


"""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 						snake_case_:






     def __init__(		self     :    Union[str, Any]    , UpperCamelCase_     :    str = "cpu"    , UpperCamelCase_     :    str = "openai/clip-vit-large-patch14" ):
           lowerCAmelCase    :		Tuple				=      device
           lowerCAmelCase    :		Dict				=      CLIPTokenizerFast.from_pretrained(UpperCamelCase_ )
           lowerCAmelCase    :		List[Any]				=      [0.48_145_466, 0.4_578_275, 0.40_821_073]
           lowerCAmelCase    :		List[str]				=      [0.26_862_954, 0.26_130_258, 0.27_577_711]
           lowerCAmelCase    :		Any				=      torchvision.transforms.Normalize(self.image_mean    , self.image_std )
           lowerCAmelCase    :		Any				=      torchvision.transforms.Resize(2_2_4 )
           lowerCAmelCase    :		Optional[int]				=      torchvision.transforms.CenterCrop(2_2_4 )






     def    lowerCamelCase__   (		self     :    Dict    , UpperCamelCase_     :    List[Any] ):
           lowerCAmelCase    :		Tuple				=      self.resize(UpperCamelCase_ )
           lowerCAmelCase    :		str				=      self.center_crop(UpperCamelCase_ )
           lowerCAmelCase    :		Tuple				=      self.normalize(UpperCamelCase_ )
           return images






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



class 						snake_case_(				nn.Module	):






     def __init__(		self     :    int    , UpperCamelCase_     :    Optional[Any]=1_0    , UpperCamelCase_     :    Optional[int]=0.01    , UpperCamelCase_     :    Union[str, Any]=None    , UpperCamelCase_     :    Union[str, Any]=None    , UpperCamelCase_     :    Dict=None    , UpperCamelCase_     :    Optional[int]=None    , UpperCamelCase_     :    Union[str, Any]=None    , UpperCamelCase_     :    Tuple=None    , UpperCamelCase_     :    Optional[Any]=False    , UpperCamelCase_     :    Optional[Any]=True    , UpperCamelCase_     :    Optional[Any]="image"    , UpperCamelCase_     :    Optional[int]=True    , UpperCamelCase_     :    Dict=False    , UpperCamelCase_     :    str=False    , UpperCamelCase_     :    Any=False    , ):
           super().__init__()
           lowerCAmelCase    :		Optional[Any]				=      None
           lowerCAmelCase    :		Optional[Any]				=      device if device else get_device()
           if vqgan:
                 lowerCAmelCase    :		Optional[Any]				=      vqgan
           else:
                 lowerCAmelCase    :		str				=      load_vqgan(self.device    , conf_path=UpperCamelCase_    , ckpt_path=UpperCamelCase_ )
           self.vqgan.eval()
           if clip:
                 lowerCAmelCase    :		int				=      clip
           else:
                 lowerCAmelCase    :		str				=      CLIPModel.from_pretrained('''openai/clip-vit-base-patch32''' )
           self.clip.to(self.device )
           lowerCAmelCase    :		str				=      ProcessorGradientFlow(device=self.device )

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






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






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






     def    lowerCamelCase__   (		self     :    List[Any]    , UpperCamelCase_     :    int ):
           lowerCAmelCase    :		int				=      self.latent.detach().requires_grad_()
           lowerCAmelCase    :		List[Any]				=      base_latent + transform_vector
           if self.quantize:
                 lowerCAmelCase,					*lowerCAmelCase    :		Any				=      self.vqgan.quantize(UpperCamelCase_ )
           else:
                 lowerCAmelCase    :		str				=      trans_latent
           return self.vqgan.decode(UpperCamelCase_ )






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






     def    lowerCamelCase__   (		self     :    int    , UpperCamelCase_     :    int    , UpperCamelCase_     :    Union[str, Any]    , UpperCamelCase_     :    Tuple ):
           lowerCAmelCase    :		Optional[Any]				=      self._get_clip_similarity(pos_prompts['''prompts''']    , UpperCamelCase_    , weights=(1 / pos_prompts['''weights''']) )
           if neg_prompts:
                 lowerCAmelCase    :		Any				=      self._get_clip_similarity(neg_prompts['''prompts''']    , UpperCamelCase_    , weights=neg_prompts['''weights'''] )
           else:
                 lowerCAmelCase    :		List[Any]				=      torch.tensor([1]    , device=self.device )
           lowerCAmelCase    :		int				=      -torch.log(UpperCamelCase_ ) + torch.log(UpperCamelCase_ )
           return loss






     def    lowerCamelCase__   (		self     :    Union[str, Any]    , UpperCamelCase_     :    str    , UpperCamelCase_     :    Any    , UpperCamelCase_     :    Optional[int] ):
           lowerCAmelCase    :		List[Any]				=      torch.randn_like(self.latent    , requires_grad=UpperCamelCase_    , device=self.device )
           lowerCAmelCase    :		List[Any]				=      torch.optim.Adam([vector]    , lr=self.lr )

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






     def    lowerCamelCase__   (		self     :    Tuple    , UpperCamelCase_     :    Tuple    , UpperCamelCase_     :    List[str]    , UpperCamelCase_     :    List[str] ):
           wandb.init(reinit=UpperCamelCase_    , 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:
                 lowerCAmelCase    :		List[str]				=      Image.open(UpperCamelCase_ )
                 lowerCAmelCase    :		Optional[int]				=      image.resize((2_5_6, 2_5_6) )
                 wandb.log('''Original Image'''    , wandb.Image(UpperCamelCase_ ) )






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






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

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

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

           lowerCAmelCase    :		Optional[int]				=      loop_post_process(UpperCamelCase_ )
           for iter, transformed_img in enumerate(self._optimize_CLIP(UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_ ) ):
                 if show_intermediate:
                       show_pil(UpperCamelCase_ )
                 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(UpperCamelCase_ )} )
           if show_final:
                 show_pil(UpperCamelCase_ )
           if save_final:
                 transformed_img.save(os.path.join(self.save_path    , F'''iter_{iter:03d}_final.png''' ) )






"""simple docstring"""





def    _snake_case  (  _snake_case :	list	):

      def merge(_snake_case :	list       ,      _snake_case :	list	) -> list:

            def _merge():
                  while left and right:
                        yield (left if left[0] <= right[0] else right).pop(0	)
                  yield from left
                  yield from right

            return list(_merge()	)

      if len(_snake_case	) <= 1:
            return collection
      lowerCAmelCase    :		Union[str, Any]				=      len(_snake_case	) // 2
      return merge(merge_sort(collection[:mid]	)       ,      merge_sort(collection[mid:]	)	)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 snake_case__						:   Optional[Any]			 = input('''Enter numbers separated by a comma:\n''').strip()
 snake_case__						:   Union[str, Any]			 = [int(item) for item in user_input.split(''',''')]
 print(*merge_sort(unsorted), sep=''',''')


