






"""simple docstring"""



from typing import List, Optional, Union

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

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


A_    =							logging.get_logger(__name__)  # pylint: disable=invalid-name

A_    =							'''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to("cuda")

        >>> prompt = "A red cartoon frog, 4k"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16
        ... )
        >>> pipe.to("cuda")

        >>> init_image = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/frog.png"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save("red_frog.png")
        ```
'''




def      _lowerCAmelCase		(							UpperCAmelCase__       :					List[str],						UpperCAmelCase__       :					List[str],						UpperCAmelCase__       :					Dict=8      )		->Any:
						A__						:  int		  =  height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						A__						:  Optional[Any]		  =  width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor




def      _lowerCAmelCase		(							UpperCAmelCase__       :					List[str],						UpperCAmelCase__       :					Union[str, Any]=5_1_2,						UpperCAmelCase__       :					List[str]=5_1_2      )		->List[Any]:
						A__						:  Optional[Any]		  =  pil_image.resize((w, h),						resample=Image.BICUBIC,						reducing_gap=1      )
						A__						:  Optional[int]		  =  np.array(pil_image.convert("""RGB"""      )      )
						A__						:  List[Any]		  =  arr.astype(np.floataa      ) / 127.5 - 1
						A__						:  Union[str, Any]		  =  np.transpose(UpperCAmelCase__,						[2, 0, 1]      )
						A__						:  List[Any]		  =  torch.from_numpy(UpperCAmelCase__      ).unsqueeze(0      )
						return image






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):


							def __init__(			self      :						Any			,    snake_case      :						UNetaDConditionModel			,    snake_case      :						DDPMScheduler			,    snake_case      :						VQModel			,    ):
													'''simple docstring'''





													super().__init__()

													self.register_modules(
													    unet=snake_case			,    scheduler=snake_case			,    movq=snake_case			,    )
													A__						:  str		  =  2 ** (len(self.movq.config.block_out_channels							) - 1)


							def 						_UpperCamelCase       (			self      :						Tuple			,    snake_case      :						Optional[int]			,    snake_case      :						List[Any]			,    snake_case      :						Optional[Any]							):
													'''simple docstring'''





													A__						:  Union[str, Any]		  =  min(int(num_inference_steps * strength							)			,    snake_case							)

													A__						:  Any		  =  max(num_inference_steps - init_timestep			,    0							)
													A__						:  Union[str, Any]		  =  self.scheduler.timesteps[t_start:]

													return timesteps, num_inference_steps - t_start


							def 						_UpperCamelCase       (			self      :						Dict			,    snake_case      :						str			,    snake_case      :						List[str]			,    snake_case      :						Any			,    snake_case      :						List[Any]			,    snake_case      :						Union[str, Any]			,    snake_case      :						List[str]			,    snake_case      :						Dict=None							):
													'''simple docstring'''





													if not isinstance(snake_case			,    (torch.Tensor, PIL.Image.Image, list)							):
																			raise ValueError(
																			    F'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(snake_case							)}'							)

													A__						:  str		  =  image.to(device=snake_case			,    dtype=snake_case							)

													A__						:  str		  =  batch_size * num_images_per_prompt

													if image.shape[1] == 4:
																			A__						:  Tuple		  =  image

													else:
																			if isinstance(snake_case			,    snake_case							) and len(snake_case							) != batch_size:
																									raise ValueError(
																									    F'You have passed a list of generators of length {len(snake_case							)}, but requested an effective batch'
																									    F' size of {batch_size}. Make sure the batch size matches the length of the generators.'							)

																			elif isinstance(snake_case			,    snake_case							):
																									A__						:  Tuple		  =  [
																									    self.movq.encode(image[i : i + 1]							).latent_dist.sample(generator[i]							) for i in range(snake_case							)
																									]
																									A__						:  Any		  =  torch.cat(snake_case			,    dim=0							)
																			else:
																									A__						:  Optional[Any]		  =  self.movq.encode(snake_case							).latent_dist.sample(snake_case							)

																			A__						:  Optional[Any]		  =  self.movq.config.scaling_factor * init_latents

													A__						:  Dict		  =  torch.cat([init_latents]			,    dim=0							)

													A__						:  Dict		  =  init_latents.shape
													A__						:  List[str]		  =  randn_tensor(snake_case			,    generator=snake_case			,    device=snake_case			,    dtype=snake_case							)

													# get latents
													A__						:  Optional[Any]		  =  self.scheduler.add_noise(snake_case			,    snake_case			,    snake_case							)

													A__						:  Dict		  =  init_latents

													return latents


							def 						_UpperCamelCase       (			self      :						Union[str, Any]			,    snake_case      :						Union[str, Any]=0							):
													'''simple docstring'''





													if is_accelerate_available():
																			from accelerate import cpu_offload
													else:
																			raise ImportError("""Please install accelerate via `pip install accelerate`"""							)

													A__						:  Dict		  =  torch.device(F'cuda:{gpu_id}'							)

													A__						:  List[str]		  =  [
													    self.unet,
													    self.movq,
													]
													for cpu_offloaded_model in models:
																			if cpu_offloaded_model is not None:
																									cpu_offload(snake_case			,    snake_case							)


							def 						_UpperCamelCase       (			self      :						Optional[int]			,    snake_case      :						Union[str, Any]=0							):
													'''simple docstring'''





													if is_accelerate_available() and is_accelerate_version(""">="""			,    """0.17.0.dev0"""							):
																			from accelerate import cpu_offload_with_hook
													else:
																			raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""							)

													A__						:  List[Any]		  =  torch.device(F'cuda:{gpu_id}'							)

													if self.device.type != "cpu":
																			self.to("""cpu"""			,    silence_dtype_warnings=snake_case							)
																			torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

													A__						:  List[Any]		  =  None
													for cpu_offloaded_model in [self.unet, self.movq]:
																			A__  ,  A__						:  Optional[Any]		  =  cpu_offload_with_hook(snake_case			,    snake_case			,    prev_module_hook=snake_case							)

													# We'll offload the last model manually.
													A__						:  Dict		  =  hook


							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def 						_UpperCamelCase       (			self      :						str							):
													'''simple docstring'''





													if not hasattr(self.unet			,    """_hf_hook"""							):
																			return self.device
													for module in self.unet.modules():
																			if (
																			    hasattr(snake_case			,    """_hf_hook"""							)
																			    and hasattr(module._hf_hook			,    """execution_device"""							)
																			    and module._hf_hook.execution_device is not None
																			):
																									return torch.device(module._hf_hook.execution_device							)
													return self.device


							@torch.no_grad()
							@replace_example_docstring(snake_case							)
							def __call__(			self      :						Optional[int]			,    snake_case      :						Union[torch.FloatTensor, List[torch.FloatTensor]]			,    snake_case      :						Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]			,    snake_case      :						Union[torch.FloatTensor, List[torch.FloatTensor]]			,    snake_case      :						int = 512			,    snake_case      :						int = 512			,    snake_case      :						int = 100			,    snake_case      :						float = 4.0			,    snake_case      :						float = 0.3			,    snake_case      :						int = 1			,    snake_case      :						Optional[Union[torch.Generator, List[torch.Generator]]] = None			,    snake_case      :						Optional[str] = "pil"			,    snake_case      :						bool = True			,    ):
													'''simple docstring'''





													A__						:  List[str]		  =  self._execution_device

													A__						:  Optional[Any]		  =  guidance_scale > 1.0

													if isinstance(snake_case			,    snake_case							):
																			A__						:  int		  =  torch.cat(snake_case			,    dim=0							)
													A__						:  List[str]		  =  image_embeds.shape[0]
													if isinstance(snake_case			,    snake_case							):
																			A__						:  List[str]		  =  torch.cat(snake_case			,    dim=0							)

													if do_classifier_free_guidance:
																			A__						:  List[Any]		  =  image_embeds.repeat_interleave(snake_case			,    dim=0							)
																			A__						:  Optional[Any]		  =  negative_image_embeds.repeat_interleave(snake_case			,    dim=0							)

													A__						:  Union[str, Any]		  =  torch.cat([negative_image_embeds, image_embeds]			,    dim=0							).to(dtype=self.unet.dtype			,    device=snake_case							)

													if not isinstance(snake_case			,    snake_case							):
																			A__						:  str		  =  [image]
													if not all(isinstance(snake_case			,    (PIL.Image.Image, torch.Tensor)							) for i in image							):
																			raise ValueError(
																			    F'Input is in incorrect format: {[type(snake_case							) for i in image]}. Currently, we only support  PIL image and pytorch tensor'							)

													A__						:  Any		  =  torch.cat([prepare_image(snake_case			,    snake_case			,    snake_case							) for i in image]			,    dim=0							)
													A__						:  str		  =  image.to(dtype=image_embeds.dtype			,    device=snake_case							)

													A__						:  List[str]		  =  self.movq.encode(snake_case							)["""latents"""]
													A__						:  Optional[Any]		  =  latents.repeat_interleave(snake_case			,    dim=0							)
													self.scheduler.set_timesteps(snake_case			,    device=snake_case							)
													A__  ,  A__						:  Optional[int]		  =  self.get_timesteps(snake_case			,    snake_case			,    snake_case							)
													A__						:  str		  =  timesteps[:1].repeat(batch_size * num_images_per_prompt							)
													A__  ,  A__						:  int		  =  downscale_height_and_width(snake_case			,    snake_case			,    self.movq_scale_factor							)
													A__						:  Tuple		  =  self.prepare_latents(
													    snake_case			,    snake_case			,    snake_case			,    snake_case			,    image_embeds.dtype			,    snake_case			,    snake_case							)
													for i, t in enumerate(self.progress_bar(snake_case							)							):
																			# expand the latents if we are doing classifier free guidance
																			A__						:  Optional[int]		  =  torch.cat([latents] * 2							) if do_classifier_free_guidance else latents

																			A__						:  Dict		  =  {"""image_embeds""": image_embeds}
																			A__						:  Dict		  =  self.unet(
																			    sample=snake_case			,    timestep=snake_case			,    encoder_hidden_states=snake_case			,    added_cond_kwargs=snake_case			,    return_dict=snake_case			,    )[0]

																			if do_classifier_free_guidance:
																									A__  ,  A__						:  int		  =  noise_pred.split(latents.shape[1]			,    dim=1							)
																									A__  ,  A__						:  Optional[int]		  =  noise_pred.chunk(2							)
																									A__  ,  A__						:  Dict		  =  variance_pred.chunk(2							)
																									A__						:  Any		  =  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																									A__						:  Optional[Any]		  =  torch.cat([noise_pred, variance_pred_text]			,    dim=1							)

																			if not (
																			    hasattr(self.scheduler.config			,    """variance_type"""							)
																			    and self.scheduler.config.variance_type in ["learned", "learned_range"]
																			):
																									A__  ,  A__						:  List[str]		  =  noise_pred.split(latents.shape[1]			,    dim=1							)

																			# compute the previous noisy sample x_t -> x_t-1
																			A__						:  Any		  =  self.scheduler.step(
																			    snake_case			,    snake_case			,    snake_case			,    generator=snake_case			,    )[0]

													# post-processing
													A__						:  Any		  =  self.movq.decode(snake_case			,    force_not_quantize=snake_case							)["""sample"""]

													if output_type not in ["pt", "np", "pil"]:
																			raise ValueError(F'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'							)

													if output_type in ["np", "pil"]:
																			A__						:  str		  =  image * 0.5 + 0.5
																			A__						:  Dict		  =  image.clamp(0			,    1							)
																			A__						:  Tuple		  =  image.cpu().permute(0			,    2			,    3			,    1							).float().numpy()

													if output_type == "pil":
																			A__						:  str		  =  self.numpy_to_pil(snake_case							)

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=snake_case							)














"""simple docstring"""



def      _lowerCAmelCase		(							UpperCAmelCase__       :					int = 1_0_0_0      )		->int:
						A__						:  Optional[int]		  =  2**power
						A__						:  Dict		  =  0
						while n:
												A__  ,  A__						:  Tuple		  =  r + n % 1_0, n // 1_0
						return r


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






