
'''simple docstring'''







import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()





def 					UpperCAmelCase_		(__a      :    List[Any] ,	__a      :    Tuple ,	__a      :    Tuple ,	__a      :    Optional[int] ):






   """simple docstring"""

   _a				:	List[Any]			=   FunnelConfig.from_json_file(A__ )
   print(f"""Building PyTorch model from configuration: {config}""" )
   _a				:	List[str]			=   FunnelBaseModel(A__ ) if base_model else FunnelModel(A__ )

   # Load weights from tf checkpoint
   load_tf_weights_in_funnel(A__ ,	A__ ,	A__ )

   # Save pytorch-model
   print(f"""Save PyTorch model to {pytorch_dump_path}""" )
   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(
      """--config_file""",
      default=None,
      type=str,
      required=True,
      help="""The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.""",
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  parser.add_argument(
      """--base_model""", action="""store_true""", help="""Whether you want just the base model (no decoder) or not."""
  )
  __lowerCAmelCase         =    parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
  )










from math import loga



def A_	(		A__				)  ->		int:
       if a < 0:
              raise ValueError('Input value must be a positive integer'				)
       elif isinstance(A__		,      A__				):
              raise TypeError('Input value must be a \'int\' type'				)
       return 0 if (a == 0) else int(loga(a & -a				)				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


