
import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available


def    a      ( *a	)     ->int:




			'''simple docstring'''


			if not isinstance(a		,						a	):
						SCREAMING_SNAKE_CASE     						= list(a	)
			for i in range(len(a	)	):
						SCREAMING_SNAKE_CASE     						= 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    a      ( a	)     ->bool:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= [
			    '''CUDA out of memory.''',  # CUDA OOM
			    '''cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.''',  # CUDNN SNAFU
			    '''DefaultCPUAllocator: can\'t allocate memory''',  # CPU OOM
			]
			if isinstance(a		,						a	) and len(exception.args	) == 1:
						return any(err in exception.args[0] for err in _statements	)
			return False





def    a      ( a = None		,						a = 128	)     ->Optional[int]:




			'''simple docstring'''


			if function is None:
						return functools.partial(a		,						starting_batch_size=a	)

			SCREAMING_SNAKE_CASE     						= starting_batch_size

			def decorator(*a		,						**a	):
						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()
						SCREAMING_SNAKE_CASE     						= list(inspect.signature(a	).parameters.keys()	)
						# Guard against user error
						if len(a	) < (len(a	) + 1):
									SCREAMING_SNAKE_CASE     						= ''', '''.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(a		,						*a		,						**a	)
									except Exception as e:
												if should_reduce_batch_size(a	):
															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

def    a      ( a	)     ->List[Any]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= len(a	)
			while cur > 1:
						# Find the maximum number in arr
						SCREAMING_SNAKE_CASE     						= arr.index(max(arr[0:cur]	)	)
						# Reverse from 0 to mi
						SCREAMING_SNAKE_CASE     						= arr[mi::-1] + arr[mi + 1 : len(a	)]
						# Reverse whole list
						SCREAMING_SNAKE_CASE     						= arr[cur - 1 :: -1] + arr[cur : len(a	)]
						cur -= 1
			return arr


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