



'''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()










'''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)





