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 functools import lru_cache
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 (
    Body,
    IncEx,
    Query,
    ModelT,
    Headers,
    Timeout,
    NotGiven,
    AnyMapping,
    HttpxRequestFiles,
)
from ._utils import (
    PropertyInfo,
    is_list,
    is_given,
    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,
)
from ._constants import RAW_RESPONSE_HEADER

if TYPE_CHECKING:
    from pydantic_core.core_schema import ModelField, 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

    @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 = True,
        ) -> 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")
            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 = True,
        ) -> 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")
            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 = 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 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


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
