


"""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 		snake_case_:






     def __init__(	self  :					int					,				UpperCamelCase_  :					str = "cpu"					,				UpperCamelCase_  :					str = "openai/clip-vit-large-patch14"				):
           lowerCAmelCase					:	int       =   device
           lowerCAmelCase					:	Optional[int]       =   CLIPTokenizerFast.from_pretrained(UpperCamelCase_				)
           lowerCAmelCase					:	Tuple       =   [0.48_145_466, 0.4_578_275, 0.40_821_073]
           lowerCAmelCase					:	List[str]       =   [0.26_862_954, 0.26_130_258, 0.27_577_711]
           lowerCAmelCase					:	Dict       =   torchvision.transforms.Normalize(self.image_mean					,				self.image_std				)
           lowerCAmelCase					:	List[Any]       =   torchvision.transforms.Resize(2_2_4				)
           lowerCAmelCase					:	str       =   torchvision.transforms.CenterCrop(2_2_4				)






     def      lowerCamelCase__     (	self  :					List[Any]					,				UpperCamelCase_  :					List[Any]				):
           lowerCAmelCase					:	Optional[Any]       =   self.resize(UpperCamelCase_				)
           lowerCAmelCase					:	Union[str, Any]       =   self.center_crop(UpperCamelCase_				)
           lowerCAmelCase					:	str       =   self.normalize(UpperCamelCase_				)
           return images






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



class 		snake_case_(       nn.Module			):






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

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






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






     def      lowerCamelCase__     (	self  :					int					,				UpperCamelCase_  :					Tuple				):
           lowerCAmelCase					:	List[str]       =   self.latent.detach().requires_grad_()
           lowerCAmelCase					:	Optional[Any]       =   base_latent + transform_vector
           if self.quantize:
                 lowerCAmelCase					:	str       =   self.vqgan.quantize(UpperCamelCase_				)
           else:
                 lowerCAmelCase					:	int       =   trans_latent
           return self.vqgan.decode(UpperCamelCase_				)






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






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






     def      lowerCamelCase__     (	self  :					List[Any]					,				UpperCamelCase_  :					Optional[int]					,				UpperCamelCase_  :					List[Any]					,				UpperCamelCase_  :					List[str]				):
           lowerCAmelCase					:	List[Any]       =   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					:	str       =   self._add_vector(UpperCamelCase_				)
                 lowerCAmelCase					:	int       =   loop_post_process(UpperCamelCase_				)
                 lowerCAmelCase					:	List[Any]       =   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      lowerCamelCase__     (	self  :					Any					,				UpperCamelCase_  :					Optional[int]					,				UpperCamelCase_  :					Any					,				UpperCamelCase_  :					Dict				):
           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					:	Optional[Any]       =   Image.open(UpperCamelCase_				)
                 lowerCAmelCase					:	str       =   image.resize((2_5_6, 2_5_6)				)
                 wandb.log('''Original Image'''					,				wandb.Image(UpperCamelCase_				)				)






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






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

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

           lowerCAmelCase					:	str       =   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'''				)				)






"""simple docstring"""





from __future__ import annotations

from PIL import Image

# Define glider example
snake_case__		:     int    				=				[
    [0, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0, 0],
    [1, 1, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
]

# Define blinker example
snake_case__		:     Any    				=				[[0, 1, 0], [0, 1, 0], [0, 1, 0]]


def      _snake_case						(						_snake_case				:			list[list[int]]			):
      lowerCAmelCase					:	Union[str, Any]       =   []
      for i in range(len(_snake_case			)			):
            lowerCAmelCase					:	Any       =   []
            for j in range(len(cells[i]			)			):
                  # Get the number of live neighbours
                  lowerCAmelCase					:	Optional[int]       =   0
                  if i > 0 and j > 0:
                        neighbour_count += cells[i - 1][j - 1]
                  if i > 0:
                        neighbour_count += cells[i - 1][j]
                  if i > 0 and j < len(cells[i]			) - 1:
                        neighbour_count += cells[i - 1][j + 1]
                  if j > 0:
                        neighbour_count += cells[i][j - 1]
                  if j < len(cells[i]			) - 1:
                        neighbour_count += cells[i][j + 1]
                  if i < len(_snake_case			) - 1 and j > 0:
                        neighbour_count += cells[i + 1][j - 1]
                  if i < len(_snake_case			) - 1:
                        neighbour_count += cells[i + 1][j]
                  if i < len(_snake_case			) - 1 and j < len(cells[i]			) - 1:
                        neighbour_count += cells[i + 1][j + 1]

                  # Rules of the game of life (excerpt from Wikipedia):
                  # 1. Any live cell with two or three live neighbours survives.
                  # 2. Any dead cell with three live neighbours becomes a live cell.
                  # 3. All other live cells die in the next generation.
                  #    Similarly, all other dead cells stay dead.
                  lowerCAmelCase					:	str       =   cells[i][j] == 1
                  if (
                      (alive and 2 <= neighbour_count <= 3)
                      or not alive
                      and neighbour_count == 3
                  ):
                        next_generation_row.append(1			)
                  else:
                        next_generation_row.append(0			)

            next_generation.append(_snake_case			)
      return next_generation


def      _snake_case						(						_snake_case				:			list[list[int]] ,   _snake_case				:			int			):
      lowerCAmelCase					:	int       =   []
      for _ in range(_snake_case			):
            # Create output image
            lowerCAmelCase					:	Union[str, Any]       =   Image.new('''RGB''' ,   (len(cells[0]			), len(_snake_case			))			)
            lowerCAmelCase					:	Union[str, Any]       =   img.load()

            # Save cells to image
            for x in range(len(_snake_case			)			):
                  for y in range(len(cells[0]			)			):
                        lowerCAmelCase					:	Optional[int]       =   255 - cells[y][x] * 255
                        lowerCAmelCase					:	List[Any]       =   (colour, colour, colour)

        # Save image
            images.append(_snake_case			)
            lowerCAmelCase					:	Union[str, Any]       =   new_generation(_snake_case			)
      return images


if __name__ == "__main__":
 snake_case__		:     Union[str, Any]    				=				generate_images(GLIDER, 16)
 images[0].save('''out.gif''', save_all=True, append_images=images[1:])


