"""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 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   Optional[int]:
				SCREAMING_SNAKE_CASE__		  =				RemBertConfig.from_json_file(__snake_case       )
				print("Building PyTorch model from configuration: {}".format(str(__snake_case       )       )       )
				SCREAMING_SNAKE_CASE__		  =				RemBertModel(__snake_case       )

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

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


if __name__ == "__main__":
		_A   					=    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   					=    parser.parse_args()
		convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)





"""simple docstring"""






from math import pi




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   float:
				return 2 * pi * radius * (angle / 360)


if __name__ == "__main__":
						print(arc_length(9_0, 1_0))




