from typing import Any, Callable, List

from langchain_core.embeddings import Embeddings
from pydantic import ConfigDict

from langchain_community.llms.self_hosted import SelfHostedPipeline


def _embed_documents(pipeline: Any, *args: Any, **kwargs: Any) -> List[List[float]]:
    """Inference function to send to the remote hardware.

    Accepts a sentence_transformer model_id and
    returns a list of embeddings for each document in the batch.
    """
    return pipeline(*args, **kwargs)


class SelfHostedEmbeddings(SelfHostedPipeline, Embeddings):
    """Custom embedding models on self-hosted remote hardware.

    Supported hardware includes auto-launched instances on AWS, GCP, Azure,
    and Lambda, as well as servers specified
    by IP address and SSH credentials (such as on-prem, or another
    cloud like Paperspace, Coreweave, etc.).

    To use, you should have the ``runhouse`` python package installed.

    Example using a model load function:
        .. code-block:: python

            from langchain_community.embeddings import SelfHostedEmbeddings
            from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
            import runhouse as rh

            gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
            def get_pipeline():
                model_id = "facebook/bart-large"
                tokenizer = AutoTokenizer.from_pretrained(model_id)
                model = AutoModelForCausalLM.from_pretrained(model_id)
                return pipeline("feature-extraction", model=model, tokenizer=tokenizer)
            embeddings = SelfHostedEmbeddings(
                model_load_fn=get_pipeline,
                hardware=gpu
                model_reqs=["./", "torch", "transformers"],
            )
    Example passing in a pipeline path:
        .. code-block:: python

            from langchain_community.embeddings import SelfHostedHFEmbeddings
            import runhouse as rh
            from transformers import pipeline

            gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
            pipeline = pipeline(model="bert-base-uncased", task="feature-extraction")
            rh.blob(pickle.dumps(pipeline),
                path="models/pipeline.pkl").save().to(gpu, path="models")
            embeddings = SelfHostedHFEmbeddings.from_pipeline(
                pipeline="models/pipeline.pkl",
                hardware=gpu,
                model_reqs=["./", "torch", "transformers"],
            )
    """

    inference_fn: Callable = _embed_documents
    """Inference function to extract the embeddings on the remote hardware."""
    inference_kwargs: Any = None
    """Any kwargs to pass to the model's inference function."""

    model_config = ConfigDict(
        extra="forbid",
    )

    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        """Compute doc embeddings using a HuggingFace transformer model.

        Args:
            texts: The list of texts to embed.s

        Returns:
            List of embeddings, one for each text.
        """
        texts = list(map(lambda x: x.replace("\n", " "), texts))
        embeddings = self.client(self.pipeline_ref, texts)
        if not isinstance(embeddings, list):
            return embeddings.tolist()
        return embeddings

    def embed_query(self, text: str) -> List[float]:
        """Compute query embeddings using a HuggingFace transformer model.

        Args:
            text: The text to embed.

        Returns:
            Embeddings for the text.
        """
        text = text.replace("\n", " ")
        embeddings = self.client(self.pipeline_ref, text)
        if not isinstance(embeddings, list):
            return embeddings.tolist()
        return embeddings