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#

# Source code for langchain.llms.predictionguard

    """Wrapper around Prediction Guard APIs."""
    import logging
    from typing import Any, Dict, List, Optional

    from pydantic import Extra, root_validator

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

    logger = logging.getLogger(__name__)

    class PredictionGuard(LLM):
        """Wrapper around Prediction Guard large language models.
        To use, you should have the ``predictionguard`` python package installed, and the
        environment variable ``PREDICTIONGUARD_TOKEN`` set with your access token, or pass
        it as a named parameter to the constructor.
        Example:
            .. code-block:: python
                pgllm = PredictionGuard(name="text-gen-proxy-name", token="my-access-token")
        """

        client: Any  #: :meta private:
        name: Optional[str] = "default-text-gen"
        """Proxy name to use."""

        max_tokens: int = 256
        """Denotes the number of tokens to predict per generation."""

        temperature: float = 0.75
        """A non-negative float that tunes the degree of randomness in generation."""

        token: Optional[str] = None

        stop: Optional[List[str]] = None

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

            extra = Extra.forbid

        @root_validator()
        def validate_environment(cls, values: Dict) -> Dict:
            """Validate that the access token and python package exists in environment."""
            token = get_from_dict_or_env(values, "token", "PREDICTIONGUARD_TOKEN")
            try:
                import predictionguard as pg

                values["client"] = pg.Client(token=token)
            except ImportError:
                raise ValueError(
                    "Could not import predictionguard python package. "
                    "Please install it with `pip install predictionguard`."
                )
            return values

        @property
        def _default_params(self) -> Dict[str, Any]:
            """Get the default parameters for calling Cohere API."""
            return {
                "max_tokens": self.max_tokens,
                "temperature": self.temperature,
            }

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

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

        def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
            """Call out to Prediction Guard's model proxy.
            Args:
                prompt: The prompt to pass into the model.
            Returns:
                The string generated by the model.
            Example:
                .. code-block:: python
                    response = pgllm("Tell me a joke.")
            """
            params = self._default_params
            if self.stop is not None and stop is not None:
                raise ValueError("`stop` found in both the input and default params.")
            elif self.stop is not None:
                params["stop_sequences"] = self.stop
            else:
                params["stop_sequences"] = stop

            response = self.client.predict(
                name=self.name,
                data={
                    "prompt": prompt,
                    "max_tokens": params["max_tokens"],
                    "temperature": params["temperature"],
                },
            )
            text = response["text"]

            # If stop tokens are provided, Prediction Guard's endpoint returns them.
            # In order to make this consistent with other endpoints, we strip them.
            if stop is not None or self.stop is not None:
                text = enforce_stop_tokens(text, params["stop_sequences"])

            return text