





from __future__ import annotations

import sys




def  A_(      A					,   A ):



	if abs(A ) == 1:
		return a, 0
	elif abs(A ) == 1:
		return 0, b

	UpperCAmelCase_    ,						UpperCAmelCase_		    =  a, b
	UpperCAmelCase_    ,						UpperCAmelCase_		    =  1, 0
	UpperCAmelCase_    ,						UpperCAmelCase_		    =  0, 1

	while remainder != 0:
		UpperCAmelCase_		    =  old_remainder // remainder
		UpperCAmelCase_    ,						UpperCAmelCase_		    =  remainder, old_remainder - quotient * remainder
		UpperCAmelCase_    ,						UpperCAmelCase_		    =  coeff_a, old_coeff_a - quotient * coeff_a
		UpperCAmelCase_    ,						UpperCAmelCase_		    =  coeff_b, old_coeff_b - quotient * coeff_b

	# sign correction for negative numbers
	if a < 0:
		UpperCAmelCase_		    =  -old_coeff_a
	if b < 0:
		UpperCAmelCase_		    =  -old_coeff_b

	return old_coeff_a, old_coeff_b




def  A_(      ):



	if len(sys.argv ) < 3:
		print("""2 integer arguments required""" )
		return 1
	UpperCAmelCase_		    =  int(sys.argv[1] )
	UpperCAmelCase_		    =  int(sys.argv[2] )
	print(extended_euclidean_algorithm(A					,   A ) )
	return 0


if __name__ == "__main__":
				raise SystemExit(main())




"""simple docstring"""






import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()
def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase ,   UpperCAmelCase     )						->Dict:

   """simple docstring"""



   a_						   =     RoFormerConfig.from_json_file(UpperCAmelCase     )
   print(F'''Building PyTorch model from configuration: {config}'''     )
   a_						   =     RoFormerForMaskedLM(UpperCAmelCase     )

   # Load weights from tf checkpoint
   load_tf_weights_in_roformer(UpperCAmelCase ,   UpperCAmelCase ,   UpperCAmelCase     )

   # Save pytorch-model
   print(F'''Save PyTorch model to {pytorch_dump_path}'''     )
   torch.save(model.state_dict() ,   UpperCAmelCase ,   _use_new_zipfile_serialization=UpperCAmelCase     )


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(
          '--bert_config_file',
          default=None,
          type=str,
          required=True,
          help=(
              'The config json file corresponding to the pre-trained BERT 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_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)