



from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
    import PIL

__UpperCAmelCase			  =			logging.get_logger(__name__)
def 						UpperCamelCase      (  snake_case__			:       Optional[int]      )      ->	List[List[ImageInput]]:
   if isinstance(snake_case__			,	(list, tuple)      ) and isinstance(videos[0]			,	(list, tuple)      ) and is_valid_image(videos[0][0]      ):
      return videos

   elif isinstance(snake_case__			,	(list, tuple)      ) and is_valid_image(videos[0]      ):
      return [videos]

   elif is_valid_image(snake_case__      ):
      return [[videos]]

   raise ValueError(F"""Could not make batched video from {videos}"""      )



class 							lowerCAmelCase_						(    a__				):
       UpperCAmelCase__							:   Tuple  	=    ["pixel_values"]
       def __init__(     self,     SCREAMING_SNAKE_CASE_ = True,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR,     SCREAMING_SNAKE_CASE_ = True,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = True,     SCREAMING_SNAKE_CASE_ = 1 / 255,     SCREAMING_SNAKE_CASE_ = True,     SCREAMING_SNAKE_CASE_ = True,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     **SCREAMING_SNAKE_CASE_,     )	->						None:
          super().__init__(**SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    List[Any]		      =      size if size is not None else {'shortest_edge': 256}
          UpperCamelCase       :    Tuple		      =      get_size_dict(SCREAMING_SNAKE_CASE_,     default_to_square=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Optional[int]		      =      crop_size if crop_size is not None else {'height': 224, 'width': 224}
          UpperCamelCase       :    List[Any]		      =      get_size_dict(SCREAMING_SNAKE_CASE_,     param_name='crop_size'			)

          UpperCamelCase       :    Optional[int]		      =      do_resize
          UpperCamelCase       :    Optional[Any]		      =      size
          UpperCamelCase       :    Any		      =      do_center_crop
          UpperCamelCase       :    Tuple		      =      crop_size
          UpperCamelCase       :    Dict		      =      resample
          UpperCamelCase       :    int		      =      do_rescale
          UpperCamelCase       :    Optional[Any]		      =      rescale_factor
          UpperCamelCase       :    List[str]		      =      offset
          UpperCamelCase       :    int		      =      do_normalize
          UpperCamelCase       :    str		      =      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          UpperCamelCase       :    str		      =      image_std if image_std is not None else IMAGENET_STANDARD_STD
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR,     SCREAMING_SNAKE_CASE_ = None,     **SCREAMING_SNAKE_CASE_,     )	->						np.ndarray:
          UpperCamelCase       :    Union[str, Any]		      =      get_size_dict(SCREAMING_SNAKE_CASE_,     default_to_square=SCREAMING_SNAKE_CASE_			)
          if "shortest_edge" in size:
             UpperCamelCase       :    List[str]		      =      get_resize_output_image_size(SCREAMING_SNAKE_CASE_,     size['shortest_edge'],     default_to_square=SCREAMING_SNAKE_CASE_			)
          elif "height" in size and "width" in size:
             UpperCamelCase       :    Dict		      =      (size['height'], size['width'])
          else:
             raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""			)
          return resize(SCREAMING_SNAKE_CASE_,     size=SCREAMING_SNAKE_CASE_,     resample=SCREAMING_SNAKE_CASE_,     data_format=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = None,     **SCREAMING_SNAKE_CASE_,     )	->						np.ndarray:
          UpperCamelCase       :    List[Any]		      =      get_size_dict(SCREAMING_SNAKE_CASE_			)
          if "height" not in size or "width" not in size:
             raise ValueError(F"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""			)
          return center_crop(SCREAMING_SNAKE_CASE_,     size=(size['height'], size['width']),     data_format=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = True,     SCREAMING_SNAKE_CASE_ = None,     **SCREAMING_SNAKE_CASE_,     )	->						Optional[int]:
          UpperCamelCase       :    Tuple		      =      image.astype(np.floataa			)
          if offset:
             UpperCamelCase       :    Tuple		      =      image - (scale / 2)
          return rescale(SCREAMING_SNAKE_CASE_,     scale=SCREAMING_SNAKE_CASE_,     data_format=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = None,     **SCREAMING_SNAKE_CASE_,     )	->						np.ndarray:
          return normalize(SCREAMING_SNAKE_CASE_,     mean=SCREAMING_SNAKE_CASE_,     std=SCREAMING_SNAKE_CASE_,     data_format=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST,     )	->						np.ndarray:
          if do_resize and size is None or resample is None:
             raise ValueError('Size and resample must be specified if do_resize is True.'			)

          if do_center_crop and crop_size is None:
             raise ValueError('Crop size must be specified if do_center_crop is True.'			)

          if do_rescale and rescale_factor is None:
             raise ValueError('Rescale factor must be specified if do_rescale is True.'			)

          if do_normalize and (image_mean is None or image_std is None):
             raise ValueError('Image mean and std must be specified if do_normalize is True.'			)

          if offset and not do_rescale:
             raise ValueError('For offset, do_rescale must also be set to True.'			)

          # All transformations expect numpy arrays.
          UpperCamelCase       :    Optional[int]		      =      to_numpy_array(SCREAMING_SNAKE_CASE_			)

          if do_resize:
             UpperCamelCase       :    Dict		      =      self.resize(image=SCREAMING_SNAKE_CASE_,     size=SCREAMING_SNAKE_CASE_,     resample=SCREAMING_SNAKE_CASE_			)

          if do_center_crop:
             UpperCamelCase       :    Union[str, Any]		      =      self.center_crop(SCREAMING_SNAKE_CASE_,     size=SCREAMING_SNAKE_CASE_			)

          if do_rescale:
             UpperCamelCase       :    Optional[Any]		      =      self.rescale(image=SCREAMING_SNAKE_CASE_,     scale=SCREAMING_SNAKE_CASE_,     offset=SCREAMING_SNAKE_CASE_			)

          if do_normalize:
             UpperCamelCase       :    int		      =      self.normalize(image=SCREAMING_SNAKE_CASE_,     mean=SCREAMING_SNAKE_CASE_,     std=SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    List[Any]		      =      to_channel_dimension_format(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
          return image






       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST,     **SCREAMING_SNAKE_CASE_,     )	->						PIL.Image.Image:
          UpperCamelCase       :    Union[str, Any]		      =      do_resize if do_resize is not None else self.do_resize
          UpperCamelCase       :    List[str]		      =      resample if resample is not None else self.resample
          UpperCamelCase       :    int		      =      do_center_crop if do_center_crop is not None else self.do_center_crop
          UpperCamelCase       :    Optional[Any]		      =      do_rescale if do_rescale is not None else self.do_rescale
          UpperCamelCase       :    List[str]		      =      rescale_factor if rescale_factor is not None else self.rescale_factor
          UpperCamelCase       :    int		      =      offset if offset is not None else self.offset
          UpperCamelCase       :    Optional[int]		      =      do_normalize if do_normalize is not None else self.do_normalize
          UpperCamelCase       :    Optional[Any]		      =      image_mean if image_mean is not None else self.image_mean
          UpperCamelCase       :    Dict		      =      image_std if image_std is not None else self.image_std

          UpperCamelCase       :    Optional[Any]		      =      size if size is not None else self.size
          UpperCamelCase       :    Tuple		      =      get_size_dict(SCREAMING_SNAKE_CASE_,     default_to_square=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Tuple		      =      crop_size if crop_size is not None else self.crop_size
          UpperCamelCase       :    Dict		      =      get_size_dict(SCREAMING_SNAKE_CASE_,     param_name='crop_size'			)

          if not valid_images(SCREAMING_SNAKE_CASE_			):
             raise ValueError(
                 'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                 'torch.Tensor, tf.Tensor or jax.ndarray.'			)

          UpperCamelCase       :    Tuple		      =      make_batched(SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    Any		      =      [
              [
                  self._preprocess_image(
                      image=SCREAMING_SNAKE_CASE_,     do_resize=SCREAMING_SNAKE_CASE_,     size=SCREAMING_SNAKE_CASE_,     resample=SCREAMING_SNAKE_CASE_,     do_center_crop=SCREAMING_SNAKE_CASE_,     crop_size=SCREAMING_SNAKE_CASE_,     do_rescale=SCREAMING_SNAKE_CASE_,     rescale_factor=SCREAMING_SNAKE_CASE_,     offset=SCREAMING_SNAKE_CASE_,     do_normalize=SCREAMING_SNAKE_CASE_,     image_mean=SCREAMING_SNAKE_CASE_,     image_std=SCREAMING_SNAKE_CASE_,     data_format=SCREAMING_SNAKE_CASE_,     )
                  for img in video
              ]
              for video in videos
          ]

          UpperCamelCase       :    str		      =      {'pixel_values': videos}
          return BatchFeature(data=SCREAMING_SNAKE_CASE_,     tensor_type=SCREAMING_SNAKE_CASE_			)






"""simple docstring"""
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class       lowerCamelCase	(       lowerCAmelCase__					,					unittest.TestCase   ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE					      =  KandinskyInpaintPipeline
  SCREAMING_SNAKE_CASE					      =  ['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
  SCREAMING_SNAKE_CASE					      =  [
      'prompt',
      'negative_prompt',
      'image_embeds',
      'negative_image_embeds',
      'image',
      'mask_image',
  ]
  SCREAMING_SNAKE_CASE					      =  [
      'generator',
      'height',
      'width',
      'latents',
      'guidance_scale',
      'negative_prompt',
      'num_inference_steps',
      'return_dict',
      'guidance_scale',
      'num_images_per_prompt',
      'output_type',
      'return_dict',
  ]
  SCREAMING_SNAKE_CASE					      =  False



  @property
  def  _a      (self	):


        """simple docstring"""

        return 32



  @property
  def  _a      (self	):


        """simple docstring"""

        return 32



  @property
  def  _a      (self	):


        """simple docstring"""

        return self.time_input_dim



  @property
  def  _a      (self	):


        """simple docstring"""

        return self.time_input_dim * 4



  @property
  def  _a      (self	):


        """simple docstring"""

        return 100



  @property
  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Optional[int]							=    XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""	)
        return tokenizer



  @property
  def  _a      (self	):


        """simple docstring"""

        torch.manual_seed(0	)
        UpperCAmelCase__			:							Optional[int]							=    MCLIPConfig(
            numDims=self.cross_attention_dim							,       transformerDimensions=self.text_embedder_hidden_size							,       hidden_size=self.text_embedder_hidden_size							,       intermediate_size=37							,       num_attention_heads=4							,       num_hidden_layers=5							,       vocab_size=1005							,       )

        UpperCAmelCase__			:							List[Any]							=    MultilingualCLIP(_lowerCamelCase	)
        UpperCAmelCase__			:							Optional[Any]							=    text_encoder.eval()

        return text_encoder



  @property
  def  _a      (self	):


        """simple docstring"""

        torch.manual_seed(0	)

        UpperCAmelCase__			:							Dict							=    {
            """in_channels""": 9,
            # Out channels is double in channels because predicts mean and variance
            """out_channels""": 8,
            """addition_embed_type""": """text_image""",
            """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
            """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
            """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
            """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
            """layers_per_block""": 1,
            """encoder_hid_dim""": self.text_embedder_hidden_size,
            """encoder_hid_dim_type""": """text_image_proj""",
            """cross_attention_dim""": self.cross_attention_dim,
            """attention_head_dim""": 4,
            """resnet_time_scale_shift""": """scale_shift""",
            """class_embed_type""": None,
        }

        UpperCAmelCase__			:							List[Any]							=    UNetaDConditionModel(**_lowerCamelCase	)
        return model



  @property
  def  _a      (self	):


        """simple docstring"""

        return {
            "block_out_channels": [32, 64],
            "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
            "in_channels": 3,
            "latent_channels": 4,
            "layers_per_block": 1,
            "norm_num_groups": 8,
            "norm_type": "spatial",
            "num_vq_embeddings": 12,
            "out_channels": 3,
            "up_block_types": [
                "AttnUpDecoderBlock2D",
                "UpDecoderBlock2D",
            ],
            "vq_embed_dim": 4,
        }



  @property
  def  _a      (self	):


        """simple docstring"""

        torch.manual_seed(0	)
        UpperCAmelCase__			:							Any							=    VQModel(**self.dummy_movq_kwargs	)
        return model



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Dict							=    self.dummy_text_encoder
        UpperCAmelCase__			:							Optional[Any]							=    self.dummy_tokenizer
        UpperCAmelCase__			:							Optional[Any]							=    self.dummy_unet
        UpperCAmelCase__			:							List[Any]							=    self.dummy_movq

        UpperCAmelCase__			:							Union[str, Any]							=    DDIMScheduler(
            num_train_timesteps=1000							,       beta_schedule="""linear"""							,       beta_start=0.00_085							,       beta_end=0.012							,       clip_sample=_lowerCamelCase							,       set_alpha_to_one=_lowerCamelCase							,       steps_offset=1							,       prediction_type="""epsilon"""							,       thresholding=_lowerCamelCase							,       )

        UpperCAmelCase__			:							Any							=    {
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """unet""": unet,
            """scheduler""": scheduler,
            """movq""": movq,
        }

        return components



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase=0	):


        """simple docstring"""

        UpperCAmelCase__			:							Dict							=    floats_tensor((1, self.cross_attention_dim)							,       rng=random.Random(_lowerCamelCase	)	).to(_lowerCamelCase	)
        UpperCAmelCase__			:							Optional[int]							=    floats_tensor((1, self.cross_attention_dim)							,       rng=random.Random(seed + 1	)	).to(_lowerCamelCase	)
        # create init_image
        UpperCAmelCase__			:							Optional[int]							=    floats_tensor((1, 3, 64, 64)							,       rng=random.Random(_lowerCamelCase	)	).to(_lowerCamelCase	)
        UpperCAmelCase__			:							Optional[int]							=    image.cpu().permute(0							,       2							,       3							,       1	)[0]
        UpperCAmelCase__			:							Optional[Any]							=    Image.fromarray(np.uinta(_lowerCamelCase	)	).convert("""RGB"""	).resize((256, 256)	)
        # create mask
        UpperCAmelCase__			:							List[Any]							=    np.ones((64, 64)							,       dtype=np.floataa	)
        UpperCAmelCase__			:							Optional[int]							=    0

        if str(_lowerCamelCase	).startswith("""mps"""	):
              UpperCAmelCase__			:							Tuple							=    torch.manual_seed(_lowerCamelCase	)
        else:
              UpperCAmelCase__			:							Optional[int]							=    torch.Generator(device=_lowerCamelCase	).manual_seed(_lowerCamelCase	)
        UpperCAmelCase__			:							Any							=    {
            """prompt""": """horse""",
            """image""": init_image,
            """mask_image""": mask,
            """image_embeds""": image_embeds,
            """negative_image_embeds""": negative_image_embeds,
            """generator""": generator,
            """height""": 64,
            """width""": 64,
            """num_inference_steps""": 2,
            """guidance_scale""": 4.0,
            """output_type""": """np""",
        }
        return inputs



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Union[str, Any]							=    """cpu"""

        UpperCAmelCase__			:							Optional[Any]							=    self.get_dummy_components()

        UpperCAmelCase__			:							Optional[Any]							=    self.pipeline_class(**_lowerCamelCase	)
        UpperCAmelCase__			:							Union[str, Any]							=    pipe.to(_lowerCamelCase	)

        pipe.set_progress_bar_config(disable=_lowerCamelCase	)

        UpperCAmelCase__			:							List[str]							=    pipe(**self.get_dummy_inputs(_lowerCamelCase	)	)
        UpperCAmelCase__			:							Optional[int]							=    output.images

        UpperCAmelCase__			:							Union[str, Any]							=    pipe(
            **self.get_dummy_inputs(_lowerCamelCase	)							,       return_dict=_lowerCamelCase							,       )[0]

        UpperCAmelCase__			:							Union[str, Any]							=    image[0, -3:, -3:, -1]
        UpperCAmelCase__			:							Optional[Any]							=    image_from_tuple[0, -3:, -3:, -1]

        print(F"""image.shape {image.shape}"""	)

        assert image.shape == (1, 64, 64, 3)

        UpperCAmelCase__			:							Any							=    np.array(
            [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786]	)

        assert (
            np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2
        ), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
        assert (
            np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1e-2
        ), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""




  def  _a      (self	):


        """simple docstring"""

        super().test_inference_batch_single_identical(expected_max_diff=3e-3	)




@slow
@require_torch_gpu
class       lowerCamelCase	(       unittest.TestCase   ):


  '''simple docstring'''







  def  _a      (self	):


        """simple docstring"""

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




  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							str							=    load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""	)

        UpperCAmelCase__			:							Union[str, Any]							=    load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""	)
        UpperCAmelCase__			:							List[str]							=    np.ones((768, 768)							,       dtype=np.floataa	)
        UpperCAmelCase__			:							Optional[int]							=    0

        UpperCAmelCase__			:							int							=    """a hat"""

        UpperCAmelCase__			:							List[Any]							=    KandinskyPriorPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-1-prior"""							,       torch_dtype=torch.floataa	)
        pipe_prior.to(_lowerCamelCase	)

        UpperCAmelCase__			:							str							=    KandinskyInpaintPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-1-inpaint"""							,       torch_dtype=torch.floataa	)
        UpperCAmelCase__			:							int							=    pipeline.to(_lowerCamelCase	)
        pipeline.set_progress_bar_config(disable=_lowerCamelCase	)

        UpperCAmelCase__			:							int							=    torch.Generator(device="""cpu"""	).manual_seed(0	)
        UpperCAmelCase__					,		UpperCAmelCase__			:							List[str]							=    pipe_prior(
            _lowerCamelCase							,       generator=_lowerCamelCase							,       num_inference_steps=5							,       negative_prompt=""""""							,       ).to_tuple()

        UpperCAmelCase__			:							Union[str, Any]							=    pipeline(
            _lowerCamelCase							,       image=_lowerCamelCase							,       mask_image=_lowerCamelCase							,       image_embeds=_lowerCamelCase							,       negative_image_embeds=_lowerCamelCase							,       generator=_lowerCamelCase							,       num_inference_steps=100							,       height=768							,       width=768							,       output_type="""np"""							,       )

        UpperCAmelCase__			:							Any							=    output.images[0]

        assert image.shape == (768, 768, 3)

        assert_mean_pixel_difference(_lowerCamelCase							,       _lowerCamelCase	)

