code: 
  
  
  import unittest
  
  from transformers import ElectraConfig, is_torch_available
  from transformers.models.auto import get_values
  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 (
          MODEL_FOR_PRETRAINING_MAPPING,
          ElectraForCausalLM,
          ElectraForMaskedLM,
          ElectraForMultipleChoice,
          ElectraForPreTraining,
          ElectraForQuestionAnswering,
          ElectraForSequenceClassification,
          ElectraForTokenClassification,
          ElectraModel,
      )
      from transformers.models.electra.modeling_electra import ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST
  
  
  
  class   __lowerCamelCase :
  
  
  
  
  
  
  
         def __init__( self:   int,A_:   Optional[int],A_:   int=13,A_:   Union[str, Any]=7,A_:   Any=True,A_:   List[Any]=True,A_:   Union[str, Any]=True,A_:   str=True,A_:   Union[str, Any]=99,A_:   List[str]=32,A_:   Optional[int]=5,A_:   List[str]=4,A_:   Dict=37,A_:   Dict="gelu",A_:   Optional[Any]=0.1,A_:   str=0.1,A_:   Any=512,A_:   str=16,A_:   Tuple=2,A_:   Any=0.0_2,A_:   Any=3,A_:   Any=4,A_:   Any=None,):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = parent
              __UpperCamelCase   = batch_size
              __UpperCamelCase   = seq_length
              __UpperCamelCase   = is_training
              __UpperCamelCase   = use_input_mask
              __UpperCamelCase   = use_token_type_ids
              __UpperCamelCase   = use_labels
              __UpperCamelCase   = vocab_size
              __UpperCamelCase   = hidden_size
              __UpperCamelCase   = num_hidden_layers
              __UpperCamelCase   = num_attention_heads
              __UpperCamelCase   = intermediate_size
              __UpperCamelCase   = hidden_act
              __UpperCamelCase   = hidden_dropout_prob
              __UpperCamelCase   = attention_probs_dropout_prob
              __UpperCamelCase   = max_position_embeddings
              __UpperCamelCase   = type_vocab_size
              __UpperCamelCase   = type_sequence_label_size
              __UpperCamelCase   = initializer_range
              __UpperCamelCase   = num_labels
              __UpperCamelCase   = num_choices
              __UpperCamelCase   = scope
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Any				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = ids_tensor([self.batch_size, self.seq_length],self.vocab_size				)
  
              __UpperCamelCase   = None
              if self.use_input_mask:
                   __UpperCamelCase   = random_attention_mask([self.batch_size, self.seq_length]				)
  
              __UpperCamelCase   = None
              if self.use_token_type_ids:
                   __UpperCamelCase   = ids_tensor([self.batch_size, self.seq_length],self.type_vocab_size				)
  
              __UpperCamelCase   = None
              __UpperCamelCase   = None
              __UpperCamelCase   = None
              if self.use_labels:
                   __UpperCamelCase   = ids_tensor([self.batch_size],self.type_sequence_label_size				)
                   __UpperCamelCase   = ids_tensor([self.batch_size, self.seq_length],self.num_labels				)
                   __UpperCamelCase   = ids_tensor([self.batch_size],self.num_choices				)
                   __UpperCamelCase   = ids_tensor([self.batch_size, self.seq_length],1				)
  
              __UpperCamelCase   = self.get_config()
  
              return (
                  config,
                  input_ids,
                  token_type_ids,
                  input_mask,
                  sequence_labels,
                  token_labels,
                  choice_labels,
                  fake_token_labels,
              )
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Dict				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              return ElectraConfig(
                  vocab_size=self.vocab_size,hidden_size=self.hidden_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=A_,initializer_range=self.initializer_range,)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Any				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              (
                  (
                  __UpperCamelCase
              ),						(
                  __UpperCamelCase
              ),						(
                  __UpperCamelCase
              ),						(
                  __UpperCamelCase
              ),						(
                  __UpperCamelCase
              ),						(
                  __UpperCamelCase
              ),						(
                  __UpperCamelCase
              ),						(
                  __UpperCamelCase
              ),						
              )   = self.prepare_config_and_inputs()
  
              __UpperCamelCase   = True
              __UpperCamelCase   = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
              __UpperCamelCase   = 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 		snake_case_  ( self:   int,A_:   List[Any],A_:   int,A_:   int,A_:   Optional[int],A_:   Union[str, Any],A_:   Tuple,A_:   str,A_:   Optional[int],):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = ElectraModel(config=A_				)
              model.to(A_				)
              model.eval()
              __UpperCamelCase   = model(A_,attention_mask=A_,token_type_ids=A_				)
              __UpperCamelCase   = model(A_,token_type_ids=A_				)
              __UpperCamelCase   = model(A_				)
              self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Tuple,A_:   Optional[int],A_:   Optional[Any],A_:   int,A_:   Any,A_:   int,A_:   Tuple,A_:   Tuple,A_:   List[Any],A_:   int,):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = True
              __UpperCamelCase   = ElectraModel(A_				)
              model.to(A_				)
              model.eval()
              __UpperCamelCase   = model(
                  A_,attention_mask=A_,token_type_ids=A_,encoder_hidden_states=A_,encoder_attention_mask=A_,)
              __UpperCamelCase   = model(
                  A_,attention_mask=A_,token_type_ids=A_,encoder_hidden_states=A_,)
              __UpperCamelCase   = model(A_,attention_mask=A_,token_type_ids=A_				)
              self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   List[Any],A_:   List[Any],A_:   List[Any],A_:   Union[str, Any],A_:   Optional[int],A_:   Union[str, Any],A_:   List[str],A_:   List[str],A_:   str,):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = ElectraForMaskedLM(config=A_				)
              model.to(A_				)
              model.eval()
              __UpperCamelCase   = model(A_,attention_mask=A_,token_type_ids=A_,labels=A_				)
              self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.vocab_size)				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Tuple,A_:   List[Any],A_:   Optional[int],A_:   Any,A_:   Optional[int],A_:   Dict,A_:   Optional[int],A_:   Any,A_:   Tuple,A_:   Dict,):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = ElectraForCausalLM(config=A_				)
              model.to(A_				)
              model.eval()
              __UpperCamelCase   = model(A_,attention_mask=A_,token_type_ids=A_,labels=A_				)
              self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.vocab_size)				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   int,A_:   List[str],A_:   Dict,A_:   Optional[int],A_:   Tuple,A_:   Optional[int],A_:   Optional[Any],A_:   Optional[int],A_:   Union[str, Any],):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self.num_labels
              __UpperCamelCase   = ElectraForTokenClassification(config=A_				)
              model.to(A_				)
              model.eval()
              __UpperCamelCase   = model(A_,attention_mask=A_,token_type_ids=A_,labels=A_				)
              self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.num_labels)				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Dict,A_:   Optional[Any],A_:   str,A_:   int,A_:   str,A_:   Tuple,A_:   str,A_:   Dict,A_:   List[Any],):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self.num_labels
              __UpperCamelCase   = ElectraForPreTraining(config=A_				)
              model.to(A_				)
              model.eval()
              __UpperCamelCase   = model(A_,attention_mask=A_,token_type_ids=A_,labels=A_				)
              self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length)				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Union[str, Any],A_:   List[Any],A_:   Union[str, Any],A_:   Dict,A_:   List[Any],A_:   Optional[int],A_:   List[Any],A_:   Dict,A_:   Dict,):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self.num_labels
              __UpperCamelCase   = ElectraForSequenceClassification(A_				)
              model.to(A_				)
              model.eval()
              __UpperCamelCase   = model(A_,attention_mask=A_,token_type_ids=A_,labels=A_				)
              self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels)				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   str,A_:   Any,A_:   Optional[Any],A_:   Union[str, Any],A_:   List[str],A_:   List[Any],A_:   Any,A_:   Optional[int],A_:   Any,):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = ElectraForQuestionAnswering(config=A_				)
              model.to(A_				)
              model.eval()
              __UpperCamelCase   = model(
                  A_,attention_mask=A_,token_type_ids=A_,start_positions=A_,end_positions=A_,)
              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 		snake_case_  ( self:   str,A_:   List[Any],A_:   Union[str, Any],A_:   Optional[int],A_:   Dict,A_:   List[str],A_:   str,A_:   Tuple,A_:   Union[str, Any],):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self.num_choices
              __UpperCamelCase   = ElectraForMultipleChoice(config=A_				)
              model.to(A_				)
              model.eval()
              __UpperCamelCase   = input_ids.unsqueeze(1				).expand(-1,self.num_choices,-1				).contiguous()
              __UpperCamelCase   = token_type_ids.unsqueeze(1				).expand(-1,self.num_choices,-1				).contiguous()
              __UpperCamelCase   = input_mask.unsqueeze(1				).expand(-1,self.num_choices,-1				).contiguous()
              __UpperCamelCase   = model(
                  A_,attention_mask=A_,token_type_ids=A_,labels=A_,)
              self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_choices)				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Optional[int]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self.prepare_config_and_inputs()
              (
                  (
                  __UpperCamelCase
              ),						(
                  __UpperCamelCase
              ),						(
                  __UpperCamelCase
              ),						(
                  __UpperCamelCase
              ),						(
                  __UpperCamelCase
              ),						(
                  __UpperCamelCase
              ),						(
                  __UpperCamelCase
              ),						(
                  __UpperCamelCase
              ),						
              )   = config_and_inputs
              __UpperCamelCase   = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
              return config, inputs_dict
  
  
  
  @require_torch
  class   __lowerCamelCase (_a							,			_a							,			unittest.TestCase	):
         _lowercase       							=    (
             (
                 ElectraModel,
                 ElectraForPreTraining,
                 ElectraForMaskedLM,
                 ElectraForCausalLM,
                 ElectraForMultipleChoice,
                 ElectraForTokenClassification,
                 ElectraForSequenceClassification,
                 ElectraForQuestionAnswering,
             )
             if is_torch_available()
             else ()
         )
         _lowercase       							=    (
             {
                 """feature-extraction""": ElectraModel,
                 """fill-mask""": ElectraForMaskedLM,
                 """question-answering""": ElectraForQuestionAnswering,
                 """text-classification""": ElectraForSequenceClassification,
                 """text-generation""": ElectraForCausalLM,
                 """token-classification""": ElectraForTokenClassification,
                 """zero-shot""": ElectraForSequenceClassification,
             }
             if is_torch_available()
             else {}
         )
         _lowercase       							=    True
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Dict,A_:   Any,A_:   List[str],A_:   Optional[Any]=False				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = super()._prepare_for_class(A_,A_,return_labels=A_				)
  
              if return_labels:
                   if model_class in get_values(A_				):
                        __UpperCamelCase   = torch.zeros(
                            (self.model_tester.batch_size, self.model_tester.seq_length),dtype=torch.long,device=A_				)
              return inputs_dict
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Dict				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = ElectraModelTester(self				)
              __UpperCamelCase   = ConfigTester(self,config_class=A_,hidden_size=37				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Union[str, Any]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              self.config_tester.run_common_tests()
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   List[Any]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_electra_model(*A_				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   List[Any]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self.model_tester.prepare_config_and_inputs_for_decoder()
              self.model_tester.create_and_check_electra_model_as_decoder(*A_				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Tuple				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self.model_tester.prepare_config_and_inputs()
              for type in ["absolute", "relative_key", "relative_key_query"]:
                   __UpperCamelCase   = type
                   self.model_tester.create_and_check_electra_model(*A_				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Tuple				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_electra_for_masked_lm(*A_				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Union[str, Any]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_electra_for_token_classification(*A_				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Union[str, Any]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_electra_for_pretraining(*A_				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Optional[Any]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_electra_for_sequence_classification(*A_				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Any				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_electra_for_question_answering(*A_				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Optional[Any]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_electra_for_multiple_choice(*A_				)
  
  
  
  
  
  
  
         @slow
         def 		snake_case_  ( self:   Optional[int]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              for model_name in ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   __UpperCamelCase   = ElectraModel.from_pretrained(A_				)
                   self.assertIsNotNone(A_				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Tuple				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self.model_tester.prepare_config_and_inputs_for_decoder()
              self.model_tester.create_and_check_electra_for_causal_lm(*A_				)
  
  
  
  @require_torch
  class   __lowerCamelCase (unittest.TestCase	):
  
  
  
  
  
  
  
         @slow
         def 		snake_case_  ( self:   List[str]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = ElectraModel.from_pretrained('google/electra-small-discriminator'				)
              __UpperCamelCase   = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]				)
              __UpperCamelCase   = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]				)
              __UpperCamelCase   = model(A_,attention_mask=A_				)[0]
              __UpperCamelCase   = torch.Size((1, 11, 256)				)
              self.assertEqual(output.shape,A_				)
              __UpperCamelCase   = torch.tensor(
                  [[[0.4_4_7_1, 0.6_8_2_1, -0.3_2_6_5], [0.4_6_2_7, 0.5_2_5_5, -0.3_6_6_8], [0.4_5_3_2, 0.3_3_1_3, -0.4_3_4_4]]]				)
  
              self.assertTrue(torch.allclose(output[:, 1:4, 1:4],A_,atol=1E-4				)				)
  
  
  
style_context: 
  
  
  import argparse
  import json
  
  from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase				,     _lowercase   )				->   List[str]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = json.load(open(_lowercase				,     'r'   )   )
       __UpperCamelCase   = GPTNeoConfig(
           hidden_size=config_json['n_embd']				,     num_layers=config_json['n_layer']				,     num_heads=config_json['n_head']				,     attention_types=config_json['attention_types']				,     max_position_embeddings=config_json['n_positions']				,     resid_dropout=config_json['res_dropout']				,     embed_dropout=config_json['embed_dropout']				,     attention_dropout=config_json['attn_dropout']				,     )
       print(f'''Building PyTorch model from configuration: {config}'''   )
       __UpperCamelCase   = GPTNeoForCausalLM(_lowercase   )
  
       # Load weights from tf checkpoint
       load_tf_weights_in_gpt_neo(_lowercase				,     _lowercase				,     _lowercase   )
  
       # Save pytorch-model
       print(f'''Save PyTorch model to {pytorch_dump_path}'''   )
       model.save_pretrained(_lowercase   )
  
  
  if __name__ == "__main__":
      __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 mesh-tf model. \n'''
              '''This specifies the model architecture.'''
          ),
      )
      parser.add_argument(
          '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
      )
      __snake_case             =	parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
  
  
  