




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    lowercase_		:



						def __init__(			self	:   List[Any]					,							snake_case__	:   str = "cpu"					,							snake_case__	:   str = "openai/clip-vit-large-patch14"  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							device
								SCREAMING_SNAKE_CASE_					      =							CLIPTokenizerFast.from_pretrained(snake_case__  )
								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(2_24  )
								SCREAMING_SNAKE_CASE_					      =							torchvision.transforms.CenterCrop(2_24  )



						def 	__a					(			self	:   Any					,							snake_case__	:   List[str]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.resize(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							self.center_crop(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							self.normalize(snake_case__  )
								return images






						def __call__(			self	:   str					,							snake_case__	:   Tuple=None					,							snake_case__	:   Union[str, Any]=None					,							**snake_case__	:   Tuple  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.tokenizer(text=snake_case__					,							**snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							self.preprocess_img(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							{key: value.to(self.device  ) for (key, value) in encoding.items()}
								return encoding



class    lowercase_		(nn.Module ):



						def __init__(			self	:   int					,							snake_case__	:   Optional[int]=10					,							snake_case__	:   Union[str, Any]=0.01					,							snake_case__	:   List[Any]=None					,							snake_case__	:   List[str]=None					,							snake_case__	:   Optional[int]=None					,							snake_case__	:   Optional[Any]=None					,							snake_case__	:   Tuple=None					,							snake_case__	:   Optional[int]=None					,							snake_case__	:   Dict=False					,							snake_case__	:   int=True					,							snake_case__	:   List[str]="image"					,							snake_case__	:   Dict=True					,							snake_case__	:   Dict=False					,							snake_case__	:   str=False					,							snake_case__	:   Optional[int]=False					,							):

								"""simple docstring"""

								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=snake_case__					,							ckpt_path=snake_case__  )
								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 	__a					(			self	:   List[Any]					,							snake_case__	:   Union[str, Any]=None					,							snake_case__	:   Optional[int]=None					,							snake_case__	:   Any=5					,							snake_case__	:   Union[str, Any]=True  ):

								"""simple docstring"""

								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(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?)'  )
								SCREAMING_SNAKE_CASE_					      =							total_duration / len(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							[frame_duration] * len(snake_case__  )
								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(snake_case__  )  )
								imageio.mimsave(snake_case__					,							snake_case__					,							duration=snake_case__  )
								print(f'''gif saved to {output_path}'''  )



						def 	__a					(			self	:   Dict					,							snake_case__	:   List[str]=None					,							snake_case__	:   Any=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_					      =							preprocess(Image.open(snake_case__  )					,							target_image_size=2_56  ).to(self.device  )
								SCREAMING_SNAKE_CASE_					      =							preprocess_vqgan(snake_case__  )
								SCREAMING_SNAKE_CASE_      ,				*SCREAMING_SNAKE_CASE_					      =							self.vqgan.encode(snake_case__  )
								return z



						def 	__a					(			self	:   List[Any]					,							snake_case__	:   str  ):

								"""simple docstring"""

								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(snake_case__  )
								else:
										SCREAMING_SNAKE_CASE_					      =							trans_latent
								return self.vqgan.decode(snake_case__  )



						def 	__a					(			self	:   Union[str, Any]					,							snake_case__	:   Optional[int]					,							snake_case__	:   List[str]					,							snake_case__	:   Optional[Any]=None  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.clip_preprocessor(text=snake_case__					,							images=snake_case__					,							return_tensors='pt'					,							padding=snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							self.clip(**snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							clip_outputs.logits_per_image
								if weights is not None:
										SCREAMING_SNAKE_CASE_					      =							similarity_logits * weights
								return similarity_logits.sum()



						def 	__a					(			self	:   Tuple					,							snake_case__	:   List[str]					,							snake_case__	:   Dict					,							snake_case__	:   Union[str, Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self._get_clip_similarity(pos_prompts['prompts']					,							snake_case__					,							weights=(1 / pos_prompts['weights'])  )
								if neg_prompts:
										SCREAMING_SNAKE_CASE_					      =							self._get_clip_similarity(neg_prompts['prompts']					,							snake_case__					,							weights=neg_prompts['weights']  )
								else:
										SCREAMING_SNAKE_CASE_					      =							torch.tensor([1]					,							device=self.device  )
								SCREAMING_SNAKE_CASE_					      =							-torch.log(snake_case__  ) + torch.log(snake_case__  )
								return loss



						def 	__a					(			self	:   Tuple					,							snake_case__	:   List[Any]					,							snake_case__	:   List[Any]					,							snake_case__	:   Dict  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							torch.randn_like(self.latent					,							requires_grad=snake_case__					,							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(snake_case__  )
										SCREAMING_SNAKE_CASE_					      =							loop_post_process(snake_case__  )
										SCREAMING_SNAKE_CASE_					      =							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 	__a					(			self	:   Union[str, Any]					,							snake_case__	:   List[Any]					,							snake_case__	:   int					,							snake_case__	:   List[Any]  ):

								"""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:
										SCREAMING_SNAKE_CASE_					      =							Image.open(snake_case__  )
										SCREAMING_SNAKE_CASE_					      =							image.resize((2_56, 2_56)  )
										wandb.log('Original Image'					,							wandb.Image(snake_case__  )  )



						def 	__a					(			self	:   int					,							snake_case__	:   Union[str, Any]  ):

								"""simple docstring"""

								if not prompts:
										return []
								SCREAMING_SNAKE_CASE_					      =							[]
								SCREAMING_SNAKE_CASE_					      =							[]
								if isinstance(snake_case__					,							snake_case__  ):
										SCREAMING_SNAKE_CASE_					      =							[prompt.strip() for prompt in prompts.split('|'  )]
								for prompt in prompts:
										if isinstance(snake_case__					,							(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(snake_case__  )
										else:
												SCREAMING_SNAKE_CASE_					      =							prompt
												SCREAMING_SNAKE_CASE_					      =							1.0
										processed_prompts.append(snake_case__  )
										weights.append(snake_case__  )
								return {
								    "prompts": processed_prompts,
								    "weights": torch.tensor(snake_case__					,							device=self.device  ),
								}






						def 	__a					(			self	:   List[Any]					,							snake_case__	:   Optional[Any]					,							snake_case__	:   List[str]=None					,							snake_case__	:   List[Any]=None					,							snake_case__	:   List[Any]=True					,							snake_case__	:   Optional[Any]=False					,							snake_case__	:   Dict=True					,							snake_case__	:   Tuple=True					,							snake_case__	:   Tuple=None					,							):

								"""simple docstring"""

								if image_path:
										SCREAMING_SNAKE_CASE_					      =							self._get_latent(snake_case__  )
								else:
										SCREAMING_SNAKE_CASE_					      =							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."
								SCREAMING_SNAKE_CASE_					      =							self.process_prompts(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							self.process_prompts(snake_case__  )
								if save_final and save_path is None:
										SCREAMING_SNAKE_CASE_					      =							os.path.join('./outputs/'					,							'_'.join(pos_prompts['prompts']  )  )
								if not os.path.exists(snake_case__  ):
										os.makedirs(snake_case__  )
								else:
										SCREAMING_SNAKE_CASE_					      =							save_path + '_' + get_timestamp()
										os.makedirs(snake_case__  )
								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(snake_case__  )  )

								SCREAMING_SNAKE_CASE_					      =							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())