




"""simple docstring"""







import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available





def 							lowercase    (*snake_case__      :       Optional[Any]       )   ->     List[Any]:


				'''simple docstring'''






				if not isinstance(snake_case__					,				snake_case__       ):
								lowerCAmelCase              = list(snake_case__       )
				for i in range(len(snake_case__       )       ):
								lowerCAmelCase              = 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 							lowercase    (snake_case__      :       Exception       )   ->     List[str]:


				'''simple docstring'''






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






def 							lowercase    (snake_case__      :       callable = None					,				snake_case__      :       int = 128       )   ->     Dict:


				'''simple docstring'''






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

				lowerCAmelCase              = starting_batch_size

				def decorator(*snake_case__      :       List[str]					,				**snake_case__      :       List[str]       ):
								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()
								lowerCAmelCase              = list(inspect.signature(snake_case__       ).parameters.keys()       )
								# Guard against user error
								if len(snake_case__       ) < (len(snake_case__       ) + 1):
												lowerCAmelCase              = """, """.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(snake_case__					,				*snake_case__					,				**snake_case__       )
												except Exception as e:
																if should_reduce_batch_size(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







def 				a						(							snake_case__:					list						,   snake_case__:					list						,   snake_case__:					int						,   snake_case__:					int						,   snake_case__:					int					):


				'''simple docstring'''
				if index == number_of_items:
								return 0
				lowercase_       =					0
				lowercase_       =					0
				lowercase_       =					knapsack(snake_case__						,   snake_case__						,   snake_case__						,   snake_case__						,   index + 1					)
				if weights[index] <= max_weight:
								lowercase_       =					values[index] + knapsack(
								    snake_case__						,   snake_case__						,   snake_case__						,   max_weight - weights[index]						,   index + 1					)
				return max(snake_case__						,   snake_case__					)


if __name__ == "__main__":
						import doctest

						doctest.testmod()




