Загрузить файлы в «venv/Lib/site-packages/pydantic/_internal»

venv/Lib/site-packages/pydantic/_internal/_config.py

@@ -0,0 +1,386 @@
+from __future__ import annotations as _annotations
+
+import warnings
+from contextlib import contextmanager
+from re import Pattern
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+    cast,
+)
+
+from pydantic_core import core_schema
+from typing_extensions import Self
+
+from ..aliases import AliasGenerator
+from ..config import ConfigDict, ExtraValues, JsonDict, JsonEncoder, JsonSchemaExtraCallable
+from ..errors import PydanticUserError
+from ..warnings import PydanticDeprecatedSince20, PydanticDeprecatedSince210
+
+if TYPE_CHECKING:
+    from .._internal._schema_generation_shared import GenerateSchema
+    from ..fields import ComputedFieldInfo, FieldInfo
+
+DEPRECATION_MESSAGE = 'Support for class-based `config` is deprecated, use ConfigDict instead.'
+
+
+class ConfigWrapper:
+    """Internal wrapper for Config which exposes ConfigDict items as attributes."""
+
+    __slots__ = ('config_dict',)
+
+    config_dict: ConfigDict
+
+    # all annotations are copied directly from ConfigDict, and should be kept up to date, a test will fail if they
+    # stop matching
+    title: str | None
+    str_to_lower: bool
+    str_to_upper: bool
+    str_strip_whitespace: bool
+    str_min_length: int
+    str_max_length: int | None
+    extra: ExtraValues | None
+    frozen: bool
+    populate_by_name: bool
+    use_enum_values: bool
+    validate_assignment: bool
+    arbitrary_types_allowed: bool
+    from_attributes: bool
+    # whether to use the actual key provided in the data (e.g. alias or first alias for "field required" errors) instead of field_names
+    # to construct error `loc`s, default `True`
+    loc_by_alias: bool
+    alias_generator: Callable[[str], str] | AliasGenerator | None
+    model_title_generator: Callable[[type], str] | None
+    field_title_generator: Callable[[str, FieldInfo | ComputedFieldInfo], str] | None
+    ignored_types: tuple[type, ...]
+    allow_inf_nan: bool
+    json_schema_extra: JsonDict | JsonSchemaExtraCallable | None
+    json_encoders: dict[type[object], JsonEncoder] | None
+
+    # new in V2
+    strict: bool
+    # whether instances of models and dataclasses (including subclass instances) should re-validate, default 'never'
+    revalidate_instances: Literal['always', 'never', 'subclass-instances']
+    ser_json_timedelta: Literal['iso8601', 'float']
+    ser_json_temporal: Literal['iso8601', 'seconds', 'milliseconds']
+    val_temporal_unit: Literal['seconds', 'milliseconds', 'infer']
+    ser_json_bytes: Literal['utf8', 'base64', 'hex']
+    val_json_bytes: Literal['utf8', 'base64', 'hex']
+    ser_json_inf_nan: Literal['null', 'constants', 'strings']
+    # whether to validate default values during validation, default False
+    validate_default: bool
+    validate_return: bool
+    protected_namespaces: tuple[str | Pattern[str], ...]
+    hide_input_in_errors: bool
+    defer_build: bool
+    plugin_settings: dict[str, object] | None
+    schema_generator: type[GenerateSchema] | None
+    json_schema_serialization_defaults_required: bool
+    json_schema_mode_override: Literal['validation', 'serialization', None]
+    coerce_numbers_to_str: bool
+    regex_engine: Literal['rust-regex', 'python-re']
+    validation_error_cause: bool
+    use_attribute_docstrings: bool
+    cache_strings: bool | Literal['all', 'keys', 'none']
+    validate_by_alias: bool
+    validate_by_name: bool
+    serialize_by_alias: bool
+    url_preserve_empty_path: bool
+    polymorphic_serialization: bool
+
+    def __init__(self, config: ConfigDict | dict[str, Any] | type[Any] | None, *, check: bool = True):
+        if check:
+            self.config_dict = prepare_config(config)
+        else:
+            self.config_dict = cast(ConfigDict, config)
+
+    @classmethod
+    def for_model(
+        cls,
+        bases: tuple[type[Any], ...],
+        namespace: dict[str, Any],
+        raw_annotations: dict[str, Any],
+        kwargs: dict[str, Any],
+    ) -> Self:
+        """Build a new `ConfigWrapper` instance for a `BaseModel`.
+
+        The config wrapper built based on (in descending order of priority):
+        - options from `kwargs`
+        - options from the `namespace`
+        - options from the base classes (`bases`)
+
+        Args:
+            bases: A tuple of base classes.
+            namespace: The namespace of the class being created.
+            raw_annotations: The (non-evaluated) annotations of the model.
+            kwargs: The kwargs passed to the class being created.
+
+        Returns:
+            A `ConfigWrapper` instance for `BaseModel`.
+        """
+        config_new = ConfigDict()
+        for base in bases:
+            config = getattr(base, 'model_config', None)
+            if config:
+                config_new.update(config.copy())
+
+        config_class_from_namespace = namespace.get('Config')
+        config_dict_from_namespace = namespace.get('model_config')
+
+        if raw_annotations.get('model_config') and config_dict_from_namespace is None:
+            raise PydanticUserError(
+                '`model_config` cannot be used as a model field name. Use `model_config` for model configuration.',
+                code='model-config-invalid-field-name',
+            )
+
+        if config_class_from_namespace and config_dict_from_namespace:
+            raise PydanticUserError('"Config" and "model_config" cannot be used together', code='config-both')
+
+        config_from_namespace = config_dict_from_namespace or prepare_config(config_class_from_namespace)
+
+        config_new.update(config_from_namespace)
+
+        for k in list(kwargs.keys()):
+            if k in config_keys:
+                config_new[k] = kwargs.pop(k)
+
+        return cls(config_new)
+
+    # we don't show `__getattr__` to type checkers so missing attributes cause errors
+    if not TYPE_CHECKING:  # pragma: no branch
+
+        def __getattr__(self, name: str) -> Any:
+            try:
+                return self.config_dict[name]
+            except KeyError:
+                try:
+                    return config_defaults[name]
+                except KeyError:
+                    raise AttributeError(f'Config has no attribute {name!r}') from None
+
+    def core_config(self, title: str | None) -> core_schema.CoreConfig:
+        """Create a pydantic-core config.
+
+        We don't use getattr here since we don't want to populate with defaults.
+
+        Args:
+            title: The title to use if not set in config.
+
+        Returns:
+            A `CoreConfig` object created from config.
+        """
+        config = self.config_dict
+
+        if config.get('schema_generator') is not None:
+            warnings.warn(
+                'The `schema_generator` setting has been deprecated since v2.10. This setting no longer has any effect.',
+                PydanticDeprecatedSince210,
+                stacklevel=2,
+            )
+
+        if (populate_by_name := config.get('populate_by_name')) is not None:
+            # We include this patch for backwards compatibility purposes, but this config setting will be deprecated in v3.0, and likely removed in v4.0.
+            # Thus, the above warning and this patch can be removed then as well.
+            if config.get('validate_by_name') is None:
+                config['validate_by_alias'] = True
+                config['validate_by_name'] = populate_by_name
+
+        # We dynamically patch validate_by_name to be True if validate_by_alias is set to False
+        # and validate_by_name is not explicitly set.
+        if config.get('validate_by_alias') is False and config.get('validate_by_name') is None:
+            config['validate_by_name'] = True
+
+        if (not config.get('validate_by_alias', True)) and (not config.get('validate_by_name', False)):
+            raise PydanticUserError(
+                'At least one of `validate_by_alias` or `validate_by_name` must be set to True.',
+                code='validate-by-alias-and-name-false',
+            )
+
+        return core_schema.CoreConfig(
+            **{  # pyright: ignore[reportArgumentType]
+                k: v
+                for k, v in (
+                    ('title', config.get('title') or title or None),
+                    ('extra_fields_behavior', config.get('extra')),
+                    ('allow_inf_nan', config.get('allow_inf_nan')),
+                    ('str_strip_whitespace', config.get('str_strip_whitespace')),
+                    ('str_to_lower', config.get('str_to_lower')),
+                    ('str_to_upper', config.get('str_to_upper')),
+                    ('strict', config.get('strict')),
+                    ('ser_json_timedelta', config.get('ser_json_timedelta')),
+                    ('ser_json_temporal', config.get('ser_json_temporal')),
+                    ('val_temporal_unit', config.get('val_temporal_unit')),
+                    ('ser_json_bytes', config.get('ser_json_bytes')),
+                    ('val_json_bytes', config.get('val_json_bytes')),
+                    ('ser_json_inf_nan', config.get('ser_json_inf_nan')),
+                    ('from_attributes', config.get('from_attributes')),
+                    ('loc_by_alias', config.get('loc_by_alias')),
+                    ('revalidate_instances', config.get('revalidate_instances')),
+                    ('validate_default', config.get('validate_default')),
+                    ('str_max_length', config.get('str_max_length')),
+                    ('str_min_length', config.get('str_min_length')),
+                    ('hide_input_in_errors', config.get('hide_input_in_errors')),
+                    ('coerce_numbers_to_str', config.get('coerce_numbers_to_str')),
+                    ('regex_engine', config.get('regex_engine')),
+                    ('validation_error_cause', config.get('validation_error_cause')),
+                    ('cache_strings', config.get('cache_strings')),
+                    ('validate_by_alias', config.get('validate_by_alias')),
+                    ('validate_by_name', config.get('validate_by_name')),
+                    ('serialize_by_alias', config.get('serialize_by_alias')),
+                    ('url_preserve_empty_path', config.get('url_preserve_empty_path')),
+                    ('polymorphic_serialization', config.get('polymorphic_serialization')),
+                )
+                if v is not None
+            }
+        )
+
+    def __repr__(self):
+        c = ', '.join(f'{k}={v!r}' for k, v in self.config_dict.items())
+        return f'ConfigWrapper({c})'
+
+
+class ConfigWrapperStack:
+    """A stack of `ConfigWrapper` instances."""
+
+    def __init__(self, config_wrapper: ConfigWrapper):
+        self._config_wrapper_stack: list[ConfigWrapper] = [config_wrapper]
+
+    @property
+    def tail(self) -> ConfigWrapper:
+        return self._config_wrapper_stack[-1]
+
+    @contextmanager
+    def push(self, config_wrapper: ConfigWrapper | ConfigDict | None):
+        if config_wrapper is None:
+            yield
+            return
+
+        if not isinstance(config_wrapper, ConfigWrapper):
+            config_wrapper = ConfigWrapper(config_wrapper, check=False)
+
+        self._config_wrapper_stack.append(config_wrapper)
+        try:
+            yield
+        finally:
+            self._config_wrapper_stack.pop()
+
+
+config_defaults = ConfigDict(
+    title=None,
+    str_to_lower=False,
+    str_to_upper=False,
+    str_strip_whitespace=False,
+    str_min_length=0,
+    str_max_length=None,
+    # let the model / dataclass decide how to handle it
+    extra=None,
+    frozen=False,
+    populate_by_name=False,
+    use_enum_values=False,
+    validate_assignment=False,
+    arbitrary_types_allowed=False,
+    from_attributes=False,
+    loc_by_alias=True,
+    alias_generator=None,
+    model_title_generator=None,
+    field_title_generator=None,
+    ignored_types=(),
+    allow_inf_nan=True,
+    json_schema_extra=None,
+    strict=False,
+    revalidate_instances='never',
+    ser_json_timedelta='iso8601',
+    ser_json_temporal='iso8601',
+    val_temporal_unit='infer',
+    ser_json_bytes='utf8',
+    val_json_bytes='utf8',
+    ser_json_inf_nan='null',
+    validate_default=False,
+    validate_return=False,
+    protected_namespaces=('model_validate', 'model_dump'),
+    hide_input_in_errors=False,
+    json_encoders=None,
+    defer_build=False,
+    schema_generator=None,
+    plugin_settings=None,
+    json_schema_serialization_defaults_required=False,
+    json_schema_mode_override=None,
+    coerce_numbers_to_str=False,
+    regex_engine='rust-regex',
+    validation_error_cause=False,
+    use_attribute_docstrings=False,
+    cache_strings=True,
+    validate_by_alias=True,
+    validate_by_name=False,
+    serialize_by_alias=False,
+    url_preserve_empty_path=False,
+    polymorphic_serialization=False,
+)
+
+
+def prepare_config(config: ConfigDict | dict[str, Any] | type[Any] | None) -> ConfigDict:
+    """Create a `ConfigDict` instance from an existing dict, a class (e.g. old class-based config) or None.
+
+    Args:
+        config: The input config.
+
+    Returns:
+        A ConfigDict object created from config.
+    """
+    if config is None:
+        return ConfigDict()
+
+    if not isinstance(config, dict):
+        warnings.warn(DEPRECATION_MESSAGE, PydanticDeprecatedSince20, stacklevel=4)
+        config = {k: getattr(config, k) for k in dir(config) if not k.startswith('__')}
+
+    config_dict = cast(ConfigDict, config)
+    check_deprecated(config_dict)
+    return config_dict
+
+
+config_keys = set(ConfigDict.__annotations__.keys())
+
+
+V2_REMOVED_KEYS = {
+    'allow_mutation',
+    'error_msg_templates',
+    'fields',
+    'getter_dict',
+    'smart_union',
+    'underscore_attrs_are_private',
+    'json_loads',
+    'json_dumps',
+    'copy_on_model_validation',
+    'post_init_call',
+}
+V2_RENAMED_KEYS = {
+    'allow_population_by_field_name': 'validate_by_name',
+    'anystr_lower': 'str_to_lower',
+    'anystr_strip_whitespace': 'str_strip_whitespace',
+    'anystr_upper': 'str_to_upper',
+    'keep_untouched': 'ignored_types',
+    'max_anystr_length': 'str_max_length',
+    'min_anystr_length': 'str_min_length',
+    'orm_mode': 'from_attributes',
+    'schema_extra': 'json_schema_extra',
+    'validate_all': 'validate_default',
+}
+
+
+def check_deprecated(config_dict: ConfigDict) -> None:
+    """Check for deprecated config keys and warn the user.
+
+    Args:
+        config_dict: The input config.
+    """
+    deprecated_removed_keys = V2_REMOVED_KEYS & config_dict.keys()
+    deprecated_renamed_keys = V2_RENAMED_KEYS.keys() & config_dict.keys()
+    if deprecated_removed_keys or deprecated_renamed_keys:
+        renamings = {k: V2_RENAMED_KEYS[k] for k in sorted(deprecated_renamed_keys)}
+        renamed_bullets = [f'* {k!r} has been renamed to {v!r}' for k, v in renamings.items()]
+        removed_bullets = [f'* {k!r} has been removed' for k in sorted(deprecated_removed_keys)]
+        message = '\n'.join(['Valid config keys have changed in V2:'] + renamed_bullets + removed_bullets)
+        warnings.warn(message, UserWarning)

venv/Lib/site-packages/pydantic/_internal/_core_metadata.py

@@ -0,0 +1,97 @@
+from __future__ import annotations as _annotations
+
+from typing import TYPE_CHECKING, Any, TypedDict, cast
+from warnings import warn
+
+if TYPE_CHECKING:
+    from ..config import JsonDict, JsonSchemaExtraCallable
+    from ._schema_generation_shared import (
+        GetJsonSchemaFunction,
+    )
+
+
+class CoreMetadata(TypedDict, total=False):
+    """A `TypedDict` for holding the metadata dict of the schema.
+
+    Attributes:
+        pydantic_js_functions: List of JSON schema functions that resolve refs during application.
+        pydantic_js_annotation_functions: List of JSON schema functions that don't resolve refs during application.
+        pydantic_js_prefer_positional_arguments: Whether JSON schema generator will
+            prefer positional over keyword arguments for an 'arguments' schema.
+            custom validation function. Only applies to before, plain, and wrap validators.
+        pydantic_js_updates: key / value pair updates to apply to the JSON schema for a type.
+        pydantic_js_extra: WIP, either key/value pair updates to apply to the JSON schema, or a custom callable.
+        pydantic_internal_union_tag_key: Used internally by the `Tag` metadata to specify the tag used for a discriminated union.
+        pydantic_internal_union_discriminator: Used internally to specify the discriminator value for a discriminated union
+            when the discriminator was applied to a `'definition-ref'` schema, and that reference was missing at the time
+            of the annotation application.
+
+    TODO: Perhaps we should move this structure to pydantic-core. At the moment, though,
+    it's easier to iterate on if we leave it in pydantic until we feel there is a semi-stable API.
+
+    TODO: It's unfortunate how functionally oriented JSON schema generation is, especially that which occurs during
+    the core schema generation process. It's inevitable that we need to store some json schema related information
+    on core schemas, given that we generate JSON schemas directly from core schemas. That being said, debugging related
+    issues is quite difficult when JSON schema information is disguised via dynamically defined functions.
+    """
+
+    pydantic_js_functions: list[GetJsonSchemaFunction]
+    pydantic_js_annotation_functions: list[GetJsonSchemaFunction]
+    pydantic_js_prefer_positional_arguments: bool
+    pydantic_js_updates: JsonDict
+    pydantic_js_extra: JsonDict | JsonSchemaExtraCallable
+    pydantic_internal_union_tag_key: str
+    pydantic_internal_union_discriminator: str
+
+
+def update_core_metadata(
+    core_metadata: Any,
+    /,
+    *,
+    pydantic_js_functions: list[GetJsonSchemaFunction] | None = None,
+    pydantic_js_annotation_functions: list[GetJsonSchemaFunction] | None = None,
+    pydantic_js_updates: JsonDict | None = None,
+    pydantic_js_extra: JsonDict | JsonSchemaExtraCallable | None = None,
+) -> None:
+    from ..json_schema import PydanticJsonSchemaWarning
+
+    """Update CoreMetadata instance in place. When we make modifications in this function, they
+    take effect on the `core_metadata` reference passed in as the first (and only) positional argument.
+
+    First, cast to `CoreMetadata`, then finish with a cast to `dict[str, Any]` for core schema compatibility.
+    We do this here, instead of before / after each call to this function so that this typing hack
+    can be easily removed if/when we move `CoreMetadata` to `pydantic-core`.
+
+    For parameter descriptions, see `CoreMetadata` above.
+    """
+    core_metadata = cast(CoreMetadata, core_metadata)
+
+    if pydantic_js_functions:
+        core_metadata.setdefault('pydantic_js_functions', []).extend(pydantic_js_functions)
+
+    if pydantic_js_annotation_functions:
+        core_metadata.setdefault('pydantic_js_annotation_functions', []).extend(pydantic_js_annotation_functions)
+
+    if pydantic_js_updates:
+        if (existing_updates := core_metadata.get('pydantic_js_updates')) is not None:
+            core_metadata['pydantic_js_updates'] = {**existing_updates, **pydantic_js_updates}
+        else:
+            core_metadata['pydantic_js_updates'] = pydantic_js_updates
+
+    if pydantic_js_extra is not None:
+        existing_pydantic_js_extra = core_metadata.get('pydantic_js_extra')
+        if existing_pydantic_js_extra is None:
+            core_metadata['pydantic_js_extra'] = pydantic_js_extra
+        if isinstance(existing_pydantic_js_extra, dict):
+            if isinstance(pydantic_js_extra, dict):
+                core_metadata['pydantic_js_extra'] = {**existing_pydantic_js_extra, **pydantic_js_extra}
+            if callable(pydantic_js_extra):
+                warn(
+                    'Composing `dict` and `callable` type `json_schema_extra` is not supported.'
+                    'The `callable` type is being ignored.'
+                    "If you'd like support for this behavior, please open an issue on pydantic.",
+                    PydanticJsonSchemaWarning,
+                )
+        if callable(existing_pydantic_js_extra):
+            # if ever there's a case of a callable, we'll just keep the last json schema extra spec
+            core_metadata['pydantic_js_extra'] = pydantic_js_extra

venv/Lib/site-packages/pydantic/_internal/_core_utils.py

@@ -0,0 +1,174 @@
+from __future__ import annotations
+
+import inspect
+from collections.abc import Mapping, Sequence
+from typing import TYPE_CHECKING, Any, Union
+
+from pydantic_core import CoreSchema, core_schema
+from typing_extensions import TypeGuard, get_args, get_origin
+from typing_inspection import typing_objects
+
+from . import _repr
+from ._typing_extra import is_generic_alias
+
+if TYPE_CHECKING:
+    from rich.console import Console
+
+AnyFunctionSchema = Union[
+    core_schema.AfterValidatorFunctionSchema,
+    core_schema.BeforeValidatorFunctionSchema,
+    core_schema.WrapValidatorFunctionSchema,
+    core_schema.PlainValidatorFunctionSchema,
+]
+
+
+FunctionSchemaWithInnerSchema = Union[
+    core_schema.AfterValidatorFunctionSchema,
+    core_schema.BeforeValidatorFunctionSchema,
+    core_schema.WrapValidatorFunctionSchema,
+]
+
+CoreSchemaField = Union[
+    core_schema.ModelField, core_schema.DataclassField, core_schema.TypedDictField, core_schema.ComputedField
+]
+CoreSchemaOrField = Union[core_schema.CoreSchema, CoreSchemaField]
+
+_CORE_SCHEMA_FIELD_TYPES = {'typed-dict-field', 'dataclass-field', 'model-field', 'computed-field'}
+_FUNCTION_WITH_INNER_SCHEMA_TYPES = {'function-before', 'function-after', 'function-wrap'}
+_LIST_LIKE_SCHEMA_WITH_ITEMS_TYPES = {'list', 'set', 'frozenset'}
+
+
+def is_core_schema(
+    schema: CoreSchemaOrField,
+) -> TypeGuard[CoreSchema]:
+    return schema['type'] not in _CORE_SCHEMA_FIELD_TYPES
+
+
+def is_core_schema_field(
+    schema: CoreSchemaOrField,
+) -> TypeGuard[CoreSchemaField]:
+    return schema['type'] in _CORE_SCHEMA_FIELD_TYPES
+
+
+def is_function_with_inner_schema(
+    schema: CoreSchemaOrField,
+) -> TypeGuard[FunctionSchemaWithInnerSchema]:
+    return schema['type'] in _FUNCTION_WITH_INNER_SCHEMA_TYPES
+
+
+def is_list_like_schema_with_items_schema(
+    schema: CoreSchema,
+) -> TypeGuard[core_schema.ListSchema | core_schema.SetSchema | core_schema.FrozenSetSchema]:
+    return schema['type'] in _LIST_LIKE_SCHEMA_WITH_ITEMS_TYPES
+
+
+def get_type_ref(type_: Any, args_override: tuple[type[Any], ...] | None = None) -> str:
+    """Produces the ref to be used for this type by pydantic_core's core schemas.
+
+    This `args_override` argument was added for the purpose of creating valid recursive references
+    when creating generic models without needing to create a concrete class.
+    """
+    origin = get_origin(type_) or type_
+
+    args = get_args(type_) if is_generic_alias(type_) else (args_override or ())
+    generic_metadata = getattr(type_, '__pydantic_generic_metadata__', None)
+    if generic_metadata:
+        origin = generic_metadata['origin'] or origin
+        args = generic_metadata['args'] or args
+
+    module_name = getattr(origin, '__module__', '<No __module__>')
+    if typing_objects.is_typealiastype(origin):
+        type_ref = f'{module_name}.{origin.__name__}:{id(origin)}'
+    else:
+        try:
+            qualname = getattr(origin, '__qualname__', f'<No __qualname__: {origin}>')
+        except Exception:
+            qualname = getattr(origin, '__qualname__', '<No __qualname__>')
+        type_ref = f'{module_name}.{qualname}:{id(origin)}'
+
+    arg_refs: list[str] = []
+    for arg in args:
+        if isinstance(arg, str):
+            # Handle string literals as a special case; we may be able to remove this special handling if we
+            # wrap them in a ForwardRef at some point.
+            arg_ref = f'{arg}:str-{id(arg)}'
+        else:
+            arg_ref = f'{_repr.display_as_type(arg)}:{id(arg)}'
+        arg_refs.append(arg_ref)
+    if arg_refs:
+        type_ref = f'{type_ref}[{",".join(arg_refs)}]'
+    return type_ref
+
+
+def get_ref(s: core_schema.CoreSchema) -> None | str:
+    """Get the ref from the schema if it has one.
+    This exists just for type checking to work correctly.
+    """
+    return s.get('ref', None)
+
+
+def _clean_schema_for_pretty_print(obj: Any, strip_metadata: bool = True) -> Any:  # pragma: no cover
+    """A utility function to remove irrelevant information from a core schema."""
+    if isinstance(obj, Mapping):
+        new_dct = {}
+        for k, v in obj.items():
+            if k == 'metadata' and strip_metadata:
+                new_metadata = {}
+
+                for meta_k, meta_v in v.items():
+                    if meta_k in ('pydantic_js_functions', 'pydantic_js_annotation_functions'):
+                        new_metadata['js_metadata'] = '<stripped>'
+                    else:
+                        new_metadata[meta_k] = _clean_schema_for_pretty_print(meta_v, strip_metadata=strip_metadata)
+
+                if list(new_metadata.keys()) == ['js_metadata']:
+                    new_metadata = {'<stripped>'}
+
+                new_dct[k] = new_metadata
+            # Remove some defaults:
+            elif k in ('custom_init', 'root_model') and not v:
+                continue
+            else:
+                new_dct[k] = _clean_schema_for_pretty_print(v, strip_metadata=strip_metadata)
+
+        return new_dct
+    elif isinstance(obj, Sequence) and not isinstance(obj, str):
+        return [_clean_schema_for_pretty_print(v, strip_metadata=strip_metadata) for v in obj]
+    else:
+        return obj
+
+
+def pretty_print_core_schema(
+    val: Any,
+    *,
+    console: Console | None = None,
+    max_depth: int | None = None,
+    strip_metadata: bool = True,
+) -> None:  # pragma: no cover
+    """Pretty-print a core schema using the `rich` library.
+
+    Args:
+        val: The core schema to print, or a Pydantic model/dataclass/type adapter
+            (in which case the cached core schema is fetched and printed).
+        console: A rich console to use when printing. Defaults to the global rich console instance.
+        max_depth: The number of nesting levels which may be printed.
+        strip_metadata: Whether to strip metadata in the output. If `True` any known core metadata
+            attributes will be stripped (but custom attributes are kept). Defaults to `True`.
+    """
+    # lazy import:
+    from rich.pretty import pprint
+
+    # circ. imports:
+    from pydantic import BaseModel, TypeAdapter
+    from pydantic.dataclasses import is_pydantic_dataclass
+
+    if (inspect.isclass(val) and issubclass(val, BaseModel)) or is_pydantic_dataclass(val):
+        val = val.__pydantic_core_schema__
+    if isinstance(val, TypeAdapter):
+        val = val.core_schema
+    cleaned_schema = _clean_schema_for_pretty_print(val, strip_metadata=strip_metadata)
+
+    pprint(cleaned_schema, console=console, max_depth=max_depth)
+
+
+pps = pretty_print_core_schema

venv/Lib/site-packages/pydantic/_internal/_dataclasses.py

@@ -0,0 +1,315 @@
+"""Private logic for creating pydantic dataclasses."""
+
+from __future__ import annotations as _annotations
+
+import copy
+import dataclasses
+import sys
+import warnings
+from collections.abc import Generator
+from contextlib import contextmanager
+from functools import partial
+from typing import TYPE_CHECKING, Any, ClassVar, Protocol, cast
+
+from pydantic_core import (
+    ArgsKwargs,
+    SchemaSerializer,
+    SchemaValidator,
+    core_schema,
+)
+from typing_extensions import TypeAlias, TypeIs
+
+from ..errors import PydanticUndefinedAnnotation
+from ..fields import FieldInfo
+from ..plugin._schema_validator import PluggableSchemaValidator, create_schema_validator
+from ..warnings import PydanticDeprecatedSince20
+from . import _config, _decorators
+from ._fields import collect_dataclass_fields
+from ._generate_schema import GenerateSchema, InvalidSchemaError
+from ._generics import get_standard_typevars_map
+from ._mock_val_ser import set_dataclass_mocks
+from ._namespace_utils import NsResolver
+from ._signature import generate_pydantic_signature
+from ._utils import LazyClassAttribute
+
+if TYPE_CHECKING:
+    from _typeshed import DataclassInstance as StandardDataclass
+
+    from ..config import ConfigDict
+
+    class PydanticDataclass(StandardDataclass, Protocol):
+        """A protocol containing attributes only available once a class has been decorated as a Pydantic dataclass.
+
+        Attributes:
+            __pydantic_config__: Pydantic-specific configuration settings for the dataclass.
+            __pydantic_complete__: Whether dataclass building is completed, or if there are still undefined fields.
+            __pydantic_core_schema__: The pydantic-core schema used to build the SchemaValidator and SchemaSerializer.
+            __pydantic_decorators__: Metadata containing the decorators defined on the dataclass.
+            __pydantic_fields__: Metadata about the fields defined on the dataclass.
+            __pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the dataclass.
+            __pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the dataclass.
+        """
+
+        __pydantic_config__: ClassVar[ConfigDict]
+        __pydantic_complete__: ClassVar[bool]
+        __pydantic_core_schema__: ClassVar[core_schema.CoreSchema]
+        __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos]
+        __pydantic_fields__: ClassVar[dict[str, FieldInfo]]
+        __pydantic_serializer__: ClassVar[SchemaSerializer]
+        __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator]
+
+        @classmethod
+        def __pydantic_fields_complete__(cls) -> bool: ...
+
+
+def set_dataclass_fields(
+    cls: type[StandardDataclass],
+    config_wrapper: _config.ConfigWrapper,
+    ns_resolver: NsResolver | None = None,
+) -> None:
+    """Collect and set `cls.__pydantic_fields__`.
+
+    Args:
+        cls: The class.
+        config_wrapper: The config wrapper instance.
+        ns_resolver: Namespace resolver to use when getting dataclass annotations.
+    """
+    typevars_map = get_standard_typevars_map(cls)
+    fields = collect_dataclass_fields(
+        cls, ns_resolver=ns_resolver, typevars_map=typevars_map, config_wrapper=config_wrapper
+    )
+
+    cls.__pydantic_fields__ = fields  # type: ignore
+
+
+def complete_dataclass(
+    cls: type[Any],
+    config_wrapper: _config.ConfigWrapper,
+    *,
+    raise_errors: bool = True,
+    ns_resolver: NsResolver | None = None,
+    _force_build: bool = False,
+) -> bool:
+    """Finish building a pydantic dataclass.
+
+    This logic is called on a class which has already been wrapped in `dataclasses.dataclass()`.
+
+    This is somewhat analogous to `pydantic._internal._model_construction.complete_model_class`.
+
+    Args:
+        cls: The class.
+        config_wrapper: The config wrapper instance.
+        raise_errors: Whether to raise errors, defaults to `True`.
+        ns_resolver: The namespace resolver instance to use when collecting dataclass fields
+            and during schema building.
+        _force_build: Whether to force building the dataclass, no matter if
+            [`defer_build`][pydantic.config.ConfigDict.defer_build] is set.
+
+    Returns:
+        `True` if building a pydantic dataclass is successfully completed, `False` otherwise.
+
+    Raises:
+        PydanticUndefinedAnnotation: If `raise_error` is `True` and there is an undefined annotations.
+    """
+    original_init = cls.__init__
+
+    # dataclass.__init__ must be defined here so its `__qualname__` can be changed since functions can't be copied,
+    # and so that the mock validator is used if building was deferred:
+    def __init__(__dataclass_self__: PydanticDataclass, *args: Any, **kwargs: Any) -> None:
+        __tracebackhide__ = True
+        s = __dataclass_self__
+        s.__pydantic_validator__.validate_python(ArgsKwargs(args, kwargs), self_instance=s)
+
+    __init__.__qualname__ = f'{cls.__qualname__}.__init__'
+
+    cls.__init__ = __init__  # type: ignore
+    cls.__pydantic_config__ = config_wrapper.config_dict  # type: ignore
+
+    set_dataclass_fields(cls, config_wrapper=config_wrapper, ns_resolver=ns_resolver)
+
+    if not _force_build and config_wrapper.defer_build:
+        set_dataclass_mocks(cls)
+        return False
+
+    if hasattr(cls, '__post_init_post_parse__'):
+        warnings.warn(
+            'Support for `__post_init_post_parse__` has been dropped, the method will not be called',
+            PydanticDeprecatedSince20,
+        )
+
+    typevars_map = get_standard_typevars_map(cls)
+    gen_schema = GenerateSchema(
+        config_wrapper,
+        ns_resolver=ns_resolver,
+        typevars_map=typevars_map,
+    )
+
+    # set __signature__ attr only for the class, but not for its instances
+    # (because instances can define `__call__`, and `inspect.signature` shouldn't
+    # use the `__signature__` attribute and instead generate from `__call__`).
+    cls.__signature__ = LazyClassAttribute(
+        '__signature__',
+        partial(
+            generate_pydantic_signature,
+            # It's important that we reference the `original_init` here,
+            # as it is the one synthesized by the stdlib `dataclass` module:
+            init=original_init,
+            fields=cls.__pydantic_fields__,  # type: ignore
+            validate_by_name=config_wrapper.validate_by_name,
+            extra=config_wrapper.extra,
+            is_dataclass=True,
+        ),
+    )
+
+    try:
+        schema = gen_schema.generate_schema(cls)
+    except PydanticUndefinedAnnotation as e:
+        if raise_errors:
+            raise
+        set_dataclass_mocks(cls, f'`{e.name}`')
+        return False
+
+    core_config = config_wrapper.core_config(title=cls.__name__)
+
+    try:
+        schema = gen_schema.clean_schema(schema)
+    except InvalidSchemaError:
+        set_dataclass_mocks(cls)
+        return False
+
+    # We are about to set all the remaining required properties expected for this cast;
+    # __pydantic_decorators__ and __pydantic_fields__ should already be set
+    cls = cast('type[PydanticDataclass]', cls)
+
+    cls.__pydantic_core_schema__ = schema
+    cls.__pydantic_validator__ = create_schema_validator(
+        schema, cls, cls.__module__, cls.__qualname__, 'dataclass', core_config, config_wrapper.plugin_settings
+    )
+    cls.__pydantic_serializer__ = SchemaSerializer(schema, core_config)
+    cls.__pydantic_complete__ = True
+    return True
+
+
+def is_stdlib_dataclass(cls: type[Any], /) -> TypeIs[type[StandardDataclass]]:
+    """Returns `True` if the class is a stdlib dataclass and *not* a Pydantic dataclass.
+
+    Unlike the stdlib `dataclasses.is_dataclass()` function, this does *not* include subclasses
+    of a dataclass that are themselves not dataclasses.
+
+    Args:
+        cls: The class.
+
+    Returns:
+        `True` if the class is a stdlib dataclass, `False` otherwise.
+    """
+    return '__dataclass_fields__' in cls.__dict__ and not hasattr(cls, '__pydantic_validator__')
+
+
+def as_dataclass_field(pydantic_field: FieldInfo) -> dataclasses.Field[Any]:
+    field_args: dict[str, Any] = {'default': pydantic_field}
+
+    # Needed because if `doc` is set, the dataclass slots will be a dict (field name -> doc) instead of a tuple:
+    if sys.version_info >= (3, 14) and pydantic_field.description is not None:
+        field_args['doc'] = pydantic_field.description
+
+    # Needed as the stdlib dataclass module processes kw_only in a specific way during class construction:
+    if sys.version_info >= (3, 10) and pydantic_field.kw_only is not None:
+        field_args['kw_only'] = pydantic_field.kw_only
+
+    # Needed as the stdlib dataclass modules generates `__repr__()` during class construction:
+    if pydantic_field.repr is not True:
+        field_args['repr'] = pydantic_field.repr
+
+    return dataclasses.field(**field_args)
+
+
+DcFields: TypeAlias = dict[str, dataclasses.Field[Any]]
+
+
+@contextmanager
+def patch_base_fields(cls: type[Any]) -> Generator[None]:
+    """Temporarily patch the stdlib dataclasses bases of `cls` if the Pydantic `Field()` function is used.
+
+    When creating a Pydantic dataclass, it is possible to inherit from stdlib dataclasses, where
+    the Pydantic `Field()` function is used. To create this Pydantic dataclass, we first apply
+    the stdlib `@dataclass` decorator on it. During the construction of the stdlib dataclass,
+    the `kw_only` and `repr` field arguments need to be understood by the stdlib *during* the
+    dataclass construction. To do so, we temporarily patch the fields dictionary of the affected
+    bases.
+
+    For instance, with the following example:
+
+    ```python {test="skip" lint="skip"}
+    import dataclasses as stdlib_dc
+
+    import pydantic
+    import pydantic.dataclasses as pydantic_dc
+
+    @stdlib_dc.dataclass
+    class A:
+        a: int = pydantic.Field(repr=False)
+
+    # Notice that the `repr` attribute of the dataclass field is `True`:
+    A.__dataclass_fields__['a']
+    #> dataclass.Field(default=FieldInfo(repr=False), repr=True, ...)
+
+    @pydantic_dc.dataclass
+    class B(A):
+        b: int = pydantic.Field(repr=False)
+    ```
+
+    When passing `B` to the stdlib `@dataclass` decorator, it will look for fields in the parent classes
+    and reuse them directly. When this context manager is active, `A` will be temporarily patched to be
+    equivalent to:
+
+    ```python {test="skip" lint="skip"}
+    @stdlib_dc.dataclass
+    class A:
+        a: int = stdlib_dc.field(default=Field(repr=False), repr=False)
+    ```
+
+    !!! note
+        This is only applied to the bases of `cls`, and not `cls` itself. The reason is that the Pydantic
+        dataclass decorator "owns" `cls` (in the previous example, `B`). As such, we instead modify the fields
+        directly (in the previous example, we simply do `setattr(B, 'b', as_dataclass_field(pydantic_field))`).
+
+    !!! note
+        This approach is far from ideal, and can probably be the source of unwanted side effects/race conditions.
+        The previous implemented approach was mutating the `__annotations__` dict of `cls`, which is no longer a
+        safe operation in Python 3.14+, and resulted in unexpected behavior with field ordering anyway.
+    """
+    # A list of two-tuples, the first element being a reference to the
+    # dataclass fields dictionary, the second element being a mapping between
+    # the field names that were modified, and their original `Field`:
+    original_fields_list: list[tuple[DcFields, DcFields]] = []
+
+    for base in cls.__mro__[1:]:
+        dc_fields: dict[str, dataclasses.Field[Any]] = base.__dict__.get('__dataclass_fields__', {})
+        dc_fields_with_pydantic_field_defaults = {
+            field_name: field
+            for field_name, field in dc_fields.items()
+            if isinstance(field.default, FieldInfo)
+            # Only do the patching if one of the affected attributes is set:
+            and (field.default.description is not None or field.default.kw_only or field.default.repr is not True)
+        }
+        if dc_fields_with_pydantic_field_defaults:
+            original_fields_list.append((dc_fields, dc_fields_with_pydantic_field_defaults))
+            for field_name, field in dc_fields_with_pydantic_field_defaults.items():
+                default = cast(FieldInfo, field.default)
+                # `dataclasses.Field` isn't documented as working with `copy.copy()`.
+                # It is a class with `__slots__`, so should work (and we hope for the best):
+                new_dc_field = copy.copy(field)
+                # For base fields, no need to set `doc` from `FieldInfo.description`, this is only relevant
+                # for the class under construction and handled in `as_dataclass_field()`.
+                if sys.version_info >= (3, 10) and default.kw_only:
+                    new_dc_field.kw_only = True
+                if default.repr is not True:
+                    new_dc_field.repr = default.repr
+                dc_fields[field_name] = new_dc_field
+
+    try:
+        yield
+    finally:
+        for fields, original_fields in original_fields_list:
+            for field_name, original_field in original_fields.items():
+                fields[field_name] = original_field