

"""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 			lowerCAmelCase_		:


				"""simple docstring"""



				def __init__(self     , SCREAMING_SNAKE_CASE__ = "cpu"     , SCREAMING_SNAKE_CASE__ = "openai/clip-vit-large-patch14"					)      ->	Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[int]     =     device
											SCREAMING_SNAKE_CASE__		: str     =     CLIPTokenizerFast.from_pretrained(a_					)
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     [0.48145466, 0.4578275, 0.40821073]
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     [0.26862954, 0.26130258, 0.27577711]
											SCREAMING_SNAKE_CASE__		: str     =     torchvision.transforms.Normalize(self.image_mean     , self.image_std					)
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     torchvision.transforms.Resize(2_24					)
											SCREAMING_SNAKE_CASE__		: str     =     torchvision.transforms.CenterCrop(2_24					)



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[int]     =     self.resize(a_					)
											SCREAMING_SNAKE_CASE__		: List[Any]     =     self.center_crop(a_					)
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     self.normalize(a_					)
											return images



				def __call__(self     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=None     , **SCREAMING_SNAKE_CASE__					)      ->	List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[int]     =     self.tokenizer(text=a_     , **a_					)
											SCREAMING_SNAKE_CASE__		: Any     =     self.preprocess_img(a_					)
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     {key: value.to(self.device					) for (key, value) in encoding.items()}
											return encoding






class 			lowerCAmelCase_		(nn.Module						):


				"""simple docstring"""



				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     , )      ->	Optional[Any]:



											"""simple docstring"""




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

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



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=5     , SCREAMING_SNAKE_CASE__=True					)      ->	Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Dict     =     []
											if output_path is None:
																		SCREAMING_SNAKE_CASE__		: Tuple     =     """./animation.gif"""
											if input_path is None:
																		SCREAMING_SNAKE_CASE__		: Any     =     self.save_path
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     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__		: List[Any]     =     total_duration / len(a_					)
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     [frame_duration] * len(a_					)
											if extend_frames:
																		SCREAMING_SNAKE_CASE__		: Optional[int]     =     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 			__magic_name__					(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
											SCREAMING_SNAKE_CASE__		: int     =     preprocess(Image.open(a_					)     , target_image_size=2_56					).to(self.device					)
											SCREAMING_SNAKE_CASE__		: int     =     preprocess_vqgan(a_					)
											SCREAMING_SNAKE_CASE__		: List[Any]     =     self.vqgan.encode(a_					)
											return z



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[int]     =     self.latent.detach().requires_grad_()
											SCREAMING_SNAKE_CASE__		: Tuple     =     base_latent + transform_vector
											if self.quantize:
																		SCREAMING_SNAKE_CASE__		: Any     =     self.vqgan.quantize(a_					)
											else:
																		SCREAMING_SNAKE_CASE__		: List[Any]     =     trans_latent
											return self.vqgan.decode(a_					)



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__=None					)      ->	Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Tuple     =     self.clip_preprocessor(text=a_     , images=a_     , return_tensors="""pt"""     , padding=a_					)
											SCREAMING_SNAKE_CASE__		: Any     =     self.clip(**a_					)
											SCREAMING_SNAKE_CASE__		: str     =     clip_outputs.logits_per_image
											if weights is not None:
																		SCREAMING_SNAKE_CASE__		: Any     =     similarity_logits * weights
											return similarity_logits.sum()



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	int:



											"""simple docstring"""




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



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Tuple     =     torch.randn_like(self.latent     , requires_grad=a_     , device=self.device					)
											SCREAMING_SNAKE_CASE__		: Dict     =     torch.optim.Adam([vector]     , lr=self.lr					)

											for i in range(self.iterations					):
																		optim.zero_grad()
																		SCREAMING_SNAKE_CASE__		: str     =     self._add_vector(a_					)
																		SCREAMING_SNAKE_CASE__		: List[Any]     =     loop_post_process(a_					)
																		SCREAMING_SNAKE_CASE__		: List[Any]     =     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 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	int:



											"""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__		: Any     =     Image.open(a_					)
																		SCREAMING_SNAKE_CASE__		: str     =     image.resize((2_56, 2_56)					)
																		wandb.log("""Original Image"""     , wandb.Image(a_					)					)



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	List[str]:



											"""simple docstring"""




											if not prompts:
																		return []
											SCREAMING_SNAKE_CASE__		: List[str]     =     []
											SCREAMING_SNAKE_CASE__		: Tuple     =     []
											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[Any]     =     prompt[0]
																									SCREAMING_SNAKE_CASE__		: Optional[Any]     =     float(prompt[1]					)
																		elif ":" in prompt:
																									SCREAMING_SNAKE_CASE__		: List[Any]     =     prompt.split(""":"""					)
																									SCREAMING_SNAKE_CASE__		: str     =     float(a_					)
																		else:
																									SCREAMING_SNAKE_CASE__		: int     =     prompt
																									SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     1.0
																		processed_prompts.append(a_					)
																		weights.append(a_					)
											return {
											    "prompts": processed_prompts,
											    "weights": torch.tensor(a_     , device=self.device					),
											}



				def 			__magic_name__					(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     , )      ->	str:



											"""simple docstring"""




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






"""simple docstring"""




UpperCAmelCase__   :					Dict												=				[
    (1_0_0_0, 'M'),
    (9_0_0, 'CM'),
    (5_0_0, 'D'),
    (4_0_0, 'CD'),
    (1_0_0, 'C'),
    (9_0, 'XC'),
    (5_0, 'L'),
    (4_0, 'XL'),
    (1_0, 'X'),
    (9, 'IX'),
    (5, 'V'),
    (4, 'IV'),
    (1, 'I'),
]

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1_000}
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     0
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     0
							while place < len(_snake_case		):
														if (place + 1 < len(_snake_case		)) and (vals[roman[place]] < vals[roman[place + 1]]):
																					total += vals[roman[place + 1]] - vals[roman[place]]
																					place += 2
														else:
																					total += vals[roman[place]]
																					place += 1
							return total

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     []
							for arabic, roman in ROMAN:
														((SCREAMING_SNAKE_CASE__)   ,   (SCREAMING_SNAKE_CASE__))		: List[Any]     =     divmod(_snake_case     ,_snake_case		)
														result.append(roman * factor		)
														if number == 0:
																					break
							return "".join(_snake_case		)


if __name__ == "__main__":
					import doctest

					doctest.testmod()




