


"""simple docstring"""


import os
from glob import glob

import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn

from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil



class   _a      :
    def __init__( self							:					Dict							,					SCREAMING_SNAKE_CASE__							:					str = "cpu"							,					SCREAMING_SNAKE_CASE__							:					str = "openai/clip-vit-large-patch14"				):
          lowerCamelCase__           =		device
          lowerCamelCase__           =		CLIPTokenizerFast.from_pretrained(__UpperCamelCase				)
          lowerCamelCase__           =		[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
          lowerCamelCase__           =		[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]
          lowerCamelCase__           =		torchvision.transforms.Normalize(self.image_mean							,					self.image_std				)
          lowerCamelCase__           =		torchvision.transforms.Resize(2_24				)
          lowerCamelCase__           =		torchvision.transforms.CenterCrop(2_24				)
    def      _UpperCamelCase       ( self							:					str							,					SCREAMING_SNAKE_CASE__							:					Dict				):
          lowerCamelCase__           =		self.resize(__UpperCamelCase				)
          lowerCamelCase__           =		self.center_crop(__UpperCamelCase				)
          lowerCamelCase__           =		self.normalize(__UpperCamelCase				)
          return images


    def __call__( self							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=None							,					SCREAMING_SNAKE_CASE__							:					List[Any]=None							,					**SCREAMING_SNAKE_CASE__							:					Union[str, Any]				):
          lowerCamelCase__           =		self.tokenizer(text=__UpperCamelCase							,					**__UpperCamelCase				)
          lowerCamelCase__           =		self.preprocess_img(__UpperCamelCase				)
          lowerCamelCase__           =		{key: value.to(self.device				) for (key, value) in encoding.items()}
          return encoding





class   _a      (      nn.Module ):
    def __init__( self							:					Dict							,					SCREAMING_SNAKE_CASE__							:					Dict=10							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=0.01							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=None							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=None							,					SCREAMING_SNAKE_CASE__							:					Any=None							,					SCREAMING_SNAKE_CASE__							:					List[Any]=None							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=None							,					SCREAMING_SNAKE_CASE__							:					Tuple=None							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=False							,					SCREAMING_SNAKE_CASE__							:					Dict=True							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]="image"							,					SCREAMING_SNAKE_CASE__							:					List[Any]=True							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=False							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=False							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=False							,					):
          super().__init__()
          lowerCamelCase__           =		None
          lowerCamelCase__           =		device if device else get_device()
          if vqgan:
                lowerCamelCase__           =		vqgan
          else:
                lowerCamelCase__           =		load_vqgan(self.device							,					conf_path=__UpperCamelCase							,					ckpt_path=__UpperCamelCase				)
          self.vqgan.eval()
          if clip:
                lowerCamelCase__           =		clip
          else:
                lowerCamelCase__           =		CLIPModel.from_pretrained('openai/clip-vit-base-patch32'				)
          self.clip.to(self.device				)
          lowerCamelCase__           =		ProcessorGradientFlow(device=self.device				)

          lowerCamelCase__           =		iterations
          lowerCamelCase__           =		lr
          lowerCamelCase__           =		log
          lowerCamelCase__           =		make_grid
          lowerCamelCase__           =		return_val
          lowerCamelCase__           =		quantize
          lowerCamelCase__           =		self.vqgan.decoder.z_shape
    def      _UpperCamelCase       ( self							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					List[str]=None							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=None							,					SCREAMING_SNAKE_CASE__							:					str=5							,					SCREAMING_SNAKE_CASE__							:					int=True				):
          lowerCamelCase__           =		[]
          if output_path is None:
                lowerCamelCase__           =		'./animation.gif'
          if input_path is None:
                lowerCamelCase__           =		self.save_path
          lowerCamelCase__           =		sorted(glob(input_path + '/*'				)				)
          if not len(__UpperCamelCase				):
                raise ValueError(
                    'No images found in save path, aborting (did you pass save_intermediate=True to the generate'
                    ' function?)'				)
          if len(__UpperCamelCase				) == 1:
                print('Only one image found in save path, (did you pass save_intermediate=True to the generate function?)'				)
          lowerCamelCase__           =		total_duration / len(__UpperCamelCase				)
          lowerCamelCase__           =		[frame_duration] * len(__UpperCamelCase				)
          if extend_frames:
                lowerCamelCase__           =		1.5
                lowerCamelCase__           =		3
          for file_name in paths:
                if file_name.endswith('.png'				):
                      images.append(imageio.imread(__UpperCamelCase				)				)
          imageio.mimsave(__UpperCamelCase							,					__UpperCamelCase							,					duration=__UpperCamelCase				)
          print(F'gif saved to {output_path}'				)
    def      _UpperCamelCase       ( self							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=None							,					SCREAMING_SNAKE_CASE__							:					Dict=None				):
          if not (path or img):
                raise ValueError('Input either path or tensor'				)
          if img is not None:
                raise NotImplementedError
          lowerCamelCase__           =		preprocess(Image.open(__UpperCamelCase				)							,					target_image_size=2_56				).to(self.device				)
          lowerCamelCase__           =		preprocess_vqgan(__UpperCamelCase				)
          lowerCamelCase__    ,  *lowerCamelCase__           =		self.vqgan.encode(__UpperCamelCase				)
          return z
    def      _UpperCamelCase       ( self							:					int							,					SCREAMING_SNAKE_CASE__							:					Dict				):
          lowerCamelCase__           =		self.latent.detach().requires_grad_()
          lowerCamelCase__           =		base_latent + transform_vector
          if self.quantize:
                lowerCamelCase__    ,  *lowerCamelCase__           =		self.vqgan.quantize(__UpperCamelCase				)
          else:
                lowerCamelCase__           =		trans_latent
          return self.vqgan.decode(__UpperCamelCase				)
    def      _UpperCamelCase       ( self							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					int							,					SCREAMING_SNAKE_CASE__							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					Dict=None				):
          lowerCamelCase__           =		self.clip_preprocessor(text=__UpperCamelCase							,					images=__UpperCamelCase							,					return_tensors='pt'							,					padding=__UpperCamelCase				)
          lowerCamelCase__           =		self.clip(**__UpperCamelCase				)
          lowerCamelCase__           =		clip_outputs.logits_per_image
          if weights is not None:
                lowerCamelCase__           =		similarity_logits * weights
          return similarity_logits.sum()
    def      _UpperCamelCase       ( self							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]				):
          lowerCamelCase__           =		self._get_clip_similarity(pos_prompts['prompts']							,					__UpperCamelCase							,					weights=(1 / pos_prompts['weights'])				)
          if neg_prompts:
                lowerCamelCase__           =		self._get_clip_similarity(neg_prompts['prompts']							,					__UpperCamelCase							,					weights=neg_prompts['weights']				)
          else:
                lowerCamelCase__           =		torch.tensor([1]							,					device=self.device				)
          lowerCamelCase__           =		-torch.log(__UpperCamelCase				) + torch.log(__UpperCamelCase				)
          return loss
    def      _UpperCamelCase       ( self							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					Any							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					Optional[int]				):
          lowerCamelCase__           =		torch.randn_like(self.latent							,					requires_grad=__UpperCamelCase							,					device=self.device				)
          lowerCamelCase__           =		torch.optim.Adam([vector]							,					lr=self.lr				)

          for i in range(self.iterations				):
                optim.zero_grad()
                lowerCamelCase__           =		self._add_vector(__UpperCamelCase				)
                lowerCamelCase__           =		loop_post_process(__UpperCamelCase				)
                lowerCamelCase__           =		self._get_CLIP_loss(__UpperCamelCase							,					__UpperCamelCase							,					__UpperCamelCase				)
                print('CLIP loss'							,					__UpperCamelCase				)
                if self.log:
                      wandb.log({'CLIP Loss': clip_loss}				)
                clip_loss.backward(retain_graph=__UpperCamelCase				)
                optim.step()
                if self.return_val == "image":
                      yield custom_to_pil(transformed_img[0]				)
                else:
                      yield vector
    def      _UpperCamelCase       ( self							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					Dict							,					SCREAMING_SNAKE_CASE__							:					Any				):
          wandb.init(reinit=__UpperCamelCase							,					project='face-editor'				)
          wandb.config.update({'Positive Prompts': positive_prompts}				)
          wandb.config.update({'Negative Prompts': negative_prompts}				)
          wandb.config.update({'lr': self.lr, 'iterations': self.iterations}				)
          if image_path:
                lowerCamelCase__           =		Image.open(__UpperCamelCase				)
                lowerCamelCase__           =		image.resize((2_56, 2_56)				)
                wandb.log('Original Image'							,					wandb.Image(__UpperCamelCase				)				)
    def      _UpperCamelCase       ( self							:					Dict							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]				):
          if not prompts:
                return []
          lowerCamelCase__           =		[]
          lowerCamelCase__           =		[]
          if isinstance(__UpperCamelCase							,					__UpperCamelCase				):
                lowerCamelCase__           =		[prompt.strip() for prompt in prompts.split('|'				)]
          for prompt in prompts:
                if isinstance(__UpperCamelCase							,					(tuple, list)				):
                      lowerCamelCase__           =		prompt[0]
                      lowerCamelCase__           =		float(prompt[1]				)
                elif ":" in prompt:
                      lowerCamelCase__    ,  lowerCamelCase__           =		prompt.split(':'				)
                      lowerCamelCase__           =		float(__UpperCamelCase				)
                else:
                      lowerCamelCase__           =		prompt
                      lowerCamelCase__           =		1.0
                processed_prompts.append(__UpperCamelCase				)
                weights.append(__UpperCamelCase				)
          return {
              "prompts": processed_prompts,
              "weights": torch.tensor(__UpperCamelCase							,					device=self.device				),
          }


    def      _UpperCamelCase       ( self							:					Any							,					SCREAMING_SNAKE_CASE__							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=None							,					SCREAMING_SNAKE_CASE__							:					List[str]=None							,					SCREAMING_SNAKE_CASE__							:					List[Any]=True							,					SCREAMING_SNAKE_CASE__							:					List[str]=False							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=True							,					SCREAMING_SNAKE_CASE__							:					List[Any]=True							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=None							,					):
          if image_path:
                lowerCamelCase__           =		self._get_latent(__UpperCamelCase				)
          else:
                lowerCamelCase__           =		torch.randn(self.latent_dim							,					device=self.device				)
          if self.log:
                self._init_logging(__UpperCamelCase							,					__UpperCamelCase							,					__UpperCamelCase				)

          assert pos_prompts, "You must provide at least one positive prompt."
          lowerCamelCase__           =		self.process_prompts(__UpperCamelCase				)
          lowerCamelCase__           =		self.process_prompts(__UpperCamelCase				)
          if save_final and save_path is None:
                lowerCamelCase__           =		os.path.join('./outputs/'							,					'_'.join(pos_prompts['prompts']				)				)
          if not os.path.exists(__UpperCamelCase				):
                os.makedirs(__UpperCamelCase				)
          else:
                lowerCamelCase__           =		save_path + '_' + get_timestamp()
                os.makedirs(__UpperCamelCase				)
          lowerCamelCase__           =		save_path

          lowerCamelCase__           =		self.vqgan.decode(self.latent				)[0]
          if show_intermediate:
                print('Original Image'				)
                show_pil(custom_to_pil(__UpperCamelCase				)				)

          lowerCamelCase__           =		loop_post_process(__UpperCamelCase				)
          for iter, transformed_img in enumerate(self._optimize_CLIP(__UpperCamelCase							,					__UpperCamelCase							,					__UpperCamelCase				)				):
                if show_intermediate:
                      show_pil(__UpperCamelCase				)
                if save_intermediate:
                      transformed_img.save(os.path.join(self.save_path							,					F'iter_{iter:03d}.png'				)				)
                if self.log:
                      wandb.log({'Image': wandb.Image(__UpperCamelCase				)}				)
          if show_final:
                show_pil(__UpperCamelCase				)
          if save_final:
                transformed_img.save(os.path.join(self.save_path							,					F'iter_{iter:03d}_final.png'				)				)







"""simple docstring"""


from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


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

_snake_case          =							"\n    Examples:\n        ```py\n        >>> from PIL import Image\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif, load_image\n\n        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n        >>> repo = \"openai/shap-e-img2img\"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 3.0\n        >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"\n        >>> image = load_image(image_url).convert(\"RGB\")\n\n        >>> images = pipe(\n        ...     image,\n        ...     guidance_scale=guidance_scale,\n        ...     num_inference_steps=64,\n        ...     frame_size=256,\n        ... ).images\n\n        >>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")\n        ```\n"



@dataclass
class   _a      (      SCREAMING_SNAKE_CASE_ ):
    a_    :				Union[PIL.Image.Image, np.ndarray]





class   _a      (      SCREAMING_SNAKE_CASE_ ):
    def __init__( self							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					PriorTransformer							,					SCREAMING_SNAKE_CASE__							:					CLIPVisionModel							,					SCREAMING_SNAKE_CASE__							:					CLIPImageProcessor							,					SCREAMING_SNAKE_CASE__							:					HeunDiscreteScheduler							,					SCREAMING_SNAKE_CASE__							:					ShapERenderer							,					):
          super().__init__()

          self.register_modules(
              prior=SCREAMING_SNAKE_CASE__							,					image_encoder=SCREAMING_SNAKE_CASE__							,					image_processor=SCREAMING_SNAKE_CASE__							,					scheduler=SCREAMING_SNAKE_CASE__							,					renderer=SCREAMING_SNAKE_CASE__							,					)
    def      _UpperCamelCase       ( self							:					str							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					int							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					List[str]				):
          if latents is None:
                lowerCamelCase__           =		randn_tensor(SCREAMING_SNAKE_CASE__							,					generator=SCREAMING_SNAKE_CASE__							,					device=SCREAMING_SNAKE_CASE__							,					dtype=SCREAMING_SNAKE_CASE__				)
          else:
                if latents.shape != shape:
                      raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}'				)
                lowerCamelCase__           =		latents.to(SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		latents * scheduler.init_noise_sigma
          return latents
    def      _UpperCamelCase       ( self							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					List[str]=0				):
          if is_accelerate_available():
                from accelerate import cpu_offload
          else:
                raise ImportError('Please install accelerate via `pip install accelerate`'				)

          lowerCamelCase__           =		torch.device(F'cuda:{gpu_id}'				)

          lowerCamelCase__           =		[self.image_encoder, self.prior]
          for cpu_offloaded_model in models:
                if cpu_offloaded_model is not None:
                      cpu_offload(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
    @property
    def      _UpperCamelCase       ( self							:					Dict				):
          if self.device != torch.device('meta'				) or not hasattr(self.image_encoder							,					'_hf_hook'				):
                return self.device
          for module in self.image_encoder.modules():
                if (
                    hasattr(SCREAMING_SNAKE_CASE__							,					'_hf_hook'				)
                    and hasattr(module._hf_hook							,					'execution_device'				)
                    and module._hf_hook.execution_device is not None
                ):
                      return torch.device(module._hf_hook.execution_device				)
          return self.device
    def      _UpperCamelCase       ( self							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Dict							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					int							,					):
          if isinstance(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				) and isinstance(image[0]							,					torch.Tensor				):
                lowerCamelCase__           =		torch.cat(SCREAMING_SNAKE_CASE__							,					axis=0				) if image[0].ndim == 4 else torch.stack(SCREAMING_SNAKE_CASE__							,					axis=0				)

          if not isinstance(SCREAMING_SNAKE_CASE__							,					torch.Tensor				):
                lowerCamelCase__           =		self.image_processor(SCREAMING_SNAKE_CASE__							,					return_tensors='pt'				).pixel_values[0].unsqueeze(0				)

          lowerCamelCase__           =		image.to(dtype=self.image_encoder.dtype							,					device=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		self.image_encoder(SCREAMING_SNAKE_CASE__				)['last_hidden_state']
          lowerCamelCase__           =		image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

          lowerCamelCase__           =		image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE__							,					dim=0				)

          if do_classifier_free_guidance:
                lowerCamelCase__           =		torch.zeros_like(SCREAMING_SNAKE_CASE__				)

                # 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__           =		torch.cat([negative_image_embeds, image_embeds]				)

          return image_embeds


    @torch.no_grad()
    @replace_example_docstring(SCREAMING_SNAKE_CASE__				)
    def __call__( self							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					Union[PIL.Image.Image, List[PIL.Image.Image]]							,					SCREAMING_SNAKE_CASE__							:					int = 1							,					SCREAMING_SNAKE_CASE__							:					int = 25							,					SCREAMING_SNAKE_CASE__							:					Optional[Union[torch.Generator, List[torch.Generator]]] = None							,					SCREAMING_SNAKE_CASE__							:					Optional[torch.FloatTensor] = None							,					SCREAMING_SNAKE_CASE__							:					float = 4.0							,					SCREAMING_SNAKE_CASE__							:					int = 64							,					SCREAMING_SNAKE_CASE__							:					Optional[str] = "pil"							,					SCREAMING_SNAKE_CASE__							:					bool = True							,					):

          if isinstance(SCREAMING_SNAKE_CASE__							,					PIL.Image.Image				):
                lowerCamelCase__           =		1
          elif isinstance(SCREAMING_SNAKE_CASE__							,					torch.Tensor				):
                lowerCamelCase__           =		image.shape[0]
          elif isinstance(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				) and isinstance(image[0]							,					(torch.Tensor, PIL.Image.Image)				):
                lowerCamelCase__           =		len(SCREAMING_SNAKE_CASE__				)
          else:
                raise ValueError(
                    F'`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(SCREAMING_SNAKE_CASE__				)}'				)

          lowerCamelCase__           =		self._execution_device

          lowerCamelCase__           =		batch_size * num_images_per_prompt

          lowerCamelCase__           =		guidance_scale > 1.0
          lowerCamelCase__           =		self._encode_image(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)

          # prior

          self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__							,					device=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		self.scheduler.timesteps

          lowerCamelCase__           =		self.prior.config.num_embeddings
          lowerCamelCase__           =		self.prior.config.embedding_dim

          lowerCamelCase__           =		self.prepare_latents(
              (batch_size, num_embeddings * embedding_dim)							,					image_embeds.dtype							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					self.scheduler							,					)

          # YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
          lowerCamelCase__           =		latents.reshape(latents.shape[0]							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)

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

                lowerCamelCase__           =		self.prior(
                    SCREAMING_SNAKE_CASE__							,					timestep=SCREAMING_SNAKE_CASE__							,					proj_embedding=SCREAMING_SNAKE_CASE__							,					).predicted_image_embedding

                # remove the variance
                lowerCamelCase__    ,  lowerCamelCase__           =		noise_pred.split(
                    scaled_model_input.shape[2]							,					dim=2				)  # batch_size, num_embeddings, embedding_dim

                if do_classifier_free_guidance is not None:
                      lowerCamelCase__    ,  lowerCamelCase__           =		noise_pred.chunk(2				)
                      lowerCamelCase__           =		noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

                lowerCamelCase__           =		self.scheduler.step(
                    SCREAMING_SNAKE_CASE__							,					timestep=SCREAMING_SNAKE_CASE__							,					sample=SCREAMING_SNAKE_CASE__							,					).prev_sample

          if output_type == "latent":
                return ShapEPipelineOutput(images=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		[]
          for i, latent in enumerate(SCREAMING_SNAKE_CASE__				):
                print()
                lowerCamelCase__           =		self.renderer.decode(
                    latent[None, :]							,					SCREAMING_SNAKE_CASE__							,					size=SCREAMING_SNAKE_CASE__							,					ray_batch_size=40_96							,					n_coarse_samples=64							,					n_fine_samples=1_28							,					)

                images.append(SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		torch.stack(SCREAMING_SNAKE_CASE__				)

          if output_type not in ["np", "pil"]:
                raise ValueError(F'Only the output types `pil` and `np` are supported not output_type={output_type}'				)

          lowerCamelCase__           =		images.cpu().numpy()

          if output_type == "pil":
                lowerCamelCase__           =		[self.numpy_to_pil(SCREAMING_SNAKE_CASE__				) for image in images]

          # Offload last model to CPU
          if hasattr(self							,					'final_offload_hook'				) and self.final_offload_hook is not None:
                self.final_offload_hook.offload()

          if not return_dict:
                return (images,)

          return ShapEPipelineOutput(images=SCREAMING_SNAKE_CASE__				)



