
"""simple docstring"""






import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


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

   """simple docstring"""



   a_						   =     CanineConfig()
   a_						   =     CanineModel(UpperCAmelCase     )
   model.eval()

   print(F'''Building PyTorch model from configuration: {config}'''     )

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

   # Save pytorch-model (weights and configuration)
   print(F'''Save PyTorch model to {pytorch_dump_path}'''     )
   model.save_pretrained(UpperCAmelCase     )

   # Save tokenizer files
   a_						   =     CanineTokenizer()
   print(F'''Save tokenizer files to {pytorch_dump_path}'''     )
   tokenizer.save_pretrained(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. Should end with model.ckpt',
      )
      parser.add_argument(
          '--pytorch_dump_path',
          default=None,
          type=str,
          required=True,
          help='Path to a folder where the PyTorch model will be placed.',
      )
      UpperCamelCase_						   =   parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)






'''simple docstring'''


from math import pi




def  __a			(  UpperCAmelCase     )   ->float:



	"""simple docstring"""







	return degree / (180 / pi)


if __name__ == "__main__":
				from doctest import testmod

				testmod()

