
"""simple docstring"""
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type

from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm


_a      :			str					=							re.compile("""[^A-Za-z_0-9]""")
# parameters used in DuplicationIndex
_a      :			Any					=							10
_a      :			str					=							256
def 				a__   (   a   :				List[str]		):

  """simple docstring"""







  if len(a		) < MIN_NUM_TOKENS:
    return None
  _snake_case     :						Tuple						=   MinHash(num_perm=a		)
  for token in set(a		):
    min_hash.update(token.encode()		)
  return min_hash
def 				a__   (   a   :				str		):

  """simple docstring"""







  return {t for t in NON_ALPHA.split(a		) if len(t.strip()		) > 0}






class      _UpperCAmelCase				:
    def __init__(    self						,    *,
        snake_case_ = 0.85						,    ):
      _snake_case     :						Optional[Any]						=   duplication_jaccard_threshold
      _snake_case     :						List[Any]						=   NUM_PERM
      _snake_case     :						List[Any]						=   MinHashLSH(threshold=self._duplication_jaccard_threshold						,    num_perm=self._num_perm     )

      _snake_case     :						str						=   defaultdict(snake_case_     )
    def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_     ):
      _snake_case     :						Optional[int]						=   self._index.query(snake_case_     )
      if code_key in self._index.keys:
        print(F'Duplicate key {code_key}'     )
        return

      self._index.insert(snake_case_						,    snake_case_     )
      if len(snake_case_     ) > 0:
        for base_duplicate in close_duplicates:
          if base_duplicate in self._duplicate_clusters:
            self._duplicate_clusters[base_duplicate].add(snake_case_     )
            break
        else:
          self._duplicate_clusters[close_duplicates[0]].add(snake_case_     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Union[str, Any]						=   []
      for base, duplicates in self._duplicate_clusters.items():
        _snake_case     :						Tuple						=   [base] + list(snake_case_     )
        # reformat the cluster to be a list of dict
        _snake_case     :						Optional[int]						=   [{"base_index": el[0], "repo_name": el[1], "path": el[2]} for el in cluster]
        duplicate_clusters.append(snake_case_     )
      return duplicate_clusters


    def 					lowerCamelCase__	(    self						,    snake_case_     ):
      _snake_case     :						int						=   self.get_duplicate_clusters()
      with open(snake_case_						,    "w"     ) as f:
        json.dump(snake_case_						,    snake_case_     )


def 				a__   (   a   :				str		):

  """simple docstring"""







  _snake_case     :						Union[str, Any]						=   element
  _snake_case     :						Union[str, Any]						=   get_min_hash([t for t in NON_ALPHA.split(data["content"]		) if len(t.strip()		) > 0]		)
  if min_hash is not None:
    return (index, data["repo_name"], data["path"]), min_hash
def 				a__   (   a   :				Type[Dataset]		):

  """simple docstring"""







  with mp.Pool() as pool:
    for data in pool.imap_unordered(
        _compute_min_hash       ,     ThreadedIterator(a       ,     max_queue_size=10_000		)       ,     chunksize=100       ,     ):
      if data is not None:
        yield data
def 				a__   (   a   :				Type[Dataset]       ,     a   :				float		):

  """simple docstring"""







  _snake_case     :						Union[str, Any]						=   DuplicationIndex(duplication_jaccard_threshold=a		)

  for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(a		)		)       ,     max_queue_size=100		)		):
    di.add(a       ,     a		)

  # Returns a List[Cluster] where Cluster is List[str] with the filenames.
  return di.get_duplicate_clusters()
def 				a__   (   a   :				str       ,     a   :				str		):

  """simple docstring"""







  _snake_case     :						Tuple						=   get_tokens(a		)
  _snake_case     :						List[Any]						=   get_tokens(a		)
  return len(tokensa & tokensa		) / len(tokensa | tokensa		)


_a      :			List[Any]					=							None
def 				a__   (   a   :				Optional[int]       ,     a   :				Union[str, Any]		):

  """simple docstring"""







  _snake_case     :						Optional[Any]						=   []
  for elementa in cluster:
    _snake_case     :						Any						=   _shared_dataset[elementa["base_index"]]["content"]
    for elementa in extremes:
      _snake_case     :						Dict						=   _shared_dataset[elementa["base_index"]]["content"]
      if jaccard_similarity(a       ,     a		) >= jaccard_threshold:
        elementa["copies"] += 1
        break
    else:
      _snake_case     :						str						=   1
      extremes.append(a		)
  return extremes
def 				a__   (   a   :				Union[str, Any]       ,     a   :				Any       ,     a   :				Optional[int]		):

  """simple docstring"""







  global _shared_dataset
  _snake_case     :						int						=   dataset
  _snake_case     :						Dict						=   []
  _snake_case     :						Union[str, Any]						=   partial(_find_cluster_extremes_shared       ,     jaccard_threshold=a		)
  with mp.Pool() as pool:
    for extremes in tqdm(
        pool.imap_unordered(
            a       ,     a       ,     )       ,     total=len(a		)       ,     ):
      extremes_list.append(a		)
  return extremes_list



def 				a__   (   a   :				Type[Dataset]       ,     a   :				float = 0.85		):

  """simple docstring"""







  _snake_case     :						Tuple						=   make_duplicate_clusters(a       ,     a		)
  _snake_case     :						Tuple						=   {x["base_index"] for cluster in duplicate_clusters for x in cluster}
  _snake_case     :						Union[str, Any]						=   {}
  _snake_case     :						Dict						=   find_extremes(a       ,     a       ,     a		)
  for extremes in extremes_clusters:
    for element in extremes:
      _snake_case     :						Optional[int]						=   element
  _snake_case     :						List[Any]						=   duplicate_indices - set(extreme_dict.keys()		)
  _snake_case     :						Tuple						=   dataset.filter(lambda a       ,     a		: idx not in remove_indices       ,     with_indices=a		)

  # update duplicate_clusters
  for cluster in duplicate_clusters:
    for element in cluster:
      _snake_case     :						Tuple						=   element["base_index"] in extreme_dict
      if element["is_extreme"]:
        _snake_case     :						Any						=   extreme_dict[element["base_index"]]["copies"]

  print(f'Original dataset size: {len(a		)}'		)
  print(f'Number of duplicate clusters: {len(a		)}'		)
  print(f'Files in duplicate cluster: {len(a		)}'		)
  print(f'Unique files in duplicate cluster: {len(a		)}'		)
  print(f'Filtered dataset size: {len(a		)}'		)

  return ds_filter, duplicate_clusters








"""simple docstring"""
from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
    _a      :			Dict					=							input("""Enter image url: """).strip()
    print(f'Downloading image from {url} ...')
    _a      :			str					=							BeautifulSoup(requests.get(url).content, """html.parser""")
    # The image URL is in the content field of the first meta tag with property og:image
    _a      :			str					=							soup.find("""meta""", {"""property""": """og:image"""})["""content"""]
    _a      :			Dict					=							requests.get(image_url).content
    _a      :			str					=							f'{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg'
    with open(file_name, """wb""") as fp:
        fp.write(image_data)
    print(f'Done. Image saved to disk as {file_name}.')

