






"""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


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



def 					_A			(__a							) ->       Optional[MinHash]:
    """simple docstring"""






    if len(__a							) < MIN_NUM_TOKENS:
        return None
    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		MinHash(num_perm=__a							)
    for token in set(__a							):
        min_hash.update(token.encode()							)
    return min_hash



def 					_A			(__a							) ->       Set[str]:
    """simple docstring"""






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




class 							lowerCAmelCase__				:






      '''simple docstring'''

      def __init__(						self     :  Dict						,	*,
          lowercase_     :  float = 0.85						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		duplication_jaccard_threshold
          SCREAMING_SNAKE_CASE_      :  Any    						=		NUM_PERM
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		MinHashLSH(threshold=self._duplication_jaccard_threshold						,	num_perm=self._num_perm)

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		defaultdict(lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]						,	lowercase_     :  Tuple						,	lowercase_     :  MinHash):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		self._index.query(lowercase_)
          if code_key in self._index.keys:
              print(F'Duplicate key {code_key}')
              return

          self._index.insert(lowercase_						,	lowercase_)
          if len(lowercase_) > 0:
              for base_duplicate in close_duplicates:
                  if base_duplicate in self._duplicate_clusters:
                      self._duplicate_clusters[base_duplicate].add(lowercase_)
                      break
              else:
                  self._duplicate_clusters[close_duplicates[0]].add(lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


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



      def _SCREAMING_SNAKE_CASE (						self     :  int						,	lowercase_     :  List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  str    						=		self.get_duplicate_clusters()
          with open(lowercase_						,	'''w''') as f:
              json.dump(lowercase_						,	lowercase_)



def 					_A			(__a							) ->       Union[str, Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  List[Any]    						=		element
    SCREAMING_SNAKE_CASE_      :  Dict    						=		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							) ->       List[Any]:
    """simple docstring"""






    with mp.Pool() as pool:
        for data in pool.imap_unordered(
            _compute_min_hash    ,				ThreadedIterator(__a    ,				max_queue_size=1_00_00							)    ,				chunksize=1_00    ,				):
            if data is not None:
                yield data



def 					_A			(__a    ,				__a							) ->       str:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  int    						=		DuplicationIndex(duplication_jaccard_threshold=__a							)

    for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(__a							)							)    ,				max_queue_size=1_00							)							):
        di.add(__a    ,				__a							)

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



def 					_A			(__a    ,				__a							) ->       float:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Any    						=		get_tokens(__a							)
    SCREAMING_SNAKE_CASE_      :  Any    						=		get_tokens(__a							)
    return len(tokensa & tokensa							) / len(tokensa | tokensa							)


UpperCAmelCase_   : List[str]   			= None



def 					_A			(__a    ,				__a							) ->       Optional[int]:
    """simple docstring"""






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



def 					_A			(__a    ,				__a    ,				__a							) ->       List[str]:
    """simple docstring"""






    global _shared_dataset
    SCREAMING_SNAKE_CASE_      :  Any    						=		dataset
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		[]
    SCREAMING_SNAKE_CASE_      :  str    						=		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    ,				__a = 0.85							) ->       Tuple[Type[Dataset], List[List[Dict]]]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		make_duplicate_clusters(__a    ,				__a							)
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		{x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		{}
    SCREAMING_SNAKE_CASE_      :  int    						=		find_extremes(__a    ,				__a    ,				__a							)
    for extremes in extremes_clusters:
        for element in extremes:
            SCREAMING_SNAKE_CASE_      :  int    						=		element
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		duplicate_indices - set(extreme_dict.keys()							)
    SCREAMING_SNAKE_CASE_      :  Any    						=		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:
            SCREAMING_SNAKE_CASE_      :  Dict    						=		element['''base_index'''] in extreme_dict
            if element["is_extreme"]:
                SCREAMING_SNAKE_CASE_      :  Union[str, 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




__A				={str(digit): digit**5 for digit in range(1_0)}





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				return sum(DIGITS_FIFTH_POWER[digit] for digit in str(lowerCamelCase__    )    )





def 			lowerCamelCase_ (				):
				return sum(
				    number
				    for number in range(1_0_0_0	, 1_0_0_0_0_0_0    )
				    if number == digits_fifth_powers_sum(lowerCamelCase__    )    )


if __name__ == "__main__":
			print(solution())


