




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



  def 						a_       ( self   ):







      '''simple docstring'''


      torch.manual_seed(0   )
      torch.manual_seed(0   )
      _a							:       int       					=			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							:       int       					=			RePaintScheduler()
      _a							:       Tuple       					=			{"""unet""": unet, """scheduler""": scheduler}
      return components



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase=0   ):







      '''simple docstring'''


      if str(__lowerCAmelCase   ).startswith("""mps"""   ):
          _a							:       Union[str, Any]       					=			torch.manual_seed(__lowerCAmelCase   )
      else:
          _a							:       List[Any]       					=			torch.Generator(device=__lowerCAmelCase   ).manual_seed(__lowerCAmelCase   )
      _a							:       Optional[Any]       					=			np.random.RandomState(__lowerCAmelCase   ).standard_normal((1, 3, 32, 32)   )
      _a							:       Optional[Any]       					=			torch.from_numpy(__lowerCAmelCase   ).to(device=__lowerCAmelCase		,    dtype=torch.floataa   )
      _a							:       Optional[Any]       					=			(image > 0).to(device=__lowerCAmelCase		,    dtype=torch.floataa   )
      _a							:       Optional[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 						a_       ( self   ):







      '''simple docstring'''


      _a							:       Optional[Any]       					=			"""cpu"""  # ensure determinism for the device-dependent torch.Generator
      _a							:       str       					=			self.get_dummy_components()
      _a							:       List[str]       					=			RePaintPipeline(**__lowerCAmelCase   )
      _a							:       List[Any]       					=			sd_pipe.to(__lowerCAmelCase   )
      sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase   )

      _a							:       Tuple       					=			self.get_dummy_inputs(__lowerCAmelCase   )
      _a							:       Dict       					=			sd_pipe(**__lowerCAmelCase   ).images
      _a							:       Tuple       					=			image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      _a							:       Tuple       					=			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 						a_       ( self   ):







      '''simple docstring'''


      return super().test_save_load_local()



  @unittest.skip("""non-deterministic pipeline"""   )
  def 						a_       ( self   ):







      '''simple docstring'''


      return super().test_inference_batch_single_identical()



  @skip_mps
  def 						a_       ( self   ):







      '''simple docstring'''


      return super().test_dict_tuple_outputs_equivalent()



  @skip_mps
  def 						a_       ( self   ):







      '''simple docstring'''


      return super().test_save_load_optional_components()



  @skip_mps
  def 						a_       ( self   ):







      '''simple docstring'''


      return super().test_attention_slicing_forward_pass()






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



  def 						a_       ( self   ):







      '''simple docstring'''


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



  def 						a_       ( self   ):







      '''simple docstring'''


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

      _a							:       Any       					=			"""google/ddpm-ema-celebahq-256"""
      _a							:       str       					=			UNetaDModel.from_pretrained(__lowerCAmelCase   )
      _a							:       str       					=			RePaintScheduler.from_pretrained(__lowerCAmelCase   )

      _a							:       Optional[Any]       					=			RePaintPipeline(unet=__lowerCAmelCase		,    scheduler=__lowerCAmelCase   ).to(__lowerCAmelCase   )
      repaint.set_progress_bar_config(disable=__lowerCAmelCase   )
      repaint.enable_attention_slicing()

      _a							:       Optional[Any]       					=			torch.manual_seed(0   )
      _a							:       Dict       					=			repaint(
          __lowerCAmelCase		,    __lowerCAmelCase		,    num_inference_steps=250		,    eta=0.0		,    jump_length=10		,    jump_n_sample=10		,    generator=__lowerCAmelCase		,    output_type="""np"""		,    )
      _a							:       int       					=			output.images[0]

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








'''simple docstring'''





def UpperCAmelCase						(					UpperCAmelCase_	):
    _a							:       List[Any]       					=			len(UpperCAmelCase_	)
    for cycle_start in range(0      ,  array_len - 1	):
        _a							:       int       					=			array[cycle_start]

        _a							:       int       					=			cycle_start
        for i in range(cycle_start + 1      ,  UpperCAmelCase_	):
            if array[i] < item:
                pos += 1

        if pos == cycle_start:
            continue

        while item == array[pos]:
            pos += 1

        _a					,   _a							:       List[str]       					=			item, array[pos]
        while pos != cycle_start:
            _a							:       Any       					=			cycle_start
            for i in range(cycle_start + 1      ,  UpperCAmelCase_	):
                if array[i] < item:
                    pos += 1

            while item == array[pos]:
                pos += 1

            _a					,   _a							:       List[Any]       					=			item, array[pos]

    return array


if __name__ == "__main__":
  assert cycle_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
  assert cycle_sort([0, 1, -1_0, 1_5, 2, -2]) == [-1_0, -2, 0, 1, 2, 1_5]


