


def 							__lowercase     (				__lowerCAmelCase							:       Any     ):
							a__    =  int(_SCREAMING_SNAKE_CASE     )
							if n_element < 1:
														a__    =  ValueError('a should be a positive number'     )
														raise my_error

							a__    =  [1]
							a__   ,      a__   ,      a__    =  (0, 0, 0)
							a__    =  1
							while index < n_element:
														while hamming_list[i] * 2 <= hamming_list[-1]:
																					i += 1
														while hamming_list[j] * 3 <= hamming_list[-1]:
																					j += 1
														while hamming_list[k] * 5 <= hamming_list[-1]:
																					k += 1
														hamming_list.append(
														    min(hamming_list[i] * 2      ,					hamming_list[j] * 3      ,					hamming_list[k] * 5     )     )
														index += 1
							return hamming_list


if __name__ == "__main__":
						snake_case							: str      =   input('''Enter the last number (nth term) of the Hamming Number Series: ''')
						print('''Formula of Hamming Number Series => 2^i * 3^j * 5^k''')
						snake_case							: str      =   hamming(int(n))
						print('''-----------------------------------------------------''')
						print(f"""The list with nth numbers is: {hamming_numbers}""")
						print('''-----------------------------------------------------''')




from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case							: Optional[Any]      =   {'''configuration_focalnet''': ['''FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FocalNetConfig''']}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							: Optional[int]      =   [
						    '''FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''FocalNetForImageClassification''',
						    '''FocalNetForMaskedImageModeling''',
						    '''FocalNetBackbone''',
						    '''FocalNetModel''',
						    '''FocalNetPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_focalnet import (
												    FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FocalNetBackbone,
												    FocalNetForImageClassification,
												    FocalNetForMaskedImageModeling,
												    FocalNetModel,
												    FocalNetPreTrainedModel,
												)

else:
						import sys

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




