



"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_			   =					logging.get_logger(__name__)

a_			   =					{
    '''facebook/dpr-ctx_encoder-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-question_encoder-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-reader-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-ctx_encoder-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-question_encoder-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-reader-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json'''
    ),
}







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Optional[int]							       =		"""dpr"""







						def __init__(self   ,		lowercase__=3_05_22   ,		lowercase__=7_68   ,		lowercase__=12   ,		lowercase__=12   ,		lowercase__=30_72   ,		lowercase__="gelu"   ,		lowercase__=0.1   ,		lowercase__=0.1   ,		lowercase__=5_12   ,		lowercase__=2   ,		lowercase__=0.02   ,		lowercase__=1e-12   ,		lowercase__=0   ,		lowercase__="absolute"   ,		lowercase__ = 0   ,		**lowercase__   ,		):
											super().__init__(pad_token_id=lowercase__   ,		**lowercase__       )

											snake_case_					:		List[Any] 					=					vocab_size
											snake_case_					:		List[str] 					=					hidden_size
											snake_case_					:		Tuple 					=					num_hidden_layers
											snake_case_					:		List[Any] 					=					num_attention_heads
											snake_case_					:		int 					=					hidden_act
											snake_case_					:		Dict 					=					intermediate_size
											snake_case_					:		int 					=					hidden_dropout_prob
											snake_case_					:		Any 					=					attention_probs_dropout_prob
											snake_case_					:		str 					=					max_position_embeddings
											snake_case_					:		List[str] 					=					type_vocab_size
											snake_case_					:		List[str] 					=					initializer_range
											snake_case_					:		Optional[int] 					=					layer_norm_eps
											snake_case_					:		Union[str, Any] 					=					projection_dim
											snake_case_					:		str 					=					position_embedding_type









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







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[str]			):



					"""simple docstring"""





					if len(SCREAMING_SNAKE_CASE__			) < MIN_NUM_TOKENS:
										return None
					snake_case_					:		Union[str, Any] 					=					MinHash(num_perm=SCREAMING_SNAKE_CASE__			)
					for token in set(SCREAMING_SNAKE_CASE__			):
										min_hash.update(token.encode()			)
					return min_hash







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  str			):



					"""simple docstring"""





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







class    __lowercase     :







						"""simple docstring"""







						def __init__(self   ,		*,
						    lowercase__ = 0.85   ,		):
											snake_case_					:		Tuple 					=					duplication_jaccard_threshold
											snake_case_					:		Optional[Any] 					=					NUM_PERM
											snake_case_					:		Tuple 					=					MinHashLSH(threshold=self._duplication_jaccard_threshold   ,		num_perm=self._num_perm       )

											snake_case_					:		List[Any] 					=					defaultdict(lowercase__       )







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







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







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[str]			):



					"""simple docstring"""





					snake_case_					,			snake_case_					:		str 					=					element
					snake_case_					:		Tuple 					=					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 							SCREAMING_SNAKE_CASE__      (		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=1_0_0_0_0			)				,    chunksize=1_0_0				,    ):
															if data is not None:
																				yield data







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Type[Dataset]				,    SCREAMING_SNAKE_CASE__ :  float			):



					"""simple docstring"""





					snake_case_					:		int 					=					DuplicationIndex(duplication_jaccard_threshold=SCREAMING_SNAKE_CASE__			)

					for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(SCREAMING_SNAKE_CASE__			)			)				,    max_queue_size=1_0_0			)			):
										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 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  str			):



					"""simple docstring"""





					snake_case_					:		int 					=					get_tokens(SCREAMING_SNAKE_CASE__			)
					snake_case_					:		Tuple 					=					get_tokens(SCREAMING_SNAKE_CASE__			)
					return len(tokensa & tokensa			) / len(tokensa | tokensa			)


a_			   =					None







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Tuple				,    SCREAMING_SNAKE_CASE__ :  List[str]			):



					"""simple docstring"""





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







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Optional[Any]				,    SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  Union[str, Any]			):



					"""simple docstring"""





					global _shared_dataset
					snake_case_					:		str 					=					dataset
					snake_case_					:		int 					=					[]
					snake_case_					:		Optional[int] 					=					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 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Type[Dataset]				,    SCREAMING_SNAKE_CASE__ :  float = 0.85			):



					"""simple docstring"""





					snake_case_					:		List[str] 					=					make_duplicate_clusters(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
					snake_case_					:		str 					=					{x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
					snake_case_					:		str 					=					{}
					snake_case_					:		Dict 					=					find_extremes(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
					for extremes in extremes_clusters:
										for element in extremes:
															snake_case_					:		int 					=					element
					snake_case_					:		Optional[int] 					=					duplicate_indices - set(extreme_dict.keys()			)
					snake_case_					:		List[Any] 					=					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:
															snake_case_					:		List[Any] 					=					element["""base_index"""] in extreme_dict
															if element["is_extreme"]:
																				snake_case_					:		str 					=					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




