


"""simple docstring"""







import argparse

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


logging.set_verbosity_info()
def  a__      (    SCREAMING_SNAKE_CASE   :   Tuple    ,				SCREAMING_SNAKE_CASE   :   Optional[int]    ,				SCREAMING_SNAKE_CASE   :   List[Any]    ):


  '''simple docstring'''







  lowerCAmelCase					:					List[str]								=							TaConfig.from_json_file(SCREAMING_SNAKE_CASE    )
  print(f"""Building PyTorch model from configuration: {config}"""    )
  lowerCAmelCase					:					List[str]								=							TaForConditionalGeneration(SCREAMING_SNAKE_CASE    )

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

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


if __name__ == "__main__":
     lowerCAmelCase__				    =       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.'''
     )
     lowerCAmelCase__				    =       parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)









"""simple docstring"""







def  a__      (    SCREAMING_SNAKE_CASE   :   list[int]    ):


  '''simple docstring'''







  lowerCAmelCase					:					str								=							len(SCREAMING_SNAKE_CASE    )
  for i in range(SCREAMING_SNAKE_CASE    ):
    for j in range(i + 1    ,				SCREAMING_SNAKE_CASE    ):
      if numbers[j] < numbers[i]:
        lowerCAmelCase						,  lowerCAmelCase					:					Any								=							numbers[j], numbers[i]
  return numbers


if __name__ == "__main__":
     lowerCAmelCase__				    =       input('''Enter numbers separated by a comma:\n''').strip()
     lowerCAmelCase__				    =       [int(item) for item in user_input.split(''',''')]
     print(exchange_sort(unsorted))





