



'''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							:Optional[Any]       = re.compile('''[^A-Za-z_0-9]''')
# parameters used in DuplicationIndex
lowerCamelCase							:Optional[int]       = 1_0
lowerCamelCase							:Dict       = 2_5_6



def      a      (			lowerCamelCase__   ):
   '''simple docstring'''





   if len(a__   ) < MIN_NUM_TOKENS:
      return None
   A_     :						Optional[int]     =					MinHash(num_perm=a__   )
   for token in set(a__   ):
      min_hash.update(token.encode()   )
   return min_hash



def      a      (			lowerCamelCase__   ):
   '''simple docstring'''





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







class      _lowerCAmelCase							:


 def __init__(self	,				*,
     lowercase = 0.85	,				):
    A_     :						Dict     =					duplication_jaccard_threshold
    A_     :						Union[str, Any]     =					NUM_PERM
    A_     :						Optional[Any]     =					MinHashLSH(threshold=self._duplication_jaccard_threshold	,				num_perm=self._num_perm							)

    A_     :						int     =					defaultdict(_lowerCamelCase							)


 def    _a     (self	,				lowercase	,				lowercase							):
    A_     :						Dict     =					self._index.query(_lowerCamelCase							)
    if code_key in self._index.keys:
       print(F'Duplicate key {code_key}'							)
       return

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


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


 def    _a     (self	,				lowercase							):
    A_     :						List[Any]     =					self.get_duplicate_clusters()
    with open(_lowerCamelCase	,				"""w"""							) as f:
       json.dump(_lowerCamelCase	,				_lowerCamelCase							)






def      a      (			lowerCamelCase__   ):
   '''simple docstring'''





   A_     :						Any     =					element
   A_     :						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      (			lowerCamelCase__   ):
   '''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      (			lowerCamelCase__						,				lowerCamelCase__   ):
   '''simple docstring'''





   A_     :						Tuple     =					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      (			lowerCamelCase__						,				lowerCamelCase__   ):
   '''simple docstring'''





   A_     :						Any     =					get_tokens(a__   )
   A_     :						Union[str, Any]     =					get_tokens(a__   )
   return len(tokensa & tokensa   ) / len(tokensa | tokensa   )


lowerCamelCase							:List[str]       = None



def      a      (			lowerCamelCase__						,				lowerCamelCase__   ):
   '''simple docstring'''





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



def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__   ):
   '''simple docstring'''





   global _shared_dataset
   A_     :						Any     =					dataset
   A_     :						List[Any]     =					[]
   A_     :						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      (			lowerCamelCase__						,				lowerCamelCase__ = 0.85   ):
   '''simple docstring'''





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

   # update duplicate_clusters
   for cluster in duplicate_clusters:
      for element in cluster:
         A_     :						Optional[Any]     =					element['''base_index'''] in extreme_dict
         if element["is_extreme"]:
            A_     :						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




'''simple docstring'''




def      a      (			lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			if density <= 0:
						raise ValueError("""Impossible fluid density"""   )
			if bulk_modulus <= 0:
						raise ValueError("""Impossible bulk modulus"""   )

			return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
						import doctest

						doctest.testmod()