


"""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


a__				:							List[Any]						=     re.compile('''[^A-Za-z_0-9]''')
# parameters used in DuplicationIndex
a__				:							List[str]						=     1_0
a__				:							Optional[Any]						=     2_5_6




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


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




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


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




class  UpperCamelCase_	:

	"""simple docstring"""





	def __init__( self			:				Tuple			,			*,
	    UpperCAmelCase__			:				float = 0.85			,			)       ->	Tuple:
						__SCREAMING_SNAKE_CASE						   =				duplication_jaccard_threshold
						__SCREAMING_SNAKE_CASE						   =				NUM_PERM
						__SCREAMING_SNAKE_CASE						   =				MinHashLSH(threshold=self._duplication_jaccard_threshold			,			num_perm=self._num_perm   )

						__SCREAMING_SNAKE_CASE						   =				defaultdict(UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				int			,			UpperCAmelCase__			:				Tuple			,			UpperCAmelCase__			:				MinHash   )       ->	None:
						__SCREAMING_SNAKE_CASE						   =				self._index.query(UpperCAmelCase__   )
						if code_key in self._index.keys:
											print(F"""Duplicate key {code_key}"""   )
											return

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




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




	def   UpperCAmelCase_      ( self			:				List[str]			,			UpperCAmelCase__			:				Dict   )       ->	None:
						__SCREAMING_SNAKE_CASE						   =				self.get_duplicate_clusters()
						with open(UpperCAmelCase__			,			"w"   ) as f:
											json.dump(UpperCAmelCase__			,			UpperCAmelCase__   )




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				element
					__SCREAMING_SNAKE_CASE						   =				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    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					with mp.Pool() as pool:
										for data in pool.imap_unordered(
										    _compute_min_hash  ,						ThreadedIterator(lowerCAmelCase_  ,						max_queue_size=1_0000   )  ,						chunksize=100  ,						):
															if data is not None:
																				yield data




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				DuplicationIndex(duplication_jaccard_threshold=lowerCAmelCase_   )

					for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(lowerCAmelCase_   )   )  ,						max_queue_size=100   )   ):
										di.add(lowerCAmelCase_  ,						lowerCAmelCase_   )

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




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				get_tokens(lowerCAmelCase_   )
					__SCREAMING_SNAKE_CASE						   =				get_tokens(lowerCAmelCase_   )
					return len(tokensa & tokensa   ) / len(tokensa | tokensa   )


a__				:							Dict						=     None




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


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




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					global _shared_dataset
					__SCREAMING_SNAKE_CASE						   =				dataset
					__SCREAMING_SNAKE_CASE						   =				[]
					__SCREAMING_SNAKE_CASE						   =				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    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_ = 0.85   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				make_duplicate_clusters(lowerCAmelCase_  ,						lowerCAmelCase_   )
					__SCREAMING_SNAKE_CASE						   =				{x["base_index"] for cluster in duplicate_clusters for x in cluster}
					__SCREAMING_SNAKE_CASE						   =				{}
					__SCREAMING_SNAKE_CASE						   =				find_extremes(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )
					for extremes in extremes_clusters:
										for element in extremes:
															__SCREAMING_SNAKE_CASE						   =				element
					__SCREAMING_SNAKE_CASE						   =				duplicate_indices - set(extreme_dict.keys()   )
					__SCREAMING_SNAKE_CASE						   =				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:
															__SCREAMING_SNAKE_CASE						   =				element["base_index"] in extreme_dict
															if element["is_extreme"]:
																				__SCREAMING_SNAKE_CASE						   =				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"""







from abc import ABC, abstractmethod
from argparse import ArgumentParser




class  UpperCamelCase_	( UpperCamelCase):

	"""simple docstring"""





	@staticmethod
	@abstractmethod
	def   UpperCAmelCase_      ( UpperCAmelCase__			:				ArgumentParser   )       ->	int:
						raise NotImplementedError()




	@abstractmethod
	def   UpperCAmelCase_      ( self			:				int   )       ->	Optional[int]:
						raise NotImplementedError()
