





'''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


lowerCAmelCase_					:    str      	=			re.compile('[^A-Za-z_0-9]')
# parameters used in DuplicationIndex
lowerCAmelCase_					:    List[str]      	=			10
lowerCAmelCase_					:    Optional[Any]      	=			2_56






def  _lowerCamelCase				( lowercase						:      List[str]						)	->	Optional[MinHash]:
				if len(lowercase						) < MIN_NUM_TOKENS:
								return None
				_a								= MinHash(num_perm=lowercase						)
				for token in set(lowercase						):
								min_hash.update(token.encode()						)
				return min_hash






def  _lowerCamelCase				( lowercase						:      str						)	->	Set[str]:
				return {t for t in NON_ALPHA.split(lowercase						) if len(t.strip()						) > 0}
class 				__SCREAMING_SNAKE_CASE  :






				"""simple docstring"""







				def __init__(  self     :					Dict					,				*,
				    __a     :					float = 0.85					,				):
								_a								= duplication_jaccard_threshold
								_a								= NUM_PERM
								_a								= MinHashLSH(threshold=self._duplication_jaccard_threshold					,				num_perm=self._num_perm					)

								_a								= defaultdict(__a					)




				def   UpperCamelCase__   (  self     :					Dict					,				__a     :					Tuple					,				__a     :					MinHash					):
								_a								= self._index.query(__a					)
								if code_key in self._index.keys:
												print(f'Duplicate key {code_key}'					)
												return

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




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




				def   UpperCamelCase__   (  self     :					Optional[int]					,				__a     :					Any					):
								_a								= self.get_duplicate_clusters()
								with open(__a					,				"w"					) as f:
												json.dump(__a					,				__a					)






def  _lowerCamelCase				( lowercase						:      Optional[int]						)	->	Optional[Any]:
				_a	,					_a								= element
				_a								= 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				( lowercase						:      Type[Dataset]						)	->	Optional[int]:
				with mp.Pool() as pool:
								for data in pool.imap_unordered(
								    _compute_min_hash    ,							ThreadedIterator(lowercase    ,							max_queue_size=1_0000						)    ,							chunksize=100    ,							):
												if data is not None:
																yield data






def  _lowerCamelCase				( lowercase						:      Type[Dataset]    ,							lowercase						:      float						)	->	Union[str, Any]:
				_a								= DuplicationIndex(duplication_jaccard_threshold=lowercase						)

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

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






def  _lowerCamelCase				( lowercase						:      str    ,							lowercase						:      str						)	->	float:
				_a								= get_tokens(lowercase						)
				_a								= get_tokens(lowercase						)
				return len(tokensa & tokensa						) / len(tokensa | tokensa						)


lowerCAmelCase_					:    Dict      	=			None






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






def  _lowerCamelCase				( lowercase						:      List[Any]    ,							lowercase						:      Dict    ,							lowercase						:      Dict						)	->	Optional[Any]:
				global _shared_dataset
				_a								= dataset
				_a								= []
				_a								= partial(_find_cluster_extremes_shared    ,							jaccard_threshold=lowercase						)
				with mp.Pool() as pool:
								for extremes in tqdm(
								    pool.imap_unordered(
								        lowercase    ,							lowercase    ,							)    ,							total=len(lowercase						)    ,							):
												extremes_list.append(lowercase						)
				return extremes_list






def  _lowerCamelCase				( lowercase						:      Type[Dataset]    ,							lowercase						:      float = 0.85						)	->	Tuple[Type[Dataset], List[List[Dict]]]:
				_a								= make_duplicate_clusters(lowercase    ,							lowercase						)
				_a								= {x["base_index"] for cluster in duplicate_clusters for x in cluster}
				_a								= {}
				_a								= find_extremes(lowercase    ,							lowercase    ,							lowercase						)
				for extremes in extremes_clusters:
								for element in extremes:
												_a								= element
				_a								= duplicate_indices - set(extreme_dict.keys()						)
				_a								= dataset.filter(lambda lowercase    ,							lowercase						: idx not in remove_indices    ,							with_indices=lowercase						)

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

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

				return ds_filter, duplicate_clusters








import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
	from transformers.models.roberta.modeling_flax_roberta import (
	    FlaxRobertaForCausalLM,
	    FlaxRobertaForMaskedLM,
	    FlaxRobertaForMultipleChoice,
	    FlaxRobertaForQuestionAnswering,
	    FlaxRobertaForSequenceClassification,
	    FlaxRobertaForTokenClassification,
	    FlaxRobertaModel,
	)




class  UpperCamelCase_      (			unittest.TestCase				):



	'''simple docstring'''




	def __init__(       self       ,			a       ,			a=13       ,			a=7       ,			a=True       ,			a=True       ,			a=True       ,			a=True       ,			a=99       ,			a=32       ,			a=5       ,			a=4       ,			a=37       ,			a="gelu"       ,			a=0.1       ,			a=0.1       ,			a=5_12       ,			a=16       ,			a=2       ,			a=0.02       ,			a=4       ,			)       ->    int:
								snake_case_				=							parent
								snake_case_				=							batch_size
								snake_case_				=							seq_length
								snake_case_				=							is_training
								snake_case_				=							use_attention_mask
								snake_case_				=							use_token_type_ids
								snake_case_				=							use_labels
								snake_case_				=							vocab_size
								snake_case_				=							hidden_size
								snake_case_				=							num_hidden_layers
								snake_case_				=							num_attention_heads
								snake_case_				=							intermediate_size
								snake_case_				=							hidden_act
								snake_case_				=							hidden_dropout_prob
								snake_case_				=							attention_probs_dropout_prob
								snake_case_				=							max_position_embeddings
								snake_case_				=							type_vocab_size
								snake_case_				=							type_sequence_label_size
								snake_case_				=							initializer_range
								snake_case_				=							num_choices




	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							ids_tensor([self.batch_size, self.seq_length]       ,			self.vocab_size					)

								snake_case_				=							None
								if self.use_attention_mask:
															snake_case_				=							random_attention_mask([self.batch_size, self.seq_length]					)

								snake_case_				=							None
								if self.use_token_type_ids:
															snake_case_				=							ids_tensor([self.batch_size, self.seq_length]       ,			self.type_vocab_size					)

								snake_case_				=							RobertaConfig(
								    vocab_size=self.vocab_size       ,			hidden_size=self.hidden_size       ,			num_hidden_layers=self.num_hidden_layers       ,			num_attention_heads=self.num_attention_heads       ,			intermediate_size=self.intermediate_size       ,			hidden_act=self.hidden_act       ,			hidden_dropout_prob=self.hidden_dropout_prob       ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,			max_position_embeddings=self.max_position_embeddings       ,			type_vocab_size=self.type_vocab_size       ,			is_decoder=a       ,			initializer_range=self.initializer_range       ,			)

								return config, input_ids, token_type_ids, attention_mask




	def 							_UpperCamelCase			(       self					)       ->    List[Any]:
								snake_case_				=							self.prepare_config_and_inputs()
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							config_and_inputs
								snake_case_				=							{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
								return config, inputs_dict






	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							self.prepare_config_and_inputs()
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							config_and_inputs

								snake_case_				=							True
								snake_case_				=							floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
								snake_case_				=							ids_tensor([self.batch_size, self.seq_length]       ,			vocab_size=2					)

								return (
								    config,
								    input_ids,
								    token_type_ids,
								    encoder_hidden_states,
								    encoder_attention_mask,
								)






@require_flax
class  UpperCamelCase_      (			snake_case_       ,					unittest.TestCase				):



	'''simple docstring'''




	lowerCAmelCase													= True

	lowerCAmelCase													= (
	    (
	        FlaxRobertaModel,
	        FlaxRobertaForCausalLM,
	        FlaxRobertaForMaskedLM,
	        FlaxRobertaForSequenceClassification,
	        FlaxRobertaForTokenClassification,
	        FlaxRobertaForMultipleChoice,
	        FlaxRobertaForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)




	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							FlaxRobertaModelTester(self					)






	@slow
	def 							_UpperCamelCase			(       self					)       ->    str:
								for model_class_name in self.all_model_classes:
															snake_case_				=							model_class_name.from_pretrained('roberta-base'       ,			from_pt=a					)
															snake_case_				=							model(np.ones((1, 1)					)					)
															self.assertIsNotNone(a					)





