



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

A__       =			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 						_UpperCAmelCase							( cls     ):





						"""simple docstring"""

						return cls


def 						_UpperCAmelCase							( snake_case      ,		snake_case = None      ,		snake_case = None      ,		snake_case = None      ,		snake_case = None      ,		):





					"""simple docstring"""

					_lowerCAmelCase         =       device
					_lowerCAmelCase         =       shape[0]

					_lowerCAmelCase         =       layout or torch.strided
					_lowerCAmelCase         =       device or torch.device("""cpu"""     )

					if generator is not None:
										_lowerCAmelCase         =       generator.device.type if not isinstance(snake_case      ,		snake_case     ) else generator[0].device.type
										if gen_device_type != device.type and gen_device_type == "cpu":
															_lowerCAmelCase         =       """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(snake_case      ,		snake_case     ):
										_lowerCAmelCase         =       (1,) + shape[1:]
										_lowerCAmelCase         =       [
										    torch.randn(snake_case      ,		generator=generator[i]      ,		device=snake_case      ,		dtype=snake_case      ,		layout=snake_case     )
										    for i in range(snake_case     )
										]
										_lowerCAmelCase         =       torch.cat(snake_case      ,		dim=0     ).to(snake_case     )
					else:
										_lowerCAmelCase         =       torch.randn(snake_case      ,		generator=snake_case      ,		device=snake_case      ,		dtype=snake_case      ,		layout=snake_case     ).to(snake_case     )

					return latents


def 						_UpperCAmelCase							( snake_case     ):





					"""simple docstring"""

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








"""simple docstring"""






def 	snake_case__  (						__lowerCamelCase							:       Union[str, Any]      ):
			"""simple docstring"""
			lowerCamelCase__						:						Dict						      =len(__lowerCamelCase      )
			for i in range(length - 1      ):
						lowerCamelCase__						:						Any						      =False
						for j in range(length - 1 - i      ):
									if collection[j] > collection[j + 1]:
												lowerCamelCase__						:						List[str]						      =True
												lowerCamelCase__      ,  lowerCamelCase__						:						List[str]						      =collection[j + 1], collection[j]
						if not swapped:
									break  # Stop iteration if the collection is sorted.
			return collection


if __name__ == "__main__":
		import doctest
		import time

		doctest.testmod()

		_lowercase					:					str 		=		input("Enter numbers separated by a comma:").strip()
		_lowercase					:					Dict 		=		[int(item) for item in user_input.split(",")]
		_lowercase					:					Optional[Any] 		=		time.process_time()
		print(*bubble_sort(unsorted), sep=",")
		print(f'Processing time: {(time.process_time() - start)%1E9 + 7}')




