





'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





def 	_a(					UpperCamelCase__			:  Dict							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			List[Any]													=image.size
      SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Any													=(x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
      SCREAMING_SNAKE_CASE__       :			List[Any]													=image.resize((w, h),		resample=PIL_INTERPOLATION['''lanczos''']							)
      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=np.array(lowerCAmelCase__							).astype(np.floataa							) / 2_5_5.0
      SCREAMING_SNAKE_CASE__       :			str													=image[None].transpose(0,		3,		1,		2							)
      SCREAMING_SNAKE_CASE__       :			List[str]													=torch.from_numpy(lowerCAmelCase__							)
      return 2.0 * image - 1.0







class        __SCREAMING_SNAKE_CASE (     __SCREAMING_SNAKE_CASE			):




 def __init__( self		:							Any   , __lowercase		:							int   , __lowercase		:							str   , __lowercase		:							Tuple   , )		->  Tuple:
       super().__init__()
       self.register_modules(vqvae=_a   , unet=_a   , scheduler=_a )




 @torch.no_grad()
 def __call__( self		:							List[Any]   , __lowercase		:							str = None   , __lowercase		:							List[Any] = 1   , __lowercase		:							List[Any] = 1_00   , __lowercase		:							List[Any] = 0.0   , __lowercase		:							Optional[Any] = None   , __lowercase		:							Tuple = "pil"   , __lowercase		:							Any = True   , )		->  Any:
       if isinstance(_a   , PIL.Image.Image ):
             SCREAMING_SNAKE_CASE__       :			List[str]													=1
       elif isinstance(_a   , torch.Tensor ):
             SCREAMING_SNAKE_CASE__       :			str													=image.shape[0]
       else:
             raise ValueError(F"`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(_a )}" )

       if isinstance(_a   , PIL.Image.Image ):
             SCREAMING_SNAKE_CASE__       :			int													=preprocess(_a )

       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			int													=image.shape[-2:]

       # in_channels should be 6: 3 for latents, 3 for low resolution image
       SCREAMING_SNAKE_CASE__       :			List[Any]													=(batch_size, self.unet.config.in_channels // 2, height, width)
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=next(self.unet.parameters() ).dtype

       SCREAMING_SNAKE_CASE__       :			Tuple													=randn_tensor(_a   , generator=_a   , device=self.device   , dtype=_a )

       SCREAMING_SNAKE_CASE__       :			List[Any]													=image.to(device=self.device   , dtype=_a )

       # set timesteps and move to the correct device
       self.scheduler.set_timesteps(_a   , device=self.device )
       SCREAMING_SNAKE_CASE__       :			List[Any]													=self.scheduler.timesteps

       # scale the initial noise by the standard deviation required by the scheduler
       SCREAMING_SNAKE_CASE__       :			List[Any]													=latents * self.scheduler.init_noise_sigma

       # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
       # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
       # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
       # and should be between [0, 1]
       SCREAMING_SNAKE_CASE__       :			str													='''eta''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													={}
       if accepts_eta:
             SCREAMING_SNAKE_CASE__       :			str													=eta

       for t in self.progress_bar(_a ):
             # concat latents and low resolution image in the channel dimension.
             SCREAMING_SNAKE_CASE__       :			Tuple													=torch.cat([latents, image]   , dim=1 )
             SCREAMING_SNAKE_CASE__       :			str													=self.scheduler.scale_model_input(_a   , _a )
             # predict the noise residual
             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=self.unet(_a   , _a ).sample
             # compute the previous noisy sample x_t -> x_t-1
             SCREAMING_SNAKE_CASE__       :			List[Any]													=self.scheduler.step(_a   , _a   , _a   , **_a ).prev_sample

       # decode the image latents with the VQVAE
       SCREAMING_SNAKE_CASE__       :			str													=self.vqvae.decode(_a ).sample
       SCREAMING_SNAKE_CASE__       :			int													=torch.clamp(_a   , -1.0   , 1.0 )
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=image / 2 + 0.5
       SCREAMING_SNAKE_CASE__       :			str													=image.cpu().permute(0   , 2   , 3   , 1 ).numpy()

       if output_type == "pil":
             SCREAMING_SNAKE_CASE__       :			int													=self.numpy_to_pil(_a )

       if not return_dict:
             return (image,)

       return ImagePipelineOutput(images=_a )







'''simple docstring'''
import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






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




 def        __magic_name__		( self		:							List[str] )		->  Optional[Any]:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 def        __magic_name__		( self		:							Union[str, Any] )		->  Dict:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4''' )
       SCREAMING_SNAKE_CASE__       :			str													=sd_pipe.to(__lowercase )
       sd_pipe.set_progress_bar_config(disable=__lowercase )

       sd_pipe.set_scheduler('''sample_euler''' )

       SCREAMING_SNAKE_CASE__       :			List[Any]													='''A painting of a squirrel eating a burger'''
       SCREAMING_SNAKE_CASE__       :			List[str]													=torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=sd_pipe([prompt]   , generator=__lowercase   , guidance_scale=9.0   , num_inference_steps=20   , output_type='''np''' )

       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=output.images

       SCREAMING_SNAKE_CASE__       :			int													=image[0, -3:, -3:, -1]

       assert image.shape == (1, 5_12, 5_12, 3)
       SCREAMING_SNAKE_CASE__       :			Any													=np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339] )

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




 def        __magic_name__		( self		:							List[str] )		->  List[Any]:
       SCREAMING_SNAKE_CASE__       :			List[str]													=StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
       SCREAMING_SNAKE_CASE__       :			int													=sd_pipe.to(__lowercase )
       sd_pipe.set_progress_bar_config(disable=__lowercase )

       sd_pipe.set_scheduler('''sample_euler''' )

       SCREAMING_SNAKE_CASE__       :			Any													='''A painting of a squirrel eating a burger'''
       SCREAMING_SNAKE_CASE__       :			Dict													=torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__       :			Tuple													=sd_pipe([prompt]   , generator=__lowercase   , guidance_scale=9.0   , num_inference_steps=20   , output_type='''np''' )

       SCREAMING_SNAKE_CASE__       :			Dict													=output.images

       SCREAMING_SNAKE_CASE__       :			Dict													=image[0, -3:, -3:, -1]

       assert image.shape == (1, 5_12, 5_12, 3)
       SCREAMING_SNAKE_CASE__       :			List[str]													=np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112] )

       assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-1




 def        __magic_name__		( self		:							List[str] )		->  List[Any]:
       SCREAMING_SNAKE_CASE__       :			Any													=StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
       SCREAMING_SNAKE_CASE__       :			Tuple													=sd_pipe.to(__lowercase )
       sd_pipe.set_progress_bar_config(disable=__lowercase )

       sd_pipe.set_scheduler('''sample_dpmpp_2m''' )

       SCREAMING_SNAKE_CASE__       :			Tuple													='''A painting of a squirrel eating a burger'''
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__       :			List[Any]													=sd_pipe(
           [prompt]   , generator=__lowercase   , guidance_scale=7.5   , num_inference_steps=15   , output_type='''np'''   , use_karras_sigmas=__lowercase   , )

       SCREAMING_SNAKE_CASE__       :			Optional[int]													=output.images

       SCREAMING_SNAKE_CASE__       :			Dict													=image[0, -3:, -3:, -1]

       assert image.shape == (1, 5_12, 5_12, 3)
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=np.array(
           [0.11381689, 0.12112921, 0.1389457, 0.12549606, 0.1244964, 0.10831517, 0.11562866, 0.10867816, 0.10499048] )

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