





'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
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, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class       a_   (				_lowerCAmelCase	,						_lowerCAmelCase	,						unittest.TestCase							):
     __A      =			CycleDiffusionPipeline
     __A      =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
         "negative_prompt",
         "height",
         "width",
         "negative_prompt_embeds",
     }
     __A      =			PipelineTesterMixin.required_optional_params - {"latents"}
     __A      =			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"}							)
     __A      =			IMAGE_TO_IMAGE_IMAGE_PARAMS
     __A      =			IMAGE_TO_IMAGE_IMAGE_PARAMS





     def 							lowercase__      ( self		:					Dict						):





        """simple docstring"""




        torch.manual_seed(0						)
        lowercase_			:str    			= UNetaDConditionModel(
            block_out_channels=(32, 64)	,      layers_per_block=2	,      sample_size=32	,      in_channels=4	,      out_channels=4	,      down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,      up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")	,      cross_attention_dim=32	,      )
        lowercase_			:Optional[int]    			= DDIMScheduler(
            beta_start=0.0_00_85	,      beta_end=0.0_12	,      beta_schedule="scaled_linear"	,      num_train_timesteps=1_000	,      clip_sample=lowercase	,      set_alpha_to_one=lowercase	,      )
        torch.manual_seed(0						)
        lowercase_			:Optional[int]    			= AutoencoderKL(
            block_out_channels=[32, 64]	,      in_channels=3	,      out_channels=3	,      down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]	,      up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]	,      latent_channels=4	,      )
        torch.manual_seed(0						)
        lowercase_			:Optional[int]    			= CLIPTextConfig(
            bos_token_id=0	,      eos_token_id=2	,      hidden_size=32	,      intermediate_size=37	,      layer_norm_eps=1e-0_5	,      num_attention_heads=4	,      num_hidden_layers=5	,      pad_token_id=1	,      vocab_size=1_000	,      )
        lowercase_			:Tuple    			= CLIPTextModel(lowercase						)
        lowercase_			:Optional[int]    			= CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

        lowercase_			:List[Any]    			= {
            "unet": unet,
            "scheduler": scheduler,
            "vae": vae,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "safety_checker": None,
            "feature_extractor": None,
        }
        return components





     def 							lowercase__      ( self		:					Optional[Any]	,      lowercase		:					Optional[int]	,      lowercase		:					List[str]=0						):





        """simple docstring"""




        lowercase_			:Optional[Any]    			= floats_tensor((1, 3, 32, 32)	,      rng=random.Random(lowercase						)						).to(lowercase						)
        lowercase_			:Any    			= image / 2 + 0.5
        if str(lowercase						).startswith("mps"						):
           lowercase_			:int    			= torch.manual_seed(lowercase						)
        else:
           lowercase_			:Tuple    			= torch.Generator(device=lowercase						).manual_seed(lowercase						)
        lowercase_			:Optional[Any]    			= {
            "prompt": "An astronaut riding an elephant",
            "source_prompt": "An astronaut riding a horse",
            "image": image,
            "generator": generator,
            "num_inference_steps": 2,
            "eta": 0.1,
            "strength": 0.8,
            "guidance_scale": 3,
            "source_guidance_scale": 1,
            "output_type": "numpy",
        }
        return inputs





     def 							lowercase__      ( self		:					Optional[Any]						):





        """simple docstring"""




        lowercase_			:Optional[int]    			= "cpu"  # ensure determinism for the device-dependent torch.Generator

        lowercase_			:Optional[int]    			= self.get_dummy_components()
        lowercase_			:Any    			= CycleDiffusionPipeline(**lowercase						)
        lowercase_			:int    			= pipe.to(lowercase						)
        pipe.set_progress_bar_config(disable=lowercase						)

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

        lowercase_			:Dict    			= images[0, -3:, -3:, -1]

        assert images.shape == (1, 32, 32, 3)
        lowercase_			:Union[str, Any]    			= np.array([0.44_59, 0.49_43, 0.45_44, 0.66_43, 0.54_74, 0.43_27, 0.57_01, 0.59_59, 0.51_79]						)

        assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2





     @unittest.skipIf(torch_device != "cuda"	,      "This test requires a GPU"						)
     def 							lowercase__      ( self		:					Optional[Any]						):





        """simple docstring"""




        lowercase_			:List[Any]    			= self.get_dummy_components()
        for name, module in components.items():
           if hasattr(lowercase	,      "half"						):
              lowercase_			:Dict    			= module.half()
        lowercase_			:Union[str, Any]    			= CycleDiffusionPipeline(**lowercase						)
        lowercase_			:Tuple    			= pipe.to(lowercase						)
        pipe.set_progress_bar_config(disable=lowercase						)

        lowercase_			:Any    			= self.get_dummy_inputs(lowercase						)
        lowercase_			:str    			= pipe(**lowercase						)
        lowercase_			:Any    			= output.images

        lowercase_			:Union[str, Any]    			= images[0, -3:, -3:, -1]

        assert images.shape == (1, 32, 32, 3)
        lowercase_			:Union[str, Any]    			= np.array([0.35_06, 0.45_43, 0.4_46, 0.45_75, 0.51_95, 0.41_55, 0.52_73, 0.5_18, 0.41_16]						)

        assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2





     @skip_mps
     def 							lowercase__      ( self		:					Dict						):





        """simple docstring"""




        return super().test_save_load_local()





     @unittest.skip("non-deterministic pipeline"						)
     def 							lowercase__      ( self		:					Optional[int]						):





        """simple docstring"""




        return super().test_inference_batch_single_identical()





     @skip_mps
     def 							lowercase__      ( self		:					int						):





        """simple docstring"""




        return super().test_dict_tuple_outputs_equivalent()





     @skip_mps
     def 							lowercase__      ( self		:					List[Any]						):





        """simple docstring"""




        return super().test_save_load_optional_components()







     @skip_mps
     def 							lowercase__      ( self		:					str						):





        """simple docstring"""




        return super().test_attention_slicing_forward_pass()







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





     def 							lowercase__      ( self		:					Dict						):





        """simple docstring"""




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





     def 							lowercase__      ( self		:					Dict						):





        """simple docstring"""




        lowercase_			:Dict    			= load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/cycle-diffusion/black_colored_car.png"						)
        lowercase_			:Dict    			= load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy"						)
        lowercase_			:List[Any]    			= init_image.resize((512, 512)						)

        lowercase_			:Tuple    			= "CompVis/stable-diffusion-v1-4"
        lowercase_			:Optional[int]    			= DDIMScheduler.from_pretrained(lowercase	,      subfolder="scheduler"						)
        lowercase_			:Any    			= CycleDiffusionPipeline.from_pretrained(
            lowercase	,      scheduler=lowercase	,      safety_checker=lowercase	,      torch_dtype=torch.floataa	,      revision="fp16"						)

        pipe.to(lowercase						)
        pipe.set_progress_bar_config(disable=lowercase						)
        pipe.enable_attention_slicing()

        lowercase_			:Optional[Any]    			= "A black colored car"
        lowercase_			:Optional[Any]    			= "A blue colored car"

        lowercase_			:Any    			= torch.manual_seed(0						)
        lowercase_			:str    			= pipe(
            prompt=lowercase	,      source_prompt=lowercase	,      image=lowercase	,      num_inference_steps=100	,      eta=0.1	,      strength=0.85	,      guidance_scale=3	,      source_guidance_scale=1	,      generator=lowercase	,      output_type="np"	,      )
        lowercase_			:str    			= output.images

        # the values aren't exactly equal, but the images look the same visually
        assert np.abs(image - expected_image						).max() < 5e-1







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





        """simple docstring"""




        lowercase_			:str    			= load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/cycle-diffusion/black_colored_car.png"						)
        lowercase_			:Tuple    			= load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"						)
        lowercase_			:Dict    			= init_image.resize((512, 512)						)

        lowercase_			:int    			= "CompVis/stable-diffusion-v1-4"
        lowercase_			:Dict    			= DDIMScheduler.from_pretrained(lowercase	,      subfolder="scheduler"						)
        lowercase_			:Dict    			= CycleDiffusionPipeline.from_pretrained(lowercase	,      scheduler=lowercase	,      safety_checker=lowercase						)

        pipe.to(lowercase						)
        pipe.set_progress_bar_config(disable=lowercase						)
        pipe.enable_attention_slicing()

        lowercase_			:Union[str, Any]    			= "A black colored car"
        lowercase_			:List[str]    			= "A blue colored car"

        lowercase_			:Union[str, Any]    			= torch.manual_seed(0						)
        lowercase_			:Any    			= pipe(
            prompt=lowercase	,      source_prompt=lowercase	,      image=lowercase	,      num_inference_steps=100	,      eta=0.1	,      strength=0.85	,      guidance_scale=3	,      source_guidance_scale=1	,      generator=lowercase	,      output_type="np"	,      )
        lowercase_			:Any    			= output.images

        assert np.abs(image - expected_image						).max() < 2e-2









'''simple docstring'''


def 							UpperCAmelCase_  (						__lowerCamelCase					:					int = 10_00 ):
   lowercase_			,							lowercase_			:str    			= 1, 1
   lowercase_			:Any    			= 2
   while True:
      lowercase_			:str    			= 0
      lowercase_			:Tuple    			= fa + fa
      lowercase_			,							lowercase_			:Tuple    			= fa, f
      index += 1
      for _ in str(__lowerCamelCase ):
         i += 1
      if i == n:
         break
   return index


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


