





"""simple docstring"""





def       a__					(					_SCREAMING_SNAKE_CASE = 10**9    ):




		"""simple docstring"""


		UpperCamelCase   =	1
		UpperCamelCase   =	2

		UpperCamelCase   =	0
		UpperCamelCase   =	0
		UpperCamelCase   =	0
		while perimeter <= max_perimeter:
				perimeters_sum += perimeter

				prev_value += 2 * value
				value += prev_value

				UpperCamelCase   =	2 * value + 2 if i % 2 == 0 else 2 * value - 2
				i += 1

		return perimeters_sum


if __name__ == "__main__":
						print(f'''{solution() = }''')













"""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


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





class        _lowerCamelCase      (       _lowercase ):


						def __init__(self   ,							__a   ,							__a   ,							__a   ,							__a   ,							__a   ,							__a   ,							__a   ,							__a   ,							__a   ,							) ->   Dict:
								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     snake_case_   (self   ,							__a = "auto"		) ->   str:
								if slice_size == "auto":
										UpperCamelCase   =	self.unet.config.attention_head_dim // 2
								self.unet.set_attention_slice(__a		)


						def     snake_case_   (self		) ->   List[Any]:
								self.enable_attention_slicing(__a		)




						@torch.no_grad()
						def __call__(self   ,							__a   ,							__a=1_60_00   ,							__a = 5_12   ,							__a = 5_12   ,							__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   ,							) ->   List[str]:
								UpperCamelCase   =	self.speech_processor.feature_extractor(
								    __a   ,							return_tensors="pt"   ,							sampling_rate=__a		).input_features.to(self.device		)
								UpperCamelCase   =	self.speech_model.generate(__a   ,							max_length=48_00_00		)

								UpperCamelCase   =	self.speech_processor.tokenizer.batch_decode(__a   ,							skip_special_tokens=__a   ,							normalize=__a		)[
								    0
								]

								if isinstance(__a   ,							__a		):
										UpperCamelCase   =	1
								elif isinstance(__a   ,							__a		):
										UpperCamelCase   =	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
								UpperCamelCase   =	self.tokenizer(
								    __a   ,							padding="max_length"   ,							max_length=self.tokenizer.model_max_length   ,							return_tensors="pt"   ,							)
								UpperCamelCase   =	text_inputs.input_ids

								if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
										UpperCamelCase   =	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}"		)
										UpperCamelCase   =	text_input_ids[:, : self.tokenizer.model_max_length]
								UpperCamelCase   =	self.text_encoder(text_input_ids.to(self.device		)		)[0]

								# duplicate text embeddings for each generation per prompt, using mps friendly method
								UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   =	text_embeddings.shape
								UpperCamelCase   =	text_embeddings.repeat(1   ,							__a   ,							1		)
								UpperCamelCase   =	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.
								UpperCamelCase   =	guidance_scale > 1.0
								# get unconditional embeddings for classifier free guidance
								if do_classifier_free_guidance:
										UpperCamelCase   =	42
										if negative_prompt is None:
												UpperCamelCase   =	[""] * 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		):
												UpperCamelCase   =	[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:
												UpperCamelCase   =	negative_prompt

										UpperCamelCase   =	text_input_ids.shape[-1]
										UpperCamelCase   =	self.tokenizer(
										    __a   ,							padding="max_length"   ,							max_length=__a   ,							truncation=__a   ,							return_tensors="pt"   ,							)
										UpperCamelCase   =	self.text_encoder(uncond_input.input_ids.to(self.device		)		)[0]

										# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
										UpperCamelCase   =	uncond_embeddings.shape[1]
										UpperCamelCase   =	uncond_embeddings.repeat(1   ,							__a   ,							1		)
										UpperCamelCase   =	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
										UpperCamelCase   =	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`.
								UpperCamelCase   =	(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
								UpperCamelCase   =	text_embeddings.dtype
								if latents is None:
										if self.device.type == "mps":
												# randn does not exist on mps
												UpperCamelCase   =	torch.randn(__a   ,							generator=__a   ,							device="cpu"   ,							dtype=__a		).to(
												    self.device		)
										else:
												UpperCamelCase   =	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}"		)
										UpperCamelCase   =	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
								UpperCamelCase   =	self.scheduler.timesteps.to(self.device		)

								# scale the initial noise by the standard deviation required by the scheduler
								UpperCamelCase   =	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]
								UpperCamelCase   =	"eta" in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
								UpperCamelCase   =	{}
								if accepts_eta:
										UpperCamelCase   =	eta

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

										# predict the noise residual
										UpperCamelCase   =	self.unet(__a   ,							__a   ,							encoder_hidden_states=__a		).sample

										# perform guidance
										if do_classifier_free_guidance:
												UpperCamelCase   ,				UpperCamelCase   =	noise_pred.chunk(2		)
												UpperCamelCase   =	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

										# compute the previous noisy sample x_t -> x_t-1
										UpperCamelCase   =	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		)

								UpperCamelCase   =	1 / 0.18215 * latents
								UpperCamelCase   =	self.vae.decode(__a		).sample

								UpperCamelCase   =	(image / 2 + 0.5).clamp(0   ,							1		)

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

								if output_type == "pil":
										UpperCamelCase   =	self.numpy_to_pil(__a		)

								if not return_dict:
										return image

								return StableDiffusionPipelineOutput(images=__a   ,							nsfw_content_detected=__a		)






