code: 
  
  
  
  '''simple docstring'''
  
  
  
  import unittest
  
  import numpy as np
  
  from transformers import ElectraConfig, is_flax_available
  from transformers.testing_utils import require_flax, slow
  
  from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
  
  
  if is_flax_available():
   from transformers.models.electra.modeling_flax_electra import (
       FlaxElectraForCausalLM,
       FlaxElectraForMaskedLM,
       FlaxElectraForMultipleChoice,
       FlaxElectraForPreTraining,
       FlaxElectraForQuestionAnswering,
       FlaxElectraForSequenceClassification,
       FlaxElectraForTokenClassification,
       FlaxElectraModel,
   )
  class        UpperCAmelCase__  (    unittest.TestCase):
  
  
  
   def __init__(  self		,	lowercase		,	lowercase=1_3		,	lowercase=7		,	lowercase=True		,	lowercase=True		,	lowercase=True		,	lowercase=True		,	lowercase=9_9		,	lowercase=2_4		,	lowercase=3_2		,	lowercase=5		,	lowercase=4		,	lowercase=3_7		,	lowercase="gelu"		,	lowercase=0.1		,	lowercase=0.1		,	lowercase=5_1_2		,	lowercase=1_6		,	lowercase=2		,	lowercase=0.02		,	lowercase=4		,	)  ->							Optional[int]:
    __UpperCamelCase				      =  parent
    __UpperCamelCase				      =  batch_size
    __UpperCamelCase				      =  seq_length
    __UpperCamelCase				      =  is_training
    __UpperCamelCase				      =  use_attention_mask
    __UpperCamelCase				      =  use_token_type_ids
    __UpperCamelCase				      =  use_labels
    __UpperCamelCase				      =  vocab_size
    __UpperCamelCase				      =  hidden_size
    __UpperCamelCase				      =  embedding_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_choices
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Tuple:
    __UpperCamelCase				      =  ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size			)
  
    __UpperCamelCase				      =  None
    if self.use_attention_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				      =  ElectraConfig(
        vocab_size=self.vocab_size		,	hidden_size=self.hidden_size		,	embedding_size=self.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		,	initializer_range=self.initializer_range		,	)
  
    return config, input_ids, token_type_ids, attention_mask
  
  
  
  
   def    __lowerCamelCase				(  self			)  ->							int:
    __UpperCamelCase				      =  self.prepare_config_and_inputs()
    __UpperCamelCase      ,		__UpperCamelCase      ,		__UpperCamelCase      ,		__UpperCamelCase				      =  config_and_inputs
    __UpperCamelCase				      =  {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
    return config, inputs_dict
  
  
  @require_flax
  class        UpperCAmelCase__  (    UpperCAmelCase_							,			unittest.TestCase):
   __SCREAMING_SNAKE_CASE					      =				True
  
   __SCREAMING_SNAKE_CASE					      =				(
       (
           FlaxElectraModel,
           FlaxElectraForCausalLM,
           FlaxElectraForMaskedLM,
           FlaxElectraForPreTraining,
           FlaxElectraForTokenClassification,
           FlaxElectraForQuestionAnswering,
           FlaxElectraForMultipleChoice,
           FlaxElectraForSequenceClassification,
       )
       if is_flax_available()
       else ()
   )
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Dict:
    __UpperCamelCase				      =  FlaxElectraModelTester(self			)
  
  
  
  
   @slow
   def    __lowerCamelCase				(  self			)  ->							Optional[int]:
    for model_class_name in self.all_model_classes:
     if model_class_name == FlaxElectraForMaskedLM:
      __UpperCamelCase				      =  model_class_name.from_pretrained("""google/electra-small-generator"""			)
     else:
      __UpperCamelCase				      =  model_class_name.from_pretrained("""google/electra-small-discriminator"""			)
     __UpperCamelCase				      =  model(np.ones((1, 1)			)			)
     self.assertIsNotNone(lowercase			)
  
  
  
  
  
  
style_context: 
  
  
  
  '''simple docstring'''
  
  
  
  import argparse
  
  from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  TaConfig.from_json_file(__A       )
   print(f"Building PyTorch model from configuration: {config}"       )
   __UpperCamelCase				      =  TaForConditionalGeneration(__A       )
  
   # Load weights from tf checkpoint
   load_tf_weights_in_ta(__A							,__A							,__A       )
  
   # Save pytorch-model
   print(f"Save PyTorch model to {pytorch_dump_path}"       )
   model.save_pretrained(__A       )
  
  
  if __name__ == "__main__":
   a__						:			int										=			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.'
   )
   a__						:			Optional[Any]										=			parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
  
  
  
  
  
  