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#

# Source code for langchain.llms.writer

    """Wrapper around Writer APIs."""
    from typing import Any, Dict, List, Mapping, Optional

    import requests
    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

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

        To use, you should have the environment variable ``WRITER_API_KEY``
        set with your API key.

        Example:
            .. code-block:: python

                from langchain import Writer
                writer = Writer(model_id="palmyra-base")
        """

        model_id: str = "palmyra-base"
        """Model name to use."""

        tokens_to_generate: int = 24
        """Max number of tokens to generate."""

        logprobs: bool = False
        """Whether to return log probabilities."""

        temperature: float = 1.0
        """What sampling temperature to use."""

        length: int = 256
        """The maximum number of tokens to generate in the completion."""

        top_p: float = 1.0
        """Total probability mass of tokens to consider at each step."""

        top_k: int = 1
        """The number of highest probability vocabulary tokens to
        keep for top-k-filtering."""

        repetition_penalty: float = 1.0
        """Penalizes repeated tokens according to frequency."""

        random_seed: int = 0
        """The model generates random results.
        Changing the random seed alone will produce a different response
        with similar characteristics. It is possible to reproduce results
        by fixing the random seed (assuming all other hyperparameters
        are also fixed)"""

        beam_search_diversity_rate: float = 1.0
        """Only applies to beam search, i.e. when the beam width is >1.
        A higher value encourages beam search to return a more diverse
        set of candidates"""

        beam_width: Optional[int] = None
        """The number of concurrent candidates to keep track of during
        beam search"""

        length_pentaly: float = 1.0
        """Only applies to beam search, i.e. when the beam width is >1.
        Larger values penalize long candidates more heavily, thus preferring
        shorter candidates"""

        writer_api_key: Optional[str] = None

        stop: Optional[List[str]] = None
        """Sequences when completion generation will stop"""

        base_url: Optional[str] = None
        """Base url to use, if None decides based on model name."""

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

            extra = Extra.forbid

        @root_validator()
        def validate_environment(cls, values: Dict) -> Dict:
            """Validate that api key exists in environment."""
            writer_api_key = get_from_dict_or_env(
                values, "writer_api_key", "WRITER_API_KEY"
            )
            values["writer_api_key"] = writer_api_key
            return values

        @property
        def _default_params(self) -> Mapping[str, Any]:
            """Get the default parameters for calling Writer API."""
            return {
                "tokens_to_generate": self.tokens_to_generate,
                "stop": self.stop,
                "logprobs": self.logprobs,
                "temperature": self.temperature,
                "top_p": self.top_p,
                "top_k": self.top_k,
                "repetition_penalty": self.repetition_penalty,
                "random_seed": self.random_seed,
                "beam_search_diversity_rate": self.beam_search_diversity_rate,
                "beam_width": self.beam_width,
                "length_pentaly": self.length_pentaly,
            }

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

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

        def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
            """Call out to Writer's complete endpoint.

            Args:
                prompt: The prompt to pass into the model.
                stop: Optional list of stop words to use when generating.

            Returns:
                The string generated by the model.

            Example:
                .. code-block:: python

                    response = Writer("Tell me a joke.")
            """
            if self.base_url is not None:
                base_url = self.base_url
            else:
                base_url = (
                    "https://api.llm.writer.com/v1/models/{self.model_id}/completions"
                )
            response = requests.post(
                url=base_url,
                headers={
                    "Authorization": f"Bearer {self.writer_api_key}",
                    "Content-Type": "application/json",
                    "Accept": "application/json",
                },
                json={"prompt": prompt, **self._default_params},
            )
            text = response.text
            if stop is not None:
                # I believe this is required since the stop tokens
                # are not enforced by the model parameters
                text = enforce_stop_tokens(text, stop)
            return text