

import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available





def  __lowerCamelCase     (			*UpperCamelCase__				):







							'''simple docstring'''






							if not isinstance(UpperCamelCase__       ,							UpperCamelCase__				):
														snake_case_          =   list(UpperCamelCase__				)
							for i in range(len(UpperCamelCase__				)				):
														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  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							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(UpperCamelCase__       ,							UpperCamelCase__				) and len(exception.args				) == 1:
														return any(err in exception.args[0] for err in _statements				)
							return False





def  __lowerCamelCase     (			UpperCamelCase__ = None       ,							UpperCamelCase__ = 128				):







							'''simple docstring'''






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

							snake_case_          =   starting_batch_size

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







							'''simple docstring'''






							snake_case_          =   len(UpperCamelCase__				)
							for _ in range(UpperCamelCase__				):
														for i in range(_ % 2       ,							arr_size - 1       ,							2				):
																					if arr[i + 1] < arr[i]:
																												snake_case_    ,   snake_case_          =   arr[i + 1], arr[i]

							return arr


if __name__ == "__main__":
					_UpperCAmelCase		:       Dict						    =	list(range(10, 0, -1))
					print(F'''Original: {arr}. Sorted: {odd_even_transposition(arr)}''')




