

'''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_      :




  """simple docstring"""




  def __init__( self  :	str   ,lowercase__  :	str = "cpu"   ,lowercase__  :	str = "openai/clip-vit-large-patch14"     ):
   __lowercase				    =					device
   __lowercase				    =					CLIPTokenizerFast.from_pretrained(lowercase__     )
   __lowercase				    =					[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]
   __lowercase				    =					[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]
   __lowercase				    =					torchvision.transforms.Normalize(self.image_mean   ,self.image_std     )
   __lowercase				    =					torchvision.transforms.Resize(2_2_4     )
   __lowercase				    =					torchvision.transforms.CenterCrop(2_2_4     )






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]   ,lowercase__  :	Dict     ):
   __lowercase				    =					self.resize(lowercase__     )
   __lowercase				    =					self.center_crop(lowercase__     )
   __lowercase				    =					self.normalize(lowercase__     )
   return images






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






class      lowercase_      (nn.Module ):




  """simple docstring"""




  def __init__( self  :	Any   ,lowercase__  :	Optional[int]=1_0   ,lowercase__  :	Optional[Any]=0.0_1   ,lowercase__  :	Optional[int]=None   ,lowercase__  :	Optional[Any]=None   ,lowercase__  :	str=None   ,lowercase__  :	Any=None   ,lowercase__  :	Optional[Any]=None   ,lowercase__  :	Union[str, Any]=None   ,lowercase__  :	Union[str, Any]=False   ,lowercase__  :	Optional[int]=True   ,lowercase__  :	Optional[Any]="image"   ,lowercase__  :	Tuple=True   ,lowercase__  :	Any=False   ,lowercase__  :	Optional[int]=False   ,lowercase__  :	Optional[int]=False   ,):
   super().__init__()
   __lowercase				    =					None
   __lowercase				    =					device if device else get_device()
   if vqgan:
    __lowercase				    =					vqgan
   else:
    __lowercase				    =					load_vqgan(self.device   ,conf_path=lowercase__   ,ckpt_path=lowercase__     )
   self.vqgan.eval()
   if clip:
    __lowercase				    =					clip
   else:
    __lowercase				    =					CLIPModel.from_pretrained('''openai/clip-vit-base-patch32'''     )
   self.clip.to(self.device     )
   __lowercase				    =					ProcessorGradientFlow(device=self.device     )

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






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






  def    SCREAMING_SNAKE_CASE ( self  :	Dict   ,lowercase__  :	List[Any]=None   ,lowercase__  :	str=None     ):
   if not (path or img):
    raise ValueError('''Input either path or tensor'''     )
   if img is not None:
    raise NotImplementedError
   __lowercase				    =					preprocess(Image.open(lowercase__     )   ,target_image_size=2_5_6     ).to(self.device     )
   __lowercase				    =					preprocess_vqgan(lowercase__     )
   __lowercase    ,      *__lowercase				    =					self.vqgan.encode(lowercase__     )
   return z






  def    SCREAMING_SNAKE_CASE ( self  :	Dict   ,lowercase__  :	Dict     ):
   __lowercase				    =					self.latent.detach().requires_grad_()
   __lowercase				    =					base_latent + transform_vector
   if self.quantize:
    __lowercase    ,      *__lowercase				    =					self.vqgan.quantize(lowercase__     )
   else:
    __lowercase				    =					trans_latent
   return self.vqgan.decode(lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	Dict   ,lowercase__  :	str   ,lowercase__  :	List[Any]   ,lowercase__  :	Optional[int]=None     ):
   __lowercase				    =					self.clip_preprocessor(text=lowercase__   ,images=lowercase__   ,return_tensors='''pt'''   ,padding=lowercase__     )
   __lowercase				    =					self.clip(**lowercase__     )
   __lowercase				    =					clip_outputs.logits_per_image
   if weights is not None:
    __lowercase				    =					similarity_logits * weights
   return similarity_logits.sum()






  def    SCREAMING_SNAKE_CASE ( self  :	Any   ,lowercase__  :	Optional[Any]   ,lowercase__  :	Optional[int]   ,lowercase__  :	Optional[int]     ):
   __lowercase				    =					self._get_clip_similarity(pos_prompts['''prompts''']   ,lowercase__   ,weights=(1 / pos_prompts['''weights'''])     )
   if neg_prompts:
    __lowercase				    =					self._get_clip_similarity(neg_prompts['''prompts''']   ,lowercase__   ,weights=neg_prompts['''weights''']     )
   else:
    __lowercase				    =					torch.tensor([1]   ,device=self.device     )
   __lowercase				    =					-torch.log(lowercase__     ) + torch.log(lowercase__     )
   return loss






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]   ,lowercase__  :	Optional[int]   ,lowercase__  :	Any   ,lowercase__  :	str     ):
   __lowercase				    =					torch.randn_like(self.latent   ,requires_grad=lowercase__   ,device=self.device     )
   __lowercase				    =					torch.optim.Adam([vector]   ,lr=self.lr     )

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






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]   ,lowercase__  :	Tuple   ,lowercase__  :	List[Any]   ,lowercase__  :	Union[str, Any]     ):
   wandb.init(reinit=lowercase__   ,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:
    __lowercase				    =					Image.open(lowercase__     )
    __lowercase				    =					image.resize((2_5_6, 2_5_6)     )
    wandb.log('''Original Image'''   ,wandb.Image(lowercase__     )     )






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






  def    SCREAMING_SNAKE_CASE ( self  :	Dict   ,lowercase__  :	Any   ,lowercase__  :	List[Any]=None   ,lowercase__  :	Dict=None   ,lowercase__  :	Optional[int]=True   ,lowercase__  :	str=False   ,lowercase__  :	Any=True   ,lowercase__  :	List[str]=True   ,lowercase__  :	int=None   ,):
   if image_path:
    __lowercase				    =					self._get_latent(lowercase__     )
   else:
    __lowercase				    =					torch.randn(self.latent_dim   ,device=self.device     )
   if self.log:
    self._init_logging(lowercase__   ,lowercase__   ,lowercase__     )

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

   __lowercase				    =					self.vqgan.decode(self.latent     )[0]
   if show_intermediate:
    print('''Original Image'''     )
    show_pil(custom_to_pil(lowercase__     )     )

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





'''simple docstring'''






import math







def 							UpperCamelCase_     (       A__   :  float				,       A__   :  float   ):
  '''simple docstring'''





  if initial_intensity < 0:
    raise ValueError("""The value of intensity cannot be negative"""   )
    # handling of negative values of initial intensity
  if angle < 0 or angle > 3_60:
    raise ValueError("""In Malus Law, the angle is in the range 0-360 degrees"""   )
    # handling of values out of allowed range
  return initial_intensity * (math.cos(math.radians(A__   )   ) ** 2)


if __name__ == "__main__":
  import doctest

  doctest.testmod(name="malus_law")



