





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_       :


							def __init__(							self    :List[Any]							,				__A    :str = "cpu"							,				__A    :str = "openai/clip-vit-large-patch14"		)   ->   None:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      device
									SCREAMING_SNAKE_CASE__			=      CLIPTokenizerFast.from_pretrained(__A		)
									SCREAMING_SNAKE_CASE__			=      [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]
									SCREAMING_SNAKE_CASE__			=      [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]
									SCREAMING_SNAKE_CASE__			=      torchvision.transforms.Normalize(self.image_mean							,				self.image_std		)
									SCREAMING_SNAKE_CASE__			=      torchvision.transforms.Resize(224		)
									SCREAMING_SNAKE_CASE__			=      torchvision.transforms.CenterCrop(224		)


							def      _snake_case  (							self    :Any							,				__A    :List[Any]		)   ->   List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.resize(__A		)
									SCREAMING_SNAKE_CASE__			=      self.center_crop(__A		)
									SCREAMING_SNAKE_CASE__			=      self.normalize(__A		)
									return images


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.tokenizer(text=__A							,				**__A		)
									SCREAMING_SNAKE_CASE__			=      self.preprocess_img(__A		)
									SCREAMING_SNAKE_CASE__			=      {key: value.to(self.device		) for (key, value) in encoding.items()}
									return encoding




class   UpperCamelCase_       (     nn.Module    ):


							def __init__(							self    :int							,				__A    :Union[str, Any]=10							,				__A    :Tuple=0.0_1							,				__A    :List[str]=None							,				__A    :int=None							,				__A    :Optional[int]=None							,				__A    :List[Any]=None							,				__A    :Optional[Any]=None							,				__A    :Any=None							,				__A    :List[str]=False							,				__A    :Optional[int]=True							,				__A    :Dict="image"							,				__A    :int=True							,				__A    :Optional[Any]=False							,				__A    :Dict=False							,				__A    :str=False							,				)   ->   None:



									"""simple docstring"""



									super().__init__()
									SCREAMING_SNAKE_CASE__			=      None
									SCREAMING_SNAKE_CASE__			=      device if device else get_device()
									if vqgan:
											SCREAMING_SNAKE_CASE__			=      vqgan
									else:
											SCREAMING_SNAKE_CASE__			=      load_vqgan(self.device							,				conf_path=__A							,				ckpt_path=__A		)
									self.vqgan.eval()
									if clip:
											SCREAMING_SNAKE_CASE__			=      clip
									else:
											SCREAMING_SNAKE_CASE__			=      CLIPModel.from_pretrained("""openai/clip-vit-base-patch32"""		)
									self.clip.to(self.device		)
									SCREAMING_SNAKE_CASE__			=      ProcessorGradientFlow(device=self.device		)

									SCREAMING_SNAKE_CASE__			=      iterations
									SCREAMING_SNAKE_CASE__			=      lr
									SCREAMING_SNAKE_CASE__			=      log
									SCREAMING_SNAKE_CASE__			=      make_grid
									SCREAMING_SNAKE_CASE__			=      return_val
									SCREAMING_SNAKE_CASE__			=      quantize
									SCREAMING_SNAKE_CASE__			=      self.vqgan.decoder.z_shape


							def      _snake_case  (							self    :Tuple							,				__A    :str=None							,				__A    :int=None							,				__A    :Union[str, Any]=5							,				__A    :List[str]=True		)   ->   Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      []
									if output_path is None:
											SCREAMING_SNAKE_CASE__			=      """./animation.gif"""
									if input_path is None:
											SCREAMING_SNAKE_CASE__			=      self.save_path
									SCREAMING_SNAKE_CASE__			=      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?)"""		)
									SCREAMING_SNAKE_CASE__			=      total_duration / len(__A		)
									SCREAMING_SNAKE_CASE__			=      [frame_duration] * len(__A		)
									if extend_frames:
											SCREAMING_SNAKE_CASE__			=      1.5
											SCREAMING_SNAKE_CASE__			=      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      _snake_case  (							self    :Dict							,				__A    :int=None							,				__A    :Optional[Any]=None		)   ->   str:



									"""simple docstring"""



									if not (path or img):
											raise ValueError("""Input either path or tensor"""		)
									if img is not None:
											raise NotImplementedError
									SCREAMING_SNAKE_CASE__			=      preprocess(Image.open(__A		)							,				target_image_size=256		).to(self.device		)
									SCREAMING_SNAKE_CASE__			=      preprocess_vqgan(__A		)
									SCREAMING_SNAKE_CASE__	,						*SCREAMING_SNAKE_CASE__			=      self.vqgan.encode(__A		)
									return z


							def      _snake_case  (							self    :Any							,				__A    :List[str]		)   ->   Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.latent.detach().requires_grad_()
									SCREAMING_SNAKE_CASE__			=      base_latent + transform_vector
									if self.quantize:
											SCREAMING_SNAKE_CASE__	,						*SCREAMING_SNAKE_CASE__			=      self.vqgan.quantize(__A		)
									else:
											SCREAMING_SNAKE_CASE__			=      trans_latent
									return self.vqgan.decode(__A		)


							def      _snake_case  (							self    :Any							,				__A    :Optional[Any]							,				__A    :Tuple							,				__A    :Optional[Any]=None		)   ->   List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.clip_preprocessor(text=__A							,				images=__A							,				return_tensors="""pt"""							,				padding=__A		)
									SCREAMING_SNAKE_CASE__			=      self.clip(**__A		)
									SCREAMING_SNAKE_CASE__			=      clip_outputs.logits_per_image
									if weights is not None:
											SCREAMING_SNAKE_CASE__			=      similarity_logits * weights
									return similarity_logits.sum()


							def      _snake_case  (							self    :Dict							,				__A    :Optional[Any]							,				__A    :Dict							,				__A    :List[Any]		)   ->   List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self._get_clip_similarity(pos_prompts["""prompts"""]							,				__A							,				weights=(1 / pos_prompts["""weights"""])		)
									if neg_prompts:
											SCREAMING_SNAKE_CASE__			=      self._get_clip_similarity(neg_prompts["""prompts"""]							,				__A							,				weights=neg_prompts["""weights"""]		)
									else:
											SCREAMING_SNAKE_CASE__			=      torch.tensor([1]							,				device=self.device		)
									SCREAMING_SNAKE_CASE__			=      -torch.log(__A		) + torch.log(__A		)
									return loss


							def      _snake_case  (							self    :Dict							,				__A    :str							,				__A    :int							,				__A    :List[str]		)   ->   Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      torch.randn_like(self.latent							,				requires_grad=__A							,				device=self.device		)
									SCREAMING_SNAKE_CASE__			=      torch.optim.Adam([vector]							,				lr=self.lr		)

									for i in range(self.iterations		):
											optim.zero_grad()
											SCREAMING_SNAKE_CASE__			=      self._add_vector(__A		)
											SCREAMING_SNAKE_CASE__			=      loop_post_process(__A		)
											SCREAMING_SNAKE_CASE__			=      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      _snake_case  (							self    :List[str]							,				__A    :List[Any]							,				__A    :Optional[Any]							,				__A    :Any		)   ->   Union[str, 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:
											SCREAMING_SNAKE_CASE__			=      Image.open(__A		)
											SCREAMING_SNAKE_CASE__			=      image.resize((256, 256)		)
											wandb.log("""Original Image"""							,				wandb.Image(__A		)		)


							def      _snake_case  (							self    :Union[str, Any]							,				__A    :Optional[Any]		)   ->   Optional[int]:



									"""simple docstring"""



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


							def      _snake_case  (							self    :List[str]							,				__A    :str							,				__A    :Any=None							,				__A    :List[Any]=None							,				__A    :Any=True							,				__A    :str=False							,				__A    :int=True							,				__A    :Tuple=True							,				__A    :Union[str, Any]=None							,				)   ->   Tuple:



									"""simple docstring"""



									if image_path:
											SCREAMING_SNAKE_CASE__			=      self._get_latent(__A		)
									else:
											SCREAMING_SNAKE_CASE__			=      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."
									SCREAMING_SNAKE_CASE__			=      self.process_prompts(__A		)
									SCREAMING_SNAKE_CASE__			=      self.process_prompts(__A		)
									if save_final and save_path is None:
											SCREAMING_SNAKE_CASE__			=      os.path.join("""./outputs/"""							,				"""_""".join(pos_prompts["""prompts"""]		)		)
									if not os.path.exists(__A		):
											os.makedirs(__A		)
									else:
											SCREAMING_SNAKE_CASE__			=      save_path + """_""" + get_timestamp()
											os.makedirs(__A		)
									SCREAMING_SNAKE_CASE__			=      save_path

									SCREAMING_SNAKE_CASE__			=      self.vqgan.decode(self.latent		)[0]
									if show_intermediate:
											print("""Original Image"""		)
											show_pil(custom_to_pil(__A		)		)

									SCREAMING_SNAKE_CASE__			=      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'''		)		)






import warnings
from functools import wraps
from typing import Callable






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Callable    ):

		@wraps(UpperCamelCase__    )
		def _inner_fn(*UpperCamelCase__:							Dict					,   **UpperCamelCase__:							Any    ):
				warnings.warn(
				    (f'''\'{fn.__name__}\' is experimental and might be subject to breaking changes in the future.''')					,   UpperCamelCase__					,   )
				return fn(*UpperCamelCase__					,   **UpperCamelCase__    )

		return _inner_fn