






'''simple docstring'''



import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase      ,      _lowerCAmelCase      ,      unittest.TestCase     ):
      __a  					=IFInpaintingSuperResolutionPipeline
      __a  					=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
      __a  					=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"original_image"}     )
      __a  					=PipelineTesterMixin.required_optional_params - {"latents"}




      def  __UpperCamelCase							(  self      )							->Any:



         '''simple docstring'''
         return self._get_superresolution_dummy_components()




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase=0      )							->Dict:



         '''simple docstring'''
         if str(lowerCamelCase      ).startswith('mps'      ):
            __a				     =    torch.manual_seed(lowerCamelCase      )
         else:
            __a				     =    torch.Generator(device=lowerCamelCase      ).manual_seed(lowerCamelCase      )

         __a				     =    floats_tensor((1, 3, 16, 16) ,   rng=random.Random(lowerCamelCase      )      ).to(lowerCamelCase      )
         __a				     =    floats_tensor((1, 3, 32, 32) ,   rng=random.Random(lowerCamelCase      )      ).to(lowerCamelCase      )
         __a				     =    floats_tensor((1, 3, 32, 32) ,   rng=random.Random(lowerCamelCase      )      ).to(lowerCamelCase      )

         __a				     =    {
             'prompt': 'A painting of a squirrel eating a burger',
             'image': image,
             'original_image': original_image,
             'mask_image': mask_image,
             'generator': generator,
             'num_inference_steps': 2,
             'output_type': 'numpy',
         }

         return inputs




      @unittest.skipIf(
          torch_device != 'cuda' or not is_xformers_available() ,   reason='XFormers attention is only available with CUDA and `xformers` installed' ,   )
      def  __UpperCamelCase							(  self      )							->Any:



         '''simple docstring'''
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3      )




      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         self._test_save_load_optional_components()




      @unittest.skipIf(torch_device != 'cuda' ,   reason='float16 requires CUDA'      )
      def  __UpperCamelCase							(  self      )							->str:



         '''simple docstring'''
         # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
         super().test_save_load_floataa(expected_max_diff=1e-1      )




      def  __UpperCamelCase							(  self      )							->str:



         '''simple docstring'''
         self._test_attention_slicing_forward_pass(expected_max_diff=1e-2      )




      def  __UpperCamelCase							(  self      )							->Dict:



         '''simple docstring'''
         self._test_save_load_local()






      def  __UpperCamelCase							(  self      )							->str:



         '''simple docstring'''
         self._test_inference_batch_single_identical(
             expected_max_diff=1e-2 ,   )







'''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    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	List[Any]    )  ->			Dict:

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



class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):




      def __init__(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   )							->Dict:



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






      @torch.no_grad()
      def __call__(  self ,   lowerCamelCase = None ,   lowerCamelCase = 1 ,   lowerCamelCase = 100 ,   lowerCamelCase = 0.0 ,   lowerCamelCase = None ,   lowerCamelCase = "pil" ,   lowerCamelCase = True ,   )							->Union[Tuple, ImagePipelineOutput]:



         '''simple docstring'''
         if isinstance(lowerCamelCase ,   PIL.Image.Image      ):
            __a				     =    1
         elif isinstance(lowerCamelCase ,   torch.Tensor      ):
            __a				     =    image.shape[0]
         else:
            raise ValueError(F"""`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(lowerCamelCase      )}"""      )

         if isinstance(lowerCamelCase ,   PIL.Image.Image      ):
            __a				     =    preprocess(lowerCamelCase      )

         __a     ,			__a				     =    image.shape[-2:]

         # in_channels should be 6: 3 for latents, 3 for low resolution image
         __a				     =    (batch_size, self.unet.config.in_channels // 2, height, width)
         __a				     =    next(self.unet.parameters()      ).dtype

         __a				     =    randn_tensor(lowerCamelCase ,   generator=lowerCamelCase ,   device=self.device ,   dtype=lowerCamelCase      )

         __a				     =    image.to(device=self.device ,   dtype=lowerCamelCase      )

         # set timesteps and move to the correct device
         self.scheduler.set_timesteps(lowerCamelCase ,   device=self.device      )
         __a				     =    self.scheduler.timesteps

         # scale the initial noise by the standard deviation required by the scheduler
         __a				     =    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]
         __a				     =    'eta' in set(inspect.signature(self.scheduler.step      ).parameters.keys()      )
         __a				     =    {}
         if accepts_eta:
            __a				     =    eta

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

         # decode the image latents with the VQVAE
         __a				     =    self.vqvae.decode(lowerCamelCase      ).sample
         __a				     =    torch.clamp(lowerCamelCase ,   -1.0 ,   1.0      )
         __a				     =    image / 2 + 0.5
         __a				     =    image.cpu().permute(0 ,   2 ,   3 ,   1      ).numpy()

         if output_type == "pil":
            __a				     =    self.numpy_to_pil(lowerCamelCase      )

         if not return_dict:
            return (image,)

         return ImagePipelineOutput(images=lowerCamelCase      )