code: 
  
  
  
  
  
  
  """simple docstring"""
  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 					UpperCamelCase__   :
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      def __init__(		self   :  Dict							,						UpperCamelCase_   :  str							,						UpperCamelCase_   :  str=1_3							,						UpperCamelCase_   :  Dict=7							,						UpperCamelCase_   :  Optional[int]=True							,						UpperCamelCase_   :  Union[str, Any]=True							,						UpperCamelCase_   :  Union[str, Any]=True							,						UpperCamelCase_   :  Optional[int]=True							,						UpperCamelCase_   :  Dict=9_9							,						UpperCamelCase_   :  List[str]=3_2							,						UpperCamelCase_   :  str=1_8							,						UpperCamelCase_   :  Tuple=4_3							,						UpperCamelCase_   :  Optional[int]=5							,						UpperCamelCase_   :  Optional[Any]=4							,						UpperCamelCase_   :  Optional[int]=3_7							,						UpperCamelCase_   :  str="gelu"							,						UpperCamelCase_   :  List[Any]=0.1							,						UpperCamelCase_   :  str=0.1							,						UpperCamelCase_   :  Optional[Any]=5_1_2							,						UpperCamelCase_   :  Tuple=1_6							,						UpperCamelCase_   :  List[str]=2							,						UpperCamelCase_   :  Dict=0.02							,						UpperCamelCase_   :  List[Any]=3							,						UpperCamelCase_   :  str=4							,						UpperCamelCase_   :  List[str]=None							,						):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						parent
             __magic_name__										=						batch_size
             __magic_name__										=						seq_length
             __magic_name__										=						is_training
             __magic_name__										=						use_input_mask
             __magic_name__										=						use_token_type_ids
             __magic_name__										=						use_labels
             __magic_name__										=						vocab_size
             __magic_name__										=						hidden_size
             __magic_name__										=						input_embedding_size
             __magic_name__										=						output_embedding_size
             __magic_name__										=						num_hidden_layers
             __magic_name__										=						num_attention_heads
             __magic_name__										=						intermediate_size
             __magic_name__										=						hidden_act
             __magic_name__										=						hidden_dropout_prob
             __magic_name__										=						attention_probs_dropout_prob
             __magic_name__										=						max_position_embeddings
             __magic_name__										=						type_vocab_size
             __magic_name__										=						type_sequence_label_size
             __magic_name__										=						initializer_range
             __magic_name__										=						num_labels
             __magic_name__										=						num_choices
             __magic_name__										=						scope
  
  
  
  
  
  
  
      def 							a__ (		self   :  List[Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						ids_tensor([self.batch_size, self.seq_length]							,						self.vocab_size   )
  
             __magic_name__										=						None
             if self.use_input_mask:
                    __magic_name__										=						random_attention_mask([self.batch_size, self.seq_length]   )
  
             __magic_name__										=						None
             if self.use_token_type_ids:
                    __magic_name__										=						ids_tensor([self.batch_size, self.seq_length]							,						self.type_vocab_size   )
  
             __magic_name__										=						None
             __magic_name__										=						None
             __magic_name__										=						None
             if self.use_labels:
                    __magic_name__										=						ids_tensor([self.batch_size]							,						self.type_sequence_label_size   )
                    __magic_name__										=						ids_tensor([self.batch_size, self.seq_length]							,						self.num_labels   )
                    __magic_name__										=						ids_tensor([self.batch_size]							,						self.num_choices   )
  
             __magic_name__										=						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 							a__ (		self   :  Optional[int]   ):
  
  
  
  
  
             '''simple docstring'''
  
             (
                 (
                 __magic_name__
             )							,     (
                 __magic_name__
             )							,     (
                 __magic_name__
             )							,     (
                 __magic_name__
             )							,     (
                 __magic_name__
             )							,     (
                 __magic_name__
             )							,     (
                 __magic_name__
             )							,     
             )										=						self.prepare_config_and_inputs()
  
             __magic_name__										=						True
             __magic_name__										=						floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
             __magic_name__										=						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 							a__ (		self   :  List[Any]							,						UpperCamelCase_   :  List[Any]							,						UpperCamelCase_   :  List[Any]							,						UpperCamelCase_   :  str							,						UpperCamelCase_   :  Tuple							,						UpperCamelCase_   :  Tuple							,						UpperCamelCase_   :  Optional[int]							,						UpperCamelCase_   :  Optional[Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						RemBertModel(config=UpperCamelCase_   )
             model.to(UpperCamelCase_   )
             model.eval()
             __magic_name__										=						model(UpperCamelCase_							,						attention_mask=UpperCamelCase_							,						token_type_ids=UpperCamelCase_   )
             __magic_name__										=						model(UpperCamelCase_							,						token_type_ids=UpperCamelCase_   )
             __magic_name__										=						model(UpperCamelCase_   )
             self.parent.assertEqual(result.last_hidden_state.shape							,						(self.batch_size, self.seq_length, self.hidden_size)   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  int							,						UpperCamelCase_   :  Tuple							,						UpperCamelCase_   :  Optional[Any]							,						UpperCamelCase_   :  Dict							,						UpperCamelCase_   :  List[str]							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  Dict							,						UpperCamelCase_   :  Dict							,						UpperCamelCase_   :  Optional[int]							,						UpperCamelCase_   :  str							,						):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						True
             __magic_name__										=						RemBertModel(UpperCamelCase_   )
             model.to(UpperCamelCase_   )
             model.eval()
             __magic_name__										=						model(
                 UpperCamelCase_							,						attention_mask=UpperCamelCase_							,						token_type_ids=UpperCamelCase_							,						encoder_hidden_states=UpperCamelCase_							,						encoder_attention_mask=UpperCamelCase_							,						)
             __magic_name__										=						model(
                 UpperCamelCase_							,						attention_mask=UpperCamelCase_							,						token_type_ids=UpperCamelCase_							,						encoder_hidden_states=UpperCamelCase_							,						)
             __magic_name__										=						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 							a__ (		self   :  Dict							,						UpperCamelCase_   :  str							,						UpperCamelCase_   :  Optional[Any]							,						UpperCamelCase_   :  Tuple							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  List[str]							,						UpperCamelCase_   :  Optional[Any]							,						UpperCamelCase_   :  Tuple							,						UpperCamelCase_   :  Dict							,						UpperCamelCase_   :  Union[str, Any]							,						):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						RemBertForCausalLM(config=UpperCamelCase_   )
             model.to(UpperCamelCase_   )
             model.eval()
             __magic_name__										=						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 							a__ (		self   :  List[Any]							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  str							,						UpperCamelCase_   :  str							,						UpperCamelCase_   :  int							,						UpperCamelCase_   :  List[str]							,						UpperCamelCase_   :  Dict							,						UpperCamelCase_   :  Optional[int]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						RemBertForMaskedLM(config=UpperCamelCase_   )
             model.to(UpperCamelCase_   )
             model.eval()
             __magic_name__										=						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 							a__ (		self   :  str							,						UpperCamelCase_   :  str							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  List[Any]							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  List[Any]							,						):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						True
             __magic_name__										=						True
             __magic_name__										=						RemBertForCausalLM(config=UpperCamelCase_   )
             model.to(UpperCamelCase_   )
             model.eval()
  
             # first forward pass
             __magic_name__										=						model(
                 UpperCamelCase_							,						attention_mask=UpperCamelCase_							,						encoder_hidden_states=UpperCamelCase_							,						encoder_attention_mask=UpperCamelCase_							,						use_cache=UpperCamelCase_							,						)
             __magic_name__										=						outputs.past_key_values
  
             # create hypothetical multiple next token and extent to next_input_ids
             __magic_name__										=						ids_tensor((self.batch_size, 3)							,						config.vocab_size   )
             __magic_name__										=						ids_tensor((self.batch_size, 3)							,						vocab_size=2   )
  
             # append to next input_ids and
             __magic_name__										=						torch.cat([input_ids, next_tokens]							,						dim=-1   )
             __magic_name__										=						torch.cat([input_mask, next_mask]							,						dim=-1   )
  
             __magic_name__										=						model(
                 UpperCamelCase_							,						attention_mask=UpperCamelCase_							,						encoder_hidden_states=UpperCamelCase_							,						encoder_attention_mask=UpperCamelCase_							,						output_hidden_states=UpperCamelCase_							,						)['hidden_states'][0]
             __magic_name__										=						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
             __magic_name__										=						ids_tensor((1,)							,						output_from_past.shape[-1]   ).item()
             __magic_name__										=						output_from_no_past[:, -3:, random_slice_idx].detach()
             __magic_name__										=						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 							a__ (		self   :  str							,						UpperCamelCase_   :  List[str]							,						UpperCamelCase_   :  Tuple							,						UpperCamelCase_   :  Any							,						UpperCamelCase_   :  Any							,						UpperCamelCase_   :  Optional[Any]							,						UpperCamelCase_   :  Tuple							,						UpperCamelCase_   :  Any   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						RemBertForQuestionAnswering(config=UpperCamelCase_   )
             model.to(UpperCamelCase_   )
             model.eval()
             __magic_name__										=						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 							a__ (		self   :  int							,						UpperCamelCase_   :  Dict							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  Tuple							,						UpperCamelCase_   :  Tuple							,						UpperCamelCase_   :  List[str]							,						UpperCamelCase_   :  List[str]							,						UpperCamelCase_   :  Union[str, Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						self.num_labels
             __magic_name__										=						RemBertForSequenceClassification(UpperCamelCase_   )
             model.to(UpperCamelCase_   )
             model.eval()
             __magic_name__										=						model(UpperCamelCase_							,						attention_mask=UpperCamelCase_							,						token_type_ids=UpperCamelCase_							,						labels=UpperCamelCase_   )
             self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.num_labels)   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  Optional[int]							,						UpperCamelCase_   :  Tuple							,						UpperCamelCase_   :  Any							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  Tuple							,						UpperCamelCase_   :  int							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  Dict   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						self.num_labels
             __magic_name__										=						RemBertForTokenClassification(config=UpperCamelCase_   )
             model.to(UpperCamelCase_   )
             model.eval()
             __magic_name__										=						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 							a__ (		self   :  Any							,						UpperCamelCase_   :  Tuple							,						UpperCamelCase_   :  Any							,						UpperCamelCase_   :  int							,						UpperCamelCase_   :  str							,						UpperCamelCase_   :  List[str]							,						UpperCamelCase_   :  List[Any]							,						UpperCamelCase_   :  List[str]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						self.num_choices
             __magic_name__										=						RemBertForMultipleChoice(config=UpperCamelCase_   )
             model.to(UpperCamelCase_   )
             model.eval()
             __magic_name__										=						input_ids.unsqueeze(1   ).expand(-1							,						self.num_choices							,						-1   ).contiguous()
             __magic_name__										=						token_type_ids.unsqueeze(1   ).expand(-1							,						self.num_choices							,						-1   ).contiguous()
             __magic_name__										=						input_mask.unsqueeze(1   ).expand(-1							,						self.num_choices							,						-1   ).contiguous()
             __magic_name__										=						model(
                 UpperCamelCase_							,						attention_mask=UpperCamelCase_							,						token_type_ids=UpperCamelCase_							,						labels=UpperCamelCase_							,						)
             self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.num_choices)   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  int   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						self.prepare_config_and_inputs()
             (
                 (
                 __magic_name__
             )							,     (
                 __magic_name__
             )							,     (
                 __magic_name__
             )							,     (
                 __magic_name__
             )							,     (
                 __magic_name__
             )							,     (
                 __magic_name__
             )							,     (
                 __magic_name__
             )							,     
             )										=						config_and_inputs
             __magic_name__										=						{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
             return config, inputs_dict
  
  
  
  @require_torch
  class 					UpperCamelCase__   (							a_       ,  a_       ,  unittest.TestCase):
  
  
  
  
  
  
  
      """simple docstring"""
      __UpperCAmelCase											=   (
          (
              RemBertModel,
              RemBertForMaskedLM,
              RemBertForCausalLM,
              RemBertForMultipleChoice,
              RemBertForQuestionAnswering,
              RemBertForSequenceClassification,
              RemBertForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      __UpperCAmelCase											=   (RemBertForCausalLM,) if is_torch_available() else ()
      __UpperCAmelCase											=   (
          {
              """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 							a__ (		self   :  Tuple   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						RemBertModelTester(self   )
             __magic_name__										=						ConfigTester(self							,						config_class=UpperCamelCase_							,						hidden_size=3_7   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  str   ):
  
  
  
  
  
             '''simple docstring'''
  
             self.config_tester.run_common_tests()
  
  
  
  
  
  
  
      def 							a__ (		self   :  str   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*UpperCamelCase_   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  Optional[Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						self.model_tester.prepare_config_and_inputs()
             for type in ["absolute", "relative_key", "relative_key_query"]:
                    __magic_name__										=						type
                    self.model_tester.create_and_check_model(*UpperCamelCase_   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  Dict   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase_   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  Dict   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_multiple_choice(*UpperCamelCase_   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  Optional[int]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						self.model_tester.prepare_config_and_inputs_for_decoder()
             self.model_tester.create_and_check_decoder_model_past_large_inputs(*UpperCamelCase_   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  Optional[int]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_question_answering(*UpperCamelCase_   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  Tuple   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase_   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  Optional[int]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_token_classification(*UpperCamelCase_   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  Any   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						self.model_tester.prepare_config_and_inputs_for_decoder()
             self.model_tester.create_and_check_model_as_decoder(*UpperCamelCase_   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  List[Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             (
                 (
                 __magic_name__
             )							,     (
                 __magic_name__
             )							,     (
                 __magic_name__
             )							,     (
                 __magic_name__
             )							,     (
                 __magic_name__
             )							,     (
                 __magic_name__
             )							,     (
                 __magic_name__
             )							,     (
                 __magic_name__
             )							,     (
                 __magic_name__
             )							,     
             )										=						self.model_tester.prepare_config_and_inputs_for_decoder()
  
             __magic_name__										=						None
  
             self.model_tester.create_and_check_model_as_decoder(
                 UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_							,						)
  
  
  
  
  
  
  
      @slow
      def 							a__ (		self   :  Optional[Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             for model_name in REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    __magic_name__										=						RemBertModel.from_pretrained(UpperCamelCase_   )
                    self.assertIsNotNone(UpperCamelCase_   )
  
  
  
  @require_torch
  class 					UpperCamelCase__   (							unittest.TestCase):
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      @slow
      def 							a__ (		self   :  Optional[int]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						RemBertModel.from_pretrained('google/rembert'   )
             __magic_name__										=						torch.tensor([[3_1_2, 5_6_4_9_8, 3_1_3, 2_1_2_5, 3_1_3]]   )
             __magic_name__										=						torch.tensor([[0, 0, 0, 1, 1]]   )
             with torch.no_grad():
                    __magic_name__										=						model(UpperCamelCase_							,						token_type_ids=UpperCamelCase_							,						output_hidden_states=UpperCamelCase_   )
  
             __magic_name__										=						1_1_5_2
  
             __magic_name__										=						torch.Size((1, 5, hidden_size)   )
             self.assertEqual(output['last_hidden_state'].shape							,						UpperCamelCase_   )
  
             __magic_name__										=						torch.tensor(
                 [
                     [
                         [0.0_754, -0.2_022, 0.1_904],
                         [-0.3_354, -0.3_692, -0.4_791],
                         [-0.2_314, -0.6_729, -0.0_749],
                         [-0.0_396, -0.3_105, -0.4_234],
                         [-0.1_571, -0.0_525, 0.5_353],
                     ]
                 ]   )
  
             # 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: 
  
  
  
  
  
  
  """simple docstring"""
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
  
  
  snake_case   :   Optional[int]    		=		{
      """configuration_splinter""": ["""SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SplinterConfig"""],
      """tokenization_splinter""": ["""SplinterTokenizer"""],
  }
  
  try:
   if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   snake_case   :   Dict    		=		["""SplinterTokenizerFast"""]
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   snake_case   :   Dict    		=		[
       """SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """SplinterForQuestionAnswering""",
       """SplinterForPreTraining""",
       """SplinterLayer""",
       """SplinterModel""",
       """SplinterPreTrainedModel""",
   ]
  
  
  if TYPE_CHECKING:
   from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
   from .tokenization_splinter import SplinterTokenizer
  
   try:
    if not is_tokenizers_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tokenization_splinter_fast import SplinterTokenizerFast
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_splinter import (
        SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
        SplinterForPreTraining,
        SplinterForQuestionAnswering,
        SplinterLayer,
        SplinterModel,
        SplinterPreTrainedModel,
    )
  
  
  else:
   import sys
  
   snake_case   :   List[str]    		=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)