

"""simple docstring"""
def       UpperCamelCase_					(     lowerCamelCase							:					int   )						->     str:



       """simple docstring"""







       if not isinstance(lowerCamelCase     , lowerCamelCase   ):
              __magic_name__			:		List[Any]        =     (
                  '''operation can not be conducted on a object of type '''
                  f"""{type(lowerCamelCase   ).__name__}"""
              )
              raise TypeError(lowerCamelCase   )
       __magic_name__			:		Union[str, Any]        =     ''''''
       for _ in range(0     , 32   ):
              bit_string += str(number % 2   )
              __magic_name__			:		Union[str, Any]        =     number >> 1
       return bit_string







def       UpperCamelCase_					(     lowerCamelCase							:					int   )						->     str:



       """simple docstring"""







       if number < 0:
              raise ValueError('''the value of input must be positive'''   )
       elif isinstance(lowerCamelCase     , lowerCamelCase   ):
              raise TypeError('''Input value must be a \'int\' type'''   )
       elif isinstance(lowerCamelCase     , lowerCamelCase   ):
              raise TypeError('''\'<\' not supported between instances of \'str\' and \'int\''''   )
       __magic_name__			:		Tuple        =     0
       # iterator over [1 to 32],since we are dealing with 32 bit integer
       for _ in range(1     , 33   ):
              # left shift the bits by unity
              __magic_name__			:		Optional[int]        =     result << 1
              # get the end bit
              __magic_name__			:		List[str]        =     number % 2
              # right shift the bits by unity
              __magic_name__			:		Union[str, Any]        =     number >> 1
              # add that bit to our ans
              __magic_name__			:		Union[str, Any]        =     result | end_bit
       return get_reverse_bit_string(lowerCamelCase   )


if __name__ == "__main__":
 import doctest

 doctest.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       UpperCamelCase_					(     lowerCamelCase							:					List[str]     , lowerCamelCase							:					Optional[int]     , lowerCamelCase							:					List[str]   )						->     Optional[int]:



       """simple docstring"""







       __magic_name__			:		int        =     TaConfig.from_json_file(lowerCamelCase   )
       print(f"""Building PyTorch model from configuration: {config}"""   )
       __magic_name__			:		Union[str, Any]        =     TaForConditionalGeneration(lowerCamelCase   )

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

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


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