


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    lowerCamelCase__     ( nn.Module):





					def __init__(self	,  UpperCAmelCase	,  UpperCAmelCase=1.0 )  ->     List[str]:
						super().__init__()

						_lowercase							   =cut_size
						_lowercase							   =cut_power





					def   __A      (self	,  UpperCAmelCase	,  UpperCAmelCase )  ->     Union[str, Any]:
						_lowercase   ,					_lowercase							   =pixel_values.shape[2:4]
						_lowercase							   =min(a_	,  a_ )
						_lowercase							   =min(a_	,  a_	,  self.cut_size )
						_lowercase							   =[]
						for _ in range(a_ ):
							_lowercase							   =int(torch.rand([] ) ** self.cut_power * (max_size - min_size) + min_size )
							_lowercase							   =torch.randint(0	,  sideX - size + 1	,  () )
							_lowercase							   =torch.randint(0	,  sideY - size + 1	,  () )
							_lowercase							   =pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
							cutouts.append(F.adaptive_avg_poolad(a_	,  self.cut_size ) )
						return torch.cat(a_ )



def        UpperCAmelCase_      (							__snake_case  ,		__snake_case		)  ->    str:







	"""simple docstring"""


	_lowercase							   =F.normalize(UpperCamelCase__  ,		dim=-1		)
	_lowercase							   =F.normalize(UpperCamelCase__  ,		dim=-1		)
	return (x - y).norm(dim=-1		).div(2		).arcsin().pow(2		).mul(2		)
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case		)  ->    str:







	"""simple docstring"""


	for param in model.parameters():
		_lowercase							   =value







class    lowerCamelCase__     ( lowerCAmelCase):





					def __init__(self	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase	,  )  ->     Optional[Any]:
						super().__init__()
						self.register_modules(
						    vae=a_	,  text_encoder=a_	,  clip_model=a_	,  tokenizer=a_	,  unet=a_	,  scheduler=a_	,  feature_extractor=a_	,  )

						_lowercase							   =transforms.Normalize(mean=feature_extractor.image_mean	,  std=feature_extractor.image_std )
						_lowercase							   =(
						    feature_extractor.size
						    if isinstance(feature_extractor.size	,  a_ )
						    else feature_extractor.size['''shortest_edge''']
						)
						_lowercase							   =MakeCutouts(self.cut_out_size )

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





					def   __A      (self	,  UpperCAmelCase = "auto" )  ->     str:
						if slice_size == "auto":
							# half the attention head size is usually a good trade-off between
							# speed and memory
							_lowercase							   =self.unet.config.attention_head_dim // 2
						self.unet.set_attention_slice(a_ )





					def   __A      (self )  ->     str:
						self.enable_attention_slicing(a_ )





					def   __A      (self )  ->     Optional[int]:
						set_requires_grad(self.vae	,  a_ )





					def   __A      (self )  ->     Tuple:
						set_requires_grad(self.vae	,  a_ )





					def   __A      (self )  ->     int:
						set_requires_grad(self.unet	,  a_ )





					def   __A      (self )  ->     List[Any]:
						set_requires_grad(self.unet	,  a_ )





					@torch.enable_grad()
					def   __A      (self	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase=True	,  )  ->     Dict:
						_lowercase							   =latents.detach().requires_grad_()

						_lowercase							   =self.scheduler.scale_model_input(a_	,  a_ )

						# predict the noise residual
						_lowercase							   =self.unet(a_	,  a_	,  encoder_hidden_states=a_ ).sample

						if isinstance(self.scheduler	,  (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
							_lowercase							   =self.scheduler.alphas_cumprod[timestep]
							_lowercase							   =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
							_lowercase							   =(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

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

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

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

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

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

						_lowercase							   =-torch.autograd.grad(a_	,  a_ )[0]

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





					@torch.no_grad()
					def __call__(self	,  UpperCAmelCase	,  UpperCAmelCase = 5_1_2	,  UpperCAmelCase = 5_1_2	,  UpperCAmelCase = 5_0	,  UpperCAmelCase = 7.5	,  UpperCAmelCase = 1	,  UpperCAmelCase = 0.0	,  UpperCAmelCase = 1_0_0	,  UpperCAmelCase = None	,  UpperCAmelCase = 4	,  UpperCAmelCase = True	,  UpperCAmelCase = None	,  UpperCAmelCase = None	,  UpperCAmelCase = "pil"	,  UpperCAmelCase = True	,  )  ->     str:
						if isinstance(a_	,  a_ ):
							_lowercase							   =1
						elif isinstance(a_	,  a_ ):
							_lowercase							   =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}." )

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

						if clip_guidance_scale > 0:
							if clip_prompt is not None:
								_lowercase							   =self.tokenizer(
								    a_	,  padding='''max_length'''	,  max_length=self.tokenizer.model_max_length	,  truncation=a_	,  return_tensors='''pt'''	,  ).input_ids.to(self.device )
							else:
								_lowercase							   =text_input.input_ids.to(self.device )
							_lowercase							   =self.clip_model.get_text_features(a_ )
							_lowercase							   =text_embeddings_clip / text_embeddings_clip.norm(p=2	,  dim=-1	,  keepdim=a_ )
							# duplicate text embeddings clip for each generation per prompt
							_lowercase							   =text_embeddings_clip.repeat_interleave(a_	,  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.
						_lowercase							   =guidance_scale > 1.0
						# get unconditional embeddings for classifier free guidance
						if do_classifier_free_guidance:
							_lowercase							   =text_input.input_ids.shape[-1]
							_lowercase							   =self.tokenizer(['''''']	,  padding='''max_length'''	,  max_length=a_	,  return_tensors='''pt''' )
							_lowercase							   =self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
							# duplicate unconditional embeddings for each generation per prompt
							_lowercase							   =uncond_embeddings.repeat_interleave(a_	,  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
							_lowercase							   =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`.
						_lowercase							   =(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
						_lowercase							   =text_embeddings.dtype
						if latents is None:
							if self.device.type == "mps":
								# randn does not work reproducibly on mps
								_lowercase							   =torch.randn(a_	,  generator=a_	,  device='''cpu'''	,  dtype=a_ ).to(
								    self.device )
							else:
								_lowercase							   =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}" )
							_lowercase							   =latents.to(self.device )

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

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

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

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

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

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

							# predict the noise residual
							_lowercase							   =self.unet(a_	,  a_	,  encoder_hidden_states=a_ ).sample

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

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

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

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

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

						if output_type == "pil":
							_lowercase							   =self.numpy_to_pil(a_ )

						if not return_dict:
							return (image, None)

						return StableDiffusionPipelineOutput(images=a_	,  nsfw_content_detected=a_ )





def        UpperCAmelCase_      (							__snake_case		)  ->    bool:







 """simple docstring"""


 if not isinstance(__snake_case  ,		__snake_case		):
  _lowercase							   =F"Input value of [number={number}] must be an integer"
  raise TypeError(__snake_case		)
 if number < 0 or number % 2 == 1:
  return False
 _lowercase							   =int(number**0.5		)
 return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
  import doctest

  doctest.testmod()

