




'''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   __UpperCamelCase       ( unittest.TestCase				):






       @property
       def      lowercase__     (							self  ):



              """simple docstring"""







              torch.manual_seed(0  )
              lowerCamelCase_							=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      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=self.dummy_uncond_unet
              lowerCamelCase_							=ScoreSdeVeScheduler()

              lowerCamelCase_							=ScoreSdeVePipeline(unet=lowerCAmelCase,		scheduler=lowerCAmelCase  )
              sde_ve.to(lowerCAmelCase  )
              sde_ve.set_progress_bar_config(disable=lowerCAmelCase  )

              lowerCamelCase_							=torch.manual_seed(0  )
              lowerCamelCase_							=sde_ve(num_inference_steps=2,		output_type='''numpy''',		generator=lowerCAmelCase  ).images

              lowerCamelCase_							=torch.manual_seed(0  )
              lowerCamelCase_							=sde_ve(num_inference_steps=2,		output_type='''numpy''',		generator=lowerCAmelCase,		return_dict=lowerCAmelCase  )[
                  0
              ]

              lowerCamelCase_							=image[0, -3:, -3:, -1]
              lowerCamelCase_							=image_from_tuple[0, -3:, -3:, -1]

              assert image.shape == (1, 32, 32, 3)
              lowerCamelCase_							=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   __UpperCamelCase       ( unittest.TestCase				):






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							='''google/ncsnpp-church-256'''
              lowerCamelCase_							=UNetaDModel.from_pretrained(lowerCAmelCase  )

              lowerCamelCase_							=ScoreSdeVeScheduler.from_pretrained(lowerCAmelCase  )

              lowerCamelCase_							=ScoreSdeVePipeline(unet=lowerCAmelCase,		scheduler=lowerCAmelCase  )
              sde_ve.to(lowerCAmelCase  )
              sde_ve.set_progress_bar_config(disable=lowerCAmelCase  )

              lowerCamelCase_							=torch.manual_seed(0  )
              lowerCamelCase_							=sde_ve(num_inference_steps=10,		output_type='''numpy''',		generator=lowerCAmelCase  ).images

              lowerCamelCase_							=image[0, -3:, -3:, -1]

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

              lowerCamelCase_							=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







import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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






def        UpperCamelCase_(							_snake_case   :  Union[List, PIL.Image.Image, torch.Tensor]	):




			"""simple docstring"""






			warnings.warn(
			    'The preprocess method is deprecated and will be removed in a future version. Please'
			    ' use VaeImageProcessor.preprocess instead'       ,     _snake_case       ,     )
			if isinstance(_snake_case       ,     torch.Tensor	):
						return image
			elif isinstance(_snake_case       ,     PIL.Image.Image	):
						__a        =[image]

			if isinstance(image[0]       ,     PIL.Image.Image	):
						__a      ,    __a        =image[0].size
						__a      ,    __a        =(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

						__a        =[np.array(i.resize((w, h)       ,     resample=PIL_INTERPOLATION['lanczos']	)	)[None, :] for i in image]
						__a        =np.concatenate(_snake_case       ,     axis=0	)
						__a        =np.array(_snake_case	).astype(np.floataa	) / 255.0
						__a        =image.transpose(0       ,     3       ,     1       ,     2	)
						__a        =2.0 * image - 1.0
						__a        =torch.from_numpy(_snake_case	)
			elif isinstance(image[0]       ,     torch.Tensor	):
						__a        =torch.cat(_snake_case       ,     dim=0	)
			return image






def        UpperCamelCase_(							_snake_case   :  Union[List, PIL.Image.Image, torch.Tensor]	):




			"""simple docstring"""






			if isinstance(_snake_case       ,     torch.Tensor	):
						return mask
			elif isinstance(_snake_case       ,     PIL.Image.Image	):
						__a        =[mask]

			if isinstance(mask[0]       ,     PIL.Image.Image	):
						__a      ,    __a        =mask[0].size
						__a      ,    __a        =(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
						__a        =[np.array(m.convert('L'	).resize((w, h)       ,     resample=PIL_INTERPOLATION['nearest']	)	)[None, :] for m in mask]
						__a        =np.concatenate(_snake_case       ,     axis=0	)
						__a        =mask.astype(np.floataa	) / 255.0
						__a        =0
						__a        =1
						__a        =torch.from_numpy(_snake_case	)
			elif isinstance(mask[0]       ,     torch.Tensor	):
						__a        =torch.cat(_snake_case       ,     dim=0	)
			return mask






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						42
			SCREAMING_SNAKE_CASE							   =						42





			def __init__(		self					,  __snake_case					,  __snake_case					)   -> Union[str, Any]:




						'''simple docstring'''
						super().__init__()
						self.register_modules(unet=__snake_case					,  scheduler=__snake_case					)





			@torch.no_grad()
			def __call__(		self					,  __snake_case					,  __snake_case					,  __snake_case = 250					,  __snake_case = 0.0					,  __snake_case = 10					,  __snake_case = 10					,  __snake_case = None					,  __snake_case = "pil"					,  __snake_case = True					,  )   -> Union[ImagePipelineOutput, Tuple]:




						'''simple docstring'''
						__a        =image

						__a        =_preprocess_image(__snake_case					)
						__a        =original_image.to(device=self.device					,  dtype=self.unet.dtype					)
						__a        =_preprocess_mask(__snake_case					)
						__a        =mask_image.to(device=self.device					,  dtype=self.unet.dtype					)

						__a        =original_image.shape[0]

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

						__a        =original_image.shape
						__a        =randn_tensor(__snake_case					,  generator=__snake_case					,  device=self.device					,  dtype=self.unet.dtype					)

						# set step values
						self.scheduler.set_timesteps(__snake_case					,  __snake_case					,  __snake_case					,  self.device					)
						__a        =eta

						__a        =self.scheduler.timesteps[0] + 1
						__a        =generator[0] if isinstance(__snake_case					,  __snake_case					) else generator
						for i, t in enumerate(self.progress_bar(self.scheduler.timesteps					)					):
									if t < t_last:
												# predict the noise residual
												__a        =self.unet(__snake_case					,  __snake_case					).sample
												# compute previous image: x_t -> x_t-1
												__a        =self.scheduler.step(__snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					).prev_sample

									else:
												# compute the reverse: x_t-1 -> x_t
												__a        =self.scheduler.undo_step(__snake_case					,  __snake_case					,  __snake_case					)
									__a        =t

						__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(__snake_case					)

						if not return_dict:
									return (image,)

						return ImagePipelineOutput(images=__snake_case					)






