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#

# Source code for langchain.retrievers.svm

    """SMV Retriever.
    Largely based on
    https://github.com/karpathy/randomfun/blob/master/knn_vs_svm.ipynb"""

    from __future__ import annotations

    import concurrent.futures
    from typing import Any, List, Optional

    import numpy as np
    from pydantic import BaseModel

    from langchain.embeddings.base import Embeddings
    from langchain.schema import BaseRetriever, Document

    def create_index(contexts: List[str], embeddings: Embeddings) -> np.ndarray:
        with concurrent.futures.ThreadPoolExecutor() as executor:
            return np.array(list(executor.map(embeddings.embed_query, contexts)))

    class SVMRetriever(BaseRetriever, BaseModel):
        embeddings: Embeddings
        index: Any
        texts: List[str]
        k: int = 4
        relevancy_threshold: Optional[float] = None

        class Config:

            """Configuration for this pydantic object."""

            arbitrary_types_allowed = True

        @classmethod
        def from_texts(
            cls, texts: List[str], embeddings: Embeddings, **kwargs: Any
        ) -> SVMRetriever:
            index = create_index(texts, embeddings)
            return cls(embeddings=embeddings, index=index, texts=texts, **kwargs)

        def get_relevant_documents(self, query: str) -> List[Document]:
            from sklearn import svm

            query_embeds = np.array(self.embeddings.embed_query(query))
            x = np.concatenate([query_embeds[None, ...], self.index])
            y = np.zeros(x.shape[0])
            y[0] = 1

            clf = svm.LinearSVC(
                class_weight="balanced", verbose=False, max_iter=10000, tol=1e-6, C=0.1
            )
            clf.fit(x, y)

            similarities = clf.decision_function(x)
            sorted_ix = np.argsort(-similarities)

            # svm.LinearSVC in scikit-learn is non-deterministic.
            # if a text is the same as a query, there is no guarantee
            # the query will be in the first index.
            # this performs a simple swap, this works because anything
            # left of the 0 should be equivalent.
            zero_index = np.where(sorted_ix == 0)[0][0]
            if zero_index != 0:
                sorted_ix[0], sorted_ix[zero_index] = sorted_ix[zero_index], sorted_ix[0]

            denominator = np.max(similarities) - np.min(similarities) + 1e-6
            normalized_similarities = (similarities - np.min(similarities)) / denominator

            top_k_results = []
            for row in sorted_ix[1 : self.k + 1]:
                if (
                    self.relevancy_threshold is None
                    or normalized_similarities[row] >= self.relevancy_threshold
                ):
                    top_k_results.append(Document(page_content=self.texts[row - 1]))
            return top_k_results

        async def aget_relevant_documents(self, query: str) -> List[Document]:
            raise NotImplementedError