





'''simple docstring'''





import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    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       (      _a ,unittest.TestCase  ):




      """simple docstring"""







      UpperCAmelCase__						=      KandinskyVaaControlnetPipeline
      UpperCAmelCase__						=      ["""image_embeds""", """negative_image_embeds""", """hint"""]
      UpperCAmelCase__						=      ["""image_embeds""", """negative_image_embeds""", """hint"""]
      UpperCAmelCase__						=      [
          """generator""",
          """height""",
          """width""",
          """latents""",
          """guidance_scale""",
          """num_inference_steps""",
          """return_dict""",
          """guidance_scale""",
          """num_images_per_prompt""",
          """output_type""",
          """return_dict""",
      ]
      UpperCAmelCase__						=      False


      @property
      def 				__snake_case			(		self):



             """simple docstring"""





             return 32


      @property
      def 				__snake_case			(		self):



             """simple docstring"""





             return 32


      @property
      def 				__snake_case			(		self):



             """simple docstring"""





             return self.time_input_dim


      @property
      def 				__snake_case			(		self):



             """simple docstring"""





             return self.time_input_dim * 4


      @property
      def 				__snake_case			(		self):



             """simple docstring"""





             return 100


      @property
      def 				__snake_case			(		self):



             """simple docstring"""





             torch.manual_seed(0)

             _lowerCamelCase							:     int	=		{
                 '''in_channels''': 8,
                 # Out channels is double in channels because predicts mean and variance
                 '''out_channels''': 8,
                 '''addition_embed_type''': '''image_hint''',
                 '''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,
             }

             _lowerCamelCase							:     Dict	=		UNetaDConditionModel(**snake_case_)
             return model


      @property
      def 				__snake_case			(		self):



             """simple docstring"""





             return {
                 "block_out_channels": [32, 32, 64, 64],
                 "down_block_types": [
                     "DownEncoderBlock2D",
                     "DownEncoderBlock2D",
                     "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
                 "vq_embed_dim": 4,
             }


      @property
      def 				__snake_case			(		self):



             """simple docstring"""





             torch.manual_seed(0)
             _lowerCamelCase							:     Union[str, Any]	=		VQModel(**self.dummy_movq_kwargs)
             return model


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[Any]	=		self.dummy_unet
             _lowerCamelCase							:     Union[str, Any]	=		self.dummy_movq

             _lowerCamelCase							:     Dict	=		DDIMScheduler(
                 num_train_timesteps=1000      ,							beta_schedule='''linear'''      ,							beta_start=0.0_0085      ,							beta_end=0.012      ,							clip_sample=snake_case_      ,							set_alpha_to_one=snake_case_      ,							steps_offset=1      ,							prediction_type='''epsilon'''      ,							thresholding=snake_case_      ,							)

             _lowerCamelCase							:     Union[str, Any]	=		{
                 '''unet''': unet,
                 '''scheduler''': scheduler,
                 '''movq''': movq,
             }
             return components


      def 				__snake_case			(		self      ,							a__      ,							a__=0):



             """simple docstring"""





             _lowerCamelCase							:     Optional[Any]	=		floats_tensor((1, self.text_embedder_hidden_size)      ,							rng=random.Random(snake_case_)).to(snake_case_)
             _lowerCamelCase							:     Optional[int]	=		floats_tensor((1, self.text_embedder_hidden_size)      ,							rng=random.Random(seed + 1)).to(
                 snake_case_)

             # create hint
             _lowerCamelCase							:     str	=		floats_tensor((1, 3, 64, 64)      ,							rng=random.Random(snake_case_)).to(snake_case_)

             if str(snake_case_).startswith('''mps'''):
                    _lowerCamelCase							:     str	=		torch.manual_seed(snake_case_)
             else:
                    _lowerCamelCase							:     int	=		torch.Generator(device=snake_case_).manual_seed(snake_case_)
             _lowerCamelCase							:     Optional[Any]	=		{
                 '''image_embeds''': image_embeds,
                 '''negative_image_embeds''': negative_image_embeds,
                 '''hint''': hint,
                 '''generator''': generator,
                 '''height''': 64,
                 '''width''': 64,
                 '''guidance_scale''': 4.0,
                 '''num_inference_steps''': 2,
                 '''output_type''': '''np''',
             }
             return inputs



      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Dict	=		'''cpu'''

             _lowerCamelCase							:     str	=		self.get_dummy_components()

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

             pipe.set_progress_bar_config(disable=snake_case_)

             _lowerCamelCase							:     Tuple	=		pipe(**self.get_dummy_inputs(snake_case_))
             _lowerCamelCase							:     Optional[int]	=		output.images

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

             _lowerCamelCase							:     Tuple	=		image[0, -3:, -3:, -1]
             _lowerCamelCase							:     Optional[Any]	=		image_from_tuple[0, -3:, -3:, -1]

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

             _lowerCamelCase							:     int	=		np.array(
                 [0.695_9826, 0.86_8279, 0.755_8092, 0.6876_9467, 0.8580_5804, 0.6597_7496, 0.4488_5302, 0.595_9111, 0.425_1595])

             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()}"""







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




      """simple docstring"""







      def 				__snake_case			(		self):



             """simple docstring"""





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



      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[Any]	=		load_numpy(
                 '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                 '''/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy''')

             _lowerCamelCase							:     Union[str, Any]	=		load_image(
                 '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                 '''/kandinskyv22/hint_image_cat.png''')
             _lowerCamelCase							:     Tuple	=		torch.from_numpy(np.array(snake_case_)).float() / 255.0
             _lowerCamelCase							:     List[Any]	=		hint.permute(2      ,							0      ,							1).unsqueeze(0)

             _lowerCamelCase							:     Any	=		KandinskyVaaPriorPipeline.from_pretrained(
                 '''kandinsky-community/kandinsky-2-2-prior'''      ,							torch_dtype=torch.floataa)
             pipe_prior.to(snake_case_)

             _lowerCamelCase							:     str	=		KandinskyVaaControlnetPipeline.from_pretrained(
                 '''kandinsky-community/kandinsky-2-2-controlnet-depth'''      ,							torch_dtype=torch.floataa)
             _lowerCamelCase							:     str	=		pipeline.to(snake_case_)
             pipeline.set_progress_bar_config(disable=snake_case_)

             _lowerCamelCase							:     int	=		'''A robot, 4k photo'''

             _lowerCamelCase							:     Dict	=		torch.Generator(device='''cuda''').manual_seed(0)
             _lowerCamelCase,  _lowerCamelCase							:     int	=		pipe_prior(
                 snake_case_      ,							generator=snake_case_      ,							num_inference_steps=5      ,							negative_prompt=''''''      ,							).to_tuple()

             _lowerCamelCase							:     Any	=		torch.Generator(device='''cuda''').manual_seed(0)
             _lowerCamelCase							:     int	=		pipeline(
                 image_embeds=snake_case_      ,							negative_image_embeds=snake_case_      ,							hint=snake_case_      ,							generator=snake_case_      ,							num_inference_steps=100      ,							output_type='''np'''      ,							)

             _lowerCamelCase							:     str	=		output.images[0]

             assert image.shape == (512, 512, 3)

             assert_mean_pixel_difference(snake_case_      ,							snake_case_)








def    __UpperCAmelCase(      lowercase_ = 10_00      ):
       _lowerCamelCase							:     Optional[int]	=		2**power
       _lowerCamelCase							:     Optional[int]	=		0
       while n:
              _lowerCamelCase,  _lowerCamelCase							:     Optional[Any]	=		r + n % 10, n // 10
       return r


if __name__ == "__main__":
       print(solution(int(str(input()).strip())))

