


'''simple docstring'''





from typing import List, Optional, Tuple, Union

import torch

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


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




class    UpperCAmelCase					(       a__    ):




			def __init__(						self							:		Optional[int]       ,       __magic_name__							:		str       ,       __magic_name__							:		List[str]						):







						"""simple docstring"""







						super().__init__()
						self.register_modules(unet=lowerCamelCase_       ,       scheduler=lowerCamelCase_						)





			@torch.no_grad()
			def __call__(						self							:		List[str]       ,       __magic_name__							:		List[str] = 1       ,       __magic_name__							:		Optional[int] = 1_0_0       ,       __magic_name__							:		str = None       ,       __magic_name__							:		Any = None       ,       __magic_name__							:		str = True       ,       ):







						"""simple docstring"""







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

						UpperCamelCase         =					audio_length_in_s * self.unet.config.sample_rate

						UpperCamelCase         =					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}.'						)

						UpperCamelCase         =					int(lowerCamelCase_						)
						if sample_size % down_scale_factor != 0:
									UpperCamelCase         =					(
									    (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."""						)
						UpperCamelCase         =					int(lowerCamelCase_						)

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

						UpperCamelCase         =					randn_tensor(lowerCamelCase_       ,       generator=lowerCamelCase_       ,       device=self.device       ,       dtype=lowerCamelCase_						)

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

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

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

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

						UpperCamelCase         =					audio[:, :, :original_sample_size]

						if not return_dict:
									return (audio,)

						return AudioPipelineOutput(audios=lowerCamelCase_						)








def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		str	):





			"""simple docstring"""




			return [
			    txt[:a] + txt[a].upper() + txt[a + 1 :]
			    for a in range(len(SCREAMING_SNAKE_CASE_	)	)
			    if txt[a].isalpha()
			]


if __name__ == "__main__":
			__import__("doctest").testmod()




