



'''simple docstring'''
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class 							lowercase__	(							unittest.TestCase   ):





	@property
	def 	UpperCamelCase_  ( self					:				int ):







							'''simple docstring'''
							torch.manual_seed(0 )
							_UpperCamelCase					:		Any 				=   UNetaDModel(
							    block_out_channels=(32, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=3 ,out_channels=3 ,down_block_types=('DownBlock2D', 'AttnDownBlock2D') ,up_block_types=('AttnUpBlock2D', 'UpBlock2D') ,)
							return model





	def 	UpperCamelCase_  ( self					:				Tuple ):







							'''simple docstring'''
							_UpperCamelCase					:		Optional[int] 				=   self.dummy_uncond_unet
							_UpperCamelCase					:		List[Any] 				=   ScoreSdeVeScheduler()

							_UpperCamelCase					:		Optional[int] 				=   ScoreSdeVePipeline(unet=lowerCamelCase__ ,scheduler=lowerCamelCase__ )
							sde_ve.to(lowerCamelCase__ )
							sde_ve.set_progress_bar_config(disable=lowerCamelCase__ )

							_UpperCamelCase					:		Tuple 				=   torch.manual_seed(0 )
							_UpperCamelCase					:		str 				=   sde_ve(num_inference_steps=2 ,output_type='numpy' ,generator=lowerCamelCase__ ).images

							_UpperCamelCase					:		Dict 				=   torch.manual_seed(0 )
							_UpperCamelCase					:		Union[str, Any] 				=   sde_ve(num_inference_steps=2 ,output_type='numpy' ,generator=lowerCamelCase__ ,return_dict=lowerCamelCase__ )[
							    0
							]

							_UpperCamelCase					:		Optional[Any] 				=   image[0, -3:, -3:, -1]
							_UpperCamelCase					:		List[Any] 				=   image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							_UpperCamelCase					:		List[str] 				=   np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )

							assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2






@slow
@require_torch
class 							lowercase__	(							unittest.TestCase   ):





	def 	UpperCamelCase_  ( self					:				int ):







							'''simple docstring'''
							_UpperCamelCase					:		List[str] 				=   'google/ncsnpp-church-256'
							_UpperCamelCase					:		Union[str, Any] 				=   UNetaDModel.from_pretrained(lowerCamelCase__ )

							_UpperCamelCase					:		List[str] 				=   ScoreSdeVeScheduler.from_pretrained(lowerCamelCase__ )

							_UpperCamelCase					:		int 				=   ScoreSdeVePipeline(unet=lowerCamelCase__ ,scheduler=lowerCamelCase__ )
							sde_ve.to(lowerCamelCase__ )
							sde_ve.set_progress_bar_config(disable=lowerCamelCase__ )

							_UpperCamelCase					:		Union[str, Any] 				=   torch.manual_seed(0 )
							_UpperCamelCase					:		int 				=   sde_ve(num_inference_steps=10 ,output_type='numpy' ,generator=lowerCamelCase__ ).images

							_UpperCamelCase					:		str 				=   image[0, -3:, -3:, -1]

							assert image.shape == (1, 256, 256, 3)

							_UpperCamelCase					:		int 				=   np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0] )

							assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2







"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

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


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

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					def __init__(       self							:  List[Any],  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  Dict     )->					Optional[int]:
								super().__init__()
								self.register_modules(unet=lowerCamelCase,  scheduler=lowerCamelCase     )




					@torch.no_grad()
					def __call__(       self							:  Optional[int],  lowerCamelCase							:  int = 1,  lowerCamelCase							:  int = 100,  lowerCamelCase							:  Optional[Union[torch.Generator, List[torch.Generator]]] = None,  lowerCamelCase							:  Optional[float] = None,  lowerCamelCase							:  bool = True,  )->					Union[AudioPipelineOutput, Tuple]:

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

								lowerCamelCase__		:		List[str]			=audio_length_in_s * self.unet.config.sample_rate

								lowerCamelCase__		:		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}.'''     )

								lowerCamelCase__		:		Optional[int]			=int(lowerCamelCase     )
								if sample_size % down_scale_factor != 0:
											lowerCamelCase__		:		Tuple			=(
											    (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			=int(lowerCamelCase     )

								lowerCamelCase__		:		str			=next(iter(self.unet.parameters()     )     ).dtype
								lowerCamelCase__		:		Union[str, Any]			=(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.'''     )

								lowerCamelCase__		:		str			=randn_tensor(lowerCamelCase,  generator=lowerCamelCase,  device=self.device,  dtype=lowerCamelCase     )

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

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

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

								lowerCamelCase__		:		Optional[int]			=audio.clamp(-1,  1     ).float().cpu().numpy()

								lowerCamelCase__		:		List[Any]			=audio[:, :, :original_sample_size]

								if not return_dict:
											return (audio,)

								return AudioPipelineOutput(audios=lowerCamelCase     )




