import os







def      _a					(   lowercase__	:      str = "input.txt"			):







     '''simple docstring'''


     with open(os.path.join(os.path.dirname(lowercase__			)  ,	lowercase__			)			) as input_file:
          SCREAMING_SNAKE_CASE__					:					List[str]      =		[
              [int(lowercase__			) for element in line.split(','			)]
              for line in input_file.readlines()
          ]

     SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		len(lowercase__			)
     SCREAMING_SNAKE_CASE__					:					str      =		len(matrix[0]			)

     SCREAMING_SNAKE_CASE__					:					List[Any]      =		[[-1 for _ in range(lowercase__			)] for _ in range(lowercase__			)]
     for i in range(lowercase__			):
          SCREAMING_SNAKE_CASE__					:					Any      =		matrix[i][0]

     for j in range(1  ,	lowercase__			):
          for i in range(lowercase__			):
               SCREAMING_SNAKE_CASE__					:					List[str]      =		minimal_path_sums[i][j - 1] + matrix[i][j]

          for i in range(1  ,	lowercase__			):
               SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		min(
                   minimal_path_sums[i][j]  ,	minimal_path_sums[i - 1][j] + matrix[i][j]			)

          for i in range(rows - 2  ,	-1  ,	-1			):
               SCREAMING_SNAKE_CASE__					:					List[Any]      =		min(
                   minimal_path_sums[i][j]  ,	minimal_path_sums[i + 1][j] + matrix[i][j]			)

     return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums			)


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









import argparse

import torch

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


logging.set_verbosity_info()


def 		lowerCamelCase__       (						_A	,      _A	,      _A				):







	'''simple docstring'''





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


if __name__ == "__main__":
		lowercase__							:	Dict											=		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."
		)
		lowercase__							:	Tuple											=		parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
