

def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						if not numbers:
												return 0

						if not isinstance(SCREAMING_SNAKE_CASE__  ,		(list, tuple)       ) or not all(
						    isinstance(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ) for number in numbers       ):
												raise ValueError('''numbers must be an iterable of integers'''       )

						snake_case_   =	snake_case_   =	snake_case_   =	numbers[0]

						for i in range(1  ,		len(SCREAMING_SNAKE_CASE__       )       ):
												# update the maximum and minimum subarray products
												snake_case_   =	numbers[i]
												if number < 0:
																		snake_case_,    snake_case_   =	min_till_now, max_till_now
												snake_case_   =	max(SCREAMING_SNAKE_CASE__  ,		max_till_now * number       )
												snake_case_   =	min(SCREAMING_SNAKE_CASE__  ,		min_till_now * number       )

												# update the maximum product found till now
												snake_case_   =	max(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )

						return max_prod


import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available


def   __SCREAMING_SNAKE_CASE					(*SCREAMING_SNAKE_CASE__       ):
						if not isinstance(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
												snake_case_   =	list(SCREAMING_SNAKE_CASE__       )
						for i in range(len(SCREAMING_SNAKE_CASE__       )       ):
												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   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						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(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ) and len(exception.args       ) == 1:
												return any(err in exception.args[0] for err in _statements       )
						return False


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__ = None  ,		SCREAMING_SNAKE_CASE__ = 128       ):
						if function is None:
												return functools.partial(SCREAMING_SNAKE_CASE__  ,		starting_batch_size=SCREAMING_SNAKE_CASE__       )

						snake_case_   =	starting_batch_size

						def decorator(*SCREAMING_SNAKE_CASE__  ,		**SCREAMING_SNAKE_CASE__       ):
												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(SCREAMING_SNAKE_CASE__       ).parameters.keys()       )
												# Guard against user error
												if len(SCREAMING_SNAKE_CASE__       ) < (len(SCREAMING_SNAKE_CASE__       ) + 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(SCREAMING_SNAKE_CASE__  ,		*SCREAMING_SNAKE_CASE__  ,		**SCREAMING_SNAKE_CASE__       )
																		except Exception as e:
																								if should_reduce_batch_size(SCREAMING_SNAKE_CASE__       ):
																														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