


"""simple docstring"""
import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput





class   __A  (nn.Module):





							'''simple docstring'''

							def __init__(					self				:   Any		,							UpperCAmelCase_				:   List[str]		,							UpperCAmelCase_				:   Any=1.0							)       ->Any:



														"""simple docstring"""



														super().__init__()

														snake_case_     							=		cut_size
														snake_case_     							=		cut_power



							def 		lowerCAmelCase							(					self				:   Any		,							UpperCAmelCase_				:   List[str]		,							UpperCAmelCase_				:   List[str]							)       ->Union[str, Any]:



														"""simple docstring"""



														snake_case_							,    snake_case_     							=		pixel_values.shape[2:4]
														snake_case_     							=		min(UpperCAmelCase_		,							UpperCAmelCase_							)
														snake_case_     							=		min(UpperCAmelCase_		,							UpperCAmelCase_		,							self.cut_size							)
														snake_case_     							=		[]
														for _ in range(UpperCAmelCase_							):
																					snake_case_     							=		int(torch.rand([]							) ** self.cut_power * (max_size - min_size) + min_size							)
																					snake_case_     							=		torch.randint(0		,							sideX - size + 1		,							()							)
																					snake_case_     							=		torch.randint(0		,							sideY - size + 1		,							()							)
																					snake_case_     							=		pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
																					cutouts.append(F.adaptive_avg_poolad(UpperCAmelCase_		,							self.cut_size							)							)
														return torch.cat(UpperCAmelCase_							)







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					int:
							snake_case_     							=		F.normalize(_SCREAMING_SNAKE_CASE					,			dim=-1					)
							snake_case_     							=		F.normalize(_SCREAMING_SNAKE_CASE					,			dim=-1					)
							return (x - y).norm(dim=-1					).div(2					).arcsin().pow(2					).mul(2					)







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					Union[str, Any]:
							for param in model.parameters():
														snake_case_     							=		value





class   __A  (snake_case__):





							'''simple docstring'''

							def __init__(					self				:   Dict		,							UpperCAmelCase_				:   AutoencoderKL		,							UpperCAmelCase_				:   CLIPTextModel		,							UpperCAmelCase_				:   CLIPModel		,							UpperCAmelCase_				:   CLIPTokenizer		,							UpperCAmelCase_				:   UNetaDConditionModel		,							UpperCAmelCase_				:   Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]		,							UpperCAmelCase_				:   CLIPImageProcessor		,							)       ->Dict:



														"""simple docstring"""



														super().__init__()
														self.register_modules(
														    vae=UpperCAmelCase_		,							text_encoder=UpperCAmelCase_		,							clip_model=UpperCAmelCase_		,							tokenizer=UpperCAmelCase_		,							unet=UpperCAmelCase_		,							scheduler=UpperCAmelCase_		,							feature_extractor=UpperCAmelCase_		,							)

														snake_case_     							=		transforms.Normalize(mean=feature_extractor.image_mean		,							std=feature_extractor.image_std							)
														snake_case_     							=		(
														    feature_extractor.size
														    if isinstance(feature_extractor.size		,							UpperCAmelCase_							)
														    else feature_extractor.size["""shortest_edge"""]
														)
														snake_case_     							=		MakeCutouts(self.cut_out_size							)

														set_requires_grad(self.text_encoder		,							UpperCAmelCase_							)
														set_requires_grad(self.clip_model		,							UpperCAmelCase_							)



							def 		lowerCAmelCase							(					self				:   List[Any]		,							UpperCAmelCase_				:   Optional[Union[str, int]] = "auto"							)       ->Tuple:



														"""simple docstring"""



														if slice_size == "auto":
																					# half the attention head size is usually a good trade-off between
																					# speed and memory
																					snake_case_     							=		self.unet.config.attention_head_dim // 2
														self.unet.set_attention_slice(UpperCAmelCase_							)



							def 		lowerCAmelCase							(					self				:   int							)       ->Optional[int]:



														"""simple docstring"""



														self.enable_attention_slicing(UpperCAmelCase_							)



							def 		lowerCAmelCase							(					self				:   str							)       ->Dict:



														"""simple docstring"""



														set_requires_grad(self.vae		,							UpperCAmelCase_							)



							def 		lowerCAmelCase							(					self				:   Optional[Any]							)       ->int:



														"""simple docstring"""



														set_requires_grad(self.vae		,							UpperCAmelCase_							)



							def 		lowerCAmelCase							(					self				:   Union[str, Any]							)       ->Optional[Any]:



														"""simple docstring"""



														set_requires_grad(self.unet		,							UpperCAmelCase_							)



							def 		lowerCAmelCase							(					self				:   Any							)       ->Any:



														"""simple docstring"""



														set_requires_grad(self.unet		,							UpperCAmelCase_							)



							@torch.enable_grad()
							def 		lowerCAmelCase							(					self				:   Union[str, Any]		,							UpperCAmelCase_				:   Union[str, Any]		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   Tuple		,							UpperCAmelCase_				:   Any		,							UpperCAmelCase_				:   Union[str, Any]		,							UpperCAmelCase_				:   str		,							UpperCAmelCase_				:   str		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   Any=True		,							)       ->List[Any]:



														"""simple docstring"""



														snake_case_     							=		latents.detach().requires_grad_()

														snake_case_     							=		self.scheduler.scale_model_input(UpperCAmelCase_		,							UpperCAmelCase_							)

														# predict the noise residual
														snake_case_     							=		self.unet(UpperCAmelCase_		,							UpperCAmelCase_		,							encoder_hidden_states=UpperCAmelCase_							).sample

														if isinstance(self.scheduler		,							(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)							):
																					snake_case_     							=		self.scheduler.alphas_cumprod[timestep]
																					snake_case_     							=		1 - alpha_prod_t
																					# compute predicted original sample from predicted noise also called
																					# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
																					snake_case_     							=		(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

																					snake_case_     							=		torch.sqrt(UpperCAmelCase_							)
																					snake_case_     							=		pred_original_sample * (fac) + latents * (1 - fac)
														elif isinstance(self.scheduler		,							UpperCAmelCase_							):
																					snake_case_     							=		self.scheduler.sigmas[index]
																					snake_case_     							=		latents - sigma * noise_pred
														else:
																					raise ValueError(F"""scheduler type {type(self.scheduler							)} not supported"""							)

														snake_case_     							=		1 / self.vae.config.scaling_factor * sample
														snake_case_     							=		self.vae.decode(UpperCAmelCase_							).sample
														snake_case_     							=		(image / 2 + 0.5).clamp(0		,							1							)

														if use_cutouts:
																					snake_case_     							=		self.make_cutouts(UpperCAmelCase_		,							UpperCAmelCase_							)
														else:
																					snake_case_     							=		transforms.Resize(self.cut_out_size							)(UpperCAmelCase_							)
														snake_case_     							=		self.normalize(UpperCAmelCase_							).to(latents.dtype							)

														snake_case_     							=		self.clip_model.get_image_features(UpperCAmelCase_							)
														snake_case_     							=		image_embeddings_clip / image_embeddings_clip.norm(p=2		,							dim=-1		,							keepdim=UpperCAmelCase_							)

														if use_cutouts:
																					snake_case_     							=		spherical_dist_loss(UpperCAmelCase_		,							UpperCAmelCase_							)
																					snake_case_     							=		dists.view([num_cutouts, sample.shape[0], -1]							)
																					snake_case_     							=		dists.sum(2							).mean(0							).sum() * clip_guidance_scale
														else:
																					snake_case_     							=		spherical_dist_loss(UpperCAmelCase_		,							UpperCAmelCase_							).mean() * clip_guidance_scale

														snake_case_     							=		-torch.autograd.grad(UpperCAmelCase_		,							UpperCAmelCase_							)[0]

														if isinstance(self.scheduler		,							UpperCAmelCase_							):
																					snake_case_     							=		latents.detach() + grads * (sigma**2)
																					snake_case_     							=		noise_pred_original
														else:
																					snake_case_     							=		noise_pred_original - torch.sqrt(UpperCAmelCase_							) * grads
														return noise_pred, latents



							@torch.no_grad()
							def __call__(					self				:   List[Any]		,							UpperCAmelCase_				:   Union[str, List[str]]		,							UpperCAmelCase_				:   Optional[int] = 512		,							UpperCAmelCase_				:   Optional[int] = 512		,							UpperCAmelCase_				:   Optional[int] = 50		,							UpperCAmelCase_				:   Optional[float] = 7.5		,							UpperCAmelCase_				:   Optional[int] = 1		,							UpperCAmelCase_				:   float = 0.0		,							UpperCAmelCase_				:   Optional[float] = 100		,							UpperCAmelCase_				:   Optional[Union[str, List[str]]] = None		,							UpperCAmelCase_				:   Optional[int] = 4		,							UpperCAmelCase_				:   Optional[bool] = True		,							UpperCAmelCase_				:   Optional[torch.Generator] = None		,							UpperCAmelCase_				:   Optional[torch.FloatTensor] = None		,							UpperCAmelCase_				:   Optional[str] = "pil"		,							UpperCAmelCase_				:   bool = True		,							)       ->Tuple:



														"""simple docstring"""



														if isinstance(UpperCAmelCase_		,							UpperCAmelCase_							):
																					snake_case_     							=		1
														elif isinstance(UpperCAmelCase_		,							UpperCAmelCase_							):
																					snake_case_     							=		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}."""							)

														# get prompt text embeddings
														snake_case_     							=		self.tokenizer(
														    UpperCAmelCase_		,							padding="""max_length"""		,							max_length=self.tokenizer.model_max_length		,							truncation=UpperCAmelCase_		,							return_tensors="""pt"""		,							)
														snake_case_     							=		self.text_encoder(text_input.input_ids.to(self.device							)							)[0]
														# duplicate text embeddings for each generation per prompt
														snake_case_     							=		text_embeddings.repeat_interleave(UpperCAmelCase_		,							dim=0							)

														if clip_guidance_scale > 0:
																					if clip_prompt is not None:
																												snake_case_     							=		self.tokenizer(
																												    UpperCAmelCase_		,							padding="""max_length"""		,							max_length=self.tokenizer.model_max_length		,							truncation=UpperCAmelCase_		,							return_tensors="""pt"""		,							).input_ids.to(self.device							)
																					else:
																												snake_case_     							=		text_input.input_ids.to(self.device							)
																					snake_case_     							=		self.clip_model.get_text_features(UpperCAmelCase_							)
																					snake_case_     							=		text_embeddings_clip / text_embeddings_clip.norm(p=2		,							dim=-1		,							keepdim=UpperCAmelCase_							)
																					# duplicate text embeddings clip for each generation per prompt
																					snake_case_     							=		text_embeddings_clip.repeat_interleave(UpperCAmelCase_		,							dim=0							)

														# 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.
														snake_case_     							=		guidance_scale > 1.0
														# get unconditional embeddings for classifier free guidance
														if do_classifier_free_guidance:
																					snake_case_     							=		text_input.input_ids.shape[-1]
																					snake_case_     							=		self.tokenizer([""""""]		,							padding="""max_length"""		,							max_length=UpperCAmelCase_		,							return_tensors="""pt"""							)
																					snake_case_     							=		self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]
																					# duplicate unconditional embeddings for each generation per prompt
																					snake_case_     							=		uncond_embeddings.repeat_interleave(UpperCAmelCase_		,							dim=0							)

																					# 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
																					snake_case_     							=		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`.
														snake_case_     							=		(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
														snake_case_     							=		text_embeddings.dtype
														if latents is None:
																					if self.device.type == "mps":
																												# randn does not work reproducibly on mps
																												snake_case_     							=		torch.randn(UpperCAmelCase_		,							generator=UpperCAmelCase_		,							device="""cpu"""		,							dtype=UpperCAmelCase_							).to(
																												    self.device							)
																					else:
																												snake_case_     							=		torch.randn(UpperCAmelCase_		,							generator=UpperCAmelCase_		,							device=self.device		,							dtype=UpperCAmelCase_							)
														else:
																					if latents.shape != latents_shape:
																												raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""							)
																					snake_case_     							=		latents.to(self.device							)

														# set timesteps
														snake_case_     							=		"""offset""" in set(inspect.signature(self.scheduler.set_timesteps							).parameters.keys()							)
														snake_case_     							=		{}
														if accepts_offset:
																					snake_case_     							=		1

														self.scheduler.set_timesteps(UpperCAmelCase_		,							**UpperCAmelCase_							)

														# Some schedulers like PNDM have timesteps as arrays
														# It's more optimized to move all timesteps to correct device beforehand
														snake_case_     							=		self.scheduler.timesteps.to(self.device							)

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

														# check if the scheduler accepts generator
														snake_case_     							=		"""generator""" in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
														if accepts_generator:
																					snake_case_     							=		generator

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

																					# predict the noise residual
																					snake_case_     							=		self.unet(UpperCAmelCase_		,							UpperCAmelCase_		,							encoder_hidden_states=UpperCAmelCase_							).sample

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

																					# perform clip guidance
																					if clip_guidance_scale > 0:
																												snake_case_     							=		(
																												    text_embeddings.chunk(2							)[1] if do_classifier_free_guidance else text_embeddings
																												)
																												snake_case_							,    snake_case_     							=		self.cond_fn(
																												    UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_		,							)

																					# compute the previous noisy sample x_t -> x_t-1
																					snake_case_     							=		self.scheduler.step(UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_		,							**UpperCAmelCase_							).prev_sample

														# scale and decode the image latents with vae
														snake_case_     							=		1 / self.vae.config.scaling_factor * latents
														snake_case_     							=		self.vae.decode(UpperCAmelCase_							).sample

														snake_case_     							=		(image / 2 + 0.5).clamp(0		,							1							)
														snake_case_     							=		image.cpu().permute(0		,							2		,							3		,							1							).numpy()

														if output_type == "pil":
																					snake_case_     							=		self.numpy_to_pil(UpperCAmelCase_							)

														if not return_dict:
																					return (image, None)

														return StableDiffusionPipelineOutput(images=UpperCAmelCase_		,							nsfw_content_detected=UpperCAmelCase_							)







"""simple docstring"""
import math







def     _a					(   _SCREAMING_SNAKE_CASE					)     ->					list[int]:
							if not isinstance(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					):
														snake_case_     							=		f"""Input value of [number={number}] must be an integer"""
														raise TypeError(_SCREAMING_SNAKE_CASE					)
							if number < 1:
														snake_case_     							=		f"""Input value of [number={number}] must be a positive integer"""
														raise ValueError(_SCREAMING_SNAKE_CASE					)
							snake_case_     							=		[number]
							while number != 1:
														if number % 2 == 0:
																					snake_case_     							=		math.floor(math.sqrt(_SCREAMING_SNAKE_CASE					)					)
														else:
																					snake_case_     							=		math.floor(
																					    math.sqrt(_SCREAMING_SNAKE_CASE					) * math.sqrt(_SCREAMING_SNAKE_CASE					) * math.sqrt(_SCREAMING_SNAKE_CASE					)					)
														sequence.append(_SCREAMING_SNAKE_CASE					)
							return sequence


if __name__ == "__main__":
				import doctest

				doctest.testmod()



