


from typing import TYPE_CHECKING

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


snake_case							:	Tuple					      =				{'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							:	Optional[int]					      =				['''DPTFeatureExtractor''']
						snake_case							:	List[Any]					      =				['''DPTImageProcessor''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							:	List[str]					      =				[
						    '''DPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''DPTForDepthEstimation''',
						    '''DPTForSemanticSegmentation''',
						    '''DPTModel''',
						    '''DPTPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_dpt import DPTFeatureExtractor
												from .image_processing_dpt import DPTImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_dpt import (
												    DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DPTForDepthEstimation,
												    DPTForSemanticSegmentation,
												    DPTModel,
												    DPTPreTrainedModel,
												)


else:
						import sys

						snake_case							:	List[str]					      =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)








def 							__lowercase       (			__lowerCAmelCase							:    int       ):

							if num <= 0:
														raise ValueError('Input must be a positive integer'       )

							a__					=					[True] * (num + 1)

							a__					=					2
							while p * p <= num:
														if primes[p]:
																					for i in range(p * p ,   num + 1 ,   __lowerCAmelCase       ):
																												a__					=					False
														p += 1

							return [prime for prime in range(2 ,   num + 1       ) if primes[prime]]


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						snake_case							:	Optional[Any]					      =				int(input('''Enter a positive integer: ''').strip())
						print(prime_sieve_eratosthenes(user_num))




