
'''simple docstring'''





def   A			(							A_				:       list     ,				A_				:       int     ,				A_				:       int							):
						if right <= left:
												return
						snake_case							:  str			=     left
						snake_case							:  Tuple			=     right
						snake_case							:  Optional[Any]			=     sorting[left]
						while i <= b:
												if sorting[i] < pivot:
																		snake_case,       snake_case							:  Optional[Any]			=     sorting[i], sorting[a]
																		a += 1
																		i += 1
												elif sorting[i] > pivot:
																		snake_case,       snake_case							:  Optional[Any]			=     sorting[i], sorting[b]
																		b -= 1
												else:
																		i += 1
						quick_sort_apartition(A_     ,				A_     ,				a - 1							)
						quick_sort_apartition(A_     ,				b + 1     ,				A_							)

def   A			(							A_				:       list     ,				A_				:       int     ,				A_				:       int							):
						if left < right:
												snake_case							:  List[Any]			=     lomuto_partition(A_     ,				A_     ,				A_							)
												quick_sort_lomuto_partition(A_     ,				A_     ,				pivot_index - 1							)
												quick_sort_lomuto_partition(A_     ,				pivot_index + 1     ,				A_							)

def   A			(							A_				:       list     ,				A_				:       int     ,				A_				:       int							):
						snake_case							:  str			=     sorting[right]
						snake_case							:  int			=     left
						for i in range(A_     ,				A_							):
												if sorting[i] < pivot:
																		snake_case,       snake_case							:  List[Any]			=     sorting[i], sorting[store_index]
																		store_index += 1
						snake_case,       snake_case							:  Union[str, Any]			=     sorting[store_index], sorting[right]
						return store_index

def   A			(							A_				:       list							):
						if len(A_							) <= 1:
												return sorting
						return (
						    three_way_radix_quicksort([i for i in sorting if i < sorting[0]]							)
						    + [i for i in sorting if i == sorting[0]]
						    + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]							)
						)


if __name__ == "__main__":
						import doctest

						doctest.testmod(verbose=True)

						UpperCAmelCase            = input("Enter numbers separated by a comma:\n").strip()
						UpperCAmelCase            = [int(item) for item in user_input.split(",")]
						quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
						print(unsorted)






"""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 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


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


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