


"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_torch_available,
    is_vision_available,
)


SCREAMING_SNAKE_CASE :    Tuple									=				{"""configuration_beit""": ["""BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BeitConfig""", """BeitOnnxConfig"""]}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE :    Dict									=				["""BeitFeatureExtractor"""]
			SCREAMING_SNAKE_CASE :    Optional[Any]									=				["""BeitImageProcessor"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE :    int									=				[
			    """BEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """BeitForImageClassification""",
			    """BeitForMaskedImageModeling""",
			    """BeitForSemanticSegmentation""",
			    """BeitModel""",
			    """BeitPreTrainedModel""",
			]


try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE :    int									=				[
			    """FlaxBeitForImageClassification""",
			    """FlaxBeitForMaskedImageModeling""",
			    """FlaxBeitModel""",
			    """FlaxBeitPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_beit import BeitFeatureExtractor
						from .image_processing_beit import BeitImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_beit import (
						    BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    BeitForImageClassification,
						    BeitForMaskedImageModeling,
						    BeitForSemanticSegmentation,
						    BeitModel,
						    BeitPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_beit import (
						    FlaxBeitForImageClassification,
						    FlaxBeitForMaskedImageModeling,
						    FlaxBeitModel,
						    FlaxBeitPreTrainedModel,
						)


else:
			import sys

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








def    __UpperCAmelCase (	__a      :			int		,__a      :			list[int]		,__a      :			int	)			->     int:

					"""simple docstring"""

					def count_of_possible_combinations(__a      :			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(__a	)



def    __UpperCAmelCase (	__a      :			int		,__a      :			list[int]		,__a      :			int	)			->     int:

					"""simple docstring"""

					def count_of_possible_combinations_with_dp_array(
					    __a      :			int		,__a      :			list[int]	) -> int:
										if target < 0:
															return 0
										if target == 0:
															return 1
										if dp_array[target] != -1:
															return dp_array[target]
										_a	:    Union[str, Any]         =		sum(
										    count_of_possible_combinations_with_dp_array(target - item		,__a	)
										    for item in array	)
										_a	:    Optional[int]         =		answer
										return answer

					_a	:    int         =		[-1] * (target + 1)
					return count_of_possible_combinations_with_dp_array(__a		,__a	)



def    __UpperCAmelCase (	__a      :			int		,__a      :			list[int]		,__a      :			int	)			->     int:

					"""simple docstring"""

					_a	:    str         =		[0] * (target + 1)
					_a	:    Optional[Any]         =		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()
			a__				=		3
			a__				=		5
			a__				=		[1, 2, 5]
			print(combination_sum_iv(n, array, target))



