






"""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      snake_case    (A_ :Optional[int]      ,     A_ :List[Any] ):







  '''simple docstring'''

  a	:     List[Any]							=  CanineConfig()
  a	:     int							=  CanineModel(A_ )
  model.eval()

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

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

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

  # Save tokenizer files
  a	:     Tuple							=  CanineTokenizer()
  print(f'''Save tokenizer files to {pytorch_dump_path}''' )
  tokenizer.save_pretrained(A_ )


if __name__ == "__main__":
   _UpperCamelCase							:       Optional[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							:       List[str]       					=		parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)












"""simple docstring"""


_UpperCamelCase							:       str       					=		10**8




def      snake_case    (A_ :int ):







  '''simple docstring'''

  if n % 1_0 == 0:
    return False
  a	:     Optional[int]							=  str(A_ )
  return s == s[::-1]




def      snake_case    ():







  '''simple docstring'''

  a	:     Tuple							=  set()
  a	:     Optional[Any]							=  1
  a	:     List[str]							=  5
  while sum_squares < LIMIT:
    a	:     int							=  first_square + 1
    while sum_squares < LIMIT:
      if is_palindrome(A_ ):
        answer.add(A_ )
      last_square += 1
      sum_squares += last_square**2
    first_square += 1
    a	:     List[str]							=  first_square**2 + (first_square + 1) ** 2

  return sum(A_ )


if __name__ == "__main__":
   print(solution())




