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


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






def _lowerCamelCase  (		snake_case  ):
							if len(snake_case  ) < MIN_NUM_TOKENS:
														return None
							_lowerCAmelCase							=							MinHash(num_perm=snake_case  )
							for token in set(snake_case  ):
														min_hash.update(token.encode()  )
							return min_hash






def _lowerCamelCase  (		snake_case  ):
							return {t for t in NON_ALPHA.split(snake_case  ) if len(t.strip()  ) > 0}



class        lowerCamelCase__				:





			def __init__(       self	:	Dict  ,  *,
			    lowercase__	:	float = 0.8_5  ,  ):
										_lowerCAmelCase							=							duplication_jaccard_threshold
										_lowerCAmelCase							=							NUM_PERM
										_lowerCAmelCase							=							MinHashLSH(threshold=self._duplication_jaccard_threshold  ,  num_perm=self._num_perm						)

										_lowerCAmelCase							=							defaultdict(lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	int  ,  lowercase__	:	Tuple  ,  lowercase__	:	MinHash						):
										_lowerCAmelCase							=							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   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										_lowerCAmelCase							=							[]
										for base, duplicates in self._duplicate_clusters.items():
																	_lowerCAmelCase							=							[base] + list(lowercase__						)
																	# reformat the cluster to be a list of dict
																	_lowerCAmelCase							=							[{'base_index': el[0], 'repo_name': el[1], 'path': el[2]} for el in cluster]
																	duplicate_clusters.append(lowercase__						)
										return duplicate_clusters





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]  ,  lowercase__	:	Optional[Any]						):
										_lowerCAmelCase							=							self.get_duplicate_clusters()
										with open(lowercase__  ,  'w'						) as f:
																	json.dump(lowercase__  ,  lowercase__						)







def _lowerCamelCase  (		snake_case  ):
							_lowerCAmelCase			,		_lowerCAmelCase							=							element
							_lowerCAmelCase							=							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  (		snake_case  ):
							with mp.Pool() as pool:
														for data in pool.imap_unordered(
														    _compute_min_hash     , ThreadedIterator(snake_case     , max_queue_size=10_000  )     , chunksize=100     , ):
																					if data is not None:
																												yield data






def _lowerCamelCase  (		snake_case     , snake_case  ):
							_lowerCAmelCase							=							DuplicationIndex(duplication_jaccard_threshold=snake_case  )

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

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






def _lowerCamelCase  (		snake_case     , snake_case  ):
							_lowerCAmelCase							=							get_tokens(snake_case  )
							_lowerCAmelCase							=							get_tokens(snake_case  )
							return len(tokensa & tokensa  ) / len(tokensa | tokensa  )


_lowercase:	Optional[int]       			=		None






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






def _lowerCamelCase  (		snake_case     , snake_case     , snake_case  ):
							global _shared_dataset
							_lowerCAmelCase							=							dataset
							_lowerCAmelCase							=							[]
							_lowerCAmelCase							=							partial(_find_cluster_extremes_shared     , jaccard_threshold=snake_case  )
							with mp.Pool() as pool:
														for extremes in tqdm(
														    pool.imap_unordered(
														        snake_case     , snake_case     , )     , total=len(snake_case  )     , ):
																					extremes_list.append(snake_case  )
							return extremes_list






def _lowerCamelCase  (		snake_case     , snake_case = 0.85  ):
							_lowerCAmelCase							=							make_duplicate_clusters(snake_case     , snake_case  )
							_lowerCAmelCase							=							{x['base_index'] for cluster in duplicate_clusters for x in cluster}
							_lowerCAmelCase							=							{}
							_lowerCAmelCase							=							find_extremes(snake_case     , snake_case     , snake_case  )
							for extremes in extremes_clusters:
														for element in extremes:
																					_lowerCAmelCase							=							element
							_lowerCAmelCase							=							duplicate_indices - set(extreme_dict.keys()  )
							_lowerCAmelCase							=							dataset.filter(lambda snake_case     , snake_case  : idx not in remove_indices     , with_indices=snake_case  )

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

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

							return ds_filter, duplicate_clusters







import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SqueezeBertForMaskedLM,
				    SqueezeBertForMultipleChoice,
				    SqueezeBertForQuestionAnswering,
				    SqueezeBertForSequenceClassification,
				    SqueezeBertForTokenClassification,
				    SqueezeBertModel,
				)



class        lowerCamelCase__				(   UpperCAmelCase				):





			def __init__(       self	:	Dict  ,  lowercase__	:	Dict  ,  lowercase__	:	Optional[Any]=13  ,  lowercase__	:	Dict=7  ,  lowercase__	:	Dict=True  ,  lowercase__	:	Optional[Any]=True  ,  lowercase__	:	Optional[int]=False  ,  lowercase__	:	Any=True  ,  lowercase__	:	Union[str, Any]=99  ,  lowercase__	:	Optional[int]=32  ,  lowercase__	:	Any=5  ,  lowercase__	:	Any=4  ,  lowercase__	:	List[str]=64  ,  lowercase__	:	Any="gelu"  ,  lowercase__	:	Optional[Any]=0.1  ,  lowercase__	:	List[str]=0.1  ,  lowercase__	:	Dict=5_12  ,  lowercase__	:	List[str]=16  ,  lowercase__	:	Union[str, Any]=2  ,  lowercase__	:	str=0.0_2  ,  lowercase__	:	Optional[int]=3  ,  lowercase__	:	Union[str, Any]=4  ,  lowercase__	:	Union[str, Any]=None  ,  lowercase__	:	Optional[int]=2  ,  lowercase__	:	Optional[int]=2  ,  lowercase__	:	List[Any]=2  ,  lowercase__	:	Optional[int]=2  ,  lowercase__	:	Union[str, Any]=4  ,  lowercase__	:	Tuple=1  ,  ):
										_lowerCAmelCase							=							parent
										_lowerCAmelCase							=							batch_size
										_lowerCAmelCase							=							seq_length
										_lowerCAmelCase							=							is_training
										_lowerCAmelCase							=							use_input_mask
										_lowerCAmelCase							=							use_token_type_ids
										_lowerCAmelCase							=							use_labels
										_lowerCAmelCase							=							vocab_size
										_lowerCAmelCase							=							hidden_size
										_lowerCAmelCase							=							num_hidden_layers
										_lowerCAmelCase							=							num_attention_heads
										_lowerCAmelCase							=							intermediate_size
										_lowerCAmelCase							=							hidden_act
										_lowerCAmelCase							=							hidden_dropout_prob
										_lowerCAmelCase							=							attention_probs_dropout_prob
										_lowerCAmelCase							=							max_position_embeddings
										_lowerCAmelCase							=							type_vocab_size
										_lowerCAmelCase							=							type_sequence_label_size
										_lowerCAmelCase							=							initializer_range
										_lowerCAmelCase							=							num_labels
										_lowerCAmelCase							=							num_choices
										_lowerCAmelCase							=							scope
										_lowerCAmelCase							=							q_groups
										_lowerCAmelCase							=							k_groups
										_lowerCAmelCase							=							v_groups
										_lowerCAmelCase							=							post_attention_groups
										_lowerCAmelCase							=							intermediate_groups
										_lowerCAmelCase							=							output_groups





			def   SCREAMING_SNAKE_CASE__     (       self	:	Any						):
										_lowerCAmelCase							=							ids_tensor([self.batch_size, self.seq_length]  ,  self.vocab_size						)

										_lowerCAmelCase							=							None
										if self.use_input_mask:
																	_lowerCAmelCase							=							random_attention_mask([self.batch_size, self.seq_length]						)

										_lowerCAmelCase							=							None
										_lowerCAmelCase							=							None
										_lowerCAmelCase							=							None
										if self.use_labels:
																	_lowerCAmelCase							=							ids_tensor([self.batch_size]  ,  self.type_sequence_label_size						)
																	_lowerCAmelCase							=							ids_tensor([self.batch_size, self.seq_length]  ,  self.num_labels						)
																	_lowerCAmelCase							=							ids_tensor([self.batch_size]  ,  self.num_choices						)

										_lowerCAmelCase							=							self.get_config()

										return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels





			def   SCREAMING_SNAKE_CASE__     (       self	:	int						):
										return SqueezeBertConfig(
										    embedding_size=self.hidden_size  ,  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  ,  attention_probs_dropout_prob=self.hidden_dropout_prob  ,  attention_dropout=self.attention_probs_dropout_prob  ,  max_position_embeddings=self.max_position_embeddings  ,  initializer_range=self.initializer_range  ,  q_groups=self.q_groups  ,  k_groups=self.k_groups  ,  v_groups=self.v_groups  ,  post_attention_groups=self.post_attention_groups  ,  intermediate_groups=self.intermediate_groups  ,  output_groups=self.output_groups  ,  )





			def   SCREAMING_SNAKE_CASE__     (       self	:	int  ,  lowercase__	:	Dict  ,  lowercase__	:	Any  ,  lowercase__	:	str  ,  lowercase__	:	Optional[Any]  ,  lowercase__	:	Any  ,  lowercase__	:	Tuple						):
										_lowerCAmelCase							=							SqueezeBertModel(config=lowercase__						)
										model.to(lowercase__						)
										model.eval()
										_lowerCAmelCase							=							model(lowercase__  ,  lowercase__						)
										_lowerCAmelCase							=							model(lowercase__						)
										self.parent.assertEqual(result.last_hidden_state.shape  ,  (self.batch_size, self.seq_length, self.hidden_size)						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]  ,  lowercase__	:	Union[str, Any]  ,  lowercase__	:	Union[str, Any]  ,  lowercase__	:	int  ,  lowercase__	:	List[Any]  ,  lowercase__	:	Dict  ,  lowercase__	:	Union[str, Any]						):
										_lowerCAmelCase							=							SqueezeBertForMaskedLM(config=lowercase__						)
										model.to(lowercase__						)
										model.eval()
										_lowerCAmelCase							=							model(lowercase__  ,  attention_mask=lowercase__  ,  labels=lowercase__						)
										self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.seq_length, self.vocab_size)						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	int  ,  lowercase__	:	Tuple  ,  lowercase__	:	Dict  ,  lowercase__	:	str  ,  lowercase__	:	Dict  ,  lowercase__	:	Tuple  ,  lowercase__	:	int						):
										_lowerCAmelCase							=							SqueezeBertForQuestionAnswering(config=lowercase__						)
										model.to(lowercase__						)
										model.eval()
										_lowerCAmelCase							=							model(
										    lowercase__  ,  attention_mask=lowercase__  ,  start_positions=lowercase__  ,  end_positions=lowercase__						)
										self.parent.assertEqual(result.start_logits.shape  ,  (self.batch_size, self.seq_length)						)
										self.parent.assertEqual(result.end_logits.shape  ,  (self.batch_size, self.seq_length)						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict  ,  lowercase__	:	Dict  ,  lowercase__	:	Tuple  ,  lowercase__	:	Any  ,  lowercase__	:	List[str]  ,  lowercase__	:	List[Any]  ,  lowercase__	:	Tuple						):
										_lowerCAmelCase							=							self.num_labels
										_lowerCAmelCase							=							SqueezeBertForSequenceClassification(lowercase__						)
										model.to(lowercase__						)
										model.eval()
										_lowerCAmelCase							=							model(lowercase__  ,  attention_mask=lowercase__  ,  labels=lowercase__						)
										self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.num_labels)						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]  ,  lowercase__	:	Union[str, Any]  ,  lowercase__	:	str  ,  lowercase__	:	Dict  ,  lowercase__	:	Tuple  ,  lowercase__	:	Any  ,  lowercase__	:	Optional[int]						):
										_lowerCAmelCase							=							self.num_labels
										_lowerCAmelCase							=							SqueezeBertForTokenClassification(config=lowercase__						)
										model.to(lowercase__						)
										model.eval()

										_lowerCAmelCase							=							model(lowercase__  ,  attention_mask=lowercase__  ,  labels=lowercase__						)
										self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.seq_length, self.num_labels)						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]  ,  lowercase__	:	Optional[int]  ,  lowercase__	:	Tuple  ,  lowercase__	:	List[Any]  ,  lowercase__	:	Optional[int]  ,  lowercase__	:	Tuple  ,  lowercase__	:	Any						):
										_lowerCAmelCase							=							self.num_choices
										_lowerCAmelCase							=							SqueezeBertForMultipleChoice(config=lowercase__						)
										model.to(lowercase__						)
										model.eval()
										_lowerCAmelCase							=							input_ids.unsqueeze(1						).expand(-1  ,  self.num_choices  ,  -1						).contiguous()
										_lowerCAmelCase							=							input_mask.unsqueeze(1						).expand(-1  ,  self.num_choices  ,  -1						).contiguous()
										_lowerCAmelCase							=							model(
										    lowercase__  ,  attention_mask=lowercase__  ,  labels=lowercase__  ,  )
										self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.num_choices)						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]						):
										_lowerCAmelCase							=							self.prepare_config_and_inputs()
										((_lowerCAmelCase)			,		(_lowerCAmelCase)			,		(_lowerCAmelCase)			,		(_lowerCAmelCase)			,		(_lowerCAmelCase)			,		(_lowerCAmelCase))							=							config_and_inputs
										_lowerCAmelCase							=							{'input_ids': input_ids, 'attention_mask': input_mask}
										return config, inputs_dict







@require_torch
class        lowerCamelCase__				(   UpperCAmelCase       ,UpperCAmelCase       ,unittest.TestCase				):
			UpperCamelCase__    							=(
			    (
			        SqueezeBertModel,
			        SqueezeBertForMaskedLM,
			        SqueezeBertForMultipleChoice,
			        SqueezeBertForQuestionAnswering,
			        SqueezeBertForSequenceClassification,
			        SqueezeBertForTokenClassification,
			    )
			    if is_torch_available()
			    else None
			)
			UpperCamelCase__    							=(
			    {
			        "feature-extraction": SqueezeBertModel,
			        "fill-mask": SqueezeBertForMaskedLM,
			        "question-answering": SqueezeBertForQuestionAnswering,
			        "text-classification": SqueezeBertForSequenceClassification,
			        "token-classification": SqueezeBertForTokenClassification,
			        "zero-shot": SqueezeBertForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			UpperCamelCase__    							=False
			UpperCamelCase__    							=True
			UpperCamelCase__    							=False





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]						):
										_lowerCAmelCase							=							SqueezeBertModelTester(self						)
										_lowerCAmelCase							=							ConfigTester(self  ,  config_class=lowercase__  ,  dim=37						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]						):
										self.config_tester.run_common_tests()





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_model(*lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Tuple						):
										_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_for_masked_lm(*lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_for_question_answering(*lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Any						):
										_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_for_sequence_classification(*lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Any						):
										_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_for_token_classification(*lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_for_multiple_choice(*lowercase__						)





			@slow
			def   SCREAMING_SNAKE_CASE__     (       self	:	int						):
										for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_lowerCAmelCase							=							SqueezeBertModel.from_pretrained(lowercase__						)
																	self.assertIsNotNone(lowercase__						)







@require_sentencepiece
@require_tokenizers
@require_torch
class        lowerCamelCase__				(   unittest.TestCase				):





			@slow
			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										_lowerCAmelCase							=							SqueezeBertForSequenceClassification.from_pretrained('squeezebert/squeezebert-mnli'						)

										_lowerCAmelCase							=							torch.tensor([[1, 2_94_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 13, 15_88, 2]]						)
										_lowerCAmelCase							=							model(lowercase__						)[0]
										_lowerCAmelCase							=							torch.Size((1, 3)						)
										self.assertEqual(output.shape  ,  lowercase__						)
										_lowerCAmelCase							=							torch.tensor([[0.6_4_0_1, -0.0_3_4_9, -0.6_0_4_1]]						)
										self.assertTrue(torch.allclose(lowercase__  ,  lowercase__  ,  atol=1e-4						)						)






