




'''simple docstring'''


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     :  str       ,		__lowerCAmelCase     :  str = "cpu"       ,		__lowerCAmelCase     :  str = "openai/clip-vit-large-patch14"      ):
							__snake_case						 =   device
							__snake_case						 =   CLIPTokenizerFast.from_pretrained(__lowerCAmelCase      )
							__snake_case						 =   [0.48145466, 0.4578275, 0.40821073]
							__snake_case						 =   [0.26862954, 0.26130258, 0.27577711]
							__snake_case						 =   torchvision.transforms.Normalize(self.image_mean       ,		self.image_std      )
							__snake_case						 =   torchvision.transforms.Resize(2_2_4      )
							__snake_case						 =   torchvision.transforms.CenterCrop(2_2_4      )




	def   lowercase__   (       self     :  int       ,		__lowerCAmelCase     :  Union[str, Any]      ):
							__snake_case						 =   self.resize(__lowerCAmelCase      )
							__snake_case						 =   self.center_crop(__lowerCAmelCase      )
							__snake_case						 =   self.normalize(__lowerCAmelCase      )
							return images




	def __call__(       self     :  Dict       ,		__lowerCAmelCase     :  List[str]=None       ,		__lowerCAmelCase     :  List[str]=None       ,		**__lowerCAmelCase     :  str      ):
							__snake_case						 =   self.tokenizer(text=__lowerCAmelCase       ,		**__lowerCAmelCase      )
							__snake_case						 =   self.preprocess_img(__lowerCAmelCase      )
							__snake_case						 =   {key: value.to(self.device      ) for (key, value) in encoding.items()}
							return encoding





class 						a_				(			nn.Module						):




	def __init__(       self     :  Optional[int]       ,		__lowerCAmelCase     :  List[str]=1_0       ,		__lowerCAmelCase     :  Union[str, Any]=0.01       ,		__lowerCAmelCase     :  Any=None       ,		__lowerCAmelCase     :  Union[str, Any]=None       ,		__lowerCAmelCase     :  Optional[Any]=None       ,		__lowerCAmelCase     :  str=None       ,		__lowerCAmelCase     :  List[str]=None       ,		__lowerCAmelCase     :  List[str]=None       ,		__lowerCAmelCase     :  Optional[int]=False       ,		__lowerCAmelCase     :  Optional[int]=True       ,		__lowerCAmelCase     :  str="image"       ,		__lowerCAmelCase     :  str=True       ,		__lowerCAmelCase     :  Any=False       ,		__lowerCAmelCase     :  int=False       ,		__lowerCAmelCase     :  List[str]=False       ,		):
							super().__init__()
							__snake_case						 =   None
							__snake_case						 =   device if device else get_device()
							if vqgan:
													__snake_case						 =   vqgan
							else:
													__snake_case						 =   load_vqgan(self.device       ,		conf_path=__lowerCAmelCase       ,		ckpt_path=__lowerCAmelCase      )
							self.vqgan.eval()
							if clip:
													__snake_case						 =   clip
							else:
													__snake_case						 =   CLIPModel.from_pretrained('openai/clip-vit-base-patch32'      )
							self.clip.to(self.device      )
							__snake_case						 =   ProcessorGradientFlow(device=self.device      )

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




	def   lowercase__   (       self     :  int       ,		__lowerCAmelCase     :  Optional[int]=None       ,		__lowerCAmelCase     :  Dict=None       ,		__lowerCAmelCase     :  List[str]=5       ,		__lowerCAmelCase     :  Any=True      ):
							__snake_case						 =   []
							if output_path is None:
													__snake_case						 =   './animation.gif'
							if input_path is None:
													__snake_case						 =   self.save_path
							__snake_case						 =   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?)'      )
							__snake_case						 =   total_duration / len(__lowerCAmelCase      )
							__snake_case						 =   [frame_duration] * len(__lowerCAmelCase      )
							if extend_frames:
													__snake_case						 =   1.5
													__snake_case						 =   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   lowercase__   (       self     :  List[Any]       ,		__lowerCAmelCase     :  List[str]=None       ,		__lowerCAmelCase     :  List[Any]=None      ):
							if not (path or img):
													raise ValueError('Input either path or tensor'      )
							if img is not None:
													raise NotImplementedError
							__snake_case						 =   preprocess(Image.open(__lowerCAmelCase      )       ,		target_image_size=2_5_6      ).to(self.device      )
							__snake_case						 =   preprocess_vqgan(__lowerCAmelCase      )
							__snake_case ,       *__snake_case						 =   self.vqgan.encode(__lowerCAmelCase      )
							return z




	def   lowercase__   (       self     :  List[Any]       ,		__lowerCAmelCase     :  Any      ):
							__snake_case						 =   self.latent.detach().requires_grad_()
							__snake_case						 =   base_latent + transform_vector
							if self.quantize:
													__snake_case ,       *__snake_case						 =   self.vqgan.quantize(__lowerCAmelCase      )
							else:
													__snake_case						 =   trans_latent
							return self.vqgan.decode(__lowerCAmelCase      )




	def   lowercase__   (       self     :  Optional[Any]       ,		__lowerCAmelCase     :  Any       ,		__lowerCAmelCase     :  List[Any]       ,		__lowerCAmelCase     :  Optional[int]=None      ):
							__snake_case						 =   self.clip_preprocessor(text=__lowerCAmelCase       ,		images=__lowerCAmelCase       ,		return_tensors='pt'       ,		padding=__lowerCAmelCase      )
							__snake_case						 =   self.clip(**__lowerCAmelCase      )
							__snake_case						 =   clip_outputs.logits_per_image
							if weights is not None:
													__snake_case						 =   similarity_logits * weights
							return similarity_logits.sum()




	def   lowercase__   (       self     :  Optional[Any]       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  List[str]       ,		__lowerCAmelCase     :  int      ):
							__snake_case						 =   self._get_clip_similarity(pos_prompts['prompts']       ,		__lowerCAmelCase       ,		weights=(1 / pos_prompts['weights'])      )
							if neg_prompts:
													__snake_case						 =   self._get_clip_similarity(neg_prompts['prompts']       ,		__lowerCAmelCase       ,		weights=neg_prompts['weights']      )
							else:
													__snake_case						 =   torch.tensor([1]       ,		device=self.device      )
							__snake_case						 =   -torch.log(__lowerCAmelCase      ) + torch.log(__lowerCAmelCase      )
							return loss




	def   lowercase__   (       self     :  Tuple       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  List[str]       ,		__lowerCAmelCase     :  Dict      ):
							__snake_case						 =   torch.randn_like(self.latent       ,		requires_grad=__lowerCAmelCase       ,		device=self.device      )
							__snake_case						 =   torch.optim.Adam([vector]       ,		lr=self.lr      )

							for i in range(self.iterations      ):
													optim.zero_grad()
													__snake_case						 =   self._add_vector(__lowerCAmelCase      )
													__snake_case						 =   loop_post_process(__lowerCAmelCase      )
													__snake_case						 =   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   lowercase__   (       self     :  Tuple       ,		__lowerCAmelCase     :  Dict       ,		__lowerCAmelCase     :  List[Any]       ,		__lowerCAmelCase     :  Tuple      ):
							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:
													__snake_case						 =   Image.open(__lowerCAmelCase      )
													__snake_case						 =   image.resize((2_5_6, 2_5_6)      )
													wandb.log('Original Image'       ,		wandb.Image(__lowerCAmelCase      )      )




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




	def   lowercase__   (       self     :  List[Any]       ,		__lowerCAmelCase     :  Union[str, Any]       ,		__lowerCAmelCase     :  Optional[int]=None       ,		__lowerCAmelCase     :  Tuple=None       ,		__lowerCAmelCase     :  str=True       ,		__lowerCAmelCase     :  Dict=False       ,		__lowerCAmelCase     :  List[Any]=True       ,		__lowerCAmelCase     :  Optional[int]=True       ,		__lowerCAmelCase     :  str=None       ,		):
							if image_path:
													__snake_case						 =   self._get_latent(__lowerCAmelCase      )
							else:
													__snake_case						 =   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."
							__snake_case						 =   self.process_prompts(__lowerCAmelCase      )
							__snake_case						 =   self.process_prompts(__lowerCAmelCase      )
							if save_final and save_path is None:
													__snake_case						 =   os.path.join('./outputs/'       ,		'_'.join(pos_prompts['prompts']      )      )
							if not os.path.exists(__lowerCAmelCase      ):
													os.makedirs(__lowerCAmelCase      )
							else:
													__snake_case						 =   save_path + '_' + get_timestamp()
													os.makedirs(__lowerCAmelCase      )
							__snake_case						 =   save_path

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

							__snake_case						 =   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'      )      )







'''simple docstring'''


def 						lowerCamelCase__   (				a							):
						assert (
						    isinstance(a    ,	a							) and number_of_steps > 0
						), f'number_of_steps needs to be positive integer, your input {number_of_steps}'
						if number_of_steps == 1:
												return 1
						__snake_case ,       __snake_case						 =   1, 1
						for _ in range(number_of_steps - 1							):
												__snake_case ,       __snake_case						 =   current + previous, current
						return current


if __name__ == "__main__":
	import doctest

	doctest.testmod()

