
'''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 					A							:





     def __init__(				self		,     SCREAMING_SNAKE_CASE = "cpu"		,     SCREAMING_SNAKE_CASE = "openai/clip-vit-large-patch14"      )					->							None:






         """simple docstring"""






         A			:  Optional[int]   =		device
         A			:  List[Any]   =		CLIPTokenizerFast.from_pretrained(SCREAMING_SNAKE_CASE      )
         A			:  Optional[Any]   =		[0.48_145_466, 0.4_578_275, 0.40_821_073]
         A			:  Optional[int]   =		[0.26_862_954, 0.26_130_258, 0.27_577_711]
         A			:  Tuple   =		torchvision.transforms.Normalize(self.image_mean		,     self.image_std      )
         A			:  List[str]   =		torchvision.transforms.Resize(224      )
         A			:  List[Any]   =		torchvision.transforms.CenterCrop(224      )





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE      )					->							Dict:






         """simple docstring"""






         A			:  str   =		self.resize(SCREAMING_SNAKE_CASE      )
         A			:  Optional[int]   =		self.center_crop(SCREAMING_SNAKE_CASE      )
         A			:  Optional[int]   =		self.normalize(SCREAMING_SNAKE_CASE      )
         return images





     def __call__(				self		,     SCREAMING_SNAKE_CASE=None		,     SCREAMING_SNAKE_CASE=None		,     **SCREAMING_SNAKE_CASE      )					->							str:






         """simple docstring"""






         A			:  str   =		self.tokenizer(text=SCREAMING_SNAKE_CASE		,     **SCREAMING_SNAKE_CASE      )
         A			:  str   =		self.preprocess_img(SCREAMING_SNAKE_CASE      )
         A			:  List[str]   =		{key: value.to(self.device      ) for (key, value) in encoding.items()}
         return encoding





class 					A							(				nn.Module		):





     def __init__(				self		,     SCREAMING_SNAKE_CASE=10		,     SCREAMING_SNAKE_CASE=0.01		,     SCREAMING_SNAKE_CASE=None		,     SCREAMING_SNAKE_CASE=None		,     SCREAMING_SNAKE_CASE=None		,     SCREAMING_SNAKE_CASE=None		,     SCREAMING_SNAKE_CASE=None		,     SCREAMING_SNAKE_CASE=None		,     SCREAMING_SNAKE_CASE=False		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE="image"		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=False		,     SCREAMING_SNAKE_CASE=False		,     SCREAMING_SNAKE_CASE=False		,     )					->							None:






         """simple docstring"""






         super().__init__()
         A			:  List[Any]   =		None
         A			:  Tuple   =		device if device else get_device()
         if vqgan:
             A			:  Optional[int]   =		vqgan
         else:
             A			:  str   =		load_vqgan(self.device		,     conf_path=SCREAMING_SNAKE_CASE		,     ckpt_path=SCREAMING_SNAKE_CASE      )
         self.vqgan.eval()
         if clip:
             A			:  int   =		clip
         else:
             A			:  Tuple   =		CLIPModel.from_pretrained('''openai/clip-vit-base-patch32'''      )
         self.clip.to(self.device      )
         A			:  Any   =		ProcessorGradientFlow(device=self.device      )

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





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE=None		,     SCREAMING_SNAKE_CASE=None		,     SCREAMING_SNAKE_CASE=5		,     SCREAMING_SNAKE_CASE=True      )					->							Dict:






         """simple docstring"""






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





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE=None		,     SCREAMING_SNAKE_CASE=None      )					->							Optional[Any]:






         """simple docstring"""






         if not (path or img):
             raise ValueError('''Input either path or tensor'''      )
         if img is not None:
             raise NotImplementedError
         A			:  Optional[int]   =		preprocess(Image.open(SCREAMING_SNAKE_CASE      )		,     target_image_size=256      ).to(self.device      )
         A			:  Tuple   =		preprocess_vqgan(SCREAMING_SNAKE_CASE      )
         A,    *A			:  int   =		self.vqgan.encode(SCREAMING_SNAKE_CASE      )
         return z





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE      )					->							Dict:






         """simple docstring"""






         A			:  Any   =		self.latent.detach().requires_grad_()
         A			:  Dict   =		base_latent + transform_vector
         if self.quantize:
             A,    *A			:  Dict   =		self.vqgan.quantize(SCREAMING_SNAKE_CASE      )
         else:
             A			:  str   =		trans_latent
         return self.vqgan.decode(SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE=None      )					->							List[str]:






         """simple docstring"""






         A			:  Tuple   =		self.clip_preprocessor(text=SCREAMING_SNAKE_CASE		,     images=SCREAMING_SNAKE_CASE		,     return_tensors='''pt'''		,     padding=SCREAMING_SNAKE_CASE      )
         A			:  Tuple   =		self.clip(**SCREAMING_SNAKE_CASE      )
         A			:  Optional[Any]   =		clip_outputs.logits_per_image
         if weights is not None:
             A			:  Tuple   =		similarity_logits * weights
         return similarity_logits.sum()





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							Union[str, Any]:






         """simple docstring"""






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





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							Optional[Any]:






         """simple docstring"""






         A			:  Dict   =		torch.randn_like(self.latent		,     requires_grad=SCREAMING_SNAKE_CASE		,     device=self.device      )
         A			:  List[Any]   =		torch.optim.Adam([vector]		,     lr=self.lr      )

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





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							Optional[int]:






         """simple docstring"""






         wandb.init(reinit=SCREAMING_SNAKE_CASE		,     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:
             A			:  Optional[Any]   =		Image.open(SCREAMING_SNAKE_CASE      )
             A			:  List[str]   =		image.resize((256, 256)      )
             wandb.log('''Original Image'''		,     wandb.Image(SCREAMING_SNAKE_CASE      )      )





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE      )					->							List[str]:






         """simple docstring"""






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





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE=None		,     SCREAMING_SNAKE_CASE=None		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=False		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=None		,     )					->							int:






         """simple docstring"""






         if image_path:
             A			:  List[str]   =		self._get_latent(SCREAMING_SNAKE_CASE      )
         else:
             A			:  List[Any]   =		torch.randn(self.latent_dim		,     device=self.device      )
         if self.log:
             self._init_logging(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )

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

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

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









def 	_lowerCAmelCase (    A__ = 50_000_000						):
      lowercase__  =	set()
      lowercase__  =	int((limit - 24) ** (1 / 2)						)

      lowercase__  =	set(range(3    ,	prime_square_limit + 1    ,	2						)						)
      primes.add(2						)
      for p in range(3    ,	prime_square_limit + 1    ,	2						):
            if p not in primes:
                  continue
            primes.difference_update(set(range(p * p    ,	prime_square_limit + 1    ,	A__						)						)						)

      for primea in primes:
            lowercase__  =	primea * primea
            for primea in primes:
                  lowercase__  =	primea * primea * primea
                  if square + cube >= limit - 16:
                        break
                  for primea in primes:
                        lowercase__  =	primea * primea * primea * primea
                        lowercase__  =	square + cube + tetr
                        if total >= limit:
                              break
                        ret.add(A__						)

      return len(A__						)


if __name__ == "__main__":
       print(F'''{solution() = }''')
