

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__						 =			re.compile("""[^A-Za-z_0-9]""")
# parameters used in DuplicationIndex
lowerCAmelCase__						 =			1_0
lowerCAmelCase__						 =			2_5_6



def        lowerCAmelCase__				(						SCREAMING_SNAKE_CASE_:				List[str]   )      ->   Tuple:




				'''simple docstring'''





				if len(__A   ) < MIN_NUM_TOKENS:
								return None
				A__          =      MinHash(num_perm=__A   )
				for token in set(__A   ):
								min_hash.update(token.encode()   )
				return min_hash



def        lowerCAmelCase__				(						SCREAMING_SNAKE_CASE_:				List[str]   )      ->   Optional[Any]:




				'''simple docstring'''





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

class        a__    :
			"""simple docstring"""







			def __init__(     self   ,   *,
			    lowercase = 0.85   ,   )	->    Optional[int]:
							'''simple docstring'''






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

							A__          =      defaultdict(lowercase							)


			def   UpperCamelCase		(     self   ,   lowercase   ,   lowercase							)	->    None:
							'''simple docstring'''






							A__          =      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   UpperCamelCase		(     self							)	->    List[List[Dict]]:
							'''simple docstring'''






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


			def   UpperCamelCase		(     self   ,   lowercase							)	->    None:
							'''simple docstring'''






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



def        lowerCAmelCase__				(						SCREAMING_SNAKE_CASE_:				str   )      ->   Tuple:




				'''simple docstring'''





				A__     ,					A__          =      element
				A__          =      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__				(						SCREAMING_SNAKE_CASE_:				Dict   )      ->   List[Any]:




				'''simple docstring'''





				with mp.Pool() as pool:
								for data in pool.imap_unordered(
								    _compute_min_hash						,							ThreadedIterator(__A						,							max_queue_size=1_0_0_0_0   )						,							chunksize=1_0_0						,							):
												if data is not None:
																yield data



def        lowerCAmelCase__				(						SCREAMING_SNAKE_CASE_:				Union[str, Any]						,							SCREAMING_SNAKE_CASE_:				List[str]   )      ->   Optional[Any]:




				'''simple docstring'''





				A__          =      DuplicationIndex(duplication_jaccard_threshold=__A   )

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

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



def        lowerCAmelCase__				(						SCREAMING_SNAKE_CASE_:				int						,							SCREAMING_SNAKE_CASE_:				List[str]   )      ->   str:




				'''simple docstring'''





				A__          =      get_tokens(__A   )
				A__          =      get_tokens(__A   )
				return len(tokensa & tokensa   ) / len(tokensa | tokensa   )


lowerCAmelCase__						 =			None



def        lowerCAmelCase__				(						SCREAMING_SNAKE_CASE_:				int						,							SCREAMING_SNAKE_CASE_:				List[Any]   )      ->   List[Any]:




				'''simple docstring'''





				A__          =      []
				for elementa in cluster:
								A__          =      _shared_dataset[elementa["base_index"]]["content"]
								for elementa in extremes:
												A__          =      _shared_dataset[elementa["base_index"]]["content"]
												if jaccard_similarity(__A						,							__A   ) >= jaccard_threshold:
																elementa["copies"] += 1
																break
								else:
												A__          =      1
												extremes.append(__A   )
				return extremes



def        lowerCAmelCase__				(						SCREAMING_SNAKE_CASE_:				Optional[int]						,							SCREAMING_SNAKE_CASE_:				str						,							SCREAMING_SNAKE_CASE_:				int   )      ->   str:




				'''simple docstring'''





				global _shared_dataset
				A__          =      dataset
				A__          =      []
				A__          =      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        lowerCAmelCase__				(						SCREAMING_SNAKE_CASE_:				List[str]						,							SCREAMING_SNAKE_CASE_:				str = 0.85   )      ->   Any:




				'''simple docstring'''





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

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





from __future__ import annotations




def lowerCAmelCase    (__A):



     """simple docstring"""



     _a           =    2
     _a           =    []
     while i * i <= n:
          if n % i:
               i += 1
          else:
               n //= i
               factors.append(__A)
     if n > 1:
          factors.append(__A)
     return factors


if __name__ == "__main__":
  import doctest

  doctest.testmod()



