






"""simple docstring"""

from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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


lowercase           =							'''
    Examples:
        ```py
        >>> import torch

        >>> from diffusers import ConsistencyModelPipeline

        >>> device = "cuda"
        >>> # Load the cd_imagenet64_l2 checkpoint.
        >>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"
        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
        >>> pipe.to(device)

        >>> # Onestep Sampling
        >>> image = pipe(num_inference_steps=1).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample.png")

        >>> # Onestep sampling, class-conditional image generation
        >>> # ImageNet-64 class label 145 corresponds to king penguins
        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")

        >>> # Multistep sampling, class-conditional image generation
        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:
        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77
        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")
        ```
'''

class     A_				(   snake_case_	):







				def __init__(				self				:    int    ,     __lowerCamelCase				:    UNetaDModel    ,     __lowerCamelCase				:    CMStochasticIterativeScheduler						)					->       None:
										super().__init__()

										self.register_modules(
										    unet=__lowerCamelCase    ,     scheduler=__lowerCamelCase    ,     )

										__magic_name__      =  None







				def      _snake_case       (				self				:    int    ,     __lowerCamelCase				:    Any=0						)					->       List[Any]:
										if is_accelerate_available() and is_accelerate_version(">="    ,     "0.14.0"						):
																from accelerate import cpu_offload
										else:
																raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher"						)

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

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

										for cpu_offloaded_model in [self.unet]:
																cpu_offload(__lowerCamelCase    ,     __lowerCamelCase						)

										if self.safety_checker is not None:
																cpu_offload(self.safety_checker    ,     execution_device=__lowerCamelCase    ,     offload_buffers=__lowerCamelCase						)







				def      _snake_case       (				self				:    List[str]    ,     __lowerCamelCase				:    List[str]=0						)					->       Optional[int]:
										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."						)

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

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

										__magic_name__      =  None
										for cpu_offloaded_model in [self.unet]:
																__magic_name__   ,     __magic_name__      =  cpu_offload_with_hook(__lowerCamelCase    ,     __lowerCamelCase    ,     prev_module_hook=__lowerCamelCase						)

										if self.safety_checker is not None:
																__magic_name__   ,     __magic_name__      =  cpu_offload_with_hook(self.safety_checker    ,     __lowerCamelCase    ,     prev_module_hook=__lowerCamelCase						)

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







				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def      _snake_case       (				self				:    Optional[int]						)					->       Optional[Any]:
										if not hasattr(self.unet    ,     "_hf_hook"						):
																return self.device
										for module in self.unet.modules():
																if (
																    hasattr(__lowerCamelCase    ,     "_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







				def      _snake_case       (				self				:    Optional[Any]    ,     __lowerCamelCase				:    Optional[int]    ,     __lowerCamelCase				:    Any    ,     __lowerCamelCase				:    Any    ,     __lowerCamelCase				:    List[Any]    ,     __lowerCamelCase				:    str    ,     __lowerCamelCase				:    Union[str, Any]    ,     __lowerCamelCase				:    Tuple    ,     __lowerCamelCase				:    int=None						)					->       int:
										__magic_name__      =  (batch_size, num_channels, height, width)
										if isinstance(__lowerCamelCase    ,     __lowerCamelCase						) and len(__lowerCamelCase						) != batch_size:
																raise ValueError(
																    f'''You have passed a list of generators of length {len(__lowerCamelCase						)}, but requested an effective batch'''
																    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''						)

										if latents is None:
																__magic_name__      =  randn_tensor(__lowerCamelCase    ,     generator=__lowerCamelCase    ,     device=__lowerCamelCase    ,     dtype=__lowerCamelCase						)
										else:
																__magic_name__      =  latents.to(device=__lowerCamelCase    ,     dtype=__lowerCamelCase						)

										# scale the initial noise by the standard deviation required by the scheduler
										__magic_name__      =  latents * self.scheduler.init_noise_sigma
										return latents







				def      _snake_case       (				self				:    Union[str, Any]    ,     __lowerCamelCase				:    torch.FloatTensor    ,     __lowerCamelCase				:    str = "pil"						)					->       Tuple:
										if output_type not in ["pt", "np", "pil"]:
																raise ValueError(
																    f'''output_type={output_type} is not supported. Make sure to choose one of [\'pt\', \'np\', or \'pil\']'''						)

										# Equivalent to diffusers.VaeImageProcessor.denormalize
										__magic_name__      =  (sample / 2 + 0.5).clamp(0    ,     1						)
										if output_type == "pt":
																return sample

										# Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
										__magic_name__      =  sample.cpu().permute(0    ,     2    ,     3    ,     1						).numpy()
										if output_type == "np":
																return sample

										# Output_type must be 'pil'
										__magic_name__      =  self.numpy_to_pil(__lowerCamelCase						)
										return sample







				def      _snake_case       (				self				:    List[Any]    ,     __lowerCamelCase				:    Optional[int]    ,     __lowerCamelCase				:    Any    ,     __lowerCamelCase				:    str=None						)					->       List[Any]:
										if self.unet.config.num_class_embeds is not None:
																if isinstance(__lowerCamelCase    ,     __lowerCamelCase						):
																						__magic_name__      =  torch.tensor(__lowerCamelCase    ,     dtype=torch.int						)
																elif isinstance(__lowerCamelCase    ,     __lowerCamelCase						):
																						assert batch_size == 1, "Batch size must be 1 if classes is an int"
																						__magic_name__      =  torch.tensor([class_labels]    ,     dtype=torch.int						)
																elif class_labels is None:
																						# Randomly generate batch_size class labels
																						# TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
																						__magic_name__      =  torch.randint(0    ,     self.unet.config.num_class_embeds    ,     size=(batch_size,)						)
																__magic_name__      =  class_labels.to(__lowerCamelCase						)
										else:
																__magic_name__      =  None
										return class_labels







				def      _snake_case       (				self				:    List[Any]    ,     __lowerCamelCase				:    Tuple    ,     __lowerCamelCase				:    Tuple    ,     __lowerCamelCase				:    Union[str, Any]    ,     __lowerCamelCase				:    Tuple    ,     __lowerCamelCase				:    List[Any]    ,     __lowerCamelCase				:    Any						)					->       List[Any]:
										if num_inference_steps is None and timesteps is None:
																raise ValueError("Exactly one of `num_inference_steps` or `timesteps` must be supplied."						)

										if num_inference_steps is not None and timesteps is not None:
																logger.warning(
																    f'''Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;'''
																    " `timesteps` will be used over `num_inference_steps`."						)

										if latents is not None:
																__magic_name__      =  (batch_size, 3, img_size, img_size)
																if latents.shape != expected_shape:
																						raise ValueError(f'''The shape of latents is {latents.shape} but is expected to be {expected_shape}.'''						)

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







				@torch.no_grad()
				@replace_example_docstring(__lowerCamelCase						)
				def __call__(				self				:    Optional[Any]    ,     __lowerCamelCase				:    int = 1    ,     __lowerCamelCase				:    Optional[Union[torch.Tensor, List[int], int]] = None    ,     __lowerCamelCase				:    int = 1    ,     __lowerCamelCase				:    List[int] = None    ,     __lowerCamelCase				:    Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,     __lowerCamelCase				:    Optional[torch.FloatTensor] = None    ,     __lowerCamelCase				:    Optional[str] = "pil"    ,     __lowerCamelCase				:    bool = True    ,     __lowerCamelCase				:    Optional[Callable[[int, int, torch.FloatTensor], None]] = None    ,     __lowerCamelCase				:    int = 1    ,     )					->       Optional[int]:
										__magic_name__      =  self.unet.config.sample_size
										__magic_name__      =  self._execution_device

										# 1. Check inputs
										self.check_inputs(__lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase						)

										# 2. Prepare image latents
										# Sample image latents x_0 ~ N(0, sigma_0^2 * I)
										__magic_name__      =  self.prepare_latents(
										    batch_size=__lowerCamelCase    ,     num_channels=self.unet.config.in_channels    ,     height=__lowerCamelCase    ,     width=__lowerCamelCase    ,     dtype=self.unet.dtype    ,     device=__lowerCamelCase    ,     generator=__lowerCamelCase    ,     latents=__lowerCamelCase    ,     )

										# 3. Handle class_labels for class-conditional models
										__magic_name__      =  self.prepare_class_labels(__lowerCamelCase    ,     __lowerCamelCase    ,     class_labels=__lowerCamelCase						)

										# 4. Prepare timesteps
										if timesteps is not None:
																self.scheduler.set_timesteps(timesteps=__lowerCamelCase    ,     device=__lowerCamelCase						)
																__magic_name__      =  self.scheduler.timesteps
																__magic_name__      =  len(__lowerCamelCase						)
										else:
																self.scheduler.set_timesteps(__lowerCamelCase						)
																__magic_name__      =  self.scheduler.timesteps

										# 5. Denoising loop
										# Multistep sampling: implements Algorithm 1 in the paper
										with self.progress_bar(total=__lowerCamelCase						) as progress_bar:
																for i, t in enumerate(__lowerCamelCase						):
																						__magic_name__      =  self.scheduler.scale_model_input(__lowerCamelCase    ,     __lowerCamelCase						)
																						__magic_name__      =  self.unet(__lowerCamelCase    ,     __lowerCamelCase    ,     class_labels=__lowerCamelCase    ,     return_dict=__lowerCamelCase						)[0]

																						__magic_name__      =  self.scheduler.step(__lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase    ,     generator=__lowerCamelCase						)[0]

																						# call the callback, if provided
																						progress_bar.update()
																						if callback is not None and i % callback_steps == 0:
																												callback(__lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase						)

        # 6. Post-process image sample
										__magic_name__      =  self.postprocess_image(__lowerCamelCase    ,     output_type=__lowerCamelCase						)

										# Offload last model to CPU
										if hasattr(self    ,     "final_offload_hook"						) and self.final_offload_hook is not None:
																self.final_offload_hook.offload()

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=__lowerCamelCase						)











"""simple docstring"""

from __future__ import annotations

lowercase           =							{1: 1}






def _lowerCAmelCase						(	__lowerCamelCase:int ):

						'''simple docstring'''






						if n in COLLATZ_SEQUENCE_LENGTHS:
												return COLLATZ_SEQUENCE_LENGTHS[n]
						__magic_name__      =  n // 2 if n % 2 == 0 else 3 * n + 1
						__magic_name__      =  collatz_sequence_length(__lowerCamelCase ) + 1
						__magic_name__      =  sequence_length
						return sequence_length






def _lowerCAmelCase						(	__lowerCamelCase:int = 1_0_0_0_0_0_0 ):

						'''simple docstring'''






						__magic_name__      =  max((collatz_sequence_length(__lowerCamelCase ), i) for i in range(1			,    __lowerCamelCase ) )
						return result[1]


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



