


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 A__     ( A__  ):



  """simple docstring"""



  def __init__( self			:  str				,				lowerCamelCase__			:  Any				,				lowerCamelCase__			:  Union[str, Any]=13				,				lowerCamelCase__			:  str=7				,				lowerCamelCase__			:  Optional[Any]=True				,				lowerCamelCase__			:  List[str]=True				,				lowerCamelCase__			:  Tuple=False				,				lowerCamelCase__			:  Any=True				,				lowerCamelCase__			:  List[str]=99				,				lowerCamelCase__			:  Optional[Any]=32				,				lowerCamelCase__			:  str=5				,				lowerCamelCase__			:  Optional[int]=4				,				lowerCamelCase__			:  Union[str, Any]=64				,				lowerCamelCase__			:  List[str]="gelu"				,				lowerCamelCase__			:  List[Any]=0.1				,				lowerCamelCase__			:  Any=0.1				,				lowerCamelCase__			:  Any=512				,				lowerCamelCase__			:  Any=16				,				lowerCamelCase__			:  Union[str, Any]=2				,				lowerCamelCase__			:  str=0.02				,				lowerCamelCase__			:  List[Any]=3				,				lowerCamelCase__			:  Dict=4				,				lowerCamelCase__			:  Optional[int]=None				,				lowerCamelCase__			:  List[str]=2				,				lowerCamelCase__			:  Optional[int]=2				,				lowerCamelCase__			:  Optional[Any]=2				,				lowerCamelCase__			:  List[str]=2				,				lowerCamelCase__			:  List[str]=4				,				lowerCamelCase__			:  Dict=1				,				):
        a__	:  Optional[int]     =   parent
        a__	:  Any     =   batch_size
        a__	:  Tuple     =   seq_length
        a__	:  Union[str, Any]     =   is_training
        a__	:  List[str]     =   use_input_mask
        a__	:  int     =   use_token_type_ids
        a__	:  Optional[Any]     =   use_labels
        a__	:  Tuple     =   vocab_size
        a__	:  Optional[int]     =   hidden_size
        a__	:  Any     =   num_hidden_layers
        a__	:  List[Any]     =   num_attention_heads
        a__	:  List[Any]     =   intermediate_size
        a__	:  int     =   hidden_act
        a__	:  List[Any]     =   hidden_dropout_prob
        a__	:  List[Any]     =   attention_probs_dropout_prob
        a__	:  Optional[int]     =   max_position_embeddings
        a__	:  Any     =   type_vocab_size
        a__	:  Union[str, Any]     =   type_sequence_label_size
        a__	:  Union[str, Any]     =   initializer_range
        a__	:  Union[str, Any]     =   num_labels
        a__	:  str     =   num_choices
        a__	:  Any     =   scope
        a__	:  Tuple     =   q_groups
        a__	:  Optional[Any]     =   k_groups
        a__	:  Optional[int]     =   v_groups
        a__	:  List[str]     =   post_attention_groups
        a__	:  Optional[int]     =   intermediate_groups
        a__	:  Union[str, Any]     =   output_groups

  def    _UpperCamelCase( self			:  List[Any]       ):
        a__	:  Any     =   ids_tensor([self.batch_size, self.seq_length]				,				self.vocab_size       )

        a__	:  Optional[Any]     =   None
        if self.use_input_mask:
              a__	:  str     =   random_attention_mask([self.batch_size, self.seq_length]       )

        a__	:  str     =   None
        a__	:  int     =   None
        a__	:  Any     =   None
        if self.use_labels:
              a__	:  List[Any]     =   ids_tensor([self.batch_size]				,				self.type_sequence_label_size       )
              a__	:  List[str]     =   ids_tensor([self.batch_size, self.seq_length]				,				self.num_labels       )
              a__	:  Tuple     =   ids_tensor([self.batch_size]				,				self.num_choices       )

        a__	:  Optional[int]     =   self.get_config()

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

  def    _UpperCamelCase( self			:  Any       ):
        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    _UpperCamelCase( self			:  Dict				,				lowerCamelCase__			:  Any				,				lowerCamelCase__			:  Dict				,				lowerCamelCase__			:  str				,				lowerCamelCase__			:  Union[str, Any]				,				lowerCamelCase__			:  List[str]				,				lowerCamelCase__			:  str       ):
        a__	:  Dict     =   SqueezeBertModel(config=lowerCamelCase__       )
        model.to(lowerCamelCase__       )
        model.eval()
        a__	:  Union[str, Any]     =   model(lowerCamelCase__				,				lowerCamelCase__       )
        a__	:  Dict     =   model(lowerCamelCase__       )
        self.parent.assertEqual(result.last_hidden_state.shape				,				(self.batch_size, self.seq_length, self.hidden_size)       )

  def    _UpperCamelCase( self			:  int				,				lowerCamelCase__			:  Union[str, Any]				,				lowerCamelCase__			:  Tuple				,				lowerCamelCase__			:  Optional[int]				,				lowerCamelCase__			:  Optional[int]				,				lowerCamelCase__			:  Optional[Any]				,				lowerCamelCase__			:  Optional[Any]       ):
        a__	:  int     =   SqueezeBertForMaskedLM(config=lowerCamelCase__       )
        model.to(lowerCamelCase__       )
        model.eval()
        a__	:  Any     =   model(lowerCamelCase__				,				attention_mask=lowerCamelCase__				,				labels=lowerCamelCase__       )
        self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.seq_length, self.vocab_size)       )

  def    _UpperCamelCase( self			:  Optional[int]				,				lowerCamelCase__			:  List[str]				,				lowerCamelCase__			:  Union[str, Any]				,				lowerCamelCase__			:  Optional[Any]				,				lowerCamelCase__			:  Any				,				lowerCamelCase__			:  Optional[Any]				,				lowerCamelCase__			:  Union[str, Any]       ):
        a__	:  Any     =   SqueezeBertForQuestionAnswering(config=lowerCamelCase__       )
        model.to(lowerCamelCase__       )
        model.eval()
        a__	:  Optional[Any]     =   model(
            lowerCamelCase__				,				attention_mask=lowerCamelCase__				,				start_positions=lowerCamelCase__				,				end_positions=lowerCamelCase__       )
        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    _UpperCamelCase( self			:  Union[str, Any]				,				lowerCamelCase__			:  List[Any]				,				lowerCamelCase__			:  Any				,				lowerCamelCase__			:  Tuple				,				lowerCamelCase__			:  str				,				lowerCamelCase__			:  str				,				lowerCamelCase__			:  Union[str, Any]       ):
        a__	:  Optional[Any]     =   self.num_labels
        a__	:  Any     =   SqueezeBertForSequenceClassification(lowerCamelCase__       )
        model.to(lowerCamelCase__       )
        model.eval()
        a__	:  List[str]     =   model(lowerCamelCase__				,				attention_mask=lowerCamelCase__				,				labels=lowerCamelCase__       )
        self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.num_labels)       )

  def    _UpperCamelCase( self			:  List[Any]				,				lowerCamelCase__			:  int				,				lowerCamelCase__			:  Union[str, Any]				,				lowerCamelCase__			:  Optional[int]				,				lowerCamelCase__			:  Union[str, Any]				,				lowerCamelCase__			:  str				,				lowerCamelCase__			:  int       ):
        a__	:  List[str]     =   self.num_labels
        a__	:  str     =   SqueezeBertForTokenClassification(config=lowerCamelCase__       )
        model.to(lowerCamelCase__       )
        model.eval()

        a__	:  List[str]     =   model(lowerCamelCase__				,				attention_mask=lowerCamelCase__				,				labels=lowerCamelCase__       )
        self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.seq_length, self.num_labels)       )

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

  def    _UpperCamelCase( self			:  Any       ):
        a__	:  Optional[Any]     =   self.prepare_config_and_inputs()
        ((a__),	(a__),	(a__),	(a__),	(a__),	(a__))	:  Any     =   config_and_inputs
        a__	:  Optional[int]     =   {"input_ids": input_ids, "attention_mask": input_mask}
        return config, inputs_dict




@require_torch
class A__     ( A__				,		A__				,		unittest.TestCase  ):



  """simple docstring"""



  _lowercase									=		(
      (
          SqueezeBertModel,
          SqueezeBertForMaskedLM,
          SqueezeBertForMultipleChoice,
          SqueezeBertForQuestionAnswering,
          SqueezeBertForSequenceClassification,
          SqueezeBertForTokenClassification,
      )
      if is_torch_available()
      else None
  )
  _lowercase									=		(
      {
          'feature-extraction': SqueezeBertModel,
          'fill-mask': SqueezeBertForMaskedLM,
          'question-answering': SqueezeBertForQuestionAnswering,
          'text-classification': SqueezeBertForSequenceClassification,
          'token-classification': SqueezeBertForTokenClassification,
          'zero-shot': SqueezeBertForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  _lowercase									=		False
  _lowercase									=		True
  _lowercase									=		False

  def    _UpperCamelCase( self			:  int       ):
        a__	:  str     =   SqueezeBertModelTester(self       )
        a__	:  List[str]     =   ConfigTester(self				,				config_class=lowerCamelCase__				,				dim=37       )

  def    _UpperCamelCase( self			:  Optional[int]       ):
        self.config_tester.run_common_tests()

  def    _UpperCamelCase( self			:  List[Any]       ):
        a__	:  Optional[int]     =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_squeezebert_model(*lowerCamelCase__       )

  def    _UpperCamelCase( self			:  Optional[int]       ):
        a__	:  int     =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_squeezebert_for_masked_lm(*lowerCamelCase__       )

  def    _UpperCamelCase( self			:  Tuple       ):
        a__	:  Dict     =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_squeezebert_for_question_answering(*lowerCamelCase__       )

  def    _UpperCamelCase( self			:  Dict       ):
        a__	:  Tuple     =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_squeezebert_for_sequence_classification(*lowerCamelCase__       )

  def    _UpperCamelCase( self			:  Tuple       ):
        a__	:  str     =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_squeezebert_for_token_classification(*lowerCamelCase__       )

  def    _UpperCamelCase( self			:  str       ):
        a__	:  Tuple     =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_squeezebert_for_multiple_choice(*lowerCamelCase__       )

  @slow
  def    _UpperCamelCase( self			:  Any       ):
        for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              a__	:  List[Any]     =   SqueezeBertModel.from_pretrained(lowerCamelCase__       )
              self.assertIsNotNone(lowerCamelCase__       )




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



  """simple docstring"""



  @slow
  def    _UpperCamelCase( self			:  Tuple       ):
        a__	:  Tuple     =   SqueezeBertForSequenceClassification.from_pretrained("squeezebert/squeezebert-mnli"       )

        a__	:  Optional[Any]     =   torch.tensor([[1, 29_414, 232, 328, 740, 1_140, 12_695, 69, 13, 1_588, 2]]       )
        a__	:  Any     =   model(lowerCamelCase__       )[0]
        a__	:  str     =   torch.Size((1, 3)       )
        self.assertEqual(output.shape				,				lowerCamelCase__       )
        a__	:  List[str]     =   torch.tensor([[0.6401, -0.0349, -0.6041]]       )
        self.assertTrue(torch.allclose(lowerCamelCase__				,				lowerCamelCase__				,				atol=1E-4       )       )





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




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




def 	_A  (       A							)  ->				Set[str]:
	return {t for t in NON_ALPHA.split(A							) if len(t.strip()							) > 0}




class        _UpperCamelCase	:


				'''simple docstring'''


				def __init__(     self							,      *,
				    a_ = 0.85							,      )    ->  List[str]:
					lowercase						:		Any   					=		duplication_jaccard_threshold
					lowercase						:		str   					=		NUM_PERM
					lowercase						:		Union[str, Any]   					=		MinHashLSH(threshold=self._duplication_jaccard_threshold							,      num_perm=self._num_perm							)

					lowercase						:		Any   					=		defaultdict(a_							)

				def 	a__		(     self							,      a_							,      a_							)    ->  None:
					lowercase						:		Dict   					=		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 	a__		(     self							)    ->  List[List[Dict]]:
					lowercase						:		str   					=		[]
					for base, duplicates in self._duplicate_clusters.items():
						lowercase						:		str   					=		[base] + list(a_							)
						# reformat the cluster to be a list of dict
						lowercase						:		Optional[Any]   					=		[{"base_index": el[0], "repo_name": el[1], "path": el[2]} for el in cluster]
						duplicate_clusters.append(a_							)
					return duplicate_clusters


				def 	a__		(     self							,      a_							)    ->  None:
					lowercase						:		Tuple   					=		self.get_duplicate_clusters()
					with open(a_							,      "w"							) as f:
						json.dump(a_							,      a_							)




def 	_A  (       A							)  ->				Dict:
	lowercase					,		lowercase						:		List[str]   					=		element
	lowercase						:		int   					=		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 	_A  (       A							)  ->				Any:
	with mp.Pool() as pool:
		for data in pool.imap_unordered(
		    _compute_min_hash							,ThreadedIterator(A							,max_queue_size=1_0_0_0_0							)							,chunksize=1_0_0							,):
			if data is not None:
				yield data




def 	_A  (       A							,A							)  ->				List[str]:
	lowercase						:		Dict   					=		DuplicationIndex(duplication_jaccard_threshold=A							)

	for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(A							)							)							,max_queue_size=1_0_0							)							):
		di.add(A							,A							)

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




def 	_A  (       A							,A							)  ->				float:
	lowercase						:		int   					=		get_tokens(A							)
	lowercase						:		Dict   					=		get_tokens(A							)
	return len(tokensa & tokensa							) / len(tokensa | tokensa							)


lowerCAmelCase   : List[str]             =				None




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




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






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

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

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

	return ds_filter, duplicate_clusters


