





'''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__							:							Optional[Any] =10
a__							:							Tuple =256
def 				lowercase__   (   __lowercase						:  List[str]     ) ->      Tuple:



       """simple docstring"""






       if len(SCREAMING_SNAKE_CASE__     ) < MIN_NUM_TOKENS:
              return None
       __UpperCamelCase       =  MinHash(num_perm=SCREAMING_SNAKE_CASE__     )
       for token in set(SCREAMING_SNAKE_CASE__     ):
              min_hash.update(token.encode()     )
       return min_hash
def 				lowercase__   (   __lowercase						:  str     ) ->      Optional[int]:



       """simple docstring"""






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

class      snake_case     :




 """simple docstring"""



 def __init__(       self       :							Any							,	*,
     __A       :							str = 0.85							,	):
        __UpperCamelCase       =  duplication_jaccard_threshold
        __UpperCamelCase       =  NUM_PERM
        __UpperCamelCase       =  MinHashLSH(threshold=self._duplication_jaccard_threshold							,	num_perm=self._num_perm	)

        __UpperCamelCase       =  defaultdict(A_	)






 def        _lowerCamelCase					(       self       :							Tuple							,	__A       :							int							,	__A       :							Any	):
        __UpperCamelCase       =  self._index.query(A_	)
        if code_key in self._index.keys:
               print(f'''Duplicate key {code_key}'''	)
               return

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






 def        _lowerCamelCase					(       self       :							Optional[Any]	):
        __UpperCamelCase       =  []
        for base, duplicates in self._duplicate_clusters.items():
               __UpperCamelCase       =  [base] + list(A_	)
               # reformat the cluster to be a list of dict
               __UpperCamelCase       =  [{'base_index': el[0], 'repo_name': el[1], 'path': el[2]} for el in cluster]
               duplicate_clusters.append(A_	)
        return duplicate_clusters






 def        _lowerCamelCase					(       self       :							str							,	__A       :							Union[str, Any]	):
        __UpperCamelCase       =  self.get_duplicate_clusters()
        with open(A_							,	'w'	) as f:
               json.dump(A_							,	A_	)






def 				lowercase__   (   __lowercase						:  Optional[int]     ) ->      Optional[Any]:



       """simple docstring"""






       __UpperCamelCase       ,						__UpperCamelCase       =  element
       __UpperCamelCase       =  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						:  Type[Dataset]     ) ->      Optional[Any]:



       """simple docstring"""






       with mp.Pool() as pool:
              for data in pool.imap_unordered(
                  _compute_min_hash				,  ThreadedIterator(SCREAMING_SNAKE_CASE__				,  max_queue_size=10000     )				,  chunksize=100				,  ):
                     if data is not None:
                            yield data
def 				lowercase__   (   __lowercase						:  Type[Dataset]				,  __lowercase						:  float     ) ->      List[Any]:



       """simple docstring"""






       __UpperCamelCase       =  DuplicationIndex(duplication_jaccard_threshold=SCREAMING_SNAKE_CASE__     )

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

       # Returns a List[Cluster] where Cluster is List[str] with the filenames.
       return di.get_duplicate_clusters()
def 				lowercase__   (   __lowercase						:  str				,  __lowercase						:  str     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  get_tokens(SCREAMING_SNAKE_CASE__     )
       __UpperCamelCase       =  get_tokens(SCREAMING_SNAKE_CASE__     )
       return len(tokensa & tokensa     ) / len(tokensa | tokensa     )


a__							:							List[Any] =None
def 				lowercase__   (   __lowercase						:  str				,  __lowercase						:  int     ) ->      List[Any]:



       """simple docstring"""






       __UpperCamelCase       =  []
       for elementa in cluster:
              __UpperCamelCase       =  _shared_dataset[elementa['base_index']]['content']
              for elementa in extremes:
                     __UpperCamelCase       =  _shared_dataset[elementa['base_index']]['content']
                     if jaccard_similarity(SCREAMING_SNAKE_CASE__				,  SCREAMING_SNAKE_CASE__     ) >= jaccard_threshold:
                            elementa["copies"] += 1
                            break
              else:
                     __UpperCamelCase       =  1
                     extremes.append(SCREAMING_SNAKE_CASE__     )
       return extremes
def 				lowercase__   (   __lowercase						:  str				,  __lowercase						:  Optional[int]				,  __lowercase						:  Any     ) ->      Union[str, Any]:



       """simple docstring"""






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


def 				lowercase__   (   __lowercase						:  Type[Dataset]				,  __lowercase						:  float = 0.8_5     ) ->      List[Any]:



       """simple docstring"""






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

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

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

       return ds_filter, duplicate_clusters




from numpy import exp, pi, sqrt

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Tuple			,     SCREAMING_SNAKE_CASE__			:					float = 0.0			,     SCREAMING_SNAKE_CASE__			:					float = 1.0				):
     return 1 / sqrt(2 * pi * sigma**2				) * exp(-((x - mu) ** 2) / (2 * sigma**2)				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()





