






import argparse

import torch

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


logging.set_verbosity_info()






def 					lowerCamelCase__					(_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase):
			# Initialise PyTorch model
			SCREAMING_SNAKE_CASE							=			RemBertConfig.from_json_file(_UpperCAmelCase)
			print('Building PyTorch model from configuration: {}'.format(str(_UpperCAmelCase)))
			SCREAMING_SNAKE_CASE							=			RemBertModel(_UpperCAmelCase)

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

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


if __name__ == "__main__":
				a_		:    List[Any]   							=     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.'
				)
				a_		:    str   							=     parser.parse_args()
				convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)








"""simple docstring"""


def    _lowerCamelCase   (     lowerCamelCase__	:							str  ):
 assert column_title.isupper()
 lowercase__       :       int				=						0
 lowercase__       :       Optional[int]				=						len(lowerCamelCase__  ) - 1
 lowercase__       :       int				=						0

 while index >= 0:
  lowercase__       :       List[str]				=						(ord(column_title[index]  ) - 64) * pow(26       ,		lowerCamelCase__  )
  answer += value
  power += 1
  index -= 1

 return answer


if __name__ == "__main__":
   from doctest import testmod

   testmod()