
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


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

_lowerCamelCase					   ="""\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__		(	__lowerCamelCase):
      _UpperCAmelCase				:    Union[PIL.Image.Image, np.ndarray]





class 							A__		(	__lowerCamelCase):
      def __init__(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  ):
       super().__init__()

       self.register_modules(
           prior=__magic_name__    ,  image_encoder=__magic_name__    ,  image_processor=__magic_name__    ,  scheduler=__magic_name__    ,  renderer=__magic_name__    ,  )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      ):
       if latents is None:
        lowerCamelCase		:	Dict							=      randn_tensor(__magic_name__    ,  generator=__magic_name__    ,  device=__magic_name__    ,  dtype=__magic_name__      )
       else:
        if latents.shape != shape:
         raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''      )
        lowerCamelCase		:	Union[str, Any]							=      latents.to(__magic_name__      )

       lowerCamelCase		:	Tuple							=      latents * scheduler.init_noise_sigma
       return latents
      def 						UpperCamelCase__							(      self    ,  __magic_name__=0      ):
       if is_accelerate_available():
        from accelerate import cpu_offload
       else:
        raise ImportError("""Please install accelerate via `pip install accelerate`"""      )

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

       lowerCamelCase		:	Any							=      [self.image_encoder, self.prior]
       for cpu_offloaded_model in models:
        if cpu_offloaded_model is not None:
         cpu_offload(__magic_name__    ,  __magic_name__      )
      @property
      def 						UpperCamelCase__							(      self      ):
       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(__magic_name__    ,  """_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    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  ):
       if isinstance(__magic_name__    ,  __magic_name__      ) and isinstance(image[0]    ,  torch.Tensor      ):
        lowerCamelCase		:	Any							=      torch.cat(__magic_name__    ,  axis=0      ) if image[0].ndim == 4 else torch.stack(__magic_name__    ,  axis=0      )

       if not isinstance(__magic_name__    ,  torch.Tensor      ):
        lowerCamelCase		:	Union[str, Any]							=      self.image_processor(__magic_name__    ,  return_tensors="""pt"""      ).pixel_values[0].unsqueeze(0      )

       lowerCamelCase		:	str							=      image.to(dtype=self.image_encoder.dtype    ,  device=__magic_name__      )

       lowerCamelCase		:	List[Any]							=      self.image_encoder(__magic_name__      )['''last_hidden_state''']
       lowerCamelCase		:	Optional[int]							=      image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

       lowerCamelCase		:	Optional[int]							=      image_embeds.repeat_interleave(__magic_name__    ,  dim=0      )

       if do_classifier_free_guidance:
        lowerCamelCase		:	int							=      torch.zeros_like(__magic_name__      )

        # 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		:	Union[str, Any]							=      torch.cat([negative_image_embeds, image_embeds]      )

       return image_embeds






      @torch.no_grad()
      @replace_example_docstring(__magic_name__      )
      def __call__(      self    ,  __magic_name__    ,  __magic_name__ = 1    ,  __magic_name__ = 2_5    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = 4.0    ,  __magic_name__ = 6_4    ,  __magic_name__ = "pil"    ,  __magic_name__ = True    ,  ):

       if isinstance(__magic_name__    ,  PIL.Image.Image      ):
        lowerCamelCase		:	Dict							=      1
       elif isinstance(__magic_name__    ,  torch.Tensor      ):
        lowerCamelCase		:	Tuple							=      image.shape[0]
       elif isinstance(__magic_name__    ,  __magic_name__      ) and isinstance(image[0]    ,  (torch.Tensor, PIL.Image.Image)      ):
        lowerCamelCase		:	Optional[Any]							=      len(__magic_name__      )
       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(__magic_name__      )}'''      )

       lowerCamelCase		:	List[Any]							=      self._execution_device

       lowerCamelCase		:	List[Any]							=      batch_size * num_images_per_prompt

       lowerCamelCase		:	List[Any]							=      guidance_scale > 1.0
       lowerCamelCase		:	List[str]							=      self._encode_image(__magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      )

       # prior

       self.scheduler.set_timesteps(__magic_name__    ,  device=__magic_name__      )
       lowerCamelCase		:	List[Any]							=      self.scheduler.timesteps

       lowerCamelCase		:	Optional[Any]							=      self.prior.config.num_embeddings
       lowerCamelCase		:	str							=      self.prior.config.embedding_dim

       lowerCamelCase		:	Tuple							=      self.prepare_latents(
           (batch_size, num_embeddings * embedding_dim)    ,  image_embeds.dtype    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  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		:	Dict							=      latents.reshape(latents.shape[0]    ,  __magic_name__    ,  __magic_name__      )

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

        lowerCamelCase		:	Dict							=      self.prior(
            __magic_name__    ,  timestep=__magic_name__    ,  proj_embedding=__magic_name__    ,  ).predicted_image_embedding

        # remove the variance
        lowerCamelCase		:	str							=      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		:	Optional[int]							=      noise_pred.chunk(2      )
         lowerCamelCase		:	Dict							=      noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

        lowerCamelCase		:	Optional[Any]							=      self.scheduler.step(
            __magic_name__    ,  timestep=__magic_name__    ,  sample=__magic_name__    ,  ).prev_sample

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

       lowerCamelCase		:	int							=      []
       for i, latent in enumerate(__magic_name__      ):
        print()
        lowerCamelCase		:	str							=      self.renderer.decode(
            latent[None, :]    ,  __magic_name__    ,  size=__magic_name__    ,  ray_batch_size=4_0_9_6    ,  n_coarse_samples=6_4    ,  n_fine_samples=1_2_8    ,  )

        images.append(__magic_name__      )

       lowerCamelCase		:	Dict							=      torch.stack(__magic_name__      )

       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		:	Optional[Any]							=      images.cpu().numpy()

       if output_type == "pil":
        lowerCamelCase		:	Optional[Any]							=      [self.numpy_to_pil(__magic_name__      ) 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=__magic_name__      )











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     lowercase__       :

   '''simple docstring'''


   def __init__(    self,		__magic_name__ = "cpu",		__magic_name__ = "openai/clip-vit-large-patch14"							) ->      None:






       """simple docstring"""
       UpperCamelCase__							:				List[str]          =      device
       UpperCamelCase__							:				Union[str, Any]          =      CLIPTokenizerFast.from_pretrained(__magic_name__							)
       UpperCamelCase__							:				Tuple          =      [0.4814_5466, 0.457_8275, 0.4082_1073]
       UpperCamelCase__							:				Union[str, Any]          =      [0.2686_2954, 0.2613_0258, 0.2757_7711]
       UpperCamelCase__							:				Dict          =      torchvision.transforms.Normalize(self.image_mean,		self.image_std							)
       UpperCamelCase__							:				List[str]          =      torchvision.transforms.Resize(224							)
       UpperCamelCase__							:				Union[str, Any]          =      torchvision.transforms.CenterCrop(224							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      List[Any]:






       """simple docstring"""
       UpperCamelCase__							:				Optional[Any]          =      self.resize(__magic_name__							)
       UpperCamelCase__							:				Dict          =      self.center_crop(__magic_name__							)
       UpperCamelCase__							:				List[str]          =      self.normalize(__magic_name__							)
       return images




   def __call__(    self,		__magic_name__=None,		__magic_name__=None,		**__magic_name__							) ->      Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:				Optional[Any]          =      self.tokenizer(text=__magic_name__,		**__magic_name__							)
       UpperCamelCase__							:				List[Any]          =      self.preprocess_img(__magic_name__							)
       UpperCamelCase__							:				Optional[Any]          =      {key: value.to(self.device							) for (key, value) in encoding.items()}
       return encoding






class     lowercase__       (	nn.Module				):

   '''simple docstring'''


   def __init__(    self,		__magic_name__=10,		__magic_name__=0.01,		__magic_name__=None,		__magic_name__=None,		__magic_name__=None,		__magic_name__=None,		__magic_name__=None,		__magic_name__=None,		__magic_name__=False,		__magic_name__=True,		__magic_name__="image",		__magic_name__=True,		__magic_name__=False,		__magic_name__=False,		__magic_name__=False,		) ->      None:






       """simple docstring"""
       super().__init__()
       UpperCamelCase__							:				Dict          =      None
       UpperCamelCase__							:				Tuple          =      device if device else get_device()
       if vqgan:
           UpperCamelCase__							:				Union[str, Any]          =      vqgan
       else:
           UpperCamelCase__							:				Any          =      load_vqgan(self.device,		conf_path=__magic_name__,		ckpt_path=__magic_name__							)
       self.vqgan.eval()
       if clip:
           UpperCamelCase__							:				Optional[Any]          =      clip
       else:
           UpperCamelCase__							:				Any          =      CLIPModel.from_pretrained('''openai/clip-vit-base-patch32'''							)
       self.clip.to(self.device							)
       UpperCamelCase__							:				str          =      ProcessorGradientFlow(device=self.device							)

       UpperCamelCase__							:				Union[str, Any]          =      iterations
       UpperCamelCase__							:				Tuple          =      lr
       UpperCamelCase__							:				Optional[int]          =      log
       UpperCamelCase__							:				List[Any]          =      make_grid
       UpperCamelCase__							:				Optional[Any]          =      return_val
       UpperCamelCase__							:				str          =      quantize
       UpperCamelCase__							:				int          =      self.vqgan.decoder.z_shape




   def       UpperCamelCase__  (    self,		__magic_name__=None,		__magic_name__=None,		__magic_name__=5,		__magic_name__=True							) ->      Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      []
       if output_path is None:
           UpperCamelCase__							:				List[str]          =      '''./animation.gif'''
       if input_path is None:
           UpperCamelCase__							:				Union[str, Any]          =      self.save_path
       UpperCamelCase__							:				Tuple          =      sorted(glob(input_path + '''/*'''							)							)
       if not len(__magic_name__							):
           raise ValueError(
               '''No images found in save path, aborting (did you pass save_intermediate=True to the generate'''
               ''' function?)'''							)
       if len(__magic_name__							) == 1:
           print('''Only one image found in save path, (did you pass save_intermediate=True to the generate function?)'''							)
       UpperCamelCase__							:				Dict          =      total_duration / len(__magic_name__							)
       UpperCamelCase__							:				List[Any]          =      [frame_duration] * len(__magic_name__							)
       if extend_frames:
           UpperCamelCase__							:				List[Any]          =      1.5
           UpperCamelCase__							:				Any          =      3
       for file_name in paths:
           if file_name.endswith('''.png'''							):
               images.append(imageio.imread(__magic_name__							)							)
       imageio.mimsave(__magic_name__,		__magic_name__,		duration=__magic_name__							)
       print(f"gif saved to {output_path}"							)




   def       UpperCamelCase__  (    self,		__magic_name__=None,		__magic_name__=None							) ->      Any:






       """simple docstring"""
       if not (path or img):
           raise ValueError('''Input either path or tensor'''							)
       if img is not None:
           raise NotImplementedError
       UpperCamelCase__							:				List[Any]          =      preprocess(Image.open(__magic_name__							),		target_image_size=256							).to(self.device							)
       UpperCamelCase__							:				str          =      preprocess_vqgan(__magic_name__							)
       UpperCamelCase__   ,*UpperCamelCase__							:				Union[str, Any]          =      self.vqgan.encode(__magic_name__							)
       return z




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Any:






       """simple docstring"""
       UpperCamelCase__							:				Optional[Any]          =      self.latent.detach().requires_grad_()
       UpperCamelCase__							:				Any          =      base_latent + transform_vector
       if self.quantize:
           UpperCamelCase__   ,*UpperCamelCase__							:				int          =      self.vqgan.quantize(__magic_name__							)
       else:
           UpperCamelCase__							:				Optional[int]          =      trans_latent
       return self.vqgan.decode(__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__=None							) ->      Tuple:






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      self.clip_preprocessor(text=__magic_name__,		images=__magic_name__,		return_tensors='''pt''',		padding=__magic_name__							)
       UpperCamelCase__							:				Optional[int]          =      self.clip(**__magic_name__							)
       UpperCamelCase__							:				Tuple          =      clip_outputs.logits_per_image
       if weights is not None:
           UpperCamelCase__							:				List[Any]          =      similarity_logits * weights
       return similarity_logits.sum()




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__							) ->      Any:






       """simple docstring"""
       UpperCamelCase__							:				List[str]          =      self._get_clip_similarity(pos_prompts['''prompts'''],		__magic_name__,		weights=(1 / pos_prompts['''weights'''])							)
       if neg_prompts:
           UpperCamelCase__							:				Tuple          =      self._get_clip_similarity(neg_prompts['''prompts'''],		__magic_name__,		weights=neg_prompts['''weights''']							)
       else:
           UpperCamelCase__							:				Optional[int]          =      torch.tensor([1],		device=self.device							)
       UpperCamelCase__							:				Tuple          =      -torch.log(__magic_name__							) + torch.log(__magic_name__							)
       return loss




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__							) ->      Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:				List[str]          =      torch.randn_like(self.latent,		requires_grad=__magic_name__,		device=self.device							)
       UpperCamelCase__							:				Optional[int]          =      torch.optim.Adam([vector],		lr=self.lr							)

       for i in range(self.iterations							):
           optim.zero_grad()
           UpperCamelCase__							:				Tuple          =      self._add_vector(__magic_name__							)
           UpperCamelCase__							:				Any          =      loop_post_process(__magic_name__							)
           UpperCamelCase__							:				Union[str, Any]          =      self._get_CLIP_loss(__magic_name__,		__magic_name__,		__magic_name__							)
           print('''CLIP loss''',		__magic_name__							)
           if self.log:
               wandb.log({'''CLIP Loss''': clip_loss}							)
           clip_loss.backward(retain_graph=__magic_name__							)
           optim.step()
           if self.return_val == "image":
               yield custom_to_pil(transformed_img[0]							)
           else:
               yield vector




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__							) ->      List[str]:






       """simple docstring"""
       wandb.init(reinit=__magic_name__,		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:
           UpperCamelCase__							:				List[str]          =      Image.open(__magic_name__							)
           UpperCamelCase__							:				List[Any]          =      image.resize((256, 256)							)
           wandb.log('''Original Image''',		wandb.Image(__magic_name__							)							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Optional[int]:






       """simple docstring"""
       if not prompts:
           return []
       UpperCamelCase__							:				int          =      []
       UpperCamelCase__							:				str          =      []
       if isinstance(__magic_name__,		__magic_name__							):
           UpperCamelCase__							:				Optional[Any]          =      [prompt.strip() for prompt in prompts.split('''|'''							)]
       for prompt in prompts:
           if isinstance(__magic_name__,		(tuple, list)							):
               UpperCamelCase__							:				Optional[int]          =      prompt[0]
               UpperCamelCase__							:				Dict          =      float(prompt[1]							)
           elif ":" in prompt:
               UpperCamelCase__   ,UpperCamelCase__							:				Optional[int]          =      prompt.split(''':'''							)
               UpperCamelCase__							:				List[Any]          =      float(__magic_name__							)
           else:
               UpperCamelCase__							:				List[str]          =      prompt
               UpperCamelCase__							:				Any          =      1.0
           processed_prompts.append(__magic_name__							)
           weights.append(__magic_name__							)
       return {
           "prompts": processed_prompts,
           "weights": torch.tensor(__magic_name__,		device=self.device							),
       }




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__=None,		__magic_name__=None,		__magic_name__=True,		__magic_name__=False,		__magic_name__=True,		__magic_name__=True,		__magic_name__=None,		) ->      str:






       """simple docstring"""
       if image_path:
           UpperCamelCase__							:				Union[str, Any]          =      self._get_latent(__magic_name__							)
       else:
           UpperCamelCase__							:				Dict          =      torch.randn(self.latent_dim,		device=self.device							)
       if self.log:
           self._init_logging(__magic_name__,		__magic_name__,		__magic_name__							)

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

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

       UpperCamelCase__							:				Optional[Any]          =      loop_post_process(__magic_name__							)
       for iter, transformed_img in enumerate(self._optimize_CLIP(__magic_name__,		__magic_name__,		__magic_name__							)							):
           if show_intermediate:
               show_pil(__magic_name__							)
           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(__magic_name__							)}							)
       if show_final:
           show_pil(__magic_name__							)
       if save_final:
           transformed_img.save(os.path.join(self.save_path,		f"iter_{iter:03d}_final.png"							)							)

