


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					:		Optional[Any]      =    re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
_snake_case					:		Optional[int]      =    10
_snake_case					:		Dict      =    256







def     lowerCAmelCase_					( __lowerCamelCase     ):



							if len(a__     ) < MIN_NUM_TOKENS:
														return None
							__snake_case		:      Optional[int]										=       MinHash(num_perm=a__     )
							for token in set(a__     ):
														min_hash.update(token.encode()     )
							return min_hash







def     lowerCAmelCase_					( __lowerCamelCase     ):
							return {t for t in NON_ALPHA.split(a__     ) if len(t.strip()     ) > 0}

class     a		:




							"""simple docstring"""


							def __init__(							self	:  Tuple   ,     *,
							    lowerCamelCase	:  List[Any] = 0.85   ,     )     ->   Dict:
														__snake_case		:      Dict										=       duplication_jaccard_threshold
														__snake_case		:      Union[str, Any]										=       NUM_PERM
														__snake_case		:      Optional[Any]										=       MinHashLSH(threshold=self._duplication_jaccard_threshold   ,     num_perm=self._num_perm    )

														__snake_case		:      int										=       defaultdict(_lowerCamelCase    )

							def    __snake_case (							self	:  Optional[int]   ,     lowerCamelCase	:  Optional[Any]   ,     lowerCamelCase	:  Any    )     ->   None:
														__snake_case		:      Dict										=       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    __snake_case (							self	:  Dict    )     ->   List[List[Dict]]:
														__snake_case		:      Tuple										=       []
														for base, duplicates in self._duplicate_clusters.items():
																					__snake_case		:      Optional[Any]										=       [base] + list(_lowerCamelCase    )
																					# reformat the cluster to be a list of dict
																					__snake_case		:      Optional[Any]										=       [{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
																					duplicate_clusters.append(_lowerCamelCase    )
														return duplicate_clusters






							def    __snake_case (							self	:  Optional[Any]   ,     lowerCamelCase	:  Any    )     ->   None:
														__snake_case		:      List[Any]										=       self.get_duplicate_clusters()
														with open(_lowerCamelCase   ,     "w"    ) as f:
																					json.dump(_lowerCamelCase   ,     _lowerCamelCase    )







def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      Any										=       element
							__snake_case		:      Dict										=       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_					( __lowerCamelCase     ):



							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_					( __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      Tuple										=       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_					( __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      Any										=       get_tokens(a__     )
							__snake_case		:      Union[str, Any]										=       get_tokens(a__     )
							return len(tokensa & tokensa     ) / len(tokensa | tokensa     )


_snake_case					:		List[str]      =    None







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      List[Any]										=       []
							for elementa in cluster:
														__snake_case		:      List[Any]										=       _shared_dataset[elementa['''base_index''']]['''content''']
														for elementa in extremes:
																					__snake_case		:      str										=       _shared_dataset[elementa['''base_index''']]['''content''']
																					if jaccard_similarity(a__							,     a__     ) >= jaccard_threshold:
																												elementa["copies"] += 1
																												break
														else:
																					__snake_case		:      Optional[Any]										=       1
																					extremes.append(a__     )
							return extremes







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							global _shared_dataset
							__snake_case		:      Any										=       dataset
							__snake_case		:      List[Any]										=       []
							__snake_case		:      str										=       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_					( __lowerCamelCase							,     __lowerCamelCase = 0.8_5     ):
							__snake_case		:      str										=       make_duplicate_clusters(a__							,     a__     )
							__snake_case		:      Optional[Any]										=       {x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
							__snake_case		:      List[Any]										=       {}
							__snake_case		:      Tuple										=       find_extremes(a__							,     a__							,     a__     )
							for extremes in extremes_clusters:
														for element in extremes:
																					__snake_case		:      Optional[int]										=       element
							__snake_case		:      int										=       duplicate_indices - set(extreme_dict.keys()     )
							__snake_case		:      Dict										=       dataset.filter(lambda __lowerCamelCase							,     __lowerCamelCase     : idx not in remove_indices							,     with_indices=a__     )

							# update duplicate_clusters
							for cluster in duplicate_clusters:
														for element in cluster:
																					__snake_case		:      Optional[Any]										=       element['''base_index'''] in extreme_dict
																					if element["is_extreme"]:
																												__snake_case		:      Union[str, Any]										=       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







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							if index == r:
														for j in range(__lowerCamelCase     ):
																					print(data[j]							,     end=" "     )
														print(" "     )
														return
							#  When no more elements are there to put in data[]
							if i >= n:
														return
							# current is included, put next at next location
							__snake_case		:      Union[str, Any]										=       arr[i]
							combination_util(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     index + 1							,     __lowerCamelCase							,     i + 1     )
							# current is excluded, replace it with
							# next (Note that i+1 is passed, but
							# index is not changed)
							combination_util(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     i + 1     )
							# The main function that prints all combinations
							# of size r in arr[] of size n. This function
							# mainly uses combinationUtil()







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							# A temporary array to store all combination one by one
							__snake_case		:      Union[str, Any]										=       [0] * r
							# Print all combination using temporary array 'data[]'
							combination_util(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     0							,     __lowerCamelCase							,     0     )


if __name__ == "__main__":
			# Driver code to check the function above
			_snake_case					:		List[str]      =    [10, 20, 30, 40, 50]
			print_combination(arr, len(arr), 3)
			# This code is contributed by Ambuj sahu

