



"""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							:				List[Any]   ,	_lowercase							:				str = "cpu"   ,	_lowercase							:				str = "openai/clip-vit-large-patch14"		)							->					None:
									snake_case   :			str						    = device
									snake_case   :			Optional[Any]						    = CLIPTokenizerFast.from_pretrained(_lowercase		)
									snake_case   :			List[Any]						    = [0.48145466, 0.4578275, 0.40821073]
									snake_case   :			str						    = [0.26862954, 0.26130258, 0.27577711]
									snake_case   :			int						    = torchvision.transforms.Normalize(self.image_mean   ,	self.image_std		)
									snake_case   :			Optional[int]						    = torchvision.transforms.Resize(224		)
									snake_case   :			Optional[int]						    = torchvision.transforms.CenterCrop(224		)



			def 				__lowercase					(			self							:				Optional[int]   ,	_lowercase							:				Any		)							->					List[str]:
									snake_case   :			int						    = self.resize(_lowercase		)
									snake_case   :			str						    = self.center_crop(_lowercase		)
									snake_case   :			List[Any]						    = self.normalize(_lowercase		)
									return images



			def __call__(			self							:				int   ,	_lowercase							:				Any=None   ,	_lowercase							:				List[Any]=None   ,	**_lowercase							:				List[str]		)							->					Optional[Any]:
									snake_case   :			List[Any]						    = self.tokenizer(text=_lowercase   ,	**_lowercase		)
									snake_case   :			Optional[Any]						    = self.preprocess_img(_lowercase		)
									snake_case   :			str						    = {key: value.to(self.device		) for (key, value) in encoding.items()}
									return encoding



class       _a  ( nn.Module):



			def __init__(			self							:				Union[str, Any]   ,	_lowercase							:				List[Any]=10   ,	_lowercase							:				Optional[Any]=0.01   ,	_lowercase							:				Dict=None   ,	_lowercase							:				Any=None   ,	_lowercase							:				Dict=None   ,	_lowercase							:				List[Any]=None   ,	_lowercase							:				int=None   ,	_lowercase							:				List[str]=None   ,	_lowercase							:				List[Any]=False   ,	_lowercase							:				Optional[int]=True   ,	_lowercase							:				List[str]="image"   ,	_lowercase							:				int=True   ,	_lowercase							:				int=False   ,	_lowercase							:				str=False   ,	_lowercase							:				List[Any]=False   ,	)							->					None:
									super().__init__()
									snake_case   :			Optional[Any]						    = None
									snake_case   :			str						    = device if device else get_device()
									if vqgan:
															snake_case   :			List[str]						    = vqgan
									else:
															snake_case   :			Tuple						    = load_vqgan(self.device   ,	conf_path=_lowercase   ,	ckpt_path=_lowercase		)
									self.vqgan.eval()
									if clip:
															snake_case   :			Dict						    = clip
									else:
															snake_case   :			Tuple						    = CLIPModel.from_pretrained("openai/clip-vit-base-patch32"		)
									self.clip.to(self.device		)
									snake_case   :			Tuple						    = ProcessorGradientFlow(device=self.device		)

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



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



			def 				__lowercase					(			self							:				Optional[Any]   ,	_lowercase							:				int=None   ,	_lowercase							:				Optional[Any]=None		)							->					Any:
									if not (path or img):
															raise ValueError("Input either path or tensor"		)
									if img is not None:
															raise NotImplementedError
									snake_case   :			int						    = preprocess(Image.open(_lowercase		)   ,	target_image_size=256		).to(self.device		)
									snake_case   :			Dict						    = preprocess_vqgan(_lowercase		)
									snake_case    ,			*snake_case   :			Optional[Any]						    = self.vqgan.encode(_lowercase		)
									return z



			def 				__lowercase					(			self							:				List[str]   ,	_lowercase							:				Optional[int]		)							->					List[Any]:
									snake_case   :			Tuple						    = self.latent.detach().requires_grad_()
									snake_case   :			str						    = base_latent + transform_vector
									if self.quantize:
															snake_case    ,			*snake_case   :			Optional[int]						    = self.vqgan.quantize(_lowercase		)
									else:
															snake_case   :			List[str]						    = trans_latent
									return self.vqgan.decode(_lowercase		)



			def 				__lowercase					(			self							:				Tuple   ,	_lowercase							:				List[Any]   ,	_lowercase							:				List[Any]   ,	_lowercase							:				Optional[Any]=None		)							->					str:
									snake_case   :			Union[str, Any]						    = self.clip_preprocessor(text=_lowercase   ,	images=_lowercase   ,	return_tensors="pt"   ,	padding=_lowercase		)
									snake_case   :			Optional[Any]						    = self.clip(**_lowercase		)
									snake_case   :			List[Any]						    = clip_outputs.logits_per_image
									if weights is not None:
															snake_case   :			Union[str, Any]						    = similarity_logits * weights
									return similarity_logits.sum()



			def 				__lowercase					(			self							:				Union[str, Any]   ,	_lowercase							:				Tuple   ,	_lowercase							:				Tuple   ,	_lowercase							:				Union[str, Any]		)							->					Tuple:
									snake_case   :			List[Any]						    = self._get_clip_similarity(pos_prompts["prompts"]   ,	_lowercase   ,	weights=(1 / pos_prompts["weights"])		)
									if neg_prompts:
															snake_case   :			List[str]						    = self._get_clip_similarity(neg_prompts["prompts"]   ,	_lowercase   ,	weights=neg_prompts["weights"]		)
									else:
															snake_case   :			Any						    = torch.tensor([1]   ,	device=self.device		)
									snake_case   :			Tuple						    = -torch.log(_lowercase		) + torch.log(_lowercase		)
									return loss



			def 				__lowercase					(			self							:				List[Any]   ,	_lowercase							:				Tuple   ,	_lowercase							:				Any   ,	_lowercase							:				List[str]		)							->					Tuple:
									snake_case   :			Dict						    = torch.randn_like(self.latent   ,	requires_grad=_lowercase   ,	device=self.device		)
									snake_case   :			Any						    = torch.optim.Adam([vector]   ,	lr=self.lr		)

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



			def 				__lowercase					(			self							:				Dict   ,	_lowercase							:				Union[str, Any]   ,	_lowercase							:				Tuple   ,	_lowercase							:				Optional[int]		)							->					List[str]:
									wandb.init(reinit=_lowercase   ,	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:
															snake_case   :			Dict						    = Image.open(_lowercase		)
															snake_case   :			Tuple						    = image.resize((256, 256)		)
															wandb.log("Original Image"   ,	wandb.Image(_lowercase		)		)



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



			def 				__lowercase					(			self							:				Any   ,	_lowercase							:				List[Any]   ,	_lowercase							:				str=None   ,	_lowercase							:				List[str]=None   ,	_lowercase							:				int=True   ,	_lowercase							:				Dict=False   ,	_lowercase							:				Optional[int]=True   ,	_lowercase							:				int=True   ,	_lowercase							:				str=None   ,	)							->					Any:
									if image_path:
															snake_case   :			Optional[Any]						    = self._get_latent(_lowercase		)
									else:
															snake_case   :			List[Any]						    = torch.randn(self.latent_dim   ,	device=self.device		)
									if self.log:
															self._init_logging(_lowercase   ,	_lowercase   ,	_lowercase		)

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

									snake_case   :			Union[str, Any]						    = self.vqgan.decode(self.latent		)[0]
									if show_intermediate:
															print("Original Image"		)
															show_pil(custom_to_pil(_lowercase		)		)

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








"""simple docstring"""







import math
import sys

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							int ):
						"""simple docstring"""






						if number != int(lowerCamelCase_ ):
												raise ValueError("the value of input must be a natural number" )
						if number < 0:
												raise ValueError("the value of input must not be a negative number" )
						if number == 0:
												return 1
						snake_case   :			List[str]						    = [-1] * (number + 1)
						snake_case   :			str						    = 0
						for i in range(1     ,		number + 1 ):
												snake_case   :			Tuple						    = sys.maxsize
												snake_case   :			Optional[Any]						    = int(math.sqrt(lowerCamelCase_ ) )
												for j in range(1     ,		root + 1 ):
																		snake_case   :			List[str]						    = 1 + answers[i - (j**2)]
																		snake_case   :			List[str]						    = min(lowerCamelCase_     ,		lowerCamelCase_ )
												snake_case   :			Dict						    = answer
						return answers[number]


if __name__ == "__main__":
			import doctest

			doctest.testmod()



