






'''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


lowerCamelCase_												=					re.compile('''[^A-Za-z_0-9]''')
# parameters used in DuplicationIndex
lowerCamelCase_												=					10
lowerCamelCase_												=					2_56

def 					__lowercase						(			__lowercase       )       ->						Optional[MinHash]:

      '''simple docstring'''


      if len(__lowercase       ) < MIN_NUM_TOKENS:
            return None
      _A			     =	MinHash(num_perm=__lowercase       )
      for token in set(__lowercase       ):
            min_hash.update(token.encode()       )
      return min_hash

def 					__lowercase						(			__lowercase       )       ->						Set[str]:

      '''simple docstring'''


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

class    _UpperCAmelCase			:




   """simple docstring"""

   def __init__(		self :	List[Any]      ,      *,
       __UpperCAmelCase :	float = 0.85      ,      ):





         '''simple docstring'''





         _A			     =	duplication_jaccard_threshold
         _A			     =	NUM_PERM
         _A			     =	MinHashLSH(threshold=self._duplication_jaccard_threshold      ,      num_perm=self._num_perm		)

         _A			     =	defaultdict(__UpperCAmelCase		)

   def 						lowerCAmelCase (		self :	List[Any]      ,      __UpperCAmelCase :	Tuple      ,      __UpperCAmelCase :	MinHash		):





         '''simple docstring'''





         _A			     =	self._index.query(__UpperCAmelCase		)
         if code_key in self._index.keys:
               print(f'''Duplicate key {code_key}'''		)
               return

         self._index.insert(__UpperCAmelCase      ,      __UpperCAmelCase		)
         if len(__UpperCAmelCase		) > 0:
               for base_duplicate in close_duplicates:
                     if base_duplicate in self._duplicate_clusters:
                           self._duplicate_clusters[base_duplicate].add(__UpperCAmelCase		)
                           break
               else:
                     self._duplicate_clusters[close_duplicates[0]].add(__UpperCAmelCase		)

   def 						lowerCAmelCase (		self :	Tuple		):





         '''simple docstring'''





         _A			     =	[]
         for base, duplicates in self._duplicate_clusters.items():
               _A			     =	[base] + list(__UpperCAmelCase		)
               # reformat the cluster to be a list of dict
               _A			     =	[{"base_index": el[0], "repo_name": el[1], "path": el[2]} for el in cluster]
               duplicate_clusters.append(__UpperCAmelCase		)
         return duplicate_clusters

   def 						lowerCAmelCase (		self :	Tuple      ,      __UpperCAmelCase :	int		):





         '''simple docstring'''





         _A			     =	self.get_duplicate_clusters()
         with open(__UpperCAmelCase      ,      "w"		) as f:
               json.dump(__UpperCAmelCase      ,      __UpperCAmelCase		)





def 					__lowercase						(			__lowercase       )       ->						Dict:

      '''simple docstring'''


      _A     ,	_A			     =	element
      _A			     =	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 					__lowercase						(			__lowercase       )       ->						Optional[int]:

      '''simple docstring'''


      with mp.Pool() as pool:
            for data in pool.imap_unordered(
                _compute_min_hash   ,  ThreadedIterator(__lowercase   ,  max_queue_size=1_0000       )   ,  chunksize=100   ,  ):
                  if data is not None:
                        yield data

def 					__lowercase						(			__lowercase   ,  __lowercase       )       ->						Union[str, Any]:

      '''simple docstring'''


      _A			     =	DuplicationIndex(duplication_jaccard_threshold=__lowercase       )

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

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

def 					__lowercase						(			__lowercase   ,  __lowercase       )       ->						float:

      '''simple docstring'''


      _A			     =	get_tokens(__lowercase       )
      _A			     =	get_tokens(__lowercase       )
      return len(tokensa & tokensa       ) / len(tokensa | tokensa       )


lowerCamelCase_												=					None

def 					__lowercase						(			__lowercase   ,  __lowercase       )       ->						int:

      '''simple docstring'''


      _A			     =	[]
      for elementa in cluster:
            _A			     =	_shared_dataset[elementa["base_index"]]["content"]
            for elementa in extremes:
                  _A			     =	_shared_dataset[elementa["base_index"]]["content"]
                  if jaccard_similarity(__lowercase   ,  __lowercase       ) >= jaccard_threshold:
                        elementa["copies"] += 1
                        break
            else:
                  _A			     =	1
                  extremes.append(__lowercase       )
      return extremes

def 					__lowercase						(			__lowercase   ,  __lowercase   ,  __lowercase       )       ->						Optional[Any]:

      '''simple docstring'''


      global _shared_dataset
      _A			     =	dataset
      _A			     =	[]
      _A			     =	partial(_find_cluster_extremes_shared   ,  jaccard_threshold=__lowercase       )
      with mp.Pool() as pool:
            for extremes in tqdm(
                pool.imap_unordered(
                    __lowercase   ,  __lowercase   ,  )   ,  total=len(__lowercase       )   ,  ):
                  extremes_list.append(__lowercase       )
      return extremes_list


def 					__lowercase						(			__lowercase   ,  __lowercase = 0.85       )       ->						Tuple[Type[Dataset], List[List[Dict]]]:

      '''simple docstring'''


      _A			     =	make_duplicate_clusters(__lowercase   ,  __lowercase       )
      _A			     =	{x["base_index"] for cluster in duplicate_clusters for x in cluster}
      _A			     =	{}
      _A			     =	find_extremes(__lowercase   ,  __lowercase   ,  __lowercase       )
      for extremes in extremes_clusters:
            for element in extremes:
                  _A			     =	element
      _A			     =	duplicate_indices - set(extreme_dict.keys()       )
      _A			     =	dataset.filter(lambda __lowercase   ,  __lowercase       : idx not in remove_indices   ,  with_indices=__lowercase       )

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

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

      return ds_filter, duplicate_clusters









from PIL import Image

def       lowercase_		(		_A			:    Image						):

      """simple docstring"""




      lowerCamelCase__    ,   lowerCamelCase__		:				List[str]    =  image.size
      lowerCamelCase__		:				Dict    =  0
      lowerCamelCase__		:				Dict    =  image.load()
      for i in range(_A						):
            for j in range(_A						):
                  lowerCamelCase__		:				List[str]    =  pixels[j, i]
                  mean += pixel
      mean //= width * height

      for j in range(_A						):
            for i in range(_A						):
                  lowerCamelCase__		:				Union[str, Any]    =  255 if pixels[i, j] > mean else 0
      return image


if __name__ == "__main__":
    A      :    int		=					mean_threshold(Image.open("path_to_image").convert("L"))
    image.save("output_image_path")


