一些兼容 pydantic<3,>=1.9.0 的代码，

model-providers/model_providers/_compat.py

@@ -0,0 +1,222 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, Union, Generic, TypeVar, Callable, cast, overload
+from datetime import date, datetime
+from typing_extensions import Self
+
+import pydantic
+from pydantic.fields import FieldInfo
+
+from ._types import StrBytesIntFloat
+
+_T = TypeVar("_T")
+_ModelT = TypeVar("_ModelT", bound=pydantic.BaseModel)
+
+# --------------- Pydantic v2 compatibility ---------------
+
+# Pyright incorrectly reports some of our functions as overriding a method when they don't
+# pyright: reportIncompatibleMethodOverride=false
+
+PYDANTIC_V2 = pydantic.VERSION.startswith("2.")
+
+# v1 re-exports
+if TYPE_CHECKING:
+
+    def parse_date(value: Union[date, StrBytesIntFloat]) -> date:  # noqa: ARG001
+        ...
+
+    def parse_datetime(value: Union[datetime, StrBytesIntFloat]) -> datetime:  # noqa: ARG001
+        ...
+
+    def get_args(t: type[Any]) -> tuple[Any, ...]:  # noqa: ARG001
+        ...
+
+    def is_union(tp: type[Any] | None) -> bool:  # noqa: ARG001
+        ...
+
+    def get_origin(t: type[Any]) -> type[Any] | None:  # noqa: ARG001
+        ...
+
+    def is_literal_type(type_: type[Any]) -> bool:  # noqa: ARG001
+        ...
+
+    def is_typeddict(type_: type[Any]) -> bool:  # noqa: ARG001
+        ...
+
+else:
+    if PYDANTIC_V2:
+        from pydantic.v1.typing import (
+            get_args as get_args,
+            is_union as is_union,
+            get_origin as get_origin,
+            is_typeddict as is_typeddict,
+            is_literal_type as is_literal_type,
+        )
+        from pydantic.v1.datetime_parse import parse_date as parse_date, parse_datetime as parse_datetime
+    else:
+        from pydantic.typing import (
+            get_args as get_args,
+            is_union as is_union,
+            get_origin as get_origin,
+            is_typeddict as is_typeddict,
+            is_literal_type as is_literal_type,
+        )
+        from pydantic.datetime_parse import parse_date as parse_date, parse_datetime as parse_datetime
+
+
+# refactored config
+if TYPE_CHECKING:
+    from pydantic import ConfigDict as ConfigDict
+else:
+    if PYDANTIC_V2:
+        from pydantic import ConfigDict
+    else:
+        # TODO: provide an error message here?
+        ConfigDict = None
+
+
+# renamed methods / properties
+def parse_obj(model: type[_ModelT], value: object) -> _ModelT:
+    if PYDANTIC_V2:
+        return model.model_validate(value)
+    else:
+        return cast(_ModelT, model.parse_obj(value))  # pyright: ignore[reportDeprecated, reportUnnecessaryCast]
+
+
+def field_is_required(field: FieldInfo) -> bool:
+    if PYDANTIC_V2:
+        return field.is_required()
+    return field.required  # type: ignore
+
+
+def field_get_default(field: FieldInfo) -> Any:
+    value = field.get_default()
+    if PYDANTIC_V2:
+        from pydantic_core import PydanticUndefined
+
+        if value == PydanticUndefined:
+            return None
+        return value
+    return value
+
+
+def field_outer_type(field: FieldInfo) -> Any:
+    if PYDANTIC_V2:
+        return field.annotation
+    return field.outer_type_  # type: ignore
+
+
+def get_model_config(model: type[pydantic.BaseModel]) -> Any:
+    if PYDANTIC_V2:
+        return model.model_config
+    return model.__config__  # type: ignore
+
+
+def get_model_fields(model: type[pydantic.BaseModel]) -> dict[str, FieldInfo]:
+    if PYDANTIC_V2:
+        return model.model_fields
+    return model.__fields__  # type: ignore
+
+
+def model_copy(model: _ModelT) -> _ModelT:
+    if PYDANTIC_V2:
+        return model.model_copy()
+    return model.copy()  # type: ignore
+
+
+def model_json(model: pydantic.BaseModel, *, indent: int | None = None) -> str:
+    if PYDANTIC_V2:
+        return model.model_dump_json(indent=indent)
+    return model.json(indent=indent)  # type: ignore
+
+
+def model_dump(
+    model: pydantic.BaseModel,
+    *,
+    exclude_unset: bool = False,
+    exclude_defaults: bool = False,
+) -> dict[str, Any]:
+    if PYDANTIC_V2:
+        return model.model_dump(
+            exclude_unset=exclude_unset,
+            exclude_defaults=exclude_defaults,
+        )
+    return cast(
+        "dict[str, Any]",
+        model.dict(  # pyright: ignore[reportDeprecated, reportUnnecessaryCast]
+            exclude_unset=exclude_unset,
+            exclude_defaults=exclude_defaults,
+        ),
+    )
+
+
+def model_parse(model: type[_ModelT], data: Any) -> _ModelT:
+    if PYDANTIC_V2:
+        return model.model_validate(data)
+    return model.parse_obj(data)  # pyright: ignore[reportDeprecated]
+
+
+# generic models
+if TYPE_CHECKING:
+
+    class GenericModel(pydantic.BaseModel):
+        ...
+
+else:
+    if PYDANTIC_V2:
+        # there no longer needs to be a distinction in v2 but
+        # we still have to create our own subclass to avoid
+        # inconsistent MRO ordering errors
+        class GenericModel(pydantic.BaseModel):
+            ...
+
+    else:
+        import pydantic.generics
+
+        class GenericModel(pydantic.generics.GenericModel, pydantic.BaseModel):
+            ...
+
+
+# cached properties
+if TYPE_CHECKING:
+    cached_property = property
+
+    # we define a separate type (copied from typeshed)
+    # that represents that `cached_property` is `set`able
+    # at runtime, which differs from `@property`.
+    #
+    # this is a separate type as editors likely special case
+    # `@property` and we don't want to cause issues just to have
+    # more helpful internal types.
+
+    class typed_cached_property(Generic[_T]):
+        func: Callable[[Any], _T]
+        attrname: str | None
+
+        def __init__(self, func: Callable[[Any], _T]) -> None:
+            ...
+
+        @overload
+        def __get__(self, instance: None, owner: type[Any] | None = None) -> Self:
+            ...
+
+        @overload
+        def __get__(self, instance: object, owner: type[Any] | None = None) -> _T:
+            ...
+
+        def __get__(self, instance: object, owner: type[Any] | None = None) -> _T | Self:
+            raise NotImplementedError()
+
+        def __set_name__(self, owner: type[Any], name: str) -> None:
+            ...
+
+        # __set__ is not defined at runtime, but @cached_property is designed to be settable
+        def __set__(self, instance: object, value: _T) -> None:
+            ...
+else:
+    try:
+        from functools import cached_property as cached_property
+    except ImportError:
+        from cached_property import cached_property as cached_property
+
+    typed_cached_property = cached_property

model-providers/model_providers/_files.py

@@ -0,0 +1,127 @@
+from __future__ import annotations
+
+import io
+import os
+import pathlib
+from typing import overload
+from typing_extensions import TypeGuard
+
+import anyio
+
+from ._types import (
+    FileTypes,
+    FileContent,
+    RequestFiles,
+    HttpxFileTypes,
+    Base64FileInput,
+    HttpxFileContent,
+    HttpxRequestFiles,
+)
+from ._utils import is_tuple_t, is_mapping_t, is_sequence_t
+
+
+def is_base64_file_input(obj: object) -> TypeGuard[Base64FileInput]:
+    return isinstance(obj, io.IOBase) or isinstance(obj, os.PathLike)
+
+
+def is_file_content(obj: object) -> TypeGuard[FileContent]:
+    return (
+        isinstance(obj, bytes) or isinstance(obj, tuple) or isinstance(obj, io.IOBase) or isinstance(obj, os.PathLike)
+    )
+
+
+def assert_is_file_content(obj: object, *, key: str | None = None) -> None:
+    if not is_file_content(obj):
+        prefix = f"Expected entry at `{key}`" if key is not None else f"Expected file input `{obj!r}`"
+        raise RuntimeError(
+            f"{prefix} to be bytes, an io.IOBase instance, PathLike or a tuple but received {type(obj)} instead. See https://github.com/openai/openai-python/tree/main#file-uploads"
+        ) from None
+
+
+@overload
+def to_httpx_files(files: None) -> None:
+    ...
+
+
+@overload
+def to_httpx_files(files: RequestFiles) -> HttpxRequestFiles:
+    ...
+
+
+def to_httpx_files(files: RequestFiles | None) -> HttpxRequestFiles | None:
+    if files is None:
+        return None
+
+    if is_mapping_t(files):
+        files = {key: _transform_file(file) for key, file in files.items()}
+    elif is_sequence_t(files):
+        files = [(key, _transform_file(file)) for key, file in files]
+    else:
+        raise TypeError(f"Unexpected file type input {type(files)}, expected mapping or sequence")
+
+    return files
+
+
+def _transform_file(file: FileTypes) -> HttpxFileTypes:
+    if is_file_content(file):
+        if isinstance(file, os.PathLike):
+            path = pathlib.Path(file)
+            return (path.name, path.read_bytes())
+
+        return file
+
+    if is_tuple_t(file):
+        return (file[0], _read_file_content(file[1]), *file[2:])
+
+    raise TypeError(f"Expected file types input to be a FileContent type or to be a tuple")
+
+
+def _read_file_content(file: FileContent) -> HttpxFileContent:
+    if isinstance(file, os.PathLike):
+        return pathlib.Path(file).read_bytes()
+    return file
+
+
+@overload
+async def async_to_httpx_files(files: None) -> None:
+    ...
+
+
+@overload
+async def async_to_httpx_files(files: RequestFiles) -> HttpxRequestFiles:
+    ...
+
+
+async def async_to_httpx_files(files: RequestFiles | None) -> HttpxRequestFiles | None:
+    if files is None:
+        return None
+
+    if is_mapping_t(files):
+        files = {key: await _async_transform_file(file) for key, file in files.items()}
+    elif is_sequence_t(files):
+        files = [(key, await _async_transform_file(file)) for key, file in files]
+    else:
+        raise TypeError("Unexpected file type input {type(files)}, expected mapping or sequence")
+
+    return files
+
+
+async def _async_transform_file(file: FileTypes) -> HttpxFileTypes:
+    if is_file_content(file):
+        if isinstance(file, os.PathLike):
+            path = anyio.Path(file)
+            return (path.name, await path.read_bytes())
+
+        return file
+
+    if is_tuple_t(file):
+        return (file[0], await _async_read_file_content(file[1]), *file[2:])
+
+    raise TypeError(f"Expected file types input to be a FileContent type or to be a tuple")
+
+
+async def _async_read_file_content(file: FileContent) -> HttpxFileContent:
+    if isinstance(file, os.PathLike):
+        return await anyio.Path(file).read_bytes()
+
+    return file

model-providers/model_providers/_models.py

@@ -0,0 +1,657 @@
+from __future__ import annotations
+
+import os
+import inspect
+from typing import TYPE_CHECKING, Any, Type, Union, Generic, TypeVar, Callable, cast
+from datetime import date, datetime
+from typing_extensions import (
+    Unpack,
+    Literal,
+    ClassVar,
+    Protocol,
+    Required,
+    TypedDict,
+    TypeGuard,
+    final,
+    override,
+    runtime_checkable,
+)
+
+import pydantic
+import pydantic.generics
+from pydantic.fields import FieldInfo
+
+from ._types import (
+    IncEx,
+    ModelT,
+)
+from ._utils import (
+    PropertyInfo,
+    is_list,
+    is_given,
+    lru_cache,
+    is_mapping,
+    parse_date,
+    coerce_boolean,
+    parse_datetime,
+    strip_not_given,
+    extract_type_arg,
+    is_annotated_type,
+    strip_annotated_type,
+)
+from ._compat import (
+    PYDANTIC_V2,
+    ConfigDict,
+    GenericModel as BaseGenericModel,
+    get_args,
+    is_union,
+    parse_obj,
+    get_origin,
+    is_literal_type,
+    get_model_config,
+    get_model_fields,
+    field_get_default,
+)
+
+if TYPE_CHECKING:
+    from pydantic_core.core_schema import ModelField, LiteralSchema, ModelFieldsSchema
+
+__all__ = ["BaseModel", "GenericModel"]
+
+_T = TypeVar("_T")
+
+
+@runtime_checkable
+class _ConfigProtocol(Protocol):
+    allow_population_by_field_name: bool
+
+
+class BaseModel(pydantic.BaseModel):
+    if PYDANTIC_V2:
+        model_config: ClassVar[ConfigDict] = ConfigDict(
+            extra="allow", defer_build=coerce_boolean(os.environ.get("DEFER_PYDANTIC_BUILD", "true"))
+        )
+    else:
+
+        @property
+        @override
+        def model_fields_set(self) -> set[str]:
+            # a forwards-compat shim for pydantic v2
+            return self.__fields_set__  # type: ignore
+
+        class Config(pydantic.BaseConfig):  # pyright: ignore[reportDeprecated]
+            extra: Any = pydantic.Extra.allow  # type: ignore
+
+    def to_dict(
+        self,
+        *,
+        mode: Literal["json", "python"] = "python",
+        use_api_names: bool = True,
+        exclude_unset: bool = True,
+        exclude_defaults: bool = False,
+        exclude_none: bool = False,
+        warnings: bool = True,
+    ) -> dict[str, object]:
+        """Recursively generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
+
+        By default, fields that were not set by the API will not be included,
+        and keys will match the API response, *not* the property names from the model.
+
+        For example, if the API responds with `"fooBar": true` but we've defined a `foo_bar: bool` property,
+        the output will use the `"fooBar"` key (unless `use_api_names=False` is passed).
+
+        Args:
+            mode:
+                If mode is 'json', the dictionary will only contain JSON serializable types. e.g. `datetime` will be turned into a string, `"2024-3-22T18:11:19.117000Z"`.
+                If mode is 'python', the dictionary may contain any Python objects. e.g. `datetime(2024, 3, 22)`
+
+            use_api_names: Whether to use the key that the API responded with or the property name. Defaults to `True`.
+            exclude_unset: Whether to exclude fields that have not been explicitly set.
+            exclude_defaults: Whether to exclude fields that are set to their default value from the output.
+            exclude_none: Whether to exclude fields that have a value of `None` from the output.
+            warnings: Whether to log warnings when invalid fields are encountered. This is only supported in Pydantic v2.
+        """
+        return self.model_dump(
+            mode=mode,
+            by_alias=use_api_names,
+            exclude_unset=exclude_unset,
+            exclude_defaults=exclude_defaults,
+            exclude_none=exclude_none,
+            warnings=warnings,
+        )
+
+    def to_json(
+        self,
+        *,
+        indent: int | None = 2,
+        use_api_names: bool = True,
+        exclude_unset: bool = True,
+        exclude_defaults: bool = False,
+        exclude_none: bool = False,
+        warnings: bool = True,
+    ) -> str:
+        """Generates a JSON string representing this model as it would be received from or sent to the API (but with indentation).
+
+        By default, fields that were not set by the API will not be included,
+        and keys will match the API response, *not* the property names from the model.
+
+        For example, if the API responds with `"fooBar": true` but we've defined a `foo_bar: bool` property,
+        the output will use the `"fooBar"` key (unless `use_api_names=False` is passed).
+
+        Args:
+            indent: Indentation to use in the JSON output. If `None` is passed, the output will be compact. Defaults to `2`
+            use_api_names: Whether to use the key that the API responded with or the property name. Defaults to `True`.
+            exclude_unset: Whether to exclude fields that have not been explicitly set.
+            exclude_defaults: Whether to exclude fields that have the default value.
+            exclude_none: Whether to exclude fields that have a value of `None`.
+            warnings: Whether to show any warnings that occurred during serialization. This is only supported in Pydantic v2.
+        """
+        return self.model_dump_json(
+            indent=indent,
+            by_alias=use_api_names,
+            exclude_unset=exclude_unset,
+            exclude_defaults=exclude_defaults,
+            exclude_none=exclude_none,
+            warnings=warnings,
+        )
+
+    @override
+    def __str__(self) -> str:
+        # mypy complains about an invalid self arg
+        return f'{self.__repr_name__()}({self.__repr_str__(", ")})'  # type: ignore[misc]
+
+    # Override the 'construct' method in a way that supports recursive parsing without validation.
+    # Based on https://github.com/samuelcolvin/pydantic/issues/1168#issuecomment-817742836.
+    @classmethod
+    @override
+    def construct(
+        cls: Type[ModelT],
+        _fields_set: set[str] | None = None,
+        **values: object,
+    ) -> ModelT:
+        m = cls.__new__(cls)
+        fields_values: dict[str, object] = {}
+
+        config = get_model_config(cls)
+        populate_by_name = (
+            config.allow_population_by_field_name
+            if isinstance(config, _ConfigProtocol)
+            else config.get("populate_by_name")
+        )
+
+        if _fields_set is None:
+            _fields_set = set()
+
+        model_fields = get_model_fields(cls)
+        for name, field in model_fields.items():
+            key = field.alias
+            if key is None or (key not in values and populate_by_name):
+                key = name
+
+            if key in values:
+                fields_values[name] = _construct_field(value=values[key], field=field, key=key)
+                _fields_set.add(name)
+            else:
+                fields_values[name] = field_get_default(field)
+
+        _extra = {}
+        for key, value in values.items():
+            if key not in model_fields:
+                if PYDANTIC_V2:
+                    _extra[key] = value
+                else:
+                    _fields_set.add(key)
+                    fields_values[key] = value
+
+        object.__setattr__(m, "__dict__", fields_values)
+
+        if PYDANTIC_V2:
+            # these properties are copied from Pydantic's `model_construct()` method
+            object.__setattr__(m, "__pydantic_private__", None)
+            object.__setattr__(m, "__pydantic_extra__", _extra)
+            object.__setattr__(m, "__pydantic_fields_set__", _fields_set)
+        else:
+            # init_private_attributes() does not exist in v2
+            m._init_private_attributes()  # type: ignore
+
+            # copied from Pydantic v1's `construct()` method
+            object.__setattr__(m, "__fields_set__", _fields_set)
+
+        return m
+
+    if not TYPE_CHECKING:
+        # type checkers incorrectly complain about this assignment
+        # because the type signatures are technically different
+        # although not in practice
+        model_construct = construct
+
+    if not PYDANTIC_V2:
+        # we define aliases for some of the new pydantic v2 methods so
+        # that we can just document these methods without having to specify
+        # a specific pydantic version as some users may not know which
+        # pydantic version they are currently using
+
+        @override
+        def model_dump(
+            self,
+            *,
+            mode: Literal["json", "python"] | str = "python",
+            include: IncEx = None,
+            exclude: IncEx = None,
+            by_alias: bool = False,
+            exclude_unset: bool = False,
+            exclude_defaults: bool = False,
+            exclude_none: bool = False,
+            round_trip: bool = False,
+            warnings: bool | Literal["none", "warn", "error"] = True,
+            context: dict[str, Any] | None = None,
+            serialize_as_any: bool = False,
+        ) -> dict[str, Any]:
+            """Usage docs: https://docs.pydantic.dev/2.4/concepts/serialization/#modelmodel_dump
+
+            Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
+
+            Args:
+                mode: The mode in which `to_python` should run.
+                    If mode is 'json', the dictionary will only contain JSON serializable types.
+                    If mode is 'python', the dictionary may contain any Python objects.
+                include: A list of fields to include in the output.
+                exclude: A list of fields to exclude from the output.
+                by_alias: Whether to use the field's alias in the dictionary key if defined.
+                exclude_unset: Whether to exclude fields that are unset or None from the output.
+                exclude_defaults: Whether to exclude fields that are set to their default value from the output.
+                exclude_none: Whether to exclude fields that have a value of `None` from the output.
+                round_trip: Whether to enable serialization and deserialization round-trip support.
+                warnings: Whether to log warnings when invalid fields are encountered.
+
+            Returns:
+                A dictionary representation of the model.
+            """
+            if mode != "python":
+                raise ValueError("mode is only supported in Pydantic v2")
+            if round_trip != False:
+                raise ValueError("round_trip is only supported in Pydantic v2")
+            if warnings != True:
+                raise ValueError("warnings is only supported in Pydantic v2")
+            if context is not None:
+                raise ValueError("context is only supported in Pydantic v2")
+            if serialize_as_any != False:
+                raise ValueError("serialize_as_any is only supported in Pydantic v2")
+            return super().dict(  # pyright: ignore[reportDeprecated]
+                include=include,
+                exclude=exclude,
+                by_alias=by_alias,
+                exclude_unset=exclude_unset,
+                exclude_defaults=exclude_defaults,
+                exclude_none=exclude_none,
+            )
+
+        @override
+        def model_dump_json(
+            self,
+            *,
+            indent: int | None = None,
+            include: IncEx = None,
+            exclude: IncEx = None,
+            by_alias: bool = False,
+            exclude_unset: bool = False,
+            exclude_defaults: bool = False,
+            exclude_none: bool = False,
+            round_trip: bool = False,
+            warnings: bool | Literal["none", "warn", "error"] = True,
+            context: dict[str, Any] | None = None,
+            serialize_as_any: bool = False,
+        ) -> str:
+            """Usage docs: https://docs.pydantic.dev/2.4/concepts/serialization/#modelmodel_dump_json
+
+            Generates a JSON representation of the model using Pydantic's `to_json` method.
+
+            Args:
+                indent: Indentation to use in the JSON output. If None is passed, the output will be compact.
+                include: Field(s) to include in the JSON output. Can take either a string or set of strings.
+                exclude: Field(s) to exclude from the JSON output. Can take either a string or set of strings.
+                by_alias: Whether to serialize using field aliases.
+                exclude_unset: Whether to exclude fields that have not been explicitly set.
+                exclude_defaults: Whether to exclude fields that have the default value.
+                exclude_none: Whether to exclude fields that have a value of `None`.
+                round_trip: Whether to use serialization/deserialization between JSON and class instance.
+                warnings: Whether to show any warnings that occurred during serialization.
+
+            Returns:
+                A JSON string representation of the model.
+            """
+            if round_trip != False:
+                raise ValueError("round_trip is only supported in Pydantic v2")
+            if warnings != True:
+                raise ValueError("warnings is only supported in Pydantic v2")
+            if context is not None:
+                raise ValueError("context is only supported in Pydantic v2")
+            if serialize_as_any != False:
+                raise ValueError("serialize_as_any is only supported in Pydantic v2")
+            return super().json(  # type: ignore[reportDeprecated]
+                indent=indent,
+                include=include,
+                exclude=exclude,
+                by_alias=by_alias,
+                exclude_unset=exclude_unset,
+                exclude_defaults=exclude_defaults,
+                exclude_none=exclude_none,
+            )
+
+
+def _construct_field(value: object, field: FieldInfo, key: str) -> object:
+    if value is None:
+        return field_get_default(field)
+
+    if PYDANTIC_V2:
+        type_ = field.annotation
+    else:
+        type_ = cast(type, field.outer_type_)  # type: ignore
+
+    if type_ is None:
+        raise RuntimeError(f"Unexpected field type is None for {key}")
+
+    return construct_type(value=value, type_=type_)
+
+
+def is_basemodel(type_: type) -> bool:
+    """Returns whether or not the given type is either a `BaseModel` or a union of `BaseModel`"""
+    if is_union(type_):
+        for variant in get_args(type_):
+            if is_basemodel(variant):
+                return True
+
+        return False
+
+    return is_basemodel_type(type_)
+
+
+def is_basemodel_type(type_: type) -> TypeGuard[type[BaseModel] | type[GenericModel]]:
+    origin = get_origin(type_) or type_
+    return issubclass(origin, BaseModel) or issubclass(origin, GenericModel)
+
+
+def construct_type(*, value: object, type_: object) -> object:
+    """Loose coercion to the expected type with construction of nested values.
+
+    If the given value does not match the expected type then it is returned as-is.
+    """
+    # we allow `object` as the input type because otherwise, passing things like
+    # `Literal['value']` will be reported as a type error by type checkers
+    type_ = cast("type[object]", type_)
+
+    # unwrap `Annotated[T, ...]` -> `T`
+    if is_annotated_type(type_):
+        meta: tuple[Any, ...] = get_args(type_)[1:]
+        type_ = extract_type_arg(type_, 0)
+    else:
+        meta = tuple()
+
+    # we need to use the origin class for any types that are subscripted generics
+    # e.g. Dict[str, object]
+    origin = get_origin(type_) or type_
+    args = get_args(type_)
+
+    if is_union(origin):
+        try:
+            return validate_type(type_=cast("type[object]", type_), value=value)
+        except Exception:
+            pass
+
+        # if the type is a discriminated union then we want to construct the right variant
+        # in the union, even if the data doesn't match exactly, otherwise we'd break code
+        # that relies on the constructed class types, e.g.
+        #
+        # class FooType:
+        #   kind: Literal['foo']
+        #   value: str
+        #
+        # class BarType:
+        #   kind: Literal['bar']
+        #   value: int
+        #
+        # without this block, if the data we get is something like `{'kind': 'bar', 'value': 'foo'}` then
+        # we'd end up constructing `FooType` when it should be `BarType`.
+        discriminator = _build_discriminated_union_meta(union=type_, meta_annotations=meta)
+        if discriminator and is_mapping(value):
+            variant_value = value.get(discriminator.field_alias_from or discriminator.field_name)
+            if variant_value and isinstance(variant_value, str):
+                variant_type = discriminator.mapping.get(variant_value)
+                if variant_type:
+                    return construct_type(type_=variant_type, value=value)
+
+        # if the data is not valid, use the first variant that doesn't fail while deserializing
+        for variant in args:
+            try:
+                return construct_type(value=value, type_=variant)
+            except Exception:
+                continue
+
+        raise RuntimeError(f"Could not convert data into a valid instance of {type_}")
+
+    if origin == dict:
+        if not is_mapping(value):
+            return value
+
+        _, items_type = get_args(type_)  # Dict[_, items_type]
+        return {key: construct_type(value=item, type_=items_type) for key, item in value.items()}
+
+    if not is_literal_type(type_) and (issubclass(origin, BaseModel) or issubclass(origin, GenericModel)):
+        if is_list(value):
+            return [cast(Any, type_).construct(**entry) if is_mapping(entry) else entry for entry in value]
+
+        if is_mapping(value):
+            if issubclass(type_, BaseModel):
+                return type_.construct(**value)  # type: ignore[arg-type]
+
+            return cast(Any, type_).construct(**value)
+
+    if origin == list:
+        if not is_list(value):
+            return value
+
+        inner_type = args[0]  # List[inner_type]
+        return [construct_type(value=entry, type_=inner_type) for entry in value]
+
+    if origin == float:
+        if isinstance(value, int):
+            coerced = float(value)
+            if coerced != value:
+                return value
+            return coerced
+
+        return value
+
+    if type_ == datetime:
+        try:
+            return parse_datetime(value)  # type: ignore
+        except Exception:
+            return value
+
+    if type_ == date:
+        try:
+            return parse_date(value)  # type: ignore
+        except Exception:
+            return value
+
+    return value
+
+
+@runtime_checkable
+class CachedDiscriminatorType(Protocol):
+    __discriminator__: DiscriminatorDetails
+
+
+class DiscriminatorDetails:
+    field_name: str
+    """The name of the discriminator field in the variant class, e.g.
+
+    ```py
+    class Foo(BaseModel):
+        type: Literal['foo']
+    ```
+
+    Will result in field_name='type'
+    """
+
+    field_alias_from: str | None
+    """The name of the discriminator field in the API response, e.g.
+
+    ```py
+    class Foo(BaseModel):
+        type: Literal['foo'] = Field(alias='type_from_api')
+    ```
+
+    Will result in field_alias_from='type_from_api'
+    """
+
+    mapping: dict[str, type]
+    """Mapping of discriminator value to variant type, e.g.
+
+    {'foo': FooVariant, 'bar': BarVariant}
+    """
+
+    def __init__(
+        self,
+        *,
+        mapping: dict[str, type],
+        discriminator_field: str,
+        discriminator_alias: str | None,
+    ) -> None:
+        self.mapping = mapping
+        self.field_name = discriminator_field
+        self.field_alias_from = discriminator_alias
+
+
+def _build_discriminated_union_meta(*, union: type, meta_annotations: tuple[Any, ...]) -> DiscriminatorDetails | None:
+    if isinstance(union, CachedDiscriminatorType):
+        return union.__discriminator__
+
+    discriminator_field_name: str | None = None
+
+    for annotation in meta_annotations:
+        if isinstance(annotation, PropertyInfo) and annotation.discriminator is not None:
+            discriminator_field_name = annotation.discriminator
+            break
+
+    if not discriminator_field_name:
+        return None
+
+    mapping: dict[str, type] = {}
+    discriminator_alias: str | None = None
+
+    for variant in get_args(union):
+        variant = strip_annotated_type(variant)
+        if is_basemodel_type(variant):
+            if PYDANTIC_V2:
+                field = _extract_field_schema_pv2(variant, discriminator_field_name)
+                if not field:
+                    continue
+
+                # Note: if one variant defines an alias then they all should
+                discriminator_alias = field.get("serialization_alias")
+
+                field_schema = field["schema"]
+
+                if field_schema["type"] == "literal":
+                    for entry in cast("LiteralSchema", field_schema)["expected"]:
+                        if isinstance(entry, str):
+                            mapping[entry] = variant
+            else:
+                field_info = cast("dict[str, FieldInfo]", variant.__fields__).get(discriminator_field_name)  # pyright: ignore[reportDeprecated, reportUnnecessaryCast]
+                if not field_info:
+                    continue
+
+                # Note: if one variant defines an alias then they all should
+                discriminator_alias = field_info.alias
+
+                if field_info.annotation and is_literal_type(field_info.annotation):
+                    for entry in get_args(field_info.annotation):
+                        if isinstance(entry, str):
+                            mapping[entry] = variant
+
+    if not mapping:
+        return None
+
+    details = DiscriminatorDetails(
+        mapping=mapping,
+        discriminator_field=discriminator_field_name,
+        discriminator_alias=discriminator_alias,
+    )
+    cast(CachedDiscriminatorType, union).__discriminator__ = details
+    return details
+
+
+def _extract_field_schema_pv2(model: type[BaseModel], field_name: str) -> ModelField | None:
+    schema = model.__pydantic_core_schema__
+    if schema["type"] != "model":
+        return None
+
+    fields_schema = schema["schema"]
+    if fields_schema["type"] != "model-fields":
+        return None
+
+    fields_schema = cast("ModelFieldsSchema", fields_schema)
+
+    field = fields_schema["fields"].get(field_name)
+    if not field:
+        return None
+
+    return cast("ModelField", field)  # pyright: ignore[reportUnnecessaryCast]
+
+
+def validate_type(*, type_: type[_T], value: object) -> _T:
+    """Strict validation that the given value matches the expected type"""
+    if inspect.isclass(type_) and issubclass(type_, pydantic.BaseModel):
+        return cast(_T, parse_obj(type_, value))
+
+    return cast(_T, _validate_non_model_type(type_=type_, value=value))
+
+
+# our use of subclasssing here causes weirdness for type checkers,
+# so we just pretend that we don't subclass
+if TYPE_CHECKING:
+    GenericModel = BaseModel
+else:
+
+    class GenericModel(BaseGenericModel, BaseModel):
+        pass
+
+
+if PYDANTIC_V2:
+    from pydantic import TypeAdapter as _TypeAdapter
+
+    _CachedTypeAdapter = cast("TypeAdapter[object]", lru_cache(maxsize=None)(_TypeAdapter))
+
+    if TYPE_CHECKING:
+        from pydantic import TypeAdapter
+    else:
+        TypeAdapter = _CachedTypeAdapter
+
+    def _validate_non_model_type(*, type_: type[_T], value: object) -> _T:
+        return TypeAdapter(type_).validate_python(value)
+
+elif not TYPE_CHECKING:  # TODO: condition is weird
+
+    class RootModel(GenericModel, Generic[_T]):
+        """Used as a placeholder to easily convert runtime types to a Pydantic format
+        to provide validation.
+
+        For example:
+        ```py
+        validated = RootModel[int](__root__="5").__root__
+        # validated: 5
+        ```
+        """
+
+        __root__: _T
+
+    def _validate_non_model_type(*, type_: type[_T], value: object) -> _T:
+        model = _create_pydantic_model(type_).validate(value)
+        return cast(_T, model.__root__)
+
+    def _create_pydantic_model(type_: _T) -> Type[RootModel[_T]]:
+        return RootModel[type_]  # type: ignore
+
+
+

model-providers/model_providers/_types.py

@@ -0,0 +1,220 @@
+from __future__ import annotations
+
+from os import PathLike
+from typing import (
+    IO,
+    TYPE_CHECKING,
+    Any,
+    Dict,
+    List,
+    Type,
+    Tuple,
+    Union,
+    Mapping,
+    TypeVar,
+    Callable,
+    Optional,
+    Sequence,
+)
+from typing_extensions import Literal, Protocol, TypeAlias, TypedDict, override, runtime_checkable
+
+import httpx
+import pydantic
+from httpx import URL, Proxy, Timeout, Response, BaseTransport, AsyncBaseTransport
+
+if TYPE_CHECKING:
+    from ._models import BaseModel
+
+Transport = BaseTransport
+AsyncTransport = AsyncBaseTransport
+Query = Mapping[str, object]
+Body = object
+AnyMapping = Mapping[str, object]
+ModelT = TypeVar("ModelT", bound=pydantic.BaseModel)
+_T = TypeVar("_T")
+
+
+# Approximates httpx internal ProxiesTypes and RequestFiles types
+# while adding support for `PathLike` instances
+ProxiesDict = Dict["str | URL", Union[None, str, URL, Proxy]]
+ProxiesTypes = Union[str, Proxy, ProxiesDict]
+if TYPE_CHECKING:
+    Base64FileInput = Union[IO[bytes], PathLike[str]]
+    FileContent = Union[IO[bytes], bytes, PathLike[str]]
+else:
+    Base64FileInput = Union[IO[bytes], PathLike]
+    FileContent = Union[IO[bytes], bytes, PathLike]  # PathLike is not subscriptable in Python 3.8.
+FileTypes = Union[
+    # file (or bytes)
+    FileContent,
+    # (filename, file (or bytes))
+    Tuple[Optional[str], FileContent],
+    # (filename, file (or bytes), content_type)
+    Tuple[Optional[str], FileContent, Optional[str]],
+    # (filename, file (or bytes), content_type, headers)
+    Tuple[Optional[str], FileContent, Optional[str], Mapping[str, str]],
+]
+RequestFiles = Union[Mapping[str, FileTypes], Sequence[Tuple[str, FileTypes]]]
+
+# duplicate of the above but without our custom file support
+HttpxFileContent = Union[IO[bytes], bytes]
+HttpxFileTypes = Union[
+    # file (or bytes)
+    HttpxFileContent,
+    # (filename, file (or bytes))
+    Tuple[Optional[str], HttpxFileContent],
+    # (filename, file (or bytes), content_type)
+    Tuple[Optional[str], HttpxFileContent, Optional[str]],
+    # (filename, file (or bytes), content_type, headers)
+    Tuple[Optional[str], HttpxFileContent, Optional[str], Mapping[str, str]],
+]
+HttpxRequestFiles = Union[Mapping[str, HttpxFileTypes], Sequence[Tuple[str, HttpxFileTypes]]]
+
+# Workaround to support (cast_to: Type[ResponseT]) -> ResponseT
+# where ResponseT includes `None`. In order to support directly
+# passing `None`, overloads would have to be defined for every
+# method that uses `ResponseT` which would lead to an unacceptable
+# amount of code duplication and make it unreadable. See _base_client.py
+# for example usage.
+#
+# This unfortunately means that you will either have
+# to import this type and pass it explicitly:
+#
+# from openai import NoneType
+# client.get('/foo', cast_to=NoneType)
+#
+# or build it yourself:
+#
+# client.get('/foo', cast_to=type(None))
+if TYPE_CHECKING:
+    NoneType: Type[None]
+else:
+    NoneType = type(None)
+
+
+class RequestOptions(TypedDict, total=False):
+    headers: Headers
+    max_retries: int
+    timeout: float | Timeout | None
+    params: Query
+    extra_json: AnyMapping
+    idempotency_key: str
+
+
+# Sentinel class used until PEP 0661 is accepted
+class NotGiven:
+    """
+    A sentinel singleton class used to distinguish omitted keyword arguments
+    from those passed in with the value None (which may have different behavior).
+
+    For example:
+
+    ```py
+    def get(timeout: Union[int, NotGiven, None] = NotGiven()) -> Response:
+        ...
+
+
+    get(timeout=1)  # 1s timeout
+    get(timeout=None)  # No timeout
+    get()  # Default timeout behavior, which may not be statically known at the method definition.
+    ```
+    """
+
+    def __bool__(self) -> Literal[False]:
+        return False
+
+    @override
+    def __repr__(self) -> str:
+        return "NOT_GIVEN"
+
+
+NotGivenOr = Union[_T, NotGiven]
+NOT_GIVEN = NotGiven()
+
+
+class Omit:
+    """In certain situations you need to be able to represent a case where a default value has
+    to be explicitly removed and `None` is not an appropriate substitute, for example:
+
+    ```py
+    # as the default `Content-Type` header is `application/json` that will be sent
+    client.post("/upload/files", files={"file": b"my raw file content"})
+
+    # you can't explicitly override the header as it has to be dynamically generated
+    # to look something like: 'multipart/form-data; boundary=0d8382fcf5f8c3be01ca2e11002d2983'
+    client.post(..., headers={"Content-Type": "multipart/form-data"})
+
+    # instead you can remove the default `application/json` header by passing Omit
+    client.post(..., headers={"Content-Type": Omit()})
+    ```
+    """
+
+    def __bool__(self) -> Literal[False]:
+        return False
+
+
+@runtime_checkable
+class ModelBuilderProtocol(Protocol):
+    @classmethod
+    def build(
+        cls: type[_T],
+        *,
+        response: Response,
+        data: object,
+    ) -> _T:
+        ...
+
+
+Headers = Mapping[str, Union[str, Omit]]
+
+
+class HeadersLikeProtocol(Protocol):
+    def get(self, __key: str) -> str | None:
+        ...
+
+
+HeadersLike = Union[Headers, HeadersLikeProtocol]
+
+ResponseT = TypeVar(
+    "ResponseT",
+    bound=Union[
+        object,
+        str,
+        None,
+        "BaseModel",
+        List[Any],
+        Dict[str, Any],
+        Response,
+        ModelBuilderProtocol,
+        "APIResponse[Any]",
+        "AsyncAPIResponse[Any]",
+        "HttpxBinaryResponseContent",
+    ],
+)
+
+StrBytesIntFloat = Union[str, bytes, int, float]
+
+# Note: copied from Pydantic
+# https://github.com/pydantic/pydantic/blob/32ea570bf96e84234d2992e1ddf40ab8a565925a/pydantic/main.py#L49
+IncEx: TypeAlias = "set[int] | set[str] | dict[int, Any] | dict[str, Any] | None"
+
+PostParser = Callable[[Any], Any]
+
+
+@runtime_checkable
+class InheritsGeneric(Protocol):
+    """Represents a type that has inherited from `Generic`
+
+    The `__orig_bases__` property can be used to determine the resolved
+    type variable for a given base class.
+    """
+
+    __orig_bases__: tuple[_GenericAlias]
+
+
+class _GenericAlias(Protocol):
+    __origin__: type[object]
+
+
+class HttpxSendArgs(TypedDict, total=False):
+    auth: httpx.Auth

model-providers/model_providers/_utils/__init__.py

@@ -0,0 +1,48 @@
+from ._utils import (
+    flatten as flatten,
+    is_dict as is_dict,
+    is_list as is_list,
+    is_given as is_given,
+    is_tuple as is_tuple,
+    lru_cache as lru_cache,
+    is_mapping as is_mapping,
+    is_tuple_t as is_tuple_t,
+    parse_date as parse_date,
+    is_iterable as is_iterable,
+    is_sequence as is_sequence,
+    coerce_float as coerce_float,
+    is_mapping_t as is_mapping_t,
+    removeprefix as removeprefix,
+    removesuffix as removesuffix,
+    extract_files as extract_files,
+    is_sequence_t as is_sequence_t,
+    required_args as required_args,
+    coerce_boolean as coerce_boolean,
+    coerce_integer as coerce_integer,
+    file_from_path as file_from_path,
+    parse_datetime as parse_datetime,
+    strip_not_given as strip_not_given,
+    deepcopy_minimal as deepcopy_minimal,
+    get_async_library as get_async_library,
+    maybe_coerce_float as maybe_coerce_float,
+    get_required_header as get_required_header,
+    maybe_coerce_boolean as maybe_coerce_boolean,
+    maybe_coerce_integer as maybe_coerce_integer,
+)
+from ._typing import (
+    is_list_type as is_list_type,
+    is_union_type as is_union_type,
+    extract_type_arg as extract_type_arg,
+    is_iterable_type as is_iterable_type,
+    is_required_type as is_required_type,
+    is_annotated_type as is_annotated_type,
+    strip_annotated_type as strip_annotated_type,
+    extract_type_var_from_base as extract_type_var_from_base,
+)
+from ._transform import (
+    PropertyInfo as PropertyInfo,
+    transform as transform,
+    async_transform as async_transform,
+    maybe_transform as maybe_transform,
+    async_maybe_transform as async_maybe_transform,
+)

model-providers/model_providers/_utils/_transform.py

@@ -0,0 +1,382 @@
+from __future__ import annotations
+
+import io
+import base64
+import pathlib
+from typing import Any, Mapping, TypeVar, cast
+from datetime import date, datetime
+from typing_extensions import Literal, get_args, override, get_type_hints
+
+import anyio
+import pydantic
+
+from ._utils import (
+    is_list,
+    is_mapping,
+    is_iterable,
+)
+from .._files import is_base64_file_input
+from ._typing import (
+    is_list_type,
+    is_union_type,
+    extract_type_arg,
+    is_iterable_type,
+    is_required_type,
+    is_annotated_type,
+    strip_annotated_type,
+)
+from .._compat import model_dump, is_typeddict
+
+_T = TypeVar("_T")
+
+
+# TODO: support for drilling globals() and locals()
+# TODO: ensure works correctly with forward references in all cases
+
+
+PropertyFormat = Literal["iso8601", "base64", "custom"]
+
+
+class PropertyInfo:
+    """Metadata class to be used in Annotated types to provide information about a given type.
+
+    For example:
+
+    class MyParams(TypedDict):
+        account_holder_name: Annotated[str, PropertyInfo(alias='accountHolderName')]
+
+    This means that {'account_holder_name': 'Robert'} will be transformed to {'accountHolderName': 'Robert'} before being sent to the API.
+    """
+
+    alias: str | None
+    format: PropertyFormat | None
+    format_template: str | None
+    discriminator: str | None
+
+    def __init__(
+        self,
+        *,
+        alias: str | None = None,
+        format: PropertyFormat | None = None,
+        format_template: str | None = None,
+        discriminator: str | None = None,
+    ) -> None:
+        self.alias = alias
+        self.format = format
+        self.format_template = format_template
+        self.discriminator = discriminator
+
+    @override
+    def __repr__(self) -> str:
+        return f"{self.__class__.__name__}(alias='{self.alias}', format={self.format}, format_template='{self.format_template}', discriminator='{self.discriminator}')"
+
+
+def maybe_transform(
+    data: object,
+    expected_type: object,
+) -> Any | None:
+    """Wrapper over `transform()` that allows `None` to be passed.
+
+    See `transform()` for more details.
+    """
+    if data is None:
+        return None
+    return transform(data, expected_type)
+
+
+# Wrapper over _transform_recursive providing fake types
+def transform(
+    data: _T,
+    expected_type: object,
+) -> _T:
+    """Transform dictionaries based off of type information from the given type, for example:
+
+    ```py
+    class Params(TypedDict, total=False):
+        card_id: Required[Annotated[str, PropertyInfo(alias="cardID")]]
+
+
+    transformed = transform({"card_id": "<my card ID>"}, Params)
+    # {'cardID': '<my card ID>'}
+    ```
+
+    Any keys / data that does not have type information given will be included as is.
+
+    It should be noted that the transformations that this function does are not represented in the type system.
+    """
+    transformed = _transform_recursive(data, annotation=cast(type, expected_type))
+    return cast(_T, transformed)
+
+
+def _get_annotated_type(type_: type) -> type | None:
+    """If the given type is an `Annotated` type then it is returned, if not `None` is returned.
+
+    This also unwraps the type when applicable, e.g. `Required[Annotated[T, ...]]`
+    """
+    if is_required_type(type_):
+        # Unwrap `Required[Annotated[T, ...]]` to `Annotated[T, ...]`
+        type_ = get_args(type_)[0]
+
+    if is_annotated_type(type_):
+        return type_
+
+    return None
+
+
+def _maybe_transform_key(key: str, type_: type) -> str:
+    """Transform the given `data` based on the annotations provided in `type_`.
+
+    Note: this function only looks at `Annotated` types that contain `PropertInfo` metadata.
+    """
+    annotated_type = _get_annotated_type(type_)
+    if annotated_type is None:
+        # no `Annotated` definition for this type, no transformation needed
+        return key
+
+    # ignore the first argument as it is the actual type
+    annotations = get_args(annotated_type)[1:]
+    for annotation in annotations:
+        if isinstance(annotation, PropertyInfo) and annotation.alias is not None:
+            return annotation.alias
+
+    return key
+
+
+def _transform_recursive(
+    data: object,
+    *,
+    annotation: type,
+    inner_type: type | None = None,
+) -> object:
+    """Transform the given data against the expected type.
+
+    Args:
+        annotation: The direct type annotation given to the particular piece of data.
+            This may or may not be wrapped in metadata types, e.g. `Required[T]`, `Annotated[T, ...]` etc
+
+        inner_type: If applicable, this is the "inside" type. This is useful in certain cases where the outside type
+            is a container type such as `List[T]`. In that case `inner_type` should be set to `T` so that each entry in
+            the list can be transformed using the metadata from the container type.
+
+            Defaults to the same value as the `annotation` argument.
+    """
+    if inner_type is None:
+        inner_type = annotation
+
+    stripped_type = strip_annotated_type(inner_type)
+    if is_typeddict(stripped_type) and is_mapping(data):
+        return _transform_typeddict(data, stripped_type)
+
+    if (
+        # List[T]
+        (is_list_type(stripped_type) and is_list(data))
+        # Iterable[T]
+        or (is_iterable_type(stripped_type) and is_iterable(data) and not isinstance(data, str))
+    ):
+        inner_type = extract_type_arg(stripped_type, 0)
+        return [_transform_recursive(d, annotation=annotation, inner_type=inner_type) for d in data]
+
+    if is_union_type(stripped_type):
+        # For union types we run the transformation against all subtypes to ensure that everything is transformed.
+        #
+        # TODO: there may be edge cases where the same normalized field name will transform to two different names
+        # in different subtypes.
+        for subtype in get_args(stripped_type):
+            data = _transform_recursive(data, annotation=annotation, inner_type=subtype)
+        return data
+
+    if isinstance(data, pydantic.BaseModel):
+        return model_dump(data, exclude_unset=True)
+
+    annotated_type = _get_annotated_type(annotation)
+    if annotated_type is None:
+        return data
+
+    # ignore the first argument as it is the actual type
+    annotations = get_args(annotated_type)[1:]
+    for annotation in annotations:
+        if isinstance(annotation, PropertyInfo) and annotation.format is not None:
+            return _format_data(data, annotation.format, annotation.format_template)
+
+    return data
+
+
+def _format_data(data: object, format_: PropertyFormat, format_template: str | None) -> object:
+    if isinstance(data, (date, datetime)):
+        if format_ == "iso8601":
+            return data.isoformat()
+
+        if format_ == "custom" and format_template is not None:
+            return data.strftime(format_template)
+
+    if format_ == "base64" and is_base64_file_input(data):
+        binary: str | bytes | None = None
+
+        if isinstance(data, pathlib.Path):
+            binary = data.read_bytes()
+        elif isinstance(data, io.IOBase):
+            binary = data.read()
+
+            if isinstance(binary, str):  # type: ignore[unreachable]
+                binary = binary.encode()
+
+        if not isinstance(binary, bytes):
+            raise RuntimeError(f"Could not read bytes from {data}; Received {type(binary)}")
+
+        return base64.b64encode(binary).decode("ascii")
+
+    return data
+
+
+def _transform_typeddict(
+    data: Mapping[str, object],
+    expected_type: type,
+) -> Mapping[str, object]:
+    result: dict[str, object] = {}
+    annotations = get_type_hints(expected_type, include_extras=True)
+    for key, value in data.items():
+        type_ = annotations.get(key)
+        if type_ is None:
+            # we do not have a type annotation for this field, leave it as is
+            result[key] = value
+        else:
+            result[_maybe_transform_key(key, type_)] = _transform_recursive(value, annotation=type_)
+    return result
+
+
+async def async_maybe_transform(
+    data: object,
+    expected_type: object,
+) -> Any | None:
+    """Wrapper over `async_transform()` that allows `None` to be passed.
+
+    See `async_transform()` for more details.
+    """
+    if data is None:
+        return None
+    return await async_transform(data, expected_type)
+
+
+async def async_transform(
+    data: _T,
+    expected_type: object,
+) -> _T:
+    """Transform dictionaries based off of type information from the given type, for example:
+
+    ```py
+    class Params(TypedDict, total=False):
+        card_id: Required[Annotated[str, PropertyInfo(alias="cardID")]]
+
+
+    transformed = transform({"card_id": "<my card ID>"}, Params)
+    # {'cardID': '<my card ID>'}
+    ```
+
+    Any keys / data that does not have type information given will be included as is.
+
+    It should be noted that the transformations that this function does are not represented in the type system.
+    """
+    transformed = await _async_transform_recursive(data, annotation=cast(type, expected_type))
+    return cast(_T, transformed)
+
+
+async def _async_transform_recursive(
+    data: object,
+    *,
+    annotation: type,
+    inner_type: type | None = None,
+) -> object:
+    """Transform the given data against the expected type.
+
+    Args:
+        annotation: The direct type annotation given to the particular piece of data.
+            This may or may not be wrapped in metadata types, e.g. `Required[T]`, `Annotated[T, ...]` etc
+
+        inner_type: If applicable, this is the "inside" type. This is useful in certain cases where the outside type
+            is a container type such as `List[T]`. In that case `inner_type` should be set to `T` so that each entry in
+            the list can be transformed using the metadata from the container type.
+
+            Defaults to the same value as the `annotation` argument.
+    """
+    if inner_type is None:
+        inner_type = annotation
+
+    stripped_type = strip_annotated_type(inner_type)
+    if is_typeddict(stripped_type) and is_mapping(data):
+        return await _async_transform_typeddict(data, stripped_type)
+
+    if (
+        # List[T]
+        (is_list_type(stripped_type) and is_list(data))
+        # Iterable[T]
+        or (is_iterable_type(stripped_type) and is_iterable(data) and not isinstance(data, str))
+    ):
+        inner_type = extract_type_arg(stripped_type, 0)
+        return [await _async_transform_recursive(d, annotation=annotation, inner_type=inner_type) for d in data]
+
+    if is_union_type(stripped_type):
+        # For union types we run the transformation against all subtypes to ensure that everything is transformed.
+        #
+        # TODO: there may be edge cases where the same normalized field name will transform to two different names
+        # in different subtypes.
+        for subtype in get_args(stripped_type):
+            data = await _async_transform_recursive(data, annotation=annotation, inner_type=subtype)
+        return data
+
+    if isinstance(data, pydantic.BaseModel):
+        return model_dump(data, exclude_unset=True)
+
+    annotated_type = _get_annotated_type(annotation)
+    if annotated_type is None:
+        return data
+
+    # ignore the first argument as it is the actual type
+    annotations = get_args(annotated_type)[1:]
+    for annotation in annotations:
+        if isinstance(annotation, PropertyInfo) and annotation.format is not None:
+            return await _async_format_data(data, annotation.format, annotation.format_template)
+
+    return data
+
+
+async def _async_format_data(data: object, format_: PropertyFormat, format_template: str | None) -> object:
+    if isinstance(data, (date, datetime)):
+        if format_ == "iso8601":
+            return data.isoformat()
+
+        if format_ == "custom" and format_template is not None:
+            return data.strftime(format_template)
+
+    if format_ == "base64" and is_base64_file_input(data):
+        binary: str | bytes | None = None
+
+        if isinstance(data, pathlib.Path):
+            binary = await anyio.Path(data).read_bytes()
+        elif isinstance(data, io.IOBase):
+            binary = data.read()
+
+            if isinstance(binary, str):  # type: ignore[unreachable]
+                binary = binary.encode()
+
+        if not isinstance(binary, bytes):
+            raise RuntimeError(f"Could not read bytes from {data}; Received {type(binary)}")
+
+        return base64.b64encode(binary).decode("ascii")
+
+    return data
+
+
+async def _async_transform_typeddict(
+    data: Mapping[str, object],
+    expected_type: type,
+) -> Mapping[str, object]:
+    result: dict[str, object] = {}
+    annotations = get_type_hints(expected_type, include_extras=True)
+    for key, value in data.items():
+        type_ = annotations.get(key)
+        if type_ is None:
+            # we do not have a type annotation for this field, leave it as is
+            result[key] = value
+        else:
+            result[_maybe_transform_key(key, type_)] = await _async_transform_recursive(value, annotation=type_)
+    return result

model-providers/model_providers/_utils/_typing.py

@@ -0,0 +1,120 @@
+from __future__ import annotations
+
+from typing import Any, TypeVar, Iterable, cast
+from collections import abc as _c_abc
+from typing_extensions import Required, Annotated, get_args, get_origin
+
+from .._types import InheritsGeneric
+from .._compat import is_union as _is_union
+
+
+def is_annotated_type(typ: type) -> bool:
+    return get_origin(typ) == Annotated
+
+
+def is_list_type(typ: type) -> bool:
+    return (get_origin(typ) or typ) == list
+
+
+def is_iterable_type(typ: type) -> bool:
+    """If the given type is `typing.Iterable[T]`"""
+    origin = get_origin(typ) or typ
+    return origin == Iterable or origin == _c_abc.Iterable
+
+
+def is_union_type(typ: type) -> bool:
+    return _is_union(get_origin(typ))
+
+
+def is_required_type(typ: type) -> bool:
+    return get_origin(typ) == Required
+
+
+def is_typevar(typ: type) -> bool:
+    # type ignore is required because type checkers
+    # think this expression will always return False
+    return type(typ) == TypeVar  # type: ignore
+
+
+# Extracts T from Annotated[T, ...] or from Required[Annotated[T, ...]]
+def strip_annotated_type(typ: type) -> type:
+    if is_required_type(typ) or is_annotated_type(typ):
+        return strip_annotated_type(cast(type, get_args(typ)[0]))
+
+    return typ
+
+
+def extract_type_arg(typ: type, index: int) -> type:
+    args = get_args(typ)
+    try:
+        return cast(type, args[index])
+    except IndexError as err:
+        raise RuntimeError(f"Expected type {typ} to have a type argument at index {index} but it did not") from err
+
+
+def extract_type_var_from_base(
+        typ: type,
+        *,
+        generic_bases: tuple[type, ...],
+        index: int,
+        failure_message: str | None = None,
+) -> type:
+    """Given a type like `Foo[T]`, returns the generic type variable `T`.
+
+    This also handles the case where a concrete subclass is given, e.g.
+    ```py
+    class MyResponse(Foo[bytes]):
+        ...
+
+    extract_type_var(MyResponse, bases=(Foo,), index=0) -> bytes
+    ```
+
+    And where a generic subclass is given:
+    ```py
+    _T = TypeVar('_T')
+    class MyResponse(Foo[_T]):
+        ...
+
+    extract_type_var(MyResponse[bytes], bases=(Foo,), index=0) -> bytes
+    ```
+    """
+    cls = cast(object, get_origin(typ) or typ)
+    if cls in generic_bases:
+        # we're given the class directly
+        return extract_type_arg(typ, index)
+
+    # if a subclass is given
+    # ---
+    # this is needed as __orig_bases__ is not present in the typeshed stubs
+    # because it is intended to be for internal use only, however there does
+    # not seem to be a way to resolve generic TypeVars for inherited subclasses
+    # without using it.
+    if isinstance(cls, InheritsGeneric):
+        target_base_class: Any | None = None
+        for base in cls.__orig_bases__:
+            if base.__origin__ in generic_bases:
+                target_base_class = base
+                break
+
+        if target_base_class is None:
+            raise RuntimeError(
+                "Could not find the generic base class;\n"
+                "This should never happen;\n"
+                f"Does {cls} inherit from one of {generic_bases} ?"
+            )
+
+        extracted = extract_type_arg(target_base_class, index)
+        if is_typevar(extracted):
+            # If the extracted type argument is itself a type variable
+            # then that means the subclass itself is generic, so we have
+            # to resolve the type argument from the class itself, not
+            # the base class.
+            #
+            # Note: if there is more than 1 type argument, the subclass could
+            # change the ordering of the type arguments, this is not currently
+            # supported.
+            return extract_type_arg(typ, index)
+
+        return extracted
+
+    raise RuntimeError(failure_message or f"Could not resolve inner type variable at index {index} for {typ}")

model-providers/model_providers/_utils/_utils.py

@@ -0,0 +1,403 @@
+from __future__ import annotations
+
+import os
+import re
+import inspect
+import functools
+from typing import (
+    Any,
+    Tuple,
+    Mapping,
+    TypeVar,
+    Callable,
+    Iterable,
+    Sequence,
+    cast,
+    overload,
+)
+from pathlib import Path
+from typing_extensions import TypeGuard
+
+import sniffio
+
+from .._types import Headers, NotGiven, FileTypes, NotGivenOr, HeadersLike
+from .._compat import parse_date as parse_date, parse_datetime as parse_datetime
+
+_T = TypeVar("_T")
+_TupleT = TypeVar("_TupleT", bound=Tuple[object, ...])
+_MappingT = TypeVar("_MappingT", bound=Mapping[str, object])
+_SequenceT = TypeVar("_SequenceT", bound=Sequence[object])
+CallableT = TypeVar("CallableT", bound=Callable[..., Any])
+
+
+def flatten(t: Iterable[Iterable[_T]]) -> list[_T]:
+    return [item for sublist in t for item in sublist]
+
+
+def extract_files(
+    # TODO: this needs to take Dict but variance issues.....
+    # create protocol type ?
+    query: Mapping[str, object],
+    *,
+    paths: Sequence[Sequence[str]],
+) -> list[tuple[str, FileTypes]]:
+    """Recursively extract files from the given dictionary based on specified paths.
+
+    A path may look like this ['foo', 'files', '<array>', 'data'].
+
+    Note: this mutates the given dictionary.
+    """
+    files: list[tuple[str, FileTypes]] = []
+    for path in paths:
+        files.extend(_extract_items(query, path, index=0, flattened_key=None))
+    return files
+
+
+def _extract_items(
+    obj: object,
+    path: Sequence[str],
+    *,
+    index: int,
+    flattened_key: str | None,
+) -> list[tuple[str, FileTypes]]:
+    try:
+        key = path[index]
+    except IndexError:
+        if isinstance(obj, NotGiven):
+            # no value was provided - we can safely ignore
+            return []
+
+        # cyclical import
+        from .._files import assert_is_file_content
+
+        # We have exhausted the path, return the entry we found.
+        assert_is_file_content(obj, key=flattened_key)
+        assert flattened_key is not None
+        return [(flattened_key, cast(FileTypes, obj))]
+
+    index += 1
+    if is_dict(obj):
+        try:
+            # We are at the last entry in the path so we must remove the field
+            if (len(path)) == index:
+                item = obj.pop(key)
+            else:
+                item = obj[key]
+        except KeyError:
+            # Key was not present in the dictionary, this is not indicative of an error
+            # as the given path may not point to a required field. We also do not want
+            # to enforce required fields as the API may differ from the spec in some cases.
+            return []
+        if flattened_key is None:
+            flattened_key = key
+        else:
+            flattened_key += f"[{key}]"
+        return _extract_items(
+            item,
+            path,
+            index=index,
+            flattened_key=flattened_key,
+        )
+    elif is_list(obj):
+        if key != "<array>":
+            return []
+
+        return flatten(
+            [
+                _extract_items(
+                    item,
+                    path,
+                    index=index,
+                    flattened_key=flattened_key + "[]" if flattened_key is not None else "[]",
+                )
+                for item in obj
+            ]
+        )
+
+    # Something unexpected was passed, just ignore it.
+    return []
+
+
+def is_given(obj: NotGivenOr[_T]) -> TypeGuard[_T]:
+    return not isinstance(obj, NotGiven)
+
+
+# Type safe methods for narrowing types with TypeVars.
+# The default narrowing for isinstance(obj, dict) is dict[unknown, unknown],
+# however this cause Pyright to rightfully report errors. As we know we don't
+# care about the contained types we can safely use `object` in it's place.
+#
+# There are two separate functions defined, `is_*` and `is_*_t` for different use cases.
+# `is_*` is for when you're dealing with an unknown input
+# `is_*_t` is for when you're narrowing a known union type to a specific subset
+
+
+def is_tuple(obj: object) -> TypeGuard[tuple[object, ...]]:
+    return isinstance(obj, tuple)
+
+
+def is_tuple_t(obj: _TupleT | object) -> TypeGuard[_TupleT]:
+    return isinstance(obj, tuple)
+
+
+def is_sequence(obj: object) -> TypeGuard[Sequence[object]]:
+    return isinstance(obj, Sequence)
+
+
+def is_sequence_t(obj: _SequenceT | object) -> TypeGuard[_SequenceT]:
+    return isinstance(obj, Sequence)
+
+
+def is_mapping(obj: object) -> TypeGuard[Mapping[str, object]]:
+    return isinstance(obj, Mapping)
+
+
+def is_mapping_t(obj: _MappingT | object) -> TypeGuard[_MappingT]:
+    return isinstance(obj, Mapping)
+
+
+def is_dict(obj: object) -> TypeGuard[dict[object, object]]:
+    return isinstance(obj, dict)
+
+
+def is_list(obj: object) -> TypeGuard[list[object]]:
+    return isinstance(obj, list)
+
+
+def is_iterable(obj: object) -> TypeGuard[Iterable[object]]:
+    return isinstance(obj, Iterable)
+
+
+def deepcopy_minimal(item: _T) -> _T:
+    """Minimal reimplementation of copy.deepcopy() that will only copy certain object types:
+
+    - mappings, e.g. `dict`
+    - list
+
+    This is done for performance reasons.
+    """
+    if is_mapping(item):
+        return cast(_T, {k: deepcopy_minimal(v) for k, v in item.items()})
+    if is_list(item):
+        return cast(_T, [deepcopy_minimal(entry) for entry in item])
+    return item
+
+
+# copied from https://github.com/Rapptz/RoboDanny
+def human_join(seq: Sequence[str], *, delim: str = ", ", final: str = "or") -> str:
+    size = len(seq)
+    if size == 0:
+        return ""
+
+    if size == 1:
+        return seq[0]
+
+    if size == 2:
+        return f"{seq[0]} {final} {seq[1]}"
+
+    return delim.join(seq[:-1]) + f" {final} {seq[-1]}"
+
+
+def quote(string: str) -> str:
+    """Add single quotation marks around the given string. Does *not* do any escaping."""
+    return f"'{string}'"
+
+
+def required_args(*variants: Sequence[str]) -> Callable[[CallableT], CallableT]:
+    """Decorator to enforce a given set of arguments or variants of arguments are passed to the decorated function.
+
+    Useful for enforcing runtime validation of overloaded functions.
+
+    Example usage:
+    ```py
+    @overload
+    def foo(*, a: str) -> str:
+        ...
+
+
+    @overload
+    def foo(*, b: bool) -> str:
+        ...
+
+
+    # This enforces the same constraints that a static type checker would
+    # i.e. that either a or b must be passed to the function
+    @required_args(["a"], ["b"])
+    def foo(*, a: str | None = None, b: bool | None = None) -> str:
+        ...
+    ```
+    """
+
+    def inner(func: CallableT) -> CallableT:
+        params = inspect.signature(func).parameters
+        positional = [
+            name
+            for name, param in params.items()
+            if param.kind
+            in {
+                param.POSITIONAL_ONLY,
+                param.POSITIONAL_OR_KEYWORD,
+            }
+        ]
+
+        @functools.wraps(func)
+        def wrapper(*args: object, **kwargs: object) -> object:
+            given_params: set[str] = set()
+            for i, _ in enumerate(args):
+                try:
+                    given_params.add(positional[i])
+                except IndexError:
+                    raise TypeError(
+                        f"{func.__name__}() takes {len(positional)} argument(s) but {len(args)} were given"
+                    ) from None
+
+            for key in kwargs.keys():
+                given_params.add(key)
+
+            for variant in variants:
+                matches = all((param in given_params for param in variant))
+                if matches:
+                    break
+            else:  # no break
+                if len(variants) > 1:
+                    variations = human_join(
+                        ["(" + human_join([quote(arg) for arg in variant], final="and") + ")" for variant in variants]
+                    )
+                    msg = f"Missing required arguments; Expected either {variations} arguments to be given"
+                else:
+                    assert len(variants) > 0
+
+                    # TODO: this error message is not deterministic
+                    missing = list(set(variants[0]) - given_params)
+                    if len(missing) > 1:
+                        msg = f"Missing required arguments: {human_join([quote(arg) for arg in missing])}"
+                    else:
+                        msg = f"Missing required argument: {quote(missing[0])}"
+                raise TypeError(msg)
+            return func(*args, **kwargs)
+
+        return wrapper  # type: ignore
+
+    return inner
+
+
+_K = TypeVar("_K")
+_V = TypeVar("_V")
+
+
+@overload
+def strip_not_given(obj: None) -> None:
+    ...
+
+
+@overload
+def strip_not_given(obj: Mapping[_K, _V | NotGiven]) -> dict[_K, _V]:
+    ...
+
+
+@overload
+def strip_not_given(obj: object) -> object:
+    ...
+
+
+def strip_not_given(obj: object | None) -> object:
+    """Remove all top-level keys where their values are instances of `NotGiven`"""
+    if obj is None:
+        return None
+
+    if not is_mapping(obj):
+        return obj
+
+    return {key: value for key, value in obj.items() if not isinstance(value, NotGiven)}
+
+
+def coerce_integer(val: str) -> int:
+    return int(val, base=10)
+
+
+def coerce_float(val: str) -> float:
+    return float(val)
+
+
+def coerce_boolean(val: str) -> bool:
+    return val == "true" or val == "1" or val == "on"
+
+
+def maybe_coerce_integer(val: str | None) -> int | None:
+    if val is None:
+        return None
+    return coerce_integer(val)
+
+
+def maybe_coerce_float(val: str | None) -> float | None:
+    if val is None:
+        return None
+    return coerce_float(val)
+
+
+def maybe_coerce_boolean(val: str | None) -> bool | None:
+    if val is None:
+        return None
+    return coerce_boolean(val)
+
+
+def removeprefix(string: str, prefix: str) -> str:
+    """Remove a prefix from a string.
+
+    Backport of `str.removeprefix` for Python < 3.9
+    """
+    if string.startswith(prefix):
+        return string[len(prefix) :]
+    return string
+
+
+def removesuffix(string: str, suffix: str) -> str:
+    """Remove a suffix from a string.
+
+    Backport of `str.removesuffix` for Python < 3.9
+    """
+    if string.endswith(suffix):
+        return string[: -len(suffix)]
+    return string
+
+
+def file_from_path(path: str) -> FileTypes:
+    contents = Path(path).read_bytes()
+    file_name = os.path.basename(path)
+    return (file_name, contents)
+
+
+def get_required_header(headers: HeadersLike, header: str) -> str:
+    lower_header = header.lower()
+    if isinstance(headers, Mapping):
+        headers = cast(Headers, headers)
+        for k, v in headers.items():
+            if k.lower() == lower_header and isinstance(v, str):
+                return v
+
+    """ to deal with the case where the header looks like Stainless-Event-Id """
+    intercaps_header = re.sub(r"([^\w])(\w)", lambda pat: pat.group(1) + pat.group(2).upper(), header.capitalize())
+
+    for normalized_header in [header, lower_header, header.upper(), intercaps_header]:
+        value = headers.get(normalized_header)
+        if value:
+            return value
+
+    raise ValueError(f"Could not find {header} header")
+
+
+def get_async_library() -> str:
+    try:
+        return sniffio.current_async_library()
+    except Exception:
+        return "false"
+
+
+def lru_cache(*, maxsize: int | None = 128) -> Callable[[CallableT], CallableT]:
+    """A version of functools.lru_cache that retains the type signature
+    for the wrapped function arguments.
+    """
+    wrapper = functools.lru_cache(  # noqa: TID251
+        maxsize=maxsize,
+    )
+    return cast(Any, wrapper)  # type: ignore[no-any-return]

model-providers/model_providers/bootstrap_web/entities/model_provider_entities.py

@@ -1,7 +1,7 @@
 from enum import Enum
 from typing import List, Literal, Optional
 
-from pydantic import BaseModel
+from ..._models import BaseModel
 
 from model_providers.core.entities.model_entities import (
     ModelStatus,

model-providers/model_providers/core/bootstrap/openai_protocol.py

@@ -1,8 +1,8 @@
 import time
 from enum import Enum
 from typing import Any, Dict, List, Optional, Union
-
+from ..._models import BaseModel
-from pydantic import BaseModel, Field, root_validator
+from pydantic import Field as FieldInfo
 from typing_extensions import Literal
 
 
@@ -81,7 +81,7 @@ class ModelCard(BaseModel):
         "tts",
         "text2img",
     ] = "llm"
-    created: int = Field(default_factory=lambda: int(time.time()))
+    created: int = FieldInfo(default_factory=lambda: int(time.time()))
     owned_by: Literal["owner"] = "owner"
 
 
@@ -171,7 +171,7 @@ class ChatCompletionStreamResponseChoice(BaseModel):
 class ChatCompletionResponse(BaseModel):
     id: str
     object: Literal["chat.completion"] = "chat.completion"
-    created: int = Field(default_factory=lambda: int(time.time()))
+    created: int = FieldInfo(default_factory=lambda: int(time.time()))
     model: str
     choices: List[ChatCompletionResponseChoice]
     usage: UsageInfo
@@ -180,7 +180,7 @@ class ChatCompletionResponse(BaseModel):
 class ChatCompletionStreamResponse(BaseModel):
     id: str
     object: Literal["chat.completion.chunk"] = "chat.completion.chunk"
-    created: int = Field(default_factory=lambda: int(time.time()))
+    created: int = FieldInfo(default_factory=lambda: int(time.time()))
     model: str
     choices: List[ChatCompletionStreamResponseChoice]
 

model-providers/model_providers/core/entities/application_entities.py

@@ -1,331 +0,0 @@
-from enum import Enum
-from typing import Any, Literal, Optional, Union
-
-from pydantic import BaseModel
-
-from model_providers.core.entities.provider_configuration import ProviderModelBundle
-from model_providers.core.file.file_obj import FileObj
-from model_providers.core.model_runtime.entities.message_entities import (
-    PromptMessageRole,
-)
-from model_providers.core.model_runtime.entities.model_entities import AIModelEntity
-
-
-class ModelConfigEntity(BaseModel):
-    """
-    Model Config Entity.
-    """
-
-    provider: str
-    model: str
-    model_schema: AIModelEntity
-    mode: str
-    provider_model_bundle: ProviderModelBundle
-    credentials: Dict[str, Any] = {}
-    parameters: Dict[str, Any] = {}
-    stop: List[str] = []
-
-
-class AdvancedChatMessageEntity(BaseModel):
-    """
-    Advanced Chat Message Entity.
-    """
-
-    text: str
-    role: PromptMessageRole
-
-
-class AdvancedChatPromptTemplateEntity(BaseModel):
-    """
-    Advanced Chat Prompt Template Entity.
-    """
-
-    messages: List[AdvancedChatMessageEntity]
-
-
-class AdvancedCompletionPromptTemplateEntity(BaseModel):
-    """
-    Advanced Completion Prompt Template Entity.
-    """
-
-    class RolePrefixEntity(BaseModel):
-        """
-        Role Prefix Entity.
-        """
-
-        user: str
-        assistant: str
-
-    prompt: str
-    role_prefix: Optional[RolePrefixEntity] = None
-
-
-class PromptTemplateEntity(BaseModel):
-    """
-    Prompt Template Entity.
-    """
-
-    class PromptType(Enum):
-        """
-        Prompt Type.
-        'simple', 'advanced'
-        """
-
-        SIMPLE = "simple"
-        ADVANCED = "advanced"
-
-        @classmethod
-        def value_of(cls, value: str) -> "PromptType":
-            """
-            Get value of given mode.
-
-            :param value: mode value
-            :return: mode
-            """
-            for mode in cls:
-                if mode.value == value:
-                    return mode
-            raise ValueError(f"invalid prompt type value {value}")
-
-    prompt_type: PromptType
-    simple_prompt_template: Optional[str] = None
-    advanced_chat_prompt_template: Optional[AdvancedChatPromptTemplateEntity] = None
-    advanced_completion_prompt_template: Optional[
-        AdvancedCompletionPromptTemplateEntity
-    ] = None
-
-
-class ExternalDataVariableEntity(BaseModel):
-    """
-    External Data Variable Entity.
-    """
-
-    variable: str
-    type: str
-    config: Dict[str, Any] = {}
-
-
-class DatasetRetrieveConfigEntity(BaseModel):
-    """
-    Dataset Retrieve Config Entity.
-    """
-
-    class RetrieveStrategy(Enum):
-        """
-        Dataset Retrieve Strategy.
-        'single' or 'multiple'
-        """
-
-        SINGLE = "single"
-        MULTIPLE = "multiple"
-
-        @classmethod
-        def value_of(cls, value: str) -> "RetrieveStrategy":
-            """
-            Get value of given mode.
-
-            :param value: mode value
-            :return: mode
-            """
-            for mode in cls:
-                if mode.value == value:
-                    return mode
-            raise ValueError(f"invalid retrieve strategy value {value}")
-
-    query_variable: Optional[str] = None  # Only when app mode is completion
-
-    retrieve_strategy: RetrieveStrategy
-    single_strategy: Optional[str] = None  # for temp
-    top_k: Optional[int] = None
-    score_threshold: Optional[float] = None
-    reranking_model: Optional[dict] = None
-
-
-class DatasetEntity(BaseModel):
-    """
-    Dataset Config Entity.
-    """
-
-    dataset_ids: List[str]
-    retrieve_config: DatasetRetrieveConfigEntity
-
-
-class SensitiveWordAvoidanceEntity(BaseModel):
-    """
-    Sensitive Word Avoidance Entity.
-    """
-
-    type: str
-    config: Dict[str, Any] = {}
-
-
-class TextToSpeechEntity(BaseModel):
-    """
-    Sensitive Word Avoidance Entity.
-    """
-
-    enabled: bool
-    voice: Optional[str] = None
-    language: Optional[str] = None
-
-
-class FileUploadEntity(BaseModel):
-    """
-    File Upload Entity.
-    """
-
-    image_config: Optional[dict[str, Any]] = None
-
-
-class AgentToolEntity(BaseModel):
-    """
-    Agent Tool Entity.
-    """
-
-    provider_type: Literal["builtin", "api"]
-    provider_id: str
-    tool_name: str
-    tool_parameters: Dict[str, Any] = {}
-
-
-class AgentPromptEntity(BaseModel):
-    """
-    Agent Prompt Entity.
-    """
-
-    first_prompt: str
-    next_iteration: str
-
-
-class AgentScratchpadUnit(BaseModel):
-    """
-    Agent First Prompt Entity.
-    """
-
-    class Action(BaseModel):
-        """
-        Action Entity.
-        """
-
-        action_name: str
-        action_input: Union[dict, str]
-
-    agent_response: Optional[str] = None
-    thought: Optional[str] = None
-    action_str: Optional[str] = None
-    observation: Optional[str] = None
-    action: Optional[Action] = None
-
-
-class AgentEntity(BaseModel):
-    """
-    Agent Entity.
-    """
-
-    class Strategy(Enum):
-        """
-        Agent Strategy.
-        """
-
-        CHAIN_OF_THOUGHT = "chain-of-thought"
-        FUNCTION_CALLING = "function-calling"
-
-    provider: str
-    model: str
-    strategy: Strategy
-    prompt: Optional[AgentPromptEntity] = None
-    tools: List[AgentToolEntity] = None
-    max_iteration: int = 5
-
-
-class AppOrchestrationConfigEntity(BaseModel):
-    """
-    App Orchestration Config Entity.
-    """
-
-    model_config: ModelConfigEntity
-    prompt_template: PromptTemplateEntity
-    external_data_variables: List[ExternalDataVariableEntity] = []
-    agent: Optional[AgentEntity] = None
-
-    # features
-    dataset: Optional[DatasetEntity] = None
-    file_upload: Optional[FileUploadEntity] = None
-    opening_statement: Optional[str] = None
-    suggested_questions_after_answer: bool = False
-    show_retrieve_source: bool = False
-    more_like_this: bool = False
-    speech_to_text: bool = False
-    text_to_speech: dict = {}
-    sensitive_word_avoidance: Optional[SensitiveWordAvoidanceEntity] = None
-
-
-class InvokeFrom(Enum):
-    """
-    Invoke From.
-    """
-
-    SERVICE_API = "service-api"
-    WEB_APP = "web-app"
-    EXPLORE = "explore"
-    DEBUGGER = "debugger"
-
-    @classmethod
-    def value_of(cls, value: str) -> "InvokeFrom":
-        """
-        Get value of given mode.
-
-        :param value: mode value
-        :return: mode
-        """
-        for mode in cls:
-            if mode.value == value:
-                return mode
-        raise ValueError(f"invalid invoke from value {value}")
-
-    def to_source(self) -> str:
-        """
-        Get source of invoke from.
-
-        :return: source
-        """
-        if self == InvokeFrom.WEB_APP:
-            return "web_app"
-        elif self == InvokeFrom.DEBUGGER:
-            return "dev"
-        elif self == InvokeFrom.EXPLORE:
-            return "explore_app"
-        elif self == InvokeFrom.SERVICE_API:
-            return "api"
-
-        return "dev"
-
-
-class ApplicationGenerateEntity(BaseModel):
-    """
-    Application Generate Entity.
-    """
-
-    task_id: str
-    tenant_id: str
-
-    app_id: str
-    app_model_config_id: str
-    # for save
-    app_model_config_dict: dict
-    app_model_config_override: bool
-
-    # Converted from app_model_config to Entity object, or directly covered by external input
-    app_orchestration_config_entity: AppOrchestrationConfigEntity
-
-    conversation_id: Optional[str] = None
-    inputs: Dict[str, str]
-    query: Optional[str] = None
-    files: List[FileObj] = []
-    user_id: str
-    # extras
-    stream: bool
-    invoke_from: InvokeFrom
-
-    # extra parameters, like: auto_generate_conversation_name
-    extras: Dict[str, Any] = {}

model-providers/model_providers/core/entities/message_entities.py

@@ -1,5 +1,5 @@
 import enum
-from typing import Any, cast
+from typing import Any, cast, List
 
 from langchain.schema import (
     AIMessage,
@@ -8,7 +8,7 @@ from langchain.schema import (
     HumanMessage,
     SystemMessage,
 )
-from pydantic import BaseModel
+from ..._models import BaseModel
 
 from model_providers.core.model_runtime.entities.message_entities import (
     AssistantPromptMessage,

model-providers/model_providers/core/entities/model_entities.py

@@ -1,7 +1,7 @@
 from enum import Enum
 from typing import List, Optional
 
-from pydantic import BaseModel
+from ..._models import BaseModel
 
 from model_providers.core.model_runtime.entities.common_entities import I18nObject
 from model_providers.core.model_runtime.entities.model_entities import (

model-providers/model_providers/core/entities/provider_configuration.py

@@ -4,7 +4,7 @@ import logging
 from json import JSONDecodeError
 from typing import Dict, Iterator, List, Optional
 
-from pydantic import BaseModel
+from ..._models import BaseModel
 
 from model_providers.core.entities.model_entities import (
     ModelStatus,

model-providers/model_providers/core/entities/provider_entities.py

@@ -1,7 +1,7 @@
 from enum import Enum
 from typing import List, Optional
 
-from pydantic import BaseModel
+from ..._models import BaseModel
 
 from model_providers.core.model_runtime.entities.model_entities import ModelType
 

model-providers/model_providers/core/entities/queue_entities.py

@@ -1,7 +1,7 @@
 from enum import Enum
 from typing import Any
 
-from pydantic import BaseModel
+from ..._models import BaseModel
 
 from model_providers.core.model_runtime.entities.llm_entities import (
     LLMResult,

model-providers/model_providers/core/model_runtime/entities/common_entities.py

@@ -1,6 +1,6 @@
 from typing import Optional
 
-from pydantic import BaseModel
+from ...._models import BaseModel
 
 
 class I18nObject(BaseModel):

model-providers/model_providers/core/model_runtime/entities/llm_entities.py

@@ -2,7 +2,7 @@ from decimal import Decimal
 from enum import Enum
 from typing import List, Optional
 
-from pydantic import BaseModel
+from ...._models import BaseModel
 
 from model_providers.core.model_runtime.entities.message_entities import (
     AssistantPromptMessage,

model-providers/model_providers/core/model_runtime/entities/message_entities.py

@@ -2,7 +2,7 @@ from abc import ABC
 from enum import Enum
 from typing import List, Optional, Union
 
-from pydantic import BaseModel
+from ...._models import BaseModel
 
 
 class PromptMessageRole(Enum):

model-providers/model_providers/core/model_runtime/entities/model_entities.py

@@ -2,7 +2,7 @@ from decimal import Decimal
 from enum import Enum
 from typing import Any, Dict, List, Optional
 
-from pydantic import BaseModel
+from ...._models import BaseModel
 
 from model_providers.core.model_runtime.entities.common_entities import I18nObject
 

model-providers/model_providers/core/model_runtime/entities/provider_entities.py

@@ -1,7 +1,7 @@
 from enum import Enum
 from typing import List, Optional
 
-from pydantic import BaseModel
+from ...._models import BaseModel
 
 from model_providers.core.model_runtime.entities.common_entities import I18nObject
 from model_providers.core.model_runtime.entities.model_entities import (

model-providers/model_providers/core/model_runtime/entities/rerank_entities.py

@@ -1,6 +1,6 @@
 from typing import List
 
-from pydantic import BaseModel
+from ...._models import BaseModel
 
 
 class RerankDocument(BaseModel):

model-providers/model_providers/core/model_runtime/entities/text_embedding_entities.py

@@ -1,7 +1,7 @@
 from decimal import Decimal
 from typing import List
 
-from pydantic import BaseModel
+from ...._models import BaseModel
 
 from model_providers.core.model_runtime.entities.model_entities import ModelUsage
 

model-providers/model_providers/core/model_runtime/model_providers/azure_openai/_constant.py

@@ -1,4 +1,4 @@
-from pydantic import BaseModel
+from ....._models import BaseModel
 
 from model_providers.core.model_runtime.entities.defaults import PARAMETER_RULE_TEMPLATE
 from model_providers.core.model_runtime.entities.llm_entities import LLMMode

model-providers/model_providers/core/model_runtime/model_providers/model_provider_factory.py

@@ -3,7 +3,7 @@ import logging
 import os
 from typing import Dict, List, Optional, Union
 
-from pydantic import BaseModel
+from ...._models import BaseModel
 
 from model_providers.core.model_runtime.entities.model_entities import ModelType
 from model_providers.core.model_runtime.entities.provider_entities import (

model-providers/model_providers/core/model_runtime/utils/_compat.py

@@ -1,21 +0,0 @@
-from typing import Any, Literal
-
-from pydantic import BaseModel
-from pydantic.version import VERSION as PYDANTIC_VERSION
-
-PYDANTIC_V2 = PYDANTIC_VERSION.startswith("2.")
-
-if PYDANTIC_V2:
-    from pydantic_core import Url as Url
-
-    def _model_dump(
-        model: BaseModel, mode: Literal["json", "python"] = "json", **kwargs: Any
-    ) -> Any:
-        return model.model_dump(mode=mode, **kwargs)
-else:
-    from pydantic import AnyUrl as Url  # noqa: F401
-
-    def _model_dump(
-        model: BaseModel, mode: Literal["json", "python"] = "json", **kwargs: Any
-    ) -> Any:
-        return model.dict(**kwargs)

model-providers/model_providers/core/model_runtime/utils/encoders.py

@@ -1,234 +0,0 @@
-import dataclasses
-import datetime
-from collections import defaultdict, deque
-from collections.abc import Callable
-from decimal import Decimal
-from enum import Enum
-from ipaddress import (
-    IPv4Address,
-    IPv4Interface,
-    IPv4Network,
-    IPv6Address,
-    IPv6Interface,
-    IPv6Network,
-)
-from pathlib import Path, PurePath
-from re import Pattern
-from types import GeneratorType
-from typing import Any, Optional, Union, Dict, Type, List, Tuple
-from uuid import UUID
-
-from pydantic import BaseModel
-from pydantic.color import Color
-from pydantic.networks import AnyUrl, NameEmail
-from pydantic.types import SecretBytes, SecretStr
-
-from ._compat import PYDANTIC_V2, Url, _model_dump
-
-
-# Taken from Pydantic v1 as is
-def isoformat(o: Union[datetime.date, datetime.time]) -> str:
-    return o.isoformat()
-
-
-# Taken from Pydantic v1 as is
-# TODO: pv2 should this return strings instead?
-def decimal_encoder(dec_value: Decimal) -> Union[int, float]:
-    """
-    Encodes a Decimal as int of there's no exponent, otherwise float
-
-    This is useful when we use ConstrainedDecimal to represent Numeric(x,0)
-    where a integer (but not int typed) is used. Encoding this as a float
-    results in failed round-tripping between encode and parse.
-    Our Id type is a prime example of this.
-
-    >>> decimal_encoder(Decimal("1.0"))
-    1.0
-
-    >>> decimal_encoder(Decimal("1"))
-    1
-    """
-    if dec_value.as_tuple().exponent >= 0:  # type: ignore[operator]
-        return int(dec_value)
-    else:
-        return float(dec_value)
-
-
-ENCODERS_BY_TYPE: Dict[Type[Any], Callable[[Any], Any]] = {
-    bytes: lambda o: o.decode(),
-    Color: str,
-    datetime.date: isoformat,
-    datetime.datetime: isoformat,
-    datetime.time: isoformat,
-    datetime.timedelta: lambda td: td.total_seconds(),
-    Decimal: decimal_encoder,
-    Enum: lambda o: o.value,
-    frozenset: list,
-    deque: list,
-    GeneratorType: list,
-    IPv4Address: str,
-    IPv4Interface: str,
-    IPv4Network: str,
-    IPv6Address: str,
-    IPv6Interface: str,
-    IPv6Network: str,
-    NameEmail: str,
-    Path: str,
-    Pattern: lambda o: o.pattern,
-    SecretBytes: str,
-    SecretStr: str,
-    set: list,
-    UUID: str,
-    Url: str,
-    AnyUrl: str,
-}
-
-
-def generate_encoders_by_class_tuples(
-    type_encoder_map: Dict[Any, Callable[[Any], Any]],
-) -> Dict[Callable[[Any], Any], Tuple[Any, ...]]:
-    encoders_by_class_tuples: Dict[Callable[[Any], Any], Tuple[Any, ...]] = defaultdict(
-        tuple
-    )
-    for type_, encoder in type_encoder_map.items():
-        encoders_by_class_tuples[encoder] += (type_,)
-    return encoders_by_class_tuples
-
-
-encoders_by_class_tuples = generate_encoders_by_class_tuples(ENCODERS_BY_TYPE)
-
-
-def jsonable_encoder(
-    obj: Any,
-    by_alias: bool = True,
-    exclude_unset: bool = False,
-    exclude_defaults: bool = False,
-    exclude_none: bool = False,
-    custom_encoder: Optional[Dict[Any, Callable[[Any], Any]]] = None,
-    sqlalchemy_safe: bool = True,
-) -> Any:
-    custom_encoder = custom_encoder or {}
-    if custom_encoder:
-        if type(obj) in custom_encoder:
-            return custom_encoder[type(obj)](obj)
-        else:
-            for encoder_type, encoder_instance in custom_encoder.items():
-                if isinstance(obj, encoder_type):
-                    return encoder_instance(obj)
-    if isinstance(obj, BaseModel):
-        # TODO: remove when deprecating Pydantic v1
-        encoders: Dict[Any, Any] = {}
-        if not PYDANTIC_V2:
-            encoders = getattr(obj.__config__, "json_encoders", {})  # type: ignore[attr-defined]
-            if custom_encoder:
-                encoders.update(custom_encoder)
-        obj_dict = _model_dump(
-            obj,
-            mode="json",
-            include=None,
-            exclude=None,
-            by_alias=by_alias,
-            exclude_unset=exclude_unset,
-            exclude_none=exclude_none,
-            exclude_defaults=exclude_defaults,
-        )
-        if "__root__" in obj_dict:
-            obj_dict = obj_dict["__root__"]
-        return jsonable_encoder(
-            obj_dict,
-            exclude_none=exclude_none,
-            exclude_defaults=exclude_defaults,
-            # TODO: remove when deprecating Pydantic v1
-            custom_encoder=encoders,
-            sqlalchemy_safe=sqlalchemy_safe,
-        )
-    if dataclasses.is_dataclass(obj):
-        obj_dict = dataclasses.asdict(obj)
-        return jsonable_encoder(
-            obj_dict,
-            by_alias=by_alias,
-            exclude_unset=exclude_unset,
-            exclude_defaults=exclude_defaults,
-            exclude_none=exclude_none,
-            custom_encoder=custom_encoder,
-            sqlalchemy_safe=sqlalchemy_safe,
-        )
-    if isinstance(obj, Enum):
-        return obj.value
-    if isinstance(obj, PurePath):
-        return str(obj)
-    if isinstance(obj, str | int | float | type(None)):
-        return obj
-    if isinstance(obj, Decimal):
-        return format(obj, "f")
-    if isinstance(obj, dict):
-        encoded_dict = {}
-        allowed_keys = set(obj.keys())
-        for key, value in obj.items():
-            if (
-                (
-                    not sqlalchemy_safe
-                    or (not isinstance(key, str))
-                    or (not key.startswith("_sa"))
-                )
-                and (value is not None or not exclude_none)
-                and key in allowed_keys
-            ):
-                encoded_key = jsonable_encoder(
-                    key,
-                    by_alias=by_alias,
-                    exclude_unset=exclude_unset,
-                    exclude_none=exclude_none,
-                    custom_encoder=custom_encoder,
-                    sqlalchemy_safe=sqlalchemy_safe,
-                )
-                encoded_value = jsonable_encoder(
-                    value,
-                    by_alias=by_alias,
-                    exclude_unset=exclude_unset,
-                    exclude_none=exclude_none,
-                    custom_encoder=custom_encoder,
-                    sqlalchemy_safe=sqlalchemy_safe,
-                )
-                encoded_dict[encoded_key] = encoded_value
-        return encoded_dict
-    if isinstance(obj, (list, set, frozenset, GeneratorType, tuple, deque)):
-        encoded_list = []
-        for item in obj:
-            encoded_list.append(
-                jsonable_encoder(
-                    item,
-                    by_alias=by_alias,
-                    exclude_unset=exclude_unset,
-                    exclude_defaults=exclude_defaults,
-                    exclude_none=exclude_none,
-                    custom_encoder=custom_encoder,
-                    sqlalchemy_safe=sqlalchemy_safe,
-                )
-            )
-        return encoded_list
-
-    if type(obj) in ENCODERS_BY_TYPE:
-        return ENCODERS_BY_TYPE[type(obj)](obj)
-    for encoder, classes_tuple in encoders_by_class_tuples.items():
-        if isinstance(obj, classes_tuple):
-            return encoder(obj)
-
-    try:
-        data = dict(obj)
-    except Exception as e:
-        errors: List[Exception] = [e]
-        try:
-            data = vars(obj)
-        except Exception as e:
-            errors.append(e)
-            raise ValueError(errors) from e
-    return jsonable_encoder(
-        data,
-        by_alias=by_alias,
-        exclude_unset=exclude_unset,
-        exclude_defaults=exclude_defaults,
-        exclude_none=exclude_none,
-        custom_encoder=custom_encoder,
-        sqlalchemy_safe=sqlalchemy_safe,
-    )

model-providers/model_providers/core/model_runtime/utils/helper.py

@@ -1,5 +1,5 @@
 import pydantic
-from pydantic import BaseModel
+from ...._models import BaseModel
 
 
 def dump_model(model: BaseModel) -> dict:

model-providers/model_providers/core/utils/generic.py

@@ -2,7 +2,7 @@ import json
 from typing import TYPE_CHECKING, Any, Dict
 
 if TYPE_CHECKING:
-    from pydantic import BaseModel
+    from ..._models import BaseModel
 
 
 def dictify(data: "BaseModel") -> Dict[str, Any]:

model-providers/model_providers/core/utils/json_dumps.py

@@ -1,7 +1,7 @@
 import os
 
 import orjson
-from pydantic import BaseModel
+from ..._models import BaseModel
 
 
 def json_dumps(o):

model-providers/tests/conftest.py

@@ -136,7 +136,7 @@ def init_server(logging_conf: dict, providers_file: str) -> None:
             yield f"http://127.0.0.1:20000"
         finally:
             print("")
-            # boot.destroy()
+            boot.destroy()
 
     except SystemExit: