





"""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   :





 """simple docstring"""


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



     '''simple docstring'''




     __A       :		Optional[int]							=	device
     __A       :		Optional[int]							=	CLIPTokenizerFast.from_pretrained(snake_case__    )
     __A       :		Any							=	[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]
     __A       :		Tuple							=	[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]
     __A       :		Optional[Any]							=	torchvision.transforms.Normalize(self.image_mean   ,		self.image_std    )
     __A       :		Dict							=	torchvision.transforms.Resize(224    )
     __A       :		int							=	torchvision.transforms.CenterCrop(224    )

 def    UpperCamelCase__(       self   ,		__lowerCamelCase    ):



     '''simple docstring'''




     __A       :		str							=	self.resize(snake_case__    )
     __A       :		Optional[Any]							=	self.center_crop(snake_case__    )
     __A       :		Optional[int]							=	self.normalize(snake_case__    )
     return images







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



     '''simple docstring'''




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






class 						__snake_case   (						nn.Module	):





 """simple docstring"""


 def __init__(       self   ,		__lowerCamelCase=10   ,		__lowerCamelCase=0.0_1   ,		__lowerCamelCase=None   ,		__lowerCamelCase=None   ,		__lowerCamelCase=None   ,		__lowerCamelCase=None   ,		__lowerCamelCase=None   ,		__lowerCamelCase=None   ,		__lowerCamelCase=False   ,		__lowerCamelCase=True   ,		__lowerCamelCase="image"   ,		__lowerCamelCase=True   ,		__lowerCamelCase=False   ,		__lowerCamelCase=False   ,		__lowerCamelCase=False   ,		):



     '''simple docstring'''




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

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

 def    UpperCamelCase__(       self   ,		__lowerCamelCase=None   ,		__lowerCamelCase=None   ,		__lowerCamelCase=5   ,		__lowerCamelCase=True    ):



     '''simple docstring'''




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

 def    UpperCamelCase__(       self   ,		__lowerCamelCase=None   ,		__lowerCamelCase=None    ):



     '''simple docstring'''




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

 def    UpperCamelCase__(       self   ,		__lowerCamelCase    ):



     '''simple docstring'''




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

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase=None    ):



     '''simple docstring'''




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

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase    ):



     '''simple docstring'''




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

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase    ):



     '''simple docstring'''




     __A       :		Tuple							=	torch.randn_like(self.latent   ,		requires_grad=snake_case__   ,		device=self.device    )
     __A       :		List[str]							=	torch.optim.Adam([vector]   ,		lr=self.lr    )

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

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase    ):



     '''simple docstring'''




     wandb.init(reinit=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       :		List[Any]							=	Image.open(snake_case__    )
         __A       :		Tuple							=	image.resize((256, 256)    )
         wandb.log('''Original Image'''   ,		wandb.Image(snake_case__    )    )

 def    UpperCamelCase__(       self   ,		__lowerCamelCase    ):



     '''simple docstring'''




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







 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase=None   ,		__lowerCamelCase=None   ,		__lowerCamelCase=True   ,		__lowerCamelCase=False   ,		__lowerCamelCase=True   ,		__lowerCamelCase=True   ,		__lowerCamelCase=None   ,		):



     '''simple docstring'''




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

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

     __A       :		Optional[int]							=	self.vqgan.decode(self.latent    )[0]
     if show_intermediate:
         print('''Original Image'''    )
         show_pil(custom_to_pil(snake_case__    )    )

     __A       :		Union[str, Any]							=	loop_post_process(snake_case__    )
     for iter, transformed_img in enumerate(self._optimize_CLIP(snake_case__   ,		snake_case__   ,		snake_case__    )    ):
         if show_intermediate:
             show_pil(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(snake_case__    )}    )
     if show_final:
         show_pil(snake_case__    )
     if save_final:
         transformed_img.save(os.path.join(self.save_path   ,		F"""iter_{iter:03d}_final.png"""    )    )










SCREAMING_SNAKE_CASE:     Optional[int]									= {str(digit): digit**5 for digit in range(1_0)}


def 					_a							(					lowerCAmelCase )->	int:
		return sum(DIGITS_FIFTH_POWER[digit] for digit in str(lowerCAmelCase ) )


def 					_a							(					)->	int:
		return sum(
		    number
		    for number in range(1000    ,						1000000 )
		    if number == digits_fifth_powers_sum(lowerCAmelCase ) )


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