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# Source code for langchain_core.output_parsers.openai_functions

    import copy
    import json
    from typing import Any, Dict, List, Optional, Type, Union

    import jsonpatch  # type: ignore[import]

    from langchain_core.exceptions import OutputParserException
    from langchain_core.output_parsers import (
        BaseCumulativeTransformOutputParser,
        BaseGenerationOutputParser,
    )
    from langchain_core.output_parsers.json import parse_partial_json
    from langchain_core.outputs import ChatGeneration, Generation
    from langchain_core.pydantic_v1 import BaseModel, root_validator

    class OutputFunctionsParser(BaseGenerationOutputParser[Any]):
        """Parse an output that is one of sets of values."""

        args_only: bool = True
        """Whether to only return the arguments to the function call."""

        def parse_result(self, result: List[Generation], *, partial: bool = False) -> Any:
            generation = result[0]
            if not isinstance(generation, ChatGeneration):
                raise OutputParserException(
                    "This output parser can only be used with a chat generation."
                )
            message = generation.message
            try:
                func_call = copy.deepcopy(message.additional_kwargs["function_call"])
            except KeyError as exc:
                raise OutputParserException(f"Could not parse function call: {exc}")

            if self.args_only:
                return func_call["arguments"]
            return func_call

    class JsonOutputFunctionsParser(BaseCumulativeTransformOutputParser[Any]):
        """Parse an output as the Json object."""

        strict: bool = False
        """Whether to allow non-JSON-compliant strings.

        See: https://docs.python.org/3/library/json.html#encoders-and-decoders

        Useful when the parsed output may include unicode characters or new lines.
        """

        args_only: bool = True
        """Whether to only return the arguments to the function call."""

        @property
        def _type(self) -> str:
            return "json_functions"

        def _diff(self, prev: Optional[Any], next: Any) -> Any:
            return jsonpatch.make_patch(prev, next).patch

        def parse_result(self, result: List[Generation], *, partial: bool = False) -> Any:
            if len(result) != 1:
                raise OutputParserException(
                    f"Expected exactly one result, but got {len(result)}"
                )
            generation = result[0]
            if not isinstance(generation, ChatGeneration):
                raise OutputParserException(
                    "This output parser can only be used with a chat generation."
                )
            message = generation.message
            try:
                function_call = message.additional_kwargs["function_call"]
            except KeyError as exc:
                if partial:
                    return None
                else:
                    raise OutputParserException(f"Could not parse function call: {exc}")
            try:
                if partial:
                    try:
                        if self.args_only:
                            return parse_partial_json(
                                function_call["arguments"], strict=self.strict
                            )
                        else:
                            return {
                                **function_call,
                                "arguments": parse_partial_json(
                                    function_call["arguments"], strict=self.strict
                                ),
                            }
                    except json.JSONDecodeError:
                        return None
                else:
                    if self.args_only:
                        try:
                            return json.loads(
                                function_call["arguments"], strict=self.strict
                            )
                        except (json.JSONDecodeError, TypeError) as exc:
                            raise OutputParserException(
                                f"Could not parse function call data: {exc}"
                            )
                    else:
                        try:
                            return {
                                **function_call,
                                "arguments": json.loads(
                                    function_call["arguments"], strict=self.strict
                                ),
                            }
                        except (json.JSONDecodeError, TypeError) as exc:
                            raise OutputParserException(
                                f"Could not parse function call data: {exc}"
                            )
            except KeyError:
                return None

        # This method would be called by the default implementation of `parse_result`
        # but we're overriding that method so it's not needed.
        def parse(self, text: str) -> Any:
            raise NotImplementedError()

    class JsonKeyOutputFunctionsParser(JsonOutputFunctionsParser):
        """Parse an output as the element of the Json object."""

        key_name: str
        """The name of the key to return."""

        def parse_result(self, result: List[Generation], *, partial: bool = False) -> Any:
            res = super().parse_result(result, partial=partial)
            if partial and res is None:
                return None
            return res.get(self.key_name) if partial else res[self.key_name]

    class PydanticOutputFunctionsParser(OutputFunctionsParser):
        """Parse an output as a pydantic object.

        This parser is used to parse the output of a ChatModel that uses
        OpenAI function format to invoke functions.

        The parser extracts the function call invocation and matches
        them to the pydantic schema provided.

        An exception will be raised if the function call does not match
        the provided schema.

        Example:

            ... code-block:: python

                message = AIMessage(
                    content="This is a test message",
                    additional_kwargs={
                        "function_call": {
                            "name": "cookie",
                            "arguments": json.dumps({"name": "value", "age": 10}),
                        }
                    },
                )
                chat_generation = ChatGeneration(message=message)

                class Cookie(BaseModel):
                    name: str
                    age: int

                class Dog(BaseModel):
                    species: str

                # Full output
                parser = PydanticOutputFunctionsParser(
                    pydantic_schema={"cookie": Cookie, "dog": Dog}
                )
                result = parser.parse_result([chat_generation])
        """

        pydantic_schema: Union[Type[BaseModel], Dict[str, Type[BaseModel]]]
        """The pydantic schema to parse the output with.

        If multiple schemas are provided, then the function name will be used to
        determine which schema to use.
        """

        @root_validator(pre=True)
        def validate_schema(cls, values: Dict) -> Dict:
            schema = values["pydantic_schema"]
            if "args_only" not in values:
                values["args_only"] = isinstance(schema, type) and issubclass(
                    schema, BaseModel
                )
            elif values["args_only"] and isinstance(schema, Dict):
                raise ValueError(
                    "If multiple pydantic schemas are provided then args_only should be"
                    " False."
                )
            return values

        def parse_result(self, result: List[Generation], *, partial: bool = False) -> Any:
            _result = super().parse_result(result)
            if self.args_only:
                pydantic_args = self.pydantic_schema.parse_raw(_result)  # type: ignore
            else:
                fn_name = _result["name"]
                _args = _result["arguments"]
                pydantic_args = self.pydantic_schema[fn_name].parse_raw(_args)  # type: ignore  # noqa: E501
            return pydantic_args

    class PydanticAttrOutputFunctionsParser(PydanticOutputFunctionsParser):
        """Parse an output as an attribute of a pydantic object."""

        attr_name: str
        """The name of the attribute to return."""

        def parse_result(self, result: List[Generation], *, partial: bool = False) -> Any:
            result = super().parse_result(result)
            return getattr(result, self.attr_name)