# Copyright (c) [2025] [OpenAI]
# Copyright (c) [2025] [ByteDance Ltd. and/or its affiliates.]
# SPDX-License-Identifier: Apache-2.0
#
# This file has been modified by [ByteDance Ltd. and/or its affiliates.] on 2025.7
#
# Original file was released under Apache License Version 2.0, with the full license text
# available at https://github.com/openai/openai-python/blob/main/LICENSE.
#
# This modified file is released under the same license.

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,
)

import pydantic
import functools
from httpx import Response
from typing_extensions import (
    Set,
    Literal,
    Protocol,
    TypeAlias,
    override,
    runtime_checkable,
)

from collections import defaultdict

if TYPE_CHECKING:
    from ._models import BaseModel
    from ._response import ArkAPIResponse, ArkAsyncAPIResponse

ModelT = TypeVar("ModelT", bound=pydantic.BaseModel)
_T = TypeVar("_T")
ResponseT = TypeVar(
    "ResponseT",
    bound=Union[
        object,
        str,
        None,
        "BaseModel",
        List[Any],
        Dict[str, Any],
        Response,
        "ArkAPIResponse[Any]",
        "ArkAsyncAPIResponse[Any]",
    ],
)

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]]
]

if TYPE_CHECKING:
    NoneType: Type[None]
else:
    NoneType = type(None)


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()

Headers = Dict[str, str]
Query = Dict[str, object]
Body = Dict[str, object]
AnyMapping = Mapping[str, object]

IncEx: TypeAlias = Union[
    Set[int],
    Set[str],
    Mapping[int, Union["IncEx", bool]],
    Mapping[str, Union["IncEx", bool]],
]

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]


_overload_registry = defaultdict(functools.partial(defaultdict, dict))


def _overload_dummy(*args, **kwds):
    """Helper for @overload to raise when called."""
    raise NotImplementedError(
        "You should not call an overloaded function. "
        "A series of @overload-decorated functions "
        "outside a stub module should always be followed "
        "by an implementation that is not @overload-ed."
    )


def overload(func):
    """Decorator for overloaded functions/methods.

    In a stub file, place two or more stub definitions for the same
    function in a row, each decorated with @overload.

    For example::

        @overload
        def utf8(value: None) -> None: ...
        @overload
        def utf8(value: bytes) -> bytes: ...
        @overload
        def utf8(value: str) -> bytes: ...

    In a non-stub file (i.e. a regular .py file), do the same but
    follow it with an implementation.  The implementation should *not*
    be decorated with @overload::

        @overload
        def utf8(value: None) -> None: ...
        @overload
        def utf8(value: bytes) -> bytes: ...
        @overload
        def utf8(value: str) -> bytes: ...
        def utf8(value):
            ...  # implementation goes here

    The overloads for a function can be retrieved at runtime using the
    get_overloads() function.
    """
    # classmethod and staticmethod
    f = getattr(func, "__func__", func)
    try:
        _overload_registry[f.__module__][f.__qualname__][f.__code__.co_firstlineno] = (
            func
        )
    except AttributeError:
        # Not a normal function; ignore.
        pass
    return _overload_dummy