

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      _a				( snake_case_    ,							unittest.TestCase							):
      _lowerCamelCase					: Optional[Any]					=					RePaintPipeline
      _lowerCamelCase					: List[Any]					=					IMAGE_INPAINTING_PARAMS - {'width', 'height', 'guidance_scale'}
      _lowerCamelCase					: List[str]					=					PipelineTesterMixin.required_optional_params - {
          'latents',
          'num_images_per_prompt',
          'callback',
          'callback_steps',
      }
      _lowerCamelCase					: Tuple					=					IMAGE_INPAINTING_BATCH_PARAMS
      _lowerCamelCase					: int					=					False





      def 		__A  (      self       :							Tuple  ):
            torch.manual_seed(0  )
            torch.manual_seed(0  )
            A_	     =							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")	, )
            A_	     =							RePaintScheduler()
            A_	     =							{"unet": unet, "scheduler": scheduler}
            return components





      def 		__A  (      self       :							List[Any]	, UpperCAmelCase       :							Optional[int]	, UpperCAmelCase       :							Dict=0  ):
            if str(UpperCAmelCase  ).startswith("mps"  ):
                  A_	     =							torch.manual_seed(UpperCAmelCase  )
            else:
                  A_	     =							torch.Generator(device=UpperCAmelCase  ).manual_seed(UpperCAmelCase  )
            A_	     =							np.random.RandomState(UpperCAmelCase  ).standard_normal((1, 3, 32, 32)  )
            A_	     =							torch.from_numpy(UpperCAmelCase  ).to(device=UpperCAmelCase	, dtype=torch.floataa  )
            A_	     =							(image > 0).to(device=UpperCAmelCase	, dtype=torch.floataa  )
            A_	     =							{
                "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 		__A  (      self       :							List[Any]  ):
            A_	     =							"cpu"  # ensure determinism for the device-dependent torch.Generator
            A_	     =							self.get_dummy_components()
            A_	     =							RePaintPipeline(**UpperCAmelCase  )
            A_	     =							sd_pipe.to(UpperCAmelCase  )
            sd_pipe.set_progress_bar_config(disable=UpperCAmelCase  )

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

            assert image.shape == (1, 32, 32, 3)
            A_	     =							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 		__A  (      self       :							Optional[Any]  ):
            return super().test_save_load_local()





      @unittest.skip("non-deterministic pipeline"  )
      def 		__A  (      self       :							Optional[int]  ):
            return super().test_inference_batch_single_identical()





      @skip_mps
      def 		__A  (      self       :							int  ):
            return super().test_dict_tuple_outputs_equivalent()





      @skip_mps
      def 		__A  (      self       :							Tuple  ):
            return super().test_save_load_optional_components()







      @skip_mps
      def 		__A  (      self       :							List[Any]  ):
            return super().test_attention_slicing_forward_pass()




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





      def 		__A  (      self       :							Any  ):
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()







      def 		__A  (      self       :							Optional[Any]  ):
            A_	     =							load_image(
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
                "repaint/celeba_hq_256.png"  )
            A_	     =							load_image(
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"  )
            A_	     =							load_numpy(
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
                "repaint/celeba_hq_256_result.npy"  )

            A_	     =							"google/ddpm-ema-celebahq-256"
            A_	     =							UNetaDModel.from_pretrained(UpperCAmelCase  )
            A_	     =							RePaintScheduler.from_pretrained(UpperCAmelCase  )

            A_	     =							RePaintPipeline(unet=UpperCAmelCase	, scheduler=UpperCAmelCase  ).to(UpperCAmelCase  )
            repaint.set_progress_bar_config(disable=UpperCAmelCase  )
            repaint.enable_attention_slicing()

            A_	     =							torch.manual_seed(0  )
            A_	     =							repaint(
                UpperCAmelCase	, UpperCAmelCase	, num_inference_steps=250	, eta=0.0	, jump_length=10	, jump_n_sample=10	, generator=UpperCAmelCase	, output_type="np"	, )
            A_	     =							output.images[0]

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


import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


__a						:Optional[int]            = logging.get_logger(__name__)

class      _a				( snake_case_							):




      """simple docstring"""

      def __init__(      self       :							List[Any]	, *UpperCAmelCase       :							Any	, **UpperCAmelCase       :							int  ):
            warnings.warn(
                "The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
                " Please use MobileNetV2ImageProcessor instead."	, UpperCAmelCase	, )
            super().__init__(*UpperCAmelCase	, **UpperCAmelCase  )