'''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


__snake_case							: Tuple			=     re.compile('''[^A-Za-z_0-9]''')
# parameters used in DuplicationIndex
__snake_case							: List[Any]			=     10
__snake_case							: int			=     2_56



def lowerCamelCase__    (							A_							):
 if len(__UpperCamelCase							) < MIN_NUM_TOKENS:
  return None
 UpperCAmelCase_												=       MinHash(num_perm=__UpperCamelCase							)
 for token in set(__UpperCamelCase							):
  min_hash.update(token.encode()							)
 return min_hash



def lowerCamelCase__    (							A_							):
 return {t for t in NON_ALPHA.split(__UpperCamelCase							) if len(t.strip()							) > 0}
class 				lowercase_     :







    def __init__(						self							,  *,
        UpperCamelCase__ = 0.85							,  )					->							List[Any]:





     """simple docstring"""

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

     UpperCAmelCase_												=       defaultdict(_UpperCamelCase					)







    def 	lowerCamelCase_     (						self							,  UpperCamelCase__							,  UpperCamelCase__					)					->							None:





     """simple docstring"""

     UpperCAmelCase_												=       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					)					->							List[List[Dict]]:





     """simple docstring"""

     UpperCAmelCase_												=       []
     for base, duplicates in self._duplicate_clusters.items():
      UpperCAmelCase_												=       [base] + list(_UpperCamelCase					)
      # 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(_UpperCamelCase					)
     return duplicate_clusters







    def 	lowerCamelCase_     (						self							,  UpperCamelCase__					)					->							None:





     """simple docstring"""

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



def lowerCamelCase__    (							A_							):
 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 lowerCamelCase__    (							A_							):
 with mp.Pool() as pool:
  for data in pool.imap_unordered(
      _compute_min_hash							,      ThreadedIterator(__UpperCamelCase							,      max_queue_size=10_000							)							,      chunksize=100							,      ):
   if data is not None:
    yield data



def lowerCamelCase__    (							A_							,      A_							):
 UpperCAmelCase_												=       DuplicationIndex(duplication_jaccard_threshold=__UpperCamelCase							)

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

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



def lowerCamelCase__    (							A_							,      A_							):
 UpperCAmelCase_												=       get_tokens(__UpperCamelCase							)
 UpperCAmelCase_												=       get_tokens(__UpperCamelCase							)
 return len(tokensa & tokensa							) / len(tokensa | tokensa							)


__snake_case							: List[str]			=     None



def lowerCamelCase__    (							A_							,      A_							):
 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(__UpperCamelCase							,      __UpperCamelCase							) >= jaccard_threshold:
    elementa["copies"] += 1
    break
  else:
   UpperCAmelCase_												=       1
   extremes.append(__UpperCamelCase							)
 return extremes



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



def lowerCamelCase__    (							A_							,      A_ = 0.85							):
 UpperCAmelCase_												=       make_duplicate_clusters(__UpperCamelCase							,      __UpperCamelCase							)
 UpperCAmelCase_												=       {x["base_index"] for cluster in duplicate_clusters for x in cluster}
 UpperCAmelCase_												=       {}
 UpperCAmelCase_												=       find_extremes(__UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							)
 for extremes in extremes_clusters:
  for element in extremes:
   UpperCAmelCase_												=       element
 UpperCAmelCase_												=       duplicate_indices - set(extreme_dict.keys()							)
 UpperCAmelCase_												=       dataset.filter(lambda A_							,      A_							: idx not in remove_indices							,      with_indices=__UpperCamelCase							)

 # 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(__UpperCamelCase							)}"""							)
 print(F"""Number of duplicate clusters: {len(__UpperCamelCase							)}"""							)
 print(F"""Files in duplicate cluster: {len(__UpperCamelCase							)}"""							)
 print(F"""Unique files in duplicate cluster: {len(__UpperCamelCase							)}"""							)
 print(F"""Filtered dataset size: {len(__UpperCamelCase							)}"""							)

 return ds_filter, duplicate_clusters




'''simple docstring'''
def 					lowerCamelCase_ ( __UpperCamelCase					:       int							,    __UpperCamelCase					:       int		)					->	int:







     """simple docstring"""
     while second != 0:
          _A				=							first & second
          first ^= second
          _A				=							c << 1
     return first


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    lowerCAmelCase						=					int(input("""Enter the first number: """).strip())
    lowerCAmelCase						=					int(input("""Enter the second number: """).strip())
    print(F'{add(first, second) = }')





