
# Unused variable in outer else
UNKNOWN_SYSTEM_MSG = "Running on an unrecognized platform"
# Function that will be used in the main code
def select_precision(precision, fallback_precision):
if precision is None:
return fallback_precision or DEFAULT_PRECISION
# Using the variables defined above
if CALCULATION_BACKEND == "numpy":
# Higher precision available with NumPy
precision_options = ["low", "medium", "high", "ultra"]
else:
# Limited precision without NumPy
precision_options = ["low", "medium", "high"]
if isinstance(precision, str):
if precision.lower() not in precision_options:
if fallback_precision:
return fallback_precision
else:
return DEFAULT_PRECISION
return precision.lower()
else:
return DEFAULT_PRECISION
# Function that won't be used
def get_system_details():
return {
"system": SYSTEM_TYPE,
"backend": CALCULATION_BACKEND,
"default_precision": DEFAULT_PRECISION,
"python_version": sys.version
}
"""
# Create a temporary directory for the test
with tempfile.TemporaryDirectory() as temp_dir:
# Set up the package structure
package_dir = Path(temp_dir) / "package"
package_dir.mkdir()
# Create the __init__.py file
with open(package_dir / "__init__.py", "w") as init_file:
init_file.write("")
# Write the utility_module.py file
with open(package_dir / "utility_module.py", "w") as utility_file:
utility_file.write(utility_module_code)
utility_file.flush()
# Write the main code file
main_file_path = package_dir / "main_module.py"
with open(main_file_path, "w") as main_file:
main_file.write(main_code)
main_file.flush()
# Set up the optimizer
file_path = main_file_path.resolve()
project_root = package_dir.resolve()
opt = Optimizer(
Namespace(
project_root=project_root,
disable_telemetry=True,
tests_root="tests",
test_framework="pytest",
pytest_cmd="pytest",
experiment_id=None,
test_project_root=Path().resolve(),
)
)
# Define the function to optimize
function_to_optimize = FunctionToOptimize(
function_name="calculate",
file_path=file_path,
parents=[FunctionParent(name="Calculator", type="ClassDef")],
starting_line=None,
ending_line=None,
)
# Get the code optimization context
code_ctx = get_code_optimization_context(function_to_optimize, opt.args.project_root)
read_write_context, read_only_context = code_ctx.read_writable_code, code_ctx.read_only_context_code
hashing_context = code_ctx.hashing_code_context
# The expected contexts
# Resolve both paths to handle symlink issues on macOS
relative_path = file_path.relative_to(project_root)
expected_read_write_context = f"""
```python:{main_file_path.resolve().relative_to(opt.args.project_root.resolve())}
import utility_module
class Calculator:
def __init__(self, precision="high", fallback_precision=None, mode="standard"):
# This is where we use the imported module
self.precision = utility_module.select_precision(precision, fallback_precision)
self.mode = mode
# Using variables from the utility module
self.backend = utility_module.CALCULATION_BACKEND
self.system = utility_module.SYSTEM_TYPE
self.default_precision = utility_module.DEFAULT_PRECISION
def add(self, a, b):
return a + b
def subtract(self, a, b):
return a - b
def calculate(self, operation, x, y):
if operation == "add":
return self.add(x, y)
elif operation == "subtract":
return self.subtract(x, y)
else:
return None
```
"""
expected_read_only_context = """
```python:utility_module.py
DEFAULT_PRECISION = "medium"
# Try-except block with variable definitions
try:
# Used variable in try block
CALCULATION_BACKEND = "numpy"
except ImportError:
# Used variable in except block
CALCULATION_BACKEND = "python"
# Function that will be used in the main code
def select_precision(precision, fallback_precision):
if precision is None:
return fallback_precision or DEFAULT_PRECISION
# Using the variables defined above
if CALCULATION_BACKEND == "numpy":
# Higher precision available with NumPy
precision_options = ["low", "medium", "high", "ultra"]
else:
# Limited precision without NumPy
precision_options = ["low", "medium", "high"]
if isinstance(precision, str):
if precision.lower() not in precision_options:
if fallback_precision:
return fallback_precision
else:
return DEFAULT_PRECISION
return precision.lower()
else:
return DEFAULT_PRECISION
```
"""
expected_hashing_context = """
```python:main_module.py
class Calculator:
def add(self, a, b):
return a + b
def subtract(self, a, b):
return a - b
def calculate(self, operation, x, y):
if operation == 'add':
return self.add(x, y)
elif operation == 'subtract':
return self.subtract(x, y)
else:
return None
```
"""
# Verify the contexts match the expected values
assert read_write_context.markdown.strip() == expected_read_write_context.strip()
assert read_only_context.strip() == expected_read_only_context.strip()
assert hashing_context.strip() == expected_hashing_context.strip()
def test_module_import_init_fto() -> None:
main_code = """
import utility_module
class Calculator:
def __init__(self, precision="high", fallback_precision=None, mode="standard"):
# This is where we use the imported module
self.precision = utility_module.select_precision(precision, fallback_precision)
self.mode = mode
# Using variables from the utility module
self.backend = utility_module.CALCULATION_BACKEND
self.system = utility_module.SYSTEM_TYPE
self.default_precision = utility_module.DEFAULT_PRECISION
def add(self, a, b):
return a + b
def subtract(self, a, b):
return a - b
def calculate(self, operation, x, y):
if operation == "add":
return self.add(x, y)
elif operation == "subtract":
return self.subtract(x, y)
else:
return None
"""
utility_module_code = """
import sys
import platform
import logging
DEFAULT_PRECISION = "medium"
DEFAULT_MODE = "standard"
# Try-except block with variable definitions
try:
import numpy as np
# Used variable in try block
CALCULATION_BACKEND = "numpy"
# Unused variable in try block
VECTOR_DIMENSIONS = 3
except ImportError:
# Used variable in except block
CALCULATION_BACKEND = "python"
# Unused variable in except block
FALLBACK_WARNING = "NumPy not available, using slower Python implementation"
# Nested if-else with variable definitions
if sys.platform.startswith('win'):
# Used variable in outer if
SYSTEM_TYPE = "windows"
if platform.architecture()[0] == '64bit':
# Unused variable in nested if
MEMORY_MODEL = "x64"
else:
# Unused variable in nested else
MEMORY_MODEL = "x86"
elif sys.platform.startswith('linux'):
# Used variable in outer elif
SYSTEM_TYPE = "linux"
# Unused variable in outer elif
KERNEL_VERSION = platform.release()
else:
# Used variable in outer else
SYSTEM_TYPE = "other"
# Unused variable in outer else
UNKNOWN_SYSTEM_MSG = "Running on an unrecognized platform"
# Function that will be used in the main code
def select_precision(precision, fallback_precision):
if precision is None:
return fallback_precision or DEFAULT_PRECISION
# Using the variables defined above
if CALCULATION_BACKEND == "numpy":
# Higher precision available with NumPy
precision_options = ["low", "medium", "high", "ultra"]
else:
# Limited precision without NumPy
precision_options = ["low", "medium", "high"]
if isinstance(precision, str):
if precision.lower() not in precision_options:
if fallback_precision:
return fallback_precision
else:
return DEFAULT_PRECISION
return precision.lower()
else:
return DEFAULT_PRECISION
# Function that won't be used
def get_system_details():
return {
"system": SYSTEM_TYPE,
"backend": CALCULATION_BACKEND,
"default_precision": DEFAULT_PRECISION,
"python_version": sys.version
}
"""
# Create a temporary directory for the test
with tempfile.TemporaryDirectory() as temp_dir:
# Set up the package structure
package_dir = Path(temp_dir) / "package"
package_dir.mkdir()
# Create the __init__.py file
with open(package_dir / "__init__.py", "w") as init_file:
init_file.write("")
# Write the utility_module.py file
with open(package_dir / "utility_module.py", "w") as utility_file:
utility_file.write(utility_module_code)
utility_file.flush()
# Write the main code file
main_file_path = package_dir / "main_module.py"
with open(main_file_path, "w") as main_file:
main_file.write(main_code)
main_file.flush()
# Set up the optimizer
file_path = main_file_path.resolve()
project_root = package_dir.resolve()
opt = Optimizer(
Namespace(
project_root=project_root,
disable_telemetry=True,
tests_root="tests",
test_framework="pytest",
pytest_cmd="pytest",
experiment_id=None,
test_project_root=Path().resolve(),
)
)
# Define the function to optimize
function_to_optimize = FunctionToOptimize(
function_name="__init__",
file_path=file_path,
parents=[FunctionParent(name="Calculator", type="ClassDef")],
starting_line=None,
ending_line=None,
)
# Get the code optimization context
code_ctx = get_code_optimization_context(function_to_optimize, opt.args.project_root)
read_write_context, read_only_context = code_ctx.read_writable_code, code_ctx.read_only_context_code
# The expected contexts
relative_path = file_path.relative_to(project_root)
expected_read_write_context = f"""
```python:utility_module.py
DEFAULT_PRECISION = "medium"
# Try-except block with variable definitions
try:
# Used variable in try block
CALCULATION_BACKEND = "numpy"
except ImportError:
# Used variable in except block
CALCULATION_BACKEND = "python"
# Function that will be used in the main code
def select_precision(precision, fallback_precision):
if precision is None:
return fallback_precision or DEFAULT_PRECISION
# Using the variables defined above
if CALCULATION_BACKEND == "numpy":
# Higher precision available with NumPy
precision_options = ["low", "medium", "high", "ultra"]
else:
# Limited precision without NumPy
precision_options = ["low", "medium", "high"]
if isinstance(precision, str):
if precision.lower() not in precision_options:
if fallback_precision:
return fallback_precision
else:
return DEFAULT_PRECISION
return precision.lower()
else:
return DEFAULT_PRECISION
```
```python:{main_file_path.resolve().relative_to(opt.args.project_root.resolve())}
import utility_module
class Calculator:
def __init__(self, precision="high", fallback_precision=None, mode="standard"):
# This is where we use the imported module
self.precision = utility_module.select_precision(precision, fallback_precision)
self.mode = mode
# Using variables from the utility module
self.backend = utility_module.CALCULATION_BACKEND
self.system = utility_module.SYSTEM_TYPE
self.default_precision = utility_module.DEFAULT_PRECISION
```
"""
expected_read_only_context = """
```python:utility_module.py
DEFAULT_PRECISION = "medium"
# Try-except block with variable definitions
try:
# Used variable in try block
CALCULATION_BACKEND = "numpy"
except ImportError:
# Used variable in except block
CALCULATION_BACKEND = "python"
```
"""
assert read_write_context.markdown.strip() == expected_read_write_context.strip()
assert read_only_context.strip() == expected_read_only_context.strip()
def test_hashing_code_context_removes_imports_docstrings_and_init(tmp_path: Path) -> None:
"""Test that hashing context removes imports, docstrings, and __init__ methods properly."""
code = '''
import os
import sys
from pathlib import Path
class MyClass:
"""A class with a docstring."""
def __init__(self, value):
"""Initialize with a value."""
self.value = value
def target_method(self):
"""Target method with docstring."""
result = self.helper_method()
helper_cls = HelperClass()
data = helper_cls.process_data()
return self.value * 2
def helper_method(self):
"""Helper method with docstring."""
return self.value + 1
class HelperClass:
"""Helper class docstring."""
def __init__(self):
"""Helper init method."""
self.data = "test"
def process_data(self):
"""Process data method."""
return self.data.upper()
def standalone_function():
"""Standalone function."""
return "standalone"
'''
# Create a temporary Python file using pytest's tmp_path fixture
file_path = tmp_path / "test_code.py"
file_path.write_text(code, encoding="utf-8")
opt = Optimizer(
Namespace(
project_root=file_path.parent.resolve(),
disable_telemetry=True,
tests_root="tests",
test_framework="pytest",
pytest_cmd="pytest",
experiment_id=None,
test_project_root=Path().resolve(),
)
)
function_to_optimize = FunctionToOptimize(
function_name="target_method",
file_path=file_path,
parents=[FunctionParent(name="MyClass", type="ClassDef")],
starting_line=None,
ending_line=None,
)
code_ctx = get_code_optimization_context(function_to_optimize, opt.args.project_root)
hashing_context = code_ctx.hashing_code_context
# Expected behavior based on current implementation:
# - Should not contain imports
# - Should remove docstrings from target functions (but currently doesn't - this is a bug)
# - Should not contain __init__ methods
# - Should contain target function and helper methods that are actually called
# - Should be formatted as markdown
# Test that it's formatted as markdown
assert hashing_context.startswith("```python:")
assert hashing_context.endswith("```")
# Test basic structure requirements
assert "import" not in hashing_context # Should not contain imports
assert "__init__" not in hashing_context # Should not contain __init__ methods
assert "target_method" in hashing_context # Should contain target function
assert "standalone_function" not in hashing_context # Should not contain unused functions
# Test that helper functions are included when they're called
assert "helper_method" in hashing_context # Should contain called helper method
assert "process_data" in hashing_context # Should contain called helper method
# Test for docstring removal (this should pass when implementation is fixed)
# Currently this will fail because docstrings are not being removed properly
assert '"""Target method with docstring."""' not in hashing_context, (
"Docstrings should be removed from target functions"
)
assert '"""Helper method with docstring."""' not in hashing_context, (
"Docstrings should be removed from helper functions"
)
assert '"""Process data method."""' not in hashing_context, "Docstrings should be removed from helper class methods"
def test_hashing_code_context_with_nested_classes(tmp_path: Path) -> None:
"""Test that hashing context handles nested classes properly (should exclude them)."""
code = '''
class OuterClass:
"""Outer class docstring."""
def __init__(self):
"""Outer init."""
self.value = 1
def target_method(self):
"""Target method."""
return self.NestedClass().nested_method()
class NestedClass:
"""Nested class - should be excluded."""
def __init__(self):
self.nested_value = 2
def nested_method(self):
return self.nested_value
'''
# Create a temporary Python file using pytest's tmp_path fixture
file_path = tmp_path / "test_code.py"
file_path.write_text(code, encoding="utf-8")
opt = Optimizer(
Namespace(
project_root=file_path.parent.resolve(),
disable_telemetry=True,
tests_root="tests",
test_framework="pytest",
pytest_cmd="pytest",
experiment_id=None,
test_project_root=Path().resolve(),
)
)
function_to_optimize = FunctionToOptimize(
function_name="target_method",
file_path=file_path,
parents=[FunctionParent(name="OuterClass", type="ClassDef")],
starting_line=None,
ending_line=None,
)
code_ctx = get_code_optimization_context(function_to_optimize, opt.args.project_root)
hashing_context = code_ctx.hashing_code_context
# Test basic requirements
assert hashing_context.startswith("```python:")
assert hashing_context.endswith("```")
assert "target_method" in hashing_context
assert "__init__" not in hashing_context # Should not contain __init__ methods
# Verify nested classes are excluded from the hashing context
# The prune_cst_for_code_hashing function should not recurse into nested classes
assert "class NestedClass:" not in hashing_context # Nested class definition should not be present
# The target method will reference NestedClass, but the actual nested class definition should not be included
# The call to self.NestedClass().nested_method() should be in the target method but the nested class itself excluded
target_method_call_present = "self.NestedClass().nested_method()" in hashing_context
assert target_method_call_present, "The target method should contain the call to nested class"
# But the actual nested method definition should not be present
nested_method_definition_present = "def nested_method(self):" in hashing_context
assert not nested_method_definition_present, "Nested method definition should not be present in hashing context"
def test_hashing_code_context_hash_consistency(tmp_path: Path) -> None:
"""Test that the same code produces the same hash."""
code = """
class TestClass:
def target_method(self):
return "test"
"""
# Create a temporary Python file using pytest's tmp_path fixture
file_path = tmp_path / "test_code.py"
file_path.write_text(code, encoding="utf-8")
opt = Optimizer(
Namespace(
project_root=file_path.parent.resolve(),
disable_telemetry=True,
tests_root="tests",
test_framework="pytest",
pytest_cmd="pytest",
experiment_id=None,
test_project_root=Path().resolve(),
)
)
function_to_optimize = FunctionToOptimize(
function_name="target_method",
file_path=file_path,
parents=[FunctionParent(name="TestClass", type="ClassDef")],
starting_line=None,
ending_line=None,
)
# Generate context twice
code_ctx1 = get_code_optimization_context(function_to_optimize, opt.args.project_root)
code_ctx2 = get_code_optimization_context(function_to_optimize, opt.args.project_root)
# Hash should be consistent
assert code_ctx1.hashing_code_context_hash == code_ctx2.hashing_code_context_hash
assert code_ctx1.hashing_code_context == code_ctx2.hashing_code_context
# Hash should be valid SHA256
import hashlib
expected_hash = hashlib.sha256(code_ctx1.hashing_code_context.encode("utf-8")).hexdigest()
assert code_ctx1.hashing_code_context_hash == expected_hash
def test_hashing_code_context_different_code_different_hash(tmp_path: Path) -> None:
"""Test that different code produces different hashes."""
code1 = """
class TestClass:
def target_method(self):
return "test1"
"""
code2 = """
class TestClass:
def target_method(self):
return "test2"
"""
# Create two temporary Python files using pytest's tmp_path fixture
file_path1 = tmp_path / "test_code1.py"
file_path2 = tmp_path / "test_code2.py"
file_path1.write_text(code1, encoding="utf-8")
file_path2.write_text(code2, encoding="utf-8")
opt1 = Optimizer(
Namespace(
project_root=file_path1.parent.resolve(),
disable_telemetry=True,
tests_root="tests",
test_framework="pytest",
pytest_cmd="pytest",
experiment_id=None,
test_project_root=Path().resolve(),
)
)
opt2 = Optimizer(
Namespace(
project_root=file_path2.parent.resolve(),
disable_telemetry=True,
tests_root="tests",
test_framework="pytest",
pytest_cmd="pytest",
experiment_id=None,
test_project_root=Path().resolve(),
)
)
function_to_optimize1 = FunctionToOptimize(
function_name="target_method",
file_path=file_path1,
parents=[FunctionParent(name="TestClass", type="ClassDef")],
starting_line=None,
ending_line=None,
)
function_to_optimize2 = FunctionToOptimize(
function_name="target_method",
file_path=file_path2,
parents=[FunctionParent(name="TestClass", type="ClassDef")],
starting_line=None,
ending_line=None,
)
code_ctx1 = get_code_optimization_context(function_to_optimize1, opt1.args.project_root)
code_ctx2 = get_code_optimization_context(function_to_optimize2, opt2.args.project_root)
# Different code should produce different hashes
assert code_ctx1.hashing_code_context_hash != code_ctx2.hashing_code_context_hash
assert code_ctx1.hashing_code_context != code_ctx2.hashing_code_context
def test_hashing_code_context_format_is_markdown(tmp_path: Path) -> None:
"""Test that hashing context is formatted as markdown."""
code = """
class SimpleClass:
def simple_method(self):
return 42
"""
# Create a temporary Python file using pytest's tmp_path fixture
file_path = tmp_path / "test_code.py"
file_path.write_text(code, encoding="utf-8")
opt = Optimizer(
Namespace(
project_root=file_path.parent.resolve(),
disable_telemetry=True,
tests_root="tests",
test_framework="pytest",
pytest_cmd="pytest",
experiment_id=None,
test_project_root=Path().resolve(),
)
)
function_to_optimize = FunctionToOptimize(
function_name="simple_method",
file_path=file_path,
parents=[FunctionParent(name="SimpleClass", type="ClassDef")],
starting_line=None,
ending_line=None,
)
code_ctx = get_code_optimization_context(function_to_optimize, opt.args.project_root)
hashing_context = code_ctx.hashing_code_context
# Should be formatted as markdown code block
assert hashing_context.startswith("```python:")
assert hashing_context.endswith("```")
# Should contain the relative file path in the markdown header
relative_path = file_path.relative_to(opt.args.project_root)
assert str(relative_path) in hashing_context
# Should contain the actual code between the markdown markers
lines = hashing_context.strip().split("\n")
assert lines[0].startswith("```python:")
assert lines[-1] == "```"
# Code should be between the markers
code_lines = lines[1:-1]
code_content = "\n".join(code_lines)
assert "class SimpleClass:" in code_content
assert "def simple_method(self):" in code_content
assert "return 42" in code_content
# This shouldn't happen as we are now using a scoped optimization context, but keep it just in case
def test_circular_deps():
path_to_root = Path(__file__).resolve().parent.parent / "code_to_optimize" / "code_directories" / "circular_deps"
file_abs_path = path_to_root / "api_client.py"
optimized_code = Path(path_to_root / "optimized.py").read_text(encoding="utf-8")
content = Path(file_abs_path).read_text(encoding="utf-8")
new_code = replace_functions_and_add_imports(
source_code=add_global_assignments(optimized_code, content),
function_names=["ApiClient.get_console_url"],
optimized_code=optimized_code,
module_abspath=Path(file_abs_path),
preexisting_objects={
("ApiClient", ()),
("get_console_url", (FunctionParent(name="ApiClient", type="ClassDef"),)),
},
project_root_path=Path(path_to_root),
)
assert "import ApiClient" not in new_code, "Error: Circular dependency found"
assert "import urllib.parse" in new_code, "Make sure imports for optimization global assignments exist"
def test_global_assignment_collector_with_async_function():
"""Test GlobalAssignmentCollector correctly identifies global assignments outside async functions."""
import libcst as cst
source_code = """
# Global assignment
GLOBAL_VAR = "global_value"
OTHER_GLOBAL = 42
async def async_function():
# This should not be collected (inside async function)
local_var = "local_value"
INNER_ASSIGNMENT = "should_not_be_global"
return local_var
# Another global assignment
ANOTHER_GLOBAL = "another_global"
"""
tree = cst.parse_module(source_code)
collector = GlobalAssignmentCollector()
tree.visit(collector)
# Should collect global assignments but not the ones inside async function
assert len(collector.assignments) == 3
assert "GLOBAL_VAR" in collector.assignments
assert "OTHER_GLOBAL" in collector.assignments
assert "ANOTHER_GLOBAL" in collector.assignments
# Should not collect assignments from inside async function
assert "local_var" not in collector.assignments
assert "INNER_ASSIGNMENT" not in collector.assignments
# Verify assignment order
expected_order = ["GLOBAL_VAR", "OTHER_GLOBAL", "ANOTHER_GLOBAL"]
assert collector.assignment_order == expected_order
def test_global_assignment_collector_nested_async_functions():
"""Test GlobalAssignmentCollector handles nested async functions correctly."""
import libcst as cst
source_code = """
# Global assignment
CONFIG = {"key": "value"}
def sync_function():
# Inside sync function - should not be collected
sync_local = "sync"
async def nested_async():
# Inside nested async function - should not be collected
nested_var = "nested"
return nested_var
return sync_local
async def async_function():
# Inside async function - should not be collected
async_local = "async"
def nested_sync():
# Inside nested function - should not be collected
deeply_nested = "deep"
return deeply_nested
return async_local
# Another global assignment
FINAL_GLOBAL = "final"
"""
tree = cst.parse_module(source_code)
collector = GlobalAssignmentCollector()
tree.visit(collector)
# Should only collect global-level assignments
assert len(collector.assignments) == 2
assert "CONFIG" in collector.assignments
assert "FINAL_GLOBAL" in collector.assignments
# Should not collect any assignments from inside functions
assert "sync_local" not in collector.assignments
assert "nested_var" not in collector.assignments
assert "async_local" not in collector.assignments
assert "deeply_nested" not in collector.assignments
def test_global_assignment_collector_mixed_async_sync_with_classes():
"""Test GlobalAssignmentCollector with async functions, sync functions, and classes."""
import libcst as cst
source_code = """
# Global assignments
GLOBAL_CONSTANT = "constant"
class TestClass:
# Class-level assignment - should not be collected
class_var = "class_value"
def sync_method(self):
# Method assignment - should not be collected
method_var = "method"
return method_var
async def async_method(self):
# Async method assignment - should not be collected
async_method_var = "async_method"
return async_method_var
def sync_function():
# Function assignment - should not be collected
func_var = "function"
return func_var
async def async_function():
# Async function assignment - should not be collected
async_func_var = "async_function"
return async_func_var
# More global assignments
ANOTHER_CONSTANT = 100
FINAL_ASSIGNMENT = {"data": "value"}
"""
tree = cst.parse_module(source_code)
collector = GlobalAssignmentCollector()
tree.visit(collector)
# Should only collect global-level assignments
assert len(collector.assignments) == 3
assert "GLOBAL_CONSTANT" in collector.assignments
assert "ANOTHER_CONSTANT" in collector.assignments
assert "FINAL_ASSIGNMENT" in collector.assignments
# Should not collect assignments from inside any scoped blocks
assert "class_var" not in collector.assignments
assert "method_var" not in collector.assignments
assert "async_method_var" not in collector.assignments
assert "func_var" not in collector.assignments
assert "async_func_var" not in collector.assignments
# Verify correct order
expected_order = ["GLOBAL_CONSTANT", "ANOTHER_CONSTANT", "FINAL_ASSIGNMENT"]
assert collector.assignment_order == expected_order
def test_global_assignment_collector_annotated_assignments():
"""Test GlobalAssignmentCollector correctly handles annotated assignments (AnnAssign)."""
import libcst as cst
source_code = """
# Regular global assignment
REGULAR_VAR = "regular"
# Annotated global assignments
TYPED_VAR: str = "typed"
CACHE: dict[str, int] = {}
SENTINEL: object = object()
# Annotated without value (type declaration only) - should NOT be collected
DECLARED_ONLY: int
def some_function():
# Annotated assignment inside function - should not be collected
local_typed: str = "local"
return local_typed
class SomeClass:
# Class-level annotated assignment - should not be collected
class_attr: str = "class"
# Another regular assignment
FINAL_VAR = 123
"""
tree = cst.parse_module(source_code)
collector = GlobalAssignmentCollector()
tree.visit(collector)
# Should collect both regular and annotated global assignments with values
assert len(collector.assignments) == 5
assert "REGULAR_VAR" in collector.assignments
assert "TYPED_VAR" in collector.assignments
assert "CACHE" in collector.assignments
assert "SENTINEL" in collector.assignments
assert "FINAL_VAR" in collector.assignments
# Should not collect type declarations without values
assert "DECLARED_ONLY" not in collector.assignments
# Should not collect assignments from inside functions or classes
assert "local_typed" not in collector.assignments
assert "class_attr" not in collector.assignments
# Verify correct order
expected_order = ["REGULAR_VAR", "TYPED_VAR", "CACHE", "SENTINEL", "FINAL_VAR"]
assert collector.assignment_order == expected_order
def test_class_instantiation_includes_init_as_helper(tmp_path: Path) -> None:
"""Test that when a class is instantiated, its __init__ method is tracked as a helper.
This test verifies the fix for the bug where class constructors were not
included in the context when only the class instantiation was called
(not any other methods). This caused LLMs to not know the constructor
signatures when generating tests.
"""
code = '''
class DataDumper:
"""A class that dumps data."""
def __init__(self, data):
"""Initialize with data."""
self.data = data
def dump(self):
"""Dump the data."""
return self.data
def target_function():
# Only instantiates DataDumper, doesn't call any other methods
dumper = DataDumper({"key": "value"})
return dumper
'''
file_path = tmp_path / "test_code.py"
file_path.write_text(code, encoding="utf-8")
opt = Optimizer(
Namespace(
project_root=file_path.parent.resolve(),
disable_telemetry=True,
tests_root="tests",
test_framework="pytest",
pytest_cmd="pytest",
experiment_id=None,
test_project_root=Path().resolve(),
)
)
function_to_optimize = FunctionToOptimize(
function_name="target_function", file_path=file_path, parents=[], starting_line=None, ending_line=None
)
code_ctx = get_code_optimization_context(function_to_optimize, opt.args.project_root)
# The __init__ method should be tracked as a helper since DataDumper() instantiates the class
qualified_names = {func.qualified_name for func in code_ctx.helper_functions}
assert "DataDumper.__init__" in qualified_names, (
"DataDumper.__init__ should be tracked as a helper when the class is instantiated"
)
# The testgen context should contain the class with __init__ (critical for LLM to know constructor)
testgen_context = code_ctx.testgen_context.markdown
assert "class DataDumper:" in testgen_context, "DataDumper class should be in testgen context"
assert "def __init__(self, data):" in testgen_context, "__init__ method should be included in testgen context"
# The hashing context should NOT contain __init__ (excluded for stability)
hashing_context = code_ctx.hashing_code_context
assert "__init__" not in hashing_context, "__init__ should NOT be in hashing context (excluded for hash stability)"
def test_class_instantiation_preserves_full_class_in_testgen(tmp_path: Path) -> None:
"""Test that instantiated classes are fully preserved in testgen context.
This is specifically for the unstructured LayoutDumper bug where helper classes
that were instantiated but had no other methods called were being excluded
from the testgen context.
"""
code = '''
class LayoutDumper:
"""Base class for layout dumpers."""
layout_source: str = "unknown"
def __init__(self, layout):
self._layout = layout
def dump(self) -> dict:
raise NotImplementedError()
class ObjectDetectionLayoutDumper(LayoutDumper):
"""Specific dumper for object detection layouts."""
def __init__(self, layout):
super().__init__(layout)
def dump(self) -> dict:
return {"type": "object_detection", "layout": self._layout}
def dump_layout(layout_type, layout):
"""Dump a layout based on its type."""
if layout_type == "object_detection":
dumper = ObjectDetectionLayoutDumper(layout)
else:
dumper = LayoutDumper(layout)
return dumper.dump()
'''
file_path = tmp_path / "test_code.py"
file_path.write_text(code, encoding="utf-8")
opt = Optimizer(
Namespace(
project_root=file_path.parent.resolve(),
disable_telemetry=True,
tests_root="tests",
test_framework="pytest",
pytest_cmd="pytest",
experiment_id=None,
test_project_root=Path().resolve(),
)
)
function_to_optimize = FunctionToOptimize(
function_name="dump_layout", file_path=file_path, parents=[], starting_line=None, ending_line=None
)
code_ctx = get_code_optimization_context(function_to_optimize, opt.args.project_root)
qualified_names = {func.qualified_name for func in code_ctx.helper_functions}
# Both class __init__ methods should be tracked as helpers
assert "ObjectDetectionLayoutDumper.__init__" in qualified_names, (
"ObjectDetectionLayoutDumper.__init__ should be tracked"
)
assert "LayoutDumper.__init__" in qualified_names, "LayoutDumper.__init__ should be tracked"
# The testgen context should include both classes with their __init__ methods
testgen_context = code_ctx.testgen_context.markdown
assert "class LayoutDumper:" in testgen_context, "LayoutDumper should be in testgen context"
assert "class ObjectDetectionLayoutDumper" in testgen_context, (
"ObjectDetectionLayoutDumper should be in testgen context"
)
# Both __init__ methods should be in the testgen context (so LLM knows constructor signatures)
assert testgen_context.count("def __init__") >= 2, "Both __init__ methods should be in testgen context"
def test_get_imported_class_definitions_extracts_project_classes(tmp_path: Path) -> None:
"""Test that get_imported_class_definitions extracts class definitions from project modules."""
# Create a package structure with two modules
package_dir = tmp_path / "mypackage"
package_dir.mkdir()
(package_dir / "__init__.py").write_text("", encoding="utf-8")
# Create a module with a class definition (simulating Element-like class)
elements_code = '''
import abc
class Element(abc.ABC):
"""An element in the document."""
def __init__(self, element_id: str = None):
self._element_id = element_id
self.text = ""
def __str__(self):
return self.text
class Text(Element):
"""A text element."""
def __init__(self, text: str, element_id: str = None):
super().__init__(element_id)
self.text = text
'''
elements_path = package_dir / "elements.py"
elements_path.write_text(elements_code, encoding="utf-8")
# Create another module that imports from elements
chunking_code = """
from mypackage.elements import Element
class PreChunk:
def __init__(self, elements: list[Element]):
self._elements = elements
class Accumulator:
def will_fit(self, chunk: PreChunk) -> bool:
return True
"""
chunking_path = package_dir / "chunking.py"
chunking_path.write_text(chunking_code, encoding="utf-8")
# Create CodeStringsMarkdown from the chunking module (simulating testgen context)
context = CodeStringsMarkdown(code_strings=[CodeString(code=chunking_code, file_path=chunking_path)])
# Call get_imported_class_definitions
result = get_imported_class_definitions(context, tmp_path)
# Verify Element class was extracted
assert len(result.code_strings) == 1, "Should extract exactly one class (Element)"
extracted_code = result.code_strings[0].code
# Verify the extracted code contains the Element class
assert "class Element" in extracted_code, "Should contain Element class definition"
assert "def __init__" in extracted_code, "Should contain __init__ method"
assert "element_id" in extracted_code, "Should contain constructor parameter"
assert "import abc" in extracted_code, "Should include necessary imports for base class"
def test_get_imported_class_definitions_skips_existing_definitions(tmp_path: Path) -> None:
"""Test that get_imported_class_definitions skips classes already defined in context."""
# Create a package structure
package_dir = tmp_path / "mypackage"
package_dir.mkdir()
(package_dir / "__init__.py").write_text("", encoding="utf-8")
# Create a module with a class definition
elements_code = """
class Element:
def __init__(self, text: str):
self.text = text
"""
elements_path = package_dir / "elements.py"
elements_path.write_text(elements_code, encoding="utf-8")
# Create code that imports Element but also redefines it locally
code_with_local_def = """
from mypackage.elements import Element
# Local redefinition (this happens when LLM redefines classes)
class Element:
def __init__(self, text: str):
self.text = text
class User:
def process(self, elem: Element):
pass
"""
code_path = package_dir / "user.py"
code_path.write_text(code_with_local_def, encoding="utf-8")
context = CodeStringsMarkdown(code_strings=[CodeString(code=code_with_local_def, file_path=code_path)])
# Call get_imported_class_definitions
result = get_imported_class_definitions(context, tmp_path)
# Should NOT extract Element since it's already defined locally
assert len(result.code_strings) == 0, "Should not extract classes already defined in context"
def test_get_imported_class_definitions_skips_third_party(tmp_path: Path) -> None:
"""Test that get_imported_class_definitions skips third-party/stdlib imports."""
# Create a simple package
package_dir = tmp_path / "mypackage"
package_dir.mkdir()
(package_dir / "__init__.py").write_text("", encoding="utf-8")
# Code with stdlib/third-party imports
code = """
from pathlib import Path
from typing import Optional
from dataclasses import dataclass
class MyClass:
def __init__(self, path: Path):
self.path = path
"""
code_path = package_dir / "main.py"
code_path.write_text(code, encoding="utf-8")
context = CodeStringsMarkdown(code_strings=[CodeString(code=code, file_path=code_path)])
# Call get_imported_class_definitions
result = get_imported_class_definitions(context, tmp_path)
# Should not extract any classes (Path, Optional, dataclass are stdlib/third-party)
assert len(result.code_strings) == 0, "Should not extract stdlib/third-party classes"
def test_get_imported_class_definitions_handles_multiple_imports(tmp_path: Path) -> None:
"""Test that get_imported_class_definitions handles multiple class imports."""
# Create a package structure
package_dir = tmp_path / "mypackage"
package_dir.mkdir()
(package_dir / "__init__.py").write_text("", encoding="utf-8")
# Create a module with multiple class definitions
types_code = """
class TypeA:
def __init__(self, value: int):
self.value = value
class TypeB:
def __init__(self, name: str):
self.name = name
class TypeC:
def __init__(self):
pass
"""
types_path = package_dir / "types.py"
types_path.write_text(types_code, encoding="utf-8")
# Create code that imports multiple classes
code = """
from mypackage.types import TypeA, TypeB
class Processor:
def process(self, a: TypeA, b: TypeB):
pass
"""
code_path = package_dir / "processor.py"
code_path.write_text(code, encoding="utf-8")
context = CodeStringsMarkdown(code_strings=[CodeString(code=code, file_path=code_path)])
# Call get_imported_class_definitions
result = get_imported_class_definitions(context, tmp_path)
# Should extract both TypeA and TypeB (but not TypeC since it's not imported)
assert len(result.code_strings) == 2, "Should extract exactly two classes (TypeA, TypeB)"
all_extracted_code = "\n".join(cs.code for cs in result.code_strings)
assert "class TypeA" in all_extracted_code, "Should contain TypeA class"
assert "class TypeB" in all_extracted_code, "Should contain TypeB class"
assert "class TypeC" not in all_extracted_code, "Should NOT contain TypeC (not imported)"
def test_get_imported_class_definitions_includes_dataclass_decorators(tmp_path: Path) -> None:
"""Test that get_imported_class_definitions includes decorators when extracting dataclasses."""
# Create a package structure
package_dir = tmp_path / "mypackage"
package_dir.mkdir()
(package_dir / "__init__.py").write_text("", encoding="utf-8")
# Create a module with dataclass definitions (like LLMConfig in skyvern)
models_code = """from dataclasses import dataclass, field
from typing import Optional
@dataclass(frozen=True)
class LLMConfigBase:
model_name: str
required_env_vars: list[str]
supports_vision: bool
add_assistant_prefix: bool
@dataclass(frozen=True)
class LLMConfig(LLMConfigBase):
litellm_params: Optional[dict] = field(default=None)
max_tokens: int | None = None
"""
models_path = package_dir / "models.py"
models_path.write_text(models_code, encoding="utf-8")
# Create code that imports the dataclass
code = """from mypackage.models import LLMConfig
class ConfigRegistry:
def get_config(self) -> LLMConfig:
pass
"""
code_path = package_dir / "registry.py"
code_path.write_text(code, encoding="utf-8")
context = CodeStringsMarkdown(code_strings=[CodeString(code=code, file_path=code_path)])
# Call get_imported_class_definitions
result = get_imported_class_definitions(context, tmp_path)
# Should extract both LLMConfigBase (base class) and LLMConfig
assert len(result.code_strings) == 2, "Should extract both LLMConfig and its base class LLMConfigBase"
# Combine extracted code to check for all required elements
all_extracted_code = "\n".join(cs.code for cs in result.code_strings)
# Verify the base class is extracted first (for proper inheritance understanding)
base_class_idx = all_extracted_code.find("class LLMConfigBase")
derived_class_idx = all_extracted_code.find("class LLMConfig(")
assert base_class_idx < derived_class_idx, "Base class should appear before derived class"
# Verify both classes include @dataclass decorators
assert all_extracted_code.count("@dataclass(frozen=True)") == 2, (
"Should include @dataclass decorator for both classes"
)
assert "class LLMConfig" in all_extracted_code, "Should contain LLMConfig class definition"
assert "class LLMConfigBase" in all_extracted_code, "Should contain LLMConfigBase class definition"
# Verify imports are included for dataclass-related items
assert "from dataclasses import" in all_extracted_code, "Should include dataclasses import"
def test_get_imported_class_definitions_extracts_imports_for_decorated_classes(tmp_path: Path) -> None:
"""Test that extract_imports_for_class includes decorator and type annotation imports."""
# Create a package structure
package_dir = tmp_path / "mypackage"
package_dir.mkdir()
(package_dir / "__init__.py").write_text("", encoding="utf-8")
# Create a module with decorated class that uses field() and various type annotations
models_code = """from dataclasses import dataclass, field
from typing import Optional, List
@dataclass
class Config:
name: str
values: List[int] = field(default_factory=list)
description: Optional[str] = None
"""
models_path = package_dir / "models.py"
models_path.write_text(models_code, encoding="utf-8")
# Create code that imports the class
code = """from mypackage.models import Config
def create_config() -> Config:
return Config(name="test")
"""
code_path = package_dir / "main.py"
code_path.write_text(code, encoding="utf-8")
context = CodeStringsMarkdown(code_strings=[CodeString(code=code, file_path=code_path)])
result = get_imported_class_definitions(context, tmp_path)
assert len(result.code_strings) == 1, "Should extract Config class"
extracted_code = result.code_strings[0].code
# The extracted code should include the decorator
assert "@dataclass" in extracted_code, "Should include @dataclass decorator"
# The imports should include dataclass and field
assert "from dataclasses import" in extracted_code, "Should include dataclasses import for decorator"
class TestCollectNamesFromAnnotation:
"""Tests for the collect_names_from_annotation helper function."""
def test_simple_name(self):
"""Test extracting a simple type name."""
import ast
code = "def f(x: MyClass): pass"
annotation = ast.parse(code).body[0].args.args[0].annotation
names: set[str] = set()
collect_names_from_annotation(annotation, names)
assert "MyClass" in names
def test_subscript_type(self):
"""Test extracting names from generic types like List[int]."""
import ast
code = "def f(x: List[int]): pass"
annotation = ast.parse(code).body[0].args.args[0].annotation
names: set[str] = set()
collect_names_from_annotation(annotation, names)
assert "List" in names
assert "int" in names
def test_optional_type(self):
"""Test extracting names from Optional[MyClass]."""
import ast
code = "def f(x: Optional[MyClass]): pass"
annotation = ast.parse(code).body[0].args.args[0].annotation
names: set[str] = set()
collect_names_from_annotation(annotation, names)
assert "Optional" in names
assert "MyClass" in names
def test_union_type_with_pipe(self):
"""Test extracting names from union types with | syntax."""
import ast
code = "def f(x: int | str | None): pass"
annotation = ast.parse(code).body[0].args.args[0].annotation
names: set[str] = set()
collect_names_from_annotation(annotation, names)
# int | str | None becomes BinOp nodes
assert "int" in names
assert "str" in names
def test_nested_generic_types(self):
"""Test extracting names from nested generics like Dict[str, List[MyClass]]."""
import ast
code = "def f(x: Dict[str, List[MyClass]]): pass"
annotation = ast.parse(code).body[0].args.args[0].annotation
names: set[str] = set()
collect_names_from_annotation(annotation, names)
assert "Dict" in names
assert "str" in names
assert "List" in names
assert "MyClass" in names
def test_tuple_annotation(self):
"""Test extracting names from tuple type hints."""
import ast
code = "def f(x: tuple[int, str, MyClass]): pass"
annotation = ast.parse(code).body[0].args.args[0].annotation
names: set[str] = set()
collect_names_from_annotation(annotation, names)
assert "tuple" in names
assert "int" in names
assert "str" in names
assert "MyClass" in names
class TestExtractImportsForClass:
"""Tests for the extract_imports_for_class helper function."""
def test_extracts_base_class_imports(self):
"""Test that base class imports are extracted."""
import ast
module_source = """from abc import ABC
from mypackage import BaseClass
class MyClass(BaseClass, ABC):
pass
"""
tree = ast.parse(module_source)
class_node = next(n for n in ast.walk(tree) if isinstance(n, ast.ClassDef))
result = extract_imports_for_class(tree, class_node, module_source)
assert "from abc import ABC" in result
assert "from mypackage import BaseClass" in result
def test_extracts_decorator_imports(self):
"""Test that decorator imports are extracted."""
import ast
module_source = """from dataclasses import dataclass
from functools import lru_cache
@dataclass
class MyClass:
name: str
"""
tree = ast.parse(module_source)
class_node = next(n for n in ast.walk(tree) if isinstance(n, ast.ClassDef))
result = extract_imports_for_class(tree, class_node, module_source)
assert "from dataclasses import dataclass" in result
def test_extracts_type_annotation_imports(self):
"""Test that type annotation imports are extracted."""
import ast
module_source = """from typing import Optional, List
from mypackage.models import Config
@dataclass
class MyClass:
config: Optional[Config]
items: List[str]
"""
tree = ast.parse(module_source)
class_node = next(n for n in ast.walk(tree) if isinstance(n, ast.ClassDef))
result = extract_imports_for_class(tree, class_node, module_source)
assert "from typing import Optional, List" in result
assert "from mypackage.models import Config" in result
def test_extracts_field_function_imports(self):
"""Test that field() function imports are extracted for dataclasses."""
import ast
module_source = """from dataclasses import dataclass, field
from typing import List
@dataclass
class MyClass:
items: List[str] = field(default_factory=list)
"""
tree = ast.parse(module_source)
class_node = next(n for n in ast.walk(tree) if isinstance(n, ast.ClassDef))
result = extract_imports_for_class(tree, class_node, module_source)
assert "from dataclasses import dataclass, field" in result
def test_no_duplicate_imports(self):
"""Test that duplicate imports are not included."""
import ast
module_source = """from typing import Optional
@dataclass
class MyClass:
field1: Optional[str]
field2: Optional[int]
"""
tree = ast.parse(module_source)
class_node = next(n for n in ast.walk(tree) if isinstance(n, ast.ClassDef))
result = extract_imports_for_class(tree, class_node, module_source)
# Should only have one import line even though Optional is used twice
assert result.count("from typing import Optional") == 1
def test_get_imported_class_definitions_multiple_decorators(tmp_path: Path) -> None:
"""Test that classes with multiple decorators are extracted correctly."""
package_dir = tmp_path / "mypackage"
package_dir.mkdir()
(package_dir / "__init__.py").write_text("", encoding="utf-8")
models_code = """from dataclasses import dataclass
from functools import total_ordering
@total_ordering
@dataclass
class OrderedConfig:
name: str
priority: int
def __lt__(self, other):
return self.priority < other.priority
"""
models_path = package_dir / "models.py"
models_path.write_text(models_code, encoding="utf-8")
code = """from mypackage.models import OrderedConfig
def sort_configs(configs: list[OrderedConfig]) -> list[OrderedConfig]:
return sorted(configs)
"""
code_path = package_dir / "main.py"
code_path.write_text(code, encoding="utf-8")
context = CodeStringsMarkdown(code_strings=[CodeString(code=code, file_path=code_path)])
result = get_imported_class_definitions(context, tmp_path)
assert len(result.code_strings) == 1
extracted_code = result.code_strings[0].code
# Both decorators should be included
assert "@total_ordering" in extracted_code, "Should include @total_ordering decorator"
assert "@dataclass" in extracted_code, "Should include @dataclass decorator"
assert "class OrderedConfig" in extracted_code
def test_get_imported_class_definitions_extracts_multilevel_inheritance(tmp_path: Path) -> None:
"""Test that base classes are recursively extracted for multi-level inheritance.
This is critical for understanding dataclass constructor signatures, as fields
from parent classes become required positional arguments in child classes.
"""
# Create a package structure
package_dir = tmp_path / "mypackage"
package_dir.mkdir()
(package_dir / "__init__.py").write_text("", encoding="utf-8")
# Create a module with multi-level inheritance like skyvern's LLM models:
# GrandParent -> Parent -> Child
models_code = '''from dataclasses import dataclass, field
from typing import Optional, Literal
@dataclass(frozen=True)
class GrandParentConfig:
"""Base config with common fields."""
model_name: str
required_env_vars: list[str]
@dataclass(frozen=True)
class ParentConfig(GrandParentConfig):
"""Intermediate config adding vision support."""
supports_vision: bool
add_assistant_prefix: bool
@dataclass(frozen=True)
class ChildConfig(ParentConfig):
"""Full config with optional parameters."""
litellm_params: Optional[dict] = field(default=None)
max_tokens: int | None = None
temperature: float | None = 0.7
@dataclass(frozen=True)
class RouterConfig(ParentConfig):
"""Router config branching from ParentConfig."""
model_list: list
main_model_group: str
routing_strategy: Literal["simple", "least-busy"] = "simple"
'''
models_path = package_dir / "models.py"
models_path.write_text(models_code, encoding="utf-8")
# Create code that imports only the child classes (not the base classes)
code = """from mypackage.models import ChildConfig, RouterConfig
class ConfigRegistry:
def get_child_config(self) -> ChildConfig:
pass
def get_router_config(self) -> RouterConfig:
pass
"""
code_path = package_dir / "registry.py"
code_path.write_text(code, encoding="utf-8")
context = CodeStringsMarkdown(code_strings=[CodeString(code=code, file_path=code_path)])
# Call get_imported_class_definitions
result = get_imported_class_definitions(context, tmp_path)
# Should extract 4 classes: GrandParentConfig, ParentConfig, ChildConfig, RouterConfig
# (all classes needed to understand the full inheritance hierarchy)
assert len(result.code_strings) == 4, (
f"Should extract 4 classes (GrandParent, Parent, Child, Router), got {len(result.code_strings)}"
)
# Combine extracted code
all_extracted_code = "\n".join(cs.code for cs in result.code_strings)
# Verify all classes are extracted
assert "class GrandParentConfig" in all_extracted_code, "Should extract GrandParentConfig base class"
assert "class ParentConfig(GrandParentConfig)" in all_extracted_code, "Should extract ParentConfig"
assert "class ChildConfig(ParentConfig)" in all_extracted_code, "Should extract ChildConfig"
assert "class RouterConfig(ParentConfig)" in all_extracted_code, "Should extract RouterConfig"
# Verify classes are ordered correctly (base classes before derived)
grandparent_idx = all_extracted_code.find("class GrandParentConfig")
parent_idx = all_extracted_code.find("class ParentConfig(")
child_idx = all_extracted_code.find("class ChildConfig(")
router_idx = all_extracted_code.find("class RouterConfig(")
assert grandparent_idx < parent_idx, "GrandParentConfig should appear before ParentConfig"
assert parent_idx < child_idx, "ParentConfig should appear before ChildConfig"
assert parent_idx < router_idx, "ParentConfig should appear before RouterConfig"
# Verify the critical fields are visible for constructor understanding
assert "model_name: str" in all_extracted_code, "Should include model_name field from GrandParent"
assert "required_env_vars: list[str]" in all_extracted_code, "Should include required_env_vars field"
assert "supports_vision: bool" in all_extracted_code, "Should include supports_vision field from Parent"
assert "litellm_params:" in all_extracted_code, "Should include litellm_params field from Child"
assert "model_list: list" in all_extracted_code, "Should include model_list field from Router"
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