





'''simple docstring'''


import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available
def 							UpperCAmelCase_  (						*__lowerCamelCase : Union[str, Any]			):
			if not isinstance(__lowerCamelCase     ,__lowerCamelCase			):
						lowercase_      :int         =			list(__lowerCamelCase			)
			for i in range(len(__lowerCamelCase			)			):
						lowercase_      :List[str]         =			None
			gc.collect()
			if is_xpu_available():
						torch.xpu.empty_cache()
			elif is_npu_available():
						torch.npu.empty_cache()
			else:
						torch.cuda.empty_cache()
			return objects
def 							UpperCAmelCase_  (						__lowerCamelCase : Exception			):
			lowercase_      :Any         =			[
			    "CUDA out of memory.",  # CUDA OOM
			    "cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.",  # CUDNN SNAFU
			    "DefaultCPUAllocator: can't allocate memory",  # CPU OOM
			]
			if isinstance(__lowerCamelCase     ,__lowerCamelCase			) and len(exception.args			) == 1:
						return any(err in exception.args[0] for err in _statements			)
			return False


def 							UpperCAmelCase_  (						__lowerCamelCase : callable = None     ,__lowerCamelCase : int = 1_28			):
			if function is None:
						return functools.partial(__lowerCamelCase     ,starting_batch_size=__lowerCamelCase			)

			lowercase_      :Optional[Any]         =			starting_batch_size

			def decorator(*__lowerCamelCase : Any     ,**__lowerCamelCase : int			):
						nonlocal batch_size
						gc.collect()
						if is_xpu_available():
									torch.xpu.empty_cache()
						elif is_npu_available():
									torch.npu.empty_cache()
						else:
									torch.cuda.empty_cache()
						lowercase_      :str         =			list(inspect.signature(__lowerCamelCase			).parameters.keys()			)
						# Guard against user error
						if len(__lowerCamelCase			) < (len(__lowerCamelCase			) + 1):
									lowercase_      :Optional[Any]         =			", ".join([F'{arg}={value}' for arg, value in zip(params[1:]     ,args[1:]			)]			)
									raise TypeError(
									    F'Batch size was passed into `{function.__name__}` as the first argument when called.'
									    F'Remove this as the decorator already does so: `{function.__name__}({arg_str})`'			)
						while True:
									if batch_size == 0:
												raise RuntimeError("No executable batch size found, reached zero."			)
									try:
												return function(__lowerCamelCase     ,*__lowerCamelCase     ,**__lowerCamelCase			)
									except Exception as e:
												if should_reduce_batch_size(__lowerCamelCase			):
															gc.collect()
															if is_xpu_available():
																		torch.xpu.empty_cache()
															elif is_npu_available():
																		torch.npu.empty_cache()
															else:
																		torch.cuda.empty_cache()
															batch_size //= 2
												else:
															raise

			return decorator









'''simple docstring'''


def 							UpperCAmelCase_  (						__lowerCamelCase : list			):
   if len(__lowerCamelCase			) <= 1:
      return lst

   lowercase_      :Optional[Any]         =			1

   while i < len(__lowerCamelCase			):
      if lst[i - 1] <= lst[i]:
         i += 1
      else:
         lowercase_      ,							lowercase_      :int         =			lst[i], lst[i - 1]
         i -= 1
         if i == 0:
            lowercase_      :Dict         =			1

   return lst


if __name__ == "__main__":
 lowerCAmelCase :					Any 							=input('''Enter numbers separated by a comma:\n''').strip()
 lowerCAmelCase :					List[str] 							=[int(item) for item in user_input.split(''',''')]
 print(gnome_sort(unsorted))


