




import math



def 							_A   (						lowerCAmelCase_     :					int			):
     """simple docstring"""

     if n <= 0:
          raise ValueError("Only positive integers have prime factors"			)
     lowerCAmelCase__		= []
     while n % 2 == 0:
          pf.append(2			)
          lowerCAmelCase__		= int(n / 2			)
     for i in range(3   ,				int(math.sqrt(lowerCAmelCase_			)			) + 1   ,				2			):
          while n % i == 0:
               pf.append(lowerCAmelCase_			)
               lowerCAmelCase__		= int(n / i			)
     if n > 2:
          pf.append(lowerCAmelCase_			)
     return pf



def 							_A   (						lowerCAmelCase_     :					int			):
     """simple docstring"""

     if n <= 0:
          raise ValueError("Only positive numbers are accepted"			)
     lowerCAmelCase__		= 1
     lowerCAmelCase__		= 1
     while n % 2 == 0:
          temp += 1
          lowerCAmelCase__		= int(n / 2			)
     div *= temp
     for i in range(3   ,				int(math.sqrt(lowerCAmelCase_			)			) + 1   ,				2			):
          lowerCAmelCase__		= 1
          while n % i == 0:
               temp += 1
               lowerCAmelCase__		= int(n / i			)
          div *= temp
     if n > 1:
          div *= 2
     return div



def 							_A   (						lowerCAmelCase_     :					int			):
     """simple docstring"""

     if n <= 0:
          raise ValueError("Only positive numbers are accepted"			)
     lowerCAmelCase__		= 1
     lowerCAmelCase__		= 1
     while n % 2 == 0:
          temp += 1
          lowerCAmelCase__		= int(n / 2			)
     if temp > 1:
          s *= (2**temp - 1) / (2 - 1)
     for i in range(3   ,				int(math.sqrt(lowerCAmelCase_			)			) + 1   ,				2			):
          lowerCAmelCase__		= 1
          while n % i == 0:
               temp += 1
               lowerCAmelCase__		= int(n / i			)
          if temp > 1:
               s *= (i**temp - 1) / (i - 1)
     return int(lowerCAmelCase_			)



def 							_A   (						lowerCAmelCase_     :					int			):
     """simple docstring"""

     lowerCAmelCase__		= n
     for x in set(prime_factors(lowerCAmelCase_			)			):
          s *= (x - 1) / x
     return int(lowerCAmelCase_			)


if __name__ == "__main__":
     import doctest

     doctest.testmod()






import argparse

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


logging.set_verbosity_info()



def 							_A   (						lowerCAmelCase_     :					Optional[Any]   ,				lowerCAmelCase_     :					Tuple			):
     """simple docstring"""

     lowerCAmelCase__		= CanineConfig()
     lowerCAmelCase__		= CanineModel(lowerCAmelCase_			)
     model.eval()

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

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

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

     # Save tokenizer files
     lowerCAmelCase__		= CanineTokenizer()
     print(F'Save tokenizer files to {pytorch_dump_path}'			)
     tokenizer.save_pretrained(lowerCAmelCase_			)


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)
