
'''simple docstring'''
def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							int:


		'''simple docstring'''


		if len(__snake_case			) != len(__snake_case			):
				raise ValueError('''The length of profit and weight must be same.'''			)
		if max_weight <= 0:
				raise ValueError('''max_weight must greater than zero.'''			)
		if any(p < 0 for p in profit			):
				raise ValueError('''Profit can not be negative.'''			)
		if any(w < 0 for w in weight			):
				raise ValueError('''Weight can not be negative.'''			)

		# List created to store profit gained for the 1kg in case of each weight
		# respectively.  Calculate and append profit/weight for each element.
		snake_case  :       str     			=						[p / w for p, w in zip(__snake_case ,       __snake_case			)]

		# Creating a copy of the list and sorting profit/weight in ascending order
		snake_case  :       Tuple     			=						sorted(__snake_case			)

		# declaring useful variables
		snake_case  :       Tuple     			=						len(__snake_case			)
		snake_case  :       Any     			=						0
		snake_case  :       str     			=						0
		snake_case  :       Optional[Any]     			=						0

		# loop till the total weight do not reach max limit e.g. 15 kg and till i<length
		while limit <= max_weight and i < length:
				# flag value for encountered greatest element in sorted_profit_by_weight
				snake_case  :       List[Any]     			=						sorted_profit_by_weight[length - i - 1]
				snake_case  :       Optional[int]     			=						profit_by_weight.index(__snake_case			)
				snake_case  :       List[Any]     			=						-1

				# check if the weight encountered is less than the total weight
				# encountered before.
				if max_weight - limit >= weight[index]:
						limit += weight[index]
						# Adding profit gained for the given weight 1 ===
						# weight[index]/weight[index]
						gain += 1 * profit[index]
				else:
						# Since the weight encountered is greater than limit, therefore take the
						# required number of remaining kgs and calculate profit for it.
						# weight remaining / weight[index]
						gain += (max_weight - limit) / weight[index] * profit[index]
						break
				i += 1
		return gain


if __name__ == "__main__":
						print(
						    "Input profits, weights, and then max_weight (all positive ints) separated by "
						    "spaces."
						)

						lowercase__						       =      [int(x) for x in input("Input profits separated by spaces: ").split()]
						lowercase__						       =      [int(x) for x in input("Input weights separated by spaces: ").split()]
						lowercase__						       =      int(input("Max weight allowed: "))

						# Function Call
						calc_profit(profit, weight, max_weight)



'''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





