





def  __UpperCamelCase    (						snake_case      )    ->		int:


   '''simple docstring'''







   __A    =  len(__UpperCAmelCase      )
   __A    =  [0] * length
   __A    =  [[False for i in range(__UpperCAmelCase      )] for j in range(__UpperCAmelCase      )]
   for i, c in enumerate(__UpperCAmelCase      ):
      __A    =  i
      for j in range(i + 1      ):
         if c == string[j] and (i - j < 2 or is_palindromic[j + 1][i - 1]):
            __A    =  True
            __A    =  min(__UpperCAmelCase							,			0 if j == 0 else (cut[j - 1] + 1)      )
      __A    =  mincut
   return cut[length - 1]


if __name__ == "__main__":
    _UpperCamelCase       :				Union[str, Any]           =			input("""Enter the string: """).strip()
    _UpperCamelCase       :				Any           =			find_minimum_partitions(s)
    print(F"""Minimum number of partitions required for the \'{s}\' is {ans}""")


import argparse

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


logging.set_verbosity_info()
def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase		)    ->     Tuple:



      '''simple docstring'''

      _lowerCamelCase           =					CanineConfig()
      _lowerCamelCase           =					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
      _lowerCamelCase           =					CanineTokenizer()
      print(F'Save tokenizer files to {pytorch_dump_path}'		)
      tokenizer.save_pretrained(__UpperCAmelCase		)


if __name__ == "__main__":
     snake_case__						      =			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.',
     )
     snake_case__						      =			parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)