



def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Dict    =    int(_lowerCamelCase  )
							if n_element < 1:
														_lowerCAmelCase     :							Tuple    =    ValueError("a should be a positive number"  )
														raise my_error

							_lowerCAmelCase     :							str    =    [1]
							_lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase     :							Tuple    =    (0, 0, 0)
							_lowerCAmelCase     :							List[Any]    =    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		=  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		=  hamming(int(n))
						print("-----------------------------------------------------")
						print(f'''The list with nth numbers is: {hamming_numbers}''')
						print("-----------------------------------------------------")





from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
						if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import (
						    VersatileDiffusionDualGuidedPipeline,
						    VersatileDiffusionImageVariationPipeline,
						    VersatileDiffusionPipeline,
						    VersatileDiffusionTextToImagePipeline,
						)
else:
						from .modeling_text_unet import UNetFlatConditionModel
						from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
						from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
						from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
						from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline
