


import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()




def       a__						(		snake_case__				:       List[Any]	,      snake_case__				:       int	,      snake_case__				:       Tuple    ):
  # Initialise PyTorch model
  _UpperCAmelCase		:       Tuple       =    TaConfig.from_json_file(snake_case__    )
  print(f'''Building PyTorch model from configuration: {config}'''    )
  _UpperCAmelCase		:       Dict       =    TaForConditionalGeneration(snake_case__    )

  # Load weights from tf checkpoint
  load_tf_weights_in_ta(snake_case__	,      snake_case__	,      snake_case__    )

  # Save pytorch-model
  print(f'''Save PyTorch model to {pytorch_dump_path}'''    )
  model.save_pretrained(snake_case__    )


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__				:    Tuple         =    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.'
 )
 SCREAMING_SNAKE_CASE__				:    Union[str, Any]         =    parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)









from typing import Any


class 		_SCREAMING_SNAKE_CASE    :


     def __init__(     self						):
       _UpperCAmelCase		:       Union[str, Any]       =    []
       _UpperCAmelCase		:       Dict       =    0


     def __str__(     self						):
       _UpperCAmelCase		:       Union[str, Any]       =    """<""" + str(self.stack						)[1:-1] + """>"""
       return printed


     def 					__snake_case(     self       ,				A_						):
       self.stack.append(A_						)
       _UpperCAmelCase		:       Optional[Any]       =    self.length + 1


     def 					__snake_case(     self						):
       self.rotate(1						)
       _UpperCAmelCase		:       List[Any]       =    self.stack[self.length - 1]
       _UpperCAmelCase		:       Optional[Any]       =    self.stack[:-1]
       self.rotate(self.length - 1						)
       _UpperCAmelCase		:       Union[str, Any]       =    self.length - 1
       return dequeued


     def 					__snake_case(     self       ,				A_						):
       for _ in range(A_						):
         _UpperCAmelCase		:       Dict       =    self.stack[0]
         _UpperCAmelCase		:       List[Any]       =    self.stack[1:]
         self.put(A_						)
         _UpperCAmelCase		:       Dict       =    self.length - 1


     def 					__snake_case(     self						):
       _UpperCAmelCase		:       Tuple       =    self.get()
       self.put(A_						)
       self.rotate(self.length - 1						)
       return front


     def 					__snake_case(     self						):
       return self.length





