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#

# Source code for langchain.llms.cerebriumai

    """Wrapper around CerebriumAI 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.llms.utils import enforce_stop_tokens
    from langchain.utils import get_from_dict_or_env

    logger = logging.getLogger(__name__)

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

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

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

        Example:
            .. code-block:: python
                from langchain.llms import CerebriumAI
                cerebrium = CerebriumAI(endpoint_url="")

        """

        endpoint_url: str = ""
        """model endpoint to use"""

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

        cerebriumai_api_key: Optional[str] = None

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

            extra = Extra.forbid

        @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"""{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."""
            cerebriumai_api_key = get_from_dict_or_env(
                values, "cerebriumai_api_key", "CEREBRIUMAI_API_KEY"
            )
            values["cerebriumai_api_key"] = cerebriumai_api_key
            return values

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

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

        def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
            """Call to CerebriumAI endpoint."""
            try:
                from cerebrium import model_api_request
            except ImportError:
                raise ValueError(
                    "Could not import cerebrium python package. "
                    "Please install it with `pip install cerebrium`."
                )

            params = self.model_kwargs or {}
            response = model_api_request(
                self.endpoint_url, {"prompt": prompt, **params}, self.cerebriumai_api_key
            )
            text = response["data"]["result"]
            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