from __future__ import annotations

UpperCAmelCase      		=					'''Alexander Joslin'''


def SCREAMING_SNAKE_CASE					(  lowercase_:						int					):

       if number <= 0:
              _UpperCamelCase  :							Union[str, Any]	    =	F"n must be greater than 0. Got n = {number}"
              raise ValueError(lowercase_					)

       _UpperCamelCase  :							List[str]	    =	[number + 1] * (number + 1)

       # starting position
       _UpperCamelCase  :							str	    =	0
       for i in range(1		,    lowercase_					):
              _UpperCamelCase  :							Optional[Any]	    =	min(table[i + 1]		,    table[i] + 1					)
              # check if out of bounds
              if i * 2 <= number:
                     _UpperCamelCase  :							Optional[Any]	    =	min(table[i * 2]		,    table[i] + 1					)
              # check if out of bounds
              if i * 3 <= number:
                     _UpperCamelCase  :							List[Any]	    =	min(table[i * 3]		,    table[i] + 1					)
       return table[number]


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   snake_case_    (			__lowercase      ,							__lowercase							):
   # Initialize PyTorch model
   UpperCAmelCase_				:   Optional[Any]							= CanineConfig()
   UpperCAmelCase_				:   Any							= 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
   UpperCAmelCase_				:   List[Any]							= CanineTokenizer()
   print(F'''Save tokenizer files to {pytorch_dump_path}'''							)
   tokenizer.save_pretrained(__lowercase							)


if __name__ == "__main__":
       __UpperCamelCase					:	int          =				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					:	str          =				parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)