
"""simple docstring"""
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   _UpperCAmelCase					(       _UpperCamelCase		,       unittest.TestCase):
    __lowercase				:			str	=       RePaintPipeline
    __lowercase				:			int	=       IMAGE_INPAINTING_PARAMS - {"""width""", """height""", """guidance_scale"""}
    __lowercase				:			List[Any]	=       PipelineTesterMixin.required_optional_params - {
        """latents""",
        """num_images_per_prompt""",
        """callback""",
        """callback_steps""",
    }
    __lowercase				:			Tuple	=       IMAGE_INPAINTING_BATCH_PARAMS
    __lowercase				:			Optional[int]	=       False
    def        lowerCamelCase__						(     self  ):
      torch.manual_seed(0  )
      torch.manual_seed(0  )
      _snake_case  :    Optional[Any]            = 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  :    Optional[int]            = RePaintScheduler()
      _snake_case  :    List[Any]            = {"unet": unet, "scheduler": scheduler}
      return components
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_=0  ):







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

      _snake_case  :    Any            = self.get_dummy_inputs(a__  )
      _snake_case  :    Any            = sd_pipe(**a__  ).images
      _snake_case  :    int            = image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      _snake_case  :    Union[str, Any]            = np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274]  )

      assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3
    @skip_mps
    def        lowerCamelCase__						(     self  ):
      return super().test_save_load_local()
    @unittest.skip("non-deterministic pipeline"  )
    def        lowerCamelCase__						(     self  ):
      return super().test_inference_batch_single_identical()
    @skip_mps
    def        lowerCamelCase__						(     self  ):
      return super().test_dict_tuple_outputs_equivalent()
    @skip_mps
    def        lowerCamelCase__						(     self  ):
      return super().test_save_load_optional_components()


    @skip_mps
    def        lowerCamelCase__						(     self  ):
      return super().test_attention_slicing_forward_pass()






@nightly
@require_torch_gpu
class   _UpperCAmelCase					(       unittest.TestCase):
    def        lowerCamelCase__						(     self  ):
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()


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

      _snake_case  :    Any            = "google/ddpm-ema-celebahq-256"
      _snake_case  :    Any            = UNetaDModel.from_pretrained(a__  )
      _snake_case  :    Union[str, Any]            = RePaintScheduler.from_pretrained(a__  )

      _snake_case  :    str            = RePaintPipeline(unet=a__    ,     scheduler=a__  ).to(a__  )
      repaint.set_progress_bar_config(disable=a__  )
      repaint.enable_attention_slicing()

      _snake_case  :    Any            = torch.manual_seed(0  )
      _snake_case  :    str            = repaint(
          a__    ,     a__    ,     num_inference_steps=2_50    ,     eta=0.0    ,     jump_length=10    ,     jump_n_sample=10    ,     generator=a__    ,     output_type="np"    ,     )
      _snake_case  :    Tuple            = output.images[0]

      assert image.shape == (2_56, 2_56, 3)
      assert np.abs(expected_image - image  ).mean() < 1E-2





"""simple docstring"""


def   a__			(					__lowercase  ,      __lowercase )						->						Tuple:
				_A       	=      1
				while b > 0:
								if b & 1:
												res *= a

								a *= a
								b >>= 1

				return res






def   a__			(					__lowercase  ,      __lowercase  ,      __lowercase )						->						Optional[Any]:
				_A       	=      1
				while b > 0:
								if b & 1:
												_A       	=      ((res % c) * (a % c)) % c

								a *= a
								b >>= 1

				return res