




'''simple docstring'''



import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class      lowerCAmelCase (			__lowerCAmelCase					, __lowerCAmelCase					, unittest.TestCase):
       __lowercase	:   List[str]										=      StableDiffusionSAGPipeline
       __lowercase	:   Union[str, Any]										=      TEXT_TO_IMAGE_PARAMS
       __lowercase	:   Dict										=      TEXT_TO_IMAGE_BATCH_PARAMS
       __lowercase	:   Dict										=      TEXT_TO_IMAGE_IMAGE_PARAMS
       __lowercase	:   List[str]										=      TEXT_TO_IMAGE_IMAGE_PARAMS
       __lowercase	:   Optional[int]										=      False


       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            torch.manual_seed(0	)
            __snake_case            =	UNetaDConditionModel(
                block_out_channels=(32, 64)     ,	layers_per_block=2     ,	sample_size=32     ,	in_channels=4     ,	out_channels=4     ,	down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')     ,	up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')     ,	cross_attention_dim=32     ,	)
            __snake_case            =	DDIMScheduler(
                beta_start=0.00_085     ,	beta_end=0.012     ,	beta_schedule='''scaled_linear'''     ,	clip_sample=__SCREAMING_SNAKE_CASE     ,	set_alpha_to_one=__SCREAMING_SNAKE_CASE     ,	)
            torch.manual_seed(0	)
            __snake_case            =	AutoencoderKL(
                block_out_channels=[32, 64]     ,	in_channels=3     ,	out_channels=3     ,	down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']     ,	up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']     ,	latent_channels=4     ,	)
            torch.manual_seed(0	)
            __snake_case            =	CLIPTextConfig(
                bos_token_id=0     ,	eos_token_id=2     ,	hidden_size=32     ,	intermediate_size=37     ,	layer_norm_eps=1E-05     ,	num_attention_heads=4     ,	num_hidden_layers=5     ,	pad_token_id=1     ,	vocab_size=1000     ,	)
            __snake_case            =	CLIPTextModel(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	)

            __snake_case            =	{
                '''unet''': unet,
                '''scheduler''': scheduler,
                '''vae''': vae,
                '''text_encoder''': text_encoder,
                '''tokenizer''': tokenizer,
                '''safety_checker''': None,
                '''feature_extractor''': None,
            }
            return components


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=0	)   ->	int:



            '''simple docstring'''




            if str(__SCREAMING_SNAKE_CASE	).startswith('''mps'''	):
                 __snake_case            =	torch.manual_seed(__SCREAMING_SNAKE_CASE	)
            else:
                 __snake_case            =	torch.Generator(device=__SCREAMING_SNAKE_CASE	).manual_seed(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	{
                '''prompt''': '''.''',
                '''generator''': generator,
                '''num_inference_steps''': 2,
                '''guidance_scale''': 1.0,
                '''sag_scale''': 1.0,
                '''output_type''': '''numpy''',
            }
            return inputs




       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            super().test_inference_batch_single_identical(expected_max_diff=3E-3	)





@slow
@require_torch_gpu
class      lowerCAmelCase (			unittest.TestCase):


       def    lowerCAmelCase   ( self	)   ->	str:



            '''simple docstring'''




            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()


       def    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            __snake_case            =	StableDiffusionSAGPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''	)
            __snake_case            =	sag_pipe.to(__SCREAMING_SNAKE_CASE	)
            sag_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE	)

            __snake_case            =	'''.'''
            __snake_case            =	torch.manual_seed(0	)
            __snake_case            =	sag_pipe(
                [prompt]     ,	generator=__SCREAMING_SNAKE_CASE     ,	guidance_scale=7.5     ,	sag_scale=1.0     ,	num_inference_steps=20     ,	output_type='''np'''	)

            __snake_case            =	output.images

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

            assert image.shape == (1, 512, 512, 3)
            __snake_case            =	np.array([0.1_568, 0.1_738, 0.1_695, 0.1_693, 0.1_507, 0.1_705, 0.1_547, 0.1_751, 0.1_949]	)

            assert np.abs(image_slice.flatten() - expected_slice	).max() < 5E-2


       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            __snake_case            =	StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''	)
            __snake_case            =	sag_pipe.to(__SCREAMING_SNAKE_CASE	)
            sag_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE	)

            __snake_case            =	'''.'''
            __snake_case            =	torch.manual_seed(0	)
            __snake_case            =	sag_pipe(
                [prompt]     ,	generator=__SCREAMING_SNAKE_CASE     ,	guidance_scale=7.5     ,	sag_scale=1.0     ,	num_inference_steps=20     ,	output_type='''np'''	)

            __snake_case            =	output.images

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

            assert image.shape == (1, 512, 512, 3)
            __snake_case            =	np.array([0.3_459, 0.2_876, 0.2_537, 0.3_002, 0.2_671, 0.2_160, 0.3_026, 0.2_262, 0.2_371]	)

            assert np.abs(image_slice.flatten() - expected_slice	).max() < 5E-2




       def    lowerCAmelCase   ( self	)   ->	str:



            '''simple docstring'''




            __snake_case            =	StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''	)
            __snake_case            =	sag_pipe.to(__SCREAMING_SNAKE_CASE	)
            sag_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE	)

            __snake_case            =	'''.'''
            __snake_case            =	torch.manual_seed(0	)
            __snake_case            =	sag_pipe(
                [prompt]     ,	width=768     ,	height=512     ,	generator=__SCREAMING_SNAKE_CASE     ,	guidance_scale=7.5     ,	sag_scale=1.0     ,	num_inference_steps=20     ,	output_type='''np'''     ,	)

            __snake_case            =	output.images

            assert image.shape == (1, 512, 768, 3)












'''simple docstring'''



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


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







def       _UpperCamelCase			(_lowerCamelCase				:  Union[List, PIL.Image.Image, torch.Tensor]						)-> Optional[Any]:




     '''simple docstring'''





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

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

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







def       _UpperCamelCase			(_lowerCamelCase				:  Union[List, PIL.Image.Image, torch.Tensor]						)-> Optional[Any]:




     '''simple docstring'''





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

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







class      lowerCAmelCase (			__lowerCAmelCase):
       __lowercase	:   UNetaDModel
       __lowercase	:   RePaintScheduler


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	str:



            '''simple docstring'''




            super().__init__()
            self.register_modules(unet=__SCREAMING_SNAKE_CASE     ,	scheduler=__SCREAMING_SNAKE_CASE	)




       @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]:



            '''simple docstring'''




            __snake_case            =	image

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

            __snake_case            =	original_image.shape[0]

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

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

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

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

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

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

            if not return_dict:
                 return (image,)

            return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE	)






