'''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 	lowerCamelCase_	:
      '''simple docstring'''

      def __init__(       self						:				Union[str, Any]						,					A						:				Dict = "cpu"						,					A						:				Union[str, Any] = "openai/clip-vit-large-patch14"       ):
             _UpperCAmelCase     :  List[str]							      =    device
             _UpperCAmelCase     :  str							      =    CLIPTokenizerFast.from_pretrained(A       )
             _UpperCAmelCase     :  int							      =    [0.48_145_466, 0.4_578_275, 0.40_821_073]
             _UpperCAmelCase     :  Union[str, Any]							      =    [0.26_862_954, 0.26_130_258, 0.27_577_711]
             _UpperCAmelCase     :  List[str]							      =    torchvision.transforms.Normalize(self.image_mean						,					self.image_std       )
             _UpperCAmelCase     :  Optional[int]							      =    torchvision.transforms.Resize(224       )
             _UpperCAmelCase     :  Optional[Any]							      =    torchvision.transforms.CenterCrop(224       )




      def      _A	(       self						:				Any						,					A						:				Tuple       ):
             _UpperCAmelCase     :  str							      =    self.resize(A       )
             _UpperCAmelCase     :  str							      =    self.center_crop(A       )
             _UpperCAmelCase     :  Dict							      =    self.normalize(A       )
             return images





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

class 	lowerCamelCase_	(nn.Module    ):
      '''simple docstring'''

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

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




      def      _A	(       self						:				Union[str, Any]						,					A						:				Optional[int]=None						,					A						:				List[Any]=None						,					A						:				List[str]=5						,					A						:				Any=True       ):
             _UpperCAmelCase     :  Dict							      =    []
             if output_path is None:
                    _UpperCAmelCase     :  int							      =    './animation.gif'
             if input_path is None:
                    _UpperCAmelCase     :  Union[str, Any]							      =    self.save_path
             _UpperCAmelCase     :  Any							      =    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     :  Dict							      =    total_duration / len(A       )
             _UpperCAmelCase     :  Optional[Any]							      =    [frame_duration] * len(A       )
             if extend_frames:
                    _UpperCAmelCase     :  Any							      =    1.5
                    _UpperCAmelCase     :  Any							      =    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						:				str						,					A						:				str=None						,					A						:				List[str]=None       ):





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




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




      def      _A	(       self						:				Optional[int]						,					A						:				Any						,					A						:				List[Any]						,					A						:				Tuple=None       ):
             _UpperCAmelCase     :  List[Any]							      =    self.clip_preprocessor(text=A						,					images=A						,					return_tensors="pt"						,					padding=A       )
             _UpperCAmelCase     :  Dict							      =    self.clip(**A       )
             _UpperCAmelCase     :  Any							      =    clip_outputs.logits_per_image
             if weights is not None:
                    _UpperCAmelCase     :  Tuple							      =    similarity_logits * weights
             return similarity_logits.sum()




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




      def      _A	(       self						:				Optional[int]						,					A						:				Optional[Any]						,					A						:				str						,					A						:				int       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    torch.randn_like(self.latent						,					requires_grad=A						,					device=self.device       )
             _UpperCAmelCase     :  int							      =    torch.optim.Adam([vector]						,					lr=self.lr       )

             for i in range(self.iterations       ):
                    optim.zero_grad()
                    _UpperCAmelCase     :  List[Any]							      =    self._add_vector(A       )
                    _UpperCAmelCase     :  Dict							      =    loop_post_process(A       )
                    _UpperCAmelCase     :  List[str]							      =    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						:				Any						,					A						:				List[Any]						,					A						:				int						,					A						:				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     :  Any							      =    Image.open(A       )
                    _UpperCAmelCase     :  Union[str, Any]							      =    image.resize((256, 256)       )
                    wandb.log("Original Image"						,					wandb.Image(A       )       )




      def      _A	(       self						:				List[Any]						,					A						:				Tuple       ):





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





      def      _A	(       self						:				Union[str, Any]						,					A						:				List[str]						,					A						:				Union[str, Any]=None						,					A						:				Optional[int]=None						,					A						:				int=True						,					A						:				List[Any]=False						,					A						:				Any=True						,					A						:				Union[str, Any]=True						,					A						:				Dict=None						,					):





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

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

             _UpperCAmelCase     :  str							      =    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 math import pow, sqrt







def 						__a   (	*_SCREAMING_SNAKE_CASE   )	->bool:
  a__:     Union[str, Any]										=	len(_SCREAMING_SNAKE_CASE   ) > 0 and all(value > 0.0 for value in values   )
  return result







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float | ValueError:
  return (
      round(sqrt(molar_mass_a / molar_mass_a   )		,     6   )
      if validate(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
      else ValueError('Input Error: Molar mass values must greater than 0.'   )
  )







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float | ValueError:
  return (
      round(effusion_rate * sqrt(molar_mass_a / molar_mass_a   )		,     6   )
      if validate(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
      else ValueError(
          'Input Error: Molar mass and effusion rate values must greater than 0.'   )
  )







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float | ValueError:
  return (
      round(effusion_rate / sqrt(molar_mass_a / molar_mass_a   )		,     6   )
      if validate(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
      else ValueError(
          'Input Error: Molar mass and effusion rate values must greater than 0.'   )
  )







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float | ValueError:
  return (
      round(molar_mass / pow(effusion_rate_a / effusion_rate_a		,     2   )		,     6   )
      if validate(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
      else ValueError(
          'Input Error: Molar mass and effusion rate values must greater than 0.'   )
  )







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float | ValueError:
  return (
      round(pow(effusion_rate_a / effusion_rate_a		,     2   ) / molar_mass		,     6   )
      if validate(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
      else ValueError(
          'Input Error: Molar mass and effusion rate values must greater than 0.'   )
  )


