





'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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 __a (   _snake_case,   unittest.TestCase			):
   __UpperCamelCase   : int	      =	KandinskyVaaInpaintPipeline
   __UpperCamelCase   : List[Any]	      =	['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
   __UpperCamelCase   : Any	      =	[
       'image_embeds',
       'negative_image_embeds',
       'image',
       'mask_image',
   ]
   __UpperCamelCase   : List[str]	      =	[
       'generator',
       'height',
       'width',
       'latents',
       'guidance_scale',
       'num_inference_steps',
       'return_dict',
       'guidance_scale',
       'num_images_per_prompt',
       'output_type',
       'return_dict',
   ]
   __UpperCamelCase   : Optional[int]	      =	False





   @property
   def      UpperCAmelCase__     (					self				:			Union[str, Any]    ):

      '''simple docstring'''

      return 32





   @property
   def      UpperCAmelCase__     (					self				:			str    ):

      '''simple docstring'''

      return 32





   @property
   def      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

      return self.time_input_dim





   @property
   def      UpperCAmelCase__     (					self				:			Tuple    ):

      '''simple docstring'''

      return self.time_input_dim * 4





   @property
   def      UpperCAmelCase__     (					self				:			Any    ):

      '''simple docstring'''

      return 100





   @property
   def      UpperCAmelCase__     (					self				:			Tuple    ):

      '''simple docstring'''

      torch.manual_seed(0    )

      __SCREAMING_SNAKE_CASE      							=       {
          """in_channels""": 9,
          # Out channels is double in channels because predicts mean and variance
          """out_channels""": 8,
          """addition_embed_type""": """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""": """image_proj""",
          """cross_attention_dim""": self.cross_attention_dim,
          """attention_head_dim""": 4,
          """resnet_time_scale_shift""": """scale_shift""",
          """class_embed_type""": None,
      }

      __SCREAMING_SNAKE_CASE      							=       UNetaDConditionModel(**lowerCamelCase    )
      return model





   @property
   def      UpperCAmelCase__     (					self				:			int    ):

      '''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      UpperCAmelCase__     (					self				:			Dict    ):

      '''simple docstring'''

      torch.manual_seed(0    )
      __SCREAMING_SNAKE_CASE      							=       VQModel(**self.dummy_movq_kwargs    )
      return model





   def      UpperCAmelCase__     (					self				:			Union[str, Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.dummy_unet
      __SCREAMING_SNAKE_CASE      							=       self.dummy_movq

      __SCREAMING_SNAKE_CASE      							=       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				,)

      __SCREAMING_SNAKE_CASE      							=       {
          """unet""": unet,
          """scheduler""": scheduler,
          """movq""": movq,
      }

      return components





   def      UpperCAmelCase__     (					self				:			Optional[Any]				,lowerCamelCase				:			Union[str, Any]				,lowerCamelCase				:			Tuple=0    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       floats_tensor((1, self.text_embedder_hidden_size)				,rng=random.Random(lowerCamelCase    )    ).to(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       floats_tensor((1, self.text_embedder_hidden_size)				,rng=random.Random(seed + 1    )    ).to(
          lowerCamelCase    )
      # create init_image
      __SCREAMING_SNAKE_CASE      							=       floats_tensor((1, 3, 64, 64)				,rng=random.Random(lowerCamelCase    )    ).to(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       image.cpu().permute(0				,2				,3				,1    )[0]
      __SCREAMING_SNAKE_CASE      							=       Image.fromarray(np.uinta(lowerCamelCase    )    ).convert("""RGB"""    ).resize((256, 256)    )
      # create mask
      __SCREAMING_SNAKE_CASE      							=       np.ones((64, 64)				,dtype=np.floataa    )
      __SCREAMING_SNAKE_CASE      							=       0

      if str(lowerCamelCase    ).startswith("""mps"""    ):
         __SCREAMING_SNAKE_CASE      							=       torch.manual_seed(lowerCamelCase    )
      else:
         __SCREAMING_SNAKE_CASE      							=       torch.Generator(device=lowerCamelCase    ).manual_seed(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       {
          """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      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       """cpu"""

      __SCREAMING_SNAKE_CASE      							=       self.get_dummy_components()

      __SCREAMING_SNAKE_CASE      							=       self.pipeline_class(**lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       pipe.to(lowerCamelCase    )

      pipe.set_progress_bar_config(disable=lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       pipe(**self.get_dummy_inputs(lowerCamelCase    )    )
      __SCREAMING_SNAKE_CASE      							=       output.images

      __SCREAMING_SNAKE_CASE      							=       pipe(
          **self.get_dummy_inputs(lowerCamelCase    )				,return_dict=lowerCamelCase				,)[0]

      __SCREAMING_SNAKE_CASE      							=       image[0, -3:, -3:, -1]
      __SCREAMING_SNAKE_CASE      							=       image_from_tuple[0, -3:, -3:, -1]

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

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

      __SCREAMING_SNAKE_CASE      							=       np.array(
          [0.50_775_903, 0.49_527_195, 0.48_824_543, 0.50_192_237, 0.48_644_906, 0.49_373_814, 0.4_780_598, 0.47_234_827, 0.48_327_848]    )

      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      UpperCAmelCase__     (					self				:			Dict    ):

      '''simple docstring'''

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







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





   def      UpperCAmelCase__     (					self				:			Dict    ):

      '''simple docstring'''

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





   def      UpperCAmelCase__     (					self				:			Union[str, Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"""    )

      __SCREAMING_SNAKE_CASE      							=       load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""    )
      __SCREAMING_SNAKE_CASE      							=       np.ones((768, 768)				,dtype=np.floataa    )
      __SCREAMING_SNAKE_CASE      							=       0

      __SCREAMING_SNAKE_CASE      							=       """a hat"""

      __SCREAMING_SNAKE_CASE      							=       KandinskyVaaPriorPipeline.from_pretrained(
          """kandinsky-community/kandinsky-2-2-prior"""				,torch_dtype=torch.floataa    )
      pipe_prior.to(lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       KandinskyVaaInpaintPipeline.from_pretrained(
          """kandinsky-community/kandinsky-2-2-decoder-inpaint"""				,torch_dtype=torch.floataa    )
      __SCREAMING_SNAKE_CASE      							=       pipeline.to(lowerCamelCase    )
      pipeline.set_progress_bar_config(disable=lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       torch.Generator(device="""cpu"""    ).manual_seed(0    )
      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       pipe_prior(
          lowerCamelCase				,generator=lowerCamelCase				,num_inference_steps=5				,negative_prompt=""""""				,).to_tuple()

      __SCREAMING_SNAKE_CASE      							=       pipeline(
          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"""				,)

      __SCREAMING_SNAKE_CASE      							=       output.images[0]

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

      assert_mean_pixel_difference(lowerCamelCase				,lowerCamelCase    )












'''simple docstring'''

def 						__magic_name__       (							__UpperCAmelCase = 1							,	__UpperCAmelCase = 1000      )						->					int:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       1
   __SCREAMING_SNAKE_CASE      							=       0

   for divide_by_number in range(__UpperCAmelCase							,	digit + 1      ):
      __SCREAMING_SNAKE_CASE      							=       []
      __SCREAMING_SNAKE_CASE      							=       numerator
      for _ in range(1							,	digit + 1      ):
         if now_divide in has_been_divided:
            if longest_list_length < len(__UpperCAmelCase      ):
               __SCREAMING_SNAKE_CASE      							=       len(__UpperCAmelCase      )
               __SCREAMING_SNAKE_CASE      							=       divide_by_number
         else:
            has_been_divided.append(__UpperCAmelCase      )
            __SCREAMING_SNAKE_CASE      							=       now_divide * 10 % divide_by_number

   return the_digit


# Tests
if __name__ == "__main__":
    import doctest

    doctest.testmod()





