def 							__UpperCAmelCase     (							UpperCAmelCase = 1000000							)->							List[Any]:



			"""simple docstring"""







			lowercase       		=		[i - 1 for i in range(limit + 1							)]

			for i in range(2,		limit + 1							):
						if phi[i] == i - 1:
									for j in range(2 * i,		limit + 1,		__UpperCamelCase							):
												phi[j] -= phi[j] // i

			return sum(phi[2 : limit + 1]							)


if __name__ == "__main__":
						print(solution())











'''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				:





       '''simple docstring'''






       def __init__(      self,							A = "cpu",							A = "openai/clip-vit-large-patch14"  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						device
             SCREAMING_SNAKE_CASE   :      Tuple    					=						CLIPTokenizerFast.from_pretrained(A  )
             SCREAMING_SNAKE_CASE   :      int    					=						[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
             SCREAMING_SNAKE_CASE   :      str    					=						[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]
             SCREAMING_SNAKE_CASE   :      Dict    					=						torchvision.transforms.Normalize(self.image_mean,							self.image_std  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						torchvision.transforms.Resize(224  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						torchvision.transforms.CenterCrop(224  )







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						self.resize(A  )
             SCREAMING_SNAKE_CASE   :      Any    					=						self.center_crop(A  )
             SCREAMING_SNAKE_CASE   :      str    					=						self.normalize(A  )
             return images







       def __call__(      self,							A=None,							A=None,							**A  ):







             '''simple docstring'''







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






class 							_a				(			nn.Module ):





       '''simple docstring'''






       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,							):







             '''simple docstring'''







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

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







       def        UpperCamelCase_							(      self,							A=None,							A=None,							A=5,							A=True  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						[]
             if output_path is None:
                   SCREAMING_SNAKE_CASE   :      int    					=						'./animation.gif'
             if input_path is None:
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.save_path
             SCREAMING_SNAKE_CASE   :      Optional[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?)'  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						total_duration / len(A  )
             SCREAMING_SNAKE_CASE   :      int    					=						[frame_duration] * len(A  )
             if extend_frames:
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						1.5
                   SCREAMING_SNAKE_CASE   :      int    					=						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        UpperCamelCase_							(      self,							A=None,							A=None  ):







             '''simple docstring'''







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







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







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







       def        UpperCamelCase_							(      self,							A,							A,							A=None  ):







             '''simple docstring'''







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







       def        UpperCamelCase_							(      self,							A,							A,							A  ):







             '''simple docstring'''







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







       def        UpperCamelCase_							(      self,							A,							A,							A  ):







             '''simple docstring'''







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

             for i in range(self.iterations  ):
                   optim.zero_grad()
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self._add_vector(A  )
                   SCREAMING_SNAKE_CASE   :      Dict    					=						loop_post_process(A  )
                   SCREAMING_SNAKE_CASE   :      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        UpperCamelCase_							(      self,							A,							A,							A  ):







             '''simple docstring'''







             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   :      Tuple    					=						Image.open(A  )
                   SCREAMING_SNAKE_CASE   :      int    					=						image.resize((256, 256)  )
                   wandb.log('Original Image',							wandb.Image(A  )  )







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







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







       def        UpperCamelCase_							(      self,							A,							A=None,							A=None,							A=True,							A=False,							A=True,							A=True,							A=None,							):







             '''simple docstring'''







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

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

             SCREAMING_SNAKE_CASE   :      int    					=						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"  )  )


