





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       ,      snake_case = "cpu"       ,      snake_case = "openai/clip-vit-large-patch14"   )					->	None:
      """simple docstring"""





      a__						:    Tuple   		=	device
      a__						:    Any   		=	CLIPTokenizerFast.from_pretrained(snake_case   )
      a__						:    str   		=	[0.48_145_466, 0.4_578_275, 0.40_821_073]
      a__						:    Optional[int]   		=	[0.26_862_954, 0.26_130_258, 0.27_577_711]
      a__						:    int   		=	torchvision.transforms.Normalize(self.image_mean       ,      self.image_std   )
      a__						:    Any   		=	torchvision.transforms.Resize(224   )
      a__						:    Union[str, Any]   		=	torchvision.transforms.CenterCrop(224   )





   def 						_snake_case			(			self       ,      snake_case   )					->	List[str]:
      """simple docstring"""





      a__						:    List[Any]   		=	self.resize(snake_case   )
      a__						:    str   		=	self.center_crop(snake_case   )
      a__						:    List[str]   		=	self.normalize(snake_case   )
      return images





   def __call__(			self       ,      snake_case=None       ,      snake_case=None       ,      **snake_case   )					->	int:
      """simple docstring"""





      a__						:    Tuple   		=	self.tokenizer(text=snake_case       ,      **snake_case   )
      a__						:    Union[str, Any]   		=	self.preprocess_img(snake_case   )
      a__						:    Tuple   		=	{key: value.to(self.device   ) for (key, value) in encoding.items()}
      return encoding







class 	__lowerCAmelCase      (       nn.Module	):





   def __init__(			self       ,      snake_case=10       ,      snake_case=0.01       ,      snake_case=None       ,      snake_case=None       ,      snake_case=None       ,      snake_case=None       ,      snake_case=None       ,      snake_case=None       ,      snake_case=False       ,      snake_case=True       ,      snake_case="image"       ,      snake_case=True       ,      snake_case=False       ,      snake_case=False       ,      snake_case=False       ,      )					->	None:
      """simple docstring"""





      super().__init__()
      a__						:    int   		=	None
      a__						:    int   		=	device if device else get_device()
      if vqgan:
         a__						:    Dict   		=	vqgan
      else:
         a__						:    List[Any]   		=	load_vqgan(self.device       ,      conf_path=snake_case       ,      ckpt_path=snake_case   )
      self.vqgan.eval()
      if clip:
         a__						:    List[str]   		=	clip
      else:
         a__						:    int   		=	CLIPModel.from_pretrained("openai/clip-vit-base-patch32"   )
      self.clip.to(self.device   )
      a__						:    Optional[Any]   		=	ProcessorGradientFlow(device=self.device   )

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





   def 						_snake_case			(			self       ,      snake_case=None       ,      snake_case=None       ,      snake_case=5       ,      snake_case=True   )					->	int:
      """simple docstring"""





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





   def 						_snake_case			(			self       ,      snake_case=None       ,      snake_case=None   )					->	Optional[int]:
      """simple docstring"""





      if not (path or img):
         raise ValueError("Input either path or tensor"   )
      if img is not None:
         raise NotImplementedError
      a__						:    List[str]   		=	preprocess(Image.open(snake_case   )       ,      target_image_size=256   ).to(self.device   )
      a__						:    Optional[Any]   		=	preprocess_vqgan(snake_case   )
      a__		,  *a__						:    int   		=	self.vqgan.encode(snake_case   )
      return z





   def 						_snake_case			(			self       ,      snake_case   )					->	Tuple:
      """simple docstring"""





      a__						:    Optional[Any]   		=	self.latent.detach().requires_grad_()
      a__						:    Optional[Any]   		=	base_latent + transform_vector
      if self.quantize:
         a__		,  *a__						:    List[Any]   		=	self.vqgan.quantize(snake_case   )
      else:
         a__						:    str   		=	trans_latent
      return self.vqgan.decode(snake_case   )





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case=None   )					->	Dict:
      """simple docstring"""





      a__						:    List[str]   		=	self.clip_preprocessor(text=snake_case       ,      images=snake_case       ,      return_tensors="pt"       ,      padding=snake_case   )
      a__						:    Union[str, Any]   		=	self.clip(**snake_case   )
      a__						:    Union[str, Any]   		=	clip_outputs.logits_per_image
      if weights is not None:
         a__						:    Union[str, Any]   		=	similarity_logits * weights
      return similarity_logits.sum()





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case   )					->	List[Any]:
      """simple docstring"""





      a__						:    Any   		=	self._get_clip_similarity(pos_prompts["prompts"]       ,      snake_case       ,      weights=(1 / pos_prompts["weights"])   )
      if neg_prompts:
         a__						:    Union[str, Any]   		=	self._get_clip_similarity(neg_prompts["prompts"]       ,      snake_case       ,      weights=neg_prompts["weights"]   )
      else:
         a__						:    str   		=	torch.tensor([1]       ,      device=self.device   )
      a__						:    Union[str, Any]   		=	-torch.log(snake_case   ) + torch.log(snake_case   )
      return loss





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case   )					->	Optional[int]:
      """simple docstring"""





      a__						:    Tuple   		=	torch.randn_like(self.latent       ,      requires_grad=snake_case       ,      device=self.device   )
      a__						:    str   		=	torch.optim.Adam([vector]       ,      lr=self.lr   )

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





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case   )					->	Union[str, Any]:
      """simple docstring"""





      wandb.init(reinit=snake_case       ,      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__						:    Optional[int]   		=	Image.open(snake_case   )
         a__						:    List[Any]   		=	image.resize((256, 256)   )
         wandb.log("Original Image"       ,      wandb.Image(snake_case   )   )





   def 						_snake_case			(			self       ,      snake_case   )					->	Tuple:
      """simple docstring"""





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





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case=None       ,      snake_case=None       ,      snake_case=True       ,      snake_case=False       ,      snake_case=True       ,      snake_case=True       ,      snake_case=None       ,      )					->	Tuple:
      """simple docstring"""





      if image_path:
         a__						:    Tuple   		=	self._get_latent(snake_case   )
      else:
         a__						:    Tuple   		=	torch.randn(self.latent_dim       ,      device=self.device   )
      if self.log:
         self._init_logging(snake_case       ,      snake_case       ,      snake_case   )

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

      a__						:    List[Any]   		=	self.vqgan.decode(self.latent   )[0]
      if show_intermediate:
         print("Original Image"   )
         show_pil(custom_to_pil(snake_case   )   )

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








from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


SCREAMING_SNAKE_CASE__							:      List[str]         =    logging.get_logger(__name__)







class 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    int  		=					["""pixel_values"""]





   def __init__(			self       ,      snake_case = True       ,      snake_case = None       ,      snake_case = PILImageResampling.BICUBIC       ,      snake_case = True       ,      snake_case = 1 / 255       ,      snake_case = True       ,      snake_case = None       ,      snake_case = None       ,      snake_case = True       ,      **snake_case       ,      )					->	None:
      """simple docstring"""





      super().__init__(**snake_case   )
      a__						:    List[str]   		=	size if size is not None else {"height": 384, "width": 384}
      a__						:    List[str]   		=	get_size_dict(snake_case       ,      default_to_square=snake_case   )

      a__						:    Any   		=	do_resize
      a__						:    Optional[int]   		=	size
      a__						:    int   		=	resample
      a__						:    Optional[Any]   		=	do_rescale
      a__						:    Dict   		=	rescale_factor
      a__						:    Dict   		=	do_normalize
      a__						:    Any   		=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
      a__						:    List[str]   		=	image_std if image_std is not None else OPENAI_CLIP_STD
      a__						:    str   		=	do_convert_rgb





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case = PILImageResampling.BICUBIC       ,      snake_case = None       ,      **snake_case       ,      )					->	np.ndarray:
      """simple docstring"""





      a__						:    Optional[int]   		=	get_size_dict(snake_case       ,      default_to_square=snake_case   )
      if "height" not in size or "width" not in size:
         raise ValueError(F"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""   )
      a__						:    Optional[int]   		=	(size["height"], size["width"])
      return resize(snake_case       ,      size=snake_case       ,      resample=snake_case       ,      data_format=snake_case       ,      **snake_case   )





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case = None       ,      **snake_case       ,      )					->	List[str]:
      """simple docstring"""





      return rescale(snake_case       ,      scale=snake_case       ,      data_format=snake_case       ,      **snake_case   )





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case = None       ,      **snake_case       ,      )					->	np.ndarray:
      """simple docstring"""





      return normalize(snake_case       ,      mean=snake_case       ,      std=snake_case       ,      data_format=snake_case       ,      **snake_case   )





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      snake_case = ChannelDimension.FIRST       ,      **snake_case       ,      )					->	PIL.Image.Image:
      """simple docstring"""





      a__						:    List[str]   		=	do_resize if do_resize is not None else self.do_resize
      a__						:    Optional[Any]   		=	resample if resample is not None else self.resample
      a__						:    int   		=	do_rescale if do_rescale is not None else self.do_rescale
      a__						:    int   		=	rescale_factor if rescale_factor is not None else self.rescale_factor
      a__						:    Union[str, Any]   		=	do_normalize if do_normalize is not None else self.do_normalize
      a__						:    List[Any]   		=	image_mean if image_mean is not None else self.image_mean
      a__						:    int   		=	image_std if image_std is not None else self.image_std
      a__						:    Tuple   		=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

      a__						:    Tuple   		=	size if size is not None else self.size
      a__						:    Any   		=	get_size_dict(snake_case       ,      default_to_square=snake_case   )

      a__						:    str   		=	make_list_of_images(snake_case   )

      if not valid_images(snake_case   ):
         raise ValueError(
             "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
             "torch.Tensor, tf.Tensor or jax.ndarray."   )

      if do_resize and size is None or resample is None:
         raise ValueError("Size and resample must be specified if do_resize is True."   )

      if do_rescale and rescale_factor is None:
         raise ValueError("Rescale factor must be specified if do_rescale is True."   )

      if do_normalize and (image_mean is None or image_std is None):
         raise ValueError("Image mean and std must be specified if do_normalize is True."   )

      # PIL RGBA images are converted to RGB
      if do_convert_rgb:
         a__						:    List[str]   		=	[convert_to_rgb(snake_case   ) for image in images]

      # All transformations expect numpy arrays.
      a__						:    Dict   		=	[to_numpy_array(snake_case   ) for image in images]

      if do_resize:
         a__						:    Union[str, Any]   		=	[self.resize(image=snake_case       ,      size=snake_case       ,      resample=snake_case   ) for image in images]

      if do_rescale:
         a__						:    Tuple   		=	[self.rescale(image=snake_case       ,      scale=snake_case   ) for image in images]

      if do_normalize:
         a__						:    List[str]   		=	[self.normalize(image=snake_case       ,      mean=snake_case       ,      std=snake_case   ) for image in images]

      a__						:    Union[str, Any]   		=	[to_channel_dimension_format(snake_case       ,      snake_case   ) for image in images]

      a__						:    List[Any]   		=	BatchFeature(data={"pixel_values": images}       ,      tensor_type=snake_case   )

      return encoded_outputs

