"""simple docstring"""




import os







def 							_UpperCAmelCase		(       lowerCamelCase__ = "matrix.txt"     ):





		"""simple docstring"""




		with open(os.path.join(os.path.dirname(lowerCamelCase__     )			,  lowerCamelCase__     )     ) as in_file:
				lowerCAmelCase__        =   in_file.read()

		lowerCAmelCase__        =   [[int(lowerCamelCase__     ) for cell in row.split(""","""     )] for row in data.strip().splitlines()]
		lowerCAmelCase__        =   [[0 for cell in row] for row in grid]
		lowerCAmelCase__        =   len(grid[0]     )

		lowerCAmelCase__        =   [[0 for i in range(lowerCamelCase__     )] for j in range(lowerCamelCase__     )]
		lowerCAmelCase__        =   grid[0][0]
		for i in range(1			,  lowerCamelCase__     ):
				lowerCAmelCase__        =   grid[0][i] + dp[0][i - 1]
		for i in range(1			,  lowerCamelCase__     ):
				lowerCAmelCase__        =   grid[i][0] + dp[i - 1][0]

		for i in range(1			,  lowerCamelCase__     ):
				for j in range(1			,  lowerCamelCase__     ):
						lowerCAmelCase__        =   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

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   MobileBertConfig.from_json_file(lowerCamelCase__     )
		print(f"""Building PyTorch model from configuration: {config}"""     )
		lowerCAmelCase__        =   MobileBertForPreTraining(lowerCamelCase__     )
		# Load weights from tf checkpoint
		lowerCAmelCase__        =   load_tf_weights_in_mobilebert(lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     )
		# Save pytorch-model
		print(f"""Save PyTorch model to {pytorch_dump_path}"""     )
		torch.save(model.state_dict()			,  lowerCamelCase__     )


if __name__ == "__main__":
			__lowerCAmelCase    :							List[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(
			    "--mobilebert_config_file",
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        "The config json file corresponding to the pre-trained MobileBERT model. \n"
			        "This specifies the model architecture."
			    ),
			)
			parser.add_argument(
			    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			__lowerCAmelCase    :							Optional[int]													=						parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)




