import functools
import heapq
import importlib
import logging
import os
import queue
import sys
from contextlib import contextmanager
from typing import Callable, Dict, List, Literal, Optional, Union, overload

import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download, snapshot_download
from torch import Tensor, device
from tqdm.autonotebook import tqdm
from transformers import is_torch_npu_available

logger = logging.getLogger(__name__)


def _convert_to_tensor(a: Union[list, np.ndarray, Tensor]) -> Tensor:
    """
    Converts the input `a` to a PyTorch tensor if it is not already a tensor.

    Args:
        a (Union[list, np.ndarray, Tensor]): The input array or tensor.

    Returns:
        Tensor: The converted tensor.
    """
    if not isinstance(a, Tensor):
        a = torch.tensor(a)
    return a


def _convert_to_batch(a: Tensor) -> Tensor:
    """
    If the tensor `a` is 1-dimensional, it is unsqueezed to add a batch dimension.

    Args:
        a (Tensor): The input tensor.

    Returns:
        Tensor: The tensor with a batch dimension.
    """
    if a.dim() == 1:
        a = a.unsqueeze(0)
    return a


def _convert_to_batch_tensor(a: Union[list, np.ndarray, Tensor]) -> Tensor:
    """
    Converts the input data to a tensor with a batch dimension.

    Args:
        a (Union[list, np.ndarray, Tensor]): The input data to be converted.

    Returns:
        Tensor: The converted tensor with a batch dimension.
    """
    a = _convert_to_tensor(a)
    a = _convert_to_batch(a)
    return a


def pytorch_cos_sim(a: Tensor, b: Tensor) -> Tensor:
    """
    Computes the cosine similarity between two tensors.

    Args:
        a (Union[list, np.ndarray, Tensor]): The first tensor.
        b (Union[list, np.ndarray, Tensor]): The second tensor.

    Returns:
        Tensor: Matrix with res[i][j] = cos_sim(a[i], b[j])
    """
    return cos_sim(a, b)


def cos_sim(a: Union[list, np.ndarray, Tensor], b: Union[list, np.ndarray, Tensor]) -> Tensor:
    """
    Computes the cosine similarity between two tensors.

    Args:
        a (Union[list, np.ndarray, Tensor]): The first tensor.
        b (Union[list, np.ndarray, Tensor]): The second tensor.

    Returns:
        Tensor: Matrix with res[i][j] = cos_sim(a[i], b[j])
    """
    a = _convert_to_batch_tensor(a)
    b = _convert_to_batch_tensor(b)

    a_norm = normalize_embeddings(a)
    b_norm = normalize_embeddings(b)
    return torch.mm(a_norm, b_norm.transpose(0, 1))


def pairwise_cos_sim(a: Tensor, b: Tensor) -> Tensor:
    """
    Computes the pairwise cosine similarity cos_sim(a[i], b[i]).

    Args:
        a (Union[list, np.ndarray, Tensor]): The first tensor.
        b (Union[list, np.ndarray, Tensor]): The second tensor.

    Returns:
        Tensor: Vector with res[i] = cos_sim(a[i], b[i])
    """
    a = _convert_to_tensor(a)
    b = _convert_to_tensor(b)

    return pairwise_dot_score(normalize_embeddings(a), normalize_embeddings(b))


def dot_score(a: Union[list, np.ndarray, Tensor], b: Union[list, np.ndarray, Tensor]) -> Tensor:
    """
    Computes the dot-product dot_prod(a[i], b[j]) for all i and j.

    Args:
        a (Union[list, np.ndarray, Tensor]): The first tensor.
        b (Union[list, np.ndarray, Tensor]): The second tensor.

    Returns:
        Tensor: Matrix with res[i][j] = dot_prod(a[i], b[j])
    """
    a = _convert_to_batch_tensor(a)
    b = _convert_to_batch_tensor(b)

    return torch.mm(a, b.transpose(0, 1))


def pairwise_dot_score(a: Tensor, b: Tensor) -> Tensor:
    """
    Computes the pairwise dot-product dot_prod(a[i], b[i]).

    Args:
        a (Union[list, np.ndarray, Tensor]): The first tensor.
        b (Union[list, np.ndarray, Tensor]): The second tensor.

    Returns:
        Tensor: Vector with res[i] = dot_prod(a[i], b[i])
    """
    a = _convert_to_tensor(a)
    b = _convert_to_tensor(b)

    return (a * b).sum(dim=-1)


def manhattan_sim(a: Union[list, np.ndarray, Tensor], b: Union[list, np.ndarray, Tensor]) -> Tensor:
    """
    Computes the manhattan similarity (i.e., negative distance) between two tensors.

    Args:
        a (Union[list, np.ndarray, Tensor]): The first tensor.
        b (Union[list, np.ndarray, Tensor]): The second tensor.

    Returns:
        Tensor: Matrix with res[i][j] = -manhattan_distance(a[i], b[j])
    """
    a = _convert_to_batch_tensor(a)
    b = _convert_to_batch_tensor(b)

    return -torch.cdist(a, b, p=1.0)


def pairwise_manhattan_sim(a: Union[list, np.ndarray, Tensor], b: Union[list, np.ndarray, Tensor]):
    """
    Computes the manhattan similarity (i.e., negative distance) between pairs of tensors.

    Args:
        a (Union[list, np.ndarray, Tensor]): The first tensor.
        b (Union[list, np.ndarray, Tensor]): The second tensor.

    Returns:
        Tensor: Vector with res[i] = -manhattan_distance(a[i], b[i])
    """
    a = _convert_to_tensor(a)
    b = _convert_to_tensor(b)

    return -torch.sum(torch.abs(a - b), dim=-1)


def euclidean_sim(a: Union[list, np.ndarray, Tensor], b: Union[list, np.ndarray, Tensor]) -> Tensor:
    """
    Computes the euclidean similarity (i.e., negative distance) between two tensors.

    Args:
        a (Union[list, np.ndarray, Tensor]): The first tensor.
        b (Union[list, np.ndarray, Tensor]): The second tensor.

    Returns:
        Tensor: Matrix with res[i][j] = -euclidean_distance(a[i], b[j])
    """
    a = _convert_to_batch_tensor(a)
    b = _convert_to_batch_tensor(b)

    return -torch.cdist(a, b, p=2.0)


def pairwise_euclidean_sim(a: Union[list, np.ndarray, Tensor], b: Union[list, np.ndarray, Tensor]):
    """
    Computes the euclidean distance (i.e., negative distance) between pairs of tensors.

    Args:
        a (Union[list, np.ndarray, Tensor]): The first tensor.
        b (Union[list, np.ndarray, Tensor]): The second tensor.

    Returns:
        Tensor: Vector with res[i] = -euclidean_distance(a[i], b[i])
    """
    a = _convert_to_tensor(a)
    b = _convert_to_tensor(b)

    return -torch.sqrt(torch.sum((a - b) ** 2, dim=-1))


def pairwise_angle_sim(x: Tensor, y: Tensor) -> Tensor:
    """
    Computes the absolute normalized angle distance. See :class:`~sentence_transformers.losses.AnglELoss`
    or https://arxiv.org/abs/2309.12871v1 for more information.

    Args:
        x (Tensor): The first tensor.
        y (Tensor): The second tensor.

    Returns:
        Tensor: Vector with res[i] = angle_sim(a[i], b[i])
    """

    x = _convert_to_tensor(x)
    y = _convert_to_tensor(y)

    # modified from https://github.com/SeanLee97/AnglE/blob/main/angle_emb/angle.py
    # chunk both tensors to obtain complex components
    a, b = torch.chunk(x, 2, dim=1)
    c, d = torch.chunk(y, 2, dim=1)

    z = torch.sum(c**2 + d**2, dim=1, keepdim=True)
    re = (a * c + b * d) / z
    im = (b * c - a * d) / z

    dz = torch.sum(a**2 + b**2, dim=1, keepdim=True) ** 0.5
    dw = torch.sum(c**2 + d**2, dim=1, keepdim=True) ** 0.5
    re /= dz / dw
    im /= dz / dw

    norm_angle = torch.sum(torch.concat((re, im), dim=1), dim=1)
    return torch.abs(norm_angle)


def normalize_embeddings(embeddings: Tensor) -> Tensor:
    """
    Normalizes the embeddings matrix, so that each sentence embedding has unit length.

    Args:
        embeddings (Tensor): The input embeddings matrix.

    Returns:
        Tensor: The normalized embeddings matrix.
    """
    return torch.nn.functional.normalize(embeddings, p=2, dim=1)


@overload
def truncate_embeddings(embeddings: np.ndarray, truncate_dim: Optional[int]) -> np.ndarray: ...


@overload
def truncate_embeddings(embeddings: torch.Tensor, truncate_dim: Optional[int]) -> torch.Tensor: ...


def truncate_embeddings(
    embeddings: Union[np.ndarray, torch.Tensor], truncate_dim: Optional[int]
) -> Union[np.ndarray, torch.Tensor]:
    """
    Truncates the embeddings matrix.

    Args:
        embeddings (Union[np.ndarray, torch.Tensor]): Embeddings to truncate.
        truncate_dim (Optional[int]): The dimension to truncate sentence embeddings to. `None` does no truncation.

    Example:
        >>> from sentence_transformers import SentenceTransformer
        >>> from sentence_transformers.util import truncate_embeddings
        >>> model = SentenceTransformer("tomaarsen/mpnet-base-nli-matryoshka")
        >>> embeddings = model.encode(["It's so nice outside!", "Today is a beautiful day.", "He drove to work earlier"])
        >>> embeddings.shape
        (3, 768)
        >>> model.similarity(embeddings, embeddings)
        tensor([[1.0000, 0.8100, 0.1426],
                [0.8100, 1.0000, 0.2121],
                [0.1426, 0.2121, 1.0000]])
        >>> truncated_embeddings = truncate_embeddings(embeddings, 128)
        >>> truncated_embeddings.shape
        >>> model.similarity(truncated_embeddings, truncated_embeddings)
        tensor([[1.0000, 0.8092, 0.1987],
                [0.8092, 1.0000, 0.2716],
                [0.1987, 0.2716, 1.0000]])

    Returns:
        Union[np.ndarray, torch.Tensor]: Truncated embeddings.
    """
    return embeddings[..., :truncate_dim]


def paraphrase_mining(
    model,
    sentences: List[str],
    show_progress_bar: bool = False,
    batch_size: int = 32,
    query_chunk_size: int = 5000,
    corpus_chunk_size: int = 100000,
    max_pairs: int = 500000,
    top_k: int = 100,
    score_function: Callable[[Tensor, Tensor], Tensor] = cos_sim,
) -> List[List[Union[float, int]]]:
    """
    Given a list of sentences / texts, this function performs paraphrase mining. It compares all sentences against all
    other sentences and returns a list with the pairs that have the highest cosine similarity score.

    Args:
        model (SentenceTransformer): SentenceTransformer model for embedding computation
        sentences (List[str]): A list of strings (texts or sentences)
        show_progress_bar (bool, optional): Plotting of a progress bar. Defaults to False.
        batch_size (int, optional): Number of texts that are encoded simultaneously by the model. Defaults to 32.
        query_chunk_size (int, optional): Search for most similar pairs for #query_chunk_size at the same time. Decrease, to lower memory footprint (increases run-time). Defaults to 5000.
        corpus_chunk_size (int, optional): Compare a sentence simultaneously against #corpus_chunk_size other sentences. Decrease, to lower memory footprint (increases run-time). Defaults to 100000.
        max_pairs (int, optional): Maximal number of text pairs returned. Defaults to 500000.
        top_k (int, optional): For each sentence, we retrieve up to top_k other sentences. Defaults to 100.
        score_function (Callable[[Tensor, Tensor], Tensor], optional): Function for computing scores. By default, cosine similarity. Defaults to cos_sim.

    Returns:
        List[List[Union[float, int]]]: Returns a list of triplets with the format [score, id1, id2]
    """

    # Compute embedding for the sentences
    embeddings = model.encode(
        sentences, show_progress_bar=show_progress_bar, batch_size=batch_size, convert_to_tensor=True
    )

    return paraphrase_mining_embeddings(
        embeddings,
        query_chunk_size=query_chunk_size,
        corpus_chunk_size=corpus_chunk_size,
        max_pairs=max_pairs,
        top_k=top_k,
        score_function=score_function,
    )


def paraphrase_mining_embeddings(
    embeddings: Tensor,
    query_chunk_size: int = 5000,
    corpus_chunk_size: int = 100000,
    max_pairs: int = 500000,
    top_k: int = 100,
    score_function: Callable[[Tensor, Tensor], Tensor] = cos_sim,
) -> List[List[Union[float, int]]]:
    """
    Given a list of sentences / texts, this function performs paraphrase mining. It compares all sentences against all
    other sentences and returns a list with the pairs that have the highest cosine similarity score.

    Args:
        embeddings (Tensor): A tensor with the embeddings
        query_chunk_size (int): Search for most similar pairs for #query_chunk_size at the same time. Decrease, to lower memory footprint (increases run-time).
        corpus_chunk_size (int): Compare a sentence simultaneously against #corpus_chunk_size other sentences. Decrease, to lower memory footprint (increases run-time).
        max_pairs (int): Maximal number of text pairs returned.
        top_k (int): For each sentence, we retrieve up to top_k other sentences
        score_function (Callable[[Tensor, Tensor], Tensor]): Function for computing scores. By default, cosine similarity.

    Returns:
        List[List[Union[float, int]]]: Returns a list of triplets with the format [score, id1, id2]
    """

    top_k += 1  # A sentence has the highest similarity to itself. Increase +1 as we are interest in distinct pairs

    # Mine for duplicates
    pairs = queue.PriorityQueue()
    min_score = -1
    num_added = 0

    for corpus_start_idx in range(0, len(embeddings), corpus_chunk_size):
        for query_start_idx in range(0, len(embeddings), query_chunk_size):
            scores = score_function(
                embeddings[query_start_idx : query_start_idx + query_chunk_size],
                embeddings[corpus_start_idx : corpus_start_idx + corpus_chunk_size],
            )

            scores_top_k_values, scores_top_k_idx = torch.topk(
                scores, min(top_k, len(scores[0])), dim=1, largest=True, sorted=False
            )
            scores_top_k_values = scores_top_k_values.cpu().tolist()
            scores_top_k_idx = scores_top_k_idx.cpu().tolist()

            for query_itr in range(len(scores)):
                for top_k_idx, corpus_itr in enumerate(scores_top_k_idx[query_itr]):
                    i = query_start_idx + query_itr
                    j = corpus_start_idx + corpus_itr

                    if i != j and scores_top_k_values[query_itr][top_k_idx] > min_score:
                        pairs.put((scores_top_k_values[query_itr][top_k_idx], i, j))
                        num_added += 1

                        if num_added >= max_pairs:
                            entry = pairs.get()
                            min_score = entry[0]

    # Get the pairs
    added_pairs = set()  # Used for duplicate detection
    pairs_list = []
    while not pairs.empty():
        score, i, j = pairs.get()
        sorted_i, sorted_j = sorted([i, j])

        if sorted_i != sorted_j and (sorted_i, sorted_j) not in added_pairs:
            added_pairs.add((sorted_i, sorted_j))
            pairs_list.append([score, sorted_i, sorted_j])

    # Highest scores first
    pairs_list = sorted(pairs_list, key=lambda x: x[0], reverse=True)
    return pairs_list


def information_retrieval(*args, **kwargs) -> List[List[Dict[str, Union[int, float]]]]:
    """This function is deprecated. Use semantic_search instead"""
    return semantic_search(*args, **kwargs)


def semantic_search(
    query_embeddings: Tensor,
    corpus_embeddings: Tensor,
    query_chunk_size: int = 100,
    corpus_chunk_size: int = 500000,
    top_k: int = 10,
    score_function: Callable[[Tensor, Tensor], Tensor] = cos_sim,
) -> List[List[Dict[str, Union[int, float]]]]:
    """
    This function performs a cosine similarity search between a list of query embeddings  and a list of corpus embeddings.
    It can be used for Information Retrieval / Semantic Search for corpora up to about 1 Million entries.

    Args:
        query_embeddings (Tensor): A 2 dimensional tensor with the query embeddings.
        corpus_embeddings (Tensor): A 2 dimensional tensor with the corpus embeddings.
        query_chunk_size (int, optional): Process 100 queries simultaneously. Increasing that value increases the speed, but requires more memory. Defaults to 100.
        corpus_chunk_size (int, optional): Scans the corpus 100k entries at a time. Increasing that value increases the speed, but requires more memory. Defaults to 500000.
        top_k (int, optional): Retrieve top k matching entries. Defaults to 10.
        score_function (Callable[[Tensor, Tensor], Tensor], optional): Function for computing scores. By default, cosine similarity.

    Returns:
        List[List[Dict[str, Union[int, float]]]]: A list with one entry for each query. Each entry is a list of dictionaries with the keys 'corpus_id' and 'score', sorted by decreasing cosine similarity scores.
    """

    if isinstance(query_embeddings, (np.ndarray, np.generic)):
        query_embeddings = torch.from_numpy(query_embeddings)
    elif isinstance(query_embeddings, list):
        query_embeddings = torch.stack(query_embeddings)

    if len(query_embeddings.shape) == 1:
        query_embeddings = query_embeddings.unsqueeze(0)

    if isinstance(corpus_embeddings, (np.ndarray, np.generic)):
        corpus_embeddings = torch.from_numpy(corpus_embeddings)
    elif isinstance(corpus_embeddings, list):
        corpus_embeddings = torch.stack(corpus_embeddings)

    # Check that corpus and queries are on the same device
    if corpus_embeddings.device != query_embeddings.device:
        query_embeddings = query_embeddings.to(corpus_embeddings.device)

    queries_result_list = [[] for _ in range(len(query_embeddings))]

    for query_start_idx in range(0, len(query_embeddings), query_chunk_size):
        # Iterate over chunks of the corpus
        for corpus_start_idx in range(0, len(corpus_embeddings), corpus_chunk_size):
            # Compute cosine similarities
            cos_scores = score_function(
                query_embeddings[query_start_idx : query_start_idx + query_chunk_size],
                corpus_embeddings[corpus_start_idx : corpus_start_idx + corpus_chunk_size],
            )

            # Get top-k scores
            cos_scores_top_k_values, cos_scores_top_k_idx = torch.topk(
                cos_scores, min(top_k, len(cos_scores[0])), dim=1, largest=True, sorted=False
            )
            cos_scores_top_k_values = cos_scores_top_k_values.cpu().tolist()
            cos_scores_top_k_idx = cos_scores_top_k_idx.cpu().tolist()

            for query_itr in range(len(cos_scores)):
                for sub_corpus_id, score in zip(cos_scores_top_k_idx[query_itr], cos_scores_top_k_values[query_itr]):
                    corpus_id = corpus_start_idx + sub_corpus_id
                    query_id = query_start_idx + query_itr
                    if len(queries_result_list[query_id]) < top_k:
                        heapq.heappush(
                            queries_result_list[query_id], (score, corpus_id)
                        )  # heaqp tracks the quantity of the first element in the tuple
                    else:
                        heapq.heappushpop(queries_result_list[query_id], (score, corpus_id))

    # change the data format and sort
    for query_id in range(len(queries_result_list)):
        for doc_itr in range(len(queries_result_list[query_id])):
            score, corpus_id = queries_result_list[query_id][doc_itr]
            queries_result_list[query_id][doc_itr] = {"corpus_id": corpus_id, "score": score}
        queries_result_list[query_id] = sorted(queries_result_list[query_id], key=lambda x: x["score"], reverse=True)

    return queries_result_list


def http_get(url, path) -> None:
    """
    Downloads a URL to a given path on disk.

    Args:
        url (str): The URL to download.
        path (str): The path to save the downloaded file.

    Raises:
        requests.HTTPError: If the HTTP request returns a non-200 status code.

    Returns:
        None
    """
    if os.path.dirname(path) != "":
        os.makedirs(os.path.dirname(path), exist_ok=True)

    req = requests.get(url, stream=True)
    if req.status_code != 200:
        print("Exception when trying to download {}. Response {}".format(url, req.status_code), file=sys.stderr)
        req.raise_for_status()
        return

    download_filepath = path + "_part"
    with open(download_filepath, "wb") as file_binary:
        content_length = req.headers.get("Content-Length")
        total = int(content_length) if content_length is not None else None
        progress = tqdm(unit="B", total=total, unit_scale=True)
        for chunk in req.iter_content(chunk_size=1024):
            if chunk:  # filter out keep-alive new chunks
                progress.update(len(chunk))
                file_binary.write(chunk)

    os.rename(download_filepath, path)
    progress.close()


def batch_to_device(batch, target_device: device):
    """
    Send a PyTorch batch (i.e., a dictionary of string keys to Tensors) to a device (e.g. "cpu", "cuda", "mps").

    Args:
        batch (Dict[str, Tensor]): The batch to send to the device.
        target_device (torch.device): The target device (e.g. "cpu", "cuda", "mps").

    Returns:
        Dict[str, Tensor]: The batch with tensors sent to the target device.
    """
    for key in batch:
        if isinstance(batch[key], Tensor):
            batch[key] = batch[key].to(target_device)
    return batch


def fullname(o) -> str:
    """
    Gives a full name (package_name.class_name) for a class / object in Python. Will
    be used to load the correct classes from JSON files

    Args:
        o: The object for which to get the full name.

    Returns:
        str: The full name of the object.

    Example:
        >>> from sentence_transformers.losses import MultipleNegativesRankingLoss
        >>> from sentence_transformers import SentenceTransformer
        >>> from sentence_transformers.util import fullname
        >>> model = SentenceTransformer('all-MiniLM-L6-v2')
        >>> loss = MultipleNegativesRankingLoss(model)
        >>> fullname(loss)
        'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss'
    """

    module = o.__class__.__module__
    if module is None or module == str.__class__.__module__:
        return o.__class__.__name__  # Avoid reporting __builtin__
    else:
        return module + "." + o.__class__.__name__


def import_from_string(dotted_path):
    """
    Import a dotted module path and return the attribute/class designated by the
    last name in the path. Raise ImportError if the import failed.

    Args:
        dotted_path (str): The dotted module path.

    Returns:
        Any: The attribute/class designated by the last name in the path.

    Raises:
        ImportError: If the import failed.

    Example:
        >>> import_from_string('sentence_transformers.losses.MultipleNegativesRankingLoss')
        <class 'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss'>
    """
    try:
        module_path, class_name = dotted_path.rsplit(".", 1)
    except ValueError:
        msg = "%s doesn't look like a module path" % dotted_path
        raise ImportError(msg)

    try:
        module = importlib.import_module(dotted_path)
    except Exception:
        module = importlib.import_module(module_path)

    try:
        return getattr(module, class_name)
    except AttributeError:
        msg = 'Module "%s" does not define a "%s" attribute/class' % (module_path, class_name)
        raise ImportError(msg)


def community_detection(
    embeddings: Union[torch.Tensor, np.ndarray],
    threshold: float = 0.75,
    min_community_size: int = 10,
    batch_size: int = 1024,
    show_progress_bar: bool = False,
) -> List[List[int]]:
    """
    Function for Fast Community Detection.

    Finds in the embeddings all communities, i.e. embeddings that are close (closer than threshold).
    Returns only communities that are larger than min_community_size. The communities are returned
    in decreasing order. The first element in each list is the central point in the community.

    Args:
        embeddings (torch.Tensor or numpy.ndarray): The input embeddings.
        threshold (float): The threshold for determining if two embeddings are close. Defaults to 0.75.
        min_community_size (int): The minimum size of a community to be considered. Defaults to 10.
        batch_size (int): The batch size for computing cosine similarity scores. Defaults to 1024.
        show_progress_bar (bool): Whether to show a progress bar during computation. Defaults to False.

    Returns:
        List[List[int]]: A list of communities, where each community is represented as a list of indices.
    """
    if not isinstance(embeddings, torch.Tensor):
        embeddings = torch.tensor(embeddings)

    threshold = torch.tensor(threshold, device=embeddings.device)
    embeddings = normalize_embeddings(embeddings)

    extracted_communities = []

    # Maximum size for community
    min_community_size = min(min_community_size, len(embeddings))
    sort_max_size = min(max(2 * min_community_size, 50), len(embeddings))

    for start_idx in tqdm(
        range(0, len(embeddings), batch_size), desc="Finding clusters", disable=not show_progress_bar
    ):
        # Compute cosine similarity scores
        cos_scores = embeddings[start_idx : start_idx + batch_size] @ embeddings.T

        # Use a torch-heavy approach if the embeddings are on CUDA, otherwise a loop-heavy one
        if embeddings.device.type in ["cuda", "npu"]:
            # Threshold the cos scores and determine how many close embeddings exist per embedding
            threshold_mask = cos_scores >= threshold
            row_wise_count = threshold_mask.sum(1)

            # Only consider embeddings with enough close other embeddings
            large_enough_mask = row_wise_count >= min_community_size
            if not large_enough_mask.any():
                continue

            row_wise_count = row_wise_count[large_enough_mask]
            cos_scores = cos_scores[large_enough_mask]

            # The max is the largest potential community, so we use that in topk
            k = row_wise_count.max()
            _, top_k_indices = cos_scores.topk(k=k, largest=True)

            # Use the row-wise count to slice the indices
            for count, indices in zip(row_wise_count, top_k_indices):
                extracted_communities.append(indices[:count].tolist())
        else:
            # Minimum size for a community
            top_k_values, _ = cos_scores.topk(k=min_community_size, largest=True)

            # Filter for rows >= min_threshold
            for i in range(len(top_k_values)):
                if top_k_values[i][-1] >= threshold:
                    # Only check top k most similar entries
                    top_val_large, top_idx_large = cos_scores[i].topk(k=sort_max_size, largest=True)

                    # Check if we need to increase sort_max_size
                    while top_val_large[-1] > threshold and sort_max_size < len(embeddings):
                        sort_max_size = min(2 * sort_max_size, len(embeddings))
                        top_val_large, top_idx_large = cos_scores[i].topk(k=sort_max_size, largest=True)

                    extracted_communities.append(top_idx_large[top_val_large >= threshold].tolist())

    # Largest cluster first
    extracted_communities = sorted(extracted_communities, key=lambda x: len(x), reverse=True)

    # Step 2) Remove overlapping communities
    unique_communities = []
    extracted_ids = set()

    for cluster_id, community in enumerate(extracted_communities):
        non_overlapped_community = []
        for idx in community:
            if idx not in extracted_ids:
                non_overlapped_community.append(idx)

        if len(non_overlapped_community) >= min_community_size:
            unique_communities.append(non_overlapped_community)
            extracted_ids.update(non_overlapped_community)

    unique_communities = sorted(unique_communities, key=lambda x: len(x), reverse=True)

    return unique_communities


##################
#
######################


class disabled_tqdm(tqdm):
    """
    Class to override `disable` argument in case progress bars are globally disabled.

    Taken from https://github.com/tqdm/tqdm/issues/619#issuecomment-619639324.
    """

    def __init__(self, *args, **kwargs):
        kwargs["disable"] = True
        super().__init__(*args, **kwargs)

    def __delattr__(self, attr: str) -> None:
        """Fix for https://github.com/huggingface/huggingface_hub/issues/1603"""
        try:
            super().__delattr__(attr)
        except AttributeError:
            if attr != "_lock":
                raise


@contextmanager
def disable_logging(highest_level=logging.CRITICAL):
    """
    A context manager that will prevent any logging messages
    triggered during the body from being processed.

    Args:
        highest_level: the maximum logging level allowed.
    """

    previous_level = logging.root.manager.disable

    logging.disable(highest_level)

    try:
        yield
    finally:
        logging.disable(previous_level)


def is_sentence_transformer_model(
    model_name_or_path: str,
    token: Optional[Union[bool, str]] = None,
    cache_folder: Optional[str] = None,
    revision: Optional[str] = None,
    local_files_only: bool = False,
) -> bool:
    """
    Checks if the given model name or path corresponds to a SentenceTransformer model.

    Args:
        model_name_or_path (str): The name or path of the model.
        token (Optional[Union[bool, str]]): The token to be used for authentication. Defaults to None.
        cache_folder (Optional[str]): The folder to cache the model files. Defaults to None.
        revision (Optional[str]): The revision of the model. Defaults to None.
        local_files_only (bool): Whether to only use local files for the model. Defaults to False.

    Returns:
        bool: True if the model is a SentenceTransformer model, False otherwise.
    """
    return bool(
        load_file_path(
            model_name_or_path,
            "modules.json",
            token,
            cache_folder,
            revision=revision,
            local_files_only=local_files_only,
        )
    )


def load_file_path(
    model_name_or_path: str,
    filename: str,
    token: Optional[Union[bool, str]],
    cache_folder: Optional[str],
    revision: Optional[str] = None,
    local_files_only: bool = False,
) -> Optional[str]:
    """
    Loads a file from a local or remote location.

    Args:
        model_name_or_path (str): The model name or path.
        filename (str): The name of the file to load.
        token (Optional[Union[bool, str]]): The token to access the remote file (if applicable).
        cache_folder (Optional[str]): The folder to cache the downloaded file (if applicable).
        revision (Optional[str], optional): The revision of the file (if applicable). Defaults to None.
        local_files_only (bool, optional): Whether to only consider local files. Defaults to False.

    Returns:
        Optional[str]: The path to the loaded file, or None if the file could not be found or loaded.
    """
    # If file is local
    file_path = os.path.join(model_name_or_path, filename)
    if os.path.exists(file_path):
        return file_path

    # If file is remote
    try:
        return hf_hub_download(
            model_name_or_path,
            filename=filename,
            revision=revision,
            library_name="sentence-transformers",
            token=token,
            cache_dir=cache_folder,
            local_files_only=local_files_only,
        )
    except Exception:
        return


def load_dir_path(
    model_name_or_path: str,
    directory: str,
    token: Optional[Union[bool, str]],
    cache_folder: Optional[str],
    revision: Optional[str] = None,
    local_files_only: bool = False,
) -> Optional[str]:
    """
    Loads the directory path for a given model name or path.

    Args:
        model_name_or_path (str): The name or path of the model.
        directory (str): The directory to load.
        token (Optional[Union[bool, str]]): The token for authentication.
        cache_folder (Optional[str]): The folder to cache the downloaded files.
        revision (Optional[str], optional): The revision of the model. Defaults to None.
        local_files_only (bool, optional): Whether to only use local files. Defaults to False.

    Returns:
        Optional[str]: The directory path if it exists, otherwise None.
    """
    # If file is local
    dir_path = os.path.join(model_name_or_path, directory)
    if os.path.exists(dir_path):
        return dir_path

    download_kwargs = {
        "repo_id": model_name_or_path,
        "revision": revision,
        "allow_patterns": f"{directory}/**",
        "library_name": "sentence-transformers",
        "token": token,
        "cache_dir": cache_folder,
        "local_files_only": local_files_only,
        "tqdm_class": disabled_tqdm,
    }
    # Try to download from the remote
    try:
        repo_path = snapshot_download(**download_kwargs)
    except Exception:
        # Otherwise, try local (i.e. cache) only
        download_kwargs["local_files_only"] = True
        repo_path = snapshot_download(**download_kwargs)
    return os.path.join(repo_path, directory)


def save_to_hub_args_decorator(func):
    @functools.wraps(func)
    def wrapper(self, *args, **kwargs):
        # If repo_id not already set, use repo_name
        repo_name = kwargs.pop("repo_name", None)
        if repo_name and "repo_id" not in kwargs:
            logger.warning(
                "Providing a `repo_name` keyword argument to `save_to_hub` is deprecated, please use `repo_id` instead."
            )
            kwargs["repo_id"] = repo_name

        # If positional args are used, adjust for the new "token" keyword argument
        if len(args) >= 2:
            args = (*args[:2], None, *args[2:])

        return func(self, *args, **kwargs)

    return wrapper


def get_device_name() -> Literal["mps", "cuda", "npu", "hpu", "cpu"]:
    """
    Returns the name of the device where this module is running on.

    It's a simple implementation that doesn't cover cases when more powerful GPUs are available and
    not a primary device ('cuda:0') or MPS device is available, but not configured properly.

    Returns:
        str: Device name, like 'cuda' or 'cpu'
    """
    if torch.cuda.is_available():
        return "cuda"
    elif torch.backends.mps.is_available():
        return "mps"
    elif is_torch_npu_available():
        return "npu"
    elif importlib.util.find_spec("habana_frameworks") is not None:
        import habana_frameworks.torch.hpu as hthpu

        if hthpu.is_available():
            return "hpu"
    return "cpu"


def is_accelerate_available() -> bool:
    """
    Returns True if the accelerate library is available.
    """
    return importlib.util.find_spec("accelerate") is not None


def is_datasets_available() -> bool:
    """
    Returns True if the datasets library is available.
    """
    return importlib.util.find_spec("datasets") is not None


def is_training_available() -> bool:
    """
    Returns True if we have the required dependencies for training Sentence Transformer models
    """
    return is_accelerate_available() and is_datasets_available()
