

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


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




def     __UpperCamelCase			(	_A :				str						)							->List[Any]:



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

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

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




def     __UpperCamelCase			(	_A :				Dict						)							->Any:



							"""simple docstring"""
							if isinstance(lowercase__	,   torch.Tensor						):
														return mask
							elif isinstance(lowercase__	,   PIL.Image.Image						):
														lowerCamelCase_				     =[mask]

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






class        _SCREAMING_SNAKE_CASE	(   _UpperCAmelCase):
						_UpperCamelCase:Any     =						42
						_UpperCamelCase:List[Any]     =						42







						def __init__(       self			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE				)->				int:
													super().__init__()
													self.register_modules(unet=A_			,		scheduler=A_				)







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

													lowerCamelCase_				     =_preprocess_image(A_				)
													lowerCamelCase_				     =original_image.to(device=self.device			,		dtype=self.unet.dtype				)
													lowerCamelCase_				     =_preprocess_mask(A_				)
													lowerCamelCase_				     =mask_image.to(device=self.device			,		dtype=self.unet.dtype				)

													lowerCamelCase_				     =original_image.shape[0]

													# sample gaussian noise to begin the loop
													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.'				)

													lowerCamelCase_				     =original_image.shape
													lowerCamelCase_				     =randn_tensor(A_			,		generator=A_			,		device=self.device			,		dtype=self.unet.dtype				)

													# set step values
													self.scheduler.set_timesteps(A_			,		A_			,		A_			,		self.device				)
													lowerCamelCase_				     =eta

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

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

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

													if not return_dict:
																				return (image,)

													return ImagePipelineOutput(images=A_				)



import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, 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					)					->Tuple:



       '''simple docstring'''





       torch.manual_seed(0					)
       __lowerCAmelCase					:						List[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					)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						List[str]					=					self.dummy_uncond_unet
       __lowerCAmelCase					:						Any					=					PNDMScheduler()

       __lowerCAmelCase					:						Dict					=					PNDMPipeline(unet=A_      ,							scheduler=A_					)
       pndm.to(A_					)
       pndm.set_progress_bar_config(disable=A_					)

       __lowerCAmelCase					:						Optional[Any]					=					torch.manual_seed(0					)
       __lowerCAmelCase					:						Any					=					pndm(generator=A_      ,							num_inference_steps=20      ,							output_type='''numpy'''					).images

       __lowerCAmelCase					:						Optional[Any]					=					torch.manual_seed(0					)
       __lowerCAmelCase					:						List[Any]					=					pndm(generator=A_      ,							num_inference_steps=20      ,							output_type='''numpy'''      ,							return_dict=A_					)[0]

       __lowerCAmelCase					:						Tuple					=					image[0, -3:, -3:, -1]
       __lowerCAmelCase					:						Optional[Any]					=					image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       __lowerCAmelCase					:						int					=					np.array([1.0, 1.0, 0.0, 1.0, 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					)					->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[int]					=					'''google/ddpm-cifar10-32'''

       __lowerCAmelCase					:						Union[str, Any]					=					UNetaDModel.from_pretrained(A_					)
       __lowerCAmelCase					:						int					=					PNDMScheduler()

       __lowerCAmelCase					:						Any					=					PNDMPipeline(unet=A_      ,							scheduler=A_					)
       pndm.to(A_					)
       pndm.set_progress_bar_config(disable=A_					)
       __lowerCAmelCase					:						Tuple					=					torch.manual_seed(0					)
       __lowerCAmelCase					:						Any					=					pndm(generator=A_      ,							output_type='''numpy'''					).images

       __lowerCAmelCase					:						Optional[int]					=					image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       __lowerCAmelCase					:						List[Any]					=					np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]					)

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


