




"""simple docstring"""
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


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







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class  _SCREAMING_SNAKE_CASE       (     SCREAMING_SNAKE_CASE__				):



 '''simple docstring'''





 __UpperCAmelCase		   =							42
 __UpperCAmelCase		   =							None






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Dict  ,	__lowerCAmelCase			:		Dict=0.999  ,	__lowerCAmelCase			:		List[Any]="cosine"  ,	)    ->       Optional[int]:
 if alpha_transform_type == "cosine":

  def alpha_bar_fn(__lowerCAmelCase			:		List[str]						):
   return math.cos((t + 0.008) / 1.008 * math.pi / 2						) ** 2

 elif alpha_transform_type == "exp":

  def alpha_bar_fn(__lowerCAmelCase			:		Optional[Any]						):
   return math.exp(t * -12.0						)

 else:
  raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}"						)

 _UpperCamelCase      :	Union[str, Any]					      =  []
 for i in range(__lowerCAmelCase						):
  _UpperCamelCase      :	Union[str, Any]					      =  i / num_diffusion_timesteps
  _UpperCamelCase      :	Optional[int]					      =  (i + 1) / num_diffusion_timesteps
  betas.append(min(1 - alpha_bar_fn(__lowerCAmelCase						) / alpha_bar_fn(__lowerCAmelCase						)  ,	__lowerCAmelCase						)						)
 return torch.tensor(__lowerCAmelCase  ,	dtype=torch.floataa						)







class  _SCREAMING_SNAKE_CASE       (     SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__				):



 '''simple docstring'''





 __UpperCAmelCase		   =							[e.name for e in KarrasDiffusionSchedulers]
 __UpperCAmelCase		   =							1






 @register_to_config
 def __init__(self					,     lowerCAmelCase__ = 10_00					,     lowerCAmelCase__ = 0.0001					,     lowerCAmelCase__ = 0.02					,     lowerCAmelCase__ = "linear"					,     lowerCAmelCase__ = None					,     lowerCAmelCase__ = "epsilon"					,     lowerCAmelCase__ = "linear"					,     lowerCAmelCase__ = False					,     lowerCAmelCase__ = "linspace"					,     lowerCAmelCase__ = 0					,     ):

  '''simple docstring'''







  if trained_betas is not None:
   _UpperCamelCase      :	List[Any]					      =  torch.tensor(lowerCAmelCase__					,     dtype=torch.floataa   )
  elif beta_schedule == "linear":
   _UpperCamelCase      :	Optional[Any]					      =  torch.linspace(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     dtype=torch.floataa   )
  elif beta_schedule == "scaled_linear":
   # this schedule is very specific to the latent diffusion model.
   _UpperCamelCase      :	Union[str, Any]					      =  (
       torch.linspace(beta_start**0.5					,     beta_end**0.5					,     lowerCAmelCase__					,     dtype=torch.floataa   ) ** 2
   )
  elif beta_schedule == "squaredcos_cap_v2":
   # Glide cosine schedule
   _UpperCamelCase      :	Optional[Any]					      =  betas_for_alpha_bar(lowerCAmelCase__   )
  else:
   raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"   )

  _UpperCamelCase      :	Dict					      =  1.0 - self.betas
  _UpperCamelCase      :	Union[str, Any]					      =  torch.cumprod(self.alphas					,     dim=0   )

  _UpperCamelCase      :	List[Any]					      =  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
  _UpperCamelCase      :	Optional[int]					      =  np.concatenate([sigmas[::-1], [0.0]]   ).astype(np.floataa   )
  _UpperCamelCase      :	List[Any]					      =  torch.from_numpy(lowerCAmelCase__   )

  # setable values
  _UpperCamelCase      :	str					      =  None
  _UpperCamelCase      :	List[Any]					      =  np.linspace(0					,     num_train_timesteps - 1					,     lowerCAmelCase__					,     dtype=lowerCAmelCase__   )[::-1].copy()
  _UpperCamelCase      :	Union[str, Any]					      =  torch.from_numpy(lowerCAmelCase__   )
  _UpperCamelCase      :	Tuple					      =  False
  _UpperCamelCase      :	int					      =  use_karras_sigmas






 @property
 def lowercase_      (self   ):

  '''simple docstring'''







  if self.config.timestep_spacing in ["linspace", "trailing"]:
   return self.sigmas.max()

  return (self.sigmas.max() ** 2 + 1) ** 0.5






 def lowercase_      (self					,     lowerCAmelCase__					,     lowerCAmelCase__   ):

  '''simple docstring'''







  if isinstance(lowerCAmelCase__					,     torch.Tensor   ):
   _UpperCamelCase      :	int					      =  timestep.to(self.timesteps.device   )
  _UpperCamelCase      :	Optional[int]					      =  (self.timesteps == timestep).nonzero().item()
  _UpperCamelCase      :	Union[str, Any]					      =  self.sigmas[step_index]

  _UpperCamelCase      :	List[Any]					      =  sample / ((sigma**2 + 1) ** 0.5)

  _UpperCamelCase      :	Any					      =  True
  return sample






 def lowercase_      (self					,     lowerCAmelCase__					,     lowerCAmelCase__ = None   ):

  '''simple docstring'''







  _UpperCamelCase      :	Optional[Any]					      =  num_inference_steps

  # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
  if self.config.timestep_spacing == "linspace":
   _UpperCamelCase      :	Union[str, Any]					      =  np.linspace(0					,     self.config.num_train_timesteps - 1					,     lowerCAmelCase__					,     dtype=lowerCAmelCase__   )[
       ::-1
   ].copy()
  elif self.config.timestep_spacing == "leading":
   _UpperCamelCase      :	List[Any]					      =  self.config.num_train_timesteps // self.num_inference_steps
   # creates integer timesteps by multiplying by ratio
   # casting to int to avoid issues when num_inference_step is power of 3
   _UpperCamelCase      :	Tuple					      =  (np.arange(0					,     lowerCAmelCase__   ) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase__   )
   timesteps += self.config.steps_offset
  elif self.config.timestep_spacing == "trailing":
   _UpperCamelCase      :	List[Any]					      =  self.config.num_train_timesteps / self.num_inference_steps
   # creates integer timesteps by multiplying by ratio
   # casting to int to avoid issues when num_inference_step is power of 3
   _UpperCamelCase      :	Tuple					      =  (np.arange(self.config.num_train_timesteps					,     0					,     -step_ratio   )).round().copy().astype(lowerCAmelCase__   )
   timesteps -= 1
  else:
   raise ValueError(
       F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."   )

  _UpperCamelCase      :	str					      =  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
  _UpperCamelCase      :	List[str]					      =  np.log(lowerCAmelCase__   )

  if self.config.interpolation_type == "linear":
   _UpperCamelCase      :	Dict					      =  np.interp(lowerCAmelCase__					,     np.arange(0					,     len(lowerCAmelCase__   )   )					,     lowerCAmelCase__   )
  elif self.config.interpolation_type == "log_linear":
   _UpperCamelCase      :	List[str]					      =  torch.linspace(np.log(sigmas[-1]   )					,     np.log(sigmas[0]   )					,     num_inference_steps + 1   ).exp()
  else:
   raise ValueError(
       F"{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either"
       " 'linear' or 'log_linear'"   )

  if self.use_karras_sigmas:
   _UpperCamelCase      :	List[str]					      =  self._convert_to_karras(in_sigmas=lowerCAmelCase__					,     num_inference_steps=self.num_inference_steps   )
   _UpperCamelCase      :	Optional[int]					      =  np.array([self._sigma_to_t(lowerCAmelCase__					,     lowerCAmelCase__   ) for sigma in sigmas]   )

  _UpperCamelCase      :	Tuple					      =  np.concatenate([sigmas, [0.0]]   ).astype(np.floataa   )
  _UpperCamelCase      :	Any					      =  torch.from_numpy(lowerCAmelCase__   ).to(device=lowerCAmelCase__   )
  if str(lowerCAmelCase__   ).startswith("mps"   ):
   # mps does not support float64
   _UpperCamelCase      :	Optional[Any]					      =  torch.from_numpy(lowerCAmelCase__   ).to(lowerCAmelCase__					,     dtype=torch.floataa   )
  else:
   _UpperCamelCase      :	List[Any]					      =  torch.from_numpy(lowerCAmelCase__   ).to(device=lowerCAmelCase__   )






 def lowercase_      (self					,     lowerCAmelCase__					,     lowerCAmelCase__   ):

  '''simple docstring'''







  _UpperCamelCase      :	str					      =  np.log(lowerCAmelCase__   )

  # get distribution
  _UpperCamelCase      :	List[Any]					      =  log_sigma - log_sigmas[:, np.newaxis]

  # get sigmas range
  _UpperCamelCase      :	Union[str, Any]					      =  np.cumsum((dists >= 0)					,     axis=0   ).argmax(axis=0   ).clip(max=log_sigmas.shape[0] - 2   )
  _UpperCamelCase      :	Any					      =  low_idx + 1

  _UpperCamelCase      :	Tuple					      =  log_sigmas[low_idx]
  _UpperCamelCase      :	str					      =  log_sigmas[high_idx]

  # interpolate sigmas
  _UpperCamelCase      :	Optional[Any]					      =  (low - log_sigma) / (low - high)
  _UpperCamelCase      :	List[str]					      =  np.clip(lowerCAmelCase__					,     0					,     1   )

  # transform interpolation to time range
  _UpperCamelCase      :	Dict					      =  (1 - w) * low_idx + w * high_idx
  _UpperCamelCase      :	List[str]					      =  t.reshape(sigma.shape   )
  return t






 def lowercase_      (self					,     lowerCAmelCase__					,     lowerCAmelCase__   ):

  '''simple docstring'''







  _UpperCamelCase      :	float					      =  in_sigmas[-1].item()
  _UpperCamelCase      :	float					      =  in_sigmas[0].item()

  _UpperCamelCase      :	int					      =  7.0  # 7.0 is the value used in the paper
  _UpperCamelCase      :	Optional[int]					      =  np.linspace(0					,     1					,     lowerCAmelCase__   )
  _UpperCamelCase      :	Union[str, Any]					      =  sigma_min ** (1 / rho)
  _UpperCamelCase      :	Optional[Any]					      =  sigma_max ** (1 / rho)
  _UpperCamelCase      :	Dict					      =  (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
  return sigmas






 def lowercase_      (self					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__ = 0.0					,     lowerCAmelCase__ = 0.0					,     lowerCAmelCase__ = float("inf"   )					,     lowerCAmelCase__ = 1.0					,     lowerCAmelCase__ = None					,     lowerCAmelCase__ = True					,     ):

  '''simple docstring'''







  if (
      isinstance(lowerCAmelCase__					,     lowerCAmelCase__   )
      or isinstance(lowerCAmelCase__					,     torch.IntTensor   )
      or isinstance(lowerCAmelCase__					,     torch.LongTensor   )
  ):
   raise ValueError(
       (
           "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
           " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
           " one of the `scheduler.timesteps` as a timestep."
       )					,     )

  if not self.is_scale_input_called:
   logger.warning(
       "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
       "See `StableDiffusionPipeline` for a usage example."   )

  if isinstance(lowerCAmelCase__					,     torch.Tensor   ):
   _UpperCamelCase      :	str					      =  timestep.to(self.timesteps.device   )

  _UpperCamelCase      :	int					      =  (self.timesteps == timestep).nonzero().item()
  _UpperCamelCase      :	Tuple					      =  self.sigmas[step_index]

  _UpperCamelCase      :	Any					      =  min(s_churn / (len(self.sigmas   ) - 1)					,     2**0.5 - 1   ) if s_tmin <= sigma <= s_tmax else 0.0

  _UpperCamelCase      :	Optional[int]					      =  randn_tensor(
      model_output.shape					,     dtype=model_output.dtype					,     device=model_output.device					,     generator=lowerCAmelCase__   )

  _UpperCamelCase      :	Union[str, Any]					      =  noise * s_noise
  _UpperCamelCase      :	int					      =  sigma * (gamma + 1)

  if gamma > 0:
   _UpperCamelCase      :	Union[str, Any]					      =  sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

  # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
  # NOTE: "original_sample" should not be an expected prediction_type but is left in for
  # backwards compatibility
  if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
   _UpperCamelCase      :	Optional[int]					      =  model_output
  elif self.config.prediction_type == "epsilon":
   _UpperCamelCase      :	Tuple					      =  sample - sigma_hat * model_output
  elif self.config.prediction_type == "v_prediction":
   # * c_out + input * c_skip
   _UpperCamelCase      :	Tuple					      =  model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
  else:
   raise ValueError(
       F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"   )

  # 2. Convert to an ODE derivative
  _UpperCamelCase      :	Any					      =  (sample - pred_original_sample) / sigma_hat

  _UpperCamelCase      :	Optional[int]					      =  self.sigmas[step_index + 1] - sigma_hat

  _UpperCamelCase      :	str					      =  sample + derivative * dt

  if not return_dict:
   return (prev_sample,)

  return EulerDiscreteSchedulerOutput(prev_sample=lowerCAmelCase__					,     pred_original_sample=lowerCAmelCase__   )






 def lowercase_      (self					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     ):

  '''simple docstring'''







  _UpperCamelCase      :	List[Any]					      =  self.sigmas.to(device=original_samples.device					,     dtype=original_samples.dtype   )
  if original_samples.device.type == "mps" and torch.is_floating_point(lowerCAmelCase__   ):
   # mps does not support float64
   _UpperCamelCase      :	Any					      =  self.timesteps.to(original_samples.device					,     dtype=torch.floataa   )
   _UpperCamelCase      :	List[str]					      =  timesteps.to(original_samples.device					,     dtype=torch.floataa   )
  else:
   _UpperCamelCase      :	List[Any]					      =  self.timesteps.to(original_samples.device   )
   _UpperCamelCase      :	Any					      =  timesteps.to(original_samples.device   )

  _UpperCamelCase      :	Union[str, Any]					      =  [(schedule_timesteps == t).nonzero().item() for t in timesteps]

  _UpperCamelCase      :	List[Any]					      =  sigmas[step_indices].flatten()
  while len(sigma.shape   ) < len(original_samples.shape   ):
   _UpperCamelCase      :	int					      =  sigma.unsqueeze(-1   )

  _UpperCamelCase      :	Union[str, Any]					      =  original_samples + noise * sigma
  return noisy_samples






 def __len__(self   ):

  '''simple docstring'''







  return self.config.num_train_timesteps









"""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  _SCREAMING_SNAKE_CASE       (     nn.Module				):



 '''simple docstring'''





 def __init__(self					,     lowerCAmelCase__					,     lowerCAmelCase__=1.0   ):

  '''simple docstring'''







  super().__init__()

  _UpperCamelCase      :	Optional[Any]					      =  cut_size
  _UpperCamelCase      :	List[str]					      =  cut_power






 def lowercase_      (self					,     lowerCAmelCase__					,     lowerCAmelCase__   ):

  '''simple docstring'''







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






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		str  ,	__lowerCAmelCase			:		Optional[int]						)    ->       Union[str, Any]:
 _UpperCamelCase      :	List[str]					      =  F.normalize(__lowerCAmelCase  ,	dim=-1						)
 _UpperCamelCase      :	Optional[Any]					      =  F.normalize(__lowerCAmelCase  ,	dim=-1						)
 return (x - y).norm(dim=-1						).div(2						).arcsin().pow(2						).mul(2						)






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Optional[Any]  ,	__lowerCAmelCase			:		str						)    ->       List[str]:
 for param in model.parameters():
  _UpperCamelCase      :	Any					      =  value







class  _SCREAMING_SNAKE_CASE       (     SCREAMING_SNAKE_CASE__				):



 '''simple docstring'''





 def __init__(self					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     ):

  '''simple docstring'''







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

  _UpperCamelCase      :	Union[str, Any]					      =  transforms.Normalize(mean=feature_extractor.image_mean					,     std=feature_extractor.image_std   )
  _UpperCamelCase      :	Optional[int]					      =  (
      feature_extractor.size
      if isinstance(feature_extractor.size					,     lowerCAmelCase__   )
      else feature_extractor.size["shortest_edge"]
  )
  _UpperCamelCase      :	Dict					      =  MakeCutouts(self.cut_out_size   )

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






 def lowercase_      (self					,     lowerCAmelCase__ = "auto"   ):

  '''simple docstring'''







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






 def lowercase_      (self   ):

  '''simple docstring'''







  self.enable_attention_slicing(lowerCAmelCase__   )






 def lowercase_      (self   ):

  '''simple docstring'''







  set_requires_grad(self.vae					,     lowerCAmelCase__   )






 def lowercase_      (self   ):

  '''simple docstring'''







  set_requires_grad(self.vae					,     lowerCAmelCase__   )






 def lowercase_      (self   ):

  '''simple docstring'''







  set_requires_grad(self.unet					,     lowerCAmelCase__   )






 def lowercase_      (self   ):

  '''simple docstring'''







  set_requires_grad(self.unet					,     lowerCAmelCase__   )






 @torch.enable_grad()
 def lowercase_      (self					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__=True					,     ):

  '''simple docstring'''







  _UpperCamelCase      :	Dict					      =  latents.detach().requires_grad_()

  _UpperCamelCase      :	Optional[Any]					      =  self.scheduler.scale_model_input(lowerCAmelCase__					,     lowerCAmelCase__   )

  # predict the noise residual
  _UpperCamelCase      :	Dict					      =  self.unet(lowerCAmelCase__					,     lowerCAmelCase__					,     encoder_hidden_states=lowerCAmelCase__   ).sample

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

   _UpperCamelCase      :	List[str]					      =  torch.sqrt(lowerCAmelCase__   )
   _UpperCamelCase      :	Optional[Any]					      =  pred_original_sample * (fac) + latents * (1 - fac)
  elif isinstance(self.scheduler					,     lowerCAmelCase__   ):
   _UpperCamelCase      :	Optional[int]					      =  self.scheduler.sigmas[index]
   _UpperCamelCase      :	Optional[Any]					      =  latents - sigma * noise_pred
  else:
   raise ValueError(F"scheduler type {type(self.scheduler   )} not supported"   )

  _UpperCamelCase      :	List[Any]					      =  1 / self.vae.config.scaling_factor * sample
  _UpperCamelCase      :	Dict					      =  self.vae.decode(lowerCAmelCase__   ).sample
  _UpperCamelCase      :	Union[str, Any]					      =  (image / 2 + 0.5).clamp(0					,     1   )

  if use_cutouts:
   _UpperCamelCase      :	Dict					      =  self.make_cutouts(lowerCAmelCase__					,     lowerCAmelCase__   )
  else:
   _UpperCamelCase      :	List[str]					      =  transforms.Resize(self.cut_out_size   )(lowerCAmelCase__   )
  _UpperCamelCase      :	str					      =  self.normalize(lowerCAmelCase__   ).to(latents.dtype   )

  _UpperCamelCase      :	List[Any]					      =  self.clip_model.get_image_features(lowerCAmelCase__   )
  _UpperCamelCase      :	Dict					      =  image_embeddings_clip / image_embeddings_clip.norm(p=2					,     dim=-1					,     keepdim=lowerCAmelCase__   )

  if use_cutouts:
   _UpperCamelCase      :	str					      =  spherical_dist_loss(lowerCAmelCase__					,     lowerCAmelCase__   )
   _UpperCamelCase      :	str					      =  dists.view([num_cutouts, sample.shape[0], -1]   )
   _UpperCamelCase      :	Optional[Any]					      =  dists.sum(2   ).mean(0   ).sum() * clip_guidance_scale
  else:
   _UpperCamelCase      :	Any					      =  spherical_dist_loss(lowerCAmelCase__					,     lowerCAmelCase__   ).mean() * clip_guidance_scale

  _UpperCamelCase      :	List[str]					      =  -torch.autograd.grad(lowerCAmelCase__					,     lowerCAmelCase__   )[0]

  if isinstance(self.scheduler					,     lowerCAmelCase__   ):
   _UpperCamelCase      :	Tuple					      =  latents.detach() + grads * (sigma**2)
   _UpperCamelCase      :	int					      =  noise_pred_original
  else:
   _UpperCamelCase      :	Union[str, Any]					      =  noise_pred_original - torch.sqrt(lowerCAmelCase__   ) * grads
  return noise_pred, latents






 @torch.no_grad()
 def __call__(self					,     lowerCAmelCase__					,     lowerCAmelCase__ = 5_12					,     lowerCAmelCase__ = 5_12					,     lowerCAmelCase__ = 50					,     lowerCAmelCase__ = 7.5					,     lowerCAmelCase__ = 1					,     lowerCAmelCase__ = 0.0					,     lowerCAmelCase__ = 1_00					,     lowerCAmelCase__ = None					,     lowerCAmelCase__ = 4					,     lowerCAmelCase__ = True					,     lowerCAmelCase__ = None					,     lowerCAmelCase__ = None					,     lowerCAmelCase__ = "pil"					,     lowerCAmelCase__ = True					,     ):

  '''simple docstring'''







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

  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
  _UpperCamelCase      :	Dict					      =  self.tokenizer(
      lowerCAmelCase__					,     padding="max_length"					,     max_length=self.tokenizer.model_max_length					,     truncation=lowerCAmelCase__					,     return_tensors="pt"					,     )
  _UpperCamelCase      :	List[str]					      =  self.text_encoder(text_input.input_ids.to(self.device   )   )[0]
  # duplicate text embeddings for each generation per prompt
  _UpperCamelCase      :	str					      =  text_embeddings.repeat_interleave(lowerCAmelCase__					,     dim=0   )

  if clip_guidance_scale > 0:
   if clip_prompt is not None:
    _UpperCamelCase      :	List[Any]					      =  self.tokenizer(
        lowerCAmelCase__					,     padding="max_length"					,     max_length=self.tokenizer.model_max_length					,     truncation=lowerCAmelCase__					,     return_tensors="pt"					,     ).input_ids.to(self.device   )
   else:
    _UpperCamelCase      :	str					      =  text_input.input_ids.to(self.device   )
   _UpperCamelCase      :	Dict					      =  self.clip_model.get_text_features(lowerCAmelCase__   )
   _UpperCamelCase      :	Optional[int]					      =  text_embeddings_clip / text_embeddings_clip.norm(p=2					,     dim=-1					,     keepdim=lowerCAmelCase__   )
   # duplicate text embeddings clip for each generation per prompt
   _UpperCamelCase      :	int					      =  text_embeddings_clip.repeat_interleave(lowerCAmelCase__					,     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.
  _UpperCamelCase      :	Optional[Any]					      =  guidance_scale > 1.0
  # get unconditional embeddings for classifier free guidance
  if do_classifier_free_guidance:
   _UpperCamelCase      :	Optional[int]					      =  text_input.input_ids.shape[-1]
   _UpperCamelCase      :	List[str]					      =  self.tokenizer([""]					,     padding="max_length"					,     max_length=lowerCAmelCase__					,     return_tensors="pt"   )
   _UpperCamelCase      :	List[str]					      =  self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]
   # duplicate unconditional embeddings for each generation per prompt
   _UpperCamelCase      :	Dict					      =  uncond_embeddings.repeat_interleave(lowerCAmelCase__					,     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
   _UpperCamelCase      :	Tuple					      =  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      :	Optional[Any]					      =  (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
  _UpperCamelCase      :	Optional[Any]					      =  text_embeddings.dtype
  if latents is None:
   if self.device.type == "mps":
    # randn does not work reproducibly on mps
    _UpperCamelCase      :	Optional[Any]					      =  torch.randn(lowerCAmelCase__					,     generator=lowerCAmelCase__					,     device="cpu"					,     dtype=lowerCAmelCase__   ).to(
        self.device   )
   else:
    _UpperCamelCase      :	Union[str, Any]					      =  torch.randn(lowerCAmelCase__					,     generator=lowerCAmelCase__					,     device=self.device					,     dtype=lowerCAmelCase__   )
  else:
   if latents.shape != latents_shape:
    raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"   )
   _UpperCamelCase      :	Optional[int]					      =  latents.to(self.device   )

  # set timesteps
  _UpperCamelCase      :	List[str]					      =  "offset" in set(inspect.signature(self.scheduler.set_timesteps   ).parameters.keys()   )
  _UpperCamelCase      :	str					      =  {}
  if accepts_offset:
   _UpperCamelCase      :	Tuple					      =  1

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

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

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

  # check if the scheduler accepts generator
  _UpperCamelCase      :	Optional[int]					      =  "generator" in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
  if accepts_generator:
   _UpperCamelCase      :	List[str]					      =  generator

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

   # predict the noise residual
   _UpperCamelCase      :	List[str]					      =  self.unet(lowerCAmelCase__					,     lowerCAmelCase__					,     encoder_hidden_states=lowerCAmelCase__   ).sample

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

   # perform clip guidance
   if clip_guidance_scale > 0:
    _UpperCamelCase      :	List[str]					      =  (
        text_embeddings.chunk(2   )[1] if do_classifier_free_guidance else text_embeddings
    )
    _UpperCamelCase      ,	_UpperCamelCase      :	Optional[Any]					      =  self.cond_fn(
        lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     )

   # compute the previous noisy sample x_t -> x_t-1
   _UpperCamelCase      :	Tuple					      =  self.scheduler.step(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     **lowerCAmelCase__   ).prev_sample

  # scale and decode the image latents with vae
  _UpperCamelCase      :	Tuple					      =  1 / self.vae.config.scaling_factor * latents
  _UpperCamelCase      :	Union[str, Any]					      =  self.vae.decode(lowerCAmelCase__   ).sample

  _UpperCamelCase      :	List[Any]					      =  (image / 2 + 0.5).clamp(0					,     1   )
  _UpperCamelCase      :	Any					      =  image.cpu().permute(0					,     2					,     3					,     1   ).numpy()

  if output_type == "pil":
   _UpperCamelCase      :	Dict					      =  self.numpy_to_pil(lowerCAmelCase__   )

  if not return_dict:
   return (image, None)

  return StableDiffusionPipelineOutput(images=lowerCAmelCase__					,     nsfw_content_detected=lowerCAmelCase__   )



