


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 						__SCREAMING_SNAKE_CASE					:






     def __init__(			self     ,							__UpperCamelCase = "cpu"     ,							__UpperCamelCase = "openai/clip-vit-large-patch14"		)       ->    None:
          _a						=		device
          _a						=		CLIPTokenizerFast.from_pretrained(__A		)
          _a						=		[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
          _a						=		[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]
          _a						=		torchvision.transforms.Normalize(self.image_mean     ,							self.image_std		)
          _a						=		torchvision.transforms.Resize(224		)
          _a						=		torchvision.transforms.CenterCrop(224		)






     def  a_					(			self     ,							__UpperCamelCase		)       ->    List[str]:
          _a						=		self.resize(__A		)
          _a						=		self.center_crop(__A		)
          _a						=		self.normalize(__A		)
          return images






     def __call__(			self     ,							__UpperCamelCase=None     ,							__UpperCamelCase=None     ,							**__UpperCamelCase		)       ->    List[Any]:
          _a						=		self.tokenizer(text=__A     ,							**__A		)
          _a						=		self.preprocess_img(__A		)
          _a						=		{key: value.to(self.device		) for (key, value) in encoding.items()}
          return encoding







class 						__SCREAMING_SNAKE_CASE					(      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     ,							)       ->    None:
          super().__init__()
          _a						=		None
          _a						=		device if device else get_device()
          if vqgan:
               _a						=		vqgan
          else:
               _a						=		load_vqgan(self.device     ,							conf_path=__A     ,							ckpt_path=__A		)
          self.vqgan.eval()
          if clip:
               _a						=		clip
          else:
               _a						=		CLIPModel.from_pretrained("openai/clip-vit-base-patch32"		)
          self.clip.to(self.device		)
          _a						=		ProcessorGradientFlow(device=self.device		)

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






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






     def  a_					(			self     ,							__UpperCamelCase=None     ,							__UpperCamelCase=None		)       ->    List[Any]:
          if not (path or img):
               raise ValueError("Input either path or tensor"		)
          if img is not None:
               raise NotImplementedError
          _a						=		preprocess(Image.open(__A		)     ,							target_image_size=256		).to(self.device		)
          _a						=		preprocess_vqgan(__A		)
          _a			,			*_a						=		self.vqgan.encode(__A		)
          return z






     def  a_					(			self     ,							__UpperCamelCase		)       ->    Any:
          _a						=		self.latent.detach().requires_grad_()
          _a						=		base_latent + transform_vector
          if self.quantize:
               _a			,			*_a						=		self.vqgan.quantize(__A		)
          else:
               _a						=		trans_latent
          return self.vqgan.decode(__A		)






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase=None		)       ->    Any:
          _a						=		self.clip_preprocessor(text=__A     ,							images=__A     ,							return_tensors="pt"     ,							padding=__A		)
          _a						=		self.clip(**__A		)
          _a						=		clip_outputs.logits_per_image
          if weights is not None:
               _a						=		similarity_logits * weights
          return similarity_logits.sum()






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    List[Any]:
          _a						=		self._get_clip_similarity(pos_prompts["prompts"]     ,							__A     ,							weights=(1 / pos_prompts["weights"])		)
          if neg_prompts:
               _a						=		self._get_clip_similarity(neg_prompts["prompts"]     ,							__A     ,							weights=neg_prompts["weights"]		)
          else:
               _a						=		torch.tensor([1]     ,							device=self.device		)
          _a						=		-torch.log(__A		) + torch.log(__A		)
          return loss






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    str:
          _a						=		torch.randn_like(self.latent     ,							requires_grad=__A     ,							device=self.device		)
          _a						=		torch.optim.Adam([vector]     ,							lr=self.lr		)

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






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    Tuple:
          wandb.init(reinit=__A     ,							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:
               _a						=		Image.open(__A		)
               _a						=		image.resize((256, 256)		)
               wandb.log("Original Image"     ,							wandb.Image(__A		)		)






     def  a_					(			self     ,							__UpperCamelCase		)       ->    int:
          if not prompts:
               return []
          _a						=		[]
          _a						=		[]
          if isinstance(__A     ,							__A		):
               _a						=		[prompt.strip() for prompt in prompts.split("|"		)]
          for prompt in prompts:
               if isinstance(__A     ,							(tuple, list)		):
                    _a						=		prompt[0]
                    _a						=		float(prompt[1]		)
               elif ":" in prompt:
                    _a			,			_a						=		prompt.split(":"		)
                    _a						=		float(__A		)
               else:
                    _a						=		prompt
                    _a						=		1.0
               processed_prompts.append(__A		)
               weights.append(__A		)
          return {
              "prompts": processed_prompts,
              "weights": torch.tensor(__A     ,							device=self.device		),
          }






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase=None     ,							__UpperCamelCase=None     ,							__UpperCamelCase=True     ,							__UpperCamelCase=False     ,							__UpperCamelCase=True     ,							__UpperCamelCase=True     ,							__UpperCamelCase=None     ,							)       ->    Dict:
          if image_path:
               _a						=		self._get_latent(__A		)
          else:
               _a						=		torch.randn(self.latent_dim     ,							device=self.device		)
          if self.log:
               self._init_logging(__A     ,							__A     ,							__A		)

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

          _a						=		self.vqgan.decode(self.latent		)[0]
          if show_intermediate:
               print("Original Image"		)
               show_pil(custom_to_pil(__A		)		)

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





'''simple docstring'''

from manim import *





class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):






     def  a_					(			self		)       ->    str:
          _a						=		Rectangle(height=0.5     ,							width=0.5		)
          _a						=		Rectangle(height=0.46     ,							width=0.46		).set_stroke(width=0		)

          _a						=		[mem.copy() for i in range(6		)]
          _a						=		[mem.copy() for i in range(6		)]
          _a						=		VGroup(*__UpperCamelCase		).arrange(__UpperCamelCase     ,							buff=0		)
          _a						=		VGroup(*__UpperCamelCase		).arrange(__UpperCamelCase     ,							buff=0		)
          _a						=		VGroup(__UpperCamelCase     ,							__UpperCamelCase		).arrange(__UpperCamelCase     ,							buff=0		)
          _a						=		Text("CPU"     ,							font_size=24		)
          _a						=		Group(__UpperCamelCase     ,							__UpperCamelCase		).arrange(__UpperCamelCase     ,							buff=0.5     ,							aligned_edge=__UpperCamelCase		)
          cpu.move_to([-2.5, -0.5, 0]		)
          self.add(__UpperCamelCase		)

          _a						=		[mem.copy() for i in range(1		)]
          _a						=		VGroup(*__UpperCamelCase		).arrange(__UpperCamelCase     ,							buff=0		)
          _a						=		Text("GPU"     ,							font_size=24		)
          _a						=		Group(__UpperCamelCase     ,							__UpperCamelCase		).arrange(__UpperCamelCase     ,							buff=0.5     ,							aligned_edge=__UpperCamelCase		)
          gpu.align_to(__UpperCamelCase     ,							__UpperCamelCase		)
          gpu.set_x(gpu.get_x() - 1		)

          self.add(__UpperCamelCase		)

          _a						=		[mem.copy() for i in range(6		)]
          _a						=		VGroup(*__UpperCamelCase		).arrange(__UpperCamelCase     ,							buff=0		)

          _a						=		Text("Model"     ,							font_size=24		)
          _a						=		Group(__UpperCamelCase     ,							__UpperCamelCase		).arrange(__UpperCamelCase     ,							buff=0.5     ,							aligned_edge=__UpperCamelCase		)
          model.move_to([3, -1.0, 0]		)

          self.play(
              Create(__UpperCamelCase     ,							run_time=1		)     ,							Create(__UpperCamelCase     ,							run_time=1		)     ,							Create(__UpperCamelCase     ,							run_time=1		)     ,							)

          _a						=		MarkupText(
              f"First, an empty model skeleton is loaded\ninto <span fgcolor='{YELLOW}'>memory</span> without using much RAM."     ,							font_size=24     ,							)

          _a						=		Square(side_length=2.2		)
          key.move_to([-5, 2, 0]		)

          _a						=		MarkupText(
              f"<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"     ,							font_size=18     ,							)

          key_text.move_to([-5, 2.4, 0]		)

          step_a.move_to([2, 2, 0]		)
          self.play(Write(__UpperCamelCase     ,							run_time=2.5		)     ,							Write(__UpperCamelCase		)     ,							Write(__UpperCamelCase		)		)

          self.add(__UpperCamelCase		)

          _a						=		[]
          _a						=		[]
          _a						=		[]
          for i, rect in enumerate(__UpperCamelCase		):
               _a						=		Rectangle(height=0.46     ,							width=0.46		).set_stroke(width=0.0		).set_fill(__UpperCamelCase     ,							opacity=0.7		)
               cpu_target.move_to(__UpperCamelCase		)
               cpu_target.generate_target()
               _a						=		0.46 / 4
               _a						=		0.46 / 3

               if i == 0:
                    cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT		)     ,							buff=0.02     ,							direction=__UpperCamelCase		)
                    cpu_target.target.set_x(cpu_target.target.get_x() + 0.1		)
               elif i == 3:
                    cpu_target.target.next_to(cpu_targs[0].target     ,							direction=__UpperCamelCase     ,							buff=0.0		)
               else:
                    cpu_target.target.next_to(cpu_targs[i - 1].target     ,							direction=__UpperCamelCase     ,							buff=0.0		)
               cpu_targs.append(__UpperCamelCase		)

               first_animations.append(rect.animate(run_time=0.5		).set_stroke(__UpperCamelCase		)		)
               second_animations.append(MoveToTarget(__UpperCamelCase     ,							run_time=1.5		)		)

          self.play(*__UpperCamelCase		)
          self.play(*__UpperCamelCase		)

          self.wait()

