code: 
  
  
  import argparse
  
  from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Union[str, Any],_SCREAMING_SNAKE_CASE				:   Union[str, Any],_SCREAMING_SNAKE_CASE				:   Any   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  TaConfig.from_json_file(_UpperCamelCase   )
  			print(f"""Building PyTorch model from configuration: {config}"""   )
  			__A=  TaForConditionalGeneration(_UpperCamelCase   )
  
  			# Load weights from tf checkpoint
  			load_tf_weights_in_ta(_UpperCamelCase,_UpperCamelCase,_UpperCamelCase   )
  
  			# Save pytorch-model
  			print(f"""Save PyTorch model to {pytorch_dump_path}"""   )
  			model.save_pretrained(_UpperCamelCase   )
  
  
  if __name__ == "__main__":
  					UpperCAmelCase__      =							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.'''
  					)
  					UpperCAmelCase__      =							parser.parse_args()
  					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  
  from typing import Any
  class 	a__				:
  
  
  
  
  
  			'''simple docstring'''
  
  			def __init__(     self       : Any  )			->		int:
  						__A=  []
  						__A=  0
  			def __str__(     self       : Tuple  )			->		Dict:
  						__A=  '<' + str(self.stack  )[1:-1] + '>'
  						return printed
  			def 				lowerCAmelCase    (     self       : Tuple ,						lowerCAmelCase_       : Any  )			->		None:
  						self.stack.append(lowerCAmelCase_  )
  						__A=  self.length + 1
  			def 				lowerCAmelCase    (     self       : int  )			->		Any:
  						self.rotate(1  )
  						__A=  self.stack[self.length - 1]
  						__A=  self.stack[:-1]
  						self.rotate(self.length - 1  )
  						__A=  self.length - 1
  						return dequeued
  			def 				lowerCAmelCase    (     self       : Optional[int] ,						lowerCAmelCase_       : int  )			->		None:
  						for _ in range(lowerCAmelCase_  ):
  									__A=  self.stack[0]
  									__A=  self.stack[1:]
  									self.put(lowerCAmelCase_  )
  									__A=  self.length - 1
  			def 				lowerCAmelCase    (     self       : Tuple  )			->		Any:
  						__A=  self.get()
  						self.put(lowerCAmelCase_  )
  						self.rotate(self.length - 1  )
  						return front
  			def 				lowerCAmelCase    (     self       : Dict  )			->		int:
  						return self.length
  
  
  
  