
import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available






def 							_lowercase							(     *UpperCamelCase_		)		->		Dict:
					'''simple docstring'''


					if not isinstance(UpperCamelCase_	,	UpperCamelCase_		):
										SCREAMING_SNAKE_CASE__													= list(UpperCamelCase_		)
					for i in range(len(UpperCamelCase_		)		):
										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 							_lowercase							(     UpperCamelCase_		)		->		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(UpperCamelCase_	,	UpperCamelCase_		) and len(exception.args		) == 1:
										return any(err in exception.args[0] for err in _statements		)
					return False






def 							_lowercase							(     UpperCamelCase_ = None	,	UpperCamelCase_ = 128		)		->		int:
					'''simple docstring'''


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

					SCREAMING_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()
										SCREAMING_SNAKE_CASE__													= list(inspect.signature(UpperCamelCase_		).parameters.keys()		)
										# Guard against user error
										if len(UpperCamelCase_		) < (len(UpperCamelCase_		) + 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(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







import random






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		tuple:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= [], [], []
					for element in data:
										if element < pivot:
															less.append(UpperCamelCase_		)
										elif element > pivot:
															greater.append(UpperCamelCase_		)
										else:
															equal.append(UpperCamelCase_		)
					return less, equal, greater






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		Any:
					'''simple docstring'''


					if index >= len(UpperCamelCase_		) or index < 0:
										return None

					SCREAMING_SNAKE_CASE__													= items[random.randint(0	,	len(UpperCamelCase_		) - 1		)]
					SCREAMING_SNAKE_CASE__													= 0
					SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= _partition(UpperCamelCase_	,	UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= len(UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= len(UpperCamelCase_		)

					# index is the pivot
					if m <= index < m + count:
										return pivot
					# must be in smaller
					elif m > index:
										return quick_select(UpperCamelCase_	,	UpperCamelCase_		)
					# must be in larger
					else:
										return quick_select(UpperCamelCase_	,	index - (m + count)		)





