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    __A				:
  
  
  
  
  
  
      def __init__(	self			:    int    ,				UpperCAmelCase_			:    List[Any]    ,				UpperCAmelCase_			:    Tuple=13    ,				UpperCAmelCase_			:    Tuple=7    ,				UpperCAmelCase_			:    int=True    ,				UpperCAmelCase_			:    Dict=True    ,				UpperCAmelCase_			:    List[Any]=True    ,				UpperCAmelCase_			:    str=True    ,				UpperCAmelCase_			:    Dict=99    ,				UpperCAmelCase_			:    str=32    ,				UpperCAmelCase_			:    Union[str, Any]=18    ,				UpperCAmelCase_			:    Any=43    ,				UpperCAmelCase_			:    Optional[int]=5    ,				UpperCAmelCase_			:    Tuple=4    ,				UpperCAmelCase_			:    List[str]=37    ,				UpperCAmelCase_			:    Tuple="gelu"    ,				UpperCAmelCase_			:    Optional[Any]=0.1    ,				UpperCAmelCase_			:    int=0.1    ,				UpperCAmelCase_			:    str=512    ,				UpperCAmelCase_			:    Optional[int]=16    ,				UpperCAmelCase_			:    List[Any]=2    ,				UpperCAmelCase_			:    int=0.02    ,				UpperCAmelCase_			:    str=3    ,				UpperCAmelCase_			:    str=4    ,				UpperCAmelCase_			:    Optional[int]=None    ,				):
          lowerCAmelCase				:			Union[str, Any]    			=      parent
          lowerCAmelCase				:			Any    			=      batch_size
          lowerCAmelCase				:			Optional[Any]    			=      seq_length
          lowerCAmelCase				:			str    			=      is_training
          lowerCAmelCase				:			List[str]    			=      use_input_mask
          lowerCAmelCase				:			Any    			=      use_token_type_ids
          lowerCAmelCase				:			Union[str, Any]    			=      use_labels
          lowerCAmelCase				:			Union[str, Any]    			=      vocab_size
          lowerCAmelCase				:			Tuple    			=      hidden_size
          lowerCAmelCase				:			Optional[Any]    			=      input_embedding_size
          lowerCAmelCase				:			Optional[Any]    			=      output_embedding_size
          lowerCAmelCase				:			Tuple    			=      num_hidden_layers
          lowerCAmelCase				:			List[str]    			=      num_attention_heads
          lowerCAmelCase				:			Union[str, Any]    			=      intermediate_size
          lowerCAmelCase				:			Union[str, Any]    			=      hidden_act
          lowerCAmelCase				:			Union[str, Any]    			=      hidden_dropout_prob
          lowerCAmelCase				:			Dict    			=      attention_probs_dropout_prob
          lowerCAmelCase				:			Optional[int]    			=      max_position_embeddings
          lowerCAmelCase				:			Dict    			=      type_vocab_size
          lowerCAmelCase				:			List[Any]    			=      type_sequence_label_size
          lowerCAmelCase				:			Optional[int]    			=      initializer_range
          lowerCAmelCase				:			Optional[Any]    			=      num_labels
          lowerCAmelCase				:			int    			=      num_choices
          lowerCAmelCase				:			Dict    			=      scope
  
  
  
  
  
  
      def 	lowercase__ (	self			:    List[Any]    ):
          lowerCAmelCase				:			int    			=      ids_tensor([self.batch_size, self.seq_length]    ,				self.vocab_size    )
  
          lowerCAmelCase				:			List[str]    			=      None
          if self.use_input_mask:
              lowerCAmelCase				:			List[Any]    			=      random_attention_mask([self.batch_size, self.seq_length]    )
  
          lowerCAmelCase				:			List[Any]    			=      None
          if self.use_token_type_ids:
              lowerCAmelCase				:			int    			=      ids_tensor([self.batch_size, self.seq_length]    ,				self.type_vocab_size    )
  
          lowerCAmelCase				:			List[Any]    			=      None
          lowerCAmelCase				:			List[str]    			=      None
          lowerCAmelCase				:			Dict    			=      None
          if self.use_labels:
              lowerCAmelCase				:			List[str]    			=      ids_tensor([self.batch_size]    ,				self.type_sequence_label_size    )
              lowerCAmelCase				:			Union[str, Any]    			=      ids_tensor([self.batch_size, self.seq_length]    ,				self.num_labels    )
              lowerCAmelCase				:			Dict    			=      ids_tensor([self.batch_size]    ,				self.num_choices    )
  
          lowerCAmelCase				:			Optional[Any]    			=      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=UpperCAmelCase_    ,				initializer_range=self.initializer_range    ,				)
  
          return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
  
  
  
  
  
  
      def 	lowercase__ (	self			:    str    ):
          (
              (
              lowerCAmelCase
          )			,  (
              lowerCAmelCase
          )			,  (
              lowerCAmelCase
          )			,  (
              lowerCAmelCase
          )			,  (
              lowerCAmelCase
          )			,  (
              lowerCAmelCase
          )			,  (
              lowerCAmelCase
          )			,  
          )				:			Optional[Any]    			=      self.prepare_config_and_inputs()
  
          lowerCAmelCase				:			Tuple    			=      True
          lowerCAmelCase				:			Optional[Any]    			=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
          lowerCAmelCase				:			str    			=      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 	lowercase__ (	self			:    Tuple    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    Tuple    ,				UpperCAmelCase_			:    Tuple    ,				UpperCAmelCase_			:    str    ,				UpperCAmelCase_			:    Dict    ,				UpperCAmelCase_			:    List[Any]    ,				UpperCAmelCase_			:    int    ):
          lowerCAmelCase				:			str    			=      RemBertModel(config=UpperCAmelCase_    )
          model.to(UpperCAmelCase_    )
          model.eval()
          lowerCAmelCase				:			Optional[int]    			=      model(UpperCAmelCase_    ,				attention_mask=UpperCAmelCase_    ,				token_type_ids=UpperCAmelCase_    )
          lowerCAmelCase				:			str    			=      model(UpperCAmelCase_    ,				token_type_ids=UpperCAmelCase_    )
          lowerCAmelCase				:			Optional[Any]    			=      model(UpperCAmelCase_    )
          self.parent.assertEqual(result.last_hidden_state.shape    ,				(self.batch_size, self.seq_length, self.hidden_size)    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    str    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    List[Any]    ,				UpperCAmelCase_			:    Tuple    ,				UpperCAmelCase_			:    int    ,				UpperCAmelCase_			:    Union[str, Any]    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    List[Any]    ,				UpperCAmelCase_			:    Union[str, Any]    ,				):
          lowerCAmelCase				:			List[Any]    			=      True
          lowerCAmelCase				:			Optional[int]    			=      RemBertModel(UpperCAmelCase_    )
          model.to(UpperCAmelCase_    )
          model.eval()
          lowerCAmelCase				:			int    			=      model(
              UpperCAmelCase_    ,				attention_mask=UpperCAmelCase_    ,				token_type_ids=UpperCAmelCase_    ,				encoder_hidden_states=UpperCAmelCase_    ,				encoder_attention_mask=UpperCAmelCase_    ,				)
          lowerCAmelCase				:			Optional[int]    			=      model(
              UpperCAmelCase_    ,				attention_mask=UpperCAmelCase_    ,				token_type_ids=UpperCAmelCase_    ,				encoder_hidden_states=UpperCAmelCase_    ,				)
          lowerCAmelCase				:			Optional[Any]    			=      model(UpperCAmelCase_    ,				attention_mask=UpperCAmelCase_    ,				token_type_ids=UpperCAmelCase_    )
          self.parent.assertEqual(result.last_hidden_state.shape    ,				(self.batch_size, self.seq_length, self.hidden_size)    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Tuple    ,				UpperCAmelCase_			:    Dict    ,				UpperCAmelCase_			:    int    ,				UpperCAmelCase_			:    str    ,				UpperCAmelCase_			:    Union[str, Any]    ,				UpperCAmelCase_			:    Optional[int]    ,				UpperCAmelCase_			:    Dict    ,				UpperCAmelCase_			:    List[Any]    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    Any    ,				):
          lowerCAmelCase				:			Any    			=      RemBertForCausalLM(config=UpperCAmelCase_    )
          model.to(UpperCAmelCase_    )
          model.eval()
          lowerCAmelCase				:			List[str]    			=      model(UpperCAmelCase_    ,				attention_mask=UpperCAmelCase_    ,				token_type_ids=UpperCAmelCase_    ,				labels=UpperCAmelCase_    )
          self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.seq_length, self.vocab_size)    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Union[str, Any]    ,				UpperCAmelCase_			:    List[str]    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    Dict    ,				UpperCAmelCase_			:    Optional[Any]    ,				UpperCAmelCase_			:    str    ,				UpperCAmelCase_			:    Union[str, Any]    ):
          lowerCAmelCase				:			Any    			=      RemBertForMaskedLM(config=UpperCAmelCase_    )
          model.to(UpperCAmelCase_    )
          model.eval()
          lowerCAmelCase				:			List[str]    			=      model(UpperCAmelCase_    ,				attention_mask=UpperCAmelCase_    ,				token_type_ids=UpperCAmelCase_    ,				labels=UpperCAmelCase_    )
          self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.seq_length, self.vocab_size)    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Tuple    ,				UpperCAmelCase_			:    Optional[int]    ,				UpperCAmelCase_			:    Optional[int]    ,				UpperCAmelCase_			:    List[str]    ,				UpperCAmelCase_			:    List[Any]    ,				UpperCAmelCase_			:    Tuple    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    List[Any]    ,				UpperCAmelCase_			:    Union[str, Any]    ,				UpperCAmelCase_			:    Tuple    ,				):
          lowerCAmelCase				:			Optional[Any]    			=      True
          lowerCAmelCase				:			Dict    			=      True
          lowerCAmelCase				:			List[Any]    			=      RemBertForCausalLM(config=UpperCAmelCase_    )
          model.to(UpperCAmelCase_    )
          model.eval()
  
          # first forward pass
          lowerCAmelCase				:			int    			=      model(
              UpperCAmelCase_    ,				attention_mask=UpperCAmelCase_    ,				encoder_hidden_states=UpperCAmelCase_    ,				encoder_attention_mask=UpperCAmelCase_    ,				use_cache=UpperCAmelCase_    ,				)
          lowerCAmelCase				:			Optional[Any]    			=      outputs.past_key_values
  
          # create hypothetical multiple next token and extent to next_input_ids
          lowerCAmelCase				:			List[Any]    			=      ids_tensor((self.batch_size, 3)    ,				config.vocab_size    )
          lowerCAmelCase				:			Tuple    			=      ids_tensor((self.batch_size, 3)    ,				vocab_size=2    )
  
          # append to next input_ids and
          lowerCAmelCase				:			int    			=      torch.cat([input_ids, next_tokens]    ,				dim=-1    )
          lowerCAmelCase				:			Any    			=      torch.cat([input_mask, next_mask]    ,				dim=-1    )
  
          lowerCAmelCase				:			List[Any]    			=      model(
              UpperCAmelCase_    ,				attention_mask=UpperCAmelCase_    ,				encoder_hidden_states=UpperCAmelCase_    ,				encoder_attention_mask=UpperCAmelCase_    ,				output_hidden_states=UpperCAmelCase_    ,				)['hidden_states'][0]
          lowerCAmelCase				:			Any    			=      model(
              UpperCAmelCase_    ,				attention_mask=UpperCAmelCase_    ,				encoder_hidden_states=UpperCAmelCase_    ,				encoder_attention_mask=UpperCAmelCase_    ,				past_key_values=UpperCAmelCase_    ,				output_hidden_states=UpperCAmelCase_    ,				)['hidden_states'][0]
  
          # select random slice
          lowerCAmelCase				:			List[Any]    			=      ids_tensor((1,)    ,				output_from_past.shape[-1]    ).item()
          lowerCAmelCase				:			Tuple    			=      output_from_no_past[:, -3:, random_slice_idx].detach()
          lowerCAmelCase				:			Optional[Any]    			=      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(UpperCAmelCase_    ,				UpperCAmelCase_    ,				atol=1E-3    )    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[int]    ,				UpperCAmelCase_			:    List[Any]    ,				UpperCAmelCase_			:    Tuple    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    Dict    ,				UpperCAmelCase_			:    Dict    ,				UpperCAmelCase_			:    Dict    ,				UpperCAmelCase_			:    Optional[Any]    ):
          lowerCAmelCase				:			List[str]    			=      RemBertForQuestionAnswering(config=UpperCAmelCase_    )
          model.to(UpperCAmelCase_    )
          model.eval()
          lowerCAmelCase				:			Tuple    			=      model(
              UpperCAmelCase_    ,				attention_mask=UpperCAmelCase_    ,				token_type_ids=UpperCAmelCase_    ,				start_positions=UpperCAmelCase_    ,				end_positions=UpperCAmelCase_    ,				)
          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 	lowercase__ (	self			:    int    ,				UpperCAmelCase_			:    Dict    ,				UpperCAmelCase_			:    List[str]    ,				UpperCAmelCase_			:    Union[str, Any]    ,				UpperCAmelCase_			:    Union[str, Any]    ,				UpperCAmelCase_			:    Optional[Any]    ,				UpperCAmelCase_			:    List[str]    ,				UpperCAmelCase_			:    Any    ):
          lowerCAmelCase				:			Any    			=      self.num_labels
          lowerCAmelCase				:			Tuple    			=      RemBertForSequenceClassification(UpperCAmelCase_    )
          model.to(UpperCAmelCase_    )
          model.eval()
          lowerCAmelCase				:			Optional[int]    			=      model(UpperCAmelCase_    ,				attention_mask=UpperCAmelCase_    ,				token_type_ids=UpperCAmelCase_    ,				labels=UpperCAmelCase_    )
          self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.num_labels)    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    List[str]    ,				UpperCAmelCase_			:    List[str]    ,				UpperCAmelCase_			:    Optional[Any]    ,				UpperCAmelCase_			:    int    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    List[str]    ,				UpperCAmelCase_			:    Tuple    ,				UpperCAmelCase_			:    List[Any]    ):
          lowerCAmelCase				:			List[str]    			=      self.num_labels
          lowerCAmelCase				:			List[Any]    			=      RemBertForTokenClassification(config=UpperCAmelCase_    )
          model.to(UpperCAmelCase_    )
          model.eval()
          lowerCAmelCase				:			Union[str, Any]    			=      model(UpperCAmelCase_    ,				attention_mask=UpperCAmelCase_    ,				token_type_ids=UpperCAmelCase_    ,				labels=UpperCAmelCase_    )
          self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.seq_length, self.num_labels)    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    List[str]    ,				UpperCAmelCase_			:    List[str]    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    Union[str, Any]    ,				UpperCAmelCase_			:    List[str]    ,				UpperCAmelCase_			:    int    ,				UpperCAmelCase_			:    List[Any]    ,				UpperCAmelCase_			:    Union[str, Any]    ):
          lowerCAmelCase				:			Optional[Any]    			=      self.num_choices
          lowerCAmelCase				:			int    			=      RemBertForMultipleChoice(config=UpperCAmelCase_    )
          model.to(UpperCAmelCase_    )
          model.eval()
          lowerCAmelCase				:			Tuple    			=      input_ids.unsqueeze(1    ).expand(-1    ,				self.num_choices    ,				-1    ).contiguous()
          lowerCAmelCase				:			Tuple    			=      token_type_ids.unsqueeze(1    ).expand(-1    ,				self.num_choices    ,				-1    ).contiguous()
          lowerCAmelCase				:			List[Any]    			=      input_mask.unsqueeze(1    ).expand(-1    ,				self.num_choices    ,				-1    ).contiguous()
          lowerCAmelCase				:			List[str]    			=      model(
              UpperCAmelCase_    ,				attention_mask=UpperCAmelCase_    ,				token_type_ids=UpperCAmelCase_    ,				labels=UpperCAmelCase_    ,				)
          self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.num_choices)    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Tuple    ):
          lowerCAmelCase				:			Optional[int]    			=      self.prepare_config_and_inputs()
          (
              (
              lowerCAmelCase
          )			,  (
              lowerCAmelCase
          )			,  (
              lowerCAmelCase
          )			,  (
              lowerCAmelCase
          )			,  (
              lowerCAmelCase
          )			,  (
              lowerCAmelCase
          )			,  (
              lowerCAmelCase
          )			,  
          )				:			List[Any]    			=      config_and_inputs
          lowerCAmelCase				:			str    			=      {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
          return config, inputs_dict
  
  
  
  
  
  
  @require_torch
  class    __A				(							lowerCAmelCase						,						lowerCAmelCase						,						unittest.TestCase ):
      lowerCAmelCase_							:		str						=							(
          (
              RemBertModel,
              RemBertForMaskedLM,
              RemBertForCausalLM,
              RemBertForMultipleChoice,
              RemBertForQuestionAnswering,
              RemBertForSequenceClassification,
              RemBertForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      lowerCAmelCase_							:		Any						=							(RemBertForCausalLM,) if is_torch_available() else ()
      lowerCAmelCase_							:		List[Any]						=							(
          {
              "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 	lowercase__ (	self			:    Optional[int]    ):
          lowerCAmelCase				:			str    			=      RemBertModelTester(self    )
          lowerCAmelCase				:			List[str]    			=      ConfigTester(self    ,				config_class=UpperCAmelCase_    ,				hidden_size=37    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Tuple    ):
          self.config_tester.run_common_tests()
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Any    ):
          lowerCAmelCase				:			Union[str, Any]    			=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    List[Any]    ):
          lowerCAmelCase				:			List[Any]    			=      self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
              lowerCAmelCase				:			str    			=      type
              self.model_tester.create_and_check_model(*UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    str    ):
          lowerCAmelCase				:			Tuple    			=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Tuple    ):
          lowerCAmelCase				:			Tuple    			=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_multiple_choice(*UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Tuple    ):
          lowerCAmelCase				:			Dict    			=      self.model_tester.prepare_config_and_inputs_for_decoder()
          self.model_tester.create_and_check_decoder_model_past_large_inputs(*UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    List[str]    ):
          lowerCAmelCase				:			Dict    			=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Any    ):
          lowerCAmelCase				:			List[Any]    			=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Dict    ):
          lowerCAmelCase				:			Any    			=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[Any]    ):
          lowerCAmelCase				:			Optional[int]    			=      self.model_tester.prepare_config_and_inputs_for_decoder()
          self.model_tester.create_and_check_model_as_decoder(*UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[int]    ):
          # This regression test was failing with PyTorch < 1.3
          (
              (
              lowerCAmelCase
          )			,  (
              lowerCAmelCase
          )			,  (
              lowerCAmelCase
          )			,  (
              lowerCAmelCase
          )			,  (
              lowerCAmelCase
          )			,  (
              lowerCAmelCase
          )			,  (
              lowerCAmelCase
          )			,  (
              lowerCAmelCase
          )			,  (
              lowerCAmelCase
          )			,  
          )				:			Union[str, Any]    			=      self.model_tester.prepare_config_and_inputs_for_decoder()
  
          lowerCAmelCase				:			List[str]    			=      None
  
          self.model_tester.create_and_check_model_as_decoder(
              UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    ,				)
  
  
  
  
  
  
      @slow
      def 	lowercase__ (	self			:    Tuple    ):
          for model_name in REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowerCAmelCase				:			int    			=      RemBertModel.from_pretrained(UpperCAmelCase_    )
              self.assertIsNotNone(UpperCAmelCase_    )
  
  
  
  
  
  
  @require_torch
  class    __A				(							unittest.TestCase ):
  
  
  
  
  
  
      @slow
      def 	lowercase__ (	self			:    List[Any]    ):
          # Test exact values at the last hidden layer
          lowerCAmelCase				:			Union[str, Any]    			=      RemBertModel.from_pretrained('google/rembert'    )
          lowerCAmelCase				:			str    			=      torch.tensor([[312, 56498, 313, 2125, 313]]    )
          lowerCAmelCase				:			List[str]    			=      torch.tensor([[0, 0, 0, 1, 1]]    )
          with torch.no_grad():
              lowerCAmelCase				:			str    			=      model(UpperCAmelCase_    ,				token_type_ids=UpperCAmelCase_    ,				output_hidden_states=UpperCAmelCase_    )
  
          lowerCAmelCase				:			int    			=      1152
  
          lowerCAmelCase				:			Union[str, Any]    			=      torch.Size((1, 5, hidden_size)    )
          self.assertEqual(output['last_hidden_state'].shape    ,				UpperCAmelCase_    )
  
          lowerCAmelCase				:			Dict    			=      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]    ,				UpperCAmelCase_    ,				atol=1E-4    )    )
  
  
  
  
  
  
style_context: 
  
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      )					-> tuple[list[list[int]], list[list[str]]]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			List[Any]    			=      list(_UpperCAmelCase				)
      lowerCAmelCase				:			Union[str, Any]    			=      list(_UpperCAmelCase				)
      lowerCAmelCase				:			Optional[int]    			=      len(_UpperCAmelCase				)
      lowerCAmelCase				:			List[Any]    			=      len(_UpperCAmelCase				)
  
      lowerCAmelCase				:			str    			=      [
          [0 for _ in range(len_destination_seq + 1				)] for _ in range(len_source_seq + 1				)
      ]
      lowerCAmelCase				:			str    			=      [
          ['0' for _ in range(len_destination_seq + 1				)] for _ in range(len_source_seq + 1				)
      ]
  
      for i in range(1,      len_source_seq + 1				):
          lowerCAmelCase				:			int    			=      i * delete_cost
          lowerCAmelCase				:			List[str]    			=      f"D{source_seq[i - 1]:c}"
  
      for i in range(1,      len_destination_seq + 1				):
          lowerCAmelCase				:			Tuple    			=      i * insert_cost
          lowerCAmelCase				:			Union[str, Any]    			=      f"I{destination_seq[i - 1]:c}"
  
      for i in range(1,      len_source_seq + 1				):
          for j in range(1,      len_destination_seq + 1				):
              if source_seq[i - 1] == destination_seq[j - 1]:
                  lowerCAmelCase				:			str    			=      costs[i - 1][j - 1] + copy_cost
                  lowerCAmelCase				:			Tuple    			=      f"C{source_seq[i - 1]:c}"
              else:
                  lowerCAmelCase				:			Optional[int]    			=      costs[i - 1][j - 1] + replace_cost
                  lowerCAmelCase				:			Optional[int]    			=      f"R{source_seq[i - 1]:c}" + str(destination_seq[j - 1]				)
  
              if costs[i - 1][j] + delete_cost < costs[i][j]:
                  lowerCAmelCase				:			List[str]    			=      costs[i - 1][j] + delete_cost
                  lowerCAmelCase				:			Optional[int]    			=      f"D{source_seq[i - 1]:c}"
  
              if costs[i][j - 1] + insert_cost < costs[i][j]:
                  lowerCAmelCase				:			Union[str, Any]    			=      costs[i][j - 1] + insert_cost
                  lowerCAmelCase				:			str    			=      f"I{destination_seq[j - 1]:c}"
  
      return costs, ops
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase				)					-> list[str]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      if i == 0 and j == 0:
          return []
      else:
          if ops[i][j][0] == "C" or ops[i][j][0] == "R":
              lowerCAmelCase				:			List[str]    			=      assemble_transformation(_UpperCAmelCase,      i - 1,      j - 1				)
              seq.append(ops[i][j]				)
              return seq
          elif ops[i][j][0] == "D":
              lowerCAmelCase				:			int    			=      assemble_transformation(_UpperCAmelCase,      i - 1,      _UpperCAmelCase				)
              seq.append(ops[i][j]				)
              return seq
          else:
              lowerCAmelCase				:			Tuple    			=      assemble_transformation(_UpperCAmelCase,      _UpperCAmelCase,      j - 1				)
              seq.append(ops[i][j]				)
              return seq
  
  
  if __name__ == "__main__":
   __A     ,      __A   :     List[str]      =			compute_transform_tables('''Python''', '''Algorithms''', -1, 1, 2, 2)
  
   __A   :     Optional[int]      =			len(operations)
   __A   :     Optional[Any]      =			len(operations[0])
   __A   :     Any      =			assemble_transformation(operations, m - 1, n - 1)
  
   __A   :     int      =			list('''Python''')
   __A   :     Tuple      =			0
   __A   :     Tuple      =			0
  
   with open('''min_cost.txt''', '''w''') as file:
    for op in sequence:
     print(''''''.join(string))
  
     if op[0] == "C":
      file.write('''%-16s''' % '''Copy %c''' % op[1])
      file.write('''\t\t\t''' + ''''''.join(string))
      file.write('''\r\n''')
  
      cost -= 1
     elif op[0] == "R":
      __A   :     Any      =			op[2]
  
      file.write('''%-16s''' % ('''Replace %c''' % op[1] + ''' with ''' + str(op[2])))
      file.write('''\t\t''' + ''''''.join(string))
      file.write('''\r\n''')
  
      cost += 1
     elif op[0] == "D":
      string.pop(i)
  
      file.write('''%-16s''' % '''Delete %c''' % op[1])
      file.write('''\t\t\t''' + ''''''.join(string))
      file.write('''\r\n''')
  
      cost += 2
     else:
      string.insert(i, op[1])
  
      file.write('''%-16s''' % '''Insert %c''' % op[1])
      file.write('''\t\t\t''' + ''''''.join(string))
      file.write('''\r\n''')
  
      cost += 2
  
     i += 1
  
    print(''''''.join(string))
    print('''Cost: ''', cost)
  
    file.write('''\r\nMinimum cost: ''' + str(cost))
  
  
  
  
  
  