




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							:					Optional[Any],		__A							:					Dict = "cpu",		__A							:					Optional[int] = "openai/clip-vit-large-patch14"						):
     UpperCAmelCase	:							Any						=			device
     UpperCAmelCase	:							Union[str, Any]						=			CLIPTokenizerFast.from_pretrained(lowerCAmelCase_						)
     UpperCAmelCase	:							int						=			[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]
     UpperCAmelCase	:							Tuple						=			[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]
     UpperCAmelCase	:							List[Any]						=			torchvision.transforms.Normalize(self.image_mean,		self.image_std						)
     UpperCAmelCase	:							Any						=			torchvision.transforms.Resize(2_2_4						)
     UpperCAmelCase	:							Tuple						=			torchvision.transforms.CenterCrop(2_2_4						)



   def 	__magic_name__   (							self							:					List[str],		__A							:					int						):
     UpperCAmelCase	:							Optional[int]						=			self.resize(lowerCAmelCase_						)
     UpperCAmelCase	:							Dict						=			self.center_crop(lowerCAmelCase_						)
     UpperCAmelCase	:							Dict						=			self.normalize(lowerCAmelCase_						)
     return images





   def __call__(							self							:					Optional[Any],		__A							:					Union[str, Any]=None,		__A							:					Any=None,		**__A							:					List[str]						):
     UpperCAmelCase	:							Optional[Any]						=			self.tokenizer(text=lowerCAmelCase_,		**lowerCAmelCase_						)
     UpperCAmelCase	:							List[str]						=			self.preprocess_img(lowerCAmelCase_						)
     UpperCAmelCase	:							Dict						=			{key: value.to(self.device						) for (key, value) in encoding.items()}
     return encoding






class  __UpperCAmelCase					(	nn.Module						):



   def __init__(							self							:					Optional[int],		__A							:					Any=1_0,		__A							:					Tuple=0.0_1,		__A							:					str=None,		__A							:					str=None,		__A							:					Union[str, Any]=None,		__A							:					Any=None,		__A							:					Optional[int]=None,		__A							:					int=None,		__A							:					Tuple=False,		__A							:					Any=True,		__A							:					Optional[Any]="image",		__A							:					Tuple=True,		__A							:					Optional[int]=False,		__A							:					Union[str, Any]=False,		__A							:					Any=False,		):
     super().__init__()
     UpperCAmelCase	:							Tuple						=			None
     UpperCAmelCase	:							Any						=			device if device else get_device()
     if vqgan:
       UpperCAmelCase	:							Any						=			vqgan
     else:
       UpperCAmelCase	:							Dict						=			load_vqgan(self.device,		conf_path=lowerCAmelCase_,		ckpt_path=lowerCAmelCase_						)
     self.vqgan.eval()
     if clip:
       UpperCAmelCase	:							Optional[int]						=			clip
     else:
       UpperCAmelCase	:							Union[str, Any]						=			CLIPModel.from_pretrained('''openai/clip-vit-base-patch32'''						)
     self.clip.to(self.device						)
     UpperCAmelCase	:							Dict						=			ProcessorGradientFlow(device=self.device						)

     UpperCAmelCase	:							Optional[Any]						=			iterations
     UpperCAmelCase	:							str						=			lr
     UpperCAmelCase	:							Any						=			log
     UpperCAmelCase	:							Any						=			make_grid
     UpperCAmelCase	:							Optional[int]						=			return_val
     UpperCAmelCase	:							Dict						=			quantize
     UpperCAmelCase	:							Optional[Any]						=			self.vqgan.decoder.z_shape



   def 	__magic_name__   (							self							:					Dict,		__A							:					str=None,		__A							:					Union[str, Any]=None,		__A							:					Any=5,		__A							:					Union[str, Any]=True						):
     UpperCAmelCase	:							List[Any]						=			[]
     if output_path is None:
       UpperCAmelCase	:							str						=			'''./animation.gif'''
     if input_path is None:
       UpperCAmelCase	:							Any						=			self.save_path
     UpperCAmelCase	:							Optional[Any]						=			sorted(glob(input_path + '''/*'''						)						)
     if not len(lowerCAmelCase_						):
       raise ValueError(
           '''No images found in save path, aborting (did you pass save_intermediate=True to the generate'''
           ''' function?)'''						)
     if len(lowerCAmelCase_						) == 1:
       print('''Only one image found in save path, (did you pass save_intermediate=True to the generate function?)'''						)
     UpperCAmelCase	:							int						=			total_duration / len(lowerCAmelCase_						)
     UpperCAmelCase	:							int						=			[frame_duration] * len(lowerCAmelCase_						)
     if extend_frames:
       UpperCAmelCase	:							int						=			1.5
       UpperCAmelCase	:							List[str]						=			3
     for file_name in paths:
       if file_name.endswith('''.png'''						):
         images.append(imageio.imread(lowerCAmelCase_						)						)
     imageio.mimsave(lowerCAmelCase_,		lowerCAmelCase_,		duration=lowerCAmelCase_						)
     print(F'''gif saved to {output_path}'''						)



   def 	__magic_name__   (							self							:					Optional[int],		__A							:					List[str]=None,		__A							:					str=None						):
     if not (path or img):
       raise ValueError('''Input either path or tensor'''						)
     if img is not None:
       raise NotImplementedError
     UpperCAmelCase	:							Dict						=			preprocess(Image.open(lowerCAmelCase_						),		target_image_size=2_5_6						).to(self.device						)
     UpperCAmelCase	:							Dict						=			preprocess_vqgan(lowerCAmelCase_						)
     UpperCAmelCase			,						*UpperCAmelCase	:							Dict						=			self.vqgan.encode(lowerCAmelCase_						)
     return z



   def 	__magic_name__   (							self							:					Optional[Any],		__A							:					Dict						):
     UpperCAmelCase	:							Optional[Any]						=			self.latent.detach().requires_grad_()
     UpperCAmelCase	:							Optional[Any]						=			base_latent + transform_vector
     if self.quantize:
       UpperCAmelCase			,						*UpperCAmelCase	:							Optional[Any]						=			self.vqgan.quantize(lowerCAmelCase_						)
     else:
       UpperCAmelCase	:							List[Any]						=			trans_latent
     return self.vqgan.decode(lowerCAmelCase_						)



   def 	__magic_name__   (							self							:					Dict,		__A							:					List[str],		__A							:					Optional[int],		__A							:					Optional[Any]=None						):
     UpperCAmelCase	:							List[str]						=			self.clip_preprocessor(text=lowerCAmelCase_,		images=lowerCAmelCase_,		return_tensors='''pt''',		padding=lowerCAmelCase_						)
     UpperCAmelCase	:							List[Any]						=			self.clip(**lowerCAmelCase_						)
     UpperCAmelCase	:							str						=			clip_outputs.logits_per_image
     if weights is not None:
       UpperCAmelCase	:							Optional[Any]						=			similarity_logits * weights
     return similarity_logits.sum()



   def 	__magic_name__   (							self							:					Optional[int],		__A							:					Optional[int],		__A							:					List[Any],		__A							:					Union[str, Any]						):
     UpperCAmelCase	:							int						=			self._get_clip_similarity(pos_prompts['''prompts'''],		lowerCAmelCase_,		weights=(1 / pos_prompts['''weights'''])						)
     if neg_prompts:
       UpperCAmelCase	:							Dict						=			self._get_clip_similarity(neg_prompts['''prompts'''],		lowerCAmelCase_,		weights=neg_prompts['''weights''']						)
     else:
       UpperCAmelCase	:							Union[str, Any]						=			torch.tensor([1],		device=self.device						)
     UpperCAmelCase	:							Optional[Any]						=			-torch.log(lowerCAmelCase_						) + torch.log(lowerCAmelCase_						)
     return loss



   def 	__magic_name__   (							self							:					Dict,		__A							:					Optional[int],		__A							:					Union[str, Any],		__A							:					Dict						):
     UpperCAmelCase	:							str						=			torch.randn_like(self.latent,		requires_grad=lowerCAmelCase_,		device=self.device						)
     UpperCAmelCase	:							str						=			torch.optim.Adam([vector],		lr=self.lr						)

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



   def 	__magic_name__   (							self							:					Optional[Any],		__A							:					Union[str, Any],		__A							:					Optional[Any],		__A							:					Union[str, Any]						):
     wandb.init(reinit=lowerCAmelCase_,		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	:							int						=			Image.open(lowerCAmelCase_						)
       UpperCAmelCase	:							Optional[int]						=			image.resize((2_5_6, 2_5_6)						)
       wandb.log('''Original Image''',		wandb.Image(lowerCAmelCase_						)						)



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





   def 	__magic_name__   (							self							:					Optional[int],		__A							:					Optional[int],		__A							:					int=None,		__A							:					int=None,		__A							:					Tuple=True,		__A							:					Tuple=False,		__A							:					Optional[Any]=True,		__A							:					Any=True,		__A							:					int=None,		):
     if image_path:
       UpperCAmelCase	:							Optional[Any]						=			self._get_latent(lowerCAmelCase_						)
     else:
       UpperCAmelCase	:							Tuple						=			torch.randn(self.latent_dim,		device=self.device						)
     if self.log:
       self._init_logging(lowerCAmelCase_,		lowerCAmelCase_,		lowerCAmelCase_						)

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

     UpperCAmelCase	:							List[Any]						=			self.vqgan.decode(self.latent						)[0]
     if show_intermediate:
       print('''Original Image'''						)
       show_pil(custom_to_pil(lowerCAmelCase_						)						)

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








import logging
import os

from .state import PartialState





class  __UpperCAmelCase					(	logging.LoggerAdapter						):



   @staticmethod
   def 	__magic_name__   (							__A							:					str						):
     UpperCAmelCase	:							Dict						=			PartialState()
     return not main_process_only or (main_process_only and state.is_main_process)





   def 	__magic_name__   (							self							:					Union[str, Any],		__A							:					Union[str, Any],		__A							:					Union[str, Any],		*__A							:					Optional[int],		**__A							:					Tuple						):
     if PartialState._shared_state == {}:
       raise RuntimeError(
           '''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'''						)
     UpperCAmelCase	:							List[str]						=			kwargs.pop('''main_process_only''',		__A						)
     UpperCAmelCase	:							int						=			kwargs.pop('''in_order''',		__A						)

     if self.isEnabledFor(__A						):
       if self._should_log(__A						):
         UpperCAmelCase			,						UpperCAmelCase	:							Dict						=			self.process(__A,		__A						)
         self.logger.log(__A,		__A,		*__A,		**__A						)

       elif in_order:
         UpperCAmelCase	:							Dict						=			PartialState()
         for i in range(state.num_processes						):
           if i == state.process_index:
             UpperCAmelCase			,						UpperCAmelCase	:							Optional[int]						=			self.process(__A,		__A						)
             self.logger.log(__A,		__A,		*__A,		**__A						)
           state.wait_for_everyone()






def       a__					(		UpperCAmelCase							:      str    ,			UpperCAmelCase							:      str = None      )  ->						Dict:
  if log_level is None:
    UpperCAmelCase	:							Union[str, Any]						=			os.environ.get('''ACCELERATE_LOG_LEVEL'''    ,			UpperCAmelCase      )
  UpperCAmelCase	:							Tuple						=			logging.getLogger(UpperCAmelCase      )
  if log_level is not None:
    logger.setLevel(log_level.upper()      )
    logger.root.setLevel(log_level.upper()      )
  return MultiProcessAdapter(UpperCAmelCase    ,			{}      )





