import requests
from torch import Tensor, device
from typing import List, Callable
from tqdm.autonotebook import tqdm
import sys
import importlib
import os
import torch
import numpy as np
import queue
import logging
from typing import Dict, Optional, Union
from pathlib import Path

import huggingface_hub
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE
from huggingface_hub import HfApi, hf_hub_url, cached_download, HfFolder
import fnmatch
from packaging import version
import heapq

logger = logging.getLogger(__name__)

def pytorch_cos_sim(a: Tensor, b: Tensor):
    """
    Computes the cosine similarity cos_sim(a[i], b[j]) for all i and j.
    :return: Matrix with res[i][j]  = cos_sim(a[i], b[j])
    """
    return cos_sim(a, b)

def cos_sim(a: Tensor, b: Tensor):
    """
    Computes the cosine similarity cos_sim(a[i], b[j]) for all i and j.
    :return: Matrix with res[i][j]  = cos_sim(a[i], b[j])
    """
    if not isinstance(a, torch.Tensor):
        a = torch.tensor(a)

    if not isinstance(b, torch.Tensor):
        b = torch.tensor(b)

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

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

    a_norm = torch.nn.functional.normalize(a, p=2, dim=1)
    b_norm = torch.nn.functional.normalize(b, p=2, dim=1)
    return torch.mm(a_norm, b_norm.transpose(0, 1))


def dot_score(a: Tensor, b: Tensor):
    """
    Computes the dot-product dot_prod(a[i], b[j]) for all i and j.
    :return: Matrix with res[i][j]  = dot_prod(a[i], b[j])
    """
    if not isinstance(a, torch.Tensor):
        a = torch.tensor(a)

    if not isinstance(b, torch.Tensor):
        b = torch.tensor(b)

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

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

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


def pairwise_dot_score(a: Tensor, b: Tensor):
    """
   Computes the pairwise dot-product dot_prod(a[i], b[i])
   :return: Vector with res[i] = dot_prod(a[i], b[i])
   """
    if not isinstance(a, torch.Tensor):
        a = torch.tensor(a)

    if not isinstance(b, torch.Tensor):
        b = torch.tensor(b)

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


def pairwise_cos_sim(a: Tensor, b: Tensor):
    """
   Computes the pairwise cossim cos_sim(a[i], b[i])
   :return: Vector with res[i] = cos_sim(a[i], b[i])
   """
    if not isinstance(a, torch.Tensor):
        a = torch.tensor(a)

    if not isinstance(b, torch.Tensor):
        b = torch.tensor(b)

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


def normalize_embeddings(embeddings: Tensor):
    """
    Normalizes the embeddings matrix, so that each sentence embedding has unit length
    """
    return torch.nn.functional.normalize(embeddings, p=2, dim=1)


def paraphrase_mining(model,
                      sentences: List[str],
                      show_progress_bar: bool = False,
                      batch_size:int = 32,
                      *args,
                      **kwargs):
    """
    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.

    :param model: SentenceTransformer model for embedding computation
    :param sentences: A list of strings (texts or sentences)
    :param show_progress_bar: Plotting of a progress bar
    :param batch_size: Number of texts that are encoded simultaneously by the model
    :param query_chunk_size: Search for most similar pairs for #query_chunk_size at the same time. Decrease, to lower memory footprint (increases run-time).
    :param corpus_chunk_size: Compare a sentence simultaneously against #corpus_chunk_size other sentences. Decrease, to lower memory footprint (increases run-time).
    :param max_pairs: Maximal number of text pairs returned.
    :param top_k: For each sentence, we retrieve up to top_k other sentences
    :param score_function: Function for computing scores. By default, cosine similarity.
    :return: 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, *args, **kwargs)


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):
    """
    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.

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

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


def information_retrieval(*args, **kwargs):
    """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):
    """
    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.

    :param query_embeddings: A 2 dimensional tensor with the query embeddings.
    :param corpus_embeddings: A 2 dimensional tensor with the corpus embeddings.
    :param query_chunk_size: Process 100 queries simultaneously. Increasing that value increases the speed, but requires more memory.
    :param corpus_chunk_size: Scans the corpus 100k entries at a time. Increasing that value increases the speed, but requires more memory.
    :param top_k: Retrieve top k matching entries.
    :param score_function: Function for computing scores. By default, cosine similarity.
    :return: Returns 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):
    """
    Downloads a URL to a given path on disc
    """
    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 to a device (CPU/GPU)
    """
    for key in batch:
        if isinstance(batch[key], Tensor):
            batch[key] = batch[key].to(target_device)
    return batch



def fullname(o):
  """
  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
  """

  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.
    """
    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:
        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, threshold=0.75, min_community_size=10, batch_size=1024):
    """
    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.
    """
    if not isinstance(embeddings, torch.Tensor):
        embeddings = torch.tensor(embeddings)

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

    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 range(0, len(embeddings), batch_size):
        # Compute cosine similarity scores
        cos_scores = cos_sim(embeddings[start_idx:start_idx + batch_size], embeddings)

        # 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:
                new_cluster = []

                # 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)

                for idx, val in zip(top_idx_large.tolist(), top_val_large):
                    if val < threshold:
                        break

                    new_cluster.append(idx)

                extracted_communities.append(new_cluster)

        del cos_scores

    # 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):
        community = sorted(community)
        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


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



def snapshot_download(
    repo_id: str,
    revision: Optional[str] = None,
    cache_dir: Union[str, Path, None] = None,
    library_name: Optional[str] = None,
    library_version: Optional[str] = None,
    user_agent: Union[Dict, str, None] = None,
    ignore_files: Optional[List[str]] = None,
    use_auth_token: Union[bool, str, None] = None
) -> str:
    """
    Method derived from huggingface_hub.
    Adds a new parameters 'ignore_files', which allows to ignore certain files / file-patterns
    """
    if cache_dir is None:
        cache_dir = HUGGINGFACE_HUB_CACHE
    if isinstance(cache_dir, Path):
        cache_dir = str(cache_dir)

    _api = HfApi()
    
    token = None 
    if isinstance(use_auth_token, str):
        token = use_auth_token
    elif use_auth_token:
        token = HfFolder.get_token()
        
    model_info = _api.model_info(repo_id=repo_id, revision=revision, token=token)

    storage_folder = os.path.join(
        cache_dir, repo_id.replace("/", "_")
    )

    all_files = model_info.siblings
    #Download modules.json as the last file
    for idx, repofile in enumerate(all_files):
        if repofile.rfilename == "modules.json":
            del all_files[idx]
            all_files.append(repofile)
            break

    for model_file in all_files:
        if ignore_files is not None:
            skip_download = False
            for pattern in ignore_files:
                if fnmatch.fnmatch(model_file.rfilename, pattern):
                    skip_download = True
                    break

            if skip_download:
                continue

        url = hf_hub_url(
            repo_id, filename=model_file.rfilename, revision=model_info.sha
        )
        relative_filepath = os.path.join(*model_file.rfilename.split("/"))

        # Create potential nested dir
        nested_dirname = os.path.dirname(
            os.path.join(storage_folder, relative_filepath)
        )
        os.makedirs(nested_dirname, exist_ok=True)

        cached_download_args = {'url': url,
            'cache_dir': storage_folder,
            'force_filename': relative_filepath,
            'library_name': library_name,
            'library_version': library_version,
            'user_agent': user_agent,
            'use_auth_token': use_auth_token}

        if version.parse(huggingface_hub.__version__) >= version.parse("0.8.1"):
            # huggingface_hub v0.8.1 introduces a new cache layout. We sill use a manual layout
            # And need to pass legacy_cache_layout=True to avoid that a warning will be printed
            cached_download_args['legacy_cache_layout'] = True

        path = cached_download(**cached_download_args)

        if os.path.exists(path + ".lock"):
            os.remove(path + ".lock")

    return storage_folder
