




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  _lowerCAmelCase     :

						def __init__(   self      ,				__UpperCAmelCase = "cpu"      ,				__UpperCAmelCase = "openai/clip-vit-large-patch14"		):
								lowerCAmelCase__				:  Union[str, Any]		       =     device
								lowerCAmelCase__				:  Optional[int]		       =     CLIPTokenizerFast.from_pretrained(__UpperCAmelCase		)
								lowerCAmelCase__				:  Optional[Any]		       =     [0.48145466, 0.4578275, 0.40821073]
								lowerCAmelCase__				:  Optional[Any]		       =     [0.26862954, 0.26130258, 0.27577711]
								lowerCAmelCase__				:  Optional[Any]		       =     torchvision.transforms.Normalize(self.image_mean      ,				self.image_std		)
								lowerCAmelCase__				:  Dict		       =     torchvision.transforms.Resize(224		)
								lowerCAmelCase__				:  Any		       =     torchvision.transforms.CenterCrop(224		)

						def 	__magic_name__(   self      ,				__UpperCAmelCase		):
								lowerCAmelCase__				:  Union[str, Any]		       =     self.resize(__UpperCAmelCase		)
								lowerCAmelCase__				:  str		       =     self.center_crop(__UpperCAmelCase		)
								lowerCAmelCase__				:  Optional[Any]		       =     self.normalize(__UpperCAmelCase		)
								return images







						def __call__(   self      ,				__UpperCAmelCase=None      ,				__UpperCAmelCase=None      ,				**__UpperCAmelCase		):
								lowerCAmelCase__				:  Tuple		       =     self.tokenizer(text=__UpperCAmelCase      ,				**__UpperCAmelCase		)
								lowerCAmelCase__				:  Optional[int]		       =     self.preprocess_img(__UpperCAmelCase		)
								lowerCAmelCase__				:  Any		       =     {key: value.to(self.device		) for (key, value) in encoding.items()}
								return encoding






class  _lowerCAmelCase     (  nn.Module	):

						def __init__(   self      ,				__UpperCAmelCase=10      ,				__UpperCAmelCase=0.01      ,				__UpperCAmelCase=None      ,				__UpperCAmelCase=None      ,				__UpperCAmelCase=None      ,				__UpperCAmelCase=None      ,				__UpperCAmelCase=None      ,				__UpperCAmelCase=None      ,				__UpperCAmelCase=False      ,				__UpperCAmelCase=True      ,				__UpperCAmelCase="image"      ,				__UpperCAmelCase=True      ,				__UpperCAmelCase=False      ,				__UpperCAmelCase=False      ,				__UpperCAmelCase=False      ,				):
								super().__init__()
								lowerCAmelCase__				:  str		       =     None
								lowerCAmelCase__				:  str		       =     device if device else get_device()
								if vqgan:
										lowerCAmelCase__				:  Union[str, Any]		       =     vqgan
								else:
										lowerCAmelCase__				:  Dict		       =     load_vqgan(self.device      ,				conf_path=__UpperCAmelCase      ,				ckpt_path=__UpperCAmelCase		)
								self.vqgan.eval()
								if clip:
										lowerCAmelCase__				:  Optional[int]		       =     clip
								else:
										lowerCAmelCase__				:  Union[str, Any]		       =     CLIPModel.from_pretrained('''openai/clip-vit-base-patch32'''		)
								self.clip.to(self.device		)
								lowerCAmelCase__				:  Optional[Any]		       =     ProcessorGradientFlow(device=self.device		)

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

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

						def 	__magic_name__(   self      ,				__UpperCAmelCase=None      ,				__UpperCAmelCase=None		):
								if not (path or img):
										raise ValueError('''Input either path or tensor'''		)
								if img is not None:
										raise NotImplementedError
								lowerCAmelCase__				:  int		       =     preprocess(Image.open(__UpperCAmelCase		)      ,				target_image_size=256		).to(self.device		)
								lowerCAmelCase__				:  Any		       =     preprocess_vqgan(__UpperCAmelCase		)
								lowerCAmelCase__				:  Dict		       =     self.vqgan.encode(__UpperCAmelCase		)
								return z

						def 	__magic_name__(   self      ,				__UpperCAmelCase		):
								lowerCAmelCase__				:  Union[str, Any]		       =     self.latent.detach().requires_grad_()
								lowerCAmelCase__				:  int		       =     base_latent + transform_vector
								if self.quantize:
										lowerCAmelCase__				:  Union[str, Any]		       =     self.vqgan.quantize(__UpperCAmelCase		)
								else:
										lowerCAmelCase__				:  Tuple		       =     trans_latent
								return self.vqgan.decode(__UpperCAmelCase		)

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

						def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
								lowerCAmelCase__				:  Dict		       =     self._get_clip_similarity(pos_prompts['''prompts''']      ,				__UpperCAmelCase      ,				weights=(1 / pos_prompts['''weights'''])		)
								if neg_prompts:
										lowerCAmelCase__				:  Union[str, Any]		       =     self._get_clip_similarity(neg_prompts['''prompts''']      ,				__UpperCAmelCase      ,				weights=neg_prompts['''weights''']		)
								else:
										lowerCAmelCase__				:  Union[str, Any]		       =     torch.tensor([1]      ,				device=self.device		)
								lowerCAmelCase__				:  Tuple		       =     -torch.log(__UpperCAmelCase		) + torch.log(__UpperCAmelCase		)
								return loss

						def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
								lowerCAmelCase__				:  Dict		       =     torch.randn_like(self.latent      ,				requires_grad=__UpperCAmelCase      ,				device=self.device		)
								lowerCAmelCase__				:  List[Any]		       =     torch.optim.Adam([vector]      ,				lr=self.lr		)

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

						def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
								wandb.init(reinit=__UpperCAmelCase      ,				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__				:  int		       =     Image.open(__UpperCAmelCase		)
										lowerCAmelCase__				:  int		       =     image.resize((256, 256)		)
										wandb.log('''Original Image'''      ,				wandb.Image(__UpperCAmelCase		)		)

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







						def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase=None      ,				__UpperCAmelCase=None      ,				__UpperCAmelCase=True      ,				__UpperCAmelCase=False      ,				__UpperCAmelCase=True      ,				__UpperCAmelCase=True      ,				__UpperCAmelCase=None      ,				):
								if image_path:
										lowerCAmelCase__				:  Union[str, Any]		       =     self._get_latent(__UpperCAmelCase		)
								else:
										lowerCAmelCase__				:  Union[str, Any]		       =     torch.randn(self.latent_dim      ,				device=self.device		)
								if self.log:
										self._init_logging(__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		)

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

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

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







from datetime import datetime

import matplotlib.pyplot as plt
import torch



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	str:
  for param in module.parameters():
    lowerCAmelCase__				:  int		       =     False



def      __lowerCAmelCase			(					)     ->	Optional[Any]:
  lowerCAmelCase__				:  List[Any]		       =     '''cuda''' if torch.cuda.is_available() else '''cpu'''
  if torch.backends.mps.is_available() and torch.backends.mps.is_built():
    lowerCAmelCase__				:  Optional[Any]		       =     '''mps'''
  if device == "mps":
    print(
        '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
        ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
        ''' with generations.'''      )
  return device



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	List[Any]:
  lowerCAmelCase__				:  str		       =     plt.imshow(UpperCamelCase      )
  fig.axes.get_xaxis().set_visible(UpperCamelCase      )
  fig.axes.get_yaxis().set_visible(UpperCamelCase      )
  plt.show()



def      __lowerCAmelCase			(					)     ->	str:
  lowerCAmelCase__				:  Dict		       =     datetime.now()
  lowerCAmelCase__				:  Optional[int]		       =     current_time.strftime('''%H:%M:%S'''      )
  return timestamp

