code: 
  
  
  
  '''simple docstring'''
  
  
  
  from __future__ import annotations
  
  from collections.abc import Iterator
  from typing import Any
  class        UpperCAmelCase__  :
  
  
  
   def __init__(  self		,	lowercase			)  ->							None:
    __UpperCamelCase				      =  data
    __UpperCamelCase				      =  None
  
  
  
  
   def __str__(  self			)  ->							str:
    return f"{self.data}"
  
  
  class        UpperCAmelCase__  :
  
  
  
   def __init__(  self			)  ->							None:
    __UpperCamelCase				      =  None
    __UpperCamelCase				      =  None
  
  
  
   def __iter__(  self			)  ->							Iterator[Any]:
    __UpperCamelCase				      =  self.front
    while node:
     yield node.data
     __UpperCamelCase				      =  node.next
  
  
  
   def __len__(  self			)  ->							int:
    return len(tuple(iter(self			)			)			)
  
  
  
   def __str__(  self			)  ->							str:
    return " <- ".join(str(lowercase_			) for item in self			)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							bool:
    return len(self			) == 0
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase			)  ->							None:
    __UpperCamelCase				      =  Node(lowercase_			)
    if self.is_empty():
     __UpperCamelCase				      =  node
    else:
     assert isinstance(self.rear		,	lowercase_			)
     __UpperCamelCase				      =  node
     __UpperCamelCase				      =  node
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Any:
  
    if self.is_empty():
     raise IndexError("""dequeue from empty queue"""			)
    assert isinstance(self.front		,	lowercase_			)
    __UpperCamelCase				      =  self.front
    __UpperCamelCase				      =  self.front.next
    if self.front is None:
     __UpperCamelCase				      =  None
    return node.data
  
  
  
  
   def    __lowerCamelCase				(  self			)  ->							None:
    __UpperCamelCase				      =  None
  
  
  if __name__ == "__main__":
   from doctest import testmod
  
   testmod()
  
  
  
  
  
  
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)
  
  
  
  
  
  