





import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

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





def      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		Any:







  """simple docstring"""





  if isinstance(__a	,    torch.Tensor		):
    return image
  elif isinstance(__a	,    PIL.Image.Image		):
    lowerCamelCase__:      str			  =[image]

  if isinstance(image[0]	,    PIL.Image.Image		):
    lowerCamelCase__:      List[str]			  =[np.array(i.resize((w, h)	,    resample=PIL_INTERPOLATION["lanczos"]		)		)[None, :] for i in image]
    lowerCamelCase__:      Any			  =np.concatenate(__a	,    axis=0		)
    lowerCamelCase__:      str			  =np.array(__a		).astype(np.floataa		) / 2_5_5.0
    lowerCamelCase__:      Tuple			  =image.transpose(0	,    3	,    1	,    2		)
    lowerCamelCase__:      Union[str, Any]			  =2.0 * image - 1.0
    lowerCamelCase__:      Any			  =torch.from_numpy(__a		)
  elif isinstance(image[0]	,    torch.Tensor		):
    lowerCamelCase__:      Dict			  =torch.cat(__a	,    dim=0		)
  return image





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a=0.9_9_9_5		)     ->		str:







  """simple docstring"""





  if not isinstance(__a	,    np.ndarray		):
    lowerCamelCase__:      int			  =True
    lowerCamelCase__:      Dict			  =va.device
    lowerCamelCase__:      List[Any]			  =va.cpu().numpy()
    lowerCamelCase__:      List[str]			  =va.cpu().numpy()

  lowerCamelCase__:      Optional[int]			  =np.sum(va * va / (np.linalg.norm(__a		) * np.linalg.norm(__a		))		)
  if np.abs(__a		) > DOT_THRESHOLD:
    lowerCamelCase__:      str			  =(1 - t) * va + t * va
  else:
    lowerCamelCase__:      List[str]			  =np.arccos(__a		)
    lowerCamelCase__:      Tuple			  =np.sin(__a		)
    lowerCamelCase__:      str			  =theta_a * t
    lowerCamelCase__:      List[Any]			  =np.sin(__a		)
    lowerCamelCase__:      Tuple			  =np.sin(theta_a - theta_t		) / sin_theta_a
    lowerCamelCase__:      int			  =sin_theta_t / sin_theta_a
    lowerCamelCase__:      List[Any]			  =sa * va + sa * va

  if inputs_are_torch:
    lowerCamelCase__:      int			  =torch.from_numpy(__a		).to(__a		)

  return va





def      lowerCAmelCase_ (       __a	,    __a		)     ->		Optional[int]:







  """simple docstring"""





  lowerCamelCase__:      str			  =F.normalize(__a	,    dim=-1		)
  lowerCamelCase__:      Union[str, Any]			  =F.normalize(__a	,    dim=-1		)
  return (x - y).norm(dim=-1		).div(2		).arcsin().pow(2		).mul(2		)





def      lowerCAmelCase_ (       __a	,    __a		)     ->		List[Any]:







  """simple docstring"""





  for param in model.parameters():
    lowerCamelCase__:      Any			  =value






class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''

 def __init__(self				:   Optional[Any]       , UpperCAmelCase_				:   AutoencoderKL       , UpperCAmelCase_				:   CLIPTextModel       , UpperCAmelCase_				:   CLIPModel       , UpperCAmelCase_				:   CLIPTokenizer       , UpperCAmelCase_				:   UNetaDConditionModel       , UpperCAmelCase_				:   Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]       , UpperCAmelCase_				:   CLIPFeatureExtractor       , UpperCAmelCase_				:   List[Any]=None       , UpperCAmelCase_				:   Union[str, Any]=None       , UpperCAmelCase_				:   List[Any]=None       , )       ->List[str]:



   '''simple docstring'''



   super().__init__()
   self.register_modules(
       vae=UpperCAmelCase_       , text_encoder=UpperCAmelCase_       , clip_model=UpperCAmelCase_       , tokenizer=UpperCAmelCase_       , unet=UpperCAmelCase_       , scheduler=UpperCAmelCase_       , feature_extractor=UpperCAmelCase_       , coca_model=UpperCAmelCase_       , coca_tokenizer=UpperCAmelCase_       , coca_transform=UpperCAmelCase_       , )
   lowerCamelCase__:      Tuple			  =(
       feature_extractor.size
       if isinstance(feature_extractor.size       , UpperCAmelCase_)
       else feature_extractor.size["shortest_edge"]
   )
   lowerCamelCase__:      int			  =transforms.Normalize(mean=feature_extractor.image_mean       , std=feature_extractor.image_std)
   set_requires_grad(self.text_encoder       , UpperCAmelCase_)
   set_requires_grad(self.clip_model       , UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict       , UpperCAmelCase_				:   Optional[Union[str, int]] = "auto")       ->Union[str, Any]:



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->Union[str, Any]:



   '''simple docstring'''



   self.enable_attention_slicing(UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   str)       ->Any:



   '''simple docstring'''



   set_requires_grad(self.vae       , UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->str:



   '''simple docstring'''



   set_requires_grad(self.vae       , UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->Dict:



   '''simple docstring'''



   set_requires_grad(self.unet       , UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->List[Any]:



   '''simple docstring'''



   set_requires_grad(self.unet       , UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   int       , UpperCAmelCase_				:   Tuple       , UpperCAmelCase_				:   Union[str, Any]       , UpperCAmelCase_				:   Tuple)       ->str:



   '''simple docstring'''



   lowerCamelCase__:      str			  =min(int(num_inference_steps * strength)       , UpperCAmelCase_)

   lowerCamelCase__:      Tuple			  =max(num_inference_steps - init_timestep       , 0)
   lowerCamelCase__:      List[str]			  =self.scheduler.timesteps[t_start:]

   return timesteps, num_inference_steps - t_start

 def 							SCREAMING_SNAKE_CASE_					(self				:   int       , UpperCAmelCase_				:   Tuple       , UpperCAmelCase_				:   Any       , UpperCAmelCase_				:   str       , UpperCAmelCase_				:   str       , UpperCAmelCase_				:   str       , UpperCAmelCase_				:   Tuple=None)       ->Union[str, Any]:



   '''simple docstring'''



   if not isinstance(UpperCAmelCase_       , torch.Tensor):
     raise ValueError(F"""`image` has to be of type `torch.Tensor` but is {type(UpperCAmelCase_)}""")

   lowerCamelCase__:      Optional[int]			  =image.to(device=UpperCAmelCase_       , dtype=UpperCAmelCase_)

   if isinstance(UpperCAmelCase_       , UpperCAmelCase_):
     lowerCamelCase__:      Any			  =[
         self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(UpperCAmelCase_)
     ]
     lowerCamelCase__:      List[Any]			  =torch.cat(UpperCAmelCase_       , dim=0)
   else:
     lowerCamelCase__:      Tuple			  =self.vae.encode(UpperCAmelCase_).latent_dist.sample(UpperCAmelCase_)

   # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
   lowerCamelCase__:      List[str]			  =0.1_8215 * init_latents
   lowerCamelCase__:      Optional[int]			  =init_latents.repeat_interleave(UpperCAmelCase_       , dim=0)

   lowerCamelCase__:      List[Any]			  =randn_tensor(init_latents.shape       , generator=UpperCAmelCase_       , device=UpperCAmelCase_       , dtype=UpperCAmelCase_)

   # get latents
   lowerCamelCase__:      str			  =self.scheduler.add_noise(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_)
   lowerCamelCase__:      Optional[int]			  =init_latents

   return latents

 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict       , UpperCAmelCase_				:   Dict)       ->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  =self.coca_transform(UpperCAmelCase_).unsqueeze(0)
   with torch.no_grad(), torch.cuda.amp.autocast():
     lowerCamelCase__:      str			  =self.coca_model.generate(transformed_image.to(device=self.device       , dtype=self.coca_model.dtype))
   lowerCamelCase__:      str			  =self.coca_tokenizer.decode(generated[0].cpu().numpy())
   return generated.split("<end_of_text>")[0].replace("<start_of_text>"       , "").rstrip(" .,")

 def 							SCREAMING_SNAKE_CASE_					(self				:   Union[str, Any]       , UpperCAmelCase_				:   Tuple       , UpperCAmelCase_				:   int)       ->str:



   '''simple docstring'''



   lowerCamelCase__:      Optional[Any]			  =self.feature_extractor.preprocess(UpperCAmelCase_)
   lowerCamelCase__:      Optional[Any]			  =torch.from_numpy(clip_image_input["pixel_values"][0]).unsqueeze(0).to(self.device).half()
   lowerCamelCase__:      Any			  =self.clip_model.get_image_features(UpperCAmelCase_)
   lowerCamelCase__:      int			  =image_embeddings_clip / image_embeddings_clip.norm(p=2       , dim=-1       , keepdim=UpperCAmelCase_)
   lowerCamelCase__:      Union[str, Any]			  =image_embeddings_clip.repeat_interleave(UpperCAmelCase_       , dim=0)
   return image_embeddings_clip

 @torch.enable_grad()
 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any]       , UpperCAmelCase_				:   List[Any]       , UpperCAmelCase_				:   List[str]       , UpperCAmelCase_				:   Optional[int]       , UpperCAmelCase_				:   Union[str, Any]       , UpperCAmelCase_				:   List[Any]       , UpperCAmelCase_				:   Union[str, Any]       , UpperCAmelCase_				:   Dict       , )       ->Any:



   '''simple docstring'''



   lowerCamelCase__:      str			  =latents.detach().requires_grad_()

   lowerCamelCase__:      int			  =self.scheduler.scale_model_input(UpperCAmelCase_       , UpperCAmelCase_)

   # predict the noise residual
   lowerCamelCase__:      str			  =self.unet(UpperCAmelCase_       , UpperCAmelCase_       , encoder_hidden_states=UpperCAmelCase_).sample

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

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

   # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
   lowerCamelCase__:      Any			  =1 / 0.1_8215 * sample
   lowerCamelCase__:      Any			  =self.vae.decode(UpperCAmelCase_).sample
   lowerCamelCase__:      List[Any]			  =(image / 2 + 0.5).clamp(0       , 1)

   lowerCamelCase__:      List[Any]			  =transforms.Resize(self.feature_extractor_size)(UpperCAmelCase_)
   lowerCamelCase__:      Dict			  =self.normalize(UpperCAmelCase_).to(latents.dtype)

   lowerCamelCase__:      Union[str, Any]			  =self.clip_model.get_image_features(UpperCAmelCase_)
   lowerCamelCase__:      List[str]			  =image_embeddings_clip / image_embeddings_clip.norm(p=2       , dim=-1       , keepdim=UpperCAmelCase_)

   lowerCamelCase__:      List[Any]			  =spherical_dist_loss(UpperCAmelCase_       , UpperCAmelCase_).mean() * clip_guidance_scale

   lowerCamelCase__:      Dict			  =-torch.autograd.grad(UpperCAmelCase_       , UpperCAmelCase_)[0]

   if isinstance(self.scheduler       , UpperCAmelCase_):
     lowerCamelCase__:      Dict			  =latents.detach() + grads * (sigma**2)
     lowerCamelCase__:      List[str]			  =noise_pred_original
   else:
     lowerCamelCase__:      int			  =noise_pred_original - torch.sqrt(UpperCAmelCase_) * grads
   return noise_pred, latents







 @torch.no_grad()
 def __call__(self				:   Dict       , UpperCAmelCase_				:   Union[torch.FloatTensor, PIL.Image.Image]       , UpperCAmelCase_				:   Union[torch.FloatTensor, PIL.Image.Image]       , UpperCAmelCase_				:   Optional[str] = None       , UpperCAmelCase_				:   Optional[str] = None       , UpperCAmelCase_				:   Optional[int] = 512       , UpperCAmelCase_				:   Optional[int] = 512       , UpperCAmelCase_				:   float = 0.6       , UpperCAmelCase_				:   Optional[int] = 50       , UpperCAmelCase_				:   Optional[float] = 7.5       , UpperCAmelCase_				:   Optional[int] = 1       , UpperCAmelCase_				:   float = 0.0       , UpperCAmelCase_				:   Optional[float] = 100       , UpperCAmelCase_				:   Optional[torch.Generator] = None       , UpperCAmelCase_				:   Optional[str] = "pil"       , UpperCAmelCase_				:   bool = True       , UpperCAmelCase_				:   float = 0.8       , UpperCAmelCase_				:   float = 0.1       , UpperCAmelCase_				:   float = 0.1       , )       ->Any:



   '''simple docstring'''



   if isinstance(UpperCAmelCase_       , UpperCAmelCase_) and len(UpperCAmelCase_) != batch_size:
     raise ValueError(F"""You have passed {batch_size} batch_size, but only {len(UpperCAmelCase_)} generators.""")

   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 isinstance(UpperCAmelCase_       , torch.Generator) and batch_size > 1:
     lowerCamelCase__:      Optional[Any]			  =[generator] + [None] * (batch_size - 1)

   lowerCamelCase__:      Optional[Any]			  =[
       ("model", self.coca_model is None),
       ("tokenizer", self.coca_tokenizer is None),
       ("transform", self.coca_transform is None),
   ]
   lowerCamelCase__:      str			  =[x[0] for x in coca_is_none if x[1]]
   lowerCamelCase__:      Optional[int]			  =", ".join(UpperCAmelCase_)
   # generate prompts with coca model if prompt is None
   if content_prompt is None:
     if len(UpperCAmelCase_):
       raise ValueError(
           F"""Content prompt is None and CoCa [{coca_is_none_str}] is None."""
           F"""Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.""")
     lowerCamelCase__:      List[str]			  =self.get_image_description(UpperCAmelCase_)
   if style_prompt is None:
     if len(UpperCAmelCase_):
       raise ValueError(
           F"""Style prompt is None and CoCa [{coca_is_none_str}] is None."""
           F""" Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.""")
     lowerCamelCase__:      Optional[int]			  =self.get_image_description(UpperCAmelCase_)

   # get prompt text embeddings for content and style
   lowerCamelCase__:      int			  =self.tokenizer(
       UpperCAmelCase_       , padding="max_length"       , max_length=self.tokenizer.model_max_length       , truncation=UpperCAmelCase_       , return_tensors="pt"       , )
   lowerCamelCase__:      Optional[Any]			  =self.text_encoder(content_text_input.input_ids.to(self.device))[0]

   lowerCamelCase__:      Tuple			  =self.tokenizer(
       UpperCAmelCase_       , padding="max_length"       , max_length=self.tokenizer.model_max_length       , truncation=UpperCAmelCase_       , return_tensors="pt"       , )
   lowerCamelCase__:      Any			  =self.text_encoder(style_text_input.input_ids.to(self.device))[0]

   lowerCamelCase__:      Tuple			  =slerp(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_)

   # duplicate text embeddings for each generation per prompt
   lowerCamelCase__:      Dict			  =text_embeddings.repeat_interleave(UpperCAmelCase_       , dim=0)

   # set timesteps
   lowerCamelCase__:      Dict			  ="offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
   lowerCamelCase__:      str			  ={}
   if accepts_offset:
     lowerCamelCase__:      Tuple			  =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
   self.scheduler.timesteps.to(self.device)

   lowerCamelCase__  ,					lowerCamelCase__:      Optional[Any]			  =self.get_timesteps(UpperCAmelCase_       , UpperCAmelCase_       , self.device)
   lowerCamelCase__:      Optional[Any]			  =timesteps[:1].repeat(UpperCAmelCase_)

   # Preprocess image
   lowerCamelCase__:      List[Any]			  =preprocess(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_)
   lowerCamelCase__:      List[str]			  =self.prepare_latents(
       UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , text_embeddings.dtype       , self.device       , UpperCAmelCase_)

   lowerCamelCase__:      Tuple			  =preprocess(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_)
   lowerCamelCase__:      int			  =self.prepare_latents(
       UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , text_embeddings.dtype       , self.device       , UpperCAmelCase_)

   lowerCamelCase__:      Tuple			  =slerp(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_)

   if clip_guidance_scale > 0:
     lowerCamelCase__:      Optional[int]			  =self.get_clip_image_embeddings(UpperCAmelCase_       , UpperCAmelCase_)
     lowerCamelCase__:      List[Any]			  =self.get_clip_image_embeddings(UpperCAmelCase_       , UpperCAmelCase_)
     lowerCamelCase__:      Optional[Any]			  =slerp(
         UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_)

   # 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__:      str			  =guidance_scale > 1.0
   # get unconditional embeddings for classifier free guidance
   if do_classifier_free_guidance:
     lowerCamelCase__:      Optional[Any]			  =content_text_input.input_ids.shape[-1]
     lowerCamelCase__:      int			  =self.tokenizer([""]       , padding="max_length"       , max_length=UpperCAmelCase_       , return_tensors="pt")
     lowerCamelCase__:      Any			  =self.text_encoder(uncond_input.input_ids.to(self.device))[0]
     # duplicate unconditional embeddings for each generation per prompt
     lowerCamelCase__:      Optional[Any]			  =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
     lowerCamelCase__:      str			  =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__:      Any			  =(batch_size, self.unet.config.in_channels, height // 8, width // 8)
   lowerCamelCase__:      Tuple			  =text_embeddings.dtype
   if latents is None:
     if self.device.type == "mps":
       # randn does not work reproducibly on mps
       lowerCamelCase__:      int			  =torch.randn(UpperCAmelCase_       , generator=UpperCAmelCase_       , device="cpu"       , dtype=UpperCAmelCase_).to(
           self.device)
     else:
       lowerCamelCase__:      Any			  =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}""")
     lowerCamelCase__:      List[str]			  =latents.to(self.device)

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

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

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

       # predict the noise residual
       lowerCamelCase__:      int			  =self.unet(UpperCAmelCase_       , UpperCAmelCase_       , encoder_hidden_states=UpperCAmelCase_).sample

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

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

       # compute the previous noisy sample x_t -> x_t-1
       lowerCamelCase__:      Optional[int]			  =self.scheduler.step(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , **UpperCAmelCase_).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
   lowerCamelCase__:      int			  =1 / 0.1_8215 * latents
   lowerCamelCase__:      str			  =self.vae.decode(UpperCAmelCase_).sample

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

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

   if not return_dict:
     return (image, None)

   return StableDiffusionPipelineOutput(images=UpperCAmelCase_       , nsfw_content_detected=UpperCAmelCase_)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A									=				{"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				[
         "IBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "IBertForMaskedLM",
         "IBertForMultipleChoice",
         "IBertForQuestionAnswering",
         "IBertForSequenceClassification",
         "IBertForTokenClassification",
         "IBertModel",
         "IBertPreTrainedModel",
     ]

if TYPE_CHECKING:
     from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_ibert import (
              IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              IBertForMaskedLM,
              IBertForMultipleChoice,
              IBertForQuestionAnswering,
              IBertForSequenceClassification,
              IBertForTokenClassification,
              IBertModel,
              IBertPreTrainedModel,
          )

else:
     import sys

     __A									=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

