
"""simple docstring"""






import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


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




class 			_SCREAMING_SNAKE_CASE					(			A__							):






			def __init__(					self       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   )			->     Optional[Any]:
						super().__init__()

						if safety_checker is None:
									logger.warning(
									    f"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
									    """ that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
									    """ results in services or applications open to the public. Both the diffusers team and Hugging Face"""
									    """ strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
									    """ it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
									    """ information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."""       )

						self.register_modules(
						    speech_model=__A       ,   speech_processor=__A       ,   vae=__A       ,   text_encoder=__A       ,   tokenizer=__A       ,   unet=__A       ,   scheduler=__A       ,   feature_extractor=__A       ,   )






			def  __lowerCAmelCase (					self       ,   __A = "auto"       )			->     List[str]:
						if slice_size == "auto":
									lowerCAmelCase_    :Any			     =  self.unet.config.attention_head_dim // 2
						self.unet.set_attention_slice(__A       )






			def  __lowerCAmelCase (					self       )			->     Optional[int]:
						self.enable_attention_slicing(__A       )






			@torch.no_grad()
			def __call__(					self       ,   __A       ,   __A=1_6000       ,   __A = 512       ,   __A = 512       ,   __A = 50       ,   __A = 7.5       ,   __A = None       ,   __A = 1       ,   __A = 0.0       ,   __A = None       ,   __A = None       ,   __A = "pil"       ,   __A = True       ,   __A = None       ,   __A = 1       ,   **__A       ,   )			->     Optional[Any]:
						lowerCAmelCase_    :Optional[Any]			     =  self.speech_processor.feature_extractor(
						    __A       ,   return_tensors="""pt"""       ,   sampling_rate=__A       ).input_features.to(self.device       )
						lowerCAmelCase_    :List[Any]			     =  self.speech_model.generate(__A       ,   max_length=48_0000       )

						lowerCAmelCase_    :Optional[int]			     =  self.speech_processor.tokenizer.batch_decode(__A       ,   skip_special_tokens=__A       ,   normalize=__A       )[
						    0
						]

						if isinstance(__A       ,   __A       ):
									lowerCAmelCase_    :List[Any]			     =  1
						elif isinstance(__A       ,   __A       ):
									lowerCAmelCase_    :Dict			     =  len(__A       )
						else:
									raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(__A       )}"""       )

						if height % 8 != 0 or width % 8 != 0:
									raise ValueError(f"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""       )

						if (callback_steps is None) or (
						    callback_steps is not None and (not isinstance(__A       ,   __A       ) or callback_steps <= 0)
						):
									raise ValueError(
									    f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
									    f""" {type(__A       )}."""       )

						# get prompt text embeddings
						lowerCAmelCase_    :List[Any]			     =  self.tokenizer(
						    __A       ,   padding="""max_length"""       ,   max_length=self.tokenizer.model_max_length       ,   return_tensors="""pt"""       ,   )
						lowerCAmelCase_    :Tuple			     =  text_inputs.input_ids

						if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
									lowerCAmelCase_    :Optional[int]			     =  self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]       )
									logger.warning(
									    """The following part of your input was truncated because CLIP can only handle sequences up to"""
									    f""" {self.tokenizer.model_max_length} tokens: {removed_text}"""       )
									lowerCAmelCase_    :int			     =  text_input_ids[:, : self.tokenizer.model_max_length]
						lowerCAmelCase_    :int			     =  self.text_encoder(text_input_ids.to(self.device       )       )[0]

						# duplicate text embeddings for each generation per prompt, using mps friendly method
						lowerCAmelCase_     ,       lowerCAmelCase_     ,       lowerCAmelCase_    :Dict			     =  text_embeddings.shape
						lowerCAmelCase_    :Optional[Any]			     =  text_embeddings.repeat(1       ,   __A       ,   1       )
						lowerCAmelCase_    :str			     =  text_embeddings.view(bs_embed * num_images_per_prompt       ,   __A       ,   -1       )

						# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
						# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
						# corresponds to doing no classifier free guidance.
						lowerCAmelCase_    :int			     =  guidance_scale > 1.0
						# get unconditional embeddings for classifier free guidance
						if do_classifier_free_guidance:
									lowerCAmelCase_    :List[str]
									if negative_prompt is None:
												lowerCAmelCase_    :Union[str, Any]			     =  [""""""] * batch_size
									elif type(__A       ) is not type(__A       ):
												raise TypeError(
												    f"""`negative_prompt` should be the same type to `prompt`, but got {type(__A       )} !="""
												    f""" {type(__A       )}."""       )
									elif isinstance(__A       ,   __A       ):
												lowerCAmelCase_    :Optional[int]			     =  [negative_prompt]
									elif batch_size != len(__A       ):
												raise ValueError(
												    f"""`negative_prompt`: {negative_prompt} has batch size {len(__A       )}, but `prompt`:"""
												    f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
												    """ the batch size of `prompt`."""       )
									else:
												lowerCAmelCase_    :Dict			     =  negative_prompt

									lowerCAmelCase_    :List[str]			     =  text_input_ids.shape[-1]
									lowerCAmelCase_    :Dict			     =  self.tokenizer(
									    __A       ,   padding="""max_length"""       ,   max_length=__A       ,   truncation=__A       ,   return_tensors="""pt"""       ,   )
									lowerCAmelCase_    :Union[str, Any]			     =  self.text_encoder(uncond_input.input_ids.to(self.device       )       )[0]

									# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
									lowerCAmelCase_    :Dict			     =  uncond_embeddings.shape[1]
									lowerCAmelCase_    :Union[str, Any]			     =  uncond_embeddings.repeat(1       ,   __A       ,   1       )
									lowerCAmelCase_    :Optional[Any]			     =  uncond_embeddings.view(batch_size * num_images_per_prompt       ,   __A       ,   -1       )

									# For classifier free guidance, we need to do two forward passes.
									# Here we concatenate the unconditional and text embeddings into a single batch
									# to avoid doing two forward passes
									lowerCAmelCase_    :List[str]			     =  torch.cat([uncond_embeddings, text_embeddings]       )

						# get the initial random noise unless the user supplied it

						# Unlike in other pipelines, latents need to be generated in the target device
						# for 1-to-1 results reproducibility with the CompVis implementation.
						# However this currently doesn't work in `mps`.
						lowerCAmelCase_    :int			     =  (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
						lowerCAmelCase_    :Optional[int]			     =  text_embeddings.dtype
						if latents is None:
									if self.device.type == "mps":
												# randn does not exist on mps
												lowerCAmelCase_    :Optional[Any]			     =  torch.randn(__A       ,   generator=__A       ,   device="""cpu"""       ,   dtype=__A       ).to(
												    self.device       )
									else:
												lowerCAmelCase_    :List[str]			     =  torch.randn(__A       ,   generator=__A       ,   device=self.device       ,   dtype=__A       )
						else:
									if latents.shape != latents_shape:
												raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""       )
									lowerCAmelCase_    :Tuple			     =  latents.to(self.device       )

						# set timesteps
						self.scheduler.set_timesteps(__A       )

						# Some schedulers like PNDM have timesteps as arrays
						# It's more optimized to move all timesteps to correct device beforehand
						lowerCAmelCase_    :Optional[int]			     =  self.scheduler.timesteps.to(self.device       )

						# scale the initial noise by the standard deviation required by the scheduler
						lowerCAmelCase_    :List[Any]			     =  latents * self.scheduler.init_noise_sigma

						# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
						# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
						# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
						# and should be between [0, 1]
						lowerCAmelCase_    :List[Any]			     =  """eta""" in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )
						lowerCAmelCase_    :List[str]			     =  {}
						if accepts_eta:
									lowerCAmelCase_    :Dict			     =  eta

						for i, t in enumerate(self.progress_bar(__A       )       ):
									# expand the latents if we are doing classifier free guidance
									lowerCAmelCase_    :int			     =  torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents
									lowerCAmelCase_    :Dict			     =  self.scheduler.scale_model_input(__A       ,   __A       )

									# predict the noise residual
									lowerCAmelCase_    :Optional[int]			     =  self.unet(__A       ,   __A       ,   encoder_hidden_states=__A       ).sample

									# perform guidance
									if do_classifier_free_guidance:
												lowerCAmelCase_     ,       lowerCAmelCase_    :List[Any]			     =  noise_pred.chunk(2       )
												lowerCAmelCase_    :Dict			     =  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

									# compute the previous noisy sample x_t -> x_t-1
									lowerCAmelCase_    :int			     =  self.scheduler.step(__A       ,   __A       ,   __A       ,   **__A       ).prev_sample

									# call the callback, if provided
									if callback is not None and i % callback_steps == 0:
												callback(__A       ,   __A       ,   __A       )

						lowerCAmelCase_    :str			     =  1 / 0.1_8_2_1_5 * latents
						lowerCAmelCase_    :Union[str, Any]			     =  self.vae.decode(__A       ).sample

						lowerCAmelCase_    :List[str]			     =  (image / 2 + 0.5).clamp(0       ,   1       )

						# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
						lowerCAmelCase_    :int			     =  image.cpu().permute(0       ,   2       ,   3       ,   1       ).float().numpy()

						if output_type == "pil":
									lowerCAmelCase_    :Dict			     =  self.numpy_to_pil(__A       )

						if not return_dict:
									return image

						return StableDiffusionPipelineOutput(images=__A       ,   nsfw_content_detected=__A       )









def      a      (			A__	:   int = 1000000     )    ->      int:





      """simple docstring"""



      _lowercase	       =1
      _lowercase	       =1
      _lowercase	       ={1: 1}

      for inputa in range(2      ,   A__     ):
            _lowercase	       =0
            _lowercase	       =inputa

            while True:
                  if number in counters:
                        counter += counters[number]
                        break
                  if number % 2 == 0:
                        number //= 2
                        counter += 1
                  else:
                        _lowercase	       =(3 * number) + 1
                        counter += 1

            if inputa not in counters:
                  _lowercase	       =counter

            if counter > pre_counter:
                  _lowercase	       =inputa
                  _lowercase	       =counter
      return largest_number


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


