


'''simple docstring'''







import os






def       lowercase					(_A = "matrix.txt"       ):







       """simple docstring"""







       with open(os.path.join(os.path.dirname(_A       ) ,    _A       )       ) as in_file:
              _lowerCAmelCase     :				Optional[int]       =							in_file.read()

       _lowerCAmelCase     :				str       =							[[int(_A       ) for cell in row.split(','       )] for row in data.strip().splitlines()]
       _lowerCAmelCase     :				int       =							[[0 for cell in row] for row in grid]
       _lowerCAmelCase     :				str       =							len(grid[0]       )

       _lowerCAmelCase     :				Optional[Any]       =							[[0 for i in range(_A       )] for j in range(_A       )]
       _lowerCAmelCase     :				Optional[int]       =							grid[0][0]
       for i in range(1 ,    _A       ):
              _lowerCAmelCase     :				Any       =							grid[0][i] + dp[0][i - 1]
       for i in range(1 ,    _A       ):
              _lowerCAmelCase     :				Optional[int]       =							grid[i][0] + dp[i - 1][0]

       for i in range(1 ,    _A       ):
              for j in range(1 ,    _A       ):
                     _lowerCAmelCase     :				Tuple       =							grid[i][j] + min(dp[i - 1][j] ,    dp[i][j - 1]       )

       return dp[-1][-1]


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)

