





"""simple docstring"""




from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class 			_UpperCAmelCase  (       UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_				:			Dict        =       ["""image_processor""", """tokenizer"""]
      lowercase_				:			Tuple        =       """EfficientNetImageProcessor"""
      lowercase_				:			Any        =       ("""BertTokenizer""", """BertTokenizerFast""")

      def __init__(					self ,				snake_case_ ,				snake_case_  ):

        """simple docstring"""

        super().__init__(snake_case_ ,				snake_case_  )

      def __call__(					self ,				snake_case_=None ,				snake_case_=None ,				snake_case_="max_length" ,				snake_case_=6_4 ,				snake_case_=None ,				**snake_case_  ):

        """simple docstring"""

        if text is None and images is None:
          raise ValueError('You have to specify either text or images. Both cannot be none.'  )

        if text is not None:
          A_  :	Dict    						=  self.tokenizer(
              snake_case_ ,				padding=snake_case_ ,				max_length=snake_case_ ,				return_tensors=snake_case_ ,				**snake_case_  )

        if images is not None:
          A_  :	List[str]    						=  self.image_processor(snake_case_ ,				return_tensors=snake_case_ ,				**snake_case_  )

        if text is not None and images is not None:
          A_  :	str    						=  image_features.pixel_values
          return encoding
        elif text is not None:
          return encoding
        else:
          return BatchEncoding(data=dict(**snake_case_  ) ,				tensor_type=snake_case_  )

      def  lowerCamelCase_      (					self ,				*snake_case_ ,				**snake_case_  ):

        """simple docstring"""

        return self.tokenizer.batch_decode(*snake_case_ ,				**snake_case_  )

      def  lowerCamelCase_      (					self ,				*snake_case_ ,				**snake_case_  ):

        """simple docstring"""

        return self.tokenizer.decode(*snake_case_ ,				**snake_case_  )

      @property
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Optional[int]    						=  self.tokenizer.model_input_names
        A_  :	Optional[Any]    						=  self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )






"""simple docstring"""




def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  A_  :	Any    						=  len(_UpperCAmelCase )
  # Create a stack and push index of fist element to it
  A_  :	List[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			, _UpperCAmelCase ):
    # Pop elements from stack while stack is not
    # empty and top of stack is smaller than price[i]
    while len(_UpperCAmelCase ) > 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_  :	Union[str, Any]    						=  i + 1 if len(_UpperCAmelCase ) <= 0 else (i - st[0])

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





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  for i in range(0			, _UpperCAmelCase ):
    print(arr[i]			, end=' ' )


# Driver program to test above function
lowerCamelCase_					:						List[Any]      = [10, 4, 5, 90, 1_20, 80]
lowerCamelCase_					:						Optional[Any]      = [0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)





print_array(S, len(price))