
'''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


__lowercase							:   Optional[int]					=     logging.get_logger(__name__)  # pylint: disable=invalid-name

class 	__lowercase				(  _lowercase    ):

			def __init__(self			,   A			,   A			,   A			,   A			,   A			,   A			,   A			,   A			,   A			,   ):
								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        UpperCAmelCase__ (self			,   A = "auto"      ):
								if slice_size == "auto":
													lowerCamelCase_					:      Optional[int]     =		self.unet.config.attention_head_dim // 2
								self.unet.set_attention_slice(A      )

			def        UpperCAmelCase__ (self      ):
								self.enable_attention_slicing(A      )






			@torch.no_grad()
			def __call__(self			,   A			,   A=1_6_0_0_0			,   A = 5_1_2			,   A = 5_1_2			,   A = 5_0			,   A = 7.5			,   A = None			,   A = 1			,   A = 0.0			,   A = None			,   A = None			,   A = "pil"			,   A = True			,   A = None			,   A = 1			,   **A			,   ):
								lowerCamelCase_					:      Any     =		self.speech_processor.feature_extractor(
								    A			,   return_tensors='''pt'''			,   sampling_rate=A      ).input_features.to(self.device      )
								lowerCamelCase_					:      Optional[Any]     =		self.speech_model.generate(A			,   max_length=4_8_0_0_0_0      )

								lowerCamelCase_					:      Union[str, Any]     =		self.speech_processor.tokenizer.batch_decode(A			,   skip_special_tokens=A			,   normalize=A      )[
								    0
								]

								if isinstance(A			,   A      ):
													lowerCamelCase_					:      List[str]     =		1
								elif isinstance(A			,   A      ):
													lowerCamelCase_					:      List[str]     =		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_					:      List[str]     =		text_inputs.input_ids

								if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
													lowerCamelCase_					:      Tuple     =		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_					:      List[str]     =		text_input_ids[:, : self.tokenizer.model_max_length]
								lowerCamelCase_					:      Optional[Any]     =		self.text_encoder(text_input_ids.to(self.device      )      )[0]

								# duplicate text embeddings for each generation per prompt, using mps friendly method
								lowerCamelCase_					:      Optional[int]     =		text_embeddings.shape
								lowerCamelCase_					:      List[str]     =		text_embeddings.repeat(1			,   A			,   1      )
								lowerCamelCase_					:      Optional[Any]     =		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_					:      Optional[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_					:      Union[str, Any]     =		[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_					:      List[Any]     =		negative_prompt

													lowerCamelCase_					:      str     =		text_input_ids.shape[-1]
													lowerCamelCase_					:      List[str]     =		self.tokenizer(
													    A			,   padding='''max_length'''			,   max_length=A			,   truncation=A			,   return_tensors='''pt'''			,   )
													lowerCamelCase_					:      List[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_					:      str     =		uncond_embeddings.shape[1]
													lowerCamelCase_					:      Tuple     =		uncond_embeddings.repeat(1			,   A			,   1      )
													lowerCamelCase_					:      str     =		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_					:      Optional[int]     =		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_					:      Optional[Any]     =		(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
								lowerCamelCase_					:      str     =		text_embeddings.dtype
								if latents is None:
													if self.device.type == "mps":
																		# randn does not exist on mps
																		lowerCamelCase_					:      Tuple     =		torch.randn(A			,   generator=A			,   device='''cpu'''			,   dtype=A      ).to(
																		    self.device      )
													else:
																		lowerCamelCase_					:      Optional[Any]     =		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_					:      Union[str, Any]     =		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_					:      Union[str, Any]     =		self.scheduler.timesteps.to(self.device      )

								# scale the initial noise by the standard deviation required by the scheduler
								lowerCamelCase_					:      List[str]     =		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_					:      Union[str, Any]     =		'''eta''' in set(inspect.signature(self.scheduler.step      ).parameters.keys()      )
								lowerCamelCase_					:      Optional[int]     =		{}
								if accepts_eta:
													lowerCamelCase_					:      List[Any]     =		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_					:      Optional[int]     =		self.scheduler.scale_model_input(A			,   A      )

													# predict the noise residual
													lowerCamelCase_					:      Optional[Any]     =		self.unet(A			,   A			,   encoder_hidden_states=A      ).sample

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

													# compute the previous noisy sample x_t -> x_t-1
													lowerCamelCase_					:      str     =		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_					:      Dict     =		1 / 0.1_82_15 * latents
								lowerCamelCase_					:      Any     =		self.vae.decode(A      ).sample

								lowerCamelCase_					:      Optional[int]     =		(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_					:      Any     =		image.cpu().permute(0			,   2			,   3			,   1      ).float().numpy()

								if output_type == "pil":
													lowerCamelCase_					:      Union[str, Any]     =		self.numpy_to_pil(A      )

								if not return_dict:
													return image

								return StableDiffusionPipelineOutput(images=A			,   nsfw_content_detected=A      )




'''simple docstring'''


def lowercase_	(   _lowercase = 1_000				)     ->							int:
					'''simple docstring'''

					lowerCamelCase_					:      Any     =		-1
					lowerCamelCase_					:      Optional[Any]     =		0
					for a in range(1			,       n // 3				):
										# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
										lowerCamelCase_					:      Any     =		(n * n - 2 * a * n) // (2 * n - 2 * a)
										lowerCamelCase_					:      int     =		n - a - b
										if c * c == (a * a + b * b):
															lowerCamelCase_					:      int     =		a * b * c
															if candidate >= product:
																				lowerCamelCase_					:      Any     =		candidate
					return product


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


