
'''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 					UpperCAmelCase_		(__a      :    Tuple ,	__a      :    List[str] ,	__a      :    Union[str, Any] ):






   """simple docstring"""

   _a				:	Tuple			=   RemBertConfig.from_json_file(__a )
   print('Building PyTorch model from configuration: {}'.format(str(__a ) ) )
   _a				:	Tuple			=   RemBertModel(__a )

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

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


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





'''simple docstring'''







def 					UpperCAmelCase_		(__a      :    list ,	__a      :    list ,	__a      :    int ):






   """simple docstring"""

   _a				:	Optional[Any]			=   len(__a )
   _a				:	int			=   [[0] * n for i in range(__a )]
   for i in range(__a ):
      _a				:	Tuple			=   y_points[i]

   for i in range(2 ,	__a ):
      for j in range(__a ,	__a ):
         _a				:	Tuple			=   (
             (xa - x_points[j - i + 1]) * q[j][i - 1]
             - (xa - x_points[j]) * q[j - 1][i - 1]
         ) / (x_points[j] - x_points[j - i + 1])

   return [q[n - 1][n - 1], q]


if __name__ == "__main__":
  import doctest

  doctest.testmod()



