

import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available

def 						UpperCamelCase (*lowercase_:			List[str] )					->				int:





	if not isinstance(__lowerCAmelCase   ,				__lowerCAmelCase ):
		A__							:   int					       =		list(__lowerCAmelCase )
	for i in range(len(__lowerCAmelCase ) ):
		A__							:   Union[str, Any]					       =		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 (lowercase_:			int )					->				bool:
	A__							:   Tuple					       =		[
	    '''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 (lowercase_:			Tuple = None   ,				lowercase_:			Any = 128 )					->				Dict:





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

	A__							:   List[Any]					       =		starting_batch_size

	def decorator(*lowercase_:			List[Any]   ,				**lowercase_:			Union[str, Any] ):
		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()
		A__							:   Union[str, Any]					       =		list(inspect.signature(__lowerCAmelCase ).parameters.keys() )
		# Guard against user error
		if len(__lowerCAmelCase ) < (len(__lowerCAmelCase ) + 1):
			A__							:   Union[str, 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






from __future__ import annotations

def 						UpperCamelCase (lowercase_:			list[int]   ,				lowercase_:			list[int]   ,				lowercase_:			int )					->				tuple[float, list[float]]:
	A__							:   Tuple					       =		list(range(len(lowercase_ ) ) )
	A__							:   Union[str, Any]					       =		[v / w for v, w in zip(lowercase_   ,				lowercase_ )]
	index.sort(key=lambda lowercase_ : ratio[i]   ,				reverse=lowercase_ )

	A__							:   float					       =		0
	A__							:   list[float]					       =		[0] * len(lowercase_ )
	for i in index:
		if weight[i] <= capacity:
			A__							:   Optional[int]					       =		1
			max_value += value[i]
			capacity -= weight[i]
		else:
			A__							:   Union[str, Any]					       =		capacity / weight[i]
			max_value += value[i] * capacity / weight[i]
			break

	return max_value, fractions


if __name__ == "__main__":
	import doctest

	doctest.testmod()



