425 lines
14 KiB
Python
Executable File
425 lines
14 KiB
Python
Executable File
#! /usr/bin/env python
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# Copyright 2022 The Matrix.org Foundation C.I.C.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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A script which enforces that Synapse always uses strict types when defining a Pydantic
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model.
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Pydantic does not yet offer a strict mode, but it is planned for pydantic v2. See
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https://github.com/pydantic/pydantic/issues/1098
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https://pydantic-docs.helpmanual.io/blog/pydantic-v2/#strict-mode
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until then, this script is a best effort to stop us from introducing type coersion bugs
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(like the infamous stringy power levels fixed in room version 10).
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"""
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import argparse
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import contextlib
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import functools
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import importlib
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import logging
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import os
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import pkgutil
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import sys
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import textwrap
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import traceback
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import unittest.mock
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from contextlib import contextmanager
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from typing import Any, Callable, Dict, Generator, List, Set, Type, TypeVar
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from parameterized import parameterized
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from pydantic import BaseModel as PydanticBaseModel, conbytes, confloat, conint, constr
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from pydantic.typing import get_args
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from typing_extensions import ParamSpec
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logger = logging.getLogger(__name__)
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CONSTRAINED_TYPE_FACTORIES_WITH_STRICT_FLAG: List[Callable] = [
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constr,
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conbytes,
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conint,
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confloat,
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]
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TYPES_THAT_PYDANTIC_WILL_COERCE_TO = [
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str,
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bytes,
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int,
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float,
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bool,
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]
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P = ParamSpec("P")
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R = TypeVar("R")
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class ModelCheckerException(Exception):
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"""Dummy exception. Allows us to detect unwanted types during a module import."""
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class MissingStrictInConstrainedTypeException(ModelCheckerException):
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factory_name: str
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def __init__(self, factory_name: str):
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self.factory_name = factory_name
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class FieldHasUnwantedTypeException(ModelCheckerException):
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message: str
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def __init__(self, message: str):
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self.message = message
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def make_wrapper(factory: Callable[P, R]) -> Callable[P, R]:
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"""We patch `constr` and friends with wrappers that enforce strict=True."""
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@functools.wraps(factory)
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def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
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if "strict" not in kwargs:
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raise MissingStrictInConstrainedTypeException(factory.__name__)
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if not kwargs["strict"]:
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raise MissingStrictInConstrainedTypeException(factory.__name__)
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return factory(*args, **kwargs)
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return wrapper
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def field_type_unwanted(type_: Any) -> bool:
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"""Very rough attempt to detect if a type is unwanted as a Pydantic annotation.
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At present, we exclude types which will coerce, or any generic type involving types
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which will coerce."""
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logger.debug("Is %s unwanted?")
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if type_ in TYPES_THAT_PYDANTIC_WILL_COERCE_TO:
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logger.debug("yes")
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return True
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logger.debug("Maybe. Subargs are %s", get_args(type_))
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rv = any(field_type_unwanted(t) for t in get_args(type_))
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logger.debug("Conclusion: %s %s unwanted", type_, "is" if rv else "is not")
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return rv
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class PatchedBaseModel(PydanticBaseModel):
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"""A patched version of BaseModel that inspects fields after models are defined.
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We complain loudly if we see an unwanted type.
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Beware: ModelField.type_ is presumably private; this is likely to be very brittle.
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"""
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@classmethod
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def __init_subclass__(cls: Type[PydanticBaseModel], **kwargs: object):
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for field in cls.__fields__.values():
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# Note that field.type_ and field.outer_type are computed based on the
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# annotation type, see pydantic.fields.ModelField._type_analysis
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if field_type_unwanted(field.outer_type_):
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# TODO: this only reports the first bad field. Can we find all bad ones
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# and report them all?
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raise FieldHasUnwantedTypeException(
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f"{cls.__module__}.{cls.__qualname__} has field '{field.name}' "
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f"with unwanted type `{field.outer_type_}`"
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)
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@contextmanager
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def monkeypatch_pydantic() -> Generator[None, None, None]:
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"""Patch pydantic with our snooping versions of BaseModel and the con* functions.
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If the snooping functions see something they don't like, they'll raise a
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ModelCheckingException instance.
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"""
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with contextlib.ExitStack() as patches:
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# Most Synapse code ought to import the patched objects directly from
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# `pydantic`. But we also patch their containing modules `pydantic.main` and
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# `pydantic.types` for completeness.
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patch_basemodel1 = unittest.mock.patch(
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"pydantic.BaseModel", new=PatchedBaseModel
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)
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patch_basemodel2 = unittest.mock.patch(
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"pydantic.main.BaseModel", new=PatchedBaseModel
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)
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patches.enter_context(patch_basemodel1)
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patches.enter_context(patch_basemodel2)
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for factory in CONSTRAINED_TYPE_FACTORIES_WITH_STRICT_FLAG:
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wrapper: Callable = make_wrapper(factory)
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patch1 = unittest.mock.patch(f"pydantic.{factory.__name__}", new=wrapper)
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patch2 = unittest.mock.patch(
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f"pydantic.types.{factory.__name__}", new=wrapper
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)
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patches.enter_context(patch1)
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patches.enter_context(patch2)
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yield
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def format_model_checker_exception(e: ModelCheckerException) -> str:
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"""Work out which line of code caused e. Format the line in a human-friendly way."""
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# TODO. FieldHasUnwantedTypeException gives better error messages. Can we ditch the
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# patches of constr() etc, and instead inspect fields to look for ConstrainedStr
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# with strict=False? There is some difficulty with the inheritance hierarchy
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# because StrictStr < ConstrainedStr < str.
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if isinstance(e, FieldHasUnwantedTypeException):
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return e.message
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elif isinstance(e, MissingStrictInConstrainedTypeException):
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frame_summary = traceback.extract_tb(e.__traceback__)[-2]
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return (
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f"Missing `strict=True` from {e.factory_name}() call \n"
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+ traceback.format_list([frame_summary])[0].lstrip()
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)
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else:
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raise ValueError(f"Unknown exception {e}") from e
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def lint() -> int:
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"""Try to import all of Synapse and see if we spot any Pydantic type coercions.
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Print any problems, then return a status code suitable for sys.exit."""
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failures = do_lint()
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if failures:
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print(f"Found {len(failures)} problem(s)")
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for failure in sorted(failures):
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print(failure)
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return os.EX_DATAERR if failures else os.EX_OK
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def do_lint() -> Set[str]:
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"""Try to import all of Synapse and see if we spot any Pydantic type coercions."""
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failures = set()
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with monkeypatch_pydantic():
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logger.debug("Importing synapse")
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try:
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# TODO: make "synapse" an argument so we can target this script at
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# a subpackage
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module = importlib.import_module("synapse")
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except ModelCheckerException as e:
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logger.warning("Bad annotation found when importing synapse")
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failures.add(format_model_checker_exception(e))
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return failures
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try:
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logger.debug("Fetching subpackages")
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module_infos = list(
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pkgutil.walk_packages(module.__path__, f"{module.__name__}.")
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)
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except ModelCheckerException as e:
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logger.warning("Bad annotation found when looking for modules to import")
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failures.add(format_model_checker_exception(e))
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return failures
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for module_info in module_infos:
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logger.debug("Importing %s", module_info.name)
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try:
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importlib.import_module(module_info.name)
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except ModelCheckerException as e:
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logger.warning(
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f"Bad annotation found when importing {module_info.name}"
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)
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failures.add(format_model_checker_exception(e))
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return failures
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def run_test_snippet(source: str) -> None:
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"""Exec a snippet of source code in an isolated environment."""
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# To emulate `source` being called at the top level of the module,
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# the globals and locals we provide apparently have to be the same mapping.
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#
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# > Remember that at the module level, globals and locals are the same dictionary.
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# > If exec gets two separate objects as globals and locals, the code will be
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# > executed as if it were embedded in a class definition.
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globals_: Dict[str, object]
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locals_: Dict[str, object]
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globals_ = locals_ = {}
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exec(textwrap.dedent(source), globals_, locals_)
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class TestConstrainedTypesPatch(unittest.TestCase):
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def test_expression_without_strict_raises(self) -> None:
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with monkeypatch_pydantic(), self.assertRaises(ModelCheckerException):
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run_test_snippet(
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"""
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from pydantic import constr
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constr()
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"""
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)
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def test_called_as_module_attribute_raises(self) -> None:
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with monkeypatch_pydantic(), self.assertRaises(ModelCheckerException):
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run_test_snippet(
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"""
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import pydantic
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pydantic.constr()
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"""
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)
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def test_wildcard_import_raises(self) -> None:
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with monkeypatch_pydantic(), self.assertRaises(ModelCheckerException):
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run_test_snippet(
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"""
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from pydantic import *
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constr()
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"""
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)
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def test_alternative_import_raises(self) -> None:
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with monkeypatch_pydantic(), self.assertRaises(ModelCheckerException):
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run_test_snippet(
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"""
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from pydantic.types import constr
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constr()
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"""
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)
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def test_alternative_import_attribute_raises(self) -> None:
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with monkeypatch_pydantic(), self.assertRaises(ModelCheckerException):
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run_test_snippet(
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"""
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import pydantic.types
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pydantic.types.constr()
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"""
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)
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def test_kwarg_but_no_strict_raises(self) -> None:
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with monkeypatch_pydantic(), self.assertRaises(ModelCheckerException):
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run_test_snippet(
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"""
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from pydantic import constr
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constr(min_length=10)
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"""
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)
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def test_kwarg_strict_False_raises(self) -> None:
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with monkeypatch_pydantic(), self.assertRaises(ModelCheckerException):
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run_test_snippet(
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"""
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from pydantic import constr
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constr(strict=False)
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"""
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)
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def test_kwarg_strict_True_doesnt_raise(self) -> None:
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with monkeypatch_pydantic():
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run_test_snippet(
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"""
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from pydantic import constr
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constr(strict=True)
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"""
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)
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def test_annotation_without_strict_raises(self) -> None:
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with monkeypatch_pydantic(), self.assertRaises(ModelCheckerException):
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run_test_snippet(
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"""
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from pydantic import constr
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x: constr()
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"""
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)
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def test_field_annotation_without_strict_raises(self) -> None:
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with monkeypatch_pydantic(), self.assertRaises(ModelCheckerException):
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run_test_snippet(
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"""
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from pydantic import BaseModel, conint
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class C:
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x: conint()
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"""
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)
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class TestFieldTypeInspection(unittest.TestCase):
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@parameterized.expand(
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[
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("str",),
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("bytes"),
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("int",),
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("float",),
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("bool"),
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("Optional[str]",),
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("Union[None, str]",),
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("List[str]",),
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("List[List[str]]",),
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("Dict[StrictStr, str]",),
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("Dict[str, StrictStr]",),
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("TypedDict('D', x=int)",),
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]
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)
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def test_field_holding_unwanted_type_raises(self, annotation: str) -> None:
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with monkeypatch_pydantic(), self.assertRaises(ModelCheckerException):
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run_test_snippet(
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f"""
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from typing import *
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from pydantic import *
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class C(BaseModel):
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f: {annotation}
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"""
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)
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@parameterized.expand(
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[
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("StrictStr",),
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("StrictBytes"),
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("StrictInt",),
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("StrictFloat",),
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("StrictBool"),
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("constr(strict=True, min_length=10)",),
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("Optional[StrictStr]",),
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("Union[None, StrictStr]",),
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("List[StrictStr]",),
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("List[List[StrictStr]]",),
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("Dict[StrictStr, StrictStr]",),
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("TypedDict('D', x=StrictInt)",),
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]
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)
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def test_field_holding_accepted_type_doesnt_raise(self, annotation: str) -> None:
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with monkeypatch_pydantic():
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run_test_snippet(
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f"""
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from typing import *
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from pydantic import *
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class C(BaseModel):
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f: {annotation}
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"""
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)
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def test_field_holding_str_raises_with_alternative_import(self) -> None:
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with monkeypatch_pydantic(), self.assertRaises(ModelCheckerException):
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run_test_snippet(
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"""
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from pydantic.main import BaseModel
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class C(BaseModel):
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f: str
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"""
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)
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parser = argparse.ArgumentParser()
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parser.add_argument("mode", choices=["lint", "test"], default="lint", nargs="?")
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parser.add_argument("-v", "--verbose", action="store_true")
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if __name__ == "__main__":
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args = parser.parse_args(sys.argv[1:])
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logging.basicConfig(
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format="%(asctime)s %(name)s:%(lineno)d %(levelname)s %(message)s",
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level=logging.DEBUG if args.verbose else logging.INFO,
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)
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# suppress logs we don't care about
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logging.getLogger("xmlschema").setLevel(logging.WARNING)
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if args.mode == "lint":
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sys.exit(lint())
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elif args.mode == "test":
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unittest.main(argv=sys.argv[:1])
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