






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      =				re.compile('[^A-Za-z_0-9]')
# parameters used in DuplicationIndex
__A      =				10
__A      =				256




def      __A					(     _lowercase ):

					'''simple docstring'''







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




def      __A					(     _lowercase ):

					'''simple docstring'''







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





class 						SCREAMING_SNAKE_CASE				:





							"""simple docstring"""

							def __init__(  self:   List[str]		,     *,
							    __A:   float = 0.85		,     )    ->				str:
												_A								= duplication_jaccard_threshold
												_A								= NUM_PERM
												_A								= MinHashLSH(threshold=self._duplication_jaccard_threshold		,     num_perm=self._num_perm   )

												_A								= defaultdict(__A   )

							def       __A (  self:   Union[str, Any]		,     __A:   Tuple		,     __A:   MinHash   )    ->				None:
												_A								= self._index.query(__A   )
												if code_key in self._index.keys:
																	print(f"""Duplicate key {code_key}"""   )
																	return

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

							def       __A (  self:   Optional[int]   )    ->				List[List[Dict]]:
												_A								= []
												for base, duplicates in self._duplicate_clusters.items():
																	_A								= [base] + list(__A   )
																	# 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(__A   )
												return duplicate_clusters



							def       __A (  self:   Dict		,     __A:   Any   )    ->				None:
												_A								= self.get_duplicate_clusters()
												with open(__A		,     '''w'''   ) as f:
																	json.dump(__A		,     __A   )




def      __A					(     _lowercase ):

					'''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      __A					(     _lowercase ):

					'''simple docstring'''







					with mp.Pool() as pool:
										for data in pool.imap_unordered(
										    _compute_min_hash					,			ThreadedIterator(_lowercase					,			max_queue_size=1_00_00 )					,			chunksize=1_00					,			):
															if data is not None:
																				yield data




def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= DuplicationIndex(duplication_jaccard_threshold=_lowercase )

					for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(_lowercase ) )					,			max_queue_size=1_00 ) ):
										di.add(_lowercase					,			_lowercase )

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




def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= get_tokens(_lowercase )
					_A								= get_tokens(_lowercase )
					return len(tokensa & tokensa ) / len(tokensa | tokensa )


__A      =				None




def      __A					(     _lowercase					,			_lowercase ):

					'''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(_lowercase					,			_lowercase ) >= jaccard_threshold:
																				elementa["copies"] += 1
																				break
										else:
															_A								= 1
															extremes.append(_lowercase )
					return extremes




def      __A					(     _lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







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




def      __A					(     _lowercase					,			_lowercase = 0.85 ):

					'''simple docstring'''







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

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

					return ds_filter, duplicate_clusters











from __future__ import annotations




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):  # noqa: E741

					'''simple docstring'''







					while r - l > 1:
										_A								= (l + r) // 2
										if v[m] >= key:
															_A								= m
										else:
															_A								= m  # noqa: E741
					return r




def      __A					(     _lowercase ):

					'''simple docstring'''







					if len(_lowercase ) == 0:
										return 0

					_A								= [0] * len(_lowercase )
					_A								= 1

					_A								= v[0]

					for i in range(1					,			len(_lowercase ) ):
										if v[i] < tail[0]:
															_A								= v[i]
										elif v[i] > tail[length - 1]:
															_A								= v[i]
															length += 1
										else:
															_A								= v[i]

					return length


if __name__ == "__main__":
				import doctest

				doctest.testmod()



