

def 	_UpperCamelCase   (    UpperCamelCase_       :					int    ,       UpperCamelCase_       :					list[int]    ,       UpperCamelCase_       :					int		)  ->   int:

						"""simple docstring"""



						def count_of_possible_combinations(UpperCamelCase_       :					int		) -> 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(UpperCamelCase_		)







def 	_UpperCamelCase   (    UpperCamelCase_       :					int    ,       UpperCamelCase_       :					list[int]    ,       UpperCamelCase_       :					int		)  ->   int:

						"""simple docstring"""



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

						lowerCAmelCase__    						=						[-1] * (target + 1)
						return count_of_possible_combinations_with_dp_array(UpperCamelCase_    ,       UpperCamelCase_		)







def 	_UpperCamelCase   (    UpperCamelCase_       :					int    ,       UpperCamelCase_       :					list[int]    ,       UpperCamelCase_       :					int		)  ->   int:

						"""simple docstring"""



						lowerCAmelCase__    						=						[0] * (target + 1)
						lowerCAmelCase__    						=						1

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

						return dp_array[target]


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					__snake_case    :    List[Any]         =     3
					__snake_case    :    Tuple         =     5
					__snake_case    :    List[str]         =     [1, 2, 5]
					print(combination_sum_iv(n, array, target))













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__snake_case    :    Tuple         =     {"""configuration_glpn""": ["""GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GLPNConfig"""]}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case    :    Dict         =     ["""GLPNFeatureExtractor"""]
					__snake_case    :    Optional[Any]         =     ["""GLPNImageProcessor"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case    :    Tuple         =     [
					    """GLPN_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """GLPNForDepthEstimation""",
					    """GLPNLayer""",
					    """GLPNModel""",
					    """GLPNPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_glpn import GLPNFeatureExtractor
										from .image_processing_glpn import GLPNImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_glpn import (
										    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GLPNForDepthEstimation,
										    GLPNLayer,
										    GLPNModel,
										    GLPNPreTrainedModel,
										)


else:
					import sys

					__snake_case    :    Optional[int]         =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





