import asyncio
import os
from tqdm.asyncio import tqdm as tqdm_async
from dataclasses import dataclass
import numpy as np
import hashlib
import uuid
from ..utils import logger
from ..base import BaseVectorStorage
import pipmaster as pm
import configparser

if not pm.is_installed("qdrant_client"):
    pm.install("qdrant_client")

from qdrant_client import QdrantClient, models

config = configparser.ConfigParser()
config.read("config.ini", "utf-8")


def compute_mdhash_id_for_qdrant(
    content: str, prefix: str = "", style: str = "simple"
) -> str:
    """
    Generate a UUID based on the content and support multiple formats.

    :param content: The content used to generate the UUID.
    :param style: The format of the UUID, optional values are "simple", "hyphenated", "urn".
    :return: A UUID that meets the requirements of Qdrant.
    """
    if not content:
        raise ValueError("Content must not be empty.")

    # Use the hash value of the content to create a UUID.
    hashed_content = hashlib.sha256((prefix + content).encode("utf-8")).digest()
    generated_uuid = uuid.UUID(bytes=hashed_content[:16], version=4)

    # Return the UUID according to the specified format.
    if style == "simple":
        return generated_uuid.hex
    elif style == "hyphenated":
        return str(generated_uuid)
    elif style == "urn":
        return f"urn:uuid:{generated_uuid}"
    else:
        raise ValueError("Invalid style. Choose from 'simple', 'hyphenated', or 'urn'.")


@dataclass
class QdrantVectorDBStorage(BaseVectorStorage):
    cosine_better_than_threshold: float = None

    @staticmethod
    def create_collection_if_not_exist(
        client: QdrantClient, collection_name: str, **kwargs
    ):
        if client.collection_exists(collection_name):
            return
        client.create_collection(collection_name, **kwargs)

    def __post_init__(self):
        kwargs = self.global_config.get("vector_db_storage_cls_kwargs", {})
        cosine_threshold = kwargs.get("cosine_better_than_threshold")
        if cosine_threshold is None:
            raise ValueError(
                "cosine_better_than_threshold must be specified in vector_db_storage_cls_kwargs"
            )
        self.cosine_better_than_threshold = cosine_threshold

        self._client = QdrantClient(
            url=os.environ.get(
                "QDRANT_URL", config.get("qdrant", "uri", fallback=None)
            ),
            api_key=os.environ.get(
                "QDRANT_API_KEY", config.get("qdrant", "apikey", fallback=None)
            ),
        )
        self._max_batch_size = self.global_config["embedding_batch_num"]
        QdrantVectorDBStorage.create_collection_if_not_exist(
            self._client,
            self.namespace,
            vectors_config=models.VectorParams(
                size=self.embedding_func.embedding_dim, distance=models.Distance.COSINE
            ),
        )

    async def upsert(self, data: dict[str, dict]):
        if not len(data):
            logger.warning("You insert an empty data to vector DB")
            return []
        list_data = [
            {
                "id": k,
                **{k1: v1 for k1, v1 in v.items() if k1 in self.meta_fields},
            }
            for k, v in data.items()
        ]
        contents = [v["content"] for v in data.values()]
        batches = [
            contents[i : i + self._max_batch_size]
            for i in range(0, len(contents), self._max_batch_size)
        ]

        async def wrapped_task(batch):
            result = await self.embedding_func(batch)
            pbar.update(1)
            return result

        embedding_tasks = [wrapped_task(batch) for batch in batches]
        pbar = tqdm_async(
            total=len(embedding_tasks), desc="Generating embeddings", unit="batch"
        )
        embeddings_list = await asyncio.gather(*embedding_tasks)

        embeddings = np.concatenate(embeddings_list)

        list_points = []
        for i, d in enumerate(list_data):
            list_points.append(
                models.PointStruct(
                    id=compute_mdhash_id_for_qdrant(d["id"]),
                    vector=embeddings[i],
                    payload=d,
                )
            )

        results = self._client.upsert(
            collection_name=self.namespace, points=list_points, wait=True
        )
        return results

    async def query(self, query, top_k=5):
        embedding = await self.embedding_func([query])
        results = self._client.search(
            collection_name=self.namespace,
            query_vector=embedding[0],
            limit=top_k,
            with_payload=True,
            score_threshold=self.cosine_better_than_threshold,
        )

        logger.debug(f"query result: {results}")

        return [{**dp.payload, "id": dp.id, "distance": dp.score} for dp in results]