




import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class     lowerCAmelCase   ( __lowerCAmelCase				,   unittest.TestCase):
       __lowercase  :      Dict        =			RePaintPipeline
       __lowercase  :      Dict        =			IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
       __lowercase  :      Any        =			PipelineTesterMixin.required_optional_params - {
           "latents",
           "num_images_per_prompt",
           "callback",
           "callback_steps",
       }
       __lowercase  :      Union[str, Any]        =			IMAGE_INPAINTING_BATCH_PARAMS
       __lowercase  :      Dict        =			False


       def       lowerCAmelCase      (   self  )      ->  Dict:



            '''simple docstring'''




            torch.manual_seed(0  )
            torch.manual_seed(0  )
            __snake_case    					=  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''')    ,  )
            __snake_case    					=  RePaintScheduler()
            __snake_case    					=  {"unet": unet, "scheduler": scheduler}
            return components


       def       lowerCAmelCase      (   self    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE=0  )      ->  List[Any]:



            '''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    					=  np.random.RandomState(__SCREAMING_SNAKE_CASE  ).standard_normal((1, 3, 32, 32)  )
            __snake_case    					=  torch.from_numpy(__SCREAMING_SNAKE_CASE  ).to(device=__SCREAMING_SNAKE_CASE    ,  dtype=torch.floataa  )
            __snake_case    					=  (image > 0).to(device=__SCREAMING_SNAKE_CASE    ,  dtype=torch.floataa  )
            __snake_case    					=  {
                "image": image,
                "mask_image": mask,
                "generator": generator,
                "num_inference_steps": 5,
                "eta": 0.0,
                "jump_length": 2,
                "jump_n_sample": 2,
                "output_type": "numpy",
            }
            return inputs


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



            '''simple docstring'''




            __snake_case    					=  "cpu"  # ensure determinism for the device-dependent torch.Generator
            __snake_case    					=  self.get_dummy_components()
            __snake_case    					=  RePaintPipeline(**__SCREAMING_SNAKE_CASE  )
            __snake_case    					=  sd_pipe.to(__SCREAMING_SNAKE_CASE  )
            sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE  )

            __snake_case    					=  self.get_dummy_inputs(__SCREAMING_SNAKE_CASE  )
            __snake_case    					=  sd_pipe(**__SCREAMING_SNAKE_CASE  ).images
            __snake_case    					=  image[0, -3:, -3:, -1]

            assert image.shape == (1, 32, 32, 3)
            __snake_case    					=  np.array([1.0_000, 0.5_426, 0.5_497, 0.2_200, 1.0_000, 1.0_000, 0.5_623, 1.0_000, 0.6_274]  )

            assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3


       @skip_mps
       def       lowerCAmelCase      (   self  )      ->  Dict:



            '''simple docstring'''




            return super().test_save_load_local()


       @unittest.skip('''non-deterministic pipeline'''  )
       def       lowerCAmelCase      (   self  )      ->  List[Any]:



            '''simple docstring'''




            return super().test_inference_batch_single_identical()


       @skip_mps
       def       lowerCAmelCase      (   self  )      ->  List[str]:



            '''simple docstring'''




            return super().test_dict_tuple_outputs_equivalent()


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



            '''simple docstring'''




            return super().test_save_load_optional_components()




       @skip_mps
       def       lowerCAmelCase      (   self  )      ->  int:



            '''simple docstring'''




            return super().test_attention_slicing_forward_pass()





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


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



            '''simple docstring'''




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




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



            '''simple docstring'''




            __snake_case    					=  load_image(
                '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
                '''repaint/celeba_hq_256.png'''  )
            __snake_case    					=  load_image(
                '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'''  )
            __snake_case    					=  load_numpy(
                '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
                '''repaint/celeba_hq_256_result.npy'''  )

            __snake_case    					=  "google/ddpm-ema-celebahq-256"
            __snake_case    					=  UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE  )
            __snake_case    					=  RePaintScheduler.from_pretrained(__SCREAMING_SNAKE_CASE  )

            __snake_case    					=  RePaintPipeline(unet=__SCREAMING_SNAKE_CASE    ,  scheduler=__SCREAMING_SNAKE_CASE  ).to(__SCREAMING_SNAKE_CASE  )
            repaint.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE  )
            repaint.enable_attention_slicing()

            __snake_case    					=  torch.manual_seed(0  )
            __snake_case    					=  repaint(
                __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  num_inference_steps=250    ,  eta=0.0    ,  jump_length=10    ,  jump_n_sample=10    ,  generator=__SCREAMING_SNAKE_CASE    ,  output_type='''np'''    ,  )
            __snake_case    					=  output.images[0]

            assert image.shape == (256, 256, 3)
            assert np.abs(expected_image - image  ).mean() < 1E-2












'''simple docstring'''



from transformers import ProcessorMixin





class     lowerCAmelCase   ( __lowerCAmelCase):
       __lowercase  :      Union[str, Any]        =			'''AutoFeatureExtractor'''
       __lowercase  :      List[Any]        =			'''AutoTokenizer'''






