

'''simple docstring'''




def 		UpperCamelCase			(					a					) ->      str:






     '''simple docstring'''
     __magic_name__		  =							1
     for i in range(1						,		num + 1					):
          fact *= i
     return fact




def 		UpperCamelCase			(					a					) ->      Tuple:






     '''simple docstring'''
     __magic_name__		  =							0
     while number > 0:
          __magic_name__		  =							number % 10
          sum_of_digits += last_digit
          __magic_name__		  =							number // 10  # Removing the last_digit from the given number
     return sum_of_digits




def 		UpperCamelCase			(					a = 100					) ->      Dict:






     '''simple docstring'''
     __magic_name__		  =							factorial(__lowerCamelCase					)
     __magic_name__		  =							split_and_add(__lowerCamelCase					)
     return result


if __name__ == "__main__":
      print(solution(int(input("Enter the Number: ").strip())))










'''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, KandinskyImgaImgPipeline, 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    __magic_name__				(						lowerCAmelCase   ,unittest.TestCase							):
       UpperCAmelCase		 =KandinskyImgaImgPipeline
       UpperCAmelCase		 =["prompt", "image_embeds", "negative_image_embeds", "image"]
       UpperCAmelCase		 =[
           "prompt",
           "negative_prompt",
           "image_embeds",
           "negative_image_embeds",
           "image",
       ]
       UpperCAmelCase		 =[
           "generator",
           "height",
           "width",
           "strength",
           "guidance_scale",
           "negative_prompt",
           "num_inference_steps",
           "return_dict",
           "guidance_scale",
           "num_images_per_prompt",
           "output_type",
           "return_dict",
       ]
       UpperCAmelCase		 =False






       @property
       def      lowerCAmelCase						(     self)		->    str:




          '''simple docstring'''
          return 3_2






       @property
       def      lowerCAmelCase						(     self)		->    List[str]:




          '''simple docstring'''
          return 3_2






       @property
       def      lowerCAmelCase						(     self)		->    Union[str, Any]:




          '''simple docstring'''
          return self.time_input_dim






       @property
       def      lowerCAmelCase						(     self)		->    Optional[int]:




          '''simple docstring'''
          return self.time_input_dim * 4






       @property
       def      lowerCAmelCase						(     self)		->    Tuple:




          '''simple docstring'''
          return 1_0_0






       @property
       def      lowerCAmelCase						(     self)		->    Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  int				       =XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base')
          return tokenizer






       @property
       def      lowerCAmelCase						(     self)		->    Dict:




          '''simple docstring'''
          torch.manual_seed(0)
          _UpperCAmelCase  :  Optional[Any]				       =MCLIPConfig(
              numDims=self.cross_attention_dim    ,				transformerDimensions=self.text_embedder_hidden_size    ,				hidden_size=self.text_embedder_hidden_size    ,				intermediate_size=3_7    ,				num_attention_heads=4    ,				num_hidden_layers=5    ,				vocab_size=1_0_0_5    ,				)

          _UpperCAmelCase  :  List[Any]				       =MultilingualCLIP(snake_case)
          _UpperCAmelCase  :  List[Any]				       =text_encoder.eval()

          return text_encoder






       @property
       def      lowerCAmelCase						(     self)		->    Optional[Any]:




          '''simple docstring'''
          torch.manual_seed(0)

          _UpperCAmelCase  :  Union[str, Any]				       ={
              'in_channels': 4,
              # 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  :  Tuple				       =UNetaDConditionModel(**snake_case)
          return model






       @property
       def      lowerCAmelCase						(     self)		->    List[Any]:




          '''simple docstring'''
          return {
              "block_out_channels": [3_2, 6_4],
              "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": 1_2,
              "out_channels": 3,
              "up_block_types": [
                  "AttnUpDecoderBlock2D",
                  "UpDecoderBlock2D",
              ],
              "vq_embed_dim": 4,
          }






       @property
       def      lowerCAmelCase						(     self)		->    int:




          '''simple docstring'''
          torch.manual_seed(0)
          _UpperCAmelCase  :  List[Any]				       =VQModel(**self.dummy_movq_kwargs)
          return model






       def      lowerCAmelCase						(     self)		->    List[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  Tuple				       =self.dummy_text_encoder
          _UpperCAmelCase  :  Any				       =self.dummy_tokenizer
          _UpperCAmelCase  :  List[str]				       =self.dummy_unet
          _UpperCAmelCase  :  Union[str, Any]				       =self.dummy_movq

          _UpperCAmelCase  :  List[str]				       ={
              'num_train_timesteps': 1_0_0_0,
              'beta_schedule': 'linear',
              'beta_start': 0.0_00_85,
              'beta_end': 0.0_12,
              'clip_sample': False,
              'set_alpha_to_one': False,
              'steps_offset': 0,
              'prediction_type': 'epsilon',
              'thresholding': False,
          }

          _UpperCAmelCase  :  Dict				       =DDIMScheduler(**snake_case)

          _UpperCAmelCase  :  Optional[int]				       ={
              'text_encoder': text_encoder,
              'tokenizer': tokenizer,
              'unet': unet,
              'scheduler': scheduler,
              'movq': movq,
          }

          return components






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case=0)		->    int:




          '''simple docstring'''
          _UpperCAmelCase  :  List[Any]				       =floats_tensor((1, self.cross_attention_dim)    ,				rng=random.Random(snake_case)).to(snake_case)
          _UpperCAmelCase  :  Tuple				       =floats_tensor((1, self.cross_attention_dim)    ,				rng=random.Random(seed + 1)).to(snake_case)
          # create init_image
          _UpperCAmelCase  :  Dict				       =floats_tensor((1, 3, 6_4, 6_4)    ,				rng=random.Random(snake_case)).to(snake_case)
          _UpperCAmelCase  :  Optional[int]				       =image.cpu().permute(0    ,				2    ,				3    ,				1)[0]
          _UpperCAmelCase  :  List[Any]				       =Image.fromarray(np.uinta(snake_case)).convert('RGB').resize((2_5_6, 2_5_6))

          if str(snake_case).startswith('mps'):
             _UpperCAmelCase  :  str				       =torch.manual_seed(snake_case)
          else:
             _UpperCAmelCase  :  Optional[Any]				       =torch.Generator(device=snake_case).manual_seed(snake_case)
          _UpperCAmelCase  :  str				       ={
              'prompt': 'horse',
              'image': init_image,
              'image_embeds': image_embeds,
              'negative_image_embeds': negative_image_embeds,
              'generator': generator,
              'height': 6_4,
              'width': 6_4,
              'num_inference_steps': 1_0,
              'guidance_scale': 7.0,
              'strength': 0.2,
              'output_type': 'np',
          }
          return inputs






       def      lowerCAmelCase						(     self)		->    str:




          '''simple docstring'''
          _UpperCAmelCase  :  List[Any]				       ='cpu'

          _UpperCAmelCase  :  Dict				       =self.get_dummy_components()

          _UpperCAmelCase  :  Optional[Any]				       =self.pipeline_class(**snake_case)
          _UpperCAmelCase  :  List[str]				       =pipe.to(snake_case)

          pipe.set_progress_bar_config(disable=snake_case)

          _UpperCAmelCase  :  List[Any]				       =pipe(**self.get_dummy_inputs(snake_case))
          _UpperCAmelCase  :  List[str]				       =output.images

          _UpperCAmelCase  :  Any				       =pipe(
              **self.get_dummy_inputs(snake_case)    ,				return_dict=snake_case    ,				)[0]

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

          assert image.shape == (1, 6_4, 6_4, 3)

          _UpperCAmelCase  :  List[Any]				       =np.array(
              [0.61_47_49_43, 0.6_07_35_39, 0.43_30_85_44, 0.5_92_82_69, 0.47_49_35_95, 0.46_75_59_73, 0.4_61_38_38, 0.45_36_87_97, 0.50_11_92_33])
          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    __magic_name__				(						unittest.TestCase							):






       def      lowerCAmelCase						(     self)		->    Optional[int]:




          '''simple docstring'''
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






       def      lowerCAmelCase						(     self)		->    Optional[int]:




          '''simple docstring'''
          _UpperCAmelCase  :  Tuple				       =load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/kandinsky/kandinsky_img2img_frog.npy')

          _UpperCAmelCase  :  List[Any]				       =load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png')
          _UpperCAmelCase  :  Union[str, Any]				       ='A red cartoon frog, 4k'

          _UpperCAmelCase  :  Union[str, Any]				       =KandinskyPriorPipeline.from_pretrained(
              'kandinsky-community/kandinsky-2-1-prior'    ,				torch_dtype=torch.floataa)
          pipe_prior.to(snake_case)

          _UpperCAmelCase  :  Optional[Any]				       =KandinskyImgaImgPipeline.from_pretrained(
              'kandinsky-community/kandinsky-2-1'    ,				torch_dtype=torch.floataa)
          _UpperCAmelCase  :  Tuple				       =pipeline.to(snake_case)

          pipeline.set_progress_bar_config(disable=snake_case)

          _UpperCAmelCase  :  List[str]				       =torch.Generator(device='cpu').manual_seed(0)
          _UpperCAmelCase       ,			_UpperCAmelCase  :  List[Any]				       =pipe_prior(
              snake_case    ,				generator=snake_case    ,				num_inference_steps=5    ,				negative_prompt=''    ,				).to_tuple()

          _UpperCAmelCase  :  str				       =pipeline(
              snake_case    ,				image=snake_case    ,				image_embeds=snake_case    ,				negative_image_embeds=snake_case    ,				generator=snake_case    ,				num_inference_steps=1_0_0    ,				height=7_6_8    ,				width=7_6_8    ,				strength=0.2    ,				output_type='np'    ,				)

          _UpperCAmelCase  :  Any				       =output.images[0]

          assert image.shape == (7_6_8, 7_6_8, 3)

          assert_mean_pixel_difference(snake_case    ,				snake_case)

