



from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
		import torch

__magic_name__       =     logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
		from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):





		def 		__snake_case						(    cls				):
							"""simple docstring"""






							return cls





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase = None       ,			_UpperCAmelCase = None       ,			_UpperCAmelCase = None       ,			_UpperCAmelCase = None       ,			):
					"""simple docstring"""






					lowercase						   =       device
					lowercase						   =       shape[0]

					lowercase						   =       layout or torch.strided
					lowercase						   =       device or torch.device('cpu'				)

					if generator is not None:
										lowercase						   =       generator.device.type if not isinstance(_UpperCAmelCase       ,			_UpperCAmelCase				) else generator[0].device.type
										if gen_device_type != device.type and gen_device_type == "cpu":
															lowercase						   =       'cpu'
															if device != "mps":
																				logger.info(
																				    f"""The passed generator was created on 'cpu' even though a tensor on {device} was expected."""
																				    f""" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"""
																				    f""" slighly speed up this function by passing a generator that was created on the {device} device."""				)
										elif gen_device_type != device.type and gen_device_type == "cuda":
															raise ValueError(f"""Cannot generate a {device} tensor from a generator of type {gen_device_type}."""				)

					if isinstance(_UpperCAmelCase       ,			_UpperCAmelCase				):
										lowercase						   =       (1,) + shape[1:]
										lowercase						   =       [
										    torch.randn(_UpperCAmelCase       ,			generator=generator[i]       ,			device=_UpperCAmelCase       ,			dtype=_UpperCAmelCase       ,			layout=_UpperCAmelCase				)
										    for i in range(_UpperCAmelCase				)
										]
										lowercase						   =       torch.cat(_UpperCAmelCase       ,			dim=0				).to(_UpperCAmelCase				)
					else:
										lowercase						   =       torch.randn(_UpperCAmelCase       ,			generator=_UpperCAmelCase       ,			device=_UpperCAmelCase       ,			dtype=_UpperCAmelCase       ,			layout=_UpperCAmelCase				).to(_UpperCAmelCase				)

					return latents





def 		__snake_case						(    _UpperCAmelCase				):
					"""simple docstring"""






					if is_torch_version('<'       ,			'2.0.0'				) or not hasattr(_UpperCAmelCase       ,			'_dynamo'				):
										return False
					return isinstance(_UpperCAmelCase       ,			torch._dynamo.eval_frame.OptimizedModule				)











def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					if right <= left:
										return
					lowercase						   =       lowercase						   =       left
					lowercase						   =       right
					lowercase						   =       sorting[left]
					while i <= b:
										if sorting[i] < pivot:
															lowercase   ,      lowercase						   =       sorting[i], sorting[a]
															a += 1
															i += 1
										elif sorting[i] > pivot:
															lowercase   ,      lowercase						   =       sorting[i], sorting[b]
															b -= 1
										else:
															i += 1
					quick_sort_apartition(_UpperCAmelCase       ,			_UpperCAmelCase       ,			a - 1				)
					quick_sort_apartition(_UpperCAmelCase       ,			b + 1       ,			_UpperCAmelCase				)





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					if left < right:
										lowercase						   =       lomuto_partition(_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				)
										quick_sort_lomuto_partition(_UpperCAmelCase       ,			_UpperCAmelCase       ,			pivot_index - 1				)
										quick_sort_lomuto_partition(_UpperCAmelCase       ,			pivot_index + 1       ,			_UpperCAmelCase				)





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					lowercase						   =       sorting[right]
					lowercase						   =       left
					for i in range(_UpperCAmelCase       ,			_UpperCAmelCase				):
										if sorting[i] < pivot:
															lowercase   ,      lowercase						   =       sorting[i], sorting[store_index]
															store_index += 1
					lowercase   ,      lowercase						   =       sorting[store_index], sorting[right]
					return store_index





def 		__snake_case						(    _UpperCAmelCase				):
					"""simple docstring"""






					if len(_UpperCAmelCase				) <= 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)

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






