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

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


_SCREAMING_SNAKE_CASE    :      List[str]          =			logging.get_logger(__name__)  # pylint: disable=invalid-name







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''





	def __init__(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       ):
					super().__init__()
					self.register_modules(
					    vae=UpperCamelCase__				,       text_encoder=UpperCamelCase__				,       tokenizer=UpperCamelCase__				,       unet=UpperCamelCase__				,       scheduler=UpperCamelCase__				,       safety_checker=UpperCamelCase__				,       feature_extractor=UpperCamelCase__				,       )





	def   __snake_case				(			self				,       UpperCamelCase__ = "auto"		):
					if slice_size == "auto":
									# half the attention head size is usually a good trade-off between
									# speed and memory
									A__  :	Union[str, Any]         =							self.unet.config.attention_head_dim // 2
					self.unet.set_attention_slice(UpperCamelCase__		)





	def   __snake_case				(			self		):
					self.enable_attention_slicing(UpperCamelCase__		)





	@torch.no_grad()
	def __call__(			self				,       UpperCamelCase__				,       UpperCamelCase__ = 512				,       UpperCamelCase__ = 512				,       UpperCamelCase__ = 50				,       UpperCamelCase__ = 7.5				,       UpperCamelCase__ = None				,       UpperCamelCase__ = 1				,       UpperCamelCase__ = 0.0				,       UpperCamelCase__ = None				,       UpperCamelCase__ = None				,       UpperCamelCase__ = "pil"				,       UpperCamelCase__ = True				,       UpperCamelCase__ = None				,       UpperCamelCase__ = 1				,       UpperCamelCase__ = None				,       **UpperCamelCase__				,       ):

					if isinstance(UpperCamelCase__				,       UpperCamelCase__		):
									A__  :	Tuple         =							1
					elif isinstance(UpperCamelCase__				,       UpperCamelCase__		):
									A__  :	Dict         =							len(UpperCamelCase__		)
					else:
									raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(UpperCamelCase__		)}"		)

					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(UpperCamelCase__				,       UpperCamelCase__		) or callback_steps <= 0)
					):
									raise ValueError(
									    F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
									    F" {type(UpperCamelCase__		)}."		)

					# get prompt text embeddings
					A__  :	Any         =							self.tokenizer(
					    UpperCamelCase__				,       padding='''max_length'''				,       max_length=self.tokenizer.model_max_length				,       return_tensors='''pt'''				,       )
					A__  :	Union[str, Any]         =							text_inputs.input_ids

					if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
									A__  :	Union[str, Any]         =							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}"		)
									A__  :	Any         =							text_input_ids[:, : self.tokenizer.model_max_length]

					if text_embeddings is None:
									A__  :	str         =							self.text_encoder(text_input_ids.to(self.device		)		)[0]

					# duplicate text embeddings for each generation per prompt, using mps friendly method
					A__  :	Dict         =							text_embeddings.shape
					A__  :	Any         =							text_embeddings.repeat(1				,       UpperCamelCase__				,       1		)
					A__  :	Any         =							text_embeddings.view(bs_embed * num_images_per_prompt				,       UpperCamelCase__				,       -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.
					A__  :	int         =							guidance_scale > 1.0
					# get unconditional embeddings for classifier free guidance
					if do_classifier_free_guidance:
									A__  :	List[str]
									if negative_prompt is None:
													A__  :	Dict         =							['''''']
									elif type(UpperCamelCase__		) is not type(UpperCamelCase__		):
													raise TypeError(
													    F"`negative_prompt` should be the same type to `prompt`, but got {type(UpperCamelCase__		)} !="
													    F" {type(UpperCamelCase__		)}."		)
									elif isinstance(UpperCamelCase__				,       UpperCamelCase__		):
													A__  :	Tuple         =							[negative_prompt]
									elif batch_size != len(UpperCamelCase__		):
													raise ValueError(
													    F"`negative_prompt`: {negative_prompt} has batch size {len(UpperCamelCase__		)}, but `prompt`:"
													    F" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
													    ''' the batch size of `prompt`.'''		)
									else:
													A__  :	Optional[Any]         =							negative_prompt

									A__  :	int         =							text_input_ids.shape[-1]
									A__  :	Dict         =							self.tokenizer(
									    UpperCamelCase__				,       padding='''max_length'''				,       max_length=UpperCamelCase__				,       truncation=UpperCamelCase__				,       return_tensors='''pt'''				,       )
									A__  :	Optional[Any]         =							self.text_encoder(uncond_input.input_ids.to(self.device		)		)[0]

									# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
									A__  :	int         =							uncond_embeddings.shape[1]
									A__  :	List[str]         =							uncond_embeddings.repeat(UpperCamelCase__				,       UpperCamelCase__				,       1		)
									A__  :	Optional[Any]         =							uncond_embeddings.view(batch_size * num_images_per_prompt				,       UpperCamelCase__				,       -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
									A__  :	List[Any]         =							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`.
					A__  :	List[Any]         =							(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
					A__  :	Any         =							(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
					A__  :	List[str]         =							text_embeddings.dtype
					if latents is None:
									if self.device.type == "mps":
													# randn does not exist on mps
													A__  :	Any         =							torch.randn(
													    UpperCamelCase__				,       generator=UpperCamelCase__				,       device='''cpu'''				,       dtype=UpperCamelCase__		).to(self.device		)
													A__  :	Dict         =							torch.randn(UpperCamelCase__				,       generator=UpperCamelCase__				,       device='''cpu'''				,       dtype=UpperCamelCase__		).to(
													    self.device		)
									else:
													A__  :	Any         =							torch.randn(
													    UpperCamelCase__				,       generator=UpperCamelCase__				,       device=self.device				,       dtype=UpperCamelCase__		)
													A__  :	Optional[Any]         =							torch.randn(UpperCamelCase__				,       generator=UpperCamelCase__				,       device=self.device				,       dtype=UpperCamelCase__		)
					else:
									if latents_reference.shape != latents_shape:
													raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"		)
									A__  :	Union[str, Any]         =							latents_reference.to(self.device		)
									A__  :	List[Any]         =							latents.to(self.device		)

					# This is the key part of the pipeline where we
					# try to ensure that the generated images w/ the same seed
					# but different sizes actually result in similar images
					A__  :	Union[str, Any]         =							(latents_shape[3] - latents_shape_reference[3]) // 2
					A__  :	Dict         =							(latents_shape[2] - latents_shape_reference[2]) // 2
					A__  :	List[Any]         =							latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
					A__  :	Optional[Any]         =							latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
					A__  :	Dict         =							0 if dx < 0 else dx
					A__  :	Optional[Any]         =							0 if dy < 0 else dy
					A__  :	Union[str, Any]         =							max(-dx				,       0		)
					A__  :	str         =							max(-dy				,       0		)
					# import pdb
					# pdb.set_trace()
					A__  :	Union[str, Any]         =							latents_reference[:, :, dy : dy + h, dx : dx + w]

					# set timesteps
					self.scheduler.set_timesteps(UpperCamelCase__		)

					# Some schedulers like PNDM have timesteps as arrays
					# It's more optimized to move all timesteps to correct device beforehand
					A__  :	Union[str, Any]         =							self.scheduler.timesteps.to(self.device		)

					# scale the initial noise by the standard deviation required by the scheduler
					A__  :	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]
					A__  :	Dict         =							'''eta''' in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
					A__  :	int         =							{}
					if accepts_eta:
									A__  :	Optional[Any]         =							eta

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

									# predict the noise residual
									A__  :	List[Any]         =							self.unet(UpperCamelCase__				,       UpperCamelCase__				,       encoder_hidden_states=UpperCamelCase__		).sample

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

									# compute the previous noisy sample x_t -> x_t-1
									A__  :	List[Any]         =							self.scheduler.step(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       **UpperCamelCase__		).prev_sample

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

					A__  :	Optional[Any]         =							1 / 0.1_8_2_1_5 * latents
					A__  :	List[Any]         =							self.vae.decode(UpperCamelCase__		).sample

					A__  :	Any         =							(image / 2 + 0.5).clamp(0				,       1		)

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

					if self.safety_checker is not None:
									A__  :	Optional[int]         =							self.feature_extractor(self.numpy_to_pil(UpperCamelCase__		)				,       return_tensors='''pt'''		).to(
									    self.device		)
									A__  :	List[Any]         =							self.safety_checker(
									    images=UpperCamelCase__				,       clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype		)		)
					else:
									A__  :	List[Any]         =							None

					if output_type == "pil":
									A__  :	Any         =							self.numpy_to_pil(UpperCamelCase__		)

					if not return_dict:
									return (image, has_nsfw_concept)

					return StableDiffusionPipelineOutput(images=UpperCamelCase__				,       nsfw_content_detected=UpperCamelCase__		)

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int       ) ->       int:

				"""simple docstring"""







				if n == 1 or not isinstance(__UpperCamelCase							,						__UpperCamelCase       ):
								return 0
				elif n == 2:
								return 1
				else:
								A__  :	Any         =							[0, 1]
								for i in range(2							,						n + 1       ):
												sequence.append(sequence[i - 1] + sequence[i - 2]       )

								return sequence[n]

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int       ) ->       int:

				"""simple docstring"""







				A__  :	Dict         =							0
				A__  :	Optional[int]         =							2

				while digits < n:
								index += 1
								A__  :	Dict         =							len(str(fibonacci(__UpperCamelCase       )       )       )

				return index

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int = 10_00       ) ->       int:

				"""simple docstring"""







				return fibonacci_digits_index(__UpperCamelCase       )


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