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#

# Source code for langchain.llms.sagemaker_endpoint

    """Wrapper around Sagemaker InvokeEndpoint API."""
    from abc import abstractmethod
    from typing import Any, Dict, Generic, List, Mapping, Optional, TypeVar, Union

    from pydantic import Extra, root_validator

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

    INPUT_TYPE = TypeVar("INPUT_TYPE", bound=Union[str, List[str]])
    OUTPUT_TYPE = TypeVar("OUTPUT_TYPE", bound=Union[str, List[List[float]]])

    class ContentHandlerBase(Generic[INPUT_TYPE, OUTPUT_TYPE]):
        """A handler class to transform input from LLM to a
        format that SageMaker endpoint expects. Similarily,
        the class also handles transforming output from the
        SageMaker endpoint to a format that LLM class expects.
        """

        """
        Example:
            .. code-block:: python

                class ContentHandler(ContentHandlerBase):
                    content_type = "application/json"
                    accepts = "application/json"

                    def transform_input(self, prompt: str, model_kwargs: Dict) -> bytes:
                        input_str = json.dumps({prompt: prompt, **model_kwargs})
                        return input_str.encode('utf-8')

                    def transform_output(self, output: bytes) -> str:
                        response_json = json.loads(output.read().decode("utf-8"))
                        return response_json[0]["generated_text"]
        """

        content_type: Optional[str] = "text/plain"
        """The MIME type of the input data passed to endpoint"""

        accepts: Optional[str] = "text/plain"
        """The MIME type of the response data returned from endpoint"""

        @abstractmethod
        def transform_input(self, prompt: INPUT_TYPE, model_kwargs: Dict) -> bytes:
            """Transforms the input to a format that model can accept
            as the request Body. Should return bytes or seekable file
            like object in the format specified in the content_type
            request header.
            """

        @abstractmethod
        def transform_output(self, output: bytes) -> OUTPUT_TYPE:
            """Transforms the output from the model to string that
            the LLM class expects.
            """

    class LLMContentHandler(ContentHandlerBase[str, str]):
        """Content handler for LLM class."""

    class SagemakerEndpoint(LLM):
        """Wrapper around custom Sagemaker Inference Endpoints.

        To use, you must supply the endpoint name from your deployed
        Sagemaker model & the region where it is deployed.

        To authenticate, the AWS client uses the following methods to
        automatically load credentials:
        https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html

        If a specific credential profile should be used, you must pass
        the name of the profile from the ~/.aws/credentials file that is to be used.

        Make sure the credentials / roles used have the required policies to
        access the Sagemaker endpoint.
        See: https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies.html
        """

        """
        Example:
            .. code-block:: python

                from langchain import SagemakerEndpoint
                endpoint_name = (
                    "my-endpoint-name"
                )
                region_name = (
                    "us-west-2"
                )
                credentials_profile_name = (
                    "default"
                )
                se = SagemakerEndpoint(
                    endpoint_name=endpoint_name,
                    region_name=region_name,
                    credentials_profile_name=credentials_profile_name
                )
        """
        client: Any  #: :meta private:

        endpoint_name: str = ""
        """The name of the endpoint from the deployed Sagemaker model.
        Must be unique within an AWS Region."""

        region_name: str = ""
        """The aws region where the Sagemaker model is deployed, eg. `us-west-2`."""

        credentials_profile_name: Optional[str] = None
        """The name of the profile in the ~/.aws/credentials or ~/.aws/config files, which
        has either access keys or role information specified.
        If not specified, the default credential profile or, if on an EC2 instance,
        credentials from IMDS will be used.
        See: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
        """

        content_handler: LLMContentHandler
        """The content handler class that provides an input and
        output transform functions to handle formats between LLM
        and the endpoint.
        """

        """
         Example:
            .. code-block:: python

            from langchain.llms.sagemaker_endpoint import LLMContentHandler

            class ContentHandler(LLMContentHandler):
                    content_type = "application/json"
                    accepts = "application/json"

                    def transform_input(self, prompt: str, model_kwargs: Dict) -> bytes:
                        input_str = json.dumps({prompt: prompt, **model_kwargs})
                        return input_str.encode('utf-8')

                    def transform_output(self, output: bytes) -> str:
                        response_json = json.loads(output.read().decode("utf-8"))
                        return response_json[0]["generated_text"]
        """

        model_kwargs: Optional[Dict] = None
        """Key word arguments to pass to the model."""

        endpoint_kwargs: Optional[Dict] = None
        """Optional attributes passed to the invoke_endpoint
        function. See `boto3`_. docs for more info.
        .. _boto3: <https://boto3.amazonaws.com/v1/documentation/api/latest/index.html>
        """

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

            extra = Extra.forbid

        @root_validator()
        def validate_environment(cls, values: Dict) -> Dict:
            """Validate that AWS credentials to and python package exists in environment."""
            try:
                import boto3

                try:
                    if values["credentials_profile_name"] is not None:
                        session = boto3.Session(
                            profile_name=values["credentials_profile_name"]
                        )
                    else:
                        # use default credentials
                        session = boto3.Session()

                    values["client"] = session.client(
                        "sagemaker-runtime", region_name=values["region_name"]
                    )

                except Exception as e:
                    raise ValueError(
                        "Could not load credentials to authenticate with AWS client. "
                        "Please check that credentials in the specified "
                        "profile name are valid."
                    ) from e

            except ImportError:
                raise ValueError(
                    "Could not import boto3 python package. "
                    "Please install it with `pip install boto3`."
                )
            return values

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

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

        def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
            """Call out to Sagemaker inference 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 = se("Tell me a joke.")
            """
            _model_kwargs = self.model_kwargs or {}
            _endpoint_kwargs = self.endpoint_kwargs or {}

            body = self.content_handler.transform_input(prompt, _model_kwargs)
            content_type = self.content_handler.content_type
            accepts = self.content_handler.accepts

            # send request
            try:
                response = self.client.invoke_endpoint(
                    EndpointName=self.endpoint_name,
                    Body=body,
                    ContentType=content_type,
                    Accept=accepts,
                    **_endpoint_kwargs,
                )
            except Exception as e:
                raise ValueError(f"Error raised by inference endpoint: {e}")

            text = self.content_handler.transform_output(response["Body"])
            if stop is not None:
                # This is a bit hacky, but I can't figure out a better way to enforce
                # stop tokens when making calls to the sagemaker endpoint.
                text = enforce_stop_tokens(text, stop)

            return text