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Diffstat (limited to '.venv/lib/python3.12/site-packages/openai/_models.py')
-rw-r--r-- | .venv/lib/python3.12/site-packages/openai/_models.py | 844 |
1 files changed, 844 insertions, 0 deletions
diff --git a/.venv/lib/python3.12/site-packages/openai/_models.py b/.venv/lib/python3.12/site-packages/openai/_models.py new file mode 100644 index 00000000..ff7c1f33 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/openai/_models.py @@ -0,0 +1,844 @@ +from __future__ import annotations + +import os +import inspect +from typing import TYPE_CHECKING, Any, Type, Tuple, Union, Generic, TypeVar, Callable, Optional, cast +from datetime import date, datetime +from typing_extensions import ( + Unpack, + Literal, + ClassVar, + Protocol, + Required, + Sequence, + ParamSpec, + TypedDict, + TypeGuard, + final, + override, + runtime_checkable, +) + +import pydantic +import pydantic.generics +from pydantic.fields import FieldInfo + +from ._types import ( + Body, + IncEx, + Query, + ModelT, + Headers, + Timeout, + NotGiven, + AnyMapping, + HttpxRequestFiles, +) +from ._utils import ( + PropertyInfo, + is_list, + is_given, + json_safe, + lru_cache, + is_mapping, + parse_date, + coerce_boolean, + parse_datetime, + strip_not_given, + extract_type_arg, + is_annotated_type, + is_type_alias_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, +) +from ._constants import RAW_RESPONSE_HEADER + +if TYPE_CHECKING: + from pydantic_core.core_schema import ModelField, ModelSchema, LiteralSchema, ModelFieldsSchema + +__all__ = ["BaseModel", "GenericModel"] + +_T = TypeVar("_T") +_BaseModelT = TypeVar("_BaseModelT", bound="BaseModel") + +P = ParamSpec("P") + +ReprArgs = Sequence[Tuple[Optional[str], Any]] + + +@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 + + @override + def __repr_args__(self) -> ReprArgs: + # we don't want these attributes to be included when something like `rich.print` is used + return [arg for arg in super().__repr_args__() if arg[0] not in {"_request_id", "__exclude_fields__"}] + + if TYPE_CHECKING: + _request_id: Optional[str] = None + """The ID of the request, returned via the X-Request-ID header. Useful for debugging requests and reporting issues to OpenAI. + + This will **only** be set for the top-level response object, it will not be defined for nested objects. For example: + + ```py + completion = await client.chat.completions.create(...) + completion._request_id # req_id_xxx + completion.usage._request_id # raises `AttributeError` + ``` + + Note: unlike other properties that use an `_` prefix, this property + *is* public. Unless documented otherwise, all other `_` prefix properties, + methods and modules are *private*. + """ + + 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( # pyright: ignore[reportIncompatibleMethodOverride] + __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 = None, + exclude: IncEx | None = 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 not in {"json", "python"}: + raise ValueError("mode must be either 'json' or 'python'") + 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") + dumped = 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, + ) + + return cast(dict[str, Any], json_safe(dumped)) if mode == "json" else dumped + + @override + def model_dump_json( + self, + *, + indent: int | None = None, + include: IncEx | None = None, + exclude: IncEx | None = 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_ + if not inspect.isclass(origin): + return False + return issubclass(origin, BaseModel) or issubclass(origin, GenericModel) + + +def build( + base_model_cls: Callable[P, _BaseModelT], + *args: P.args, + **kwargs: P.kwargs, +) -> _BaseModelT: + """Construct a BaseModel class without validation. + + This is useful for cases where you need to instantiate a `BaseModel` + from an API response as this provides type-safe params which isn't supported + by helpers like `construct_type()`. + + ```py + build(MyModel, my_field_a="foo", my_field_b=123) + ``` + """ + if args: + raise TypeError( + "Received positional arguments which are not supported; Keyword arguments must be used instead", + ) + + return cast(_BaseModelT, construct_type(type_=base_model_cls, value=kwargs)) + + +def construct_type_unchecked(*, value: object, type_: type[_T]) -> _T: + """Loose coercion to the expected type with construction of nested values. + + Note: the returned value from this function is not guaranteed to match the + given type. + """ + return cast(_T, construct_type(value=value, type_=type_)) + + +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. + """ + + # store a reference to the original type we were given before we extract any inner + # types so that we can properly resolve forward references in `TypeAliasType` annotations + original_type = None + + # 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_) + if is_type_alias_type(type_): + original_type = type_ # type: ignore[unreachable] + type_ = type_.__value__ # type: ignore[unreachable] + + # 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]", original_type or 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 inspect.isclass(origin) + 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"] == "definitions": + schema = schema["schema"] + + if schema["type"] != "model": + return None + + schema = cast("ModelSchema", schema) + 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)) + + +def set_pydantic_config(typ: Any, config: pydantic.ConfigDict) -> None: + """Add a pydantic config for the given type. + + Note: this is a no-op on Pydantic v1. + """ + setattr(typ, "__pydantic_config__", config) # noqa: B010 + + +def add_request_id(obj: BaseModel, request_id: str | None) -> None: + obj._request_id = request_id + + # in Pydantic v1, using setattr like we do above causes the attribute + # to be included when serializing the model which we don't want in this + # case so we need to explicitly exclude it + if not PYDANTIC_V2: + try: + exclude_fields = obj.__exclude_fields__ # type: ignore + except AttributeError: + cast(Any, obj).__exclude_fields__ = {"_request_id", "__exclude_fields__"} + else: + cast(Any, obj).__exclude_fields__ = {*(exclude_fields or {}), "_request_id", "__exclude_fields__"} + + +# 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 + + +class FinalRequestOptionsInput(TypedDict, total=False): + method: Required[str] + url: Required[str] + params: Query + headers: Headers + max_retries: int + timeout: float | Timeout | None + files: HttpxRequestFiles | None + idempotency_key: str + json_data: Body + extra_json: AnyMapping + + +@final +class FinalRequestOptions(pydantic.BaseModel): + method: str + url: str + params: Query = {} + headers: Union[Headers, NotGiven] = NotGiven() + max_retries: Union[int, NotGiven] = NotGiven() + timeout: Union[float, Timeout, None, NotGiven] = NotGiven() + files: Union[HttpxRequestFiles, None] = None + idempotency_key: Union[str, None] = None + post_parser: Union[Callable[[Any], Any], NotGiven] = NotGiven() + + # It should be noted that we cannot use `json` here as that would override + # a BaseModel method in an incompatible fashion. + json_data: Union[Body, None] = None + extra_json: Union[AnyMapping, None] = None + + if PYDANTIC_V2: + model_config: ClassVar[ConfigDict] = ConfigDict(arbitrary_types_allowed=True) + else: + + class Config(pydantic.BaseConfig): # pyright: ignore[reportDeprecated] + arbitrary_types_allowed: bool = True + + def get_max_retries(self, max_retries: int) -> int: + if isinstance(self.max_retries, NotGiven): + return max_retries + return self.max_retries + + def _strip_raw_response_header(self) -> None: + if not is_given(self.headers): + return + + if self.headers.get(RAW_RESPONSE_HEADER): + self.headers = {**self.headers} + self.headers.pop(RAW_RESPONSE_HEADER) + + # override the `construct` method so that we can run custom transformations. + # this is necessary as we don't want to do any actual runtime type checking + # (which means we can't use validators) but we do want to ensure that `NotGiven` + # values are not present + # + # type ignore required because we're adding explicit types to `**values` + @classmethod + def construct( # type: ignore + cls, + _fields_set: set[str] | None = None, + **values: Unpack[FinalRequestOptionsInput], + ) -> FinalRequestOptions: + kwargs: dict[str, Any] = { + # we unconditionally call `strip_not_given` on any value + # as it will just ignore any non-mapping types + key: strip_not_given(value) + for key, value in values.items() + } + if PYDANTIC_V2: + return super().model_construct(_fields_set, **kwargs) + return cast(FinalRequestOptions, super().construct(_fields_set, **kwargs)) # pyright: ignore[reportDeprecated] + + if not TYPE_CHECKING: + # type checkers incorrectly complain about this assignment + model_construct = construct |