from collections.abc import Sequence
from functools import lru_cache
from typing import (
    Any,
)

from fastapi._compat.shared import lenient_issubclass
from fastapi.types import ModelNameMap
from pydantic import BaseModel
from typing_extensions import Literal

from . import v2
from .v2 import BaseConfig as BaseConfig
from .v2 import FieldInfo as FieldInfo
from .v2 import ModelField
from .v2 import PydanticSchemaGenerationError as PydanticSchemaGenerationError
from .v2 import RequiredParam as RequiredParam
from .v2 import Undefined as Undefined
from .v2 import UndefinedType as UndefinedType
from .v2 import Url as Url
from .v2 import Validator as Validator
from .v2 import evaluate_forwardref as evaluate_forwardref
from .v2 import get_missing_field_error as get_missing_field_error
from .v2 import get_model_fields as get_model_fields
from .v2 import (
    with_info_plain_validator_function as with_info_plain_validator_function,
)


@lru_cache
def get_cached_model_fields(model: type[BaseModel]) -> list[ModelField]:
    return get_model_fields(model)  # type: ignore[return-value]


def _is_undefined(value: object) -> bool:
    return isinstance(value, v2.UndefinedType)


def _get_model_config(model: BaseModel) -> Any:
    return v2._get_model_config(model)


def _model_dump(
    model: BaseModel, mode: Literal["json", "python"] = "json", **kwargs: Any
) -> Any:
    return v2._model_dump(model, mode=mode, **kwargs)


def copy_field_info(*, field_info: FieldInfo, annotation: Any) -> FieldInfo:
    return v2.copy_field_info(field_info=field_info, annotation=annotation)


def create_body_model(
    *, fields: Sequence[ModelField], model_name: str
) -> type[BaseModel]:
    return v2.create_body_model(fields=fields, model_name=model_name)  # type: ignore[arg-type]


def get_annotation_from_field_info(
    annotation: Any, field_info: FieldInfo, field_name: str
) -> Any:
    return v2.get_annotation_from_field_info(
        annotation=annotation, field_info=field_info, field_name=field_name
    )


def is_bytes_field(field: ModelField) -> bool:
    return v2.is_bytes_field(field)  # type: ignore[arg-type]


def is_bytes_sequence_field(field: ModelField) -> bool:
    return v2.is_bytes_sequence_field(field)  # type: ignore[arg-type]


def is_scalar_field(field: ModelField) -> bool:
    return v2.is_scalar_field(field)  # type: ignore[arg-type]


def is_scalar_sequence_field(field: ModelField) -> bool:
    return v2.is_scalar_sequence_field(field)  # type: ignore[arg-type]


def is_sequence_field(field: ModelField) -> bool:
    return v2.is_sequence_field(field)  # type: ignore[arg-type]


def serialize_sequence_value(*, field: ModelField, value: Any) -> Sequence[Any]:
    return v2.serialize_sequence_value(field=field, value=value)  # type: ignore[arg-type]


def get_compat_model_name_map(fields: list[ModelField]) -> ModelNameMap:
    all_flat_models = set()

    v2_model_fields = [field for field in fields if isinstance(field, v2.ModelField)]
    v2_flat_models = v2.get_flat_models_from_fields(v2_model_fields, known_models=set())
    all_flat_models = all_flat_models.union(v2_flat_models)  # type: ignore[arg-type]

    model_name_map = v2.get_model_name_map(all_flat_models)  # type: ignore[arg-type]
    return model_name_map


def get_schema_from_model_field(
    *,
    field: ModelField,
    model_name_map: ModelNameMap,
    field_mapping: dict[
        tuple[ModelField, Literal["validation", "serialization"]],
        dict[str, Any],
    ],
    separate_input_output_schemas: bool = True,
) -> dict[str, Any]:
    return v2.get_schema_from_model_field(
        field=field,  # type: ignore[arg-type]
        model_name_map=model_name_map,
        field_mapping=field_mapping,  # type: ignore[arg-type]
        separate_input_output_schemas=separate_input_output_schemas,
    )


def _is_model_field(value: Any) -> bool:
    return isinstance(value, v2.ModelField)


def _is_model_class(value: Any) -> bool:
    return lenient_issubclass(value, v2.BaseModel)  # type: ignore[attr-defined]