





"""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 			snake_case		:

      def __init__(			self   :List[Any]						,							_lowerCamelCase   :List[Any]						,							_lowerCamelCase   :str=1_3						,							_lowerCamelCase   :List[Any]=7						,							_lowerCamelCase   :List[Any]=True						,							_lowerCamelCase   :Dict=True						,							_lowerCamelCase   :str=True						,							_lowerCamelCase   :List[str]=True						,							_lowerCamelCase   :Dict=9_9						,							_lowerCamelCase   :int=3_2						,							_lowerCamelCase   :Optional[int]=1_8						,							_lowerCamelCase   :Union[str, Any]=4_3						,							_lowerCamelCase   :str=5						,							_lowerCamelCase   :Optional[Any]=4						,							_lowerCamelCase   :int=3_7						,							_lowerCamelCase   :Tuple="gelu"						,							_lowerCamelCase   :List[str]=0.1						,							_lowerCamelCase   :Tuple=0.1						,							_lowerCamelCase   :int=5_1_2						,							_lowerCamelCase   :List[str]=1_6						,							_lowerCamelCase   :List[Any]=2						,							_lowerCamelCase   :Optional[Any]=0.0_2						,							_lowerCamelCase   :Optional[Any]=3						,							_lowerCamelCase   :Tuple=4						,							_lowerCamelCase   :str=None						,							):
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       parent
       __SCREAMING_SNAKE_CASE :			int												=       batch_size
       __SCREAMING_SNAKE_CASE :			Any												=       seq_length
       __SCREAMING_SNAKE_CASE :			Dict												=       is_training
       __SCREAMING_SNAKE_CASE :			Any												=       use_input_mask
       __SCREAMING_SNAKE_CASE :			List[str]												=       use_token_type_ids
       __SCREAMING_SNAKE_CASE :			Tuple												=       use_labels
       __SCREAMING_SNAKE_CASE :			str												=       vocab_size
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       hidden_size
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       input_embedding_size
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       output_embedding_size
       __SCREAMING_SNAKE_CASE :			Any												=       num_hidden_layers
       __SCREAMING_SNAKE_CASE :			List[str]												=       num_attention_heads
       __SCREAMING_SNAKE_CASE :			Tuple												=       intermediate_size
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       hidden_act
       __SCREAMING_SNAKE_CASE :			int												=       hidden_dropout_prob
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       attention_probs_dropout_prob
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       max_position_embeddings
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       type_vocab_size
       __SCREAMING_SNAKE_CASE :			List[Any]												=       type_sequence_label_size
       __SCREAMING_SNAKE_CASE :			int												=       initializer_range
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       num_labels
       __SCREAMING_SNAKE_CASE :			List[Any]												=       num_choices
       __SCREAMING_SNAKE_CASE :			List[str]												=       scope

      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[Any]						):
       __SCREAMING_SNAKE_CASE :			str												=       ids_tensor([self.batch_size, self.seq_length]						,							self.vocab_size						)

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

       __SCREAMING_SNAKE_CASE :			List[Any]												=       None
       if self.use_token_type_ids:
        __SCREAMING_SNAKE_CASE :			int												=       ids_tensor([self.batch_size, self.seq_length]						,							self.type_vocab_size						)

       __SCREAMING_SNAKE_CASE :			List[str]												=       None
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       None
       __SCREAMING_SNAKE_CASE :			Any												=       None
       if self.use_labels:
        __SCREAMING_SNAKE_CASE :			Dict												=       ids_tensor([self.batch_size]						,							self.type_sequence_label_size						)
        __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       ids_tensor([self.batch_size, self.seq_length]						,							self.num_labels						)
        __SCREAMING_SNAKE_CASE :			List[str]												=       ids_tensor([self.batch_size]						,							self.num_choices						)

       __SCREAMING_SNAKE_CASE :			Tuple												=       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=_lowerCamelCase						,							initializer_range=self.initializer_range						,							)

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

      def 						SCREAMING_SNAKE_CASE_			(			self   :Any						):
       (
           (
           __SCREAMING_SNAKE_CASE
       )							,      (
           __SCREAMING_SNAKE_CASE
       )							,      (
           __SCREAMING_SNAKE_CASE
       )							,      (
           __SCREAMING_SNAKE_CASE
       )							,      (
           __SCREAMING_SNAKE_CASE
       )							,      (
           __SCREAMING_SNAKE_CASE
       )							,      (
           __SCREAMING_SNAKE_CASE
       )							,      
       ) :			Optional[Any]												=       self.prepare_config_and_inputs()

       __SCREAMING_SNAKE_CASE :			int												=       True
       __SCREAMING_SNAKE_CASE :			List[str]												=       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
       __SCREAMING_SNAKE_CASE :			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 						SCREAMING_SNAKE_CASE_			(			self   :List[Any]						,							_lowerCamelCase   :Optional[Any]						,							_lowerCamelCase   :List[str]						,							_lowerCamelCase   :Tuple						,							_lowerCamelCase   :Optional[Any]						,							_lowerCamelCase   :Optional[int]						,							_lowerCamelCase   :List[Any]						,							_lowerCamelCase   :Any						):
       __SCREAMING_SNAKE_CASE :			Tuple												=       RemBertModel(config=_lowerCamelCase						)
       model.to(_lowerCamelCase						)
       model.eval()
       __SCREAMING_SNAKE_CASE :			List[Any]												=       model(_lowerCamelCase						,							attention_mask=_lowerCamelCase						,							token_type_ids=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Tuple												=       model(_lowerCamelCase						,							token_type_ids=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			str												=       model(_lowerCamelCase						)
       self.parent.assertEqual(result.last_hidden_state.shape						,							(self.batch_size, self.seq_length, self.hidden_size)						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Dict						,							_lowerCamelCase   :Dict						,							_lowerCamelCase   :Union[str, Any]						,							_lowerCamelCase   :Optional[Any]						,							_lowerCamelCase   :Tuple						,							_lowerCamelCase   :int						,							_lowerCamelCase   :Optional[Any]						,							_lowerCamelCase   :Optional[int]						,							_lowerCamelCase   :str						,							_lowerCamelCase   :Any						,							):
       __SCREAMING_SNAKE_CASE :			int												=       True
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       RemBertModel(_lowerCamelCase						)
       model.to(_lowerCamelCase						)
       model.eval()
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       model(
           _lowerCamelCase						,							attention_mask=_lowerCamelCase						,							token_type_ids=_lowerCamelCase						,							encoder_hidden_states=_lowerCamelCase						,							encoder_attention_mask=_lowerCamelCase						,							)
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       model(
           _lowerCamelCase						,							attention_mask=_lowerCamelCase						,							token_type_ids=_lowerCamelCase						,							encoder_hidden_states=_lowerCamelCase						,							)
       __SCREAMING_SNAKE_CASE :			int												=       model(_lowerCamelCase						,							attention_mask=_lowerCamelCase						,							token_type_ids=_lowerCamelCase						)
       self.parent.assertEqual(result.last_hidden_state.shape						,							(self.batch_size, self.seq_length, self.hidden_size)						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[int]						,							_lowerCamelCase   :Union[str, Any]						,							_lowerCamelCase   :Union[str, Any]						,							_lowerCamelCase   :Tuple						,							_lowerCamelCase   :Dict						,							_lowerCamelCase   :List[Any]						,							_lowerCamelCase   :str						,							_lowerCamelCase   :List[str]						,							_lowerCamelCase   :List[str]						,							_lowerCamelCase   :Optional[Any]						,							):
       __SCREAMING_SNAKE_CASE :			Tuple												=       RemBertForCausalLM(config=_lowerCamelCase						)
       model.to(_lowerCamelCase						)
       model.eval()
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       model(_lowerCamelCase						,							attention_mask=_lowerCamelCase						,							token_type_ids=_lowerCamelCase						,							labels=_lowerCamelCase						)
       self.parent.assertEqual(result.logits.shape						,							(self.batch_size, self.seq_length, self.vocab_size)						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :int						,							_lowerCamelCase   :Union[str, Any]						,							_lowerCamelCase   :Tuple						,							_lowerCamelCase   :List[str]						,							_lowerCamelCase   :Union[str, Any]						,							_lowerCamelCase   :Dict						,							_lowerCamelCase   :List[Any]						,							_lowerCamelCase   :Union[str, Any]						):
       __SCREAMING_SNAKE_CASE :			List[Any]												=       RemBertForMaskedLM(config=_lowerCamelCase						)
       model.to(_lowerCamelCase						)
       model.eval()
       __SCREAMING_SNAKE_CASE :			int												=       model(_lowerCamelCase						,							attention_mask=_lowerCamelCase						,							token_type_ids=_lowerCamelCase						,							labels=_lowerCamelCase						)
       self.parent.assertEqual(result.logits.shape						,							(self.batch_size, self.seq_length, self.vocab_size)						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Union[str, Any]						,							_lowerCamelCase   :List[str]						,							_lowerCamelCase   :Dict						,							_lowerCamelCase   :Optional[int]						,							_lowerCamelCase   :List[Any]						,							_lowerCamelCase   :Optional[Any]						,							_lowerCamelCase   :Tuple						,							_lowerCamelCase   :int						,							_lowerCamelCase   :List[Any]						,							_lowerCamelCase   :Tuple						,							):
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       True
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       True
       __SCREAMING_SNAKE_CASE :			List[Any]												=       RemBertForCausalLM(config=_lowerCamelCase						)
       model.to(_lowerCamelCase						)
       model.eval()

       # first forward pass
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       model(
           _lowerCamelCase						,							attention_mask=_lowerCamelCase						,							encoder_hidden_states=_lowerCamelCase						,							encoder_attention_mask=_lowerCamelCase						,							use_cache=_lowerCamelCase						,							)
       __SCREAMING_SNAKE_CASE :			int												=       outputs.past_key_values

       # create hypothetical multiple next token and extent to next_input_ids
       __SCREAMING_SNAKE_CASE :			List[Any]												=       ids_tensor((self.batch_size, 3)						,							config.vocab_size						)
       __SCREAMING_SNAKE_CASE :			List[Any]												=       ids_tensor((self.batch_size, 3)						,							vocab_size=2						)

       # append to next input_ids and
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       torch.cat([input_ids, next_tokens]						,							dim=-1						)
       __SCREAMING_SNAKE_CASE :			str												=       torch.cat([input_mask, next_mask]						,							dim=-1						)

       __SCREAMING_SNAKE_CASE :			List[str]												=       model(
           _lowerCamelCase						,							attention_mask=_lowerCamelCase						,							encoder_hidden_states=_lowerCamelCase						,							encoder_attention_mask=_lowerCamelCase						,							output_hidden_states=_lowerCamelCase						,							)['''hidden_states'''][0]
       __SCREAMING_SNAKE_CASE :			str												=       model(
           _lowerCamelCase						,							attention_mask=_lowerCamelCase						,							encoder_hidden_states=_lowerCamelCase						,							encoder_attention_mask=_lowerCamelCase						,							past_key_values=_lowerCamelCase						,							output_hidden_states=_lowerCamelCase						,							)['''hidden_states'''][0]

       # select random slice
       __SCREAMING_SNAKE_CASE :			Any												=       ids_tensor((1,)						,							output_from_past.shape[-1]						).item()
       __SCREAMING_SNAKE_CASE :			str												=       output_from_no_past[:, -3:, random_slice_idx].detach()
       __SCREAMING_SNAKE_CASE :			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(_lowerCamelCase						,							_lowerCamelCase						,							atol=1e-3						)						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Tuple						,							_lowerCamelCase   :int						,							_lowerCamelCase   :Tuple						,							_lowerCamelCase   :int						,							_lowerCamelCase   :Union[str, Any]						,							_lowerCamelCase   :Tuple						,							_lowerCamelCase   :Optional[Any]						,							_lowerCamelCase   :int						):
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       RemBertForQuestionAnswering(config=_lowerCamelCase						)
       model.to(_lowerCamelCase						)
       model.eval()
       __SCREAMING_SNAKE_CASE :			int												=       model(
           _lowerCamelCase						,							attention_mask=_lowerCamelCase						,							token_type_ids=_lowerCamelCase						,							start_positions=_lowerCamelCase						,							end_positions=_lowerCamelCase						,							)
       self.parent.assertEqual(result.start_logits.shape						,							(self.batch_size, self.seq_length)						)
       self.parent.assertEqual(result.end_logits.shape						,							(self.batch_size, self.seq_length)						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :int						,							_lowerCamelCase   :Union[str, Any]						,							_lowerCamelCase   :Any						,							_lowerCamelCase   :int						,							_lowerCamelCase   :List[Any]						,							_lowerCamelCase   :List[str]						,							_lowerCamelCase   :Dict						,							_lowerCamelCase   :Dict						):
       __SCREAMING_SNAKE_CASE :			Tuple												=       self.num_labels
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       RemBertForSequenceClassification(_lowerCamelCase						)
       model.to(_lowerCamelCase						)
       model.eval()
       __SCREAMING_SNAKE_CASE :			str												=       model(_lowerCamelCase						,							attention_mask=_lowerCamelCase						,							token_type_ids=_lowerCamelCase						,							labels=_lowerCamelCase						)
       self.parent.assertEqual(result.logits.shape						,							(self.batch_size, self.num_labels)						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :str						,							_lowerCamelCase   :Optional[Any]						,							_lowerCamelCase   :int						,							_lowerCamelCase   :List[str]						,							_lowerCamelCase   :Union[str, Any]						,							_lowerCamelCase   :Tuple						,							_lowerCamelCase   :str						,							_lowerCamelCase   :List[Any]						):
       __SCREAMING_SNAKE_CASE :			int												=       self.num_labels
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       RemBertForTokenClassification(config=_lowerCamelCase						)
       model.to(_lowerCamelCase						)
       model.eval()
       __SCREAMING_SNAKE_CASE :			Any												=       model(_lowerCamelCase						,							attention_mask=_lowerCamelCase						,							token_type_ids=_lowerCamelCase						,							labels=_lowerCamelCase						)
       self.parent.assertEqual(result.logits.shape						,							(self.batch_size, self.seq_length, self.num_labels)						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :int						,							_lowerCamelCase   :str						,							_lowerCamelCase   :Union[str, Any]						,							_lowerCamelCase   :str						,							_lowerCamelCase   :str						,							_lowerCamelCase   :int						,							_lowerCamelCase   :str						,							_lowerCamelCase   :List[str]						):
       __SCREAMING_SNAKE_CASE :			List[str]												=       self.num_choices
       __SCREAMING_SNAKE_CASE :			Tuple												=       RemBertForMultipleChoice(config=_lowerCamelCase						)
       model.to(_lowerCamelCase						)
       model.eval()
       __SCREAMING_SNAKE_CASE :			List[Any]												=       input_ids.unsqueeze(1						).expand(-1						,							self.num_choices						,							-1						).contiguous()
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       token_type_ids.unsqueeze(1						).expand(-1						,							self.num_choices						,							-1						).contiguous()
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       input_mask.unsqueeze(1						).expand(-1						,							self.num_choices						,							-1						).contiguous()
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       model(
           _lowerCamelCase						,							attention_mask=_lowerCamelCase						,							token_type_ids=_lowerCamelCase						,							labels=_lowerCamelCase						,							)
       self.parent.assertEqual(result.logits.shape						,							(self.batch_size, self.num_choices)						)



      def 						SCREAMING_SNAKE_CASE_			(			self   :Union[str, Any]						):
       __SCREAMING_SNAKE_CASE :			str												=       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
       )							,      
       ) :			List[str]												=       config_and_inputs
       __SCREAMING_SNAKE_CASE :			List[Any]												=       {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
       return config, inputs_dict







@require_torch
class 			snake_case		(   __UpperCAmelCase ,     __UpperCAmelCase ,     unittest.TestCase   ):
      lowerCamelCase__						= (
          (
              RemBertModel,
              RemBertForMaskedLM,
              RemBertForCausalLM,
              RemBertForMultipleChoice,
              RemBertForQuestionAnswering,
              RemBertForSequenceClassification,
              RemBertForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      lowerCamelCase__						= (RemBertForCausalLM,) if is_torch_available() else ()
      lowerCamelCase__						= (
          {
              '''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 						SCREAMING_SNAKE_CASE_			(			self   :Any						):
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       RemBertModelTester(self						)
       __SCREAMING_SNAKE_CASE :			Any												=       ConfigTester(self						,							config_class=_lowerCamelCase						,							hidden_size=3_7						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :str						):
       self.config_tester.run_common_tests()

      def 						SCREAMING_SNAKE_CASE_			(			self   :Tuple						):
       __SCREAMING_SNAKE_CASE :			Any												=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :List[Any]						):
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
        __SCREAMING_SNAKE_CASE :			List[Any]												=       type
        self.model_tester.create_and_check_model(*_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Tuple						):
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[int]						):
       __SCREAMING_SNAKE_CASE :			Tuple												=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_multiple_choice(*_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :List[str]						):
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       self.model_tester.prepare_config_and_inputs_for_decoder()
       self.model_tester.create_and_check_decoder_model_past_large_inputs(*_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Any						):
       __SCREAMING_SNAKE_CASE :			List[str]												=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :List[str]						):
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Union[str, Any]						):
       __SCREAMING_SNAKE_CASE :			List[str]												=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :str						):
       __SCREAMING_SNAKE_CASE :			List[str]												=       self.model_tester.prepare_config_and_inputs_for_decoder()
       self.model_tester.create_and_check_model_as_decoder(*_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[int]						):
       # This regression test was failing with PyTorch < 1.3
       (
           (
           __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
       )							,      
       ) :			List[str]												=       self.model_tester.prepare_config_and_inputs_for_decoder()

       __SCREAMING_SNAKE_CASE :			List[Any]												=       None

       self.model_tester.create_and_check_model_as_decoder(
           _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							)



      @slow
      def 						SCREAMING_SNAKE_CASE_			(			self   :int						):
       for model_name in REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        __SCREAMING_SNAKE_CASE :			str												=       RemBertModel.from_pretrained(_lowerCamelCase						)
        self.assertIsNotNone(_lowerCamelCase						)







@require_torch
class 			snake_case		(   unittest.TestCase   ):

      @slow
      def 						SCREAMING_SNAKE_CASE_			(			self   :int						):
       # Test exact values at the last hidden layer
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       RemBertModel.from_pretrained('''google/rembert'''						)
       __SCREAMING_SNAKE_CASE :			str												=       torch.tensor([[3_1_2, 5_6_4_9_8, 3_1_3, 2_1_2_5, 3_1_3]]						)
       __SCREAMING_SNAKE_CASE :			List[Any]												=       torch.tensor([[0, 0, 0, 1, 1]]						)
       with torch.no_grad():
        __SCREAMING_SNAKE_CASE :			List[str]												=       model(_lowerCamelCase						,							token_type_ids=_lowerCamelCase						,							output_hidden_states=_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			int												=       1_1_5_2

       __SCREAMING_SNAKE_CASE :			int												=       torch.Size((1, 5, hidden_size)						)
       self.assertEqual(output['''last_hidden_state'''].shape						,							_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			Tuple												=       torch.tensor(
           [
               [
                   [0.0_7_5_4, -0.2_0_2_2, 0.1_9_0_4],
                   [-0.3_3_5_4, -0.3_6_9_2, -0.4_7_9_1],
                   [-0.2_3_1_4, -0.6_7_2_9, -0.0_7_4_9],
                   [-0.0_3_9_6, -0.3_1_0_5, -0.4_2_3_4],
                   [-0.1_5_7_1, -0.0_5_2_5, 0.5_3_5_3],
               ]
           ]						)

       # 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]						,							_lowerCamelCase						,							atol=1e-4						)						)








"""simple docstring"""





from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
    from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore





class 			snake_case		(   __UpperCAmelCase   ):
      lowerCamelCase__						= c_float

      @property
      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[Any]						):
       return sizeof(self.value						)







def 		lowerCAmelCase_	(  lowercase_   :							int ,      lowercase_   :							int ,      lowercase_   :							int = 0		):

 '''simple docstring'''






 if dimension <= 0:
  __SCREAMING_SNAKE_CASE :			str												=       fixed_dimension

 dimension -= num_token_to_add
 return dimension




def 		lowerCAmelCase_	(  lowercase_   :							int ,      lowercase_   :							ParameterFormat		):

 '''simple docstring'''






 return num_parameters * dtype.size






def 		lowerCAmelCase_	(  lowercase_   :							str		):

 '''simple docstring'''






 from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

 try:
  return AutoProcessor.from_pretrained(lowercase_		)
 except (ValueError, OSError, KeyError):
  __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			int												=       None, None
  try:
   __SCREAMING_SNAKE_CASE :			Optional[Any]												=       AutoTokenizer.from_pretrained(lowercase_		)
  except (OSError, KeyError):
   pass
  try:
   __SCREAMING_SNAKE_CASE :			Optional[Any]												=       AutoFeatureExtractor.from_pretrained(lowercase_		)
  except (OSError, KeyError):
   pass

  if tokenizer is not None and feature_extractor is not None:
   raise ValueError(
       F'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''		)
  elif tokenizer is None and feature_extractor is None:
   return None
  elif tokenizer is not None:
   return tokenizer
  else:
   return feature_extractor

