code: 
  
  def     UpperCAmelCase__       (    _A   :				Optional[Any]			,   _A   :				List[str]							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=len(_A							)
    # Create a stack and push index of fist element to it
    a__						=[]
    st.append(0							)
  
    # Span value of first element is always 1
    a__						=1
  
    # Calculate span values for rest of the elements
    for i in range(1			,   _A							):
      # Pop elements from stack while stack is not
      # empty and top of stack is smaller than price[i]
      while len(_A							) > 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__						=i + 1 if len(_A							) <= 0 else (i - st[0])
  
      # Push this element to stack
      st.append(_A							)
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				str			,   _A   :				Union[str, Any]							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    for i in range(0			,   _A							):
      print(arr[i]			,   end=''' '''							)
  
  
  # Driver program to test above function
  lowerCamelCase     =      [10, 4, 5, 90, 120, 80]
  lowerCamelCase     =      [0 for i in range(len(price) + 1)]
  
  # Fill the span values in array S[]
  calculation_span(price, S)
  
  
  
  print_array(S, len(price))
  
style_context: 
  
  from dataclasses import dataclass
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  from PIL import Image
  
  from ...utils import BaseOutput, is_torch_available, is_transformers_available
  
  
  
  
  
  
  
  @dataclass
  # Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
  class 		__magic_name__    (							lowerCamelCase__							):
  
     '''simple docstring'''
  
  
  
  
     lowerCamelCase__				:			Union[List[PIL.Image.Image], np.ndarray]
     lowerCamelCase__				:			Optional[List[bool]]
  
  
  if is_transformers_available() and is_torch_available():
         from .modeling_roberta_series import RobertaSeriesModelWithTransformation
         from .pipeline_alt_diffusion import AltDiffusionPipeline
         from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline
  