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#

# Source code for langchain.llms.nlpcloud

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

    from pydantic import Extra, root_validator

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

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

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

        Example:
            .. code-block:: python

                from langchain.llms import NLPCloud
                nlpcloud = NLPCloud(model="gpt-neox-20b")
        """

        client: Any  #: :meta private:
        model_name: str = "finetuned-gpt-neox-20b"
        """Model name to use."""
        temperature: float = 0.7
        """What sampling temperature to use."""
        min_length: int = 1
        """The minimum number of tokens to generate in the completion."""
        max_length: int = 256
        """The maximum number of tokens to generate in the completion."""
        length_no_input: bool = True
        """Whether min_length and max_length should include the length of the input."""
        remove_input: bool = True
        """Remove input text from API response"""
        remove_end_sequence: bool = True
        """Whether or not to remove the end sequence token."""
        bad_words: List[str] = []
        """List of tokens not allowed to be generated."""
        top_p: int = 1
        """Total probability mass of tokens to consider at each step."""
        top_k: int = 50
        """The number of highest probability tokens to keep for top-k filtering."""
        repetition_penalty: float = 1.0
        """Penalizes repeated tokens. 1.0 means no penalty."""
        length_penalty: float = 1.0
        """Exponential penalty to the length."""
        do_sample: bool = True
        """Whether to use sampling (True) or greedy decoding."""
        num_beams: int = 1
        """Number of beams for beam search."""
        early_stopping: bool = False
        """Whether to stop beam search at num_beams sentences."""
        num_return_sequences: int = 1
        """How many completions to generate for each prompt."""

        nlpcloud_api_key: Optional[str] = None

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

            extra = Extra.forbid

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

                values["client"] = nlpcloud.Client(
                    values["model_name"], nlpcloud_api_key, gpu=True, lang="en"
                )
            except ImportError:
                raise ValueError(
                    "Could not import nlpcloud python package. "
                    "Please install it with `pip install nlpcloud`."
                )
            return values

        @property
        def _default_params(self) -> Mapping[str, Any]:
            """Get the default parameters for calling NLPCloud API."""
            return {
                "temperature": self.temperature,
                "min_length": self.min_length,
                "max_length": self.max_length,
                "length_no_input": self.length_no_input,
                "remove_input": self.remove_input,
                "remove_end_sequence": self.remove_end_sequence,
                "bad_words": self.bad_words,
                "top_p": self.top_p,
                "top_k": self.top_k,
                "repetition_penalty": self.repetition_penalty,
                "length_penalty": self.length_penalty,
                "do_sample": self.do_sample,
                "num_beams": self.num_beams,
                "early_stopping": self.early_stopping,
                "num_return_sequences": self.num_return_sequences,
            }

        @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 "nlpcloud"

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

            Args:
                prompt: The prompt to pass into the model.
                stop: Not supported by this interface (pass in init method)

            Returns:
                The string generated by the model.

            Example:
                .. code-block:: python

                    response = nlpcloud("Tell me a joke.")
            """
            if stop and len(stop) > 1:
                raise ValueError(
                    "NLPCloud only supports a single stop sequence per generation."
                    "Pass in a list of length 1."
                )
            elif stop and len(stop) == 1:
                end_sequence = stop[0]
            else:
                end_sequence = None
            response = self.client.generation(
                prompt, end_sequence=end_sequence, **self._default_params
            )
            return response["generated_text"]