


'''simple docstring'''







def       lowercase					(_A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Union[str, Any]       =							0
       while num > 0:
              digit_sum += num % 1_0
              num //= 1_0
       return digit_sum






def       lowercase					(_A = 1_0_0       ):







       """simple docstring"""







       _lowerCAmelCase     :				List[Any]       =							1
       _lowerCAmelCase     :				int       =							2

       for i in range(2 ,    max_n + 1       ):
              _lowerCAmelCase     :				List[Any]       =							pre_numerator
              _lowerCAmelCase     :				Optional[int]       =							2 * i // 3 if i % 3 == 0 else 1
              _lowerCAmelCase     :				Tuple       =							cur_numerator
              _lowerCAmelCase     :				Optional[int]       =							e_cont * pre_numerator + temp

       return sum_digits(_A       )


if __name__ == "__main__":
       print(F'''{solution() = }''')





'''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"""







       _lowerCAmelCase     :				Optional[Any]       =							TaConfig.from_json_file(_A       )
       print(f'Building PyTorch model from configuration: {config}'       )
       _lowerCAmelCase     :				Union[str, Any]       =							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__":
       lowerCAmelCase     :  str											=    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     :  int											=    parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

