




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,
)


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

UpperCamelCase								=     """
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")

        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""



def 							_A   (						lowerCAmelCase_     :					List[str]   ,				lowerCAmelCase_     :					Any   ,				lowerCAmelCase_     :					List[str]=8			):
     """simple docstring"""

     lowerCAmelCase__		= height // scale_factor**2
     if height % scale_factor**2 != 0:
          new_height += 1
     lowerCAmelCase__		= width // scale_factor**2
     if width % scale_factor**2 != 0:
          new_width += 1
     return new_height * scale_factor, new_width * scale_factor



def 							_A   (						lowerCAmelCase_     :					List[Any]   ,				lowerCAmelCase_     :					Union[str, Any]=512   ,				lowerCAmelCase_     :					Tuple=512			):
     """simple docstring"""

     lowerCAmelCase__		= pil_image.resize((w, h)   ,				resample=Image.BICUBIC   ,				reducing_gap=1			)
     lowerCAmelCase__		= np.array(pil_image.convert("RGB"			)			)
     lowerCAmelCase__		= arr.astype(np.floataa			) / 127.5 - 1
     lowerCAmelCase__		= np.transpose(lowerCAmelCase_   ,				[2, 0, 1]			)
     lowerCAmelCase__		= torch.from_numpy(lowerCAmelCase_			).unsqueeze(0			)
     return image






class  __lowerCamelCase       (    SCREAMING_SNAKE_CASE__    ):




    """simple docstring"""

    def __init__(       self     :     List[Any]   ,	SCREAMING_SNAKE_CASE__     :     str   ,	SCREAMING_SNAKE_CASE__     :     List[Any]   ,	SCREAMING_SNAKE_CASE__     :     Optional[Any]   ,	)      ->       int:
         super().__init__()

         self.register_modules(
             unet=snake_case__   ,	scheduler=snake_case__   ,	movq=snake_case__   ,	)
         lowerCAmelCase__		= 2 ** (len(self.movq.config.block_out_channels   ) - 1)


    def    a      (       self     :     int   ,	SCREAMING_SNAKE_CASE__     :     int   ,	SCREAMING_SNAKE_CASE__     :     Union[str, Any]   ,	SCREAMING_SNAKE_CASE__     :     Tuple   )      ->       List[Any]:
         lowerCAmelCase__		= min(int(num_inference_steps * strength   )   ,	snake_case__   )

         lowerCAmelCase__		= max(num_inference_steps - init_timestep   ,	0   )
         lowerCAmelCase__		= self.scheduler.timesteps[t_start:]

         return timesteps, num_inference_steps - t_start


    def    a      (       self     :     Dict   ,	SCREAMING_SNAKE_CASE__     :     int   ,	SCREAMING_SNAKE_CASE__     :     int   ,	SCREAMING_SNAKE_CASE__     :     str   ,	SCREAMING_SNAKE_CASE__     :     List[str]   ,	SCREAMING_SNAKE_CASE__     :     int   ,	SCREAMING_SNAKE_CASE__     :     Dict   ,	SCREAMING_SNAKE_CASE__     :     Dict=None   )      ->       Tuple:
         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__   )}'   )

         lowerCAmelCase__		= image.to(device=snake_case__   ,	dtype=snake_case__   )

         lowerCAmelCase__		= batch_size * num_images_per_prompt

         if image.shape[1] == 4:
              lowerCAmelCase__		= 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__   ):
                   lowerCAmelCase__		= [
                       self.movq.encode(image[i : i + 1]   ).latent_dist.sample(generator[i]   ) for i in range(snake_case__   )
                   ]
                   lowerCAmelCase__		= torch.cat(snake_case__   ,	dim=0   )
              else:
                   lowerCAmelCase__		= self.movq.encode(snake_case__   ).latent_dist.sample(snake_case__   )

              lowerCAmelCase__		= self.movq.config.scaling_factor * init_latents

         lowerCAmelCase__		= torch.cat([init_latents]   ,	dim=0   )

         lowerCAmelCase__		= init_latents.shape
         lowerCAmelCase__		= randn_tensor(snake_case__   ,	generator=snake_case__   ,	device=snake_case__   ,	dtype=snake_case__   )

         # get latents
         lowerCAmelCase__		= self.scheduler.add_noise(snake_case__   ,	snake_case__   ,	snake_case__   )

         lowerCAmelCase__		= init_latents

         return latents


    def    a      (       self     :     List[Any]   ,	SCREAMING_SNAKE_CASE__     :     Union[str, Any]=0   )      ->       Union[str, Any]:
         if is_accelerate_available():
              from accelerate import cpu_offload
         else:
              raise ImportError("Please install accelerate via `pip install accelerate`"   )

         lowerCAmelCase__		= torch.device(f'cuda:{gpu_id}'   )

         lowerCAmelCase__		= [
             self.unet,
             self.movq,
         ]
         for cpu_offloaded_model in models:
              if cpu_offloaded_model is not None:
                   cpu_offload(snake_case__   ,	snake_case__   )


    def    a      (       self     :     List[Any]   ,	SCREAMING_SNAKE_CASE__     :     int=0   )      ->       Any:
         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."   )

         lowerCAmelCase__		= 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)

         lowerCAmelCase__		= None
         for cpu_offloaded_model in [self.unet, self.movq]:
              lowerCAmelCase__		= cpu_offload_with_hook(snake_case__   ,	snake_case__   ,	prev_module_hook=snake_case__   )

         # We'll offload the last model manually.
         lowerCAmelCase__		= hook


    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def    a      (       self     :     str   )      ->       Any:
         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[Any]   ,	SCREAMING_SNAKE_CASE__     :     Optional[int]   ,	SCREAMING_SNAKE_CASE__     :     Union[str, Any]   ,	SCREAMING_SNAKE_CASE__     :     Any   ,	SCREAMING_SNAKE_CASE__     :     List[Any]   ,	SCREAMING_SNAKE_CASE__     :     int = 512   ,	SCREAMING_SNAKE_CASE__     :     List[str] = 512   ,	SCREAMING_SNAKE_CASE__     :     List[Any] = 100   ,	SCREAMING_SNAKE_CASE__     :     Any = 4.0   ,	SCREAMING_SNAKE_CASE__     :     List[Any] = 0.3   ,	SCREAMING_SNAKE_CASE__     :     Tuple = 1   ,	SCREAMING_SNAKE_CASE__     :     Optional[int] = None   ,	SCREAMING_SNAKE_CASE__     :     int = "pil"   ,	SCREAMING_SNAKE_CASE__     :     Any = True   ,	)      ->       Dict:
         lowerCAmelCase__		= self._execution_device

         lowerCAmelCase__		= guidance_scale > 1.0

         if isinstance(snake_case__   ,	snake_case__   ):
              lowerCAmelCase__		= torch.cat(snake_case__   ,	dim=0   )
         if isinstance(snake_case__   ,	snake_case__   ):
              lowerCAmelCase__		= torch.cat(snake_case__   ,	dim=0   )
         if isinstance(snake_case__   ,	snake_case__   ):
              lowerCAmelCase__		= torch.cat(snake_case__   ,	dim=0   )

         lowerCAmelCase__		= image_embeds.shape[0]

         if do_classifier_free_guidance:
              lowerCAmelCase__		= image_embeds.repeat_interleave(snake_case__   ,	dim=0   )
              lowerCAmelCase__		= negative_image_embeds.repeat_interleave(snake_case__   ,	dim=0   )
              lowerCAmelCase__		= hint.repeat_interleave(snake_case__   ,	dim=0   )

         lowerCAmelCase__		= torch.cat([negative_image_embeds, image_embeds]   ,	dim=0   ).to(dtype=self.unet.dtype   ,	device=snake_case__   )
         lowerCAmelCase__		= torch.cat([hint, hint]   ,	dim=0   ).to(dtype=self.unet.dtype   ,	device=snake_case__   )

         if not isinstance(snake_case__   ,	snake_case__   ):
              lowerCAmelCase__		= [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'   )

         lowerCAmelCase__		= torch.cat([prepare_image(snake_case__   ,	snake_case__   ,	snake_case__   ) for i in image]   ,	dim=0   )
         lowerCAmelCase__		= image.to(dtype=image_embeds.dtype   ,	device=snake_case__   )

         lowerCAmelCase__		= self.movq.encode(snake_case__   )["latents"]
         lowerCAmelCase__		= latents.repeat_interleave(snake_case__   ,	dim=0   )
         self.scheduler.set_timesteps(snake_case__   ,	device=snake_case__   )
         lowerCAmelCase__		= self.get_timesteps(snake_case__   ,	snake_case__   ,	snake_case__   )
         lowerCAmelCase__		= timesteps[:1].repeat(batch_size * num_images_per_prompt   )
         lowerCAmelCase__		= downscale_height_and_width(snake_case__   ,	snake_case__   ,	self.movq_scale_factor   )
         lowerCAmelCase__		= 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
              lowerCAmelCase__		= torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents

              lowerCAmelCase__		= {"image_embeds": image_embeds, "hint": hint}
              lowerCAmelCase__		= 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:
                   lowerCAmelCase__		= noise_pred.split(latents.shape[1]   ,	dim=1   )
                   lowerCAmelCase__		= noise_pred.chunk(2   )
                   lowerCAmelCase__		= variance_pred.chunk(2   )
                   lowerCAmelCase__		= noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                   lowerCAmelCase__		= 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"]
              ):
                   lowerCAmelCase__		= noise_pred.split(latents.shape[1]   ,	dim=1   )

              # compute the previous noisy sample x_t -> x_t-1

              lowerCAmelCase__		= self.scheduler.step(
                  snake_case__   ,	snake_case__   ,	snake_case__   ,	generator=snake_case__   ,	)[0]

         # post-processing
         lowerCAmelCase__		= 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"]:
              lowerCAmelCase__		= image * 0.5 + 0.5
              lowerCAmelCase__		= image.clamp(0   ,	1   )
              lowerCAmelCase__		= image.cpu().permute(0   ,	2   ,	3   ,	1   ).float().numpy()

         if output_type == "pil":
              lowerCAmelCase__		= self.numpy_to_pil(snake_case__   )

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=snake_case__   )








"""simple docstring"""







def 						lowercase__(A = 1_000						)				->int:




						"""simple docstring"""



						lowercase__				:      Any=       [0, 3, 3, 5, 4, 4, 3, 5, 5, 4, 3, 6, 6, 8, 8, 7, 7, 9, 8, 8]
						# number of letters in twenty, thirty, ..., ninety (0 for numbers less than
						# 20 due to inconsistency in teens)
						lowercase__				:      List[str]=       [0, 0, 6, 6, 5, 5, 5, 7, 6, 6]

						lowercase__				:      Dict=       0

						for i in range(1					,		n + 1						):
												if i < 1_000:
																		if i >= 100:
																								# add number of letters for "n hundred"
																								count += ones_counts[i // 100] + 7

																								if i % 100 != 0:
																														# add number of letters for "and" if number is not multiple
																														# of 100
																														count += 3

																		if 0 < i % 100 < 20:
																								# add number of letters for one, two, three, ..., nineteen
																								# (could be combined with below if not for inconsistency in
																								# teens)
																								count += ones_counts[i % 100]
																		else:
																								# add number of letters for twenty, twenty one, ..., ninety
																								# nine
																								count += ones_counts[i % 10]
																								count += tens_counts[(i % 100 - i % 10) // 10]
												else:
																		count += ones_counts[i // 1_000] + 8
						return count


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






