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Diffstat (limited to '.venv/lib/python3.12/site-packages/pydantic_core')
| -rw-r--r-- | .venv/lib/python3.12/site-packages/pydantic_core/__init__.py | 139 | ||||
| -rwxr-xr-x | .venv/lib/python3.12/site-packages/pydantic_core/_pydantic_core.cpython-312-x86_64-linux-gnu.so | bin | 0 -> 4616736 bytes | |||
| -rw-r--r-- | .venv/lib/python3.12/site-packages/pydantic_core/_pydantic_core.pyi | 1013 | ||||
| -rw-r--r-- | .venv/lib/python3.12/site-packages/pydantic_core/core_schema.py | 4211 | ||||
| -rw-r--r-- | .venv/lib/python3.12/site-packages/pydantic_core/py.typed | 0 |
5 files changed, 5363 insertions, 0 deletions
diff --git a/.venv/lib/python3.12/site-packages/pydantic_core/__init__.py b/.venv/lib/python3.12/site-packages/pydantic_core/__init__.py new file mode 100644 index 00000000..791de9d9 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/pydantic_core/__init__.py @@ -0,0 +1,139 @@ +from __future__ import annotations + +import sys as _sys +from typing import Any as _Any + +from ._pydantic_core import ( + ArgsKwargs, + MultiHostUrl, + PydanticCustomError, + PydanticKnownError, + PydanticOmit, + PydanticSerializationError, + PydanticSerializationUnexpectedValue, + PydanticUndefined, + PydanticUndefinedType, + PydanticUseDefault, + SchemaError, + SchemaSerializer, + SchemaValidator, + Some, + TzInfo, + Url, + ValidationError, + __version__, + from_json, + to_json, + to_jsonable_python, + validate_core_schema, +) +from .core_schema import CoreConfig, CoreSchema, CoreSchemaType, ErrorType + +if _sys.version_info < (3, 11): + from typing_extensions import NotRequired as _NotRequired +else: + from typing import NotRequired as _NotRequired + +if _sys.version_info < (3, 12): + from typing_extensions import TypedDict as _TypedDict +else: + from typing import TypedDict as _TypedDict + +__all__ = [ + '__version__', + 'CoreConfig', + 'CoreSchema', + 'CoreSchemaType', + 'SchemaValidator', + 'SchemaSerializer', + 'Some', + 'Url', + 'MultiHostUrl', + 'ArgsKwargs', + 'PydanticUndefined', + 'PydanticUndefinedType', + 'SchemaError', + 'ErrorDetails', + 'InitErrorDetails', + 'ValidationError', + 'PydanticCustomError', + 'PydanticKnownError', + 'PydanticOmit', + 'PydanticUseDefault', + 'PydanticSerializationError', + 'PydanticSerializationUnexpectedValue', + 'TzInfo', + 'to_json', + 'from_json', + 'to_jsonable_python', + 'validate_core_schema', +] + + +class ErrorDetails(_TypedDict): + type: str + """ + The type of error that occurred, this is an identifier designed for + programmatic use that will change rarely or never. + + `type` is unique for each error message, and can hence be used as an identifier to build custom error messages. + """ + loc: tuple[int | str, ...] + """Tuple of strings and ints identifying where in the schema the error occurred.""" + msg: str + """A human readable error message.""" + input: _Any + """The input data at this `loc` that caused the error.""" + ctx: _NotRequired[dict[str, _Any]] + """ + Values which are required to render the error message, and could hence be useful in rendering custom error messages. + Also useful for passing custom error data forward. + """ + + +class InitErrorDetails(_TypedDict): + type: str | PydanticCustomError + """The type of error that occurred, this should a "slug" identifier that changes rarely or never.""" + loc: _NotRequired[tuple[int | str, ...]] + """Tuple of strings and ints identifying where in the schema the error occurred.""" + input: _Any + """The input data at this `loc` that caused the error.""" + ctx: _NotRequired[dict[str, _Any]] + """ + Values which are required to render the error message, and could hence be useful in rendering custom error messages. + Also useful for passing custom error data forward. + """ + + +class ErrorTypeInfo(_TypedDict): + """ + Gives information about errors. + """ + + type: ErrorType + """The type of error that occurred, this should a "slug" identifier that changes rarely or never.""" + message_template_python: str + """String template to render a human readable error message from using context, when the input is Python.""" + example_message_python: str + """Example of a human readable error message, when the input is Python.""" + message_template_json: _NotRequired[str] + """String template to render a human readable error message from using context, when the input is JSON data.""" + example_message_json: _NotRequired[str] + """Example of a human readable error message, when the input is JSON data.""" + example_context: dict[str, _Any] | None + """Example of context values.""" + + +class MultiHostHost(_TypedDict): + """ + A host part of a multi-host URL. + """ + + username: str | None + """The username part of this host, or `None`.""" + password: str | None + """The password part of this host, or `None`.""" + host: str | None + """The host part of this host, or `None`.""" + port: int | None + """The port part of this host, or `None`.""" diff --git a/.venv/lib/python3.12/site-packages/pydantic_core/_pydantic_core.cpython-312-x86_64-linux-gnu.so b/.venv/lib/python3.12/site-packages/pydantic_core/_pydantic_core.cpython-312-x86_64-linux-gnu.so Binary files differnew file mode 100755 index 00000000..a05d9ab0 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/pydantic_core/_pydantic_core.cpython-312-x86_64-linux-gnu.so diff --git a/.venv/lib/python3.12/site-packages/pydantic_core/_pydantic_core.pyi b/.venv/lib/python3.12/site-packages/pydantic_core/_pydantic_core.pyi new file mode 100644 index 00000000..f3103f28 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/pydantic_core/_pydantic_core.pyi @@ -0,0 +1,1013 @@ +import datetime +from collections.abc import Mapping +from typing import Any, Callable, Generic, Literal, TypeVar, final + +from _typeshed import SupportsAllComparisons +from typing_extensions import LiteralString, Self, TypeAlias + +from pydantic_core import ErrorDetails, ErrorTypeInfo, InitErrorDetails, MultiHostHost +from pydantic_core.core_schema import CoreConfig, CoreSchema, ErrorType + +__all__ = [ + '__version__', + 'build_profile', + 'build_info', + '_recursion_limit', + 'ArgsKwargs', + 'SchemaValidator', + 'SchemaSerializer', + 'Url', + 'MultiHostUrl', + 'SchemaError', + 'ValidationError', + 'PydanticCustomError', + 'PydanticKnownError', + 'PydanticOmit', + 'PydanticUseDefault', + 'PydanticSerializationError', + 'PydanticSerializationUnexpectedValue', + 'PydanticUndefined', + 'PydanticUndefinedType', + 'Some', + 'to_json', + 'from_json', + 'to_jsonable_python', + 'list_all_errors', + 'TzInfo', + 'validate_core_schema', +] +__version__: str +build_profile: str +build_info: str +_recursion_limit: int + +_T = TypeVar('_T', default=Any, covariant=True) + +_StringInput: TypeAlias = 'dict[str, _StringInput]' + +@final +class Some(Generic[_T]): + """ + Similar to Rust's [`Option::Some`](https://doc.rust-lang.org/std/option/enum.Option.html) type, this + identifies a value as being present, and provides a way to access it. + + Generally used in a union with `None` to different between "some value which could be None" and no value. + """ + + __match_args__ = ('value',) + + @property + def value(self) -> _T: + """ + Returns the value wrapped by `Some`. + """ + @classmethod + def __class_getitem__(cls, item: Any, /) -> type[Self]: ... + +@final +class SchemaValidator: + """ + `SchemaValidator` is the Python wrapper for `pydantic-core`'s Rust validation logic, internally it owns one + `CombinedValidator` which may in turn own more `CombinedValidator`s which make up the full schema validator. + """ + + # note: pyo3 currently supports __new__, but not __init__, though we include __init__ stubs + # and docstrings here (and in the following classes) for documentation purposes + + def __init__(self, schema: CoreSchema, config: CoreConfig | None = None) -> None: + """Initializes the `SchemaValidator`. + + Arguments: + schema: The `CoreSchema` to use for validation. + config: Optionally a [`CoreConfig`][pydantic_core.core_schema.CoreConfig] to configure validation. + """ + + def __new__(cls, schema: CoreSchema, config: CoreConfig | None = None) -> Self: ... + @property + def title(self) -> str: + """ + The title of the schema, as used in the heading of [`ValidationError.__str__()`][pydantic_core.ValidationError]. + """ + def validate_python( + self, + input: Any, + *, + strict: bool | None = None, + from_attributes: bool | None = None, + context: Any | None = None, + self_instance: Any | None = None, + allow_partial: bool | Literal['off', 'on', 'trailing-strings'] = False, + ) -> Any: + """ + Validate a Python object against the schema and return the validated object. + + Arguments: + input: The Python object to validate. + strict: Whether to validate the object in strict mode. + If `None`, the value of [`CoreConfig.strict`][pydantic_core.core_schema.CoreConfig] is used. + from_attributes: Whether to validate objects as inputs to models by extracting attributes. + If `None`, the value of [`CoreConfig.from_attributes`][pydantic_core.core_schema.CoreConfig] is used. + context: The context to use for validation, this is passed to functional validators as + [`info.context`][pydantic_core.core_schema.ValidationInfo.context]. + self_instance: An instance of a model set attributes on from validation, this is used when running + validation from the `__init__` method of a model. + allow_partial: Whether to allow partial validation; if `True` errors in the last element of sequences + and mappings are ignored. + `'trailing-strings'` means any final unfinished JSON string is included in the result. + + Raises: + ValidationError: If validation fails. + Exception: Other error types maybe raised if internal errors occur. + + Returns: + The validated object. + """ + def isinstance_python( + self, + input: Any, + *, + strict: bool | None = None, + from_attributes: bool | None = None, + context: Any | None = None, + self_instance: Any | None = None, + ) -> bool: + """ + Similar to [`validate_python()`][pydantic_core.SchemaValidator.validate_python] but returns a boolean. + + Arguments match `validate_python()`. This method will not raise `ValidationError`s but will raise internal + errors. + + Returns: + `True` if validation succeeds, `False` if validation fails. + """ + def validate_json( + self, + input: str | bytes | bytearray, + *, + strict: bool | None = None, + context: Any | None = None, + self_instance: Any | None = None, + allow_partial: bool | Literal['off', 'on', 'trailing-strings'] = False, + ) -> Any: + """ + Validate JSON data directly against the schema and return the validated Python object. + + This method should be significantly faster than `validate_python(json.loads(json_data))` as it avoids the + need to create intermediate Python objects + + It also handles constructing the correct Python type even in strict mode, where + `validate_python(json.loads(json_data))` would fail validation. + + Arguments: + input: The JSON data to validate. + strict: Whether to validate the object in strict mode. + If `None`, the value of [`CoreConfig.strict`][pydantic_core.core_schema.CoreConfig] is used. + context: The context to use for validation, this is passed to functional validators as + [`info.context`][pydantic_core.core_schema.ValidationInfo.context]. + self_instance: An instance of a model set attributes on from validation. + allow_partial: Whether to allow partial validation; if `True` incomplete JSON will be parsed successfully + and errors in the last element of sequences and mappings are ignored. + `'trailing-strings'` means any final unfinished JSON string is included in the result. + + Raises: + ValidationError: If validation fails or if the JSON data is invalid. + Exception: Other error types maybe raised if internal errors occur. + + Returns: + The validated Python object. + """ + def validate_strings( + self, + input: _StringInput, + *, + strict: bool | None = None, + context: Any | None = None, + allow_partial: bool | Literal['off', 'on', 'trailing-strings'] = False, + ) -> Any: + """ + Validate a string against the schema and return the validated Python object. + + This is similar to `validate_json` but applies to scenarios where the input will be a string but not + JSON data, e.g. URL fragments, query parameters, etc. + + Arguments: + input: The input as a string, or bytes/bytearray if `strict=False`. + strict: Whether to validate the object in strict mode. + If `None`, the value of [`CoreConfig.strict`][pydantic_core.core_schema.CoreConfig] is used. + context: The context to use for validation, this is passed to functional validators as + [`info.context`][pydantic_core.core_schema.ValidationInfo.context]. + allow_partial: Whether to allow partial validation; if `True` errors in the last element of sequences + and mappings are ignored. + `'trailing-strings'` means any final unfinished JSON string is included in the result. + + Raises: + ValidationError: If validation fails or if the JSON data is invalid. + Exception: Other error types maybe raised if internal errors occur. + + Returns: + The validated Python object. + """ + def validate_assignment( + self, + obj: Any, + field_name: str, + field_value: Any, + *, + strict: bool | None = None, + from_attributes: bool | None = None, + context: Any | None = None, + ) -> dict[str, Any] | tuple[dict[str, Any], dict[str, Any] | None, set[str]]: + """ + Validate an assignment to a field on a model. + + Arguments: + obj: The model instance being assigned to. + field_name: The name of the field to validate assignment for. + field_value: The value to assign to the field. + strict: Whether to validate the object in strict mode. + If `None`, the value of [`CoreConfig.strict`][pydantic_core.core_schema.CoreConfig] is used. + from_attributes: Whether to validate objects as inputs to models by extracting attributes. + If `None`, the value of [`CoreConfig.from_attributes`][pydantic_core.core_schema.CoreConfig] is used. + context: The context to use for validation, this is passed to functional validators as + [`info.context`][pydantic_core.core_schema.ValidationInfo.context]. + + Raises: + ValidationError: If validation fails. + Exception: Other error types maybe raised if internal errors occur. + + Returns: + Either the model dict or a tuple of `(model_data, model_extra, fields_set)` + """ + def get_default_value(self, *, strict: bool | None = None, context: Any = None) -> Some | None: + """ + Get the default value for the schema, including running default value validation. + + Arguments: + strict: Whether to validate the default value in strict mode. + If `None`, the value of [`CoreConfig.strict`][pydantic_core.core_schema.CoreConfig] is used. + context: The context to use for validation, this is passed to functional validators as + [`info.context`][pydantic_core.core_schema.ValidationInfo.context]. + + Raises: + ValidationError: If validation fails. + Exception: Other error types maybe raised if internal errors occur. + + Returns: + `None` if the schema has no default value, otherwise a [`Some`][pydantic_core.Some] containing the default. + """ + +# In reality, `bool` should be replaced by `Literal[True]` but mypy fails to correctly apply bidirectional type inference +# (e.g. when using `{'a': {'b': True}}`). +_IncEx: TypeAlias = set[int] | set[str] | Mapping[int, _IncEx | bool] | Mapping[str, _IncEx | bool] + +@final +class SchemaSerializer: + """ + `SchemaSerializer` is the Python wrapper for `pydantic-core`'s Rust serialization logic, internally it owns one + `CombinedSerializer` which may in turn own more `CombinedSerializer`s which make up the full schema serializer. + """ + + def __init__(self, schema: CoreSchema, config: CoreConfig | None = None) -> None: + """Initializes the `SchemaSerializer`. + + Arguments: + schema: The `CoreSchema` to use for serialization. + config: Optionally a [`CoreConfig`][pydantic_core.core_schema.CoreConfig] to to configure serialization. + """ + + def __new__(cls, schema: CoreSchema, config: CoreConfig | None = None) -> Self: ... + def to_python( + self, + value: Any, + *, + mode: str | None = None, + include: _IncEx | None = None, + exclude: _IncEx | None = None, + by_alias: bool = True, + exclude_unset: bool = False, + exclude_defaults: bool = False, + exclude_none: bool = False, + round_trip: bool = False, + warnings: bool | Literal['none', 'warn', 'error'] = True, + fallback: Callable[[Any], Any] | None = None, + serialize_as_any: bool = False, + context: Any | None = None, + ) -> Any: + """ + Serialize/marshal a Python object to a Python object including transforming and filtering data. + + Arguments: + value: The Python object to serialize. + mode: The serialization mode to use, either `'python'` or `'json'`, defaults to `'python'`. In JSON mode, + all values are converted to JSON compatible types, e.g. `None`, `int`, `float`, `str`, `list`, `dict`. + include: A set of fields to include, if `None` all fields are included. + exclude: A set of fields to exclude, if `None` no fields are excluded. + by_alias: Whether to use the alias names of fields. + exclude_unset: Whether to exclude fields that are not set, + e.g. are not included in `__pydantic_fields_set__`. + exclude_defaults: Whether to exclude fields that are equal to their default value. + exclude_none: Whether to exclude fields that have a value of `None`. + round_trip: Whether to enable serialization and validation round-trip support. + warnings: How to handle invalid fields. False/"none" ignores them, True/"warn" logs errors, + "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError]. + fallback: A function to call when an unknown value is encountered, + if `None` a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError] error is raised. + serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. + context: The context to use for serialization, this is passed to functional serializers as + [`info.context`][pydantic_core.core_schema.SerializationInfo.context]. + + Raises: + PydanticSerializationError: If serialization fails and no `fallback` function is provided. + + Returns: + The serialized Python object. + """ + def to_json( + self, + value: Any, + *, + indent: int | None = None, + include: _IncEx | None = None, + exclude: _IncEx | None = None, + by_alias: bool = True, + exclude_unset: bool = False, + exclude_defaults: bool = False, + exclude_none: bool = False, + round_trip: bool = False, + warnings: bool | Literal['none', 'warn', 'error'] = True, + fallback: Callable[[Any], Any] | None = None, + serialize_as_any: bool = False, + context: Any | None = None, + ) -> bytes: + """ + Serialize a Python object to JSON including transforming and filtering data. + + Arguments: + value: The Python object to serialize. + indent: If `None`, the JSON will be compact, otherwise it will be pretty-printed with the indent provided. + include: A set of fields to include, if `None` all fields are included. + exclude: A set of fields to exclude, if `None` no fields are excluded. + by_alias: Whether to use the alias names of fields. + exclude_unset: Whether to exclude fields that are not set, + e.g. are not included in `__pydantic_fields_set__`. + exclude_defaults: Whether to exclude fields that are equal to their default value. + exclude_none: Whether to exclude fields that have a value of `None`. + round_trip: Whether to enable serialization and validation round-trip support. + warnings: How to handle invalid fields. False/"none" ignores them, True/"warn" logs errors, + "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError]. + fallback: A function to call when an unknown value is encountered, + if `None` a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError] error is raised. + serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. + context: The context to use for serialization, this is passed to functional serializers as + [`info.context`][pydantic_core.core_schema.SerializationInfo.context]. + + Raises: + PydanticSerializationError: If serialization fails and no `fallback` function is provided. + + Returns: + JSON bytes. + """ + +def to_json( + value: Any, + *, + indent: int | None = None, + include: _IncEx | None = None, + exclude: _IncEx | None = None, + by_alias: bool = True, + exclude_none: bool = False, + round_trip: bool = False, + timedelta_mode: Literal['iso8601', 'float'] = 'iso8601', + bytes_mode: Literal['utf8', 'base64', 'hex'] = 'utf8', + inf_nan_mode: Literal['null', 'constants', 'strings'] = 'constants', + serialize_unknown: bool = False, + fallback: Callable[[Any], Any] | None = None, + serialize_as_any: bool = False, + context: Any | None = None, +) -> bytes: + """ + Serialize a Python object to JSON including transforming and filtering data. + + This is effectively a standalone version of [`SchemaSerializer.to_json`][pydantic_core.SchemaSerializer.to_json]. + + Arguments: + value: The Python object to serialize. + indent: If `None`, the JSON will be compact, otherwise it will be pretty-printed with the indent provided. + include: A set of fields to include, if `None` all fields are included. + exclude: A set of fields to exclude, if `None` no fields are excluded. + by_alias: Whether to use the alias names of fields. + exclude_none: Whether to exclude fields that have a value of `None`. + round_trip: Whether to enable serialization and validation round-trip support. + timedelta_mode: How to serialize `timedelta` objects, either `'iso8601'` or `'float'`. + bytes_mode: How to serialize `bytes` objects, either `'utf8'`, `'base64'`, or `'hex'`. + inf_nan_mode: How to serialize `Infinity`, `-Infinity` and `NaN` values, either `'null'`, `'constants'`, or `'strings'`. + serialize_unknown: Attempt to serialize unknown types, `str(value)` will be used, if that fails + `"<Unserializable {value_type} object>"` will be used. + fallback: A function to call when an unknown value is encountered, + if `None` a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError] error is raised. + serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. + context: The context to use for serialization, this is passed to functional serializers as + [`info.context`][pydantic_core.core_schema.SerializationInfo.context]. + + Raises: + PydanticSerializationError: If serialization fails and no `fallback` function is provided. + + Returns: + JSON bytes. + """ + +def from_json( + data: str | bytes | bytearray, + *, + allow_inf_nan: bool = True, + cache_strings: bool | Literal['all', 'keys', 'none'] = True, + allow_partial: bool | Literal['off', 'on', 'trailing-strings'] = False, +) -> Any: + """ + Deserialize JSON data to a Python object. + + This is effectively a faster version of `json.loads()`, with some extra functionality. + + Arguments: + data: The JSON data to deserialize. + allow_inf_nan: Whether to allow `Infinity`, `-Infinity` and `NaN` values as `json.loads()` does by default. + cache_strings: Whether to cache strings to avoid constructing new Python objects, + this should have a significant impact on performance while increasing memory usage slightly, + `all/True` means cache all strings, `keys` means cache only dict keys, `none/False` means no caching. + allow_partial: Whether to allow partial deserialization, if `True` JSON data is returned if the end of the + input is reached before the full object is deserialized, e.g. `["aa", "bb", "c` would return `['aa', 'bb']`. + `'trailing-strings'` means any final unfinished JSON string is included in the result. + + Raises: + ValueError: If deserialization fails. + + Returns: + The deserialized Python object. + """ + +def to_jsonable_python( + value: Any, + *, + include: _IncEx | None = None, + exclude: _IncEx | None = None, + by_alias: bool = True, + exclude_none: bool = False, + round_trip: bool = False, + timedelta_mode: Literal['iso8601', 'float'] = 'iso8601', + bytes_mode: Literal['utf8', 'base64', 'hex'] = 'utf8', + inf_nan_mode: Literal['null', 'constants', 'strings'] = 'constants', + serialize_unknown: bool = False, + fallback: Callable[[Any], Any] | None = None, + serialize_as_any: bool = False, + context: Any | None = None, +) -> Any: + """ + Serialize/marshal a Python object to a JSON-serializable Python object including transforming and filtering data. + + This is effectively a standalone version of + [`SchemaSerializer.to_python(mode='json')`][pydantic_core.SchemaSerializer.to_python]. + + Args: + value: The Python object to serialize. + include: A set of fields to include, if `None` all fields are included. + exclude: A set of fields to exclude, if `None` no fields are excluded. + by_alias: Whether to use the alias names of fields. + exclude_none: Whether to exclude fields that have a value of `None`. + round_trip: Whether to enable serialization and validation round-trip support. + timedelta_mode: How to serialize `timedelta` objects, either `'iso8601'` or `'float'`. + bytes_mode: How to serialize `bytes` objects, either `'utf8'`, `'base64'`, or `'hex'`. + inf_nan_mode: How to serialize `Infinity`, `-Infinity` and `NaN` values, either `'null'`, `'constants'`, or `'strings'`. + serialize_unknown: Attempt to serialize unknown types, `str(value)` will be used, if that fails + `"<Unserializable {value_type} object>"` will be used. + fallback: A function to call when an unknown value is encountered, + if `None` a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError] error is raised. + serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. + context: The context to use for serialization, this is passed to functional serializers as + [`info.context`][pydantic_core.core_schema.SerializationInfo.context]. + + Raises: + PydanticSerializationError: If serialization fails and no `fallback` function is provided. + + Returns: + The serialized Python object. + """ + +class Url(SupportsAllComparisons): + """ + A URL type, internal logic uses the [url rust crate](https://docs.rs/url/latest/url/) originally developed + by Mozilla. + """ + + def __init__(self, url: str) -> None: ... + def __new__(cls, url: str) -> Self: ... + @property + def scheme(self) -> str: ... + @property + def username(self) -> str | None: ... + @property + def password(self) -> str | None: ... + @property + def host(self) -> str | None: ... + def unicode_host(self) -> str | None: ... + @property + def port(self) -> int | None: ... + @property + def path(self) -> str | None: ... + @property + def query(self) -> str | None: ... + def query_params(self) -> list[tuple[str, str]]: ... + @property + def fragment(self) -> str | None: ... + def unicode_string(self) -> str: ... + def __repr__(self) -> str: ... + def __str__(self) -> str: ... + def __deepcopy__(self, memo: dict) -> str: ... + @classmethod + def build( + cls, + *, + scheme: str, + username: str | None = None, + password: str | None = None, + host: str, + port: int | None = None, + path: str | None = None, + query: str | None = None, + fragment: str | None = None, + ) -> Self: ... + +class MultiHostUrl(SupportsAllComparisons): + """ + A URL type with support for multiple hosts, as used by some databases for DSNs, e.g. `https://foo.com,bar.com/path`. + + Internal URL logic uses the [url rust crate](https://docs.rs/url/latest/url/) originally developed + by Mozilla. + """ + + def __init__(self, url: str) -> None: ... + def __new__(cls, url: str) -> Self: ... + @property + def scheme(self) -> str: ... + @property + def path(self) -> str | None: ... + @property + def query(self) -> str | None: ... + def query_params(self) -> list[tuple[str, str]]: ... + @property + def fragment(self) -> str | None: ... + def hosts(self) -> list[MultiHostHost]: ... + def unicode_string(self) -> str: ... + def __repr__(self) -> str: ... + def __str__(self) -> str: ... + def __deepcopy__(self, memo: dict) -> Self: ... + @classmethod + def build( + cls, + *, + scheme: str, + hosts: list[MultiHostHost] | None = None, + username: str | None = None, + password: str | None = None, + host: str | None = None, + port: int | None = None, + path: str | None = None, + query: str | None = None, + fragment: str | None = None, + ) -> Self: ... + +@final +class SchemaError(Exception): + """ + Information about errors that occur while building a [`SchemaValidator`][pydantic_core.SchemaValidator] + or [`SchemaSerializer`][pydantic_core.SchemaSerializer]. + """ + + def error_count(self) -> int: + """ + Returns: + The number of errors in the schema. + """ + def errors(self) -> list[ErrorDetails]: + """ + Returns: + A list of [`ErrorDetails`][pydantic_core.ErrorDetails] for each error in the schema. + """ + +class ValidationError(ValueError): + """ + `ValidationError` is the exception raised by `pydantic-core` when validation fails, it contains a list of errors + which detail why validation failed. + """ + @classmethod + def from_exception_data( + cls, + title: str, + line_errors: list[InitErrorDetails], + input_type: Literal['python', 'json'] = 'python', + hide_input: bool = False, + ) -> Self: + """ + Python constructor for a Validation Error. + + The API for constructing validation errors will probably change in the future, + hence the static method rather than `__init__`. + + Arguments: + title: The title of the error, as used in the heading of `str(validation_error)` + line_errors: A list of [`InitErrorDetails`][pydantic_core.InitErrorDetails] which contain information + about errors that occurred during validation. + input_type: Whether the error is for a Python object or JSON. + hide_input: Whether to hide the input value in the error message. + """ + @property + def title(self) -> str: + """ + The title of the error, as used in the heading of `str(validation_error)`. + """ + def error_count(self) -> int: + """ + Returns: + The number of errors in the validation error. + """ + def errors( + self, *, include_url: bool = True, include_context: bool = True, include_input: bool = True + ) -> list[ErrorDetails]: + """ + Details about each error in the validation error. + + Args: + include_url: Whether to include a URL to documentation on the error each error. + include_context: Whether to include the context of each error. + include_input: Whether to include the input value of each error. + + Returns: + A list of [`ErrorDetails`][pydantic_core.ErrorDetails] for each error in the validation error. + """ + def json( + self, + *, + indent: int | None = None, + include_url: bool = True, + include_context: bool = True, + include_input: bool = True, + ) -> str: + """ + Same as [`errors()`][pydantic_core.ValidationError.errors] but returns a JSON string. + + Args: + indent: The number of spaces to indent the JSON by, or `None` for no indentation - compact JSON. + include_url: Whether to include a URL to documentation on the error each error. + include_context: Whether to include the context of each error. + include_input: Whether to include the input value of each error. + + Returns: + a JSON string. + """ + + def __repr__(self) -> str: + """ + A string representation of the validation error. + + Whether or not documentation URLs are included in the repr is controlled by the + environment variable `PYDANTIC_ERRORS_INCLUDE_URL` being set to `1` or + `true`; by default, URLs are shown. + + Due to implementation details, this environment variable can only be set once, + before the first validation error is created. + """ + +class PydanticCustomError(ValueError): + """A custom exception providing flexible error handling for Pydantic validators. + + You can raise this error in custom validators when you'd like flexibility in regards to the error type, message, and context. + + Example: + ```py + from pydantic_core import PydanticCustomError + + def custom_validator(v) -> None: + if v <= 10: + raise PydanticCustomError('custom_value_error', 'Value must be greater than {value}', {'value': 10, 'extra_context': 'extra_data'}) + return v + ``` + """ + + def __init__( + self, error_type: LiteralString, message_template: LiteralString, context: dict[str, Any] | None = None + ) -> None: + """Initializes the `PydanticCustomError`. + + Arguments: + error_type: The error type. + message_template: The message template. + context: The data to inject into the message template. + """ + + def __new__( + cls, error_type: LiteralString, message_template: LiteralString, context: dict[str, Any] | None = None + ) -> Self: ... + @property + def context(self) -> dict[str, Any] | None: + """Values which are required to render the error message, and could hence be useful in passing error data forward.""" + + @property + def type(self) -> str: + """The error type associated with the error. For consistency with Pydantic, this is typically a snake_case string.""" + + @property + def message_template(self) -> str: + """The message template associated with the error. This is a string that can be formatted with context variables in `{curly_braces}`.""" + + def message(self) -> str: + """The formatted message associated with the error. This presents as the message template with context variables appropriately injected.""" + +@final +class PydanticKnownError(ValueError): + """A helper class for raising exceptions that mimic Pydantic's built-in exceptions, with more flexibility in regards to context. + + Unlike [`PydanticCustomError`][pydantic_core.PydanticCustomError], the `error_type` argument must be a known `ErrorType`. + + Example: + ```py + from pydantic_core import PydanticKnownError + + def custom_validator(v) -> None: + if v <= 10: + raise PydanticKnownError(error_type='greater_than', context={'gt': 10}) + return v + ``` + """ + + def __init__(self, error_type: ErrorType, context: dict[str, Any] | None = None) -> None: + """Initializes the `PydanticKnownError`. + + Arguments: + error_type: The error type. + context: The data to inject into the message template. + """ + + def __new__(cls, error_type: ErrorType, context: dict[str, Any] | None = None) -> Self: ... + @property + def context(self) -> dict[str, Any] | None: + """Values which are required to render the error message, and could hence be useful in passing error data forward.""" + + @property + def type(self) -> ErrorType: + """The type of the error.""" + + @property + def message_template(self) -> str: + """The message template associated with the provided error type. This is a string that can be formatted with context variables in `{curly_braces}`.""" + + def message(self) -> str: + """The formatted message associated with the error. This presents as the message template with context variables appropriately injected.""" + +@final +class PydanticOmit(Exception): + """An exception to signal that a field should be omitted from a generated result. + + This could span from omitting a field from a JSON Schema to omitting a field from a serialized result. + Upcoming: more robust support for using PydanticOmit in custom serializers is still in development. + Right now, this is primarily used in the JSON Schema generation process. + + Example: + ```py + from typing import Callable + + from pydantic_core import PydanticOmit + + from pydantic import BaseModel + from pydantic.json_schema import GenerateJsonSchema, JsonSchemaValue + + + class MyGenerateJsonSchema(GenerateJsonSchema): + def handle_invalid_for_json_schema(self, schema, error_info) -> JsonSchemaValue: + raise PydanticOmit + + + class Predicate(BaseModel): + name: str = 'no-op' + func: Callable = lambda x: x + + + instance_example = Predicate() + + validation_schema = instance_example.model_json_schema(schema_generator=MyGenerateJsonSchema, mode='validation') + print(validation_schema) + ''' + {'properties': {'name': {'default': 'no-op', 'title': 'Name', 'type': 'string'}}, 'title': 'Predicate', 'type': 'object'} + ''' + ``` + + For a more in depth example / explanation, see the [customizing JSON schema](../concepts/json_schema.md#customizing-the-json-schema-generation-process) docs. + """ + + def __new__(cls) -> Self: ... + +@final +class PydanticUseDefault(Exception): + """An exception to signal that standard validation either failed or should be skipped, and the default value should be used instead. + + This warning can be raised in custom valiation functions to redirect the flow of validation. + + Example: + ```py + from pydantic_core import PydanticUseDefault + from datetime import datetime + from pydantic import BaseModel, field_validator + + + class Event(BaseModel): + name: str = 'meeting' + time: datetime + + @field_validator('name', mode='plain') + def name_must_be_present(cls, v) -> str: + if not v or not isinstance(v, str): + raise PydanticUseDefault() + return v + + + event1 = Event(name='party', time=datetime(2024, 1, 1, 12, 0, 0)) + print(repr(event1)) + # > Event(name='party', time=datetime.datetime(2024, 1, 1, 12, 0)) + event2 = Event(time=datetime(2024, 1, 1, 12, 0, 0)) + print(repr(event2)) + # > Event(name='meeting', time=datetime.datetime(2024, 1, 1, 12, 0)) + ``` + + For an additional example, seethe [validating partial json data](../concepts/json.md#partial-json-parsing) section of the Pydantic documentation. + """ + + def __new__(cls) -> Self: ... + +@final +class PydanticSerializationError(ValueError): + """An error raised when an issue occurs during serialization. + + In custom serializers, this error can be used to indicate that serialization has failed. + """ + + def __init__(self, message: str) -> None: + """Initializes the `PydanticSerializationError`. + + Arguments: + message: The message associated with the error. + """ + + def __new__(cls, message: str) -> Self: ... + +@final +class PydanticSerializationUnexpectedValue(ValueError): + """An error raised when an unexpected value is encountered during serialization. + + This error is often caught and coerced into a warning, as `pydantic-core` generally makes a best attempt + at serializing values, in contrast with validation where errors are eagerly raised. + + Example: + ```py + from pydantic import BaseModel, field_serializer + from pydantic_core import PydanticSerializationUnexpectedValue + + class BasicPoint(BaseModel): + x: int + y: int + + @field_serializer('*') + def serialize(self, v): + if not isinstance(v, int): + raise PydanticSerializationUnexpectedValue(f'Expected type `int`, got {type(v)} with value {v}') + return v + + point = BasicPoint(x=1, y=2) + # some sort of mutation + point.x = 'a' + + print(point.model_dump()) + ''' + UserWarning: Pydantic serializer warnings: + PydanticSerializationUnexpectedValue(Expected type `int`, got <class 'str'> with value a) + return self.__pydantic_serializer__.to_python( + {'x': 'a', 'y': 2} + ''' + ``` + + This is often used internally in `pydantic-core` when unexpected types are encountered during serialization, + but it can also be used by users in custom serializers, as seen above. + """ + + def __init__(self, message: str) -> None: + """Initializes the `PydanticSerializationUnexpectedValue`. + + Arguments: + message: The message associated with the unexpected value. + """ + + def __new__(cls, message: str | None = None) -> Self: ... + +@final +class ArgsKwargs: + """A construct used to store arguments and keyword arguments for a function call. + + This data structure is generally used to store information for core schemas associated with functions (like in an arguments schema). + This data structure is also currently used for some validation against dataclasses. + + Example: + ```py + from pydantic.dataclasses import dataclass + from pydantic import model_validator + + + @dataclass + class Model: + a: int + b: int + + @model_validator(mode="before") + @classmethod + def no_op_validator(cls, values): + print(values) + return values + + Model(1, b=2) + #> ArgsKwargs((1,), {"b": 2}) + + Model(1, 2) + #> ArgsKwargs((1, 2), {}) + + Model(a=1, b=2) + #> ArgsKwargs((), {"a": 1, "b": 2}) + ``` + """ + + def __init__(self, args: tuple[Any, ...], kwargs: dict[str, Any] | None = None) -> None: + """Initializes the `ArgsKwargs`. + + Arguments: + args: The arguments (inherently ordered) for a function call. + kwargs: The keyword arguments for a function call + """ + + def __new__(cls, args: tuple[Any, ...], kwargs: dict[str, Any] | None = None) -> Self: ... + @property + def args(self) -> tuple[Any, ...]: + """The arguments (inherently ordered) for a function call.""" + + @property + def kwargs(self) -> dict[str, Any] | None: + """The keyword arguments for a function call.""" + +@final +class PydanticUndefinedType: + """A type used as a sentinel for undefined values.""" + + def __copy__(self) -> Self: ... + def __deepcopy__(self, memo: Any) -> Self: ... + +PydanticUndefined: PydanticUndefinedType + +def list_all_errors() -> list[ErrorTypeInfo]: + """ + Get information about all built-in errors. + + Returns: + A list of `ErrorTypeInfo` typed dicts. + """ +@final +class TzInfo(datetime.tzinfo): + """An `pydantic-core` implementation of the abstract [`datetime.tzinfo`] class.""" + + # Docstrings for attributes sourced from the abstract base class, [`datetime.tzinfo`](https://docs.python.org/3/library/datetime.html#datetime.tzinfo). + + def tzname(self, dt: datetime.datetime | None) -> str | None: + """Return the time zone name corresponding to the [`datetime`][datetime.datetime] object _dt_, as a string. + + For more info, see [`tzinfo.tzname`][datetime.tzinfo.tzname]. + """ + + def utcoffset(self, dt: datetime.datetime | None) -> datetime.timedelta | None: + """Return offset of local time from UTC, as a [`timedelta`][datetime.timedelta] object that is positive east of UTC. If local time is west of UTC, this should be negative. + + More info can be found at [`tzinfo.utcoffset`][datetime.tzinfo.utcoffset]. + """ + + def dst(self, dt: datetime.datetime | None) -> datetime.timedelta | None: + """Return the daylight saving time (DST) adjustment, as a [`timedelta`][datetime.timedelta] object or `None` if DST information isn’t known. + + More info can be found at[`tzinfo.dst`][datetime.tzinfo.dst].""" + + def fromutc(self, dt: datetime.datetime) -> datetime.datetime: + """Adjust the date and time data associated datetime object _dt_, returning an equivalent datetime in self’s local time. + + More info can be found at [`tzinfo.fromutc`][datetime.tzinfo.fromutc].""" + + def __deepcopy__(self, _memo: dict[Any, Any]) -> TzInfo: ... + +def validate_core_schema(schema: CoreSchema, *, strict: bool | None = None) -> CoreSchema: + """Validate a core schema. + + This currently uses lax mode for validation (i.e. will coerce strings to dates and such) + but may use strict mode in the future. + We may also remove this function altogether, do not rely on it being present if you are + using pydantic-core directly. + """ diff --git a/.venv/lib/python3.12/site-packages/pydantic_core/core_schema.py b/.venv/lib/python3.12/site-packages/pydantic_core/core_schema.py new file mode 100644 index 00000000..e588e772 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/pydantic_core/core_schema.py @@ -0,0 +1,4211 @@ +""" +This module contains definitions to build schemas which `pydantic_core` can +validate and serialize. +""" + +from __future__ import annotations as _annotations + +import sys +import warnings +from collections.abc import Mapping +from datetime import date, datetime, time, timedelta +from decimal import Decimal +from typing import TYPE_CHECKING, Any, Callable, Dict, Hashable, List, Pattern, Set, Tuple, Type, Union + +from typing_extensions import deprecated + +if sys.version_info < (3, 12): + from typing_extensions import TypedDict +else: + from typing import TypedDict + +if sys.version_info < (3, 11): + from typing_extensions import Protocol, Required, TypeAlias +else: + from typing import Protocol, Required, TypeAlias + +if sys.version_info < (3, 9): + from typing_extensions import Literal +else: + from typing import Literal + +if TYPE_CHECKING: + from pydantic_core import PydanticUndefined +else: + # The initial build of pydantic_core requires PydanticUndefined to generate + # the core schema; so we need to conditionally skip it. mypy doesn't like + # this at all, hence the TYPE_CHECKING branch above. + try: + from pydantic_core import PydanticUndefined + except ImportError: + PydanticUndefined = object() + + +ExtraBehavior = Literal['allow', 'forbid', 'ignore'] + + +class CoreConfig(TypedDict, total=False): + """ + Base class for schema configuration options. + + Attributes: + title: The name of the configuration. + strict: Whether the configuration should strictly adhere to specified rules. + extra_fields_behavior: The behavior for handling extra fields. + typed_dict_total: Whether the TypedDict should be considered total. Default is `True`. + from_attributes: Whether to use attributes for models, dataclasses, and tagged union keys. + loc_by_alias: Whether to use the used alias (or first alias for "field required" errors) instead of + `field_names` to construct error `loc`s. Default is `True`. + revalidate_instances: Whether instances of models and dataclasses should re-validate. Default is 'never'. + validate_default: Whether to validate default values during validation. Default is `False`. + populate_by_name: Whether an aliased field may be populated by its name as given by the model attribute, + as well as the alias. (Replaces 'allow_population_by_field_name' in Pydantic v1.) Default is `False`. + str_max_length: The maximum length for string fields. + str_min_length: The minimum length for string fields. + str_strip_whitespace: Whether to strip whitespace from string fields. + str_to_lower: Whether to convert string fields to lowercase. + str_to_upper: Whether to convert string fields to uppercase. + allow_inf_nan: Whether to allow infinity and NaN values for float fields. Default is `True`. + ser_json_timedelta: The serialization option for `timedelta` values. Default is 'iso8601'. + ser_json_bytes: The serialization option for `bytes` values. Default is 'utf8'. + ser_json_inf_nan: The serialization option for infinity and NaN values + in float fields. Default is 'null'. + val_json_bytes: The validation option for `bytes` values, complementing ser_json_bytes. Default is 'utf8'. + hide_input_in_errors: Whether to hide input data from `ValidationError` representation. + validation_error_cause: Whether to add user-python excs to the __cause__ of a ValidationError. + Requires exceptiongroup backport pre Python 3.11. + coerce_numbers_to_str: Whether to enable coercion of any `Number` type to `str` (not applicable in `strict` mode). + regex_engine: The regex engine to use for regex pattern validation. Default is 'rust-regex'. See `StringSchema`. + cache_strings: Whether to cache strings. Default is `True`, `True` or `'all'` is required to cache strings + during general validation since validators don't know if they're in a key or a value. + """ + + title: str + strict: bool + # settings related to typed dicts, model fields, dataclass fields + extra_fields_behavior: ExtraBehavior + typed_dict_total: bool # default: True + # used for models, dataclasses, and tagged union keys + from_attributes: bool + # whether to use the used alias (or first alias for "field required" errors) instead of field_names + # to construct error `loc`s, default True + loc_by_alias: bool + # whether instances of models and dataclasses (including subclass instances) should re-validate, default 'never' + revalidate_instances: Literal['always', 'never', 'subclass-instances'] + # whether to validate default values during validation, default False + validate_default: bool + # used on typed-dicts and arguments + populate_by_name: bool # replaces `allow_population_by_field_name` in pydantic v1 + # fields related to string fields only + str_max_length: int + str_min_length: int + str_strip_whitespace: bool + str_to_lower: bool + str_to_upper: bool + # fields related to float fields only + allow_inf_nan: bool # default: True + # the config options are used to customise serialization to JSON + ser_json_timedelta: Literal['iso8601', 'float'] # default: 'iso8601' + ser_json_bytes: Literal['utf8', 'base64', 'hex'] # default: 'utf8' + ser_json_inf_nan: Literal['null', 'constants', 'strings'] # default: 'null' + val_json_bytes: Literal['utf8', 'base64', 'hex'] # default: 'utf8' + # used to hide input data from ValidationError repr + hide_input_in_errors: bool + validation_error_cause: bool # default: False + coerce_numbers_to_str: bool # default: False + regex_engine: Literal['rust-regex', 'python-re'] # default: 'rust-regex' + cache_strings: Union[bool, Literal['all', 'keys', 'none']] # default: 'True' + + +IncExCall: TypeAlias = 'set[int | str] | dict[int | str, IncExCall] | None' + + +class SerializationInfo(Protocol): + @property + def include(self) -> IncExCall: ... + + @property + def exclude(self) -> IncExCall: ... + + @property + def context(self) -> Any | None: + """Current serialization context.""" + + @property + def mode(self) -> str: ... + + @property + def by_alias(self) -> bool: ... + + @property + def exclude_unset(self) -> bool: ... + + @property + def exclude_defaults(self) -> bool: ... + + @property + def exclude_none(self) -> bool: ... + + @property + def serialize_as_any(self) -> bool: ... + + def round_trip(self) -> bool: ... + + def mode_is_json(self) -> bool: ... + + def __str__(self) -> str: ... + + def __repr__(self) -> str: ... + + +class FieldSerializationInfo(SerializationInfo, Protocol): + @property + def field_name(self) -> str: ... + + +class ValidationInfo(Protocol): + """ + Argument passed to validation functions. + """ + + @property + def context(self) -> Any | None: + """Current validation context.""" + ... + + @property + def config(self) -> CoreConfig | None: + """The CoreConfig that applies to this validation.""" + ... + + @property + def mode(self) -> Literal['python', 'json']: + """The type of input data we are currently validating""" + ... + + @property + def data(self) -> Dict[str, Any]: + """The data being validated for this model.""" + ... + + @property + def field_name(self) -> str | None: + """ + The name of the current field being validated if this validator is + attached to a model field. + """ + ... + + +ExpectedSerializationTypes = Literal[ + 'none', + 'int', + 'bool', + 'float', + 'str', + 'bytes', + 'bytearray', + 'list', + 'tuple', + 'set', + 'frozenset', + 'generator', + 'dict', + 'datetime', + 'date', + 'time', + 'timedelta', + 'url', + 'multi-host-url', + 'json', + 'uuid', + 'any', +] + + +class SimpleSerSchema(TypedDict, total=False): + type: Required[ExpectedSerializationTypes] + + +def simple_ser_schema(type: ExpectedSerializationTypes) -> SimpleSerSchema: + """ + Returns a schema for serialization with a custom type. + + Args: + type: The type to use for serialization + """ + return SimpleSerSchema(type=type) + + +# (input_value: Any, /) -> Any +GeneralPlainNoInfoSerializerFunction = Callable[[Any], Any] +# (input_value: Any, info: FieldSerializationInfo, /) -> Any +GeneralPlainInfoSerializerFunction = Callable[[Any, SerializationInfo], Any] +# (model: Any, input_value: Any, /) -> Any +FieldPlainNoInfoSerializerFunction = Callable[[Any, Any], Any] +# (model: Any, input_value: Any, info: FieldSerializationInfo, /) -> Any +FieldPlainInfoSerializerFunction = Callable[[Any, Any, FieldSerializationInfo], Any] +SerializerFunction = Union[ + GeneralPlainNoInfoSerializerFunction, + GeneralPlainInfoSerializerFunction, + FieldPlainNoInfoSerializerFunction, + FieldPlainInfoSerializerFunction, +] + +WhenUsed = Literal['always', 'unless-none', 'json', 'json-unless-none'] +""" +Values have the following meanings: + +* `'always'` means always use +* `'unless-none'` means use unless the value is `None` +* `'json'` means use when serializing to JSON +* `'json-unless-none'` means use when serializing to JSON and the value is not `None` +""" + + +class PlainSerializerFunctionSerSchema(TypedDict, total=False): + type: Required[Literal['function-plain']] + function: Required[SerializerFunction] + is_field_serializer: bool # default False + info_arg: bool # default False + return_schema: CoreSchema # if omitted, AnySchema is used + when_used: WhenUsed # default: 'always' + + +def plain_serializer_function_ser_schema( + function: SerializerFunction, + *, + is_field_serializer: bool | None = None, + info_arg: bool | None = None, + return_schema: CoreSchema | None = None, + when_used: WhenUsed = 'always', +) -> PlainSerializerFunctionSerSchema: + """ + Returns a schema for serialization with a function, can be either a "general" or "field" function. + + Args: + function: The function to use for serialization + is_field_serializer: Whether the serializer is for a field, e.g. takes `model` as the first argument, + and `info` includes `field_name` + info_arg: Whether the function takes an `info` argument + return_schema: Schema to use for serializing return value + when_used: When the function should be called + """ + if when_used == 'always': + # just to avoid extra elements in schema, and to use the actual default defined in rust + when_used = None # type: ignore + return _dict_not_none( + type='function-plain', + function=function, + is_field_serializer=is_field_serializer, + info_arg=info_arg, + return_schema=return_schema, + when_used=when_used, + ) + + +class SerializerFunctionWrapHandler(Protocol): # pragma: no cover + def __call__(self, input_value: Any, index_key: int | str | None = None, /) -> Any: ... + + +# (input_value: Any, serializer: SerializerFunctionWrapHandler, /) -> Any +GeneralWrapNoInfoSerializerFunction = Callable[[Any, SerializerFunctionWrapHandler], Any] +# (input_value: Any, serializer: SerializerFunctionWrapHandler, info: SerializationInfo, /) -> Any +GeneralWrapInfoSerializerFunction = Callable[[Any, SerializerFunctionWrapHandler, SerializationInfo], Any] +# (model: Any, input_value: Any, serializer: SerializerFunctionWrapHandler, /) -> Any +FieldWrapNoInfoSerializerFunction = Callable[[Any, Any, SerializerFunctionWrapHandler], Any] +# (model: Any, input_value: Any, serializer: SerializerFunctionWrapHandler, info: FieldSerializationInfo, /) -> Any +FieldWrapInfoSerializerFunction = Callable[[Any, Any, SerializerFunctionWrapHandler, FieldSerializationInfo], Any] +WrapSerializerFunction = Union[ + GeneralWrapNoInfoSerializerFunction, + GeneralWrapInfoSerializerFunction, + FieldWrapNoInfoSerializerFunction, + FieldWrapInfoSerializerFunction, +] + + +class WrapSerializerFunctionSerSchema(TypedDict, total=False): + type: Required[Literal['function-wrap']] + function: Required[WrapSerializerFunction] + is_field_serializer: bool # default False + info_arg: bool # default False + schema: CoreSchema # if omitted, the schema on which this serializer is defined is used + return_schema: CoreSchema # if omitted, AnySchema is used + when_used: WhenUsed # default: 'always' + + +def wrap_serializer_function_ser_schema( + function: WrapSerializerFunction, + *, + is_field_serializer: bool | None = None, + info_arg: bool | None = None, + schema: CoreSchema | None = None, + return_schema: CoreSchema | None = None, + when_used: WhenUsed = 'always', +) -> WrapSerializerFunctionSerSchema: + """ + Returns a schema for serialization with a wrap function, can be either a "general" or "field" function. + + Args: + function: The function to use for serialization + is_field_serializer: Whether the serializer is for a field, e.g. takes `model` as the first argument, + and `info` includes `field_name` + info_arg: Whether the function takes an `info` argument + schema: The schema to use for the inner serialization + return_schema: Schema to use for serializing return value + when_used: When the function should be called + """ + if when_used == 'always': + # just to avoid extra elements in schema, and to use the actual default defined in rust + when_used = None # type: ignore + return _dict_not_none( + type='function-wrap', + function=function, + is_field_serializer=is_field_serializer, + info_arg=info_arg, + schema=schema, + return_schema=return_schema, + when_used=when_used, + ) + + +class FormatSerSchema(TypedDict, total=False): + type: Required[Literal['format']] + formatting_string: Required[str] + when_used: WhenUsed # default: 'json-unless-none' + + +def format_ser_schema(formatting_string: str, *, when_used: WhenUsed = 'json-unless-none') -> FormatSerSchema: + """ + Returns a schema for serialization using python's `format` method. + + Args: + formatting_string: String defining the format to use + when_used: Same meaning as for [general_function_plain_ser_schema], but with a different default + """ + if when_used == 'json-unless-none': + # just to avoid extra elements in schema, and to use the actual default defined in rust + when_used = None # type: ignore + return _dict_not_none(type='format', formatting_string=formatting_string, when_used=when_used) + + +class ToStringSerSchema(TypedDict, total=False): + type: Required[Literal['to-string']] + when_used: WhenUsed # default: 'json-unless-none' + + +def to_string_ser_schema(*, when_used: WhenUsed = 'json-unless-none') -> ToStringSerSchema: + """ + Returns a schema for serialization using python's `str()` / `__str__` method. + + Args: + when_used: Same meaning as for [general_function_plain_ser_schema], but with a different default + """ + s = dict(type='to-string') + if when_used != 'json-unless-none': + # just to avoid extra elements in schema, and to use the actual default defined in rust + s['when_used'] = when_used + return s # type: ignore + + +class ModelSerSchema(TypedDict, total=False): + type: Required[Literal['model']] + cls: Required[Type[Any]] + schema: Required[CoreSchema] + + +def model_ser_schema(cls: Type[Any], schema: CoreSchema) -> ModelSerSchema: + """ + Returns a schema for serialization using a model. + + Args: + cls: The expected class type, used to generate warnings if the wrong type is passed + schema: Internal schema to use to serialize the model dict + """ + return ModelSerSchema(type='model', cls=cls, schema=schema) + + +SerSchema = Union[ + SimpleSerSchema, + PlainSerializerFunctionSerSchema, + WrapSerializerFunctionSerSchema, + FormatSerSchema, + ToStringSerSchema, + ModelSerSchema, +] + + +class InvalidSchema(TypedDict, total=False): + type: Required[Literal['invalid']] + ref: str + metadata: Dict[str, Any] + # note, we never plan to use this, but include it for type checking purposes to match + # all other CoreSchema union members + serialization: SerSchema + + +def invalid_schema(ref: str | None = None, metadata: Dict[str, Any] | None = None) -> InvalidSchema: + """ + Returns an invalid schema, used to indicate that a schema is invalid. + + Returns a schema that matches any value, e.g.: + + Args: + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + """ + + return _dict_not_none(type='invalid', ref=ref, metadata=metadata) + + +class ComputedField(TypedDict, total=False): + type: Required[Literal['computed-field']] + property_name: Required[str] + return_schema: Required[CoreSchema] + alias: str + metadata: Dict[str, Any] + + +def computed_field( + property_name: str, return_schema: CoreSchema, *, alias: str | None = None, metadata: Dict[str, Any] | None = None +) -> ComputedField: + """ + ComputedFields are properties of a model or dataclass that are included in serialization. + + Args: + property_name: The name of the property on the model or dataclass + return_schema: The schema used for the type returned by the computed field + alias: The name to use in the serialized output + metadata: Any other information you want to include with the schema, not used by pydantic-core + """ + return _dict_not_none( + type='computed-field', property_name=property_name, return_schema=return_schema, alias=alias, metadata=metadata + ) + + +class AnySchema(TypedDict, total=False): + type: Required[Literal['any']] + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + + +def any_schema( + *, ref: str | None = None, metadata: Dict[str, Any] | None = None, serialization: SerSchema | None = None +) -> AnySchema: + """ + Returns a schema that matches any value, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.any_schema() + v = SchemaValidator(schema) + assert v.validate_python(1) == 1 + ``` + + Args: + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none(type='any', ref=ref, metadata=metadata, serialization=serialization) + + +class NoneSchema(TypedDict, total=False): + type: Required[Literal['none']] + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + + +def none_schema( + *, ref: str | None = None, metadata: Dict[str, Any] | None = None, serialization: SerSchema | None = None +) -> NoneSchema: + """ + Returns a schema that matches a None value, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.none_schema() + v = SchemaValidator(schema) + assert v.validate_python(None) is None + ``` + + Args: + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none(type='none', ref=ref, metadata=metadata, serialization=serialization) + + +class BoolSchema(TypedDict, total=False): + type: Required[Literal['bool']] + strict: bool + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + + +def bool_schema( + strict: bool | None = None, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> BoolSchema: + """ + Returns a schema that matches a bool value, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.bool_schema() + v = SchemaValidator(schema) + assert v.validate_python('True') is True + ``` + + Args: + strict: Whether the value should be a bool or a value that can be converted to a bool + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none(type='bool', strict=strict, ref=ref, metadata=metadata, serialization=serialization) + + +class IntSchema(TypedDict, total=False): + type: Required[Literal['int']] + multiple_of: int + le: int + ge: int + lt: int + gt: int + strict: bool + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + + +def int_schema( + *, + multiple_of: int | None = None, + le: int | None = None, + ge: int | None = None, + lt: int | None = None, + gt: int | None = None, + strict: bool | None = None, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> IntSchema: + """ + Returns a schema that matches a int value, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.int_schema(multiple_of=2, le=6, ge=2) + v = SchemaValidator(schema) + assert v.validate_python('4') == 4 + ``` + + Args: + multiple_of: The value must be a multiple of this number + le: The value must be less than or equal to this number + ge: The value must be greater than or equal to this number + lt: The value must be strictly less than this number + gt: The value must be strictly greater than this number + strict: Whether the value should be a int or a value that can be converted to a int + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='int', + multiple_of=multiple_of, + le=le, + ge=ge, + lt=lt, + gt=gt, + strict=strict, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class FloatSchema(TypedDict, total=False): + type: Required[Literal['float']] + allow_inf_nan: bool # whether 'NaN', '+inf', '-inf' should be forbidden. default: True + multiple_of: float + le: float + ge: float + lt: float + gt: float + strict: bool + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + + +def float_schema( + *, + allow_inf_nan: bool | None = None, + multiple_of: float | None = None, + le: float | None = None, + ge: float | None = None, + lt: float | None = None, + gt: float | None = None, + strict: bool | None = None, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> FloatSchema: + """ + Returns a schema that matches a float value, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.float_schema(le=0.8, ge=0.2) + v = SchemaValidator(schema) + assert v.validate_python('0.5') == 0.5 + ``` + + Args: + allow_inf_nan: Whether to allow inf and nan values + multiple_of: The value must be a multiple of this number + le: The value must be less than or equal to this number + ge: The value must be greater than or equal to this number + lt: The value must be strictly less than this number + gt: The value must be strictly greater than this number + strict: Whether the value should be a float or a value that can be converted to a float + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='float', + allow_inf_nan=allow_inf_nan, + multiple_of=multiple_of, + le=le, + ge=ge, + lt=lt, + gt=gt, + strict=strict, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class DecimalSchema(TypedDict, total=False): + type: Required[Literal['decimal']] + allow_inf_nan: bool # whether 'NaN', '+inf', '-inf' should be forbidden. default: False + multiple_of: Decimal + le: Decimal + ge: Decimal + lt: Decimal + gt: Decimal + max_digits: int + decimal_places: int + strict: bool + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + + +def decimal_schema( + *, + allow_inf_nan: bool | None = None, + multiple_of: Decimal | None = None, + le: Decimal | None = None, + ge: Decimal | None = None, + lt: Decimal | None = None, + gt: Decimal | None = None, + max_digits: int | None = None, + decimal_places: int | None = None, + strict: bool | None = None, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> DecimalSchema: + """ + Returns a schema that matches a decimal value, e.g.: + + ```py + from decimal import Decimal + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.decimal_schema(le=0.8, ge=0.2) + v = SchemaValidator(schema) + assert v.validate_python('0.5') == Decimal('0.5') + ``` + + Args: + allow_inf_nan: Whether to allow inf and nan values + multiple_of: The value must be a multiple of this number + le: The value must be less than or equal to this number + ge: The value must be greater than or equal to this number + lt: The value must be strictly less than this number + gt: The value must be strictly greater than this number + max_digits: The maximum number of decimal digits allowed + decimal_places: The maximum number of decimal places allowed + strict: Whether the value should be a float or a value that can be converted to a float + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='decimal', + gt=gt, + ge=ge, + lt=lt, + le=le, + max_digits=max_digits, + decimal_places=decimal_places, + multiple_of=multiple_of, + allow_inf_nan=allow_inf_nan, + strict=strict, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class ComplexSchema(TypedDict, total=False): + type: Required[Literal['complex']] + strict: bool + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + + +def complex_schema( + *, + strict: bool | None = None, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> ComplexSchema: + """ + Returns a schema that matches a complex value, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.complex_schema() + v = SchemaValidator(schema) + assert v.validate_python('1+2j') == complex(1, 2) + assert v.validate_python(complex(1, 2)) == complex(1, 2) + ``` + + Args: + strict: Whether the value should be a complex object instance or a value that can be converted to a complex object + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='complex', + strict=strict, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class StringSchema(TypedDict, total=False): + type: Required[Literal['str']] + pattern: Union[str, Pattern[str]] + max_length: int + min_length: int + strip_whitespace: bool + to_lower: bool + to_upper: bool + regex_engine: Literal['rust-regex', 'python-re'] # default: 'rust-regex' + strict: bool + coerce_numbers_to_str: bool + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + + +def str_schema( + *, + pattern: str | Pattern[str] | None = None, + max_length: int | None = None, + min_length: int | None = None, + strip_whitespace: bool | None = None, + to_lower: bool | None = None, + to_upper: bool | None = None, + regex_engine: Literal['rust-regex', 'python-re'] | None = None, + strict: bool | None = None, + coerce_numbers_to_str: bool | None = None, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> StringSchema: + """ + Returns a schema that matches a string value, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.str_schema(max_length=10, min_length=2) + v = SchemaValidator(schema) + assert v.validate_python('hello') == 'hello' + ``` + + Args: + pattern: A regex pattern that the value must match + max_length: The value must be at most this length + min_length: The value must be at least this length + strip_whitespace: Whether to strip whitespace from the value + to_lower: Whether to convert the value to lowercase + to_upper: Whether to convert the value to uppercase + regex_engine: The regex engine to use for pattern validation. Default is 'rust-regex'. + - `rust-regex` uses the [`regex`](https://docs.rs/regex) Rust + crate, which is non-backtracking and therefore more DDoS + resistant, but does not support all regex features. + - `python-re` use the [`re`](https://docs.python.org/3/library/re.html) module, + which supports all regex features, but may be slower. + strict: Whether the value should be a string or a value that can be converted to a string + coerce_numbers_to_str: Whether to enable coercion of any `Number` type to `str` (not applicable in `strict` mode). + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='str', + pattern=pattern, + max_length=max_length, + min_length=min_length, + strip_whitespace=strip_whitespace, + to_lower=to_lower, + to_upper=to_upper, + regex_engine=regex_engine, + strict=strict, + coerce_numbers_to_str=coerce_numbers_to_str, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class BytesSchema(TypedDict, total=False): + type: Required[Literal['bytes']] + max_length: int + min_length: int + strict: bool + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + + +def bytes_schema( + *, + max_length: int | None = None, + min_length: int | None = None, + strict: bool | None = None, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> BytesSchema: + """ + Returns a schema that matches a bytes value, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.bytes_schema(max_length=10, min_length=2) + v = SchemaValidator(schema) + assert v.validate_python(b'hello') == b'hello' + ``` + + Args: + max_length: The value must be at most this length + min_length: The value must be at least this length + strict: Whether the value should be a bytes or a value that can be converted to a bytes + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='bytes', + max_length=max_length, + min_length=min_length, + strict=strict, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class DateSchema(TypedDict, total=False): + type: Required[Literal['date']] + strict: bool + le: date + ge: date + lt: date + gt: date + now_op: Literal['past', 'future'] + # defaults to current local utc offset from `time.localtime().tm_gmtoff` + # value is restricted to -86_400 < offset < 86_400 by bounds in generate_self_schema.py + now_utc_offset: int + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + + +def date_schema( + *, + strict: bool | None = None, + le: date | None = None, + ge: date | None = None, + lt: date | None = None, + gt: date | None = None, + now_op: Literal['past', 'future'] | None = None, + now_utc_offset: int | None = None, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> DateSchema: + """ + Returns a schema that matches a date value, e.g.: + + ```py + from datetime import date + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.date_schema(le=date(2020, 1, 1), ge=date(2019, 1, 1)) + v = SchemaValidator(schema) + assert v.validate_python(date(2019, 6, 1)) == date(2019, 6, 1) + ``` + + Args: + strict: Whether the value should be a date or a value that can be converted to a date + le: The value must be less than or equal to this date + ge: The value must be greater than or equal to this date + lt: The value must be strictly less than this date + gt: The value must be strictly greater than this date + now_op: The value must be in the past or future relative to the current date + now_utc_offset: The value must be in the past or future relative to the current date with this utc offset + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='date', + strict=strict, + le=le, + ge=ge, + lt=lt, + gt=gt, + now_op=now_op, + now_utc_offset=now_utc_offset, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class TimeSchema(TypedDict, total=False): + type: Required[Literal['time']] + strict: bool + le: time + ge: time + lt: time + gt: time + tz_constraint: Union[Literal['aware', 'naive'], int] + microseconds_precision: Literal['truncate', 'error'] + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + + +def time_schema( + *, + strict: bool | None = None, + le: time | None = None, + ge: time | None = None, + lt: time | None = None, + gt: time | None = None, + tz_constraint: Literal['aware', 'naive'] | int | None = None, + microseconds_precision: Literal['truncate', 'error'] = 'truncate', + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> TimeSchema: + """ + Returns a schema that matches a time value, e.g.: + + ```py + from datetime import time + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.time_schema(le=time(12, 0, 0), ge=time(6, 0, 0)) + v = SchemaValidator(schema) + assert v.validate_python(time(9, 0, 0)) == time(9, 0, 0) + ``` + + Args: + strict: Whether the value should be a time or a value that can be converted to a time + le: The value must be less than or equal to this time + ge: The value must be greater than or equal to this time + lt: The value must be strictly less than this time + gt: The value must be strictly greater than this time + tz_constraint: The value must be timezone aware or naive, or an int to indicate required tz offset + microseconds_precision: The behavior when seconds have more than 6 digits or microseconds is too large + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='time', + strict=strict, + le=le, + ge=ge, + lt=lt, + gt=gt, + tz_constraint=tz_constraint, + microseconds_precision=microseconds_precision, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class DatetimeSchema(TypedDict, total=False): + type: Required[Literal['datetime']] + strict: bool + le: datetime + ge: datetime + lt: datetime + gt: datetime + now_op: Literal['past', 'future'] + tz_constraint: Union[Literal['aware', 'naive'], int] + # defaults to current local utc offset from `time.localtime().tm_gmtoff` + # value is restricted to -86_400 < offset < 86_400 by bounds in generate_self_schema.py + now_utc_offset: int + microseconds_precision: Literal['truncate', 'error'] # default: 'truncate' + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + + +def datetime_schema( + *, + strict: bool | None = None, + le: datetime | None = None, + ge: datetime | None = None, + lt: datetime | None = None, + gt: datetime | None = None, + now_op: Literal['past', 'future'] | None = None, + tz_constraint: Literal['aware', 'naive'] | int | None = None, + now_utc_offset: int | None = None, + microseconds_precision: Literal['truncate', 'error'] = 'truncate', + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> DatetimeSchema: + """ + Returns a schema that matches a datetime value, e.g.: + + ```py + from datetime import datetime + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.datetime_schema() + v = SchemaValidator(schema) + now = datetime.now() + assert v.validate_python(str(now)) == now + ``` + + Args: + strict: Whether the value should be a datetime or a value that can be converted to a datetime + le: The value must be less than or equal to this datetime + ge: The value must be greater than or equal to this datetime + lt: The value must be strictly less than this datetime + gt: The value must be strictly greater than this datetime + now_op: The value must be in the past or future relative to the current datetime + tz_constraint: The value must be timezone aware or naive, or an int to indicate required tz offset + TODO: use of a tzinfo where offset changes based on the datetime is not yet supported + now_utc_offset: The value must be in the past or future relative to the current datetime with this utc offset + microseconds_precision: The behavior when seconds have more than 6 digits or microseconds is too large + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='datetime', + strict=strict, + le=le, + ge=ge, + lt=lt, + gt=gt, + now_op=now_op, + tz_constraint=tz_constraint, + now_utc_offset=now_utc_offset, + microseconds_precision=microseconds_precision, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class TimedeltaSchema(TypedDict, total=False): + type: Required[Literal['timedelta']] + strict: bool + le: timedelta + ge: timedelta + lt: timedelta + gt: timedelta + microseconds_precision: Literal['truncate', 'error'] + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + + +def timedelta_schema( + *, + strict: bool | None = None, + le: timedelta | None = None, + ge: timedelta | None = None, + lt: timedelta | None = None, + gt: timedelta | None = None, + microseconds_precision: Literal['truncate', 'error'] = 'truncate', + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> TimedeltaSchema: + """ + Returns a schema that matches a timedelta value, e.g.: + + ```py + from datetime import timedelta + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.timedelta_schema(le=timedelta(days=1), ge=timedelta(days=0)) + v = SchemaValidator(schema) + assert v.validate_python(timedelta(hours=12)) == timedelta(hours=12) + ``` + + Args: + strict: Whether the value should be a timedelta or a value that can be converted to a timedelta + le: The value must be less than or equal to this timedelta + ge: The value must be greater than or equal to this timedelta + lt: The value must be strictly less than this timedelta + gt: The value must be strictly greater than this timedelta + microseconds_precision: The behavior when seconds have more than 6 digits or microseconds is too large + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='timedelta', + strict=strict, + le=le, + ge=ge, + lt=lt, + gt=gt, + microseconds_precision=microseconds_precision, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class LiteralSchema(TypedDict, total=False): + type: Required[Literal['literal']] + expected: Required[List[Any]] + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + + +def literal_schema( + expected: list[Any], + *, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> LiteralSchema: + """ + Returns a schema that matches a literal value, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.literal_schema(['hello', 'world']) + v = SchemaValidator(schema) + assert v.validate_python('hello') == 'hello' + ``` + + Args: + expected: The value must be one of these values + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none(type='literal', expected=expected, ref=ref, metadata=metadata, serialization=serialization) + + +class EnumSchema(TypedDict, total=False): + type: Required[Literal['enum']] + cls: Required[Any] + members: Required[List[Any]] + sub_type: Literal['str', 'int', 'float'] + missing: Callable[[Any], Any] + strict: bool + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + + +def enum_schema( + cls: Any, + members: list[Any], + *, + sub_type: Literal['str', 'int', 'float'] | None = None, + missing: Callable[[Any], Any] | None = None, + strict: bool | None = None, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> EnumSchema: + """ + Returns a schema that matches an enum value, e.g.: + + ```py + from enum import Enum + from pydantic_core import SchemaValidator, core_schema + + class Color(Enum): + RED = 1 + GREEN = 2 + BLUE = 3 + + schema = core_schema.enum_schema(Color, list(Color.__members__.values())) + v = SchemaValidator(schema) + assert v.validate_python(2) is Color.GREEN + ``` + + Args: + cls: The enum class + members: The members of the enum, generally `list(MyEnum.__members__.values())` + sub_type: The type of the enum, either 'str' or 'int' or None for plain enums + missing: A function to use when the value is not found in the enum, from `_missing_` + strict: Whether to use strict mode, defaults to False + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='enum', + cls=cls, + members=members, + sub_type=sub_type, + missing=missing, + strict=strict, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +# must match input/parse_json.rs::JsonType::try_from +JsonType = Literal['null', 'bool', 'int', 'float', 'str', 'list', 'dict'] + + +class IsInstanceSchema(TypedDict, total=False): + type: Required[Literal['is-instance']] + cls: Required[Any] + cls_repr: str + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + + +def is_instance_schema( + cls: Any, + *, + cls_repr: str | None = None, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> IsInstanceSchema: + """ + Returns a schema that checks if a value is an instance of a class, equivalent to python's `isinstance` method, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + class A: + pass + + schema = core_schema.is_instance_schema(cls=A) + v = SchemaValidator(schema) + v.validate_python(A()) + ``` + + Args: + cls: The value must be an instance of this class + cls_repr: If provided this string is used in the validator name instead of `repr(cls)` + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='is-instance', cls=cls, cls_repr=cls_repr, ref=ref, metadata=metadata, serialization=serialization + ) + + +class IsSubclassSchema(TypedDict, total=False): + type: Required[Literal['is-subclass']] + cls: Required[Type[Any]] + cls_repr: str + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + + +def is_subclass_schema( + cls: Type[Any], + *, + cls_repr: str | None = None, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> IsInstanceSchema: + """ + Returns a schema that checks if a value is a subtype of a class, equivalent to python's `issubclass` method, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + class A: + pass + + class B(A): + pass + + schema = core_schema.is_subclass_schema(cls=A) + v = SchemaValidator(schema) + v.validate_python(B) + ``` + + Args: + cls: The value must be a subclass of this class + cls_repr: If provided this string is used in the validator name instead of `repr(cls)` + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='is-subclass', cls=cls, cls_repr=cls_repr, ref=ref, metadata=metadata, serialization=serialization + ) + + +class CallableSchema(TypedDict, total=False): + type: Required[Literal['callable']] + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + + +def callable_schema( + *, ref: str | None = None, metadata: Dict[str, Any] | None = None, serialization: SerSchema | None = None +) -> CallableSchema: + """ + Returns a schema that checks if a value is callable, equivalent to python's `callable` method, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.callable_schema() + v = SchemaValidator(schema) + v.validate_python(min) + ``` + + Args: + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none(type='callable', ref=ref, metadata=metadata, serialization=serialization) + + +class UuidSchema(TypedDict, total=False): + type: Required[Literal['uuid']] + version: Literal[1, 3, 4, 5] + strict: bool + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + + +def uuid_schema( + *, + version: Literal[1, 3, 4, 5] | None = None, + strict: bool | None = None, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> UuidSchema: + return _dict_not_none( + type='uuid', version=version, strict=strict, ref=ref, metadata=metadata, serialization=serialization + ) + + +class IncExSeqSerSchema(TypedDict, total=False): + type: Required[Literal['include-exclude-sequence']] + include: Set[int] + exclude: Set[int] + + +def filter_seq_schema(*, include: Set[int] | None = None, exclude: Set[int] | None = None) -> IncExSeqSerSchema: + return _dict_not_none(type='include-exclude-sequence', include=include, exclude=exclude) + + +IncExSeqOrElseSerSchema = Union[IncExSeqSerSchema, SerSchema] + + +class ListSchema(TypedDict, total=False): + type: Required[Literal['list']] + items_schema: CoreSchema + min_length: int + max_length: int + fail_fast: bool + strict: bool + ref: str + metadata: Dict[str, Any] + serialization: IncExSeqOrElseSerSchema + + +def list_schema( + items_schema: CoreSchema | None = None, + *, + min_length: int | None = None, + max_length: int | None = None, + fail_fast: bool | None = None, + strict: bool | None = None, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: IncExSeqOrElseSerSchema | None = None, +) -> ListSchema: + """ + Returns a schema that matches a list value, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.list_schema(core_schema.int_schema(), min_length=0, max_length=10) + v = SchemaValidator(schema) + assert v.validate_python(['4']) == [4] + ``` + + Args: + items_schema: The value must be a list of items that match this schema + min_length: The value must be a list with at least this many items + max_length: The value must be a list with at most this many items + fail_fast: Stop validation on the first error + strict: The value must be a list with exactly this many items + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='list', + items_schema=items_schema, + min_length=min_length, + max_length=max_length, + fail_fast=fail_fast, + strict=strict, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +# @deprecated('tuple_positional_schema is deprecated. Use pydantic_core.core_schema.tuple_schema instead.') +def tuple_positional_schema( + items_schema: list[CoreSchema], + *, + extras_schema: CoreSchema | None = None, + strict: bool | None = None, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: IncExSeqOrElseSerSchema | None = None, +) -> TupleSchema: + """ + Returns a schema that matches a tuple of schemas, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.tuple_positional_schema( + [core_schema.int_schema(), core_schema.str_schema()] + ) + v = SchemaValidator(schema) + assert v.validate_python((1, 'hello')) == (1, 'hello') + ``` + + Args: + items_schema: The value must be a tuple with items that match these schemas + extras_schema: The value must be a tuple with items that match this schema + This was inspired by JSON schema's `prefixItems` and `items` fields. + In python's `typing.Tuple`, you can't specify a type for "extra" items -- they must all be the same type + if the length is variable. So this field won't be set from a `typing.Tuple` annotation on a pydantic model. + strict: The value must be a tuple with exactly this many items + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + if extras_schema is not None: + variadic_item_index = len(items_schema) + items_schema = items_schema + [extras_schema] + else: + variadic_item_index = None + return tuple_schema( + items_schema=items_schema, + variadic_item_index=variadic_item_index, + strict=strict, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +# @deprecated('tuple_variable_schema is deprecated. Use pydantic_core.core_schema.tuple_schema instead.') +def tuple_variable_schema( + items_schema: CoreSchema | None = None, + *, + min_length: int | None = None, + max_length: int | None = None, + strict: bool | None = None, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: IncExSeqOrElseSerSchema | None = None, +) -> TupleSchema: + """ + Returns a schema that matches a tuple of a given schema, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.tuple_variable_schema( + items_schema=core_schema.int_schema(), min_length=0, max_length=10 + ) + v = SchemaValidator(schema) + assert v.validate_python(('1', 2, 3)) == (1, 2, 3) + ``` + + Args: + items_schema: The value must be a tuple with items that match this schema + min_length: The value must be a tuple with at least this many items + max_length: The value must be a tuple with at most this many items + strict: The value must be a tuple with exactly this many items + ref: Optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return tuple_schema( + items_schema=[items_schema or any_schema()], + variadic_item_index=0, + min_length=min_length, + max_length=max_length, + strict=strict, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class TupleSchema(TypedDict, total=False): + type: Required[Literal['tuple']] + items_schema: Required[List[CoreSchema]] + variadic_item_index: int + min_length: int + max_length: int + fail_fast: bool + strict: bool + ref: str + metadata: Dict[str, Any] + serialization: IncExSeqOrElseSerSchema + + +def tuple_schema( + items_schema: list[CoreSchema], + *, + variadic_item_index: int | None = None, + min_length: int | None = None, + max_length: int | None = None, + fail_fast: bool | None = None, + strict: bool | None = None, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: IncExSeqOrElseSerSchema | None = None, +) -> TupleSchema: + """ + Returns a schema that matches a tuple of schemas, with an optional variadic item, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.tuple_schema( + [core_schema.int_schema(), core_schema.str_schema(), core_schema.float_schema()], + variadic_item_index=1, + ) + v = SchemaValidator(schema) + assert v.validate_python((1, 'hello', 'world', 1.5)) == (1, 'hello', 'world', 1.5) + ``` + + Args: + items_schema: The value must be a tuple with items that match these schemas + variadic_item_index: The index of the schema in `items_schema` to be treated as variadic (following PEP 646) + min_length: The value must be a tuple with at least this many items + max_length: The value must be a tuple with at most this many items + fail_fast: Stop validation on the first error + strict: The value must be a tuple with exactly this many items + ref: Optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='tuple', + items_schema=items_schema, + variadic_item_index=variadic_item_index, + min_length=min_length, + max_length=max_length, + fail_fast=fail_fast, + strict=strict, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class SetSchema(TypedDict, total=False): + type: Required[Literal['set']] + items_schema: CoreSchema + min_length: int + max_length: int + fail_fast: bool + strict: bool + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + + +def set_schema( + items_schema: CoreSchema | None = None, + *, + min_length: int | None = None, + max_length: int | None = None, + fail_fast: bool | None = None, + strict: bool | None = None, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> SetSchema: + """ + Returns a schema that matches a set of a given schema, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.set_schema( + items_schema=core_schema.int_schema(), min_length=0, max_length=10 + ) + v = SchemaValidator(schema) + assert v.validate_python({1, '2', 3}) == {1, 2, 3} + ``` + + Args: + items_schema: The value must be a set with items that match this schema + min_length: The value must be a set with at least this many items + max_length: The value must be a set with at most this many items + fail_fast: Stop validation on the first error + strict: The value must be a set with exactly this many items + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='set', + items_schema=items_schema, + min_length=min_length, + max_length=max_length, + fail_fast=fail_fast, + strict=strict, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class FrozenSetSchema(TypedDict, total=False): + type: Required[Literal['frozenset']] + items_schema: CoreSchema + min_length: int + max_length: int + fail_fast: bool + strict: bool + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + + +def frozenset_schema( + items_schema: CoreSchema | None = None, + *, + min_length: int | None = None, + max_length: int | None = None, + fail_fast: bool | None = None, + strict: bool | None = None, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> FrozenSetSchema: + """ + Returns a schema that matches a frozenset of a given schema, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.frozenset_schema( + items_schema=core_schema.int_schema(), min_length=0, max_length=10 + ) + v = SchemaValidator(schema) + assert v.validate_python(frozenset(range(3))) == frozenset({0, 1, 2}) + ``` + + Args: + items_schema: The value must be a frozenset with items that match this schema + min_length: The value must be a frozenset with at least this many items + max_length: The value must be a frozenset with at most this many items + fail_fast: Stop validation on the first error + strict: The value must be a frozenset with exactly this many items + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='frozenset', + items_schema=items_schema, + min_length=min_length, + max_length=max_length, + fail_fast=fail_fast, + strict=strict, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class GeneratorSchema(TypedDict, total=False): + type: Required[Literal['generator']] + items_schema: CoreSchema + min_length: int + max_length: int + ref: str + metadata: Dict[str, Any] + serialization: IncExSeqOrElseSerSchema + + +def generator_schema( + items_schema: CoreSchema | None = None, + *, + min_length: int | None = None, + max_length: int | None = None, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: IncExSeqOrElseSerSchema | None = None, +) -> GeneratorSchema: + """ + Returns a schema that matches a generator value, e.g.: + + ```py + from typing import Iterator + from pydantic_core import SchemaValidator, core_schema + + def gen() -> Iterator[int]: + yield 1 + + schema = core_schema.generator_schema(items_schema=core_schema.int_schema()) + v = SchemaValidator(schema) + v.validate_python(gen()) + ``` + + Unlike other types, validated generators do not raise ValidationErrors eagerly, + but instead will raise a ValidationError when a violating value is actually read from the generator. + This is to ensure that "validated" generators retain the benefit of lazy evaluation. + + Args: + items_schema: The value must be a generator with items that match this schema + min_length: The value must be a generator that yields at least this many items + max_length: The value must be a generator that yields at most this many items + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='generator', + items_schema=items_schema, + min_length=min_length, + max_length=max_length, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +IncExDict = Set[Union[int, str]] + + +class IncExDictSerSchema(TypedDict, total=False): + type: Required[Literal['include-exclude-dict']] + include: IncExDict + exclude: IncExDict + + +def filter_dict_schema(*, include: IncExDict | None = None, exclude: IncExDict | None = None) -> IncExDictSerSchema: + return _dict_not_none(type='include-exclude-dict', include=include, exclude=exclude) + + +IncExDictOrElseSerSchema = Union[IncExDictSerSchema, SerSchema] + + +class DictSchema(TypedDict, total=False): + type: Required[Literal['dict']] + keys_schema: CoreSchema # default: AnySchema + values_schema: CoreSchema # default: AnySchema + min_length: int + max_length: int + strict: bool + ref: str + metadata: Dict[str, Any] + serialization: IncExDictOrElseSerSchema + + +def dict_schema( + keys_schema: CoreSchema | None = None, + values_schema: CoreSchema | None = None, + *, + min_length: int | None = None, + max_length: int | None = None, + strict: bool | None = None, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> DictSchema: + """ + Returns a schema that matches a dict value, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.dict_schema( + keys_schema=core_schema.str_schema(), values_schema=core_schema.int_schema() + ) + v = SchemaValidator(schema) + assert v.validate_python({'a': '1', 'b': 2}) == {'a': 1, 'b': 2} + ``` + + Args: + keys_schema: The value must be a dict with keys that match this schema + values_schema: The value must be a dict with values that match this schema + min_length: The value must be a dict with at least this many items + max_length: The value must be a dict with at most this many items + strict: Whether the keys and values should be validated with strict mode + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='dict', + keys_schema=keys_schema, + values_schema=values_schema, + min_length=min_length, + max_length=max_length, + strict=strict, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +# (input_value: Any, /) -> Any +NoInfoValidatorFunction = Callable[[Any], Any] + + +class NoInfoValidatorFunctionSchema(TypedDict): + type: Literal['no-info'] + function: NoInfoValidatorFunction + + +# (input_value: Any, info: ValidationInfo, /) -> Any +WithInfoValidatorFunction = Callable[[Any, ValidationInfo], Any] + + +class WithInfoValidatorFunctionSchema(TypedDict, total=False): + type: Required[Literal['with-info']] + function: Required[WithInfoValidatorFunction] + field_name: str + + +ValidationFunction = Union[NoInfoValidatorFunctionSchema, WithInfoValidatorFunctionSchema] + + +class _ValidatorFunctionSchema(TypedDict, total=False): + function: Required[ValidationFunction] + schema: Required[CoreSchema] + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + + +class BeforeValidatorFunctionSchema(_ValidatorFunctionSchema, total=False): + type: Required[Literal['function-before']] + json_schema_input_schema: CoreSchema + + +def no_info_before_validator_function( + function: NoInfoValidatorFunction, + schema: CoreSchema, + *, + ref: str | None = None, + json_schema_input_schema: CoreSchema | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> BeforeValidatorFunctionSchema: + """ + Returns a schema that calls a validator function before validating, no `info` argument is provided, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + def fn(v: bytes) -> str: + return v.decode() + 'world' + + func_schema = core_schema.no_info_before_validator_function( + function=fn, schema=core_schema.str_schema() + ) + schema = core_schema.typed_dict_schema({'a': core_schema.typed_dict_field(func_schema)}) + + v = SchemaValidator(schema) + assert v.validate_python({'a': b'hello '}) == {'a': 'hello world'} + ``` + + Args: + function: The validator function to call + schema: The schema to validate the output of the validator function + ref: optional unique identifier of the schema, used to reference the schema in other places + json_schema_input_schema: The core schema to be used to generate the corresponding JSON Schema input type + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='function-before', + function={'type': 'no-info', 'function': function}, + schema=schema, + ref=ref, + json_schema_input_schema=json_schema_input_schema, + metadata=metadata, + serialization=serialization, + ) + + +def with_info_before_validator_function( + function: WithInfoValidatorFunction, + schema: CoreSchema, + *, + field_name: str | None = None, + ref: str | None = None, + json_schema_input_schema: CoreSchema | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> BeforeValidatorFunctionSchema: + """ + Returns a schema that calls a validator function before validation, the function is called with + an `info` argument, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + def fn(v: bytes, info: core_schema.ValidationInfo) -> str: + assert info.data is not None + assert info.field_name is not None + return v.decode() + 'world' + + func_schema = core_schema.with_info_before_validator_function( + function=fn, schema=core_schema.str_schema(), field_name='a' + ) + schema = core_schema.typed_dict_schema({'a': core_schema.typed_dict_field(func_schema)}) + + v = SchemaValidator(schema) + assert v.validate_python({'a': b'hello '}) == {'a': 'hello world'} + ``` + + Args: + function: The validator function to call + field_name: The name of the field + schema: The schema to validate the output of the validator function + ref: optional unique identifier of the schema, used to reference the schema in other places + json_schema_input_schema: The core schema to be used to generate the corresponding JSON Schema input type + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='function-before', + function=_dict_not_none(type='with-info', function=function, field_name=field_name), + schema=schema, + ref=ref, + json_schema_input_schema=json_schema_input_schema, + metadata=metadata, + serialization=serialization, + ) + + +class AfterValidatorFunctionSchema(_ValidatorFunctionSchema, total=False): + type: Required[Literal['function-after']] + + +def no_info_after_validator_function( + function: NoInfoValidatorFunction, + schema: CoreSchema, + *, + ref: str | None = None, + json_schema_input_schema: CoreSchema | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> AfterValidatorFunctionSchema: + """ + Returns a schema that calls a validator function after validating, no `info` argument is provided, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + def fn(v: str) -> str: + return v + 'world' + + func_schema = core_schema.no_info_after_validator_function(fn, core_schema.str_schema()) + schema = core_schema.typed_dict_schema({'a': core_schema.typed_dict_field(func_schema)}) + + v = SchemaValidator(schema) + assert v.validate_python({'a': b'hello '}) == {'a': 'hello world'} + ``` + + Args: + function: The validator function to call after the schema is validated + schema: The schema to validate before the validator function + ref: optional unique identifier of the schema, used to reference the schema in other places + json_schema_input_schema: The core schema to be used to generate the corresponding JSON Schema input type + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='function-after', + function={'type': 'no-info', 'function': function}, + schema=schema, + ref=ref, + json_schema_input_schema=json_schema_input_schema, + metadata=metadata, + serialization=serialization, + ) + + +def with_info_after_validator_function( + function: WithInfoValidatorFunction, + schema: CoreSchema, + *, + field_name: str | None = None, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> AfterValidatorFunctionSchema: + """ + Returns a schema that calls a validator function after validation, the function is called with + an `info` argument, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + def fn(v: str, info: core_schema.ValidationInfo) -> str: + assert info.data is not None + assert info.field_name is not None + return v + 'world' + + func_schema = core_schema.with_info_after_validator_function( + function=fn, schema=core_schema.str_schema(), field_name='a' + ) + schema = core_schema.typed_dict_schema({'a': core_schema.typed_dict_field(func_schema)}) + + v = SchemaValidator(schema) + assert v.validate_python({'a': b'hello '}) == {'a': 'hello world'} + ``` + + Args: + function: The validator function to call after the schema is validated + schema: The schema to validate before the validator function + field_name: The name of the field this validators is applied to, if any + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='function-after', + function=_dict_not_none(type='with-info', function=function, field_name=field_name), + schema=schema, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class ValidatorFunctionWrapHandler(Protocol): + def __call__(self, input_value: Any, outer_location: str | int | None = None, /) -> Any: # pragma: no cover + ... + + +# (input_value: Any, validator: ValidatorFunctionWrapHandler, /) -> Any +NoInfoWrapValidatorFunction = Callable[[Any, ValidatorFunctionWrapHandler], Any] + + +class NoInfoWrapValidatorFunctionSchema(TypedDict): + type: Literal['no-info'] + function: NoInfoWrapValidatorFunction + + +# (input_value: Any, validator: ValidatorFunctionWrapHandler, info: ValidationInfo, /) -> Any +WithInfoWrapValidatorFunction = Callable[[Any, ValidatorFunctionWrapHandler, ValidationInfo], Any] + + +class WithInfoWrapValidatorFunctionSchema(TypedDict, total=False): + type: Required[Literal['with-info']] + function: Required[WithInfoWrapValidatorFunction] + field_name: str + + +WrapValidatorFunction = Union[NoInfoWrapValidatorFunctionSchema, WithInfoWrapValidatorFunctionSchema] + + +class WrapValidatorFunctionSchema(TypedDict, total=False): + type: Required[Literal['function-wrap']] + function: Required[WrapValidatorFunction] + schema: Required[CoreSchema] + ref: str + json_schema_input_schema: CoreSchema + metadata: Dict[str, Any] + serialization: SerSchema + + +def no_info_wrap_validator_function( + function: NoInfoWrapValidatorFunction, + schema: CoreSchema, + *, + ref: str | None = None, + json_schema_input_schema: CoreSchema | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> WrapValidatorFunctionSchema: + """ + Returns a schema which calls a function with a `validator` callable argument which can + optionally be used to call inner validation with the function logic, this is much like the + "onion" implementation of middleware in many popular web frameworks, no `info` argument is passed, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + def fn( + v: str, + validator: core_schema.ValidatorFunctionWrapHandler, + ) -> str: + return validator(input_value=v) + 'world' + + schema = core_schema.no_info_wrap_validator_function( + function=fn, schema=core_schema.str_schema() + ) + v = SchemaValidator(schema) + assert v.validate_python('hello ') == 'hello world' + ``` + + Args: + function: The validator function to call + schema: The schema to validate the output of the validator function + ref: optional unique identifier of the schema, used to reference the schema in other places + json_schema_input_schema: The core schema to be used to generate the corresponding JSON Schema input type + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='function-wrap', + function={'type': 'no-info', 'function': function}, + schema=schema, + json_schema_input_schema=json_schema_input_schema, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +def with_info_wrap_validator_function( + function: WithInfoWrapValidatorFunction, + schema: CoreSchema, + *, + field_name: str | None = None, + json_schema_input_schema: CoreSchema | None = None, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> WrapValidatorFunctionSchema: + """ + Returns a schema which calls a function with a `validator` callable argument which can + optionally be used to call inner validation with the function logic, this is much like the + "onion" implementation of middleware in many popular web frameworks, an `info` argument is also passed, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + def fn( + v: str, + validator: core_schema.ValidatorFunctionWrapHandler, + info: core_schema.ValidationInfo, + ) -> str: + return validator(input_value=v) + 'world' + + schema = core_schema.with_info_wrap_validator_function( + function=fn, schema=core_schema.str_schema() + ) + v = SchemaValidator(schema) + assert v.validate_python('hello ') == 'hello world' + ``` + + Args: + function: The validator function to call + schema: The schema to validate the output of the validator function + field_name: The name of the field this validators is applied to, if any + json_schema_input_schema: The core schema to be used to generate the corresponding JSON Schema input type + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='function-wrap', + function=_dict_not_none(type='with-info', function=function, field_name=field_name), + schema=schema, + json_schema_input_schema=json_schema_input_schema, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class PlainValidatorFunctionSchema(TypedDict, total=False): + type: Required[Literal['function-plain']] + function: Required[ValidationFunction] + ref: str + json_schema_input_schema: CoreSchema + metadata: Dict[str, Any] + serialization: SerSchema + + +def no_info_plain_validator_function( + function: NoInfoValidatorFunction, + *, + ref: str | None = None, + json_schema_input_schema: CoreSchema | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> PlainValidatorFunctionSchema: + """ + Returns a schema that uses the provided function for validation, no `info` argument is passed, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + def fn(v: str) -> str: + assert 'hello' in v + return v + 'world' + + schema = core_schema.no_info_plain_validator_function(function=fn) + v = SchemaValidator(schema) + assert v.validate_python('hello ') == 'hello world' + ``` + + Args: + function: The validator function to call + ref: optional unique identifier of the schema, used to reference the schema in other places + json_schema_input_schema: The core schema to be used to generate the corresponding JSON Schema input type + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='function-plain', + function={'type': 'no-info', 'function': function}, + ref=ref, + json_schema_input_schema=json_schema_input_schema, + metadata=metadata, + serialization=serialization, + ) + + +def with_info_plain_validator_function( + function: WithInfoValidatorFunction, + *, + field_name: str | None = None, + ref: str | None = None, + json_schema_input_schema: CoreSchema | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> PlainValidatorFunctionSchema: + """ + Returns a schema that uses the provided function for validation, an `info` argument is passed, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + def fn(v: str, info: core_schema.ValidationInfo) -> str: + assert 'hello' in v + return v + 'world' + + schema = core_schema.with_info_plain_validator_function(function=fn) + v = SchemaValidator(schema) + assert v.validate_python('hello ') == 'hello world' + ``` + + Args: + function: The validator function to call + field_name: The name of the field this validators is applied to, if any + ref: optional unique identifier of the schema, used to reference the schema in other places + json_schema_input_schema: The core schema to be used to generate the corresponding JSON Schema input type + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='function-plain', + function=_dict_not_none(type='with-info', function=function, field_name=field_name), + ref=ref, + json_schema_input_schema=json_schema_input_schema, + metadata=metadata, + serialization=serialization, + ) + + +class WithDefaultSchema(TypedDict, total=False): + type: Required[Literal['default']] + schema: Required[CoreSchema] + default: Any + default_factory: Union[Callable[[], Any], Callable[[Dict[str, Any]], Any]] + default_factory_takes_data: bool + on_error: Literal['raise', 'omit', 'default'] # default: 'raise' + validate_default: bool # default: False + strict: bool + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + + +def with_default_schema( + schema: CoreSchema, + *, + default: Any = PydanticUndefined, + default_factory: Union[Callable[[], Any], Callable[[Dict[str, Any]], Any], None] = None, + default_factory_takes_data: bool | None = None, + on_error: Literal['raise', 'omit', 'default'] | None = None, + validate_default: bool | None = None, + strict: bool | None = None, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> WithDefaultSchema: + """ + Returns a schema that adds a default value to the given schema, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.with_default_schema(core_schema.str_schema(), default='hello') + wrapper_schema = core_schema.typed_dict_schema( + {'a': core_schema.typed_dict_field(schema)} + ) + v = SchemaValidator(wrapper_schema) + assert v.validate_python({}) == v.validate_python({'a': 'hello'}) + ``` + + Args: + schema: The schema to add a default value to + default: The default value to use + default_factory: A callable that returns the default value to use + default_factory_takes_data: Whether the default factory takes a validated data argument + on_error: What to do if the schema validation fails. One of 'raise', 'omit', 'default' + validate_default: Whether the default value should be validated + strict: Whether the underlying schema should be validated with strict mode + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + s = _dict_not_none( + type='default', + schema=schema, + default_factory=default_factory, + default_factory_takes_data=default_factory_takes_data, + on_error=on_error, + validate_default=validate_default, + strict=strict, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + if default is not PydanticUndefined: + s['default'] = default + return s + + +class NullableSchema(TypedDict, total=False): + type: Required[Literal['nullable']] + schema: Required[CoreSchema] + strict: bool + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + + +def nullable_schema( + schema: CoreSchema, + *, + strict: bool | None = None, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> NullableSchema: + """ + Returns a schema that matches a nullable value, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.nullable_schema(core_schema.str_schema()) + v = SchemaValidator(schema) + assert v.validate_python(None) is None + ``` + + Args: + schema: The schema to wrap + strict: Whether the underlying schema should be validated with strict mode + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='nullable', schema=schema, strict=strict, ref=ref, metadata=metadata, serialization=serialization + ) + + +class UnionSchema(TypedDict, total=False): + type: Required[Literal['union']] + choices: Required[List[Union[CoreSchema, Tuple[CoreSchema, str]]]] + # default true, whether to automatically collapse unions with one element to the inner validator + auto_collapse: bool + custom_error_type: str + custom_error_message: str + custom_error_context: Dict[str, Union[str, int, float]] + mode: Literal['smart', 'left_to_right'] # default: 'smart' + strict: bool + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + + +def union_schema( + choices: list[CoreSchema | tuple[CoreSchema, str]], + *, + auto_collapse: bool | None = None, + custom_error_type: str | None = None, + custom_error_message: str | None = None, + custom_error_context: dict[str, str | int] | None = None, + mode: Literal['smart', 'left_to_right'] | None = None, + strict: bool | None = None, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> UnionSchema: + """ + Returns a schema that matches a union value, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.union_schema([core_schema.str_schema(), core_schema.int_schema()]) + v = SchemaValidator(schema) + assert v.validate_python('hello') == 'hello' + assert v.validate_python(1) == 1 + ``` + + Args: + choices: The schemas to match. If a tuple, the second item is used as the label for the case. + auto_collapse: whether to automatically collapse unions with one element to the inner validator, default true + custom_error_type: The custom error type to use if the validation fails + custom_error_message: The custom error message to use if the validation fails + custom_error_context: The custom error context to use if the validation fails + mode: How to select which choice to return + * `smart` (default) will try to return the choice which is the closest match to the input value + * `left_to_right` will return the first choice in `choices` which succeeds validation + strict: Whether the underlying schemas should be validated with strict mode + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='union', + choices=choices, + auto_collapse=auto_collapse, + custom_error_type=custom_error_type, + custom_error_message=custom_error_message, + custom_error_context=custom_error_context, + mode=mode, + strict=strict, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class TaggedUnionSchema(TypedDict, total=False): + type: Required[Literal['tagged-union']] + choices: Required[Dict[Hashable, CoreSchema]] + discriminator: Required[Union[str, List[Union[str, int]], List[List[Union[str, int]]], Callable[[Any], Hashable]]] + custom_error_type: str + custom_error_message: str + custom_error_context: Dict[str, Union[str, int, float]] + strict: bool + from_attributes: bool # default: True + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + + +def tagged_union_schema( + choices: Dict[Any, CoreSchema], + discriminator: str | list[str | int] | list[list[str | int]] | Callable[[Any], Any], + *, + custom_error_type: str | None = None, + custom_error_message: str | None = None, + custom_error_context: dict[str, int | str | float] | None = None, + strict: bool | None = None, + from_attributes: bool | None = None, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> TaggedUnionSchema: + """ + Returns a schema that matches a tagged union value, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + apple_schema = core_schema.typed_dict_schema( + { + 'foo': core_schema.typed_dict_field(core_schema.str_schema()), + 'bar': core_schema.typed_dict_field(core_schema.int_schema()), + } + ) + banana_schema = core_schema.typed_dict_schema( + { + 'foo': core_schema.typed_dict_field(core_schema.str_schema()), + 'spam': core_schema.typed_dict_field( + core_schema.list_schema(items_schema=core_schema.int_schema()) + ), + } + ) + schema = core_schema.tagged_union_schema( + choices={ + 'apple': apple_schema, + 'banana': banana_schema, + }, + discriminator='foo', + ) + v = SchemaValidator(schema) + assert v.validate_python({'foo': 'apple', 'bar': '123'}) == {'foo': 'apple', 'bar': 123} + assert v.validate_python({'foo': 'banana', 'spam': [1, 2, 3]}) == { + 'foo': 'banana', + 'spam': [1, 2, 3], + } + ``` + + Args: + choices: The schemas to match + When retrieving a schema from `choices` using the discriminator value, if the value is a str, + it should be fed back into the `choices` map until a schema is obtained + (This approach is to prevent multiple ownership of a single schema in Rust) + discriminator: The discriminator to use to determine the schema to use + * If `discriminator` is a str, it is the name of the attribute to use as the discriminator + * If `discriminator` is a list of int/str, it should be used as a "path" to access the discriminator + * If `discriminator` is a list of lists, each inner list is a path, and the first path that exists is used + * If `discriminator` is a callable, it should return the discriminator when called on the value to validate; + the callable can return `None` to indicate that there is no matching discriminator present on the input + custom_error_type: The custom error type to use if the validation fails + custom_error_message: The custom error message to use if the validation fails + custom_error_context: The custom error context to use if the validation fails + strict: Whether the underlying schemas should be validated with strict mode + from_attributes: Whether to use the attributes of the object to retrieve the discriminator value + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='tagged-union', + choices=choices, + discriminator=discriminator, + custom_error_type=custom_error_type, + custom_error_message=custom_error_message, + custom_error_context=custom_error_context, + strict=strict, + from_attributes=from_attributes, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class ChainSchema(TypedDict, total=False): + type: Required[Literal['chain']] + steps: Required[List[CoreSchema]] + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + + +def chain_schema( + steps: list[CoreSchema], + *, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> ChainSchema: + """ + Returns a schema that chains the provided validation schemas, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + def fn(v: str, info: core_schema.ValidationInfo) -> str: + assert 'hello' in v + return v + ' world' + + fn_schema = core_schema.with_info_plain_validator_function(function=fn) + schema = core_schema.chain_schema( + [fn_schema, fn_schema, fn_schema, core_schema.str_schema()] + ) + v = SchemaValidator(schema) + assert v.validate_python('hello') == 'hello world world world' + ``` + + Args: + steps: The schemas to chain + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none(type='chain', steps=steps, ref=ref, metadata=metadata, serialization=serialization) + + +class LaxOrStrictSchema(TypedDict, total=False): + type: Required[Literal['lax-or-strict']] + lax_schema: Required[CoreSchema] + strict_schema: Required[CoreSchema] + strict: bool + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + + +def lax_or_strict_schema( + lax_schema: CoreSchema, + strict_schema: CoreSchema, + *, + strict: bool | None = None, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> LaxOrStrictSchema: + """ + Returns a schema that uses the lax or strict schema, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + def fn(v: str, info: core_schema.ValidationInfo) -> str: + assert 'hello' in v + return v + ' world' + + lax_schema = core_schema.int_schema(strict=False) + strict_schema = core_schema.int_schema(strict=True) + + schema = core_schema.lax_or_strict_schema( + lax_schema=lax_schema, strict_schema=strict_schema, strict=True + ) + v = SchemaValidator(schema) + assert v.validate_python(123) == 123 + + schema = core_schema.lax_or_strict_schema( + lax_schema=lax_schema, strict_schema=strict_schema, strict=False + ) + v = SchemaValidator(schema) + assert v.validate_python('123') == 123 + ``` + + Args: + lax_schema: The lax schema to use + strict_schema: The strict schema to use + strict: Whether the strict schema should be used + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='lax-or-strict', + lax_schema=lax_schema, + strict_schema=strict_schema, + strict=strict, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class JsonOrPythonSchema(TypedDict, total=False): + type: Required[Literal['json-or-python']] + json_schema: Required[CoreSchema] + python_schema: Required[CoreSchema] + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + + +def json_or_python_schema( + json_schema: CoreSchema, + python_schema: CoreSchema, + *, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> JsonOrPythonSchema: + """ + Returns a schema that uses the Json or Python schema depending on the input: + + ```py + from pydantic_core import SchemaValidator, ValidationError, core_schema + + v = SchemaValidator( + core_schema.json_or_python_schema( + json_schema=core_schema.int_schema(), + python_schema=core_schema.int_schema(strict=True), + ) + ) + + assert v.validate_json('"123"') == 123 + + try: + v.validate_python('123') + except ValidationError: + pass + else: + raise AssertionError('Validation should have failed') + ``` + + Args: + json_schema: The schema to use for Json inputs + python_schema: The schema to use for Python inputs + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='json-or-python', + json_schema=json_schema, + python_schema=python_schema, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class TypedDictField(TypedDict, total=False): + type: Required[Literal['typed-dict-field']] + schema: Required[CoreSchema] + required: bool + validation_alias: Union[str, List[Union[str, int]], List[List[Union[str, int]]]] + serialization_alias: str + serialization_exclude: bool # default: False + metadata: Dict[str, Any] + + +def typed_dict_field( + schema: CoreSchema, + *, + required: bool | None = None, + validation_alias: str | list[str | int] | list[list[str | int]] | None = None, + serialization_alias: str | None = None, + serialization_exclude: bool | None = None, + metadata: Dict[str, Any] | None = None, +) -> TypedDictField: + """ + Returns a schema that matches a typed dict field, e.g.: + + ```py + from pydantic_core import core_schema + + field = core_schema.typed_dict_field(schema=core_schema.int_schema(), required=True) + ``` + + Args: + schema: The schema to use for the field + required: Whether the field is required, otherwise uses the value from `total` on the typed dict + validation_alias: The alias(es) to use to find the field in the validation data + serialization_alias: The alias to use as a key when serializing + serialization_exclude: Whether to exclude the field when serializing + metadata: Any other information you want to include with the schema, not used by pydantic-core + """ + return _dict_not_none( + type='typed-dict-field', + schema=schema, + required=required, + validation_alias=validation_alias, + serialization_alias=serialization_alias, + serialization_exclude=serialization_exclude, + metadata=metadata, + ) + + +class TypedDictSchema(TypedDict, total=False): + type: Required[Literal['typed-dict']] + fields: Required[Dict[str, TypedDictField]] + cls: Type[Any] + computed_fields: List[ComputedField] + strict: bool + extras_schema: CoreSchema + # all these values can be set via config, equivalent fields have `typed_dict_` prefix + extra_behavior: ExtraBehavior + total: bool # default: True + populate_by_name: bool # replaces `allow_population_by_field_name` in pydantic v1 + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + config: CoreConfig + + +def typed_dict_schema( + fields: Dict[str, TypedDictField], + *, + cls: Type[Any] | None = None, + computed_fields: list[ComputedField] | None = None, + strict: bool | None = None, + extras_schema: CoreSchema | None = None, + extra_behavior: ExtraBehavior | None = None, + total: bool | None = None, + populate_by_name: bool | None = None, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, + config: CoreConfig | None = None, +) -> TypedDictSchema: + """ + Returns a schema that matches a typed dict, e.g.: + + ```py + from typing_extensions import TypedDict + + from pydantic_core import SchemaValidator, core_schema + + class MyTypedDict(TypedDict): + a: str + + wrapper_schema = core_schema.typed_dict_schema( + {'a': core_schema.typed_dict_field(core_schema.str_schema())}, cls=MyTypedDict + ) + v = SchemaValidator(wrapper_schema) + assert v.validate_python({'a': 'hello'}) == {'a': 'hello'} + ``` + + Args: + fields: The fields to use for the typed dict + cls: The class to use for the typed dict + computed_fields: Computed fields to use when serializing the model, only applies when directly inside a model + strict: Whether the typed dict is strict + extras_schema: The extra validator to use for the typed dict + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + extra_behavior: The extra behavior to use for the typed dict + total: Whether the typed dict is total, otherwise uses `typed_dict_total` from config + populate_by_name: Whether the typed dict should populate by name + serialization: Custom serialization schema + """ + return _dict_not_none( + type='typed-dict', + fields=fields, + cls=cls, + computed_fields=computed_fields, + strict=strict, + extras_schema=extras_schema, + extra_behavior=extra_behavior, + total=total, + populate_by_name=populate_by_name, + ref=ref, + metadata=metadata, + serialization=serialization, + config=config, + ) + + +class ModelField(TypedDict, total=False): + type: Required[Literal['model-field']] + schema: Required[CoreSchema] + validation_alias: Union[str, List[Union[str, int]], List[List[Union[str, int]]]] + serialization_alias: str + serialization_exclude: bool # default: False + frozen: bool + metadata: Dict[str, Any] + + +def model_field( + schema: CoreSchema, + *, + validation_alias: str | list[str | int] | list[list[str | int]] | None = None, + serialization_alias: str | None = None, + serialization_exclude: bool | None = None, + frozen: bool | None = None, + metadata: Dict[str, Any] | None = None, +) -> ModelField: + """ + Returns a schema for a model field, e.g.: + + ```py + from pydantic_core import core_schema + + field = core_schema.model_field(schema=core_schema.int_schema()) + ``` + + Args: + schema: The schema to use for the field + validation_alias: The alias(es) to use to find the field in the validation data + serialization_alias: The alias to use as a key when serializing + serialization_exclude: Whether to exclude the field when serializing + frozen: Whether the field is frozen + metadata: Any other information you want to include with the schema, not used by pydantic-core + """ + return _dict_not_none( + type='model-field', + schema=schema, + validation_alias=validation_alias, + serialization_alias=serialization_alias, + serialization_exclude=serialization_exclude, + frozen=frozen, + metadata=metadata, + ) + + +class ModelFieldsSchema(TypedDict, total=False): + type: Required[Literal['model-fields']] + fields: Required[Dict[str, ModelField]] + model_name: str + computed_fields: List[ComputedField] + strict: bool + extras_schema: CoreSchema + # all these values can be set via config, equivalent fields have `typed_dict_` prefix + extra_behavior: ExtraBehavior + populate_by_name: bool # replaces `allow_population_by_field_name` in pydantic v1 + from_attributes: bool + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + + +def model_fields_schema( + fields: Dict[str, ModelField], + *, + model_name: str | None = None, + computed_fields: list[ComputedField] | None = None, + strict: bool | None = None, + extras_schema: CoreSchema | None = None, + extra_behavior: ExtraBehavior | None = None, + populate_by_name: bool | None = None, + from_attributes: bool | None = None, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> ModelFieldsSchema: + """ + Returns a schema that matches a typed dict, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + wrapper_schema = core_schema.model_fields_schema( + {'a': core_schema.model_field(core_schema.str_schema())} + ) + v = SchemaValidator(wrapper_schema) + print(v.validate_python({'a': 'hello'})) + #> ({'a': 'hello'}, None, {'a'}) + ``` + + Args: + fields: The fields to use for the typed dict + model_name: The name of the model, used for error messages, defaults to "Model" + computed_fields: Computed fields to use when serializing the model, only applies when directly inside a model + strict: Whether the typed dict is strict + extras_schema: The extra validator to use for the typed dict + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + extra_behavior: The extra behavior to use for the typed dict + populate_by_name: Whether the typed dict should populate by name + from_attributes: Whether the typed dict should be populated from attributes + serialization: Custom serialization schema + """ + return _dict_not_none( + type='model-fields', + fields=fields, + model_name=model_name, + computed_fields=computed_fields, + strict=strict, + extras_schema=extras_schema, + extra_behavior=extra_behavior, + populate_by_name=populate_by_name, + from_attributes=from_attributes, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class ModelSchema(TypedDict, total=False): + type: Required[Literal['model']] + cls: Required[Type[Any]] + generic_origin: Type[Any] + schema: Required[CoreSchema] + custom_init: bool + root_model: bool + post_init: str + revalidate_instances: Literal['always', 'never', 'subclass-instances'] # default: 'never' + strict: bool + frozen: bool + extra_behavior: ExtraBehavior + config: CoreConfig + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + + +def model_schema( + cls: Type[Any], + schema: CoreSchema, + *, + generic_origin: Type[Any] | None = None, + custom_init: bool | None = None, + root_model: bool | None = None, + post_init: str | None = None, + revalidate_instances: Literal['always', 'never', 'subclass-instances'] | None = None, + strict: bool | None = None, + frozen: bool | None = None, + extra_behavior: ExtraBehavior | None = None, + config: CoreConfig | None = None, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> ModelSchema: + """ + A model schema generally contains a typed-dict schema. + It will run the typed dict validator, then create a new class + and set the dict and fields set returned from the typed dict validator + to `__dict__` and `__pydantic_fields_set__` respectively. + + Example: + + ```py + from pydantic_core import CoreConfig, SchemaValidator, core_schema + + class MyModel: + __slots__ = ( + '__dict__', + '__pydantic_fields_set__', + '__pydantic_extra__', + '__pydantic_private__', + ) + + schema = core_schema.model_schema( + cls=MyModel, + config=CoreConfig(str_max_length=5), + schema=core_schema.model_fields_schema( + fields={'a': core_schema.model_field(core_schema.str_schema())}, + ), + ) + v = SchemaValidator(schema) + assert v.isinstance_python({'a': 'hello'}) is True + assert v.isinstance_python({'a': 'too long'}) is False + ``` + + Args: + cls: The class to use for the model + schema: The schema to use for the model + generic_origin: The origin type used for this model, if it's a parametrized generic. Ex, + if this model schema represents `SomeModel[int]`, generic_origin is `SomeModel` + custom_init: Whether the model has a custom init method + root_model: Whether the model is a `RootModel` + post_init: The call after init to use for the model + revalidate_instances: whether instances of models and dataclasses (including subclass instances) + should re-validate defaults to config.revalidate_instances, else 'never' + strict: Whether the model is strict + frozen: Whether the model is frozen + extra_behavior: The extra behavior to use for the model, used in serialization + config: The config to use for the model + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='model', + cls=cls, + generic_origin=generic_origin, + schema=schema, + custom_init=custom_init, + root_model=root_model, + post_init=post_init, + revalidate_instances=revalidate_instances, + strict=strict, + frozen=frozen, + extra_behavior=extra_behavior, + config=config, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class DataclassField(TypedDict, total=False): + type: Required[Literal['dataclass-field']] + name: Required[str] + schema: Required[CoreSchema] + kw_only: bool # default: True + init: bool # default: True + init_only: bool # default: False + frozen: bool # default: False + validation_alias: Union[str, List[Union[str, int]], List[List[Union[str, int]]]] + serialization_alias: str + serialization_exclude: bool # default: False + metadata: Dict[str, Any] + + +def dataclass_field( + name: str, + schema: CoreSchema, + *, + kw_only: bool | None = None, + init: bool | None = None, + init_only: bool | None = None, + validation_alias: str | list[str | int] | list[list[str | int]] | None = None, + serialization_alias: str | None = None, + serialization_exclude: bool | None = None, + metadata: Dict[str, Any] | None = None, + frozen: bool | None = None, +) -> DataclassField: + """ + Returns a schema for a dataclass field, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + field = core_schema.dataclass_field( + name='a', schema=core_schema.str_schema(), kw_only=False + ) + schema = core_schema.dataclass_args_schema('Foobar', [field]) + v = SchemaValidator(schema) + assert v.validate_python({'a': 'hello'}) == ({'a': 'hello'}, None) + ``` + + Args: + name: The name to use for the argument parameter + schema: The schema to use for the argument parameter + kw_only: Whether the field can be set with a positional argument as well as a keyword argument + init: Whether the field should be validated during initialization + init_only: Whether the field should be omitted from `__dict__` and passed to `__post_init__` + validation_alias: The alias(es) to use to find the field in the validation data + serialization_alias: The alias to use as a key when serializing + serialization_exclude: Whether to exclude the field when serializing + metadata: Any other information you want to include with the schema, not used by pydantic-core + frozen: Whether the field is frozen + """ + return _dict_not_none( + type='dataclass-field', + name=name, + schema=schema, + kw_only=kw_only, + init=init, + init_only=init_only, + validation_alias=validation_alias, + serialization_alias=serialization_alias, + serialization_exclude=serialization_exclude, + metadata=metadata, + frozen=frozen, + ) + + +class DataclassArgsSchema(TypedDict, total=False): + type: Required[Literal['dataclass-args']] + dataclass_name: Required[str] + fields: Required[List[DataclassField]] + computed_fields: List[ComputedField] + populate_by_name: bool # default: False + collect_init_only: bool # default: False + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + extra_behavior: ExtraBehavior + + +def dataclass_args_schema( + dataclass_name: str, + fields: list[DataclassField], + *, + computed_fields: List[ComputedField] | None = None, + populate_by_name: bool | None = None, + collect_init_only: bool | None = None, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, + extra_behavior: ExtraBehavior | None = None, +) -> DataclassArgsSchema: + """ + Returns a schema for validating dataclass arguments, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + field_a = core_schema.dataclass_field( + name='a', schema=core_schema.str_schema(), kw_only=False + ) + field_b = core_schema.dataclass_field( + name='b', schema=core_schema.bool_schema(), kw_only=False + ) + schema = core_schema.dataclass_args_schema('Foobar', [field_a, field_b]) + v = SchemaValidator(schema) + assert v.validate_python({'a': 'hello', 'b': True}) == ({'a': 'hello', 'b': True}, None) + ``` + + Args: + dataclass_name: The name of the dataclass being validated + fields: The fields to use for the dataclass + computed_fields: Computed fields to use when serializing the dataclass + populate_by_name: Whether to populate by name + collect_init_only: Whether to collect init only fields into a dict to pass to `__post_init__` + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + extra_behavior: How to handle extra fields + """ + return _dict_not_none( + type='dataclass-args', + dataclass_name=dataclass_name, + fields=fields, + computed_fields=computed_fields, + populate_by_name=populate_by_name, + collect_init_only=collect_init_only, + ref=ref, + metadata=metadata, + serialization=serialization, + extra_behavior=extra_behavior, + ) + + +class DataclassSchema(TypedDict, total=False): + type: Required[Literal['dataclass']] + cls: Required[Type[Any]] + generic_origin: Type[Any] + schema: Required[CoreSchema] + fields: Required[List[str]] + cls_name: str + post_init: bool # default: False + revalidate_instances: Literal['always', 'never', 'subclass-instances'] # default: 'never' + strict: bool # default: False + frozen: bool # default False + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + slots: bool + config: CoreConfig + + +def dataclass_schema( + cls: Type[Any], + schema: CoreSchema, + fields: List[str], + *, + generic_origin: Type[Any] | None = None, + cls_name: str | None = None, + post_init: bool | None = None, + revalidate_instances: Literal['always', 'never', 'subclass-instances'] | None = None, + strict: bool | None = None, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, + frozen: bool | None = None, + slots: bool | None = None, + config: CoreConfig | None = None, +) -> DataclassSchema: + """ + Returns a schema for a dataclass. As with `ModelSchema`, this schema can only be used as a field within + another schema, not as the root type. + + Args: + cls: The dataclass type, used to perform subclass checks + schema: The schema to use for the dataclass fields + fields: Fields of the dataclass, this is used in serialization and in validation during re-validation + and while validating assignment + generic_origin: The origin type used for this dataclass, if it's a parametrized generic. Ex, + if this model schema represents `SomeDataclass[int]`, generic_origin is `SomeDataclass` + cls_name: The name to use in error locs, etc; this is useful for generics (default: `cls.__name__`) + post_init: Whether to call `__post_init__` after validation + revalidate_instances: whether instances of models and dataclasses (including subclass instances) + should re-validate defaults to config.revalidate_instances, else 'never' + strict: Whether to require an exact instance of `cls` + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + frozen: Whether the dataclass is frozen + slots: Whether `slots=True` on the dataclass, means each field is assigned independently, rather than + simply setting `__dict__`, default false + """ + return _dict_not_none( + type='dataclass', + cls=cls, + generic_origin=generic_origin, + fields=fields, + cls_name=cls_name, + schema=schema, + post_init=post_init, + revalidate_instances=revalidate_instances, + strict=strict, + ref=ref, + metadata=metadata, + serialization=serialization, + frozen=frozen, + slots=slots, + config=config, + ) + + +class ArgumentsParameter(TypedDict, total=False): + name: Required[str] + schema: Required[CoreSchema] + mode: Literal['positional_only', 'positional_or_keyword', 'keyword_only'] # default positional_or_keyword + alias: Union[str, List[Union[str, int]], List[List[Union[str, int]]]] + + +def arguments_parameter( + name: str, + schema: CoreSchema, + *, + mode: Literal['positional_only', 'positional_or_keyword', 'keyword_only'] | None = None, + alias: str | list[str | int] | list[list[str | int]] | None = None, +) -> ArgumentsParameter: + """ + Returns a schema that matches an argument parameter, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + param = core_schema.arguments_parameter( + name='a', schema=core_schema.str_schema(), mode='positional_only' + ) + schema = core_schema.arguments_schema([param]) + v = SchemaValidator(schema) + assert v.validate_python(('hello',)) == (('hello',), {}) + ``` + + Args: + name: The name to use for the argument parameter + schema: The schema to use for the argument parameter + mode: The mode to use for the argument parameter + alias: The alias to use for the argument parameter + """ + return _dict_not_none(name=name, schema=schema, mode=mode, alias=alias) + + +VarKwargsMode: TypeAlias = Literal['uniform', 'unpacked-typed-dict'] + + +class ArgumentsSchema(TypedDict, total=False): + type: Required[Literal['arguments']] + arguments_schema: Required[List[ArgumentsParameter]] + populate_by_name: bool + var_args_schema: CoreSchema + var_kwargs_mode: VarKwargsMode + var_kwargs_schema: CoreSchema + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + + +def arguments_schema( + arguments: list[ArgumentsParameter], + *, + populate_by_name: bool | None = None, + var_args_schema: CoreSchema | None = None, + var_kwargs_mode: VarKwargsMode | None = None, + var_kwargs_schema: CoreSchema | None = None, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> ArgumentsSchema: + """ + Returns a schema that matches an arguments schema, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + param_a = core_schema.arguments_parameter( + name='a', schema=core_schema.str_schema(), mode='positional_only' + ) + param_b = core_schema.arguments_parameter( + name='b', schema=core_schema.bool_schema(), mode='positional_only' + ) + schema = core_schema.arguments_schema([param_a, param_b]) + v = SchemaValidator(schema) + assert v.validate_python(('hello', True)) == (('hello', True), {}) + ``` + + Args: + arguments: The arguments to use for the arguments schema + populate_by_name: Whether to populate by name + var_args_schema: The variable args schema to use for the arguments schema + var_kwargs_mode: The validation mode to use for variadic keyword arguments. If `'uniform'`, every value of the + keyword arguments will be validated against the `var_kwargs_schema` schema. If `'unpacked-typed-dict'`, + the `var_kwargs_schema` argument must be a [`typed_dict_schema`][pydantic_core.core_schema.typed_dict_schema] + var_kwargs_schema: The variable kwargs schema to use for the arguments schema + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='arguments', + arguments_schema=arguments, + populate_by_name=populate_by_name, + var_args_schema=var_args_schema, + var_kwargs_mode=var_kwargs_mode, + var_kwargs_schema=var_kwargs_schema, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class CallSchema(TypedDict, total=False): + type: Required[Literal['call']] + arguments_schema: Required[CoreSchema] + function: Required[Callable[..., Any]] + function_name: str # default function.__name__ + return_schema: CoreSchema + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + + +def call_schema( + arguments: CoreSchema, + function: Callable[..., Any], + *, + function_name: str | None = None, + return_schema: CoreSchema | None = None, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> CallSchema: + """ + Returns a schema that matches an arguments schema, then calls a function, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + param_a = core_schema.arguments_parameter( + name='a', schema=core_schema.str_schema(), mode='positional_only' + ) + param_b = core_schema.arguments_parameter( + name='b', schema=core_schema.bool_schema(), mode='positional_only' + ) + args_schema = core_schema.arguments_schema([param_a, param_b]) + + schema = core_schema.call_schema( + arguments=args_schema, + function=lambda a, b: a + str(not b), + return_schema=core_schema.str_schema(), + ) + v = SchemaValidator(schema) + assert v.validate_python((('hello', True))) == 'helloFalse' + ``` + + Args: + arguments: The arguments to use for the arguments schema + function: The function to use for the call schema + function_name: The function name to use for the call schema, if not provided `function.__name__` is used + return_schema: The return schema to use for the call schema + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='call', + arguments_schema=arguments, + function=function, + function_name=function_name, + return_schema=return_schema, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class CustomErrorSchema(TypedDict, total=False): + type: Required[Literal['custom-error']] + schema: Required[CoreSchema] + custom_error_type: Required[str] + custom_error_message: str + custom_error_context: Dict[str, Union[str, int, float]] + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + + +def custom_error_schema( + schema: CoreSchema, + custom_error_type: str, + *, + custom_error_message: str | None = None, + custom_error_context: dict[str, Any] | None = None, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> CustomErrorSchema: + """ + Returns a schema that matches a custom error value, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.custom_error_schema( + schema=core_schema.int_schema(), + custom_error_type='MyError', + custom_error_message='Error msg', + ) + v = SchemaValidator(schema) + v.validate_python(1) + ``` + + Args: + schema: The schema to use for the custom error schema + custom_error_type: The custom error type to use for the custom error schema + custom_error_message: The custom error message to use for the custom error schema + custom_error_context: The custom error context to use for the custom error schema + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='custom-error', + schema=schema, + custom_error_type=custom_error_type, + custom_error_message=custom_error_message, + custom_error_context=custom_error_context, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class JsonSchema(TypedDict, total=False): + type: Required[Literal['json']] + schema: CoreSchema + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + + +def json_schema( + schema: CoreSchema | None = None, + *, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> JsonSchema: + """ + Returns a schema that matches a JSON value, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + dict_schema = core_schema.model_fields_schema( + { + 'field_a': core_schema.model_field(core_schema.str_schema()), + 'field_b': core_schema.model_field(core_schema.bool_schema()), + }, + ) + + class MyModel: + __slots__ = ( + '__dict__', + '__pydantic_fields_set__', + '__pydantic_extra__', + '__pydantic_private__', + ) + field_a: str + field_b: bool + + json_schema = core_schema.json_schema(schema=dict_schema) + schema = core_schema.model_schema(cls=MyModel, schema=json_schema) + v = SchemaValidator(schema) + m = v.validate_python('{"field_a": "hello", "field_b": true}') + assert isinstance(m, MyModel) + ``` + + Args: + schema: The schema to use for the JSON schema + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none(type='json', schema=schema, ref=ref, metadata=metadata, serialization=serialization) + + +class UrlSchema(TypedDict, total=False): + type: Required[Literal['url']] + max_length: int + allowed_schemes: List[str] + host_required: bool # default False + default_host: str + default_port: int + default_path: str + strict: bool + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + + +def url_schema( + *, + max_length: int | None = None, + allowed_schemes: list[str] | None = None, + host_required: bool | None = None, + default_host: str | None = None, + default_port: int | None = None, + default_path: str | None = None, + strict: bool | None = None, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> UrlSchema: + """ + Returns a schema that matches a URL value, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.url_schema() + v = SchemaValidator(schema) + print(v.validate_python('https://example.com')) + #> https://example.com/ + ``` + + Args: + max_length: The maximum length of the URL + allowed_schemes: The allowed URL schemes + host_required: Whether the URL must have a host + default_host: The default host to use if the URL does not have a host + default_port: The default port to use if the URL does not have a port + default_path: The default path to use if the URL does not have a path + strict: Whether to use strict URL parsing + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='url', + max_length=max_length, + allowed_schemes=allowed_schemes, + host_required=host_required, + default_host=default_host, + default_port=default_port, + default_path=default_path, + strict=strict, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class MultiHostUrlSchema(TypedDict, total=False): + type: Required[Literal['multi-host-url']] + max_length: int + allowed_schemes: List[str] + host_required: bool # default False + default_host: str + default_port: int + default_path: str + strict: bool + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + + +def multi_host_url_schema( + *, + max_length: int | None = None, + allowed_schemes: list[str] | None = None, + host_required: bool | None = None, + default_host: str | None = None, + default_port: int | None = None, + default_path: str | None = None, + strict: bool | None = None, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> MultiHostUrlSchema: + """ + Returns a schema that matches a URL value with possibly multiple hosts, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.multi_host_url_schema() + v = SchemaValidator(schema) + print(v.validate_python('redis://localhost,0.0.0.0,127.0.0.1')) + #> redis://localhost,0.0.0.0,127.0.0.1 + ``` + + Args: + max_length: The maximum length of the URL + allowed_schemes: The allowed URL schemes + host_required: Whether the URL must have a host + default_host: The default host to use if the URL does not have a host + default_port: The default port to use if the URL does not have a port + default_path: The default path to use if the URL does not have a path + strict: Whether to use strict URL parsing + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='multi-host-url', + max_length=max_length, + allowed_schemes=allowed_schemes, + host_required=host_required, + default_host=default_host, + default_port=default_port, + default_path=default_path, + strict=strict, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class DefinitionsSchema(TypedDict, total=False): + type: Required[Literal['definitions']] + schema: Required[CoreSchema] + definitions: Required[List[CoreSchema]] + metadata: Dict[str, Any] + serialization: SerSchema + + +def definitions_schema(schema: CoreSchema, definitions: list[CoreSchema]) -> DefinitionsSchema: + """ + Build a schema that contains both an inner schema and a list of definitions which can be used + within the inner schema. + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.definitions_schema( + core_schema.list_schema(core_schema.definition_reference_schema('foobar')), + [core_schema.int_schema(ref='foobar')], + ) + v = SchemaValidator(schema) + assert v.validate_python([1, 2, '3']) == [1, 2, 3] + ``` + + Args: + schema: The inner schema + definitions: List of definitions which can be referenced within inner schema + """ + return DefinitionsSchema(type='definitions', schema=schema, definitions=definitions) + + +class DefinitionReferenceSchema(TypedDict, total=False): + type: Required[Literal['definition-ref']] + schema_ref: Required[str] + ref: str + metadata: Dict[str, Any] + serialization: SerSchema + + +def definition_reference_schema( + schema_ref: str, + ref: str | None = None, + metadata: Dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> DefinitionReferenceSchema: + """ + Returns a schema that points to a schema stored in "definitions", this is useful for nested recursive + models and also when you want to define validators separately from the main schema, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema_definition = core_schema.definition_reference_schema('list-schema') + schema = core_schema.definitions_schema( + schema=schema_definition, + definitions=[ + core_schema.list_schema(items_schema=schema_definition, ref='list-schema'), + ], + ) + v = SchemaValidator(schema) + assert v.validate_python([()]) == [[]] + ``` + + Args: + schema_ref: The schema ref to use for the definition reference schema + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='definition-ref', schema_ref=schema_ref, ref=ref, metadata=metadata, serialization=serialization + ) + + +MYPY = False +# See https://github.com/python/mypy/issues/14034 for details, in summary mypy is extremely slow to process this +# union which kills performance not just for pydantic, but even for code using pydantic +if not MYPY: + CoreSchema = Union[ + InvalidSchema, + AnySchema, + NoneSchema, + BoolSchema, + IntSchema, + FloatSchema, + DecimalSchema, + StringSchema, + BytesSchema, + DateSchema, + TimeSchema, + DatetimeSchema, + TimedeltaSchema, + LiteralSchema, + EnumSchema, + IsInstanceSchema, + IsSubclassSchema, + CallableSchema, + ListSchema, + TupleSchema, + SetSchema, + FrozenSetSchema, + GeneratorSchema, + DictSchema, + AfterValidatorFunctionSchema, + BeforeValidatorFunctionSchema, + WrapValidatorFunctionSchema, + PlainValidatorFunctionSchema, + WithDefaultSchema, + NullableSchema, + UnionSchema, + TaggedUnionSchema, + ChainSchema, + LaxOrStrictSchema, + JsonOrPythonSchema, + TypedDictSchema, + ModelFieldsSchema, + ModelSchema, + DataclassArgsSchema, + DataclassSchema, + ArgumentsSchema, + CallSchema, + CustomErrorSchema, + JsonSchema, + UrlSchema, + MultiHostUrlSchema, + DefinitionsSchema, + DefinitionReferenceSchema, + UuidSchema, + ComplexSchema, + ] +elif False: + CoreSchema: TypeAlias = Mapping[str, Any] + + +# to update this, call `pytest -k test_core_schema_type_literal` and copy the output +CoreSchemaType = Literal[ + 'invalid', + 'any', + 'none', + 'bool', + 'int', + 'float', + 'decimal', + 'str', + 'bytes', + 'date', + 'time', + 'datetime', + 'timedelta', + 'literal', + 'enum', + 'is-instance', + 'is-subclass', + 'callable', + 'list', + 'tuple', + 'set', + 'frozenset', + 'generator', + 'dict', + 'function-after', + 'function-before', + 'function-wrap', + 'function-plain', + 'default', + 'nullable', + 'union', + 'tagged-union', + 'chain', + 'lax-or-strict', + 'json-or-python', + 'typed-dict', + 'model-fields', + 'model', + 'dataclass-args', + 'dataclass', + 'arguments', + 'call', + 'custom-error', + 'json', + 'url', + 'multi-host-url', + 'definitions', + 'definition-ref', + 'uuid', + 'complex', +] + +CoreSchemaFieldType = Literal['model-field', 'dataclass-field', 'typed-dict-field', 'computed-field'] + + +# used in _pydantic_core.pyi::PydanticKnownError +# to update this, call `pytest -k test_all_errors` and copy the output +ErrorType = Literal[ + 'no_such_attribute', + 'json_invalid', + 'json_type', + 'needs_python_object', + 'recursion_loop', + 'missing', + 'frozen_field', + 'frozen_instance', + 'extra_forbidden', + 'invalid_key', + 'get_attribute_error', + 'model_type', + 'model_attributes_type', + 'dataclass_type', + 'dataclass_exact_type', + 'none_required', + 'greater_than', + 'greater_than_equal', + 'less_than', + 'less_than_equal', + 'multiple_of', + 'finite_number', + 'too_short', + 'too_long', + 'iterable_type', + 'iteration_error', + 'string_type', + 'string_sub_type', + 'string_unicode', + 'string_too_short', + 'string_too_long', + 'string_pattern_mismatch', + 'enum', + 'dict_type', + 'mapping_type', + 'list_type', + 'tuple_type', + 'set_type', + 'bool_type', + 'bool_parsing', + 'int_type', + 'int_parsing', + 'int_parsing_size', + 'int_from_float', + 'float_type', + 'float_parsing', + 'bytes_type', + 'bytes_too_short', + 'bytes_too_long', + 'bytes_invalid_encoding', + 'value_error', + 'assertion_error', + 'literal_error', + 'date_type', + 'date_parsing', + 'date_from_datetime_parsing', + 'date_from_datetime_inexact', + 'date_past', + 'date_future', + 'time_type', + 'time_parsing', + 'datetime_type', + 'datetime_parsing', + 'datetime_object_invalid', + 'datetime_from_date_parsing', + 'datetime_past', + 'datetime_future', + 'timezone_naive', + 'timezone_aware', + 'timezone_offset', + 'time_delta_type', + 'time_delta_parsing', + 'frozen_set_type', + 'is_instance_of', + 'is_subclass_of', + 'callable_type', + 'union_tag_invalid', + 'union_tag_not_found', + 'arguments_type', + 'missing_argument', + 'unexpected_keyword_argument', + 'missing_keyword_only_argument', + 'unexpected_positional_argument', + 'missing_positional_only_argument', + 'multiple_argument_values', + 'url_type', + 'url_parsing', + 'url_syntax_violation', + 'url_too_long', + 'url_scheme', + 'uuid_type', + 'uuid_parsing', + 'uuid_version', + 'decimal_type', + 'decimal_parsing', + 'decimal_max_digits', + 'decimal_max_places', + 'decimal_whole_digits', + 'complex_type', + 'complex_str_parsing', +] + + +def _dict_not_none(**kwargs: Any) -> Any: + return {k: v for k, v in kwargs.items() if v is not None} + + +############################################################################### +# All this stuff is deprecated by #980 and will be removed eventually +# They're kept because some code external code will be using them + + +@deprecated('`field_before_validator_function` is deprecated, use `with_info_before_validator_function` instead.') +def field_before_validator_function(function: WithInfoValidatorFunction, field_name: str, schema: CoreSchema, **kwargs): + warnings.warn( + '`field_before_validator_function` is deprecated, use `with_info_before_validator_function` instead.', + DeprecationWarning, + ) + return with_info_before_validator_function(function, schema, field_name=field_name, **kwargs) + + +@deprecated('`general_before_validator_function` is deprecated, use `with_info_before_validator_function` instead.') +def general_before_validator_function(*args, **kwargs): + warnings.warn( + '`general_before_validator_function` is deprecated, use `with_info_before_validator_function` instead.', + DeprecationWarning, + ) + return with_info_before_validator_function(*args, **kwargs) + + +@deprecated('`field_after_validator_function` is deprecated, use `with_info_after_validator_function` instead.') +def field_after_validator_function(function: WithInfoValidatorFunction, field_name: str, schema: CoreSchema, **kwargs): + warnings.warn( + '`field_after_validator_function` is deprecated, use `with_info_after_validator_function` instead.', + DeprecationWarning, + ) + return with_info_after_validator_function(function, schema, field_name=field_name, **kwargs) + + +@deprecated('`general_after_validator_function` is deprecated, use `with_info_after_validator_function` instead.') +def general_after_validator_function(*args, **kwargs): + warnings.warn( + '`general_after_validator_function` is deprecated, use `with_info_after_validator_function` instead.', + DeprecationWarning, + ) + return with_info_after_validator_function(*args, **kwargs) + + +@deprecated('`field_wrap_validator_function` is deprecated, use `with_info_wrap_validator_function` instead.') +def field_wrap_validator_function( + function: WithInfoWrapValidatorFunction, field_name: str, schema: CoreSchema, **kwargs +): + warnings.warn( + '`field_wrap_validator_function` is deprecated, use `with_info_wrap_validator_function` instead.', + DeprecationWarning, + ) + return with_info_wrap_validator_function(function, schema, field_name=field_name, **kwargs) + + +@deprecated('`general_wrap_validator_function` is deprecated, use `with_info_wrap_validator_function` instead.') +def general_wrap_validator_function(*args, **kwargs): + warnings.warn( + '`general_wrap_validator_function` is deprecated, use `with_info_wrap_validator_function` instead.', + DeprecationWarning, + ) + return with_info_wrap_validator_function(*args, **kwargs) + + +@deprecated('`field_plain_validator_function` is deprecated, use `with_info_plain_validator_function` instead.') +def field_plain_validator_function(function: WithInfoValidatorFunction, field_name: str, **kwargs): + warnings.warn( + '`field_plain_validator_function` is deprecated, use `with_info_plain_validator_function` instead.', + DeprecationWarning, + ) + return with_info_plain_validator_function(function, field_name=field_name, **kwargs) + + +@deprecated('`general_plain_validator_function` is deprecated, use `with_info_plain_validator_function` instead.') +def general_plain_validator_function(*args, **kwargs): + warnings.warn( + '`general_plain_validator_function` is deprecated, use `with_info_plain_validator_function` instead.', + DeprecationWarning, + ) + return with_info_plain_validator_function(*args, **kwargs) + + +_deprecated_import_lookup = { + 'FieldValidationInfo': ValidationInfo, + 'FieldValidatorFunction': WithInfoValidatorFunction, + 'GeneralValidatorFunction': WithInfoValidatorFunction, + 'FieldWrapValidatorFunction': WithInfoWrapValidatorFunction, +} + +if TYPE_CHECKING: + FieldValidationInfo = ValidationInfo + + +def __getattr__(attr_name: str) -> object: + new_attr = _deprecated_import_lookup.get(attr_name) + if new_attr is None: + raise AttributeError(f"module 'pydantic_core' has no attribute '{attr_name}'") + else: + import warnings + + msg = f'`{attr_name}` is deprecated, use `{new_attr.__name__}` instead.' + warnings.warn(msg, DeprecationWarning, stacklevel=1) + return new_attr diff --git a/.venv/lib/python3.12/site-packages/pydantic_core/py.typed b/.venv/lib/python3.12/site-packages/pydantic_core/py.typed new file mode 100644 index 00000000..e69de29b --- /dev/null +++ b/.venv/lib/python3.12/site-packages/pydantic_core/py.typed |