def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_							)						->						Dict:
     a	:     int						      =    len(lowerCAmelCase_							)
     # Create a stack and push index of fist element to it
     a	:     Union[str, Any]						      =    []
     st.append(0							)

     # Span value of first element is always 1
     a	:     List[str]						      =    1

     # Calculate span values for rest of the elements
     for i in range(1    ,   lowerCAmelCase_							):
          # Pop elements from stack while stack is not
          # empty and top of stack is smaller than price[i]
          while len(lowerCAmelCase_							) > 0 and price[st[0]] <= price[i]:
               st.pop()

          # If stack becomes empty, then price[i] is greater
          # than all elements on left of it, i.e. price[0],
          # price[1], ..price[i-1]. Else the price[i]  is
          # greater than elements after top of stack
          a	:     Tuple						      =    i + 1 if len(lowerCAmelCase_							) <= 0 else (i - st[0])

          # Push this element to stack
          st.append(lowerCAmelCase_							)







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_							)						->						Any:
     for i in range(0    ,   lowerCAmelCase_							):
          print(arr[i]    ,   end=''' '''							)


# Driver program to test above function
UpperCAmelCase				 =   [10, 4, 5, 90, 120, 80]
UpperCAmelCase				 =   [0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)
print_array(S, len(price))
import torch
from torch import autograd






class 				_SCREAMING_SNAKE_CASE( autograd.Function						):
   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_     )						->List[str]:
        a	:     str						      =    inputs.numel()
        a	:     str						      =    int(0.0_05 * nb_elems     ) + 1
        if sigmoid:
             a	:     Optional[Any]						      =    (torch.sigmoid(lowerCAmelCase_     ) > threshold).type(inputs.type()     )
        else:
             a	:     Union[str, Any]						      =    (inputs > threshold).type(inputs.type()     )
        if mask.sum() < nb_min:
             # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
             a	:     str						      =    inputs.flatten().kthvalue(max(nb_elems - nb_min				,  1     )     ).values
             a	:     Tuple						      =    (inputs > k_threshold).type(inputs.type()     )
        return mask


   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_     )						->Union[str, Any]:
        return gradOutput, None, None






class 				_SCREAMING_SNAKE_CASE( autograd.Function						):
   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_     )						->List[Any]:
        a	:     List[Any]						      =    inputs.clone()
        a      ,				a	:     Optional[Any]						      =    inputs.flatten().sort(descending=lowerCAmelCase_     )
        a	:     Tuple						      =    int(threshold * inputs.numel()     )

        # flat_out and mask access the same memory.
        a	:     Dict						      =    mask.flatten()
        a	:     Optional[Any]						      =    0
        a	:     List[Any]						      =    1
        return mask


   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_     )						->Tuple:
        return gradOutput, None






class 				_SCREAMING_SNAKE_CASE( snake_case__						):
   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_     )						->Tuple:
        a	:     Union[str, Any]						      =    inputs.clone()
        a      ,				a	:     List[Any]						      =    inputs.abs().flatten().sort(descending=lowerCAmelCase_     )
        a	:     Optional[int]						      =    int(threshold * inputs.numel()     )

        # flat_out and mask access the same memory.
        a	:     Dict						      =    mask.flatten()
        a	:     Tuple						      =    0
        a	:     List[str]						      =    1
        return mask