




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      ,     SCREAMING_SNAKE_CASE = "cpu"      ,     SCREAMING_SNAKE_CASE = "openai/clip-vit-large-patch14"      )  ->					None:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						device
				SCREAMING_SNAKE_CASE_								=						CLIPTokenizerFast.from_pretrained(SCREAMING_SNAKE_CASE      )
				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 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.resize(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						self.center_crop(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						self.normalize(SCREAMING_SNAKE_CASE      )
				return images





	def __call__(							self      ,     SCREAMING_SNAKE_CASE=None      ,     SCREAMING_SNAKE_CASE=None      ,     **SCREAMING_SNAKE_CASE      )  ->					int:




				"""simple docstring"""



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




class       a_       (     nn.Module     ):





	def __init__(							self      ,     SCREAMING_SNAKE_CASE=10      ,     SCREAMING_SNAKE_CASE=0.0_1      ,     SCREAMING_SNAKE_CASE=None      ,     SCREAMING_SNAKE_CASE=None      ,     SCREAMING_SNAKE_CASE=None      ,     SCREAMING_SNAKE_CASE=None      ,     SCREAMING_SNAKE_CASE=None      ,     SCREAMING_SNAKE_CASE=None      ,     SCREAMING_SNAKE_CASE=False      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE="image"      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=False      ,     SCREAMING_SNAKE_CASE=False      ,     SCREAMING_SNAKE_CASE=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=SCREAMING_SNAKE_CASE      ,     ckpt_path=SCREAMING_SNAKE_CASE      )
				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 						A_(							self      ,     SCREAMING_SNAKE_CASE=None      ,     SCREAMING_SNAKE_CASE=None      ,     SCREAMING_SNAKE_CASE=5      ,     SCREAMING_SNAKE_CASE=True      )  ->					List[Any]:




				"""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(SCREAMING_SNAKE_CASE      ):
							raise ValueError(
							    'No images found in save path, aborting (did you pass save_intermediate=True to the generate'
							    ' function?)'      )
				if len(SCREAMING_SNAKE_CASE      ) == 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(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						[frame_duration] * len(SCREAMING_SNAKE_CASE      )
				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(SCREAMING_SNAKE_CASE      )      )
				imageio.mimsave(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     duration=SCREAMING_SNAKE_CASE      )
				print(f'gif saved to {output_path}'      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE=None      ,     SCREAMING_SNAKE_CASE=None      )  ->					Tuple:




				"""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(SCREAMING_SNAKE_CASE      )      ,     target_image_size=256      ).to(self.device      )
				SCREAMING_SNAKE_CASE_								=						preprocess_vqgan(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_						,		*SCREAMING_SNAKE_CASE_								=						self.vqgan.encode(SCREAMING_SNAKE_CASE      )
				return z





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					int:




				"""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(SCREAMING_SNAKE_CASE      )
				else:
							SCREAMING_SNAKE_CASE_								=						trans_latent
				return self.vqgan.decode(SCREAMING_SNAKE_CASE      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE=None      )  ->					Optional[int]:




				"""simple docstring"""



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





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




				"""simple docstring"""



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





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						torch.randn_like(self.latent      ,     requires_grad=SCREAMING_SNAKE_CASE      ,     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(SCREAMING_SNAKE_CASE      )
							SCREAMING_SNAKE_CASE_								=						loop_post_process(SCREAMING_SNAKE_CASE      )
							SCREAMING_SNAKE_CASE_								=						self._get_CLIP_loss(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )
							print('CLIP loss'      ,     SCREAMING_SNAKE_CASE      )
							if self.log:
										wandb.log({'CLIP Loss': clip_loss}      )
							clip_loss.backward(retain_graph=SCREAMING_SNAKE_CASE      )
							optim.step()
							if self.return_val == "image":
										yield custom_to_pil(transformed_img[0]      )
							else:
										yield vector





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




				"""simple docstring"""



				wandb.init(reinit=SCREAMING_SNAKE_CASE      ,     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(SCREAMING_SNAKE_CASE      )
							SCREAMING_SNAKE_CASE_								=						image.resize((256, 256)      )
							wandb.log('Original Image'      ,     wandb.Image(SCREAMING_SNAKE_CASE      )      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Any:




				"""simple docstring"""



				if not prompts:
							return []
				SCREAMING_SNAKE_CASE_								=						[]
				SCREAMING_SNAKE_CASE_								=						[]
				if isinstance(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ):
							SCREAMING_SNAKE_CASE_								=						[prompt.strip() for prompt in prompts.split('|'      )]
				for prompt in prompts:
							if isinstance(SCREAMING_SNAKE_CASE      ,     (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(SCREAMING_SNAKE_CASE      )
							else:
										SCREAMING_SNAKE_CASE_								=						prompt
										SCREAMING_SNAKE_CASE_								=						1.0
							processed_prompts.append(SCREAMING_SNAKE_CASE      )
							weights.append(SCREAMING_SNAKE_CASE      )
				return {
				    "prompts": processed_prompts,
				    "weights": torch.tensor(SCREAMING_SNAKE_CASE      ,     device=self.device      ),
				}





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE=None      ,     SCREAMING_SNAKE_CASE=None      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=False      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=None      ,     )  ->					int:




				"""simple docstring"""



				if image_path:
							SCREAMING_SNAKE_CASE_								=						self._get_latent(SCREAMING_SNAKE_CASE      )
				else:
							SCREAMING_SNAKE_CASE_								=						torch.randn(self.latent_dim      ,     device=self.device      )
				if self.log:
							self._init_logging(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )

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

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






from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
				import accelerate

def lowercase    (  SCREAMING_SNAKE_CASE )			->							Union[str, Any]:


			'''simple docstring'''







			if not is_accelerate_available():
						return method
			SCREAMING_SNAKE_CASE_								=						version.parse(accelerate.__version__ ).base_version
			if version.parse(SCREAMING_SNAKE_CASE ) < version.parse('0.17.0' ):
						return method

			def wrapper(self       ,    *SCREAMING_SNAKE_CASE       ,    **SCREAMING_SNAKE_CASE ):
						if hasattr(self       ,    '_hf_hook' ) and hasattr(self._hf_hook       ,    'pre_forward' ):
									self._hf_hook.pre_forward(self )
						return method(self       ,    *SCREAMING_SNAKE_CASE       ,    **SCREAMING_SNAKE_CASE )

			return wrapper
