code: 
  import unittest
  
  from transformers import is_torch_available
  from transformers.testing_utils import require_torch, slow, torch_device
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_torch_available():
  							import torch
  
  							from transformers import (
  							    RemBertConfig,
  							    RemBertForCausalLM,
  							    RemBertForMaskedLM,
  							    RemBertForMultipleChoice,
  							    RemBertForQuestionAnswering,
  							    RemBertForSequenceClassification,
  							    RemBertForTokenClassification,
  							    RemBertModel,
  							)
  							from transformers.models.rembert.modeling_rembert import REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST
  
  
  
  
  
  class 						lowerCamelCase			:
  
  
  
  
  
  
  
  	def __init__(    self			:List[str]      ,    lowercase			:str      ,    lowercase			:List[Any]=1_3      ,    lowercase			:Dict=7      ,    lowercase			:Any=True      ,    lowercase			:int=True      ,    lowercase			:Optional[int]=True      ,    lowercase			:Dict=True      ,    lowercase			:str=9_9      ,    lowercase			:List[str]=3_2      ,    lowercase			:Optional[int]=1_8      ,    lowercase			:Tuple=4_3      ,    lowercase			:List[str]=5      ,    lowercase			:Optional[Any]=4      ,    lowercase			:List[Any]=3_7      ,    lowercase			:Union[str, Any]="gelu"      ,    lowercase			:Optional[int]=0.1      ,    lowercase			:int=0.1      ,    lowercase			:str=5_1_2      ,    lowercase			:Dict=1_6      ,    lowercase			:Union[str, Any]=2      ,    lowercase			:Any=0.02      ,    lowercase			:str=3      ,    lowercase			:Union[str, Any]=4      ,    lowercase			:List[Any]=None      ,    )		->			str:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   parent
  				SCREAMING_SNAKE_CASE    	=   batch_size
  				SCREAMING_SNAKE_CASE    	=   seq_length
  				SCREAMING_SNAKE_CASE    	=   is_training
  				SCREAMING_SNAKE_CASE    	=   use_input_mask
  				SCREAMING_SNAKE_CASE    	=   use_token_type_ids
  				SCREAMING_SNAKE_CASE    	=   use_labels
  				SCREAMING_SNAKE_CASE    	=   vocab_size
  				SCREAMING_SNAKE_CASE    	=   hidden_size
  				SCREAMING_SNAKE_CASE    	=   input_embedding_size
  				SCREAMING_SNAKE_CASE    	=   output_embedding_size
  				SCREAMING_SNAKE_CASE    	=   num_hidden_layers
  				SCREAMING_SNAKE_CASE    	=   num_attention_heads
  				SCREAMING_SNAKE_CASE    	=   intermediate_size
  				SCREAMING_SNAKE_CASE    	=   hidden_act
  				SCREAMING_SNAKE_CASE    	=   hidden_dropout_prob
  				SCREAMING_SNAKE_CASE    	=   attention_probs_dropout_prob
  				SCREAMING_SNAKE_CASE    	=   max_position_embeddings
  				SCREAMING_SNAKE_CASE    	=   type_vocab_size
  				SCREAMING_SNAKE_CASE    	=   type_sequence_label_size
  				SCREAMING_SNAKE_CASE    	=   initializer_range
  				SCREAMING_SNAKE_CASE    	=   num_labels
  				SCREAMING_SNAKE_CASE    	=   num_choices
  				SCREAMING_SNAKE_CASE    	=   scope
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Dict )		->			Union[str, Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   ids_tensor([self.batch_size, self.seq_length]      ,    self.vocab_size )
  
  				SCREAMING_SNAKE_CASE    	=   None
  				if self.use_input_mask:
  							SCREAMING_SNAKE_CASE    	=   random_attention_mask([self.batch_size, self.seq_length] )
  
  				SCREAMING_SNAKE_CASE    	=   None
  				if self.use_token_type_ids:
  							SCREAMING_SNAKE_CASE    	=   ids_tensor([self.batch_size, self.seq_length]      ,    self.type_vocab_size )
  
  				SCREAMING_SNAKE_CASE    	=   None
  				SCREAMING_SNAKE_CASE    	=   None
  				SCREAMING_SNAKE_CASE    	=   None
  				if self.use_labels:
  							SCREAMING_SNAKE_CASE    	=   ids_tensor([self.batch_size]      ,    self.type_sequence_label_size )
  							SCREAMING_SNAKE_CASE    	=   ids_tensor([self.batch_size, self.seq_length]      ,    self.num_labels )
  							SCREAMING_SNAKE_CASE    	=   ids_tensor([self.batch_size]      ,    self.num_choices )
  
  				SCREAMING_SNAKE_CASE    	=   RemBertConfig(
  				    vocab_size=self.vocab_size      ,    hidden_size=self.hidden_size      ,    input_embedding_size=self.input_embedding_size      ,    output_embedding_size=self.output_embedding_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=lowercase      ,    initializer_range=self.initializer_range      ,    )
  
  				return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Tuple )		->			Dict:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				(
  				    (
  				    SCREAMING_SNAKE_CASE
  				)	,       (
  				    SCREAMING_SNAKE_CASE
  				)	,       (
  				    SCREAMING_SNAKE_CASE
  				)	,       (
  				    SCREAMING_SNAKE_CASE
  				)	,       (
  				    SCREAMING_SNAKE_CASE
  				)	,       (
  				    SCREAMING_SNAKE_CASE
  				)	,       (
  				    SCREAMING_SNAKE_CASE
  				)	,       
  				)    	=   self.prepare_config_and_inputs()
  
  				SCREAMING_SNAKE_CASE    	=   True
  				SCREAMING_SNAKE_CASE    	=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
  				SCREAMING_SNAKE_CASE    	=   ids_tensor([self.batch_size, self.seq_length]      ,    vocab_size=2 )
  
  				return (
  				    config,
  				    input_ids,
  				    token_type_ids,
  				    input_mask,
  				    sequence_labels,
  				    token_labels,
  				    choice_labels,
  				    encoder_hidden_states,
  				    encoder_attention_mask,
  				)
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:List[str]      ,    lowercase			:Optional[Any]      ,    lowercase			:Optional[int]      ,    lowercase			:List[str]      ,    lowercase			:Optional[int]      ,    lowercase			:Dict      ,    lowercase			:List[str]      ,    lowercase			:Optional[Any] )		->			Tuple:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   RemBertModel(config=lowercase )
  				model.to(lowercase )
  				model.eval()
  				SCREAMING_SNAKE_CASE    	=   model(lowercase      ,    attention_mask=lowercase      ,    token_type_ids=lowercase )
  				SCREAMING_SNAKE_CASE    	=   model(lowercase      ,    token_type_ids=lowercase )
  				SCREAMING_SNAKE_CASE    	=   model(lowercase )
  				self.parent.assertEqual(result.last_hidden_state.shape      ,    (self.batch_size, self.seq_length, self.hidden_size) )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:str      ,    lowercase			:List[str]      ,    lowercase			:Tuple      ,    lowercase			:str      ,    lowercase			:Optional[int]      ,    lowercase			:Tuple      ,    lowercase			:Dict      ,    lowercase			:List[str]      ,    lowercase			:str      ,    lowercase			:List[str]      ,    )		->			Dict:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   True
  				SCREAMING_SNAKE_CASE    	=   RemBertModel(lowercase )
  				model.to(lowercase )
  				model.eval()
  				SCREAMING_SNAKE_CASE    	=   model(
  				    lowercase      ,    attention_mask=lowercase      ,    token_type_ids=lowercase      ,    encoder_hidden_states=lowercase      ,    encoder_attention_mask=lowercase      ,    )
  				SCREAMING_SNAKE_CASE    	=   model(
  				    lowercase      ,    attention_mask=lowercase      ,    token_type_ids=lowercase      ,    encoder_hidden_states=lowercase      ,    )
  				SCREAMING_SNAKE_CASE    	=   model(lowercase      ,    attention_mask=lowercase      ,    token_type_ids=lowercase )
  				self.parent.assertEqual(result.last_hidden_state.shape      ,    (self.batch_size, self.seq_length, self.hidden_size) )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Optional[int]      ,    lowercase			:int      ,    lowercase			:Tuple      ,    lowercase			:Optional[Any]      ,    lowercase			:int      ,    lowercase			:Optional[int]      ,    lowercase			:Optional[int]      ,    lowercase			:Dict      ,    lowercase			:Optional[int]      ,    lowercase			:int      ,    )		->			int:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   RemBertForCausalLM(config=lowercase )
  				model.to(lowercase )
  				model.eval()
  				SCREAMING_SNAKE_CASE    	=   model(lowercase      ,    attention_mask=lowercase      ,    token_type_ids=lowercase      ,    labels=lowercase )
  				self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.seq_length, self.vocab_size) )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Tuple      ,    lowercase			:Optional[Any]      ,    lowercase			:Union[str, Any]      ,    lowercase			:Dict      ,    lowercase			:Dict      ,    lowercase			:Dict      ,    lowercase			:List[str]      ,    lowercase			:Union[str, Any] )		->			Dict:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   RemBertForMaskedLM(config=lowercase )
  				model.to(lowercase )
  				model.eval()
  				SCREAMING_SNAKE_CASE    	=   model(lowercase      ,    attention_mask=lowercase      ,    token_type_ids=lowercase      ,    labels=lowercase )
  				self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.seq_length, self.vocab_size) )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Optional[int]      ,    lowercase			:Optional[Any]      ,    lowercase			:Tuple      ,    lowercase			:Optional[int]      ,    lowercase			:Any      ,    lowercase			:Optional[int]      ,    lowercase			:List[Any]      ,    lowercase			:str      ,    lowercase			:Any      ,    lowercase			:int      ,    )		->			Optional[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   True
  				SCREAMING_SNAKE_CASE    	=   True
  				SCREAMING_SNAKE_CASE    	=   RemBertForCausalLM(config=lowercase )
  				model.to(lowercase )
  				model.eval()
  
  				# first forward pass
  				SCREAMING_SNAKE_CASE    	=   model(
  				    lowercase      ,    attention_mask=lowercase      ,    encoder_hidden_states=lowercase      ,    encoder_attention_mask=lowercase      ,    use_cache=lowercase      ,    )
  				SCREAMING_SNAKE_CASE    	=   outputs.past_key_values
  
  				# create hypothetical multiple next token and extent to next_input_ids
  				SCREAMING_SNAKE_CASE    	=   ids_tensor((self.batch_size, 3)      ,    config.vocab_size )
  				SCREAMING_SNAKE_CASE    	=   ids_tensor((self.batch_size, 3)      ,    vocab_size=2 )
  
  				# append to next input_ids and
  				SCREAMING_SNAKE_CASE    	=   torch.cat([input_ids, next_tokens]      ,    dim=-1 )
  				SCREAMING_SNAKE_CASE    	=   torch.cat([input_mask, next_mask]      ,    dim=-1 )
  
  				SCREAMING_SNAKE_CASE    	=   model(
  				    lowercase      ,    attention_mask=lowercase      ,    encoder_hidden_states=lowercase      ,    encoder_attention_mask=lowercase      ,    output_hidden_states=lowercase      ,    )['''hidden_states'''][0]
  				SCREAMING_SNAKE_CASE    	=   model(
  				    lowercase      ,    attention_mask=lowercase      ,    encoder_hidden_states=lowercase      ,    encoder_attention_mask=lowercase      ,    past_key_values=lowercase      ,    output_hidden_states=lowercase      ,    )['''hidden_states'''][0]
  
  				# select random slice
  				SCREAMING_SNAKE_CASE    	=   ids_tensor((1,)      ,    output_from_past.shape[-1] ).item()
  				SCREAMING_SNAKE_CASE    	=   output_from_no_past[:, -3:, random_slice_idx].detach()
  				SCREAMING_SNAKE_CASE    	=   output_from_past[:, :, random_slice_idx].detach()
  
  				self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
  
  				# test that outputs are equal for slice
  				self.parent.assertTrue(torch.allclose(lowercase      ,    lowercase      ,    atol=1e-3 ) )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Any      ,    lowercase			:Any      ,    lowercase			:Union[str, Any]      ,    lowercase			:Dict      ,    lowercase			:Dict      ,    lowercase			:Union[str, Any]      ,    lowercase			:Any      ,    lowercase			:Tuple )		->			List[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   RemBertForQuestionAnswering(config=lowercase )
  				model.to(lowercase )
  				model.eval()
  				SCREAMING_SNAKE_CASE    	=   model(
  				    lowercase      ,    attention_mask=lowercase      ,    token_type_ids=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    snake_case__    (    self			:int      ,    lowercase			:Union[str, Any]      ,    lowercase			:int      ,    lowercase			:List[str]      ,    lowercase			:Union[str, Any]      ,    lowercase			:Optional[int]      ,    lowercase			:Union[str, Any]      ,    lowercase			:List[str] )		->			List[str]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.num_labels
  				SCREAMING_SNAKE_CASE    	=   RemBertForSequenceClassification(lowercase )
  				model.to(lowercase )
  				model.eval()
  				SCREAMING_SNAKE_CASE    	=   model(lowercase      ,    attention_mask=lowercase      ,    token_type_ids=lowercase      ,    labels=lowercase )
  				self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.num_labels) )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Union[str, Any]      ,    lowercase			:Tuple      ,    lowercase			:List[Any]      ,    lowercase			:Tuple      ,    lowercase			:Optional[Any]      ,    lowercase			:str      ,    lowercase			:Dict      ,    lowercase			:int )		->			Dict:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.num_labels
  				SCREAMING_SNAKE_CASE    	=   RemBertForTokenClassification(config=lowercase )
  				model.to(lowercase )
  				model.eval()
  				SCREAMING_SNAKE_CASE    	=   model(lowercase      ,    attention_mask=lowercase      ,    token_type_ids=lowercase      ,    labels=lowercase )
  				self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.seq_length, self.num_labels) )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:int      ,    lowercase			:int      ,    lowercase			:int      ,    lowercase			:Tuple      ,    lowercase			:List[str]      ,    lowercase			:Tuple      ,    lowercase			:List[Any]      ,    lowercase			:Dict )		->			Optional[int]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.num_choices
  				SCREAMING_SNAKE_CASE    	=   RemBertForMultipleChoice(config=lowercase )
  				model.to(lowercase )
  				model.eval()
  				SCREAMING_SNAKE_CASE    	=   input_ids.unsqueeze(1 ).expand(-1      ,    self.num_choices      ,    -1 ).contiguous()
  				SCREAMING_SNAKE_CASE    	=   token_type_ids.unsqueeze(1 ).expand(-1      ,    self.num_choices      ,    -1 ).contiguous()
  				SCREAMING_SNAKE_CASE    	=   input_mask.unsqueeze(1 ).expand(-1      ,    self.num_choices      ,    -1 ).contiguous()
  				SCREAMING_SNAKE_CASE    	=   model(
  				    lowercase      ,    attention_mask=lowercase      ,    token_type_ids=lowercase      ,    labels=lowercase      ,    )
  				self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.num_choices) )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:int )		->			Dict:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.prepare_config_and_inputs()
  				(
  				    (
  				    SCREAMING_SNAKE_CASE
  				)	,       (
  				    SCREAMING_SNAKE_CASE
  				)	,       (
  				    SCREAMING_SNAKE_CASE
  				)	,       (
  				    SCREAMING_SNAKE_CASE
  				)	,       (
  				    SCREAMING_SNAKE_CASE
  				)	,       (
  				    SCREAMING_SNAKE_CASE
  				)	,       (
  				    SCREAMING_SNAKE_CASE
  				)	,       
  				)    	=   config_and_inputs
  				SCREAMING_SNAKE_CASE    	=   {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
  				return config, inputs_dict
  
  
  
  
  
  @require_torch
  class 						lowerCamelCase			(					__lowerCamelCase   ,		__lowerCamelCase   ,		unittest.TestCase				):
  	UpperCamelCase_     :				str								=  (
  	    (
  	        RemBertModel,
  	        RemBertForMaskedLM,
  	        RemBertForCausalLM,
  	        RemBertForMultipleChoice,
  	        RemBertForQuestionAnswering,
  	        RemBertForSequenceClassification,
  	        RemBertForTokenClassification,
  	    )
  	    if is_torch_available()
  	    else ()
  	)
  	UpperCamelCase_     :				Optional[Any]								=  (RemBertForCausalLM,) if is_torch_available() else ()
  	UpperCamelCase_     :				Dict								=  (
  	    {
  	        'feature-extraction': RemBertModel,
  	        'fill-mask': RemBertForMaskedLM,
  	        'question-answering': RemBertForQuestionAnswering,
  	        'text-classification': RemBertForSequenceClassification,
  	        'text-generation': RemBertForCausalLM,
  	        'token-classification': RemBertForTokenClassification,
  	        'zero-shot': RemBertForSequenceClassification,
  	    }
  	    if is_torch_available()
  	    else {}
  	)
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Optional[Any] )		->			int:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   RemBertModelTester(self )
  				SCREAMING_SNAKE_CASE    	=   ConfigTester(self      ,    config_class=lowercase      ,    hidden_size=3_7 )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Dict )		->			str:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				self.config_tester.run_common_tests()
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Dict )		->			List[str]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_model(*lowercase )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Dict )		->			int:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.model_tester.prepare_config_and_inputs()
  				for type in ["absolute", "relative_key", "relative_key_query"]:
  							SCREAMING_SNAKE_CASE    	=   type
  							self.model_tester.create_and_check_model(*lowercase )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Union[str, Any] )		->			Tuple:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_for_masked_lm(*lowercase )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Tuple )		->			Optional[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_for_multiple_choice(*lowercase )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:List[Any] )		->			List[str]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.model_tester.prepare_config_and_inputs_for_decoder()
  				self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowercase )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:str )		->			Tuple:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_for_question_answering(*lowercase )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Dict )		->			Dict:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_for_sequence_classification(*lowercase )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Any )		->			int:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_for_token_classification(*lowercase )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:List[Any] )		->			List[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.model_tester.prepare_config_and_inputs_for_decoder()
  				self.model_tester.create_and_check_model_as_decoder(*lowercase )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:List[str] )		->			List[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				(
  				    (
  				    SCREAMING_SNAKE_CASE
  				)	,       (
  				    SCREAMING_SNAKE_CASE
  				)	,       (
  				    SCREAMING_SNAKE_CASE
  				)	,       (
  				    SCREAMING_SNAKE_CASE
  				)	,       (
  				    SCREAMING_SNAKE_CASE
  				)	,       (
  				    SCREAMING_SNAKE_CASE
  				)	,       (
  				    SCREAMING_SNAKE_CASE
  				)	,       (
  				    SCREAMING_SNAKE_CASE
  				)	,       (
  				    SCREAMING_SNAKE_CASE
  				)	,       
  				)    	=   self.model_tester.prepare_config_and_inputs_for_decoder()
  
  				SCREAMING_SNAKE_CASE    	=   None
  
  				self.model_tester.create_and_check_model_as_decoder(
  				    lowercase      ,    lowercase      ,    lowercase      ,    lowercase      ,    lowercase      ,    lowercase      ,    lowercase      ,    lowercase      ,    lowercase      ,    )
  
  
  
  
  
  
  
  	@slow
  	def    snake_case__    (    self			:Tuple )		->			List[str]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				for model_name in REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  							SCREAMING_SNAKE_CASE    	=   RemBertModel.from_pretrained(lowercase )
  							self.assertIsNotNone(lowercase )
  
  
  
  
  
  @require_torch
  class 						lowerCamelCase			(					unittest.TestCase				):
  
  
  
  
  
  
  
  	@slow
  	def    snake_case__    (    self			:List[Any] )		->			str:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   RemBertModel.from_pretrained('''google/rembert''' )
  				SCREAMING_SNAKE_CASE    	=   torch.tensor([[3_1_2, 5_6_4_9_8, 3_1_3, 2_1_2_5, 3_1_3]] )
  				SCREAMING_SNAKE_CASE    	=   torch.tensor([[0, 0, 0, 1, 1]] )
  				with torch.no_grad():
  							SCREAMING_SNAKE_CASE    	=   model(lowercase      ,    token_type_ids=lowercase      ,    output_hidden_states=lowercase )
  
  				SCREAMING_SNAKE_CASE    	=   1_1_5_2
  
  				SCREAMING_SNAKE_CASE    	=   torch.Size((1, 5, hidden_size) )
  				self.assertEqual(output['''last_hidden_state'''].shape      ,    lowercase )
  
  				SCREAMING_SNAKE_CASE    	=   torch.tensor(
  				    [
  				        [
  				            [0.07_54, -0.20_22, 0.19_04],
  				            [-0.33_54, -0.36_92, -0.47_91],
  				            [-0.23_14, -0.67_29, -0.07_49],
  				            [-0.03_96, -0.31_05, -0.42_34],
  				            [-0.15_71, -0.05_25, 0.53_53],
  				        ]
  				    ] )
  
  				# Running on the original tf implementation gives slightly different results here.
  				# Not clear why this variations is present
  				# TODO: Find reason for discrepancy
  				# expected_original_implementation = [[
  				#     [0.07630594074726105, -0.20146065950393677, 0.19107051193714142],
  				#     [-0.3405614495277405, -0.36971670389175415, -0.4808273911476135],
  				#     [-0.22587086260318756, -0.6656315922737122, -0.07844287157058716],
  				#     [-0.04145475849509239, -0.3077218234539032, -0.42316967248916626],
  				#     [-0.15887849032878876, -0.054529931396245956, 0.5356100797653198]
  				# ]]
  
  				self.assertTrue(torch.allclose(output['''last_hidden_state'''][:, :, :3]      ,    lowercase      ,    atol=1e-4 ) )
style_context: 
  import argparse
  import os
  from dataclasses import dataclass
  
  import torch
  import torchaudio
  from tqdm import tqdm
  
  from transformers import AutoConfig, AutoModelForCTC, AutoProcessor
  
  
  
  
  
  class 						lowerCamelCase			:
  
  
  
  
  
  
  
  	def __init__(    self			:Optional[int]      ,    lowercase			:Optional[Any]      ,    lowercase			:Optional[int]      ,    lowercase			:int )		->			Optional[int]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   cuda
  				SCREAMING_SNAKE_CASE    	=   AutoConfig.from_pretrained(lowercase )
  				SCREAMING_SNAKE_CASE    	=   AutoModelForCTC.from_pretrained(lowercase )
  				self.model.eval()
  				if self.cuda:
  							self.model.to(device='''cuda''' )
  				SCREAMING_SNAKE_CASE    	=   AutoProcessor.from_pretrained(lowercase )
  				SCREAMING_SNAKE_CASE    	=   torchaudio.transforms.Resample(lowercase      ,    1_6_0_0_0 )
  				SCREAMING_SNAKE_CASE    	=   0
  				SCREAMING_SNAKE_CASE    	=   list(self.processor.tokenizer.get_vocab().keys() )
  				for i in range(len(lowercase ) ):
  							if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
  										SCREAMING_SNAKE_CASE    	=   i
  				print('''Blank Token id [PAD]/<pad>'''      ,    lowercase )
  				SCREAMING_SNAKE_CASE    	=   blank_id
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Any      ,    lowercase			:int )		->			Optional[int]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   torchaudio.load(lowercase )
  				SCREAMING_SNAKE_CASE    	=   self.resampler(lowercase ).squeeze().numpy()
  				return speech
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Union[str, Any]      ,    lowercase			:Optional[Any] )		->			int:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.blank_id
  				SCREAMING_SNAKE_CASE    	=   '''|'''.join(item['''sent'''].split(''' ''' ) )
  				if not os.path.isfile(item['''wav_path'''] ):
  							print(item['''wav_path''']      ,    '''not found in wavs directory''' )
  
  				SCREAMING_SNAKE_CASE    	=   self.speech_file_to_array_fn(item['''wav_path'''] )
  				SCREAMING_SNAKE_CASE    	=   self.processor(lowercase      ,    sampling_rate=1_6_0_0_0      ,    return_tensors='''pt'''      ,    padding=lowercase )
  				if self.cuda:
  							SCREAMING_SNAKE_CASE    	=   inputs.to(device='''cuda''' )
  
  				with torch.no_grad():
  							SCREAMING_SNAKE_CASE    	=   self.model(inputs.input_values ).logits
  
  				# get the emission probability at frame level
  				SCREAMING_SNAKE_CASE    	=   torch.log_softmax(lowercase      ,    dim=-1 )
  				SCREAMING_SNAKE_CASE    	=   emissions[0].cpu().detach()
  
  				# get labels from vocab
  				SCREAMING_SNAKE_CASE    	=   ([''''''] + list(self.processor.tokenizer.get_vocab().keys() ))[
  				    :-1
  				]  # logits don't align with the tokenizer's vocab
  
  				SCREAMING_SNAKE_CASE    	=   {c: i for i, c in enumerate(lowercase )}
  				SCREAMING_SNAKE_CASE    	=   []
  				for c in transcript:
  							if c in dictionary:
  										tokens.append(dictionary[c] )
  
  				def get_trellis(lowercase			:List[Any]      ,    lowercase			:Any      ,    lowercase			:int=0 ):
  							SCREAMING_SNAKE_CASE    	=   emission.size(0 )
  							SCREAMING_SNAKE_CASE    	=   len(lowercase )
  
  							# Trellis has extra diemsions for both time axis and tokens.
  							# The extra dim for tokens represents <SoS> (start-of-sentence)
  							# The extra dim for time axis is for simplification of the code.
  							SCREAMING_SNAKE_CASE    	=   torch.full((num_frames + 1, num_tokens + 1)      ,    -float('''inf''' ) )
  							SCREAMING_SNAKE_CASE    	=   0
  							for t in range(lowercase ):
  										SCREAMING_SNAKE_CASE    	=   torch.maximum(
  										    # Score for staying at the same token
  										    trellis[t, 1:] + emission[t, blank_id]      ,    trellis[t, :-1] + emission[t, tokens]      ,    )
  							return trellis
  
  				SCREAMING_SNAKE_CASE    	=   get_trellis(lowercase      ,    lowercase      ,    lowercase )
  
  
  
  
  
  				@dataclass
  				class 						lowerCamelCase			:
  					UpperCamelCase_     :				int
  					UpperCamelCase_     :				int
  					UpperCamelCase_     :				float
  
  				def backtrack(lowercase			:Optional[int]      ,    lowercase			:List[str]      ,    lowercase			:Union[str, Any]      ,    lowercase			:Optional[Any]=0 ):
  							SCREAMING_SNAKE_CASE    	=   trellis.size(1 ) - 1
  							SCREAMING_SNAKE_CASE    	=   torch.argmax(trellis[:, j] ).item()
  
  							SCREAMING_SNAKE_CASE    	=   []
  							for t in range(lowercase      ,    0      ,    -1 ):
  										# 1. Figure out if the current position was stay or change
  										# Note (again):
  										# `emission[J-1]` is the emission at time frame `J` of trellis dimension.
  										# Score for token staying the same from time frame J-1 to T.
  										SCREAMING_SNAKE_CASE    	=   trellis[t - 1, j] + emission[t - 1, blank_id]
  										# Score for token changing from C-1 at T-1 to J at T.
  										SCREAMING_SNAKE_CASE    	=   trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]
  
  										# 2. Store the path with frame-wise probability.
  										SCREAMING_SNAKE_CASE    	=   emission[t - 1, tokens[j - 1] if changed > stayed else 0].exp().item()
  										# Return token index and time index in non-trellis coordinate.
  										path.append(Point(j - 1      ,    t - 1      ,    lowercase ) )
  
  										# 3. Update the token
  										if changed > stayed:
  													j -= 1
  													if j == 0:
  																break
  							else:
  										raise ValueError('''Failed to align''' )
  							return path[::-1]
  
  				SCREAMING_SNAKE_CASE    	=   backtrack(lowercase      ,    lowercase      ,    lowercase      ,    lowercase )
  
  
  
  
  
  				@dataclass
  				class 						lowerCamelCase			:
  					UpperCamelCase_     :				str
  					UpperCamelCase_     :				int
  					UpperCamelCase_     :				int
  					UpperCamelCase_     :				float
  
  
  
  
  
  
  
  					def __repr__(    self			:Optional[Any] )		->			str:
  
  
  
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								return f"""{self.label}\t{self.score:4.2f}\t{self.start*2_0:5d}\t{self.end*2_0:5d}"""
  
  
  
  
  
  
  
  					@property
  					def    snake_case__    (    self			:List[str] )		->			Any:
  
  
  
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								return self.end - self.start
  
  				def merge_repeats(lowercase			:List[str] ):
  							SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   0, 0
  							SCREAMING_SNAKE_CASE    	=   []
  							while ia < len(lowercase ):
  										while ia < len(lowercase ) and path[ia].token_index == path[ia].token_index:
  													ia += 1
  										SCREAMING_SNAKE_CASE    	=   sum(path[k].score for k in range(lowercase      ,    lowercase ) ) / (ia - ia)
  										segments.append(
  										    Segment(
  										        transcript[path[ia].token_index]      ,    path[ia].time_index      ,    path[ia - 1].time_index + 1      ,    lowercase      ,    ) )
  										SCREAMING_SNAKE_CASE    	=   ia
  							return segments
  
  				SCREAMING_SNAKE_CASE    	=   merge_repeats(lowercase )
  				with open(item['''out_path''']      ,    '''w''' ) as out_align:
  							for seg in segments:
  										out_align.write(str(lowercase ) + '''\n''' )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:List[Any]      ,    lowercase			:Dict      ,    lowercase			:Tuple      ,    lowercase			:str )		->			Dict:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				if not os.path.exists(lowercase ):
  							os.makedirs(lowercase )
  
  				# load text file
  				SCREAMING_SNAKE_CASE    	=   open(lowercase      ,    encoding='''utf8''' ).readlines()
  
  				SCREAMING_SNAKE_CASE    	=   []
  				for line in lines:
  							if len(line.strip().split('''\t''' ) ) != 2:
  										print('''Script must be in format: 00001  this is my sentence''' )
  										exit()
  
  							SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   line.strip().split('''\t''' )
  							SCREAMING_SNAKE_CASE    	=   os.path.join(lowercase      ,    wav_name + '''.wav''' )
  							SCREAMING_SNAKE_CASE    	=   os.path.join(lowercase      ,    wav_name + '''.txt''' )
  
  							items.append({'''sent''': sentence, '''wav_path''': wav_path, '''out_path''': out_path} )
  				print('''Number of samples found in script file'''      ,    len(lowercase ) )
  
  				for item in tqdm(lowercase ):
  							self.align_single_sample(lowercase )
  
  
  
  def       a			(   )    ->Optional[int]:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE    	=   argparse.ArgumentParser()
  
  			parser.add_argument(
  			    '''--model_name'''		,	type=a		,	default='''arijitx/wav2vec2-xls-r-300m-bengali'''		,	help='''wav2vec model name'''  )
  			parser.add_argument('''--wav_dir'''		,	type=a		,	default='''./wavs'''		,	help='''directory containing wavs'''  )
  			parser.add_argument('''--text_file'''		,	type=a		,	default='''script.txt'''		,	help='''file containing text'''  )
  			parser.add_argument('''--input_wavs_sr'''		,	type=a		,	default=1_6000		,	help='''sampling rate of input audios'''  )
  			parser.add_argument(
  			    '''--output_dir'''		,	type=a		,	default='''./out_alignment'''		,	help='''output directory containing the alignment files'''  )
  			parser.add_argument('''--cuda'''		,	action='''store_true'''  )
  
  			SCREAMING_SNAKE_CASE    	=   parser.parse_args()
  
  			SCREAMING_SNAKE_CASE    	=   WavaVecaAligner(args.model_name		,	args.input_wavs_sr		,	args.cuda  )
  			aligner.align_data(args.wav_dir		,	args.text_file		,	args.output_dir  )
  
  
  if __name__ == "__main__":
  							main()