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  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     A_										=  42





class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     def __init__(					self				,				A_				,				A_				,				A_				,				A_				,				A_				,				) ->				Dict:






           """simple docstring"""
           super().__init__()

           self.register_modules(
               prior=A_				,				image_encoder=A_				,				image_processor=A_				,				scheduler=A_				,				renderer=A_				,				)



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_				,				A_				,				A_				,				A_						) ->				List[Any]:






           """simple docstring"""
           if latents is None:
                 _lowerCamelCase			    =				randn_tensor(A_				,				generator=A_				,				device=A_				,				dtype=A_						)
           else:
                 if latents.shape != shape:
                       raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}'						)
                 _lowerCamelCase			    =				latents.to(A_						)

           _lowerCamelCase			    =				latents * scheduler.init_noise_sigma
           return latents



     def 		UpperCamelCase_						(					self				,				A_=0						) ->				List[Any]:






           """simple docstring"""
           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(A_				,				A_						)



     @property
     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           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(A_				,				'''_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				,				A_				,				A_				,				A_				,				A_				,				) ->				Optional[int]:






           """simple docstring"""
           if isinstance(A_				,				A_						) and isinstance(image[0]				,				torch.Tensor						):
                 _lowerCamelCase			    =				torch.cat(A_				,				axis=0						) if image[0].ndim == 4 else torch.stack(A_				,				axis=0						)

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

           _lowerCamelCase			    =				image.to(dtype=self.image_encoder.dtype				,				device=A_						)

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

           _lowerCamelCase			    =				image_embeds.repeat_interleave(A_				,				dim=0						)

           if do_classifier_free_guidance:
                 _lowerCamelCase			    =				torch.zeros_like(A_						)

                 # 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(A_						)
     def __call__(					self				,				A_				,				A_ = 1				,				A_ = 25				,				A_ = None				,				A_ = None				,				A_ = 4.0				,				A_ = 64				,				A_ = "pil"				,				A_ = True				,				) ->				Any:






           """simple docstring"""

           if isinstance(A_				,				PIL.Image.Image						):
                 _lowerCamelCase			    =				1
           elif isinstance(A_				,				torch.Tensor						):
                 _lowerCamelCase			    =				image.shape[0]
           elif isinstance(A_				,				A_						) and isinstance(image[0]				,				(torch.Tensor, PIL.Image.Image)						):
                 _lowerCamelCase			    =				len(A_						)
           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(A_						)}'						)

           _lowerCamelCase			    =				self._execution_device

           _lowerCamelCase			    =				batch_size * num_images_per_prompt

           _lowerCamelCase			    =				guidance_scale > 1.0
           _lowerCamelCase			    =				self._encode_image(A_				,				A_				,				A_				,				A_						)

           # prior

           self.scheduler.set_timesteps(A_				,				device=A_						)
           _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				,				A_				,				A_				,				A_				,				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]				,				A_				,				A_						)

           for i, t in enumerate(self.progress_bar(A_						)						):
                 # 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(A_				,				A_						)

                 _lowerCamelCase			    =				self.prior(
                     A_				,				timestep=A_				,				proj_embedding=A_				,				).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(
                     A_				,				timestep=A_				,				sample=A_				,				).prev_sample

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

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

                 images.append(A_						)

           _lowerCamelCase			    =				torch.stack(A_						)

           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(A_						) 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=A_						)
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  UpperCamelCase				:

     '''simple docstring'''

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






           """simple docstring"""
           _lowerCamelCase			    =				device
           _lowerCamelCase			    =				CLIPTokenizerFast.from_pretrained(A_						)
           _lowerCamelCase			    =				[0.48145466, 0.4578275, 0.40821073]
           _lowerCamelCase			    =				[0.26862954, 0.26130258, 0.27577711]
           _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				,				A_						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				self.resize(A_						)
           _lowerCamelCase			    =				self.center_crop(A_						)
           _lowerCamelCase			    =				self.normalize(A_						)
           return images





     def __call__(					self				,				A_=None				,				A_=None				,				**A_						) ->				Optional[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				self.tokenizer(text=A_				,				**A_						)
           _lowerCamelCase			    =				self.preprocess_img(A_						)
           _lowerCamelCase			    =				{key: value.to(self.device						) for (key, value) in encoding.items()}
           return encoding





class  UpperCamelCase				( nn.Module ):

     '''simple docstring'''

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






           """simple docstring"""
           super().__init__()
           _lowerCamelCase			    =				None
           _lowerCamelCase			    =				device if device else get_device()
           if vqgan:
                 _lowerCamelCase			    =				vqgan
           else:
                 _lowerCamelCase			    =				load_vqgan(self.device				,				conf_path=A_				,				ckpt_path=A_						)
           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				,				A_=None				,				A_=None				,				A_=5				,				A_=True						) ->				Any:






           """simple docstring"""
           _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(A_						):
                 raise ValueError(
                     '''No images found in save path, aborting (did you pass save_intermediate=True to the generate'''
                     ''' function?)'''						)
           if len(A_						) == 1:
                 print('''Only one image found in save path, (did you pass save_intermediate=True to the generate function?)'''						)
           _lowerCamelCase			    =				total_duration / len(A_						)
           _lowerCamelCase			    =				[frame_duration] * len(A_						)
           if extend_frames:
                 _lowerCamelCase			    =				1.5
                 _lowerCamelCase			    =				3
           for file_name in paths:
                 if file_name.endswith('''.png'''						):
                       images.append(imageio.imread(A_						)						)
           imageio.mimsave(A_				,				A_				,				duration=A_						)
           print(F'gif saved to {output_path}'						)



     def 		UpperCamelCase_						(					self				,				A_=None				,				A_=None						) ->				Union[str, Any]:






           """simple docstring"""
           if not (path or img):
                 raise ValueError('''Input either path or tensor'''						)
           if img is not None:
                 raise NotImplementedError
           _lowerCamelCase			    =				preprocess(Image.open(A_						)				,				target_image_size=2_56						).to(self.device						)
           _lowerCamelCase			    =				preprocess_vqgan(A_						)
           _lowerCamelCase    ,					*_lowerCamelCase			    =				self.vqgan.encode(A_						)
           return z



     def 		UpperCamelCase_						(					self				,				A_						) ->				Optional[int]:






           """simple docstring"""
           _lowerCamelCase			    =				self.latent.detach().requires_grad_()
           _lowerCamelCase			    =				base_latent + transform_vector
           if self.quantize:
                 _lowerCamelCase    ,					*_lowerCamelCase			    =				self.vqgan.quantize(A_						)
           else:
                 _lowerCamelCase			    =				trans_latent
           return self.vqgan.decode(A_						)



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_=None						) ->				Any:






           """simple docstring"""
           _lowerCamelCase			    =				self.clip_preprocessor(text=A_				,				images=A_				,				return_tensors='''pt'''				,				padding=A_						)
           _lowerCamelCase			    =				self.clip(**A_						)
           _lowerCamelCase			    =				clip_outputs.logits_per_image
           if weights is not None:
                 _lowerCamelCase			    =				similarity_logits * weights
           return similarity_logits.sum()



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_						) ->				Dict:






           """simple docstring"""
           _lowerCamelCase			    =				self._get_clip_similarity(pos_prompts['''prompts''']				,				A_				,				weights=(1 / pos_prompts['''weights'''])						)
           if neg_prompts:
                 _lowerCamelCase			    =				self._get_clip_similarity(neg_prompts['''prompts''']				,				A_				,				weights=neg_prompts['''weights''']						)
           else:
                 _lowerCamelCase			    =				torch.tensor([1]				,				device=self.device						)
           _lowerCamelCase			    =				-torch.log(A_						) + torch.log(A_						)
           return loss



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_						) ->				str:






           """simple docstring"""
           _lowerCamelCase			    =				torch.randn_like(self.latent				,				requires_grad=A_				,				device=self.device						)
           _lowerCamelCase			    =				torch.optim.Adam([vector]				,				lr=self.lr						)

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



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_						) ->				Any:






           """simple docstring"""
           wandb.init(reinit=A_				,				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(A_						)
                 _lowerCamelCase			    =				image.resize((2_56, 2_56)						)
                 wandb.log('''Original Image'''				,				wandb.Image(A_						)						)



     def 		UpperCamelCase_						(					self				,				A_						) ->				int:






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





     def 		UpperCamelCase_						(					self				,				A_				,				A_=None				,				A_=None				,				A_=True				,				A_=False				,				A_=True				,				A_=True				,				A_=None				,				) ->				str:






           """simple docstring"""
           if image_path:
                 _lowerCamelCase			    =				self._get_latent(A_						)
           else:
                 _lowerCamelCase			    =				torch.randn(self.latent_dim				,				device=self.device						)
           if self.log:
                 self._init_logging(A_				,				A_				,				A_						)

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

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

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