


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_   :







 """simple docstring"""





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

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

      UpperCAmelCase						:				Union[str, Any]		  =			None
      if self.use_token_type_ids:
           UpperCAmelCase						:				int		  =			ids_tensor([self.batch_size, self.seq_length]       ,    self.type_vocab_size				)

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

      UpperCAmelCase						:				int		  =			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=__SCREAMING_SNAKE_CASE       ,    initializer_range=self.initializer_range       ,    )

      return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
 def   _UpperCamelCase    (			self					: Optional[Any]				)    ->			int:
      (
          (
          UpperCAmelCase   
      )  , (
          UpperCAmelCase   
      )  , (
          UpperCAmelCase   
      )  , (
          UpperCAmelCase   
      )  , (
          UpperCAmelCase   
      )  , (
          UpperCAmelCase   
      )  , (
          UpperCAmelCase   
      )  ,    
      )						:				Any		  =			self.prepare_config_and_inputs()

      UpperCAmelCase						:				Optional[Any]		  =			True
      UpperCAmelCase						:				str		  =			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
      UpperCAmelCase						:				List[Any]		  =			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   _UpperCamelCase    (			self					: Optional[int]       ,    _a					: int       ,    _a					: List[Any]       ,    _a					: List[str]       ,    _a					: int       ,    _a					: int       ,    _a					: str       ,    _a					: Union[str, Any]				)    ->			str:
      UpperCAmelCase						:				Any		  =			RemBertModel(config=__SCREAMING_SNAKE_CASE				)
      model.to(__SCREAMING_SNAKE_CASE				)
      model.eval()
      UpperCAmelCase						:				str		  =			model(__SCREAMING_SNAKE_CASE       ,    attention_mask=__SCREAMING_SNAKE_CASE       ,    token_type_ids=__SCREAMING_SNAKE_CASE				)
      UpperCAmelCase						:				Optional[Any]		  =			model(__SCREAMING_SNAKE_CASE       ,    token_type_ids=__SCREAMING_SNAKE_CASE				)
      UpperCAmelCase						:				str		  =			model(__SCREAMING_SNAKE_CASE				)
      self.parent.assertEqual(result.last_hidden_state.shape       ,    (self.batch_size, self.seq_length, self.hidden_size)				)
 def   _UpperCamelCase    (			self					: Any       ,    _a					: int       ,    _a					: List[Any]       ,    _a					: Union[str, Any]       ,    _a					: Optional[Any]       ,    _a					: int       ,    _a					: Union[str, Any]       ,    _a					: List[str]       ,    _a					: List[Any]       ,    _a					: Dict       ,    )    ->			str:
      UpperCAmelCase						:				Optional[Any]		  =			True
      UpperCAmelCase						:				Optional[Any]		  =			RemBertModel(__SCREAMING_SNAKE_CASE				)
      model.to(__SCREAMING_SNAKE_CASE				)
      model.eval()
      UpperCAmelCase						:				List[Any]		  =			model(
          __SCREAMING_SNAKE_CASE       ,    attention_mask=__SCREAMING_SNAKE_CASE       ,    token_type_ids=__SCREAMING_SNAKE_CASE       ,    encoder_hidden_states=__SCREAMING_SNAKE_CASE       ,    encoder_attention_mask=__SCREAMING_SNAKE_CASE       ,    )
      UpperCAmelCase						:				Any		  =			model(
          __SCREAMING_SNAKE_CASE       ,    attention_mask=__SCREAMING_SNAKE_CASE       ,    token_type_ids=__SCREAMING_SNAKE_CASE       ,    encoder_hidden_states=__SCREAMING_SNAKE_CASE       ,    )
      UpperCAmelCase						:				Dict		  =			model(__SCREAMING_SNAKE_CASE       ,    attention_mask=__SCREAMING_SNAKE_CASE       ,    token_type_ids=__SCREAMING_SNAKE_CASE				)
      self.parent.assertEqual(result.last_hidden_state.shape       ,    (self.batch_size, self.seq_length, self.hidden_size)				)
 def   _UpperCamelCase    (			self					: Any       ,    _a					: str       ,    _a					: Any       ,    _a					: Dict       ,    _a					: List[str]       ,    _a					: Union[str, Any]       ,    _a					: Union[str, Any]       ,    _a					: Tuple       ,    _a					: Optional[int]       ,    _a					: int       ,    )    ->			int:
      UpperCAmelCase						:				Optional[Any]		  =			RemBertForCausalLM(config=__SCREAMING_SNAKE_CASE				)
      model.to(__SCREAMING_SNAKE_CASE				)
      model.eval()
      UpperCAmelCase						:				Optional[Any]		  =			model(__SCREAMING_SNAKE_CASE       ,    attention_mask=__SCREAMING_SNAKE_CASE       ,    token_type_ids=__SCREAMING_SNAKE_CASE       ,    labels=__SCREAMING_SNAKE_CASE				)
      self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.seq_length, self.vocab_size)				)
 def   _UpperCamelCase    (			self					: Tuple       ,    _a					: Optional[Any]       ,    _a					: Dict       ,    _a					: Union[str, Any]       ,    _a					: Union[str, Any]       ,    _a					: Tuple       ,    _a					: int       ,    _a					: str				)    ->			Optional[Any]:
      UpperCAmelCase						:				Optional[int]		  =			RemBertForMaskedLM(config=__SCREAMING_SNAKE_CASE				)
      model.to(__SCREAMING_SNAKE_CASE				)
      model.eval()
      UpperCAmelCase						:				List[str]		  =			model(__SCREAMING_SNAKE_CASE       ,    attention_mask=__SCREAMING_SNAKE_CASE       ,    token_type_ids=__SCREAMING_SNAKE_CASE       ,    labels=__SCREAMING_SNAKE_CASE				)
      self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.seq_length, self.vocab_size)				)
 def   _UpperCamelCase    (			self					: str       ,    _a					: Tuple       ,    _a					: str       ,    _a					: Optional[Any]       ,    _a					: List[Any]       ,    _a					: List[Any]       ,    _a					: Optional[int]       ,    _a					: Optional[int]       ,    _a					: Tuple       ,    _a					: Optional[Any]       ,    )    ->			Optional[int]:
      UpperCAmelCase						:				Optional[int]		  =			True
      UpperCAmelCase						:				Tuple		  =			True
      UpperCAmelCase						:				int		  =			RemBertForCausalLM(config=__SCREAMING_SNAKE_CASE				)
      model.to(__SCREAMING_SNAKE_CASE				)
      model.eval()

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

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

      # append to next input_ids and
      UpperCAmelCase						:				Optional[Any]		  =			torch.cat([input_ids, next_tokens]       ,    dim=-1				)
      UpperCAmelCase						:				Optional[int]		  =			torch.cat([input_mask, next_mask]       ,    dim=-1				)

      UpperCAmelCase						:				Optional[Any]		  =			model(
          __SCREAMING_SNAKE_CASE       ,    attention_mask=__SCREAMING_SNAKE_CASE       ,    encoder_hidden_states=__SCREAMING_SNAKE_CASE       ,    encoder_attention_mask=__SCREAMING_SNAKE_CASE       ,    output_hidden_states=__SCREAMING_SNAKE_CASE       ,    )["hidden_states"][0]
      UpperCAmelCase						:				Dict		  =			model(
          __SCREAMING_SNAKE_CASE       ,    attention_mask=__SCREAMING_SNAKE_CASE       ,    encoder_hidden_states=__SCREAMING_SNAKE_CASE       ,    encoder_attention_mask=__SCREAMING_SNAKE_CASE       ,    past_key_values=__SCREAMING_SNAKE_CASE       ,    output_hidden_states=__SCREAMING_SNAKE_CASE       ,    )["hidden_states"][0]

      # select random slice
      UpperCAmelCase						:				List[str]		  =			ids_tensor((1,)       ,    output_from_past.shape[-1]				).item()
      UpperCAmelCase						:				List[str]		  =			output_from_no_past[:, -3:, random_slice_idx].detach()
      UpperCAmelCase						:				int		  =			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(__SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    atol=1E-3				)				)
 def   _UpperCamelCase    (			self					: Dict       ,    _a					: List[Any]       ,    _a					: List[str]       ,    _a					: Optional[Any]       ,    _a					: int       ,    _a					: int       ,    _a					: Tuple       ,    _a					: List[str]				)    ->			Optional[Any]:
      UpperCAmelCase						:				Any		  =			RemBertForQuestionAnswering(config=__SCREAMING_SNAKE_CASE				)
      model.to(__SCREAMING_SNAKE_CASE				)
      model.eval()
      UpperCAmelCase						:				Optional[int]		  =			model(
          __SCREAMING_SNAKE_CASE       ,    attention_mask=__SCREAMING_SNAKE_CASE       ,    token_type_ids=__SCREAMING_SNAKE_CASE       ,    start_positions=__SCREAMING_SNAKE_CASE       ,    end_positions=__SCREAMING_SNAKE_CASE       ,    )
      self.parent.assertEqual(result.start_logits.shape       ,    (self.batch_size, self.seq_length)				)
      self.parent.assertEqual(result.end_logits.shape       ,    (self.batch_size, self.seq_length)				)
 def   _UpperCamelCase    (			self					: Tuple       ,    _a					: Optional[Any]       ,    _a					: Tuple       ,    _a					: Optional[Any]       ,    _a					: int       ,    _a					: Optional[int]       ,    _a					: Tuple       ,    _a					: Union[str, Any]				)    ->			List[str]:
      UpperCAmelCase						:				int		  =			self.num_labels
      UpperCAmelCase						:				Union[str, Any]		  =			RemBertForSequenceClassification(__SCREAMING_SNAKE_CASE				)
      model.to(__SCREAMING_SNAKE_CASE				)
      model.eval()
      UpperCAmelCase						:				Optional[Any]		  =			model(__SCREAMING_SNAKE_CASE       ,    attention_mask=__SCREAMING_SNAKE_CASE       ,    token_type_ids=__SCREAMING_SNAKE_CASE       ,    labels=__SCREAMING_SNAKE_CASE				)
      self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.num_labels)				)
 def   _UpperCamelCase    (			self					: Optional[Any]       ,    _a					: Union[str, Any]       ,    _a					: Any       ,    _a					: List[Any]       ,    _a					: Optional[int]       ,    _a					: int       ,    _a					: Optional[Any]       ,    _a					: Tuple				)    ->			List[Any]:
      UpperCAmelCase						:				Dict		  =			self.num_labels
      UpperCAmelCase						:				List[str]		  =			RemBertForTokenClassification(config=__SCREAMING_SNAKE_CASE				)
      model.to(__SCREAMING_SNAKE_CASE				)
      model.eval()
      UpperCAmelCase						:				Any		  =			model(__SCREAMING_SNAKE_CASE       ,    attention_mask=__SCREAMING_SNAKE_CASE       ,    token_type_ids=__SCREAMING_SNAKE_CASE       ,    labels=__SCREAMING_SNAKE_CASE				)
      self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.seq_length, self.num_labels)				)
 def   _UpperCamelCase    (			self					: str       ,    _a					: Any       ,    _a					: int       ,    _a					: Dict       ,    _a					: int       ,    _a					: Optional[Any]       ,    _a					: Dict       ,    _a					: Optional[int]				)    ->			Optional[Any]:
      UpperCAmelCase						:				Dict		  =			self.num_choices
      UpperCAmelCase						:				str		  =			RemBertForMultipleChoice(config=__SCREAMING_SNAKE_CASE				)
      model.to(__SCREAMING_SNAKE_CASE				)
      model.eval()
      UpperCAmelCase						:				Tuple		  =			input_ids.unsqueeze(1				).expand(-1       ,    self.num_choices       ,    -1				).contiguous()
      UpperCAmelCase						:				Optional[Any]		  =			token_type_ids.unsqueeze(1				).expand(-1       ,    self.num_choices       ,    -1				).contiguous()
      UpperCAmelCase						:				Union[str, Any]		  =			input_mask.unsqueeze(1				).expand(-1       ,    self.num_choices       ,    -1				).contiguous()
      UpperCAmelCase						:				Optional[int]		  =			model(
          __SCREAMING_SNAKE_CASE       ,    attention_mask=__SCREAMING_SNAKE_CASE       ,    token_type_ids=__SCREAMING_SNAKE_CASE       ,    labels=__SCREAMING_SNAKE_CASE       ,    )
      self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.num_choices)				)

 def   _UpperCamelCase    (			self					: Dict				)    ->			Any:
      UpperCAmelCase						:				str		  =			self.prepare_config_and_inputs()
      (
          (
          UpperCAmelCase   
      )  , (
          UpperCAmelCase   
      )  , (
          UpperCAmelCase   
      )  , (
          UpperCAmelCase   
      )  , (
          UpperCAmelCase   
      )  , (
          UpperCAmelCase   
      )  , (
          UpperCAmelCase   
      )  ,    
      )						:				List[Any]		  =			config_and_inputs
      UpperCAmelCase						:				Optional[Any]		  =			{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
      return config, inputs_dict




@require_torch
class    A_   ( UpperCamelCase_      ,						UpperCamelCase_      ,						unittest.TestCase     ):







 """simple docstring"""





 _UpperCamelCase				: int      =					(
     (
         RemBertModel,
         RemBertForMaskedLM,
         RemBertForCausalLM,
         RemBertForMultipleChoice,
         RemBertForQuestionAnswering,
         RemBertForSequenceClassification,
         RemBertForTokenClassification,
     )
     if is_torch_available()
     else ()
 )
 _UpperCamelCase				: Dict      =					(RemBertForCausalLM,) if is_torch_available() else ()
 _UpperCamelCase				: 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   _UpperCamelCase    (			self					: List[Any]				)    ->			int:
      UpperCAmelCase						:				Dict		  =			RemBertModelTester(self				)
      UpperCAmelCase						:				Optional[int]		  =			ConfigTester(self       ,    config_class=__SCREAMING_SNAKE_CASE       ,    hidden_size=3_7				)
 def   _UpperCamelCase    (			self					: Any				)    ->			str:
      self.config_tester.run_common_tests()
 def   _UpperCamelCase    (			self					: Union[str, Any]				)    ->			List[Any]:
      UpperCAmelCase						:				List[str]		  =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE				)
 def   _UpperCamelCase    (			self					: List[Any]				)    ->			Union[str, Any]:
      UpperCAmelCase						:				Optional[Any]		  =			self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
           UpperCAmelCase						:				List[Any]		  =			type
           self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE				)
 def   _UpperCamelCase    (			self					: Tuple				)    ->			Any:
      UpperCAmelCase						:				str		  =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*__SCREAMING_SNAKE_CASE				)
 def   _UpperCamelCase    (			self					: Any				)    ->			Tuple:
      UpperCAmelCase						:				str		  =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_multiple_choice(*__SCREAMING_SNAKE_CASE				)
 def   _UpperCamelCase    (			self					: List[Any]				)    ->			Any:
      UpperCAmelCase						:				Optional[Any]		  =			self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_decoder_model_past_large_inputs(*__SCREAMING_SNAKE_CASE				)
 def   _UpperCamelCase    (			self					: Dict				)    ->			Union[str, Any]:
      UpperCAmelCase						:				Optional[int]		  =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*__SCREAMING_SNAKE_CASE				)
 def   _UpperCamelCase    (			self					: Optional[int]				)    ->			Any:
      UpperCAmelCase						:				Optional[int]		  =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*__SCREAMING_SNAKE_CASE				)
 def   _UpperCamelCase    (			self					: Optional[Any]				)    ->			int:
      UpperCAmelCase						:				Optional[Any]		  =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*__SCREAMING_SNAKE_CASE				)
 def   _UpperCamelCase    (			self					: List[str]				)    ->			int:
      UpperCAmelCase						:				Union[str, Any]		  =			self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_model_as_decoder(*__SCREAMING_SNAKE_CASE				)
 def   _UpperCamelCase    (			self					: Any				)    ->			List[str]:
      # This regression test was failing with PyTorch < 1.3
      (
          (
          UpperCAmelCase   
      )  , (
          UpperCAmelCase   
      )  , (
          UpperCAmelCase   
      )  , (
          UpperCAmelCase   
      )  , (
          UpperCAmelCase   
      )  , (
          UpperCAmelCase   
      )  , (
          UpperCAmelCase   
      )  , (
          UpperCAmelCase   
      )  , (
          UpperCAmelCase   
      )  ,    
      )						:				int		  =			self.model_tester.prepare_config_and_inputs_for_decoder()

      UpperCAmelCase						:				Optional[int]		  =			None

      self.model_tester.create_and_check_model_as_decoder(
          __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       ,    )

 @slow
 def   _UpperCamelCase    (			self					: Tuple				)    ->			int:
      for model_name in REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCAmelCase						:				Any		  =			RemBertModel.from_pretrained(__SCREAMING_SNAKE_CASE				)
           self.assertIsNotNone(__SCREAMING_SNAKE_CASE				)




@require_torch
class    A_   ( unittest.TestCase     ):







 """simple docstring"""





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

      UpperCAmelCase						:				Tuple		  =			1_1_5_2

      UpperCAmelCase						:				Tuple		  =			torch.Size((1, 5, hidden_size)				)
      self.assertEqual(output["last_hidden_state"].shape       ,    __SCREAMING_SNAKE_CASE				)

      UpperCAmelCase						:				int		  =			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]       ,    __SCREAMING_SNAKE_CASE       ,    atol=1E-4				)				)








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_snake_case   = {"configuration_t5": ["T5_PRETRAINED_CONFIG_ARCHIVE_MAP", "T5Config", "T5OnnxConfig"]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case   = ["T5Tokenizer"]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case   = ["T5TokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case   = [
          "T5_PRETRAINED_MODEL_ARCHIVE_LIST",
          "T5EncoderModel",
          "T5ForConditionalGeneration",
          "T5Model",
          "T5PreTrainedModel",
          "load_tf_weights_in_t5",
          "T5ForQuestionAnswering",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case   = [
          "TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFT5EncoderModel",
          "TFT5ForConditionalGeneration",
          "TFT5Model",
          "TFT5PreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case   = [
          "FlaxT5EncoderModel",
          "FlaxT5ForConditionalGeneration",
          "FlaxT5Model",
          "FlaxT5PreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_ta import TaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_ta_fast import TaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_ta import (
                T5_PRETRAINED_MODEL_ARCHIVE_LIST,
                TaEncoderModel,
                TaForConditionalGeneration,
                TaForQuestionAnswering,
                TaModel,
                TaPreTrainedModel,
                load_tf_weights_in_ta,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_ta import (
                TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFTaEncoderModel,
                TFTaForConditionalGeneration,
                TFTaModel,
                TFTaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_ta import (
                FlaxTaEncoderModel,
                FlaxTaForConditionalGeneration,
                FlaxTaModel,
                FlaxTaPreTrainedModel,
            )


else:
      import sys

      _snake_case   = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



