





"""simple docstring"""






import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()






def      lowercase__ (					lowerCAmelCase__   :			Tuple							,			lowerCAmelCase__   :			List[Any]							,			lowerCAmelCase__   :			Dict			)    ->			Union[str, Any]:



			'''simple docstring'''
			# Initialise PyTorch model
			a__  :	Dict		 =			RemBertConfig.from_json_file(lowerCAmelCase__			)
			print("Building PyTorch model from configuration: {}".format(str(lowerCAmelCase__			)			)			)
			a__  :	Tuple		 =			RemBertModel(lowerCAmelCase__			)

			# Load weights from tf checkpoint
			load_tf_weights_in_rembert(lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__			)

			# Save pytorch-model
			print("Save PyTorch model to {}".format(lowerCAmelCase__			)			)
			torch.save(model.state_dict()							,			lowerCAmelCase__			)


if __name__ == "__main__":
		__UpperCAmelCase						=			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(
		    '''--rembert_config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained RemBERT 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.'''
		)
		__UpperCAmelCase						=			parser.parse_args()
		convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)






"""simple docstring"""






import os






def      lowercase__ (					)    ->			Optional[Any]:



			'''simple docstring'''
			with open(os.path.dirname(lowerCAmelCase__			) + "/p022_names.txt"			) as file:
						a__  :	Optional[int]		 =			str(file.readlines()[0]			)
						a__  :	Optional[int]		 =			names.replace("\""							,			""			).split(","			)

			names.sort()

			a__  :	int		 =			0
			a__  :	Dict		 =			0

			for i, name in enumerate(lowerCAmelCase__			):
						for letter in name:
									name_score += ord(lowerCAmelCase__			) - 6_4

						total_score += (i + 1) * name_score
						a__  :	Tuple		 =			0
			return total_score


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