
from typing import List, Optional, Tuple, Union

import torch

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


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





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	def __init__(	self :			Any							,A :			Optional[Any]							,A :			List[str]			):
					super().__init__()
					self.register_modules(unet=A							,scheduler=A			)







	@torch.no_grad()
	def __call__(	self :			Optional[int]							,A :			int = 1							,A :			int = 1_00							,A :			Optional[Union[torch.Generator, List[torch.Generator]]] = None							,A :			Optional[float] = None							,A :			bool = True							,):

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

					__A             =			audio_length_in_s * self.unet.config.sample_rate

					__A             =			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}.'''			)

					__A             =			int(A			)
					if sample_size % down_scale_factor != 0:
									__A             =			(
									    (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."			)
					__A             =			int(A			)

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

					__A             =			randn_tensor(A							,generator=A							,device=self.device							,dtype=A			)

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

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

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

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

					__A             =			audio[:, :, :original_sample_size]

					if not return_dict:
									return (audio,)

					return AudioPipelineOutput(audios=A			)


from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	def __init__(	self :			List[str]							,A :			Optional[Any]							,A :			List[Any]			):
					super().__init__()

					# make sure scheduler can always be converted to DDIM
					__A             =			DDIMScheduler.from_config(scheduler.config			)

					self.register_modules(unet=A							,scheduler=A			)







	@torch.no_grad()
	def __call__(	self :			Tuple							,A :			int = 1							,A :			Optional[Union[torch.Generator, List[torch.Generator]]] = None							,A :			float = 0.0							,A :			int = 50							,A :			Optional[bool] = None							,A :			Optional[str] = "pil"							,A :			bool = True							,):

					# Sample gaussian noise to begin loop
					if isinstance(self.unet.config.sample_size							,A			):
									__A             =			(
									    batch_size,
									    self.unet.config.in_channels,
									    self.unet.config.sample_size,
									    self.unet.config.sample_size,
									)
					else:
									__A             =			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

					if isinstance(A							,A			) and len(A			) != batch_size:
									raise ValueError(
									    f'''You have passed a list of generators of length {len(A			)}, but requested an effective batch'''
									    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''			)

					__A             =			randn_tensor(A							,generator=A							,device=self.device							,dtype=self.unet.dtype			)

					# set step values
					self.scheduler.set_timesteps(A			)

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

									# 2. predict previous mean of image x_t-1 and add variance depending on eta
									# eta corresponds to η in paper and should be between [0, 1]
									# do x_t -> x_t-1
									__A             =			self.scheduler.step(
									    A							,A							,A							,eta=A							,use_clipped_model_output=A							,generator=A			).prev_sample

					__A             =			(image / 2 + 0.5).clamp(0							,1			)
					__A             =			image.cpu().permute(0							,2							,3							,1			).numpy()
					if output_type == "pil":
									__A             =			self.numpy_to_pil(A			)

					if not return_dict:
									return (image,)

					return ImagePipelineOutput(images=A			)
