





from typing import List, Optional, Tuple, Union

import torch

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


snake_case_     :       List[Any]						=  logging.get_logger(__name__)  # pylint: disable=invalid-name


class 					__a      (lowercase__     ):

							def __init__(							self							:     Tuple       ,     __magic_name__							:     List[Any]       ,     __magic_name__							:     Optional[int]   )					->       Any:







													"""simple docstring"""
													super().__init__()
													self.register_modules(unet=__magic_name__       ,     scheduler=__magic_name__   )



							@torch.no_grad()
							def __call__(							self							:     str       ,     __magic_name__							:     int = 1       ,     __magic_name__							:     int = 1_00       ,     __magic_name__							:     Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,     __magic_name__							:     Optional[float] = None       ,     __magic_name__							:     bool = True       ,     )					->       Union[AudioPipelineOutput, Tuple]:







													"""simple docstring"""

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

													UpperCAmelCase_			:      Tuple     						=  audio_length_in_s * self.unet.config.sample_rate

													UpperCAmelCase_			:      List[Any]     						=  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_			:      str     						=  int(__magic_name__   )
													if sample_size % down_scale_factor != 0:
																			UpperCAmelCase_			:      List[Any]     						=  (
																			    (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_			:      Tuple     						=  int(__magic_name__   )

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

													UpperCAmelCase_			:      Any     						=  randn_tensor(__magic_name__       ,     generator=__magic_name__       ,     device=self.device       ,     dtype=__magic_name__   )

													# set step values
													self.scheduler.set_timesteps(__magic_name__       ,     device=audio.device   )
													UpperCAmelCase_			:      Optional[int]     						=  self.scheduler.timesteps.to(__magic_name__   )

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

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

													UpperCAmelCase_			:      List[str]     						=  audio.clamp(-1       ,     1   ).float().cpu().numpy()

													UpperCAmelCase_			:      List[str]     						=  audio[:, :, :original_sample_size]

													if not return_dict:
																			return (audio,)

													return AudioPipelineOutput(audios=__magic_name__   )












'''simple docstring'''







def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			list[int]   ) ->	list[list[int]]:
						UpperCAmelCase_			:      int     						=  []
						if len(SCREAMING_SNAKE_CASE__   ) == 1:
												return [nums.copy()]
						for _ in range(len(SCREAMING_SNAKE_CASE__   )   ):
												UpperCAmelCase_			:      List[Any]     						=  nums.pop(0   )
												UpperCAmelCase_			:      Optional[Any]     						=  permute(SCREAMING_SNAKE_CASE__   )
												for perm in permutations:
																		perm.append(SCREAMING_SNAKE_CASE__   )
												result.extend(SCREAMING_SNAKE_CASE__   )
												nums.append(SCREAMING_SNAKE_CASE__   )
						return result





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			List[str]   ) ->	Any:

						def backtrack(SCREAMING_SNAKE_CASE__      :			Union[str, Any]   ):
												if start == len(SCREAMING_SNAKE_CASE__   ) - 1:
																		output.append(nums[:]   )
												else:
																		for i in range(SCREAMING_SNAKE_CASE__,			len(SCREAMING_SNAKE_CASE__   )   ):
																								UpperCAmelCase_						,       UpperCAmelCase_			:      Tuple     						=  nums[i], nums[start]
																								backtrack(start + 1   )
																								UpperCAmelCase_						,       UpperCAmelCase_			:      int     						=  nums[i], nums[start]  # backtrack

						UpperCAmelCase_			:      Optional[int]     						=  []
						backtrack(0   )
						return output


if __name__ == "__main__":
					import doctest

					# use res to print the data in permute2 function
					snake_case_     :       Tuple						=  permutea([1, 2, 3])
					print(res)
					doctest.testmod()





