code: 
  
  
  
  import argparse
  
  import torch
  
  from transformers import ElectraConfig, ElectraForMaskedLM, ElectraForPreTraining, load_tf_weights_in_electra
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  def  __a  (					lowerCAmelCase_     :      str    ,lowerCAmelCase_     :      Optional[Any]    ,lowerCAmelCase_     :      List[str]    ,lowerCAmelCase_     :      Optional[Any]       )   ->				Union[str, Any]:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						UpperCAmelCase_=     ElectraConfig.from_json_file(lowerCAmelCase__       )
  						print(F"""Building PyTorch model from configuration: {config}"""       )
  
  						if discriminator_or_generator == "discriminator":
  												UpperCAmelCase_=     ElectraForPreTraining(lowerCAmelCase__       )
  						elif discriminator_or_generator == "generator":
  												UpperCAmelCase_=     ElectraForMaskedLM(lowerCAmelCase__       )
  						else:
  												raise ValueError("""The discriminator_or_generator argument should be either \'discriminator\' or \'generator\'"""       )
  
  						# Load weights from tf checkpoint
  						load_tf_weights_in_electra(
  						    lowerCAmelCase__    ,lowerCAmelCase__    ,lowerCAmelCase__    ,discriminator_or_generator=lowerCAmelCase__       )
  
  						# Save pytorch-model
  						print(F"""Save PyTorch model to {pytorch_dump_path}"""       )
  						torch.save(model.state_dict()    ,lowerCAmelCase__       )
  
  
  if __name__ == "__main__":
  	__A       					=		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 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.'''
  	)
  	parser.add_argument(
  	    '''--discriminator_or_generator''',
  	    default=None,
  	    type=str,
  	    required=True,
  	    help=(
  	        '''Whether to export the generator or the discriminator. Should be a string, either \'discriminator\' or '''
  	        '''\'generator\'.'''
  	    ),
  	)
  	__A       					=		parser.parse_args()
  	convert_tf_checkpoint_to_pytorch(
  	    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
  	)
  
  
  
  
  
style_context: 
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  __A       					=		logging.get_logger(__name__)
  
  __A       					=		{
      '''ZinengTang/tvlt-base''': '''https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json''',
  }
  
  
  
  
  
  
  class 	lowercase		(				snake_case__):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      Tuple	     =							"tvlt"
  
  
  
  
  	def __init__(			self			:		List[str]		,						__UpperCAmelCase			:		List[str]=224		,						__UpperCAmelCase			:		List[str]=2_048		,						__UpperCAmelCase			:		Tuple=128		,						__UpperCAmelCase			:		Union[str, Any]=[16, 16]		,						__UpperCAmelCase			:		str=[16, 16]		,						__UpperCAmelCase			:		str=3		,						__UpperCAmelCase			:		List[str]=1		,						__UpperCAmelCase			:		Tuple=8		,						__UpperCAmelCase			:		Optional[Any]=768		,						__UpperCAmelCase			:		List[Any]=12		,						__UpperCAmelCase			:		Tuple=12		,						__UpperCAmelCase			:		List[str]=3_072		,						__UpperCAmelCase			:		List[Any]="gelu"		,						__UpperCAmelCase			:		str=0.0		,						__UpperCAmelCase			:		str=0.0		,						__UpperCAmelCase			:		List[Any]=0.02		,						__UpperCAmelCase			:		Optional[int]=1E-6		,						__UpperCAmelCase			:		Tuple=True		,						__UpperCAmelCase			:		List[Any]=False		,						__UpperCAmelCase			:		Union[str, Any]=16		,						__UpperCAmelCase			:		Any=512		,						__UpperCAmelCase			:		Optional[Any]=8		,						__UpperCAmelCase			:		Dict=2_048		,						__UpperCAmelCase			:		Dict=0.75		,						__UpperCAmelCase			:		Union[str, Any]=0.15		,						__UpperCAmelCase			:		Any="frame-level"		,						__UpperCAmelCase			:		int=True		,						__UpperCAmelCase			:		List[Any]=True		,						__UpperCAmelCase			:		Union[str, Any]="classification"		,						**__UpperCAmelCase			:		Any		,						)    ->				Tuple:
  							super().__init__(**__UpperCAmelCase  )
  
  							if audio_mask_type not in ("frame-level", "patch_level"):
  													raise ValueError(
  													    """audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') """
  													    F"""got {audio_mask_type}"""  )
  
  							UpperCAmelCase_=     image_size
  							UpperCAmelCase_=     spectrogram_length
  							UpperCAmelCase_=     frequency_length
  							UpperCAmelCase_=     image_patch_size
  							UpperCAmelCase_=     audio_patch_size
  							UpperCAmelCase_=     num_image_channels
  							UpperCAmelCase_=     num_audio_channels
  							UpperCAmelCase_=     num_frames
  
  							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_=     initializer_range
  							UpperCAmelCase_=     layer_norm_eps
  							UpperCAmelCase_=     qkv_bias
  							UpperCAmelCase_=     use_mean_pooling
  
  							UpperCAmelCase_=     decoder_num_attention_heads
  							UpperCAmelCase_=     decoder_hidden_size
  							UpperCAmelCase_=     decoder_num_hidden_layers
  							UpperCAmelCase_=     decoder_intermediate_size
  							UpperCAmelCase_=     pixel_mask_ratio
  							UpperCAmelCase_=     audio_mask_ratio
  							UpperCAmelCase_=     audio_mask_type
  
  							UpperCAmelCase_=     task_matching
  							UpperCAmelCase_=     task_mae
  							UpperCAmelCase_=     loss_type
  
  
  
  
  