






"""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    lowercase_    :






      def __init__(    self     ,     a_ = "cpu"     ,     a_ = "openai/clip-vit-large-patch14"	)	->None:







        '''simple docstring'''


        _a							=	device
        _a							=	CLIPTokenizerFast.from_pretrained(__A	)
        _a							=	[0.48_145_466, 0.4_578_275, 0.40_821_073]
        _a							=	[0.26_862_954, 0.26_130_258, 0.27_577_711]
        _a							=	torchvision.transforms.Normalize(self.image_mean     ,     self.image_std	)
        _a							=	torchvision.transforms.Resize(2_2_4	)
        _a							=	torchvision.transforms.CenterCrop(2_2_4	)






      def 			lowerCamelCase__    (    self     ,     a_	)	->List[str]:







        '''simple docstring'''


        _a							=	self.resize(__A	)
        _a							=	self.center_crop(__A	)
        _a							=	self.normalize(__A	)
        return images






      def __call__(    self     ,     a_=None     ,     a_=None     ,     **a_	)	->Any:







        '''simple docstring'''


        _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    lowercase_    (nn.Module     ):






      def __init__(    self     ,     a_=1_0     ,     a_=0.01     ,     a_=None     ,     a_=None     ,     a_=None     ,     a_=None     ,     a_=None     ,     a_=None     ,     a_=False     ,     a_=True     ,     a_="image"     ,     a_=True     ,     a_=False     ,     a_=False     ,     a_=False     ,     )	->None:







        '''simple docstring'''


        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 			lowerCamelCase__    (    self     ,     a_=None     ,     a_=None     ,     a_=5     ,     a_=True	)	->Optional[int]:







        '''simple docstring'''


        _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 			lowerCamelCase__    (    self     ,     a_=None     ,     a_=None	)	->str:







        '''simple docstring'''


        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=2_5_6	).to(self.device	)
        _a							=	preprocess_vqgan(__A	)
        _a			,	*_a							=	self.vqgan.encode(__A	)
        return z






      def 			lowerCamelCase__    (    self     ,     a_	)	->Any:







        '''simple docstring'''


        _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 			lowerCamelCase__    (    self     ,     a_     ,     a_     ,     a_=None	)	->List[str]:







        '''simple docstring'''


        _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 			lowerCamelCase__    (    self     ,     a_     ,     a_     ,     a_	)	->List[str]:







        '''simple docstring'''


        _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 			lowerCamelCase__    (    self     ,     a_     ,     a_     ,     a_	)	->Dict:







        '''simple docstring'''


        _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 			lowerCamelCase__    (    self     ,     a_     ,     a_     ,     a_	)	->Union[str, Any]:







        '''simple docstring'''


        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((2_5_6, 2_5_6)	)
          wandb.log("Original Image"     ,     wandb.Image(__A	)	)






      def 			lowerCamelCase__    (    self     ,     a_	)	->Optional[int]:







        '''simple docstring'''


        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 			lowerCamelCase__    (    self     ,     a_     ,     a_=None     ,     a_=None     ,     a_=True     ,     a_=False     ,     a_=True     ,     a_=True     ,     a_=None     ,     )	->Tuple:







        '''simple docstring'''


        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 __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

UpperCamelCase							       =    TypeVar("""T""")
UpperCamelCase							       =    TypeVar("""U""")




class    lowercase_    (Generic[T, U]     ):






      def __init__(    self     ,     a_     ,     a_	)	->Any:







        '''simple docstring'''


        _a							=	key
        _a							=	val
        _a							=	None
        _a							=	None






      def __repr__(    self	)	->str:







        '''simple docstring'''


        return (
            f'''Node: key: {self.key}, val: {self.val}, '''
            f'''has next: {bool(self.next	)}, has prev: {bool(self.prev	)}'''
        )







class    lowercase_    (Generic[T, U]     ):






      def __init__(    self	)	->None:







        '''simple docstring'''


        _a							=	DoubleLinkedListNode(a_     ,     a_	)
        _a							=	DoubleLinkedListNode(a_     ,     a_	)
        _a			,	_a							=	self.rear, self.head






      def __repr__(    self	)	->str:







        '''simple docstring'''


        _a							=	["DoubleLinkedList"]
        _a							=	self.head
        while node.next is not None:
          rep.append(str(a_	)	)
          _a							=	node.next
        rep.append(str(self.rear	)	)
        return ",\n    ".join(a_	)






      def 			lowerCamelCase__    (    self     ,     a_	)	->None:







        '''simple docstring'''


        _a							=	self.rear.prev

        # All nodes other than self.head are guaranteed to have non-None previous
        assert previous is not None

        _a							=	node
        _a							=	previous
        _a							=	node
        _a							=	self.rear






      def 			lowerCamelCase__    (    self     ,     a_	)	->DoubleLinkedListNode[T, U] | None:







        '''simple docstring'''


        if node.prev is None or node.next is None:
          return None

        _a							=	node.next
        _a							=	node.prev
        _a							=	None
        _a							=	None
        return node







class    lowercase_    (Generic[T, U]     ):
      A__	:		dict[Callable[[T], U], LRUCache[T, U]]       			=				{}






      def __init__(    self     ,     a_	)	->int:







        '''simple docstring'''


        _a							=	DoubleLinkedList()
        _a							=	capacity
        _a							=	0
        _a							=	0
        _a							=	0
        _a							=	{}






      def __repr__(    self	)	->str:







        '''simple docstring'''


        return (
            f'''CacheInfo(hits={self.hits}, misses={self.miss}, '''
            f'''capacity={self.capacity}, current size={self.num_keys})'''
        )






      def __contains__(    self     ,     a_	)	->bool:







        '''simple docstring'''


        return key in self.cache






      def 			lowerCamelCase__    (    self     ,     a_	)	->U | None:







        '''simple docstring'''


        if key in self.cache:
          self.hits += 1
          _a							=	self.cache[key]
          _a							=	self.list.remove(self.cache[key]	)
          assert node == value_node

          # node is guaranteed not None because it is in self.cache
          assert node is not None
          self.list.add(a_	)
          return node.val
        self.miss += 1
        return None






      def 			lowerCamelCase__    (    self     ,     a_     ,     a_	)	->None:







        '''simple docstring'''


        if key not in self.cache:
          if self.num_keys >= self.capacity:
            # delete first node (oldest) when over capacity
            _a							=	self.list.head.next

            # guaranteed to have a non-None first node when num_keys > 0
            # explain to type checker via assertions
            assert first_node is not None
            assert first_node.key is not None
            assert (
                self.list.remove(a_	) is not None
            )  # node guaranteed to be in list assert node.key is not None

            del self.cache[first_node.key]
            self.num_keys -= 1
          _a							=	DoubleLinkedListNode(a_     ,     a_	)
          self.list.add(self.cache[key]	)
          self.num_keys += 1

        else:
          # bump node to the end of the list, update value
          _a							=	self.list.remove(self.cache[key]	)
          assert node is not None  # node guaranteed to be in list
          _a							=	value
          self.list.add(a_	)






      @classmethod
      def 			lowerCamelCase__    (    cls     ,     a_ = 1_2_8	)	->Callable[[Callable[[T], U]], Callable[..., U]]:







        '''simple docstring'''


        def cache_decorator_inner(a_	) -> Callable[..., U]:
          def cache_decorator_wrapper(*a_	) -> U:
            if func not in cls.decorator_function_to_instance_map:
              _a							=	LRUCache(a_	)

            _a							=	cls.decorator_function_to_instance_map[func].get(args[0]	)
            if result is None:
              _a							=	func(*a_	)
              cls.decorator_function_to_instance_map[func].put(args[0]     ,     a_	)
            return result

          def cache_info() -> LRUCache[T, U]:
            return cls.decorator_function_to_instance_map[func]

          setattr(a_     ,     "cache_info"     ,     a_	)  # noqa: B010

          return cache_decorator_wrapper

        return cache_decorator_inner


if __name__ == "__main__":
     import doctest

     doctest.testmod()


