


"""simple docstring"""







def _A		(		lowercase ,					lowercase	):







    """simple docstring"""
    a					=len(lowercase	)
    # 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 ,					lowercase	):
        # Pop elements from stack while stack is not
        # empty and top of stack is smaller than price[i]
        while len(lowercase	) > 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(lowercase	) <= 0 else (i - st[0])

        # Push this element to stack
        st.append(lowercase	)
def _A		(		lowercase ,					lowercase	):







    """simple docstring"""
    for i in range(0 ,					lowercase	):
        print(arr[i] ,					end=''' '''	)


# Driver program to test above function
lowerCamelCase_     :     Any    		=						[1_0, 4, 5, 9_0, 1_2_0, 8_0]
lowerCamelCase_     :     Tuple    		=						[0 for i in range(len(price) + 1)]

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





print_array(S, len(price))



"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_     :     int    		=						logging.get_logger(__name__)

lowerCamelCase_     :     Optional[Any]    		=						{
    """dandelin/vilt-b32-mlm""": """https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"""
}






class    __A       ( _SCREAMING_SNAKE_CASE    ):






 """simple docstring"""




 __lowerCAmelCase				      =						"vilt"


 def __init__(	self				,					__A=3_0522				,					__A=2				,					__A=2				,					__A=40				,					__A=768				,					__A=12				,					__A=12				,					__A=3072				,					__A="gelu"				,					__A=0.0				,					__A=0.0				,					__A=0.02				,					__A=1E-1_2				,					__A=384				,					__A=32				,					__A=3				,					__A=True				,					__A=-1				,					__A=False				,					__A=-1				,					**__A				,					)      ->  Dict:
     super().__init__(tie_word_embeddings=__A				,					**__A		)

     a					=vocab_size
     a					=type_vocab_size
     a					=modality_type_vocab_size
     a					=max_position_embeddings

     a					=hidden_size
     a					=num_hidden_layers
     a					=num_attention_heads
     a					=intermediate_size
     a					=hidden_act
     a					=hidden_dropout_prob
     a					=attention_probs_dropout_prob
     a					=initializer_range
     a					=layer_norm_eps

     a					=image_size
     a					=patch_size
     a					=num_channels
     a					=qkv_bias
     a					=max_image_length
     a					=num_images