
"""simple docstring"""





_a :    Optional[int]														=		[0, 2, 4, 6, 8]
_a :    Tuple														=		[1, 3, 5, 7, 9]



def 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case,					__snake_case     )							->		int:


					"""simple docstring"""





					if remaining_length == 0:
										if digits[0] == 0 or digits[-1] == 0:
															return 0

										for i in range(length // 2 - 1,					-1,					-1     ):
															remainder += digits[i] + digits[length - i - 1]

															if remainder % 2 == 0:
																				return 0

															remainder //= 10

										return 1

					if remaining_length == 1:
										if remainder % 2 == 0:
															return 0

										_UpperCamelCase										=  0
										for digit in range(10     ):
															_UpperCamelCase										=  digit
															result += reversible_numbers(
															    0,					(remainder + 2 * digit) // 10,					__snake_case,					__snake_case     )
										return result

					_UpperCamelCase										=  0
					for digita in range(10     ):
										_UpperCamelCase										=  digita

										if (remainder + digita) % 2 == 0:
															_UpperCamelCase										=  ODD_DIGITS
										else:
															_UpperCamelCase										=  EVEN_DIGITS

										for digita in other_parity_digits:
															_UpperCamelCase										=  digita
															result += reversible_numbers(
															    remaining_length - 2,					(remainder + digita + digita) // 10,					__snake_case,					__snake_case,					)
					return result



def 				lowerCamelCase__		(      __snake_case = 9     )							->		int:


					"""simple docstring"""





					_UpperCamelCase										=  0
					for length in range(1,					max_power + 1     ):
										result += reversible_numbers(__snake_case,					0,					[0] * length,					__snake_case     )
					return result


if __name__ == "__main__":
				print(F"""{solution() = }""")








"""simple docstring"""





from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
				from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline






