import argparse

import torch

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


logging.set_verbosity_info()







def     _a				(      lowercase__  :			Optional[int]     ,  lowercase__  :			Optional[int]     ,  lowercase__  :			Optional[Any] ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Any			=		RemBertConfig.from_json_file(lowercase__ )
     print('Building PyTorch model from configuration: {}'.format(str(lowercase__ ) ) )
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		RemBertModel(lowercase__ )

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

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


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE__		:					Union[str, 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."
      )
      SCREAMING_SNAKE_CASE__		:					Dict						=					parser.parse_args()
      convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)




from math import factorial, radians







def     _a				(      lowercase__  :			float     ,  lowercase__  :			int = 18     ,  lowercase__  :			int = 10 ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)

     # Converting from degrees to radians
     SCREAMING_SNAKE_CASE__				:				int			=		radians(lowercase__ )

     SCREAMING_SNAKE_CASE__				:				List[Any]			=		angle_in_radians
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		3
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		-1

     for _ in range(lowercase__ ):
          result += (b * (angle_in_radians**a)) / factorial(lowercase__ )

          SCREAMING_SNAKE_CASE__				:				Any			=		-b  # One positive term and the next will be negative and so on...
          a += 2  # Increased by 2 for every term.

     return round(lowercase__     ,  lowercase__ )


if __name__ == "__main__":
      __import__("doctest").testmod()



