





def        _lowercase   (  _UpperCAmelCase = 60_08_51_47_51_43			)  ->					int:

				try:
								lowerCamelCase			=int(_UpperCAmelCase			)
				except (TypeError, ValueError):
								raise TypeError("""Parameter n must be int or castable to int."""			)
				if n <= 0:
								raise ValueError("""Parameter n must be greater than or equal to one."""			)
				lowerCamelCase			=2
				lowerCamelCase			=0
				if n == 2:
								return 2
				while n > 2:
								while n % i != 0:
												i += 1
								lowerCamelCase			=i
								while n % i == 0:
												lowerCamelCase			=n // i
								i += 1
				return int(_UpperCAmelCase			)


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












from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


UpperCAmelCase__  :     Dict   			=logging.get_logger(__name__)  # pylint: disable=invalid-name

class  __A       (  a      ):


				def __init__(						self				,	UpperCAmelCase_				,	UpperCAmelCase_							):
								super().__init__()
								self.register_modules(unet=UpperCAmelCase_				,	scheduler=UpperCAmelCase_							)


				@torch.no_grad()
				def __call__(						self				,	UpperCAmelCase_ = 1				,	UpperCAmelCase_ = 100				,	UpperCAmelCase_ = None				,	UpperCAmelCase_ = None				,	UpperCAmelCase_ = True				,	):

								if audio_length_in_s is None:
												lowerCamelCase			=self.unet.config.sample_size / self.unet.config.sample_rate

								lowerCamelCase			=audio_length_in_s * self.unet.config.sample_rate

								lowerCamelCase			=2 ** len(self.unet.up_blocks							)
								if sample_size < 3 * down_scale_factor:
												raise ValueError(
												    f"""{audio_length_in_s} is too small. Make sure it's bigger or equal to"""
												    f""" {3 * down_scale_factor / self.unet.config.sample_rate}."""							)

								lowerCamelCase			=int(UpperCAmelCase_							)
								if sample_size % down_scale_factor != 0:
												lowerCamelCase			=(
												    (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
												) * down_scale_factor
												logger.info(
												    f"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
												    f""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
												    """ process."""							)
								lowerCamelCase			=int(UpperCAmelCase_							)

								lowerCamelCase			=next(iter(self.unet.parameters()							)							).dtype
								lowerCamelCase			=(batch_size, self.unet.config.in_channels, sample_size)
								if isinstance(UpperCAmelCase_				,	UpperCAmelCase_							) and len(UpperCAmelCase_							) != batch_size:
												raise ValueError(
												    f"""You have passed a list of generators of length {len(UpperCAmelCase_							)}, but requested an effective batch"""
												    f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""							)

								lowerCamelCase			=randn_tensor(UpperCAmelCase_				,	generator=UpperCAmelCase_				,	device=self.device				,	dtype=UpperCAmelCase_							)

								# set step values
								self.scheduler.set_timesteps(UpperCAmelCase_				,	device=audio.device							)
								lowerCamelCase			=self.scheduler.timesteps.to(UpperCAmelCase_							)

								for t in self.progress_bar(self.scheduler.timesteps							):
												# 1. predict noise model_output
												lowerCamelCase			=self.unet(UpperCAmelCase_				,	UpperCAmelCase_							).sample

												# 2. compute previous image: x_t -> t_t-1
												lowerCamelCase			=self.scheduler.step(UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_							).prev_sample

								lowerCamelCase			=audio.clamp(-1				,	1							).float().cpu().numpy()

								lowerCamelCase			=audio[:, :, :original_sample_size]

								if not return_dict:
												return (audio,)

								return AudioPipelineOutput(audios=UpperCAmelCase_							)





