from abc import ABC, abstractmethod
import os
from sentence_transformers import SentenceTransformer
from langchain_openai import OpenAIEmbeddings
from application.core.settings import settings

class EmbeddingsWrapper:
    def __init__(self, model_name, *args, **kwargs):
        self.model = SentenceTransformer(model_name, config_kwargs={'allow_dangerous_deserialization': True}, *args, **kwargs)
        self.dimension = self.model.get_sentence_embedding_dimension()

    def embed_query(self, query: str):
        return self.model.encode(query).tolist()
    
    def embed_documents(self, documents: list):
        return self.model.encode(documents).tolist()
    
    def __call__(self, text):
        if isinstance(text, str):
            return self.embed_query(text)
        elif isinstance(text, list):
            return self.embed_documents(text)
        else:
            raise ValueError("Input must be a string or a list of strings")



class EmbeddingsSingleton:
    _instances = {}

    @staticmethod
    def get_instance(embeddings_name, *args, **kwargs):
        if embeddings_name not in EmbeddingsSingleton._instances:
            EmbeddingsSingleton._instances[embeddings_name] = EmbeddingsSingleton._create_instance(
                embeddings_name, *args, **kwargs
            )
        return EmbeddingsSingleton._instances[embeddings_name]

    @staticmethod
    def _create_instance(embeddings_name, *args, **kwargs):
        embeddings_factory = {
            "openai_text-embedding-ada-002": OpenAIEmbeddings,
            "huggingface_sentence-transformers/all-mpnet-base-v2": lambda: EmbeddingsWrapper("sentence-transformers/all-mpnet-base-v2"),
            "huggingface_sentence-transformers-all-mpnet-base-v2": lambda: EmbeddingsWrapper("sentence-transformers/all-mpnet-base-v2"),
            "huggingface_hkunlp/instructor-large": lambda: EmbeddingsWrapper("hkunlp/instructor-large"),
        }

        if embeddings_name in embeddings_factory:
            return embeddings_factory[embeddings_name](*args, **kwargs)
        else:
            return EmbeddingsWrapper(embeddings_name, *args, **kwargs)

class BaseVectorStore(ABC):
    def __init__(self):
        pass

    @abstractmethod
    def search(self, *args, **kwargs):
        pass

    def is_azure_configured(self):
        return settings.OPENAI_API_BASE and settings.OPENAI_API_VERSION and settings.AZURE_DEPLOYMENT_NAME

    def _get_embeddings(self, embeddings_name, embeddings_key=None):
        if embeddings_name == "openai_text-embedding-ada-002":
            if self.is_azure_configured():
                os.environ["OPENAI_API_TYPE"] = "azure"
                embedding_instance = EmbeddingsSingleton.get_instance(
                    embeddings_name,
                    model=settings.AZURE_EMBEDDINGS_DEPLOYMENT_NAME
                )
            else:
                embedding_instance = EmbeddingsSingleton.get_instance(
                    embeddings_name,
                    openai_api_key=embeddings_key
                )
        elif embeddings_name == "huggingface_sentence-transformers/all-mpnet-base-v2":
            if os.path.exists("./models/all-mpnet-base-v2"):
                embedding_instance = EmbeddingsSingleton.get_instance(
                    embeddings_name = "./models/all-mpnet-base-v2",
                )
            else:
                embedding_instance = EmbeddingsSingleton.get_instance(
                    embeddings_name,
                )
        else:
            embedding_instance = EmbeddingsSingleton.get_instance(embeddings_name)

        return embedding_instance