





"""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


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



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							List[str]   ):


							'''simple docstring'''







							if len(SCREAMING_SNAKE_CASE   ) < MIN_NUM_TOKENS:
														return None
							__lowerCamelCase      :      List[str]         =MinHash(num_perm=SCREAMING_SNAKE_CASE   )
							for token in set(SCREAMING_SNAKE_CASE   ):
														min_hash.update(token.encode()   )
							return min_hash



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							str   ):


							'''simple docstring'''







							return {t for t in NON_ALPHA.split(SCREAMING_SNAKE_CASE   ) if len(t.strip()   ) > 0}
class 		SCREAMING_SNAKE_CASE_							:
				"""simple docstring"""




				def __init__(     self  :Tuple						,    *,
				    __lowercase  :float = 0.85						,    ):
											__lowerCamelCase      :      Union[str, Any]         =duplication_jaccard_threshold
											__lowerCamelCase      :      Dict         =NUM_PERM
											__lowerCamelCase      :      Dict         =MinHashLSH(threshold=self._duplication_jaccard_threshold						,    num_perm=self._num_perm			)

											__lowerCamelCase      :      Optional[Any]         =defaultdict(__lowercase			)




				def 					__lowercase  (     self  :Any						,    __lowercase  :Tuple						,    __lowercase  :MinHash			):
											__lowerCamelCase      :      Optional[int]         =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 					__lowercase  (     self  :Tuple			):
											__lowerCamelCase      :      List[Any]         =[]
											for base, duplicates in self._duplicate_clusters.items():
																		__lowerCamelCase      :      Union[str, Any]         =[base] + list(__lowercase			)
																		# reformat the cluster to be a list of dict
																		__lowerCamelCase      :      Optional[int]         =[{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
																		duplicate_clusters.append(__lowercase			)
											return duplicate_clusters




				def 					__lowercase  (     self  :Optional[Any]						,    __lowercase  :Union[str, Any]			):
											__lowerCamelCase      :      Any         =self.get_duplicate_clusters()
											with open(__lowercase						,    '''w'''			) as f:
																		json.dump(__lowercase						,    __lowercase			)



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							List[str]   ):


							'''simple docstring'''







							__lowerCamelCase    ,						__lowerCamelCase      :      Union[str, Any]         =element
							__lowerCamelCase      :      List[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 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							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=10000   )    ,						chunksize=100    ,						):
																					if data is not None:
																												yield data



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Type[Dataset]    ,						SCREAMING_SNAKE_CASE       :							float   ):


							'''simple docstring'''







							__lowerCamelCase      :      Any         =DuplicationIndex(duplication_jaccard_threshold=SCREAMING_SNAKE_CASE   )

							for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(SCREAMING_SNAKE_CASE   )   )    ,						max_queue_size=100   )   ):
														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 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							str    ,						SCREAMING_SNAKE_CASE       :							str   ):


							'''simple docstring'''







							__lowerCamelCase      :      Optional[int]         =get_tokens(SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      Union[str, Any]         =get_tokens(SCREAMING_SNAKE_CASE   )
							return len(tokensa & tokensa   ) / len(tokensa | tokensa   )


_UpperCamelCase							=      None



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							List[str]    ,						SCREAMING_SNAKE_CASE       :							Union[str, Any]   ):


							'''simple docstring'''







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



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							List[Any]    ,						SCREAMING_SNAKE_CASE       :							Any    ,						SCREAMING_SNAKE_CASE       :							List[Any]   ):


							'''simple docstring'''







							global _shared_dataset
							__lowerCamelCase      :      str         =dataset
							__lowerCamelCase      :      str         =[]
							__lowerCamelCase      :      Dict         =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 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Type[Dataset]    ,						SCREAMING_SNAKE_CASE       :							float = 0.85   ):


							'''simple docstring'''







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

							# update duplicate_clusters
							for cluster in duplicate_clusters:
														for element in cluster:
																					__lowerCamelCase      :      Dict         =element['''base_index'''] in extreme_dict
																					if element["is_extreme"]:
																												__lowerCamelCase      :      int         =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 warnings

from ...utils import logging
from .image_processing_flava import FlavaImageProcessor


_UpperCamelCase							=      logging.get_logger(__name__)
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""




				def __init__(     self  :List[str]						,    *__lowercase  :int						,    **__lowercase  :Any			):
											warnings.warn(
											    '''The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
											    ''' use FlavaImageProcessor instead.'''						,    __lowercase						,    )
											super().__init__(*__lowercase						,    **__lowercase			)




