

"""simple docstring"""




import warnings

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





class      __UpperCamelCase					(  _A		):
		SCREAMING_SNAKE_CASE														=       ["image_processor", "tokenizer"]
		SCREAMING_SNAKE_CASE														=       "AutoImageProcessor"
		SCREAMING_SNAKE_CASE														=       "AutoTokenizer"


		def __init__(self					:						Optional[Any]	,	__SCREAMING_SNAKE_CASE					:						Union[str, Any]=None	,	__SCREAMING_SNAKE_CASE					:						int=None	,	**__SCREAMING_SNAKE_CASE					:						Any):
							A     =   None
							if "feature_extractor" in kwargs:
												warnings.warn(
												    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
												    " instead."	,	__SCREAMING_SNAKE_CASE	,	)
												A     =   kwargs.pop("feature_extractor")

							A     =   image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
												raise ValueError("You have to specify an image_processor.")
							if tokenizer is None:
												raise ValueError("You have to specify a tokenizer.")

							super().__init__(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE)
							A     =   self.image_processor


		def __call__(self					:						int	,	__SCREAMING_SNAKE_CASE					:						Optional[Any]=None	,	__SCREAMING_SNAKE_CASE					:						List[Any]=None	,	__SCREAMING_SNAKE_CASE					:						Tuple=None	,	**__SCREAMING_SNAKE_CASE					:						str):

							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     =   self.tokenizer(__SCREAMING_SNAKE_CASE	,	return_tensors=__SCREAMING_SNAKE_CASE	,	**__SCREAMING_SNAKE_CASE)

							if images is not None:
												A     =   self.image_processor(__SCREAMING_SNAKE_CASE	,	return_tensors=__SCREAMING_SNAKE_CASE	,	**__SCREAMING_SNAKE_CASE)

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


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]	,	*__SCREAMING_SNAKE_CASE					:						Optional[Any]	,	**__SCREAMING_SNAKE_CASE					:						Tuple):
							return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE	,	**__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						List[str]	,	*__SCREAMING_SNAKE_CASE					:						int	,	**__SCREAMING_SNAKE_CASE					:						int):
							return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE	,	**__SCREAMING_SNAKE_CASE)


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							A     =   self.tokenizer.model_input_names
							A     =   self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							warnings.warn(
							    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."	,	__SCREAMING_SNAKE_CASE	,	)
							return self.image_processor_class


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						int):
							warnings.warn(
							    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."	,	__SCREAMING_SNAKE_CASE	,	)
							return self.image_processor






"""simple docstring"""


def 						UpperCamelCase				(	_lowerCAmelCase   :							Dict				, _lowerCAmelCase   :							Tuple						):
    __a			      =						len(_lowerCAmelCase						)
    # 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				, _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			      =						i + 1 if len(_lowerCAmelCase						) <= 0 else (i - st[0])

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

def 						UpperCamelCase				(	_lowerCAmelCase   :							Dict				, _lowerCAmelCase   :							Optional[Any]						):
    for i in range(0				, _lowerCAmelCase						):
        print(arr[i]				, end=""" """						)


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

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






print_array(S, len(price))
