
"""simple docstring"""




def        _lowerCamelCase ( __a,							__a,							__a    ):

		def count_of_possible_combinations(__a    ) -> int:
				if target < 0:
						return 0
				if target == 0:
						return 1
				return sum(count_of_possible_combinations(target - item    ) for item in array    )

		return count_of_possible_combinations(__a    )



def        _lowerCamelCase ( __a,							__a,							__a    ):

		def count_of_possible_combinations_with_dp_array(
		    __a,							__a    ) -> int:
				if target < 0:
						return 0
				if target == 0:
						return 1
				if dp_array[target] != -1:
						return dp_array[target]
				SCREAMING_SNAKE_CASE_						 = sum(
				    count_of_possible_combinations_with_dp_array(target - item,							__a    )
				    for item in array    )
				SCREAMING_SNAKE_CASE_						 = answer
				return answer

		SCREAMING_SNAKE_CASE_						 = [-1] * (target + 1)
		return count_of_possible_combinations_with_dp_array(__a,							__a    )



def        _lowerCamelCase ( __a,							__a,							__a    ):
		SCREAMING_SNAKE_CASE_						 = [0] * (target + 1)
		SCREAMING_SNAKE_CASE_						 = 1

		for i in range(1,							target + 1    ):
				for j in range(__a    ):
						if i - array[j] >= 0:
								dp_array[i] += dp_array[i - array[j]]

		return dp_array[target]


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						lowerCAmelCase__							=				3
						lowerCAmelCase__							=				5
						lowerCAmelCase__							=				[1, 2, 5]
						print(combination_sum_iv(n, array, target))

"""simple docstring"""




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


lowerCAmelCase__							=				logging.get_logger(__name__)

lowerCAmelCase__							=				{
    'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}





class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      '''glpn'''

							def __init__(self       ,       SCREAMING_SNAKE_CASE_=3       ,       SCREAMING_SNAKE_CASE_=4       ,       SCREAMING_SNAKE_CASE_=[2, 2, 2, 2]       ,       SCREAMING_SNAKE_CASE_=[8, 4, 2, 1]       ,       SCREAMING_SNAKE_CASE_=[32, 64, 1_60, 2_56]       ,       SCREAMING_SNAKE_CASE_=[7, 3, 3, 3]       ,       SCREAMING_SNAKE_CASE_=[4, 2, 2, 2]       ,       SCREAMING_SNAKE_CASE_=[1, 2, 5, 8]       ,       SCREAMING_SNAKE_CASE_=[4, 4, 4, 4]       ,       SCREAMING_SNAKE_CASE_="gelu"       ,       SCREAMING_SNAKE_CASE_=0.0       ,       SCREAMING_SNAKE_CASE_=0.0       ,       SCREAMING_SNAKE_CASE_=0.02       ,       SCREAMING_SNAKE_CASE_=0.1       ,       SCREAMING_SNAKE_CASE_=1e-6       ,       SCREAMING_SNAKE_CASE_=64       ,       SCREAMING_SNAKE_CASE_=10       ,       SCREAMING_SNAKE_CASE_=-1       ,       **SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									super().__init__(**SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = num_channels
									SCREAMING_SNAKE_CASE_						 = num_encoder_blocks
									SCREAMING_SNAKE_CASE_						 = depths
									SCREAMING_SNAKE_CASE_						 = sr_ratios
									SCREAMING_SNAKE_CASE_						 = hidden_sizes
									SCREAMING_SNAKE_CASE_						 = patch_sizes
									SCREAMING_SNAKE_CASE_						 = strides
									SCREAMING_SNAKE_CASE_						 = mlp_ratios
									SCREAMING_SNAKE_CASE_						 = num_attention_heads
									SCREAMING_SNAKE_CASE_						 = hidden_act
									SCREAMING_SNAKE_CASE_						 = hidden_dropout_prob
									SCREAMING_SNAKE_CASE_						 = attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE_						 = initializer_range
									SCREAMING_SNAKE_CASE_						 = drop_path_rate
									SCREAMING_SNAKE_CASE_						 = layer_norm_eps
									SCREAMING_SNAKE_CASE_						 = decoder_hidden_size
									SCREAMING_SNAKE_CASE_						 = max_depth
									SCREAMING_SNAKE_CASE_						 = head_in_index