




'''simple docstring'''


import colorsys
import io

import cva
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg

from utils import img_tensorize


UpperCamelCase			     =					1000






class 						lowerCAmelCase_	:







  '''simple docstring'''

  def __init__( self				:							Dict					,  SCREAMING_SNAKE_CASE_				:							Dict					,  SCREAMING_SNAKE_CASE_				:							List[str]=1.2					,  SCREAMING_SNAKE_CASE_				:							Dict="g"					,  SCREAMING_SNAKE_CASE_				:							Dict=0.5					,  SCREAMING_SNAKE_CASE_				:							Dict="-"					,  SCREAMING_SNAKE_CASE_				:							int="test_out.jpg"					,  SCREAMING_SNAKE_CASE_				:							Dict=True					,  SCREAMING_SNAKE_CASE_				:							Optional[int]=False					,  SCREAMING_SNAKE_CASE_				:							int=None					,  SCREAMING_SNAKE_CASE_				:							int=None					,  SCREAMING_SNAKE_CASE_				:							Tuple=0.7					,  )			->		int:







       '''simple docstring'''



       if isinstance(SCREAMING_SNAKE_CASE_					,  torch.Tensor	):
            A:		Union[str, Any]          =		img.numpy().astype('''np.uint8'''	)
       if isinstance(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	):
            A:		List[Any]          =		img_tensorize(SCREAMING_SNAKE_CASE_	)
       assert isinstance(SCREAMING_SNAKE_CASE_					,  np.ndarray	)

       A     ,    A:		int          =		img.shape[1], img.shape[0]
       A:		Tuple          =		mplfigure.Figure(frameon=SCREAMING_SNAKE_CASE_	)
       A:		Tuple          =		fig.get_dpi()
       A:		Union[str, Any]          =		(width * scale + 1E-2) / dpi
       A:		Optional[int]          =		(height * scale + 1E-2) / dpi
       fig.set_size_inches(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)
       A:		Union[str, Any]          =		fig.add_axes([0.0, 0.0, 1.0, 1.0]	)
       ax.axis('''off'''	)
       ax.set_xlim(0.0					,  SCREAMING_SNAKE_CASE_	)
       ax.set_ylim(SCREAMING_SNAKE_CASE_	)

       A:		List[str]          =		saveas
       A:		Tuple          =		rgb
       A:		Dict          =		pynb
       A:		Union[str, Any]          =		img
       A:		int          =		edgecolor
       A:		Tuple          =		0.5
       A:		str          =		linestyle
       A:		List[Any]          =		int(np.sqrt(min(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)	) * scale // 3	)
       A:		Optional[int]          =		width
       A:		List[str]          =		height
       A:		int          =		scale
       A:		Tuple          =		fig
       A:		Union[str, Any]          =		ax
       A:		Optional[int]          =		pad
       A:		int          =		idaobj
       A:		List[Any]          =		idaattr
       A:		Dict          =		FigureCanvasAgg(SCREAMING_SNAKE_CASE_	)



  def 					_snake_case     ( self				:							Dict					,  SCREAMING_SNAKE_CASE_				:							int					,  SCREAMING_SNAKE_CASE_				:							Dict=None	)			->		Tuple:







       '''simple docstring'''



       if color is None:
            A:		Union[str, Any]          =		self.edgecolor
       ((A)     ,    (A)     ,    (A)     ,    (A)):		Dict          =		box
       A:		List[Any]          =		xa - xa
       A:		Union[str, Any]          =		ya - ya
       self.ax.add_patch(
           mpl.patches.Rectangle(
               (xa, ya)					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  fill=SCREAMING_SNAKE_CASE_					,  edgecolor=SCREAMING_SNAKE_CASE_					,  linewidth=self.font_size // 3					,  alpha=self.alpha					,  linestyle=self.linestyle					,  )	)



  def 					_snake_case     ( self				:							Any					,  SCREAMING_SNAKE_CASE_				:							Optional[int]					,  SCREAMING_SNAKE_CASE_				:							List[str]=None					,  SCREAMING_SNAKE_CASE_				:							Tuple=None					,  SCREAMING_SNAKE_CASE_				:							Any=None					,  SCREAMING_SNAKE_CASE_				:							List[str]=None	)			->		Tuple:







       '''simple docstring'''



       if len(boxes.shape	) > 2:
            A:		Optional[int]          =		boxes[0]
       if len(obj_ids.shape	) > 1:
            A:		Tuple          =		obj_ids[0]
       if len(obj_scores.shape	) > 1:
            A:		int          =		obj_scores[0]
       if len(attr_ids.shape	) > 1:
            A:		Tuple          =		attr_ids[0]
       if len(attr_scores.shape	) > 1:
            A:		Tuple          =		attr_scores[0]
       if isinstance(SCREAMING_SNAKE_CASE_					,  torch.Tensor	):
            A:		Any          =		boxes.numpy()
       if isinstance(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	):
            A:		List[Any]          =		np.array(SCREAMING_SNAKE_CASE_	)
       assert isinstance(SCREAMING_SNAKE_CASE_					,  np.ndarray	)
       A:		str          =		np.prod(boxes[:, 2:] - boxes[:, :2]					,  axis=1	)
       A:		int          =		np.argsort(-areas	).tolist()
       A:		Dict          =		boxes[sorted_idxs] if boxes is not None else None
       A:		Any          =		obj_ids[sorted_idxs] if obj_ids is not None else None
       A:		Optional[int]          =		obj_scores[sorted_idxs] if obj_scores is not None else None
       A:		List[Any]          =		attr_ids[sorted_idxs] if attr_ids is not None else None
       A:		Any          =		attr_scores[sorted_idxs] if attr_scores is not None else None

       A:		Optional[Any]          =		[self._random_color(maximum=1	) for _ in range(len(SCREAMING_SNAKE_CASE_	)	)]
       A:		List[Any]          =		[assigned_colors[idx] for idx in sorted_idxs]
       if obj_ids is not None:
            A:		Optional[int]          =		self._create_text_labels_attr(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)
            for i in range(len(SCREAMING_SNAKE_CASE_	)	):
                 A:		Tuple          =		assigned_colors[i]
                 self.add_box(boxes[i]					,  SCREAMING_SNAKE_CASE_	)
                 self.draw_labels(labels[i]					,  boxes[i]					,  SCREAMING_SNAKE_CASE_	)



  def 					_snake_case     ( self				:							Tuple					,  SCREAMING_SNAKE_CASE_				:							int					,  SCREAMING_SNAKE_CASE_				:							Any					,  SCREAMING_SNAKE_CASE_				:							Union[str, Any]	)			->		Union[str, Any]:







       '''simple docstring'''



       A     ,    A     ,    A     ,    A:		Any          =		box
       A:		Optional[Any]          =		(xa, ya)
       A:		Tuple          =		(ya - ya) * (xa - xa)
       A:		int          =		_SMALL_OBJ * self.scale
       if instance_area < small or ya - ya < 40 * self.scale:
            if ya >= self.height - 5:
                 A:		Union[str, Any]          =		(xa, ya)
            else:
                 A:		Optional[Any]          =		(xa, ya)

       A:		Optional[Any]          =		(ya - ya) / np.sqrt(self.height * self.width	)
       A:		Dict          =		self._change_color_brightness(SCREAMING_SNAKE_CASE_					,  brightness_factor=0.7	)
       A:		List[Any]          =		np.clip((height_ratio - 0.02) / 0.08 + 1					,  1.2					,  2	)
       font_size *= 0.75 * self.font_size

       self.draw_text(
           text=SCREAMING_SNAKE_CASE_					,  position=SCREAMING_SNAKE_CASE_					,  color=SCREAMING_SNAKE_CASE_					,  )



  def 					_snake_case     ( self				:							List[Any]					,  SCREAMING_SNAKE_CASE_				:							Any					,  SCREAMING_SNAKE_CASE_				:							Tuple					,  SCREAMING_SNAKE_CASE_				:							List[Any]="g"					,  SCREAMING_SNAKE_CASE_				:							int="left"					,  )			->		List[str]:







       '''simple docstring'''



       A:		Dict          =		0
       A:		Tuple          =		self.font_size
       A:		List[Any]          =		np.maximum(list(mplc.to_rgb(SCREAMING_SNAKE_CASE_	)	)					,  0.2	)
       A:		int          =		max(0.8					,  np.max(SCREAMING_SNAKE_CASE_	)	)
       A:		Optional[int]          =		{
           '''facecolor''': '''black''',
           '''alpha''': self.alpha,
           '''pad''': self.pad,
           '''edgecolor''': '''none''',
       }
       A     ,    A:		Union[str, Any]          =		position
       self.ax.text(
           SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  size=font_size * self.scale					,  family='''sans-serif'''					,  bbox=SCREAMING_SNAKE_CASE_					,  verticalalignment='''top'''					,  horizontalalignment=SCREAMING_SNAKE_CASE_					,  color=SCREAMING_SNAKE_CASE_					,  zorder=10					,  rotation=SCREAMING_SNAKE_CASE_					,  )



  def 					_snake_case     ( self				:							Tuple					,  SCREAMING_SNAKE_CASE_				:							Union[str, Any]=None	)			->		Any:







       '''simple docstring'''



       if saveas is None:
            A:		List[Any]          =		self.saveas
       if saveas.lower().endswith('''.jpg'''	) or saveas.lower().endswith('''.png'''	):
            cva.imwrite(
                SCREAMING_SNAKE_CASE_					,  self._get_buffer()[:, :, ::-1]					,  )
       else:
            self.fig.savefig(SCREAMING_SNAKE_CASE_	)



  def 					_snake_case     ( self				:							Optional[Any]					,  SCREAMING_SNAKE_CASE_				:							int					,  SCREAMING_SNAKE_CASE_				:							int					,  SCREAMING_SNAKE_CASE_				:							int					,  SCREAMING_SNAKE_CASE_				:							Optional[int]	)			->		int:







       '''simple docstring'''



       A:		Optional[Any]          =		[self.idaobj[i] for i in classes]
       A:		Optional[int]          =		[self.idaattr[i] for i in attr_classes]
       A:		int          =		[
           f"""{label} {score:.2f} {attr} {attr_score:.2f}"""
           for label, score, attr, attr_score in zip(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)
       ]
       return labels



  def 					_snake_case     ( self				:							Optional[int]					,  SCREAMING_SNAKE_CASE_				:							Tuple					,  SCREAMING_SNAKE_CASE_				:							List[Any]	)			->		Union[str, Any]:







       '''simple docstring'''



       A:		Optional[int]          =		[self.idaobj[i] for i in classes]
       if scores is not None:
            if labels is None:
                 A:		List[str]          =		['''{:.0f}%'''.format(s * 1_00	) for s in scores]
            else:
                 A:		List[Any]          =		['''{} {:.0f}%'''.format(SCREAMING_SNAKE_CASE_					,  s * 1_00	) for li, s in zip(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)]
       return labels



  def 					_snake_case     ( self				:							Union[str, Any]					,  SCREAMING_SNAKE_CASE_				:							str=2_55	)			->		Optional[Any]:







       '''simple docstring'''



       A:		Any          =		np.random.randint(0					,  len(_COLORS	)	)
       A:		int          =		_COLORS[idx] * maximum
       if not self.rgb:
            A:		Dict          =		ret[::-1]
       return ret



  def 					_snake_case     ( self				:							Dict	)			->		Union[str, Any]:







       '''simple docstring'''



       if not self.pynb:
            A     ,    A:		List[str]          =		self.canvas.print_to_buffer()
            if (width, height) != (self.width, self.height):
                 A:		List[str]          =		cva.resize(self.img					,  (width, height)	)
            else:
                 A:		str          =		self.img
       else:
            A:		str          =		io.BytesIO()  # works for cairo backend
            self.canvas.print_rgba(SCREAMING_SNAKE_CASE_	)
            A     ,    A:		Optional[int]          =		self.width, self.height
            A:		int          =		buf.getvalue()
            A:		List[str]          =		self.img

       A:		List[Any]          =		np.frombuffer(SCREAMING_SNAKE_CASE_					,  dtype='''uint8'''	)
       A:		str          =		buffer.reshape(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  4	)
       A     ,    A:		Union[str, Any]          =		np.split(SCREAMING_SNAKE_CASE_					,  [3]					,  axis=2	)

       try:
            import numexpr as ne  # fuse them with numexpr

            A:		Dict          =		ne.evaluate('''img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'''	)
       except ImportError:
            A:		List[Any]          =		alpha.astype('''float32'''	) / 255.0
            A:		Any          =		img * (1 - alpha) + rgb * alpha

       return visualized_image.astype('''uint8'''	)




  def 					_snake_case     ( self				:							Union[str, Any]					,  SCREAMING_SNAKE_CASE_				:							List[str]					,  SCREAMING_SNAKE_CASE_				:							int	)			->		Union[str, Any]:







       '''simple docstring'''



       assert brightness_factor >= -1.0 and brightness_factor <= 1.0
       A:		List[Any]          =		mplc.to_rgb(SCREAMING_SNAKE_CASE_	)
       A:		Dict          =		colorsys.rgb_to_hls(*mplc.to_rgb(SCREAMING_SNAKE_CASE_	)	)
       A:		str          =		polygon_color[1] + (brightness_factor * polygon_color[1])
       A:		Optional[int]          =		0.0 if modified_lightness < 0.0 else modified_lightness
       A:		Union[str, Any]          =		1.0 if modified_lightness > 1.0 else modified_lightness
       A:		Any          =		colorsys.hls_to_rgb(polygon_color[0]					,  SCREAMING_SNAKE_CASE_					,  polygon_color[2]	)
       return modified_color






UpperCamelCase			     =					(
    np.array(
        [
            0.0_00,
            0.4_47,
            0.7_41,
            0.8_50,
            0.3_25,
            0.0_98,
            0.9_29,
            0.6_94,
            0.1_25,
            0.4_94,
            0.1_84,
            0.5_56,
            0.4_66,
            0.6_74,
            0.1_88,
            0.3_01,
            0.7_45,
            0.9_33,
            0.6_35,
            0.0_78,
            0.1_84,
            0.3_00,
            0.3_00,
            0.3_00,
            0.6_00,
            0.6_00,
            0.6_00,
            1.0_00,
            0.0_00,
            0.0_00,
            1.0_00,
            0.5_00,
            0.0_00,
            0.7_49,
            0.7_49,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            0.3_33,
            0.3_33,
            0.0_00,
            0.3_33,
            0.6_67,
            0.0_00,
            0.3_33,
            1.0_00,
            0.0_00,
            0.6_67,
            0.3_33,
            0.0_00,
            0.6_67,
            0.6_67,
            0.0_00,
            0.6_67,
            1.0_00,
            0.0_00,
            1.0_00,
            0.3_33,
            0.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.3_33,
            0.5_00,
            0.0_00,
            0.6_67,
            0.5_00,
            0.0_00,
            1.0_00,
            0.5_00,
            0.3_33,
            0.0_00,
            0.5_00,
            0.3_33,
            0.3_33,
            0.5_00,
            0.3_33,
            0.6_67,
            0.5_00,
            0.3_33,
            1.0_00,
            0.5_00,
            0.6_67,
            0.0_00,
            0.5_00,
            0.6_67,
            0.3_33,
            0.5_00,
            0.6_67,
            0.6_67,
            0.5_00,
            0.6_67,
            1.0_00,
            0.5_00,
            1.0_00,
            0.0_00,
            0.5_00,
            1.0_00,
            0.3_33,
            0.5_00,
            1.0_00,
            0.6_67,
            0.5_00,
            1.0_00,
            1.0_00,
            0.5_00,
            0.0_00,
            0.3_33,
            1.0_00,
            0.0_00,
            0.6_67,
            1.0_00,
            0.0_00,
            1.0_00,
            1.0_00,
            0.3_33,
            0.0_00,
            1.0_00,
            0.3_33,
            0.3_33,
            1.0_00,
            0.3_33,
            0.6_67,
            1.0_00,
            0.3_33,
            1.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            0.6_67,
            0.3_33,
            1.0_00,
            0.6_67,
            0.6_67,
            1.0_00,
            0.6_67,
            1.0_00,
            1.0_00,
            1.0_00,
            0.0_00,
            1.0_00,
            1.0_00,
            0.3_33,
            1.0_00,
            1.0_00,
            0.6_67,
            1.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_67,
            0.0_00,
            0.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_67,
            0.0_00,
            0.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_43,
            0.1_43,
            0.1_43,
            0.8_57,
            0.8_57,
            0.8_57,
            1.0_00,
            1.0_00,
            1.0_00,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)












'''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 						lowerCAmelCase_	(       UpperCAmelCase_       ,					unittest.TestCase							):







  '''simple docstring'''

  UpperCamelCase_			:      Any										=				RePaintPipeline
  UpperCamelCase_			:      int										=				IMAGE_INPAINTING_PARAMS - {"""width""", """height""", """guidance_scale"""}
  UpperCamelCase_			:      List[str]										=				PipelineTesterMixin.required_optional_params - {
      """latents""",
      """num_images_per_prompt""",
      """callback""",
      """callback_steps""",
  }
  UpperCamelCase_			:      List[str]										=				IMAGE_INPAINTING_BATCH_PARAMS
  UpperCamelCase_			:      str										=				False



  def 					_snake_case     ( self				:							Dict	)			->		Optional[Any]:







       '''simple docstring'''



       torch.manual_seed(0	)
       torch.manual_seed(0	)
       A:		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''')					,  )
       A:		Optional[int]          =		RePaintScheduler()
       A:		Optional[int]          =		{'''unet''': unet, '''scheduler''': scheduler}
       return components



  def 					_snake_case     ( self				:							Optional[Any]					,  SCREAMING_SNAKE_CASE_				:							List[Any]					,  SCREAMING_SNAKE_CASE_				:							Tuple=0	)			->		Optional[int]:







       '''simple docstring'''



       if str(SCREAMING_SNAKE_CASE_	).startswith('''mps'''	):
            A:		str          =		torch.manual_seed(SCREAMING_SNAKE_CASE_	)
       else:
            A:		Any          =		torch.Generator(device=SCREAMING_SNAKE_CASE_	).manual_seed(SCREAMING_SNAKE_CASE_	)
       A:		str          =		np.random.RandomState(SCREAMING_SNAKE_CASE_	).standard_normal((1, 3, 32, 32)	)
       A:		Tuple          =		torch.from_numpy(SCREAMING_SNAKE_CASE_	).to(device=SCREAMING_SNAKE_CASE_					,  dtype=torch.floataa	)
       A:		List[Any]          =		(image > 0).to(device=SCREAMING_SNAKE_CASE_					,  dtype=torch.floataa	)
       A:		Tuple          =		{
           '''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 					_snake_case     ( self				:							int	)			->		Tuple:







       '''simple docstring'''



       A:		int          =		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       A:		List[str]          =		self.get_dummy_components()
       A:		int          =		RePaintPipeline(**SCREAMING_SNAKE_CASE_	)
       A:		List[Any]          =		sd_pipe.to(SCREAMING_SNAKE_CASE_	)
       sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

       A:		Dict          =		self.get_dummy_inputs(SCREAMING_SNAKE_CASE_	)
       A:		List[str]          =		sd_pipe(**SCREAMING_SNAKE_CASE_	).images
       A:		Optional[Any]          =		image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       A:		List[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 					_snake_case     ( self				:							Union[str, Any]	)			->		Optional[Any]:







       '''simple docstring'''



       return super().test_save_load_local()



  @unittest.skip('''non-deterministic pipeline'''	)
  def 					_snake_case     ( self				:							Tuple	)			->		Optional[Any]:







       '''simple docstring'''



       return super().test_inference_batch_single_identical()



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







       '''simple docstring'''



       return super().test_dict_tuple_outputs_equivalent()



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







       '''simple docstring'''



       return super().test_save_load_optional_components()




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







       '''simple docstring'''



       return super().test_attention_slicing_forward_pass()






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







  '''simple docstring'''

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







       '''simple docstring'''



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




  def 					_snake_case     ( self				:							Optional[int]	)			->		List[Any]:







       '''simple docstring'''



       A:		str          =		load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
           '''repaint/celeba_hq_256.png'''	)
       A:		Union[str, Any]          =		load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'''	)
       A:		Optional[Any]          =		load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
           '''repaint/celeba_hq_256_result.npy'''	)

       A:		Dict          =		'''google/ddpm-ema-celebahq-256'''
       A:		Tuple          =		UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE_	)
       A:		List[Any]          =		RePaintScheduler.from_pretrained(SCREAMING_SNAKE_CASE_	)

       A:		Optional[Any]          =		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()

       A:		str          =		torch.manual_seed(0	)
       A:		str          =		repaint(
           SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  num_inference_steps=2_50					,  eta=0.0					,  jump_length=10					,  jump_n_sample=10					,  generator=SCREAMING_SNAKE_CASE_					,  output_type='''np'''					,  )
       A:		Optional[Any]          =		output.images[0]

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






