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#

# Source code for langchain.llms.gooseai

    """Wrapper around GooseAI API."""
    import logging
    from typing import Any, Dict, List, Mapping, Optional

    from pydantic import Extra, Field, root_validator

    from langchain.llms.base import LLM
    from langchain.utils import get_from_dict_or_env

    logger = logging.getLogger(__name__)

    class GooseAI(LLM):
        """Wrapper around OpenAI large language models.

        To use, you should have the ``openai`` python package installed, and the
        environment variable ``GOOSEAI_API_KEY`` set with your API key.

        Any parameters that are valid to be passed to the openai.create call can be passed
        in, even if not explicitly saved on this class.

        Example:
            .. code-block:: python
                from langchain.llms import GooseAI
                gooseai = GooseAI(model_name="gpt-neo-20b")

        """

        client: Any

        model_name: str = "gpt-neo-20b"
        """Model name to use"""

        temperature: float = 0.7
        """What sampling temperature to use"""

        max_tokens: int = 256
        """The maximum number of tokens to generate in the completion.
        -1 returns as many tokens as possible given the prompt and
        the models maximal context size."""

        top_p: float = 1
        """Total probability mass of tokens to consider at each step."""

        min_tokens: int = 1
        """The minimum number of tokens to generate in the completion."""

        frequency_penalty: float = 0
        """Penalizes repeated tokens according to frequency."""

        presence_penalty: float = 0
        """Penalizes repeated tokens."""

        n: int = 1
        """How many completions to generate for each prompt."""

        model_kwargs: Dict[str, Any] = Field(default_factory=dict)
        """Holds any model parameters valid for `create` call not explicitly specified."""

        logit_bias: Optional[Dict[str, float]] = Field(default_factory=dict)
        """Adjust the probability of specific tokens being generated."""

        gooseai_api_key: Optional[str] = None

        class Config:
            """Configuration for this pydantic config."""

            extra = Extra.ignore

        @root_validator(pre=True)
        def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
            """Build extra kwargs from additional params that were passed in."""
            all_required_field_names = {field.alias for field in cls.__fields__.values()}

            extra = values.get("model_kwargs", {})
            for field_name in list(values):
                if field_name not in all_required_field_names:
                    if field_name in extra:
                        raise ValueError(f"Found {field_name} supplied twice.")
                    logger.warning(
                        f"""WARNING! {field_name} is not default parameter.
                        {field_name} was transfered to model_kwargs.
                        Please confirm that {field_name} is what you intended."""
                    )
                    extra[field_name] = values.pop(field_name)
            values["model_kwargs"] = extra
            return values

        @root_validator()
        def validate_environment(cls, values: Dict) -> Dict:
            """Validate that api key and python package exists in environment."""
            gooseai_api_key = get_from_dict_or_env(
                values, "gooseai_api_key", "GOOSEAI_API_KEY"
            )
            try:
                import openai

                openai.api_key = gooseai_api_key
                openai.api_base = "https://api.goose.ai/v1"
                values["client"] = openai.Completion
            except ImportError:
                raise ValueError(
                    "Could not import openai python package. "
                    "Please install it with `pip install openai`."
                )
            return values

        @property
        def _default_params(self) -> Dict[str, Any]:
            """Get the default parameters for calling GooseAI API."""
            normal_params = {
                "temperature": self.temperature,
                "max_tokens": self.max_tokens,
                "top_p": self.top_p,
                "min_tokens": self.min_tokens,
                "frequency_penalty": self.frequency_penalty,
                "presence_penalty": self.presence_penalty,
                "n": self.n,
                "logit_bias": self.logit_bias,
            }
            return {**normal_params, **self.model_kwargs}

        @property
        def _identifying_params(self) -> Mapping[str, Any]:
            """Get the identifying parameters."""
            return {**{"model_name": self.model_name}, **self._default_params}

        @property
        def _llm_type(self) -> str:
            """Return type of llm."""
            return "gooseai"

        def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
            """Call the GooseAI API."""
            params = self._default_params
            if stop is not None:
                if "stop" in params:
                    raise ValueError("`stop` found in both the input and default params.")
                params["stop"] = stop

            response = self.client.create(engine=self.model_name, prompt=prompt, **params)
            text = response.choices[0].text
            return text