

import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     List[Any]     ,					lowerCAmelCase:     List[str]       )      ->				Union[str, Any]:
				# Initialize PyTorch model
				_UpperCAmelCase				:						List[Any]       						=			CanineConfig()
				_UpperCAmelCase				:						str       						=			CanineModel(lowerCAmelCase       )
				model.eval()

				print(F'Building PyTorch model from configuration: {config}'       )

				# Load weights from tf checkpoint
				load_tf_weights_in_canine(lowerCAmelCase     ,					lowerCAmelCase     ,					lowerCAmelCase       )

				# Save pytorch-model (weights and configuration)
				print(F'Save PyTorch model to {pytorch_dump_path}'       )
				model.save_pretrained(lowerCAmelCase       )

				# Save tokenizer files
				_UpperCAmelCase				:						int       						=			CanineTokenizer()
				print(F'Save tokenizer files to {pytorch_dump_path}'       )
				tokenizer.save_pretrained(lowerCAmelCase       )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_						=					argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--tf_checkpoint_path',
					    default=None,
					    type=str,
					    required=True,
					    help='Path to the TensorFlow checkpoint. Should end with model.ckpt',
					)
					parser.add_argument(
					    '--pytorch_dump_path',
					    default=None,
					    type=str,
					    required=True,
					    help='Path to a folder where the PyTorch model will be placed.',
					)
					SCREAMING_SNAKE_CASE_						=					parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)





import os
def 						UpperCamelCase_(			)       -> int:
 UpperCAmelCase__							=				os.path.dirname(os.path.realpath(snake_case__   )   )
 UpperCAmelCase__							=				os.path.join(snake_case__  ,    'triangle.txt'   )

 with open(snake_case__   ) as in_file:
  UpperCAmelCase__							=				[[int(snake_case__   ) for i in line.split()] for line in in_file]

 while len(snake_case__   ) != 1:
  UpperCAmelCase__							=				triangle.pop()
  UpperCAmelCase__							=				triangle[-1]
  for j in range(len(snake_case__   ) - 1   ):
   curr_row[j] += max(last_row[j]  ,    last_row[j + 1]   )
 return triangle[0][0]


if __name__ == "__main__":
 print(solution())



