
'''simple docstring'''
import os







def 		A_(     ):
							UpperCamelCase   			=     os.path.dirname(os.path.realpath(__lowercase))
							UpperCamelCase   			=     os.path.join(__lowercase		,     'triangle.txt')

							with open(__lowercase) as f:
														UpperCamelCase   			=     f.readlines()

							UpperCamelCase   			=     []
							for line in triangle:
														UpperCamelCase   			=     []
														for number in line.strip().split(' '):
																					numbers_from_line.append(int(__lowercase))
														a.append(__lowercase)

							for i in range(1		,     len(__lowercase)):
														for j in range(len(a[i])):
																					UpperCamelCase   			=     a[i - 1][j] if j != len(a[i - 1]) else 0
																					UpperCamelCase   			=     a[i - 1][j - 1] if j > 0 else 0
																					a[i][j] += max(__lowercase		,     __lowercase)
							return max(a[-1])


if __name__ == "__main__":
						print(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      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase):
   # Initialise PyTorch model
   UpperCamelCase_						=  MobileBertConfig.from_json_file(__lowercase)
   print(f"""Building PyTorch model from configuration: {config}""")
   UpperCamelCase_						=  MobileBertForPreTraining(__lowercase)
   # Load weights from tf checkpoint
   UpperCamelCase_						=  load_tf_weights_in_mobilebert(__lowercase     ,   __lowercase     ,   __lowercase)
   # Save pytorch-model
   print(f"""Save PyTorch model to {pytorch_dump_path}""")
   torch.save(model.state_dict()     ,   __lowercase)


if __name__ == "__main__":
 snake_case__   :						int													=   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."""
 )
 snake_case__   :						Optional[Any]													=   parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)





