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This PR changes `from pydantic import BaseModel` to `from synapse._pydantic_compat import BaseModel` (as well as `constr`, `conbytes`, `conint`, `confloat`). It allows `check_pydantic_models.py` to mock those pydantic objects only in the synapse module, and not interfere with pydantic objects in external dependencies. This should solve the CI problems for #17144, which breaks because `check_pydantic_models.py` patches pydantic models from [scim2-models](https://scim2-models.readthedocs.io/). /cc @DMRobertson @gotmax23 fixes #17659 ### Pull Request Checklist <!-- Please read https://element-hq.github.io/synapse/latest/development/contributing_guide.html before submitting your pull request --> * [x] Pull request is based on the develop branch * [x] Pull request includes a [changelog file](https://element-hq.github.io/synapse/latest/development/contributing_guide.html#changelog). The entry should: - Be a short description of your change which makes sense to users. "Fixed a bug that prevented receiving messages from other servers." instead of "Moved X method from `EventStore` to `EventWorkerStore`.". - Use markdown where necessary, mostly for `code blocks`. - End with either a period (.) or an exclamation mark (!). - Start with a capital letter. - Feel free to credit yourself, by adding a sentence "Contributed by @github_username." or "Contributed by [Your Name]." to the end of the entry. * [x] [Code style](https://element-hq.github.io/synapse/latest/code_style.html) is correct (run the [linters](https://element-hq.github.io/synapse/latest/development/contributing_guide.html#run-the-linters))
100 lines
3.2 KiB
Python
100 lines
3.2 KiB
Python
#
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# This file is licensed under the Affero General Public License (AGPL) version 3.
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#
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# Copyright 2020 The Matrix.org Foundation C.I.C.
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# Copyright (C) 2023 New Vector, Ltd
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#
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU Affero General Public License as
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# published by the Free Software Foundation, either version 3 of the
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# License, or (at your option) any later version.
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#
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# See the GNU Affero General Public License for more details:
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# <https://www.gnu.org/licenses/agpl-3.0.html>.
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#
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# Originally licensed under the Apache License, Version 2.0:
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# <http://www.apache.org/licenses/LICENSE-2.0>.
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#
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# [This file includes modifications made by New Vector Limited]
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#
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#
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from typing import Any, Dict, Type, TypeVar
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import jsonschema
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from synapse._pydantic_compat import BaseModel, ValidationError, parse_obj_as
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from synapse.config._base import ConfigError
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from synapse.types import JsonDict, StrSequence
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def validate_config(
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json_schema: JsonDict, config: Any, config_path: StrSequence
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) -> None:
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"""Validates a config setting against a JsonSchema definition
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This can be used to validate a section of the config file against a schema
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definition. If the validation fails, a ConfigError is raised with a textual
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description of the problem.
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Args:
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json_schema: the schema to validate against
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config: the configuration value to be validated
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config_path: the path within the config file. This will be used as a basis
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for the error message.
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Raises:
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ConfigError, if validation fails.
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"""
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try:
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jsonschema.validate(config, json_schema)
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except jsonschema.ValidationError as e:
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raise json_error_to_config_error(e, config_path)
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def json_error_to_config_error(
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e: jsonschema.ValidationError, config_path: StrSequence
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) -> ConfigError:
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"""Converts a json validation error to a user-readable ConfigError
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Args:
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e: the exception to be converted
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config_path: the path within the config file. This will be used as a basis
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for the error message.
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Returns:
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a ConfigError
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"""
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# copy `config_path` before modifying it.
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path = list(config_path)
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for p in list(e.absolute_path):
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if isinstance(p, int):
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path.append("<item %i>" % p)
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else:
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path.append(str(p))
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return ConfigError(e.message, path)
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Model = TypeVar("Model", bound=BaseModel)
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def parse_and_validate_mapping(
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config: Any,
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model_type: Type[Model],
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) -> Dict[str, Model]:
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"""Parse `config` as a mapping from strings to a given `Model` type.
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Args:
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config: The configuration data to check
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model_type: The BaseModel to validate and parse against.
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Returns:
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Fully validated and parsed Dict[str, Model].
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Raises:
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ConfigError, if given improper input.
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"""
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try:
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# type-ignore: mypy doesn't like constructing `Dict[str, model_type]` because
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# `model_type` is a runtime variable. Pydantic is fine with this.
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instances = parse_obj_as(Dict[str, model_type], config) # type: ignore[valid-type]
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except ValidationError as e:
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raise ConfigError(str(e)) from e
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return instances
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