





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		:       int		,lowerCamelCase__		:       Dict		,lowerCamelCase__		:       List[Any]=13		,lowerCamelCase__		:       List[str]=7		,lowerCamelCase__		:       List[str]=True		,lowerCamelCase__		:       Tuple=True		,lowerCamelCase__		:       Optional[int]=True		,lowerCamelCase__		:       Union[str, Any]=True		,lowerCamelCase__		:       Dict=99		,lowerCamelCase__		:       Optional[Any]=32		,lowerCamelCase__		:       Tuple=18		,lowerCamelCase__		:       Union[str, Any]=43		,lowerCamelCase__		:       List[Any]=5		,lowerCamelCase__		:       Optional[Any]=4		,lowerCamelCase__		:       Optional[Any]=37		,lowerCamelCase__		:       Dict="gelu"		,lowerCamelCase__		:       Optional[Any]=0.1		,lowerCamelCase__		:       Tuple=0.1		,lowerCamelCase__		:       int=512		,lowerCamelCase__		:       Union[str, Any]=16		,lowerCamelCase__		:       List[Any]=2		,lowerCamelCase__		:       Optional[int]=0.02		,lowerCamelCase__		:       Optional[Any]=3		,lowerCamelCase__		:       Any=4		,lowerCamelCase__		:       Any=None		,)	->						int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		parent
           SCREAMING_SNAKE_CASE						=		batch_size
           SCREAMING_SNAKE_CASE						=		seq_length
           SCREAMING_SNAKE_CASE						=		is_training
           SCREAMING_SNAKE_CASE						=		use_input_mask
           SCREAMING_SNAKE_CASE						=		use_token_type_ids
           SCREAMING_SNAKE_CASE						=		use_labels
           SCREAMING_SNAKE_CASE						=		vocab_size
           SCREAMING_SNAKE_CASE						=		hidden_size
           SCREAMING_SNAKE_CASE						=		input_embedding_size
           SCREAMING_SNAKE_CASE						=		output_embedding_size
           SCREAMING_SNAKE_CASE						=		num_hidden_layers
           SCREAMING_SNAKE_CASE						=		num_attention_heads
           SCREAMING_SNAKE_CASE						=		intermediate_size
           SCREAMING_SNAKE_CASE						=		hidden_act
           SCREAMING_SNAKE_CASE						=		hidden_dropout_prob
           SCREAMING_SNAKE_CASE						=		attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE						=		max_position_embeddings
           SCREAMING_SNAKE_CASE						=		type_vocab_size
           SCREAMING_SNAKE_CASE						=		type_sequence_label_size
           SCREAMING_SNAKE_CASE						=		initializer_range
           SCREAMING_SNAKE_CASE						=		num_labels
           SCREAMING_SNAKE_CASE						=		num_choices
           SCREAMING_SNAKE_CASE						=		scope






     def 							SCREAMING_SNAKE_CASE__    (				self		:       int			)	->						int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		ids_tensor([self.batch_size, self.seq_length]		,self.vocab_size			)

           SCREAMING_SNAKE_CASE						=		None
           if self.use_input_mask:
                 SCREAMING_SNAKE_CASE						=		random_attention_mask([self.batch_size, self.seq_length]			)

           SCREAMING_SNAKE_CASE						=		None
           if self.use_token_type_ids:
                 SCREAMING_SNAKE_CASE						=		ids_tensor([self.batch_size, self.seq_length]		,self.type_vocab_size			)

           SCREAMING_SNAKE_CASE						=		None
           SCREAMING_SNAKE_CASE						=		None
           SCREAMING_SNAKE_CASE						=		None
           if self.use_labels:
                 SCREAMING_SNAKE_CASE						=		ids_tensor([self.batch_size]		,self.type_sequence_label_size			)
                 SCREAMING_SNAKE_CASE						=		ids_tensor([self.batch_size, self.seq_length]		,self.num_labels			)
                 SCREAMING_SNAKE_CASE						=		ids_tensor([self.batch_size]		,self.num_choices			)

           SCREAMING_SNAKE_CASE						=		RemBertConfig(
               vocab_size=self.vocab_size		,hidden_size=self.hidden_size		,input_embedding_size=self.input_embedding_size		,output_embedding_size=self.output_embedding_size		,num_hidden_layers=self.num_hidden_layers		,num_attention_heads=self.num_attention_heads		,intermediate_size=self.intermediate_size		,hidden_act=self.hidden_act		,hidden_dropout_prob=self.hidden_dropout_prob		,attention_probs_dropout_prob=self.attention_probs_dropout_prob		,max_position_embeddings=self.max_position_embeddings		,type_vocab_size=self.type_vocab_size		,is_decoder=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		:       Tuple			)	->						Any:




           '''simple docstring'''


           (
               (
               SCREAMING_SNAKE_CASE
           ),   (
               SCREAMING_SNAKE_CASE
           ),   (
               SCREAMING_SNAKE_CASE
           ),   (
               SCREAMING_SNAKE_CASE
           ),   (
               SCREAMING_SNAKE_CASE
           ),   (
               SCREAMING_SNAKE_CASE
           ),   (
               SCREAMING_SNAKE_CASE
           ),   
           )						=		self.prepare_config_and_inputs()

           SCREAMING_SNAKE_CASE						=		True
           SCREAMING_SNAKE_CASE						=		floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
           SCREAMING_SNAKE_CASE						=		ids_tensor([self.batch_size, self.seq_length]		,vocab_size=2			)

           return (
               config,
               input_ids,
               token_type_ids,
               input_mask,
               sequence_labels,
               token_labels,
               choice_labels,
               encoder_hidden_states,
               encoder_attention_mask,
           )






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Optional[int]		,lowerCamelCase__		:       str		,lowerCamelCase__		:       Optional[Any]		,lowerCamelCase__		:       List[str]		,lowerCamelCase__		:       Optional[Any]		,lowerCamelCase__		:       Optional[int]		,lowerCamelCase__		:       Optional[Any]		,lowerCamelCase__		:       str			)	->						Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		RemBertModel(config=lowerCamelCase__			)
           model.to(lowerCamelCase__			)
           model.eval()
           SCREAMING_SNAKE_CASE						=		model(lowerCamelCase__		,attention_mask=lowerCamelCase__		,token_type_ids=lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		model(lowerCamelCase__		,token_type_ids=lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		model(lowerCamelCase__			)
           self.parent.assertEqual(result.last_hidden_state.shape		,(self.batch_size, self.seq_length, self.hidden_size)			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Union[str, Any]		,lowerCamelCase__		:       List[str]		,lowerCamelCase__		:       Any		,lowerCamelCase__		:       Optional[int]		,lowerCamelCase__		:       Optional[int]		,lowerCamelCase__		:       Optional[Any]		,lowerCamelCase__		:       Optional[Any]		,lowerCamelCase__		:       List[str]		,lowerCamelCase__		:       Any		,lowerCamelCase__		:       Optional[Any]		,)	->						List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		True
           SCREAMING_SNAKE_CASE						=		RemBertModel(lowerCamelCase__			)
           model.to(lowerCamelCase__			)
           model.eval()
           SCREAMING_SNAKE_CASE						=		model(
               lowerCamelCase__		,attention_mask=lowerCamelCase__		,token_type_ids=lowerCamelCase__		,encoder_hidden_states=lowerCamelCase__		,encoder_attention_mask=lowerCamelCase__		,)
           SCREAMING_SNAKE_CASE						=		model(
               lowerCamelCase__		,attention_mask=lowerCamelCase__		,token_type_ids=lowerCamelCase__		,encoder_hidden_states=lowerCamelCase__		,)
           SCREAMING_SNAKE_CASE						=		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		:       List[Any]		,lowerCamelCase__		:       Optional[int]		,lowerCamelCase__		:       str		,lowerCamelCase__		:       Any		,lowerCamelCase__		:       List[Any]		,lowerCamelCase__		:       Optional[int]		,lowerCamelCase__		:       List[str]		,lowerCamelCase__		:       int		,lowerCamelCase__		:       List[str]		,lowerCamelCase__		:       Tuple		,)	->						List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		RemBertForCausalLM(config=lowerCamelCase__			)
           model.to(lowerCamelCase__			)
           model.eval()
           SCREAMING_SNAKE_CASE						=		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__		:       str		,lowerCamelCase__		:       int		,lowerCamelCase__		:       Union[str, Any]		,lowerCamelCase__		:       int		,lowerCamelCase__		:       Union[str, Any]		,lowerCamelCase__		:       Any			)	->						Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		RemBertForMaskedLM(config=lowerCamelCase__			)
           model.to(lowerCamelCase__			)
           model.eval()
           SCREAMING_SNAKE_CASE						=		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__		:       Tuple		,lowerCamelCase__		:       str		,lowerCamelCase__		:       Dict		,lowerCamelCase__		:       Optional[Any]		,lowerCamelCase__		:       Optional[int]		,lowerCamelCase__		:       str		,lowerCamelCase__		:       Union[str, Any]		,lowerCamelCase__		:       List[Any]		,lowerCamelCase__		:       int		,)	->						List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		True
           SCREAMING_SNAKE_CASE						=		True
           SCREAMING_SNAKE_CASE						=		RemBertForCausalLM(config=lowerCamelCase__			)
           model.to(lowerCamelCase__			)
           model.eval()

           # first forward pass
           SCREAMING_SNAKE_CASE						=		model(
               lowerCamelCase__		,attention_mask=lowerCamelCase__		,encoder_hidden_states=lowerCamelCase__		,encoder_attention_mask=lowerCamelCase__		,use_cache=lowerCamelCase__		,)
           SCREAMING_SNAKE_CASE						=		outputs.past_key_values

           # create hypothetical multiple next token and extent to next_input_ids
           SCREAMING_SNAKE_CASE						=		ids_tensor((self.batch_size, 3)		,config.vocab_size			)
           SCREAMING_SNAKE_CASE						=		ids_tensor((self.batch_size, 3)		,vocab_size=2			)

           # append to next input_ids and
           SCREAMING_SNAKE_CASE						=		torch.cat([input_ids, next_tokens]		,dim=-1			)
           SCREAMING_SNAKE_CASE						=		torch.cat([input_mask, next_mask]		,dim=-1			)

           SCREAMING_SNAKE_CASE						=		model(
               lowerCamelCase__		,attention_mask=lowerCamelCase__		,encoder_hidden_states=lowerCamelCase__		,encoder_attention_mask=lowerCamelCase__		,output_hidden_states=lowerCamelCase__		,)["""hidden_states"""][0]
           SCREAMING_SNAKE_CASE						=		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						=		ids_tensor((1,)		,output_from_past.shape[-1]			).item()
           SCREAMING_SNAKE_CASE						=		output_from_no_past[:, -3:, random_slice_idx].detach()
           SCREAMING_SNAKE_CASE						=		output_from_past[:, :, random_slice_idx].detach()

           self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]			)

           # test that outputs are equal for slice
           self.parent.assertTrue(torch.allclose(lowerCamelCase__		,lowerCamelCase__		,atol=1e-3			)			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Any		,lowerCamelCase__		:       Optional[Any]		,lowerCamelCase__		:       List[str]		,lowerCamelCase__		:       Union[str, Any]		,lowerCamelCase__		:       Optional[int]		,lowerCamelCase__		:       Optional[int]		,lowerCamelCase__		:       Optional[Any]		,lowerCamelCase__		:       int			)	->						int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		RemBertForQuestionAnswering(config=lowerCamelCase__			)
           model.to(lowerCamelCase__			)
           model.eval()
           SCREAMING_SNAKE_CASE						=		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__		:       List[Any]		,lowerCamelCase__		:       int		,lowerCamelCase__		:       Optional[int]		,lowerCamelCase__		:       Union[str, Any]		,lowerCamelCase__		:       List[str]		,lowerCamelCase__		:       Any		,lowerCamelCase__		:       Any			)	->						Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.num_labels
           SCREAMING_SNAKE_CASE						=		RemBertForSequenceClassification(lowerCamelCase__			)
           model.to(lowerCamelCase__			)
           model.eval()
           SCREAMING_SNAKE_CASE						=		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		:       Dict		,lowerCamelCase__		:       Tuple		,lowerCamelCase__		:       Union[str, Any]		,lowerCamelCase__		:       str		,lowerCamelCase__		:       List[str]		,lowerCamelCase__		:       List[Any]		,lowerCamelCase__		:       Any		,lowerCamelCase__		:       Union[str, Any]			)	->						Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.num_labels
           SCREAMING_SNAKE_CASE						=		RemBertForTokenClassification(config=lowerCamelCase__			)
           model.to(lowerCamelCase__			)
           model.eval()
           SCREAMING_SNAKE_CASE						=		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		:       Union[str, Any]		,lowerCamelCase__		:       List[str]		,lowerCamelCase__		:       int		,lowerCamelCase__		:       List[str]		,lowerCamelCase__		:       Tuple		,lowerCamelCase__		:       List[str]		,lowerCamelCase__		:       Optional[Any]		,lowerCamelCase__		:       List[str]			)	->						int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.num_choices
           SCREAMING_SNAKE_CASE						=		RemBertForMultipleChoice(config=lowerCamelCase__			)
           model.to(lowerCamelCase__			)
           model.eval()
           SCREAMING_SNAKE_CASE						=		input_ids.unsqueeze(1			).expand(-1		,self.num_choices		,-1			).contiguous()
           SCREAMING_SNAKE_CASE						=		token_type_ids.unsqueeze(1			).expand(-1		,self.num_choices		,-1			).contiguous()
           SCREAMING_SNAKE_CASE						=		input_mask.unsqueeze(1			).expand(-1		,self.num_choices		,-1			).contiguous()
           SCREAMING_SNAKE_CASE						=		model(
               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		:       Optional[Any]			)	->						Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.prepare_config_and_inputs()
           (
               (
               SCREAMING_SNAKE_CASE
           ),   (
               SCREAMING_SNAKE_CASE
           ),   (
               SCREAMING_SNAKE_CASE
           ),   (
               SCREAMING_SNAKE_CASE
           ),   (
               SCREAMING_SNAKE_CASE
           ),   (
               SCREAMING_SNAKE_CASE
           ),   (
               SCREAMING_SNAKE_CASE
           ),   
           )						=		config_and_inputs
           SCREAMING_SNAKE_CASE						=		{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
           return config, inputs_dict

@require_torch
class 						UpperCamelCase__					( lowerCAmelCase_				, lowerCAmelCase_				, unittest.TestCase  ):



     '''simple docstring'''

     __snake_case       :      Optional[int]						      =     (
         (
             RemBertModel,
             RemBertForMaskedLM,
             RemBertForCausalLM,
             RemBertForMultipleChoice,
             RemBertForQuestionAnswering,
             RemBertForSequenceClassification,
             RemBertForTokenClassification,
         )
         if is_torch_available()
         else ()
     )
     __snake_case       :      Any						      =     (RemBertForCausalLM,) if is_torch_available() else ()
     __snake_case       :      Dict						      =     (
         {
             "feature-extraction": RemBertModel,
             "fill-mask": RemBertForMaskedLM,
             "question-answering": RemBertForQuestionAnswering,
             "text-classification": RemBertForSequenceClassification,
             "text-generation": RemBertForCausalLM,
             "token-classification": RemBertForTokenClassification,
             "zero-shot": RemBertForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )






     def 							SCREAMING_SNAKE_CASE__    (				self		:       int			)	->						Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		RemBertModelTester(self			)
           SCREAMING_SNAKE_CASE						=		ConfigTester(self		,config_class=lowerCamelCase__		,hidden_size=37			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       int			)	->						int:




           '''simple docstring'''


           self.config_tester.run_common_tests()






     def 							SCREAMING_SNAKE_CASE__    (				self		:       int			)	->						str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase__			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       str			)	->						Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
                 SCREAMING_SNAKE_CASE						=		type
                 self.model_tester.create_and_check_model(*lowerCamelCase__			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Any			)	->						List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase__			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Dict			)	->						Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase__			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[str]			)	->						Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.model_tester.prepare_config_and_inputs_for_decoder()
           self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowerCamelCase__			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Tuple			)	->						List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*lowerCamelCase__			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Tuple			)	->						Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		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]			)	->						Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*lowerCamelCase__			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Tuple			)	->						List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.model_tester.prepare_config_and_inputs_for_decoder()
           self.model_tester.create_and_check_model_as_decoder(*lowerCamelCase__			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[str]			)	->						Optional[Any]:




           '''simple docstring'''


           (
               (
               SCREAMING_SNAKE_CASE
           ),   (
               SCREAMING_SNAKE_CASE
           ),   (
               SCREAMING_SNAKE_CASE
           ),   (
               SCREAMING_SNAKE_CASE
           ),   (
               SCREAMING_SNAKE_CASE
           ),   (
               SCREAMING_SNAKE_CASE
           ),   (
               SCREAMING_SNAKE_CASE
           ),   (
               SCREAMING_SNAKE_CASE
           ),   (
               SCREAMING_SNAKE_CASE
           ),   
           )						=		self.model_tester.prepare_config_and_inputs_for_decoder()

           SCREAMING_SNAKE_CASE						=		None

           self.model_tester.create_and_check_model_as_decoder(
               lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__		,)






     @slow
     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[Any]			)	->						Optional[int]:




           '''simple docstring'''


           for model_name in REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 SCREAMING_SNAKE_CASE						=		RemBertModel.from_pretrained(lowerCamelCase__			)
                 self.assertIsNotNone(lowerCamelCase__			)

@require_torch
class 						UpperCamelCase__					( unittest.TestCase  ):



     '''simple docstring'''

     @slow
     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[str]			)	->						Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		RemBertModel.from_pretrained("""google/rembert"""			)
           SCREAMING_SNAKE_CASE						=		torch.tensor([[312, 56498, 313, 2125, 313]]			)
           SCREAMING_SNAKE_CASE						=		torch.tensor([[0, 0, 0, 1, 1]]			)
           with torch.no_grad():
                 SCREAMING_SNAKE_CASE						=		model(lowerCamelCase__		,token_type_ids=lowerCamelCase__		,output_hidden_states=lowerCamelCase__			)

           SCREAMING_SNAKE_CASE						=		1152

           SCREAMING_SNAKE_CASE						=		torch.Size((1, 5, hidden_size)			)
           self.assertEqual(output["""last_hidden_state"""].shape		,lowerCamelCase__			)

           SCREAMING_SNAKE_CASE						=		torch.tensor(
               [
                   [
                       [0.0754, -0.2022, 0.1904],
                       [-0.3354, -0.3692, -0.4791],
                       [-0.2314, -0.6729, -0.0749],
                       [-0.0396, -0.3105, -0.4234],
                       [-0.1571, -0.0525, 0.5353],
                   ]
               ]			)

           # 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			)			)









import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()



def   __lowercase  ( _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE      )					->  List[Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		TaConfig.from_json_file(_SCREAMING_SNAKE_CASE      )
      print(F"""Building PyTorch model from configuration: {config}"""      )
      SCREAMING_SNAKE_CASE						=		TaForConditionalGeneration(_SCREAMING_SNAKE_CASE      )

      # Load weights from tf checkpoint
      load_tf_weights_in_ta(_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE      )

      # Save pytorch-model
      print(F"""Save PyTorch model to {pytorch_dump_path}"""      )
      model.save_pretrained(_SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_		  =			argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
      )
      parser.add_argument(
          """--config_file""",
          default=None,
          type=str,
          required=True,
          help=(
              """The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."""
          ),
      )
      parser.add_argument(
          """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
      )
      SCREAMING_SNAKE_CASE_		  =			parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


