






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 		_snake_case  :






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






   def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       List[Any]:
      SCREAMING_SNAKE_CASE						= self.resize(a)
      SCREAMING_SNAKE_CASE						= self.center_crop(a)
      SCREAMING_SNAKE_CASE						= self.normalize(a)
      return images







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



class 		_snake_case  (  nn.Module					):






   def __init__(       self		,					a=10		,					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:
      super().__init__()
      SCREAMING_SNAKE_CASE						= None
      SCREAMING_SNAKE_CASE						= device if device else get_device()
      if vqgan:
         SCREAMING_SNAKE_CASE						= vqgan
      else:
         SCREAMING_SNAKE_CASE						= load_vqgan(self.device		,					conf_path=a		,					ckpt_path=a)
      self.vqgan.eval()
      if clip:
         SCREAMING_SNAKE_CASE						= clip
      else:
         SCREAMING_SNAKE_CASE						= CLIPModel.from_pretrained('openai/clip-vit-base-patch32')
      self.clip.to(self.device)
      SCREAMING_SNAKE_CASE						= ProcessorGradientFlow(device=self.device)

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






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






   def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Union[str, Any]:
      SCREAMING_SNAKE_CASE						= self.latent.detach().requires_grad_()
      SCREAMING_SNAKE_CASE						= base_latent + transform_vector
      if self.quantize:
         SCREAMING_SNAKE_CASE				,						*SCREAMING_SNAKE_CASE						= self.vqgan.quantize(a)
      else:
         SCREAMING_SNAKE_CASE						= trans_latent
      return self.vqgan.decode(a)






   def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a=None)      ->       Any:
      SCREAMING_SNAKE_CASE						= self.clip_preprocessor(text=a		,					images=a		,					return_tensors='pt'		,					padding=a)
      SCREAMING_SNAKE_CASE						= self.clip(**a)
      SCREAMING_SNAKE_CASE						= clip_outputs.logits_per_image
      if weights is not None:
         SCREAMING_SNAKE_CASE						= similarity_logits * weights
      return similarity_logits.sum()






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






   def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a)      ->       str:
      SCREAMING_SNAKE_CASE						= torch.randn_like(self.latent		,					requires_grad=a		,					device=self.device)
      SCREAMING_SNAKE_CASE						= torch.optim.Adam([vector]		,					lr=self.lr)

      for i in range(self.iterations):
         optim.zero_grad()
         SCREAMING_SNAKE_CASE						= self._add_vector(a)
         SCREAMING_SNAKE_CASE						= loop_post_process(a)
         SCREAMING_SNAKE_CASE						= 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		,					a		,					a		,					a)      ->       int:
      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:
         SCREAMING_SNAKE_CASE						= Image.open(a)
         SCREAMING_SNAKE_CASE						= image.resize((256, 256))
         wandb.log('Original Image'		,					wandb.Image(a))






   def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       int:
      if not prompts:
         return []
      SCREAMING_SNAKE_CASE						= []
      SCREAMING_SNAKE_CASE						= []
      if isinstance(a		,					a):
         SCREAMING_SNAKE_CASE						= [prompt.strip() for prompt in prompts.split('|')]
      for prompt in prompts:
         if isinstance(a		,					(tuple, list)):
            SCREAMING_SNAKE_CASE						= prompt[0]
            SCREAMING_SNAKE_CASE						= float(prompt[1])
         elif ":" in prompt:
            SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= prompt.split(':')
            SCREAMING_SNAKE_CASE						= float(a)
         else:
            SCREAMING_SNAKE_CASE						= prompt
            SCREAMING_SNAKE_CASE						= 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		,					a		,					a=None		,					a=None		,					a=True		,					a=False		,					a=True		,					a=True		,					a=None		,					)      ->       str:
      if image_path:
         SCREAMING_SNAKE_CASE						= self._get_latent(a)
      else:
         SCREAMING_SNAKE_CASE						= 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."
      SCREAMING_SNAKE_CASE						= self.process_prompts(a)
      SCREAMING_SNAKE_CASE						= self.process_prompts(a)
      if save_final and save_path is None:
         SCREAMING_SNAKE_CASE						= os.path.join('./outputs/'		,					'_'.join(pos_prompts['prompts']))
      if not os.path.exists(a):
         os.makedirs(a)
      else:
         SCREAMING_SNAKE_CASE						= save_path + '_' + get_timestamp()
         os.makedirs(a)
      SCREAMING_SNAKE_CASE						= save_path

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

      SCREAMING_SNAKE_CASE						= 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'''))












a_		:						Tuple             =    {
    'A': '.-', 'B': '-...', 'C': '-.-.', 'D': '-..', 'E': '.', 'F': '..-.', 'G': '--.',
    'H': '....', 'I': '..', 'J': '.---', 'K': '-.-', 'L': '.-..', 'M': '--', 'N': '-.',
    'O': '---', 'P': '.--.', 'Q': '--.-', 'R': '.-.', 'S': '...', 'T': '-', 'U': '..-',
    'V': '...-', 'W': '.--', 'X': '-..-', 'Y': '-.--', 'Z': '--..', '1': '.----',
    '2': '..---', '3': '...--', '4': '....-', '5': '.....', '6': '-....', '7': '--...',
    '8': '---..', '9': '----.', '0': '-----', '&': '.-...', '@': '.--.-.',
    ':': '---...', ',': '--..--', '.': '.-.-.-', '\'': '.----.', '"': '.-..-.',
    '?': '..--..', '/': '-..-.', '=': '-...-', '+': '.-.-.', '-': '-....-',
    '(': '-.--.', ')': '-.--.-', '!': '-.-.--', ' ': '/'
}  # Exclamation mark is not in ITU-R recommendation
# fmt: on
a_		:						List[Any]             =    {value: key for key, value in MORSE_CODE_DICT.items()}






def 				lowerCamelCase__				(_UpperCAmelCase):
			return " ".join(MORSE_CODE_DICT[char] for char in message.upper())






def 				lowerCamelCase__				(_UpperCAmelCase):
			return "".join(REVERSE_DICT[char] for char in message.split())






def 				lowerCamelCase__				():
			SCREAMING_SNAKE_CASE						= 'Morse code here!'
			print(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= encrypt(_UpperCAmelCase)
			print(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= decrypt(_UpperCAmelCase)
			print(_UpperCAmelCase)


if __name__ == "__main__":
				main()




