






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:     Optional[int]   					=					re.compile('''[^A-Za-z_0-9]''')
# parameters used in DuplicationIndex
__a:     List[str]   					=					10
__a:     List[str]   					=					256




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Optional[MinHash]:
       if len(__snake_case ) < MIN_NUM_TOKENS:
              return None
       _UpperCAmelCase							=    MinHash(num_perm=__snake_case )
       for token in set(__snake_case ):
              min_hash.update(token.encode() )
       return min_hash




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Set[str]:
       return {t for t in NON_ALPHA.split(__snake_case ) if len(t.strip() ) > 0}
class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  def __init__(      self		:			Any       ,				*,
      lowerCamelCase		:			float = 0.85       ,				)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    duplication_jaccard_threshold
         _UpperCAmelCase							=    NUM_PERM
         _UpperCAmelCase							=    MinHashLSH(threshold=self._duplication_jaccard_threshold       ,				num_perm=self._num_perm							)

         _UpperCAmelCase							=    defaultdict(lowerCamelCase							)


  def  lowerCamelCase       (      self		:			List[Any]       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			MinHash							)						->	None:


         """simple docstring"""
         _UpperCAmelCase							=    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  lowerCamelCase       (      self		:			List[Any]							)						->	List[List[Dict]]:


         """simple docstring"""
         _UpperCAmelCase							=    []
         for base, duplicates in self._duplicate_clusters.items():
                _UpperCAmelCase							=    [base] + list(lowerCamelCase							)
                # 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(lowerCamelCase							)
         return duplicate_clusters





  def  lowerCamelCase       (      self		:			Optional[int]       ,				lowerCamelCase		:			int							)						->	None:


         """simple docstring"""
         _UpperCAmelCase							=    self.get_duplicate_clusters()
         with open(lowerCamelCase       ,				"""w"""							) as f:
                json.dump(lowerCamelCase       ,				lowerCamelCase							)




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Optional[Any]:
       _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       _SCREAMING_SNAKE_CASE (							__snake_case )				->						List[Any]:
       with mp.Pool() as pool:
              for data in pool.imap_unordered(
                  _compute_min_hash   ,      ThreadedIterator(__snake_case   ,      max_queue_size=1_0_0_0_0 )   ,      chunksize=1_0_0   ,      ):
                     if data is not None:
                            yield data




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						Tuple:
       _UpperCAmelCase							=    DuplicationIndex(duplication_jaccard_threshold=__snake_case )

       for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(__snake_case ) )   ,      max_queue_size=1_0_0 ) ):
              di.add(__snake_case   ,      __snake_case )

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




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						float:
       _UpperCAmelCase							=    get_tokens(__snake_case )
       _UpperCAmelCase							=    get_tokens(__snake_case )
       return len(tokensa & tokensa ) / len(tokensa | tokensa )


__a:     List[str]   					=					None




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						Union[str, Any]:
       _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(__snake_case   ,      __snake_case ) >= jaccard_threshold:
                            elementa["copies"] += 1
                            break
              else:
                     _UpperCAmelCase							=    1
                     extremes.append(__snake_case )
       return extremes




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case )				->						List[str]:
       global _shared_dataset
       _UpperCAmelCase							=    dataset
       _UpperCAmelCase							=    []
       _UpperCAmelCase							=    partial(_find_cluster_extremes_shared   ,      jaccard_threshold=__snake_case )
       with mp.Pool() as pool:
              for extremes in tqdm(
                  pool.imap_unordered(
                      __snake_case   ,      __snake_case   ,      )   ,      total=len(__snake_case )   ,      ):
                     extremes_list.append(__snake_case )
       return extremes_list





def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case = 0.85 )				->						Tuple[Type[Dataset], List[List[Dict]]]:
       _UpperCAmelCase							=    make_duplicate_clusters(__snake_case   ,      __snake_case )
       _UpperCAmelCase							=    {x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
       _UpperCAmelCase							=    {}
       _UpperCAmelCase							=    find_extremes(__snake_case   ,      __snake_case   ,      __snake_case )
       for extremes in extremes_clusters:
              for element in extremes:
                     _UpperCAmelCase							=    element
       _UpperCAmelCase							=    duplicate_indices - set(extreme_dict.keys() )
       _UpperCAmelCase							=    dataset.filter(lambda __snake_case   ,      __snake_case : idx not in remove_indices   ,      with_indices=__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(__snake_case )}""" )
       print(f"""Number of duplicate clusters: {len(__snake_case )}""" )
       print(f"""Files in duplicate cluster: {len(__snake_case )}""" )
       print(f"""Unique files in duplicate cluster: {len(__snake_case )}""" )
       print(f"""Filtered dataset size: {len(__snake_case )}""" )

       return ds_filter, duplicate_clusters




'''simple docstring'''





def 							lowerCamelCase	(					_snake_case	:    int = 50_000_000							):





    '''simple docstring'''


    lowercase__									=    set()
    lowercase__									=    int((limit - 24) ** (1 / 2)							)

    lowercase__									=    set(range(3  ,prime_square_limit + 1  ,2							)							)
    primes.add(2							)
    for p in range(3  ,prime_square_limit + 1  ,2							):
        if p not in primes:
            continue
        primes.difference_update(set(range(p * p  ,prime_square_limit + 1  ,_snake_case							)							)							)

    for primea in primes:
        lowercase__									=    primea * primea
        for primea in primes:
            lowercase__									=    primea * primea * primea
            if square + cube >= limit - 16:
                break
            for primea in primes:
                lowercase__									=    primea * primea * primea * primea
                lowercase__									=    square + cube + tetr
                if total >= limit:
                    break
                ret.add(_snake_case							)

    return len(_snake_case							)


if __name__ == "__main__":
 print(f'''{solution() = }''')




