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



def   a_						(					lowerCamelCase       ):
				if len(lowerCamelCase       ) < MIN_NUM_TOKENS:
								return None
				UpperCAmelCase__             =							MinHash(num_perm=lowerCamelCase       )
				for token in set(lowerCamelCase       ):
								min_hash.update(token.encode()       )
				return min_hash



def   a_						(					lowerCamelCase       ):
				return {t for t in NON_ALPHA.split(lowerCamelCase       ) if len(t.strip()       ) > 0}
class 				snake_case       :




					"""simple docstring"""

					def __init__(				self   :	Any    ,*,
					    lowerCamelCase__   :	float = 0.8_5    ,):
									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			):
									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]			):
									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   :	Dict    ,lowerCamelCase__   :	Optional[int]			):
									UpperCAmelCase__             =							self.get_duplicate_clusters()
									with open(lowerCamelCase__    ,'w'			) as f:
													json.dump(lowerCamelCase__    ,lowerCamelCase__			)






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



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							DuplicationIndex(duplication_jaccard_threshold=lowerCamelCase       )

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

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



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							get_tokens(lowerCamelCase       )
				UpperCAmelCase__             =							get_tokens(lowerCamelCase       )
				return len(tokensa & tokensa       ) / len(tokensa | tokensa       )


lowerCAmelCase__  :  Any						     =	None



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				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(lowerCamelCase					,			lowerCamelCase       ) >= jaccard_threshold:
																elementa["copies"] += 1
																break
								else:
												UpperCAmelCase__             =							1
												extremes.append(lowerCamelCase       )
				return extremes



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







def   a_						(					lowerCamelCase					,			lowerCamelCase = 0.85       ):
				UpperCAmelCase__             =							make_duplicate_clusters(lowerCamelCase					,			lowerCamelCase       )
				UpperCAmelCase__             =							{x['base_index'] for cluster in duplicate_clusters for x in cluster}
				UpperCAmelCase__             =							{}
				UpperCAmelCase__             =							find_extremes(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )
				for extremes in extremes_clusters:
								for element in extremes:
												UpperCAmelCase__             =							element
				UpperCAmelCase__             =							duplicate_indices - set(extreme_dict.keys()       )
				UpperCAmelCase__             =							dataset.filter(lambda lowerCamelCase					,			lowerCamelCase       : idx not in remove_indices					,			with_indices=lowerCamelCase       )

				# 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(lowerCamelCase       )}'''       )
				print(f'''Number of duplicate clusters: {len(lowerCamelCase       )}'''       )
				print(f'''Files in duplicate cluster: {len(lowerCamelCase       )}'''       )
				print(f'''Unique files in duplicate cluster: {len(lowerCamelCase       )}'''       )
				print(f'''Filtered dataset size: {len(lowerCamelCase       )}'''       )

				return ds_filter, duplicate_clusters




"""simple docstring"""






def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				if a < 0 or b < 0:
								raise ValueError('the value of both inputs must be positive'       )

				UpperCAmelCase__             =							str(bin(lowerCamelCase       )       )[2:]  # remove the leading "0b"
				UpperCAmelCase__             =							str(bin(lowerCamelCase       )       )[2:]  # remove the leading "0b"

				UpperCAmelCase__             =							max(len(lowerCamelCase       )					,			len(lowerCamelCase       )       )

				return "0b" + "".join(
				    str(int(char_a == '1' and char_b == '1'       )       )
				    for char_a, char_b in zip(a_binary.zfill(lowerCamelCase       )					,			b_binary.zfill(lowerCamelCase       )       )       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



