


'''simple docstring'''






def    _snake_case  (	A					,       A			)		->       Tuple:
							lowerCAmelCase__			  =      0
							lowerCAmelCase__			  =      len(A			) - 1

							while left <= right:
														# avoid divided by 0 during interpolation
														if sorted_collection[left] == sorted_collection[right]:
																					if sorted_collection[left] == item:
																												return left
																					else:
																												return None

														lowerCAmelCase__			  =      left + ((item - sorted_collection[left]) * (right - left)) // (
														    sorted_collection[right] - sorted_collection[left]
														)

														# out of range check
														if point < 0 or point >= len(A			):
																					return None

														lowerCAmelCase__			  =      sorted_collection[point]
														if current_item == item:
																					return point
														else:
																					if point < left:
																												lowerCAmelCase__			  =      left
																												lowerCAmelCase__			  =      point
																					elif point > right:
																												lowerCAmelCase__			  =      right
																												lowerCAmelCase__			  =      point
																					else:
																												if item < current_item:
																																			lowerCAmelCase__			  =      point - 1
																												else:
																																			lowerCAmelCase__			  =      point + 1
							return None


def    _snake_case  (	A					,       A					,       A					,       A			)		->       List[str]:

							# avoid divided by 0 during interpolation
							if sorted_collection[left] == sorted_collection[right]:
														if sorted_collection[left] == item:
																					return left
														else:
																					return None

							lowerCAmelCase__			  =      left + ((item - sorted_collection[left]) * (right - left)) // (
							    sorted_collection[right] - sorted_collection[left]
							)

							# out of range check
							if point < 0 or point >= len(A			):
														return None

							if sorted_collection[point] == item:
														return point
							elif point < left:
														return interpolation_search_by_recursion(A					,       A					,       A					,       A			)
							elif point > right:
														return interpolation_search_by_recursion(A					,       A					,       A					,       A			)
							else:
														if sorted_collection[point] > item:
																					return interpolation_search_by_recursion(
																					    A					,       A					,       A					,       point - 1			)
														else:
																					return interpolation_search_by_recursion(
																					    A					,       A					,       point + 1					,       A			)


def    _snake_case  (	A			)		->       Dict:
							if collection != sorted(A			):
														raise ValueError('''Collection must be ascending sorted'''			)
							return True


if __name__ == "__main__":
		import sys
		__UpperCAmelCase       							=				0
		if debug == 1:
				__UpperCAmelCase       							=				[10, 30, 40, 45, 50, 66, 77, 93]
				try:
						__assert_sorted(collection)
				except ValueError:
						sys.exit('''Sequence must be ascending sorted to apply interpolation search''')
				__UpperCAmelCase       							=				67

		__UpperCAmelCase       							=				interpolation_search(collection, target)
		if result is not None:
				print(f"""{target} found at positions: {result}""")
		else:
				print('''Not found''')
'''simple docstring'''

import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available



def snake_case_      (					*__snake_case  :							Optional[int])			->					int:
   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 snake_case_      (					__snake_case  :							Exception)			->					bool:
   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 snake_case_      (					__snake_case  :							callable = None	,   __snake_case  :							int = 128)			->					List[Any]:
   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  :							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()
      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





