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# Source code for langchain_community.embeddings.self_hosted

    from typing import Any, Callable, List

    from langchain_core.embeddings import Embeddings
    from langchain_core.pydantic_v1 import Extra

    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."""

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

            extra = 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