





def        __SCREAMING_SNAKE_CASE	(				__lowercase:		list):



  '''simple docstring'''






  __lowerCAmelCase								=					len(__lowercase)
  for cycle_start in range(0  ,				array_len - 1):
    __lowerCAmelCase								=					array[cycle_start]

    __lowerCAmelCase								=					cycle_start
    for i in range(cycle_start + 1  ,				__lowercase):
      if array[i] < item:
        pos += 1

    if pos == cycle_start:
      continue

    while item == array[pos]:
      pos += 1

    __lowerCAmelCase								=					item, array[pos]
    while pos != cycle_start:
      __lowerCAmelCase								=					cycle_start
      for i in range(cycle_start + 1  ,				__lowercase):
        if array[i] < item:
          pos += 1

      while item == array[pos]:
        pos += 1

      __lowerCAmelCase								=					item, array[pos]

  return array


if __name__ == "__main__":
      assert cycle_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
      assert cycle_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]




import argparse

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


logging.set_verbosity_info()



def      __lowerCAmelCase    (				snake_case						:     Union[str, Any]    ,   snake_case						:     Optional[int]			)							->				List[Any]:
   # Initialize PyTorch model
   __lowerCamelCase:					str	 =						CanineConfig()
   __lowerCamelCase:					Union[str, Any]	 =						CanineModel(snake_case			)
   model.eval()

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

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

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

   # Save tokenizer files
   __lowerCamelCase:					Optional[int]	 =						CanineTokenizer()
   print(f'Save tokenizer files to {pytorch_dump_path}'			)
   tokenizer.save_pretrained(snake_case			)


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


