





"""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_   :   str        =       1_0
snake_case_   :   str        =       2_5_6



def   lowercase_	(    _lowercase       :		List[str]	):




					'''simple docstring'''






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



def   lowercase_	(    _lowercase       :		str	):




					'''simple docstring'''






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







class       snake_case__  :







			def __init__(			self			: Union[str, Any]			,  *,
			    lowercase			: Union[str, Any] = 0.8_5			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[int]      =				duplication_jaccard_threshold
								UpperCAmelCase			:   List[Any]      =				NUM_PERM
								UpperCAmelCase			:   Union[str, Any]      =				MinHashLSH(threshold=self._duplication_jaccard_threshold			,  num_perm=self._num_perm  )

								UpperCAmelCase			:   Optional[Any]      =				defaultdict(lowercase__  )







			def 			__lowerCAmelCase				(			self			: str			,  lowercase			: Optional[Any]			,  lowercase			: Optional[int]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Dict      =				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 			__lowerCAmelCase				(			self			: Union[str, Any]  ):





								'''simple docstring'''



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







			def 			__lowerCAmelCase				(			self			: Tuple			,  lowercase			: Union[str, Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   int      =				self.get_duplicate_clusters()
								with open(lowercase__			,  "w"  ) as f:
													json.dump(lowercase__			,  lowercase__  )



def   lowercase_	(    _lowercase       :		Union[str, Any]	):




					'''simple docstring'''






					UpperCAmelCase			:   Optional[Any]      =				element
					UpperCAmelCase			:   Any      =				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   lowercase_	(    _lowercase       :		Type[Dataset]	):




					'''simple docstring'''






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



def   lowercase_	(    _lowercase       :		Type[Dataset]							,      _lowercase       :		float	):




					'''simple docstring'''






					UpperCAmelCase			:   Dict      =				DuplicationIndex(duplication_jaccard_threshold=SCREAMING_SNAKE_CASE_	)

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

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



def   lowercase_	(    _lowercase       :		str							,      _lowercase       :		str	):




					'''simple docstring'''






					UpperCAmelCase			:   int      =				get_tokens(SCREAMING_SNAKE_CASE_	)
					UpperCAmelCase			:   Dict      =				get_tokens(SCREAMING_SNAKE_CASE_	)
					return len(tokensa & tokensa	) / len(tokensa | tokensa	)


snake_case_   :   int        =       None



def   lowercase_	(    _lowercase       :		Any							,      _lowercase       :		Optional[Any]	):




					'''simple docstring'''






					UpperCAmelCase			:   str      =				[]
					for elementa in cluster:
										UpperCAmelCase			:   Optional[Any]      =				_shared_dataset[elementa["base_index"]]["content"]
										for elementa in extremes:
															UpperCAmelCase			:   Optional[int]      =				_shared_dataset[elementa["base_index"]]["content"]
															if jaccard_similarity(SCREAMING_SNAKE_CASE_							,      SCREAMING_SNAKE_CASE_	) >= jaccard_threshold:
																				elementa["copies"] += 1
																				break
										else:
															UpperCAmelCase			:   Dict      =				1
															extremes.append(SCREAMING_SNAKE_CASE_	)
					return extremes



def   lowercase_	(    _lowercase       :		List[Any]							,      _lowercase       :		Union[str, Any]							,      _lowercase       :		Optional[int]	):




					'''simple docstring'''






					global _shared_dataset
					UpperCAmelCase			:   Optional[int]      =				dataset
					UpperCAmelCase			:   Union[str, Any]      =				[]
					UpperCAmelCase			:   Tuple      =				partial(_find_cluster_extremes_shared							,      jaccard_threshold=SCREAMING_SNAKE_CASE_	)
					with mp.Pool() as pool:
										for extremes in tqdm(
										    pool.imap_unordered(
										        SCREAMING_SNAKE_CASE_							,      SCREAMING_SNAKE_CASE_							,      )							,      total=len(SCREAMING_SNAKE_CASE_	)							,      ):
															extremes_list.append(SCREAMING_SNAKE_CASE_	)
					return extremes_list




def   lowercase_	(    _lowercase       :		Type[Dataset]							,      _lowercase       :		float = 0.8_5	):




					'''simple docstring'''






					UpperCAmelCase			:   int      =				make_duplicate_clusters(SCREAMING_SNAKE_CASE_							,      SCREAMING_SNAKE_CASE_	)
					UpperCAmelCase			:   List[str]      =				{x["base_index"] for cluster in duplicate_clusters for x in cluster}
					UpperCAmelCase			:   Optional[int]      =				{}
					UpperCAmelCase			:   Optional[int]      =				find_extremes(SCREAMING_SNAKE_CASE_							,      SCREAMING_SNAKE_CASE_							,      SCREAMING_SNAKE_CASE_	)
					for extremes in extremes_clusters:
										for element in extremes:
															UpperCAmelCase			:   Dict      =				element
					UpperCAmelCase			:   Any      =				duplicate_indices - set(extreme_dict.keys()	)
					UpperCAmelCase			:   Any      =				dataset.filter(lambda _lowercase							,      _lowercase	: idx not in remove_indices							,      with_indices=SCREAMING_SNAKE_CASE_	)

					# update duplicate_clusters
					for cluster in duplicate_clusters:
										for element in cluster:
															UpperCAmelCase			:   str      =				element["base_index"] in extreme_dict
															if element["is_extreme"]:
																				UpperCAmelCase			:   Any      =				extreme_dict[element["base_index"]]["copies"]

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

					return ds_filter, duplicate_clusters





"""simple docstring"""






import math



def   lowercase_	(    _lowercase       :		int	):




					'''simple docstring'''






					return math.sqrt(_lowercase	) * math.sqrt(_lowercase	) == num



def   lowercase_	(    _lowercase       :		int	):




					'''simple docstring'''






					UpperCAmelCase			:   List[Any]      =				0
					UpperCAmelCase			:   Tuple      =				n
					while left <= right:
										UpperCAmelCase			:   Tuple      =				(left + right) // 2
										if mid**2 == n:
															return True
										elif mid**2 > n:
															UpperCAmelCase			:   List[str]      =				mid - 1
										else:
															UpperCAmelCase			:   Any      =				mid + 1
					return False


if __name__ == "__main__":
	import doctest

	doctest.testmod()


