






import argparse

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


logging.set_verbosity_info()





def 		A_			(		lowercase_					,       lowercase_	)							->int:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						CanineConfig()
 SCREAMING_SNAKE_CASE							  =						CanineModel(lowercase_	)
 model.eval()

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

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

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

 # Save tokenizer files
 SCREAMING_SNAKE_CASE							  =						CanineTokenizer()
 print(f'''Save tokenizer files to {pytorch_dump_path}'''	)
 tokenizer.save_pretrained(lowercase_	)


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)












def 		A_			(		lowercase_	)							->int:







 """simple docstring"""

 if not isinstance(lowercase_					,       lowercase_	):
  SCREAMING_SNAKE_CASE							  =						f'''Input value of [number={number}] must be an integer'''
  raise TypeError(lowercase_	)
 if number < 1:
  raise ValueError('Input must be a positive integer'	)
 return number * (2 * number - 1)


if __name__ == "__main__":
      import doctest

      doctest.testmod()




