





"""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   __SCREAMING_SNAKE_CASE     :







  '''simple docstring'''


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

  def  __SCREAMING_SNAKE_CASE					(		self :							Tuple       , __a :							int						)		->					Tuple:
        _UpperCamelCase   :						Optional[Any]    =							self.resize(__a						)
        _UpperCamelCase   :						List[Any]    =							self.center_crop(__a						)
        _UpperCamelCase   :						int    =							self.normalize(__a						)
        return images


  def __call__(		self :							Tuple       , __a :							Tuple=None       , __a :							Any=None       , **__a :							Optional[Any]						)		->					str:
        _UpperCamelCase   :						Optional[Any]    =							self.tokenizer(text=__a       , **__a						)
        _UpperCamelCase   :						int    =							self.preprocess_img(__a						)
        _UpperCamelCase   :						str    =							{key: value.to(self.device						) for (key, value) in encoding.items()}
        return encoding





class   __SCREAMING_SNAKE_CASE     (    nn.Module ):







  '''simple docstring'''


  def __init__(		self :							int       , __a :							int=10       , __a :							List[str]=0.01       , __a :							str=None       , __a :							Optional[Any]=None       , __a :							Dict=None       , __a :							Optional[int]=None       , __a :							List[str]=None       , __a :							Any=None       , __a :							List[Any]=False       , __a :							Tuple=True       , __a :							Optional[int]="image"       , __a :							Optional[int]=True       , __a :							Optional[int]=False       , __a :							Any=False       , __a :							Any=False       , )		->					None:
        super().__init__()
        _UpperCamelCase   :						Optional[Any]    =							None
        _UpperCamelCase   :						List[str]    =							device if device else get_device()
        if vqgan:
              _UpperCamelCase   :						List[Any]    =							vqgan
        else:
              _UpperCamelCase   :						Union[str, Any]    =							load_vqgan(self.device       , conf_path=__a       , ckpt_path=__a						)
        self.vqgan.eval()
        if clip:
              _UpperCamelCase   :						int    =							clip
        else:
              _UpperCamelCase   :						Union[str, Any]    =							CLIPModel.from_pretrained("openai/clip-vit-base-patch32"						)
        self.clip.to(self.device						)
        _UpperCamelCase   :						Tuple    =							ProcessorGradientFlow(device=self.device						)

        _UpperCamelCase   :						Dict    =							iterations
        _UpperCamelCase   :						str    =							lr
        _UpperCamelCase   :						List[Any]    =							log
        _UpperCamelCase   :						str    =							make_grid
        _UpperCamelCase   :						str    =							return_val
        _UpperCamelCase   :						Any    =							quantize
        _UpperCamelCase   :						str    =							self.vqgan.decoder.z_shape

  def  __SCREAMING_SNAKE_CASE					(		self :							Any       , __a :							int=None       , __a :							List[str]=None       , __a :							Union[str, Any]=5       , __a :							str=True						)		->					List[Any]:
        _UpperCamelCase   :						str    =							[]
        if output_path is None:
              _UpperCamelCase   :						Optional[int]    =							"./animation.gif"
        if input_path is None:
              _UpperCamelCase   :						List[Any]    =							self.save_path
        _UpperCamelCase   :						int    =							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?)"						)
        _UpperCamelCase   :						int    =							total_duration / len(__a						)
        _UpperCamelCase   :						Optional[int]    =							[frame_duration] * len(__a						)
        if extend_frames:
              _UpperCamelCase   :						Optional[int]    =							1.5
              _UpperCamelCase   :						Optional[int]    =							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  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]       , __a :							Any=None       , __a :							Dict=None						)		->					List[Any]:
        if not (path or img):
              raise ValueError("Input either path or tensor"						)
        if img is not None:
              raise NotImplementedError
        _UpperCamelCase   :						int    =							preprocess(Image.open(__a						)       , target_image_size=256						).to(self.device						)
        _UpperCamelCase   :						str    =							preprocess_vqgan(__a						)
        _UpperCamelCase,	*_UpperCamelCase   :						List[Any]    =							self.vqgan.encode(__a						)
        return z

  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]       , __a :							Optional[int]						)		->					Any:
        _UpperCamelCase   :						Optional[int]    =							self.latent.detach().requires_grad_()
        _UpperCamelCase   :						List[Any]    =							base_latent + transform_vector
        if self.quantize:
              _UpperCamelCase,	*_UpperCamelCase   :						int    =							self.vqgan.quantize(__a						)
        else:
              _UpperCamelCase   :						Tuple    =							trans_latent
        return self.vqgan.decode(__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[Any]       , __a :							int       , __a :							Optional[int]       , __a :							Dict=None						)		->					Dict:
        _UpperCamelCase   :						List[str]    =							self.clip_preprocessor(text=__a       , images=__a       , return_tensors="pt"       , padding=__a						)
        _UpperCamelCase   :						Union[str, Any]    =							self.clip(**__a						)
        _UpperCamelCase   :						Any    =							clip_outputs.logits_per_image
        if weights is not None:
              _UpperCamelCase   :						str    =							similarity_logits * weights
        return similarity_logits.sum()

  def  __SCREAMING_SNAKE_CASE					(		self :							int       , __a :							List[Any]       , __a :							Optional[int]       , __a :							int						)		->					List[str]:
        _UpperCamelCase   :						Any    =							self._get_clip_similarity(pos_prompts["prompts"]       , __a       , weights=(1 / pos_prompts["weights"])						)
        if neg_prompts:
              _UpperCamelCase   :						Dict    =							self._get_clip_similarity(neg_prompts["prompts"]       , __a       , weights=neg_prompts["weights"]						)
        else:
              _UpperCamelCase   :						List[Any]    =							torch.tensor([1]       , device=self.device						)
        _UpperCamelCase   :						Any    =							-torch.log(__a						) + torch.log(__a						)
        return loss

  def  __SCREAMING_SNAKE_CASE					(		self :							List[str]       , __a :							Optional[Any]       , __a :							List[Any]       , __a :							Dict						)		->					Dict:
        _UpperCamelCase   :						List[str]    =							torch.randn_like(self.latent       , requires_grad=__a       , device=self.device						)
        _UpperCamelCase   :						Union[str, Any]    =							torch.optim.Adam([vector]       , lr=self.lr						)

        for i in range(self.iterations						):
              optim.zero_grad()
              _UpperCamelCase   :						Any    =							self._add_vector(__a						)
              _UpperCamelCase   :						Tuple    =							loop_post_process(__a						)
              _UpperCamelCase   :						int    =							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  __SCREAMING_SNAKE_CASE					(		self :							List[Any]       , __a :							Union[str, Any]       , __a :							str       , __a :							int						)		->					str:
        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:
              _UpperCamelCase   :						List[str]    =							Image.open(__a						)
              _UpperCamelCase   :						Tuple    =							image.resize((256, 256)						)
              wandb.log("Original Image"       , wandb.Image(__a						)						)

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


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

        _UpperCamelCase   :						List[str]    =							self.vqgan.decode(self.latent						)[0]
        if show_intermediate:
              print("Original Image"						)
              show_pil(custom_to_pil(__a						)						)

        _UpperCamelCase   :						List[Any]    =							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 typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    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


lowerCamelCase__						= logging.get_logger(__name__)


if is_vision_available():
  import PIL



class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:Optional[int]						  =       ["pixel_values"]

  def __init__(		self :							Tuple       , __a :							bool = True       , __a :							Dict[str, int] = None       , __a :							PILImageResampling = PILImageResampling.BICUBIC       , __a :							bool = True       , __a :							Dict[str, int] = None       , __a :							bool = True       , __a :							Union[int, float] = 1 / 255       , __a :							bool = True       , __a :							Optional[Union[float, List[float]]] = None       , __a :							Optional[Union[float, List[float]]] = None       , __a :							bool = True       , **__a :							List[Any]       , )		->					None:
        super().__init__(**__a						)
        _UpperCamelCase   :						Optional[int]    =							size if size is not None else {"shortest_edge": 224}
        _UpperCamelCase   :						Optional[int]    =							get_size_dict(__a       , default_to_square=__a						)
        _UpperCamelCase   :						Union[str, Any]    =							crop_size if crop_size is not None else {"height": 224, "width": 224}
        _UpperCamelCase   :						Tuple    =							get_size_dict(__a       , default_to_square=__a       , param_name="crop_size"						)

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

  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]       , __a :							np.ndarray       , __a :							Dict[str, int]       , __a :							PILImageResampling = PILImageResampling.BICUBIC       , __a :							Optional[Union[str, ChannelDimension]] = None       , **__a :							str       , )		->					np.ndarray:
        _UpperCamelCase   :						Any    =							get_size_dict(__a       , default_to_square=__a						)
        if "shortest_edge" not in size:
              raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''						)
        _UpperCamelCase   :						Dict    =							get_resize_output_image_size(__a       , size=size["shortest_edge"]       , default_to_square=__a						)
        return resize(__a       , size=__a       , resample=__a       , data_format=__a       , **__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							List[Any]       , __a :							np.ndarray       , __a :							Dict[str, int]       , __a :							Optional[Union[str, ChannelDimension]] = None       , **__a :							Tuple       , )		->					np.ndarray:
        _UpperCamelCase   :						List[str]    =							get_size_dict(__a						)
        if "height" not in size or "width" not in size:
              raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''						)
        return center_crop(__a       , size=(size["height"], size["width"])       , data_format=__a       , **__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Union[str, Any]       , __a :							np.ndarray       , __a :							Union[int, float]       , __a :							Optional[Union[str, ChannelDimension]] = None       , **__a :							Optional[Any]       , )		->					List[str]:
        return rescale(__a       , scale=__a       , data_format=__a       , **__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Tuple       , __a :							np.ndarray       , __a :							Union[float, List[float]]       , __a :							Union[float, List[float]]       , __a :							Optional[Union[str, ChannelDimension]] = None       , **__a :							Tuple       , )		->					np.ndarray:
        return normalize(__a       , mean=__a       , std=__a       , data_format=__a       , **__a						)


  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]       , __a :							ImageInput       , __a :							bool = None       , __a :							Dict[str, int] = None       , __a :							PILImageResampling = None       , __a :							bool = None       , __a :							int = None       , __a :							bool = None       , __a :							float = None       , __a :							bool = None       , __a :							Optional[Union[float, List[float]]] = None       , __a :							Optional[Union[float, List[float]]] = None       , __a :							bool = None       , __a :							Optional[Union[str, TensorType]] = None       , __a :							Optional[ChannelDimension] = ChannelDimension.FIRST       , **__a :							Union[str, Any]       , )		->					PIL.Image.Image:
        _UpperCamelCase   :						Tuple    =							do_resize if do_resize is not None else self.do_resize
        _UpperCamelCase   :						int    =							size if size is not None else self.size
        _UpperCamelCase   :						int    =							get_size_dict(__a       , param_name="size"       , default_to_square=__a						)
        _UpperCamelCase   :						List[Any]    =							resample if resample is not None else self.resample
        _UpperCamelCase   :						List[Any]    =							do_center_crop if do_center_crop is not None else self.do_center_crop
        _UpperCamelCase   :						Union[str, Any]    =							crop_size if crop_size is not None else self.crop_size
        _UpperCamelCase   :						List[Any]    =							get_size_dict(__a       , param_name="crop_size"       , default_to_square=__a						)
        _UpperCamelCase   :						Dict    =							do_rescale if do_rescale is not None else self.do_rescale
        _UpperCamelCase   :						Union[str, Any]    =							rescale_factor if rescale_factor is not None else self.rescale_factor
        _UpperCamelCase   :						Union[str, Any]    =							do_normalize if do_normalize is not None else self.do_normalize
        _UpperCamelCase   :						List[Any]    =							image_mean if image_mean is not None else self.image_mean
        _UpperCamelCase   :						List[Any]    =							image_std if image_std is not None else self.image_std
        _UpperCamelCase   :						List[str]    =							do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        _UpperCamelCase   :						Any    =							make_list_of_images(__a						)

        if not valid_images(__a						):
              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:
              raise ValueError("Size must be specified if do_resize is True."						)

        if do_center_crop and crop_size is None:
              raise ValueError("Crop size must be specified if do_center_crop 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:
              _UpperCamelCase   :						int    =							[convert_to_rgb(__a						) for image in images]

        # All transformations expect numpy arrays.
        _UpperCamelCase   :						List[Any]    =							[to_numpy_array(__a						) for image in images]

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

        if do_center_crop:
              _UpperCamelCase   :						Optional[Any]    =							[self.center_crop(image=__a       , size=__a						) for image in images]

        if do_rescale:
              _UpperCamelCase   :						Dict    =							[self.rescale(image=__a       , scale=__a						) for image in images]

        if do_normalize:
              _UpperCamelCase   :						List[Any]    =							[self.normalize(image=__a       , mean=__a       , std=__a						) for image in images]

        _UpperCamelCase   :						List[Any]    =							[to_channel_dimension_format(__a       , __a						) for image in images]

        _UpperCamelCase   :						str    =							{"pixel_values": images}
        return BatchFeature(data=__a       , tensor_type=__a						)
