





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   :    Dict							,	__lowerCAmelCase   :    str = "cpu"							,	__lowerCAmelCase   :    str = "openai/clip-vit-large-patch14"   )	->	None:







					"""simple docstring"""






					A__        =   device
					A__        =   CLIPTokenizerFast.from_pretrained(__lowerCAmelCase   )
					A__        =   [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__        =   [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__        =   torchvision.transforms.Normalize(self.image_mean							,	self.image_std   )
					A__        =   torchvision.transforms.Resize(2_24   )
					A__        =   torchvision.transforms.CenterCrop(2_24   )






			def    a_       (			self   :    Dict							,	__lowerCAmelCase   :    Optional[int]   )	->	Optional[int]:







					"""simple docstring"""






					A__        =   self.resize(__lowerCAmelCase   )
					A__        =   self.center_crop(__lowerCAmelCase   )
					A__        =   self.normalize(__lowerCAmelCase   )
					return images







			def __call__(			self   :    Dict							,	__lowerCAmelCase   :    Tuple=None							,	__lowerCAmelCase   :    Any=None							,	**__lowerCAmelCase   :    Dict   )	->	Optional[int]:







					"""simple docstring"""






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


class   A			(nn.Module    ):
			'''simple docstring'''




			def __init__(			self   :    Dict							,	__lowerCAmelCase   :    Any=10							,	__lowerCAmelCase   :    Union[str, Any]=0.0_1							,	__lowerCAmelCase   :    Tuple=None							,	__lowerCAmelCase   :    int=None							,	__lowerCAmelCase   :    List[str]=None							,	__lowerCAmelCase   :    List[str]=None							,	__lowerCAmelCase   :    List[str]=None							,	__lowerCAmelCase   :    str=None							,	__lowerCAmelCase   :    str=False							,	__lowerCAmelCase   :    Optional[int]=True							,	__lowerCAmelCase   :    List[Any]="image"							,	__lowerCAmelCase   :    List[Any]=True							,	__lowerCAmelCase   :    List[Any]=False							,	__lowerCAmelCase   :    int=False							,	__lowerCAmelCase   :    int=False							,	)	->	None:







					"""simple docstring"""






					super().__init__()
					A__        =   None
					A__        =   device if device else get_device()
					if vqgan:
							A__        =   vqgan
					else:
							A__        =   load_vqgan(self.device							,	conf_path=__lowerCAmelCase							,	ckpt_path=__lowerCAmelCase   )
					self.vqgan.eval()
					if clip:
							A__        =   clip
					else:
							A__        =   CLIPModel.from_pretrained("""openai/clip-vit-base-patch32"""   )
					self.clip.to(self.device   )
					A__        =   ProcessorGradientFlow(device=self.device   )

					A__        =   iterations
					A__        =   lr
					A__        =   log
					A__        =   make_grid
					A__        =   return_val
					A__        =   quantize
					A__        =   self.vqgan.decoder.z_shape






			def    a_       (			self   :    List[Any]							,	__lowerCAmelCase   :    int=None							,	__lowerCAmelCase   :    Optional[Any]=None							,	__lowerCAmelCase   :    List[Any]=5							,	__lowerCAmelCase   :    Optional[int]=True   )	->	Optional[int]:







					"""simple docstring"""






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






			def    a_       (			self   :    int							,	__lowerCAmelCase   :    Optional[Any]=None							,	__lowerCAmelCase   :    Any=None   )	->	List[Any]:







					"""simple docstring"""






					if not (path or img):
							raise ValueError("""Input either path or tensor"""   )
					if img is not None:
							raise NotImplementedError
					A__        =   preprocess(Image.open(__lowerCAmelCase   )							,	target_image_size=2_56   ).to(self.device   )
					A__        =   preprocess_vqgan(__lowerCAmelCase   )
					A__      ,			*A__        =   self.vqgan.encode(__lowerCAmelCase   )
					return z






			def    a_       (			self   :    Optional[Any]							,	__lowerCAmelCase   :    Any   )	->	Tuple:







					"""simple docstring"""






					A__        =   self.latent.detach().requires_grad_()
					A__        =   base_latent + transform_vector
					if self.quantize:
							A__      ,			*A__        =   self.vqgan.quantize(__lowerCAmelCase   )
					else:
							A__        =   trans_latent
					return self.vqgan.decode(__lowerCAmelCase   )






			def    a_       (			self   :    Optional[int]							,	__lowerCAmelCase   :    Dict							,	__lowerCAmelCase   :    Dict							,	__lowerCAmelCase   :    Optional[Any]=None   )	->	Optional[Any]:







					"""simple docstring"""






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






			def    a_       (			self   :    str							,	__lowerCAmelCase   :    Any							,	__lowerCAmelCase   :    str							,	__lowerCAmelCase   :    Optional[Any]   )	->	Union[str, Any]:







					"""simple docstring"""






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






			def    a_       (			self   :    Union[str, Any]							,	__lowerCAmelCase   :    List[Any]							,	__lowerCAmelCase   :    Any							,	__lowerCAmelCase   :    Tuple   )	->	str:







					"""simple docstring"""






					A__        =   torch.randn_like(self.latent							,	requires_grad=__lowerCAmelCase							,	device=self.device   )
					A__        =   torch.optim.Adam([vector]							,	lr=self.lr   )

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






			def    a_       (			self   :    List[str]							,	__lowerCAmelCase   :    str							,	__lowerCAmelCase   :    Optional[int]							,	__lowerCAmelCase   :    str   )	->	Dict:







					"""simple docstring"""






					wandb.init(reinit=__lowerCAmelCase							,	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__        =   Image.open(__lowerCAmelCase   )
							A__        =   image.resize((2_56, 2_56)   )
							wandb.log("""Original Image"""							,	wandb.Image(__lowerCAmelCase   )   )






			def    a_       (			self   :    List[Any]							,	__lowerCAmelCase   :    Tuple   )	->	Optional[int]:







					"""simple docstring"""






					if not prompts:
							return []
					A__        =   []
					A__        =   []
					if isinstance(__lowerCAmelCase							,	__lowerCAmelCase   ):
							A__        =   [prompt.strip() for prompt in prompts.split("""|"""   )]
					for prompt in prompts:
							if isinstance(__lowerCAmelCase							,	(tuple, list)   ):
									A__        =   prompt[0]
									A__        =   float(prompt[1]   )
							elif ":" in prompt:
									A__      ,			A__        =   prompt.split(""":"""   )
									A__        =   float(__lowerCAmelCase   )
							else:
									A__        =   prompt
									A__        =   1.0
							processed_prompts.append(__lowerCAmelCase   )
							weights.append(__lowerCAmelCase   )
					return {
					    "prompts": processed_prompts,
					    "weights": torch.tensor(__lowerCAmelCase							,	device=self.device   ),
					}







			def    a_       (			self   :    Dict							,	__lowerCAmelCase   :    Dict							,	__lowerCAmelCase   :    Any=None							,	__lowerCAmelCase   :    List[Any]=None							,	__lowerCAmelCase   :    List[str]=True							,	__lowerCAmelCase   :    Dict=False							,	__lowerCAmelCase   :    Optional[Any]=True							,	__lowerCAmelCase   :    Optional[Any]=True							,	__lowerCAmelCase   :    Dict=None							,	)	->	Dict:







					"""simple docstring"""






					if image_path:
							A__        =   self._get_latent(__lowerCAmelCase   )
					else:
							A__        =   torch.randn(self.latent_dim							,	device=self.device   )
					if self.log:
							self._init_logging(__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase   )

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

					A__        =   self.vqgan.decode(self.latent   )[0]
					if show_intermediate:
							print("""Original Image"""   )
							show_pil(custom_to_pil(__lowerCAmelCase   )   )

					A__        =   loop_post_process(__lowerCAmelCase   )
					for iter, transformed_img in enumerate(self._optimize_CLIP(__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase   )   ):
							if show_intermediate:
									show_pil(__lowerCAmelCase   )
							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(__lowerCAmelCase   )}   )
					if show_final:
							show_pil(__lowerCAmelCase   )
					if save_final:
							transformed_img.save(os.path.join(self.save_path							,	f'iter_{iter:03d}_final.png'   )   )










def    __lowerCamelCase					(							__a   :str    )		->				list:






		"""simple docstring"""

		A__        =   [0] * len(__a    )

		for i in range(1    ,     len(__a    )    ):
				# use last results for better performance - dynamic programming
				A__        =   prefix_result[i - 1]
				while j > 0 and input_string[i] != input_string[j]:
						A__        =   prefix_result[j - 1]

				if input_string[i] == input_string[j]:
						j += 1
				A__        =   j

		return prefix_result







def    __lowerCamelCase					(							__a   :str    )		->				int:






		"""simple docstring"""

		return max(prefix_function(__a    )    )


if __name__ == "__main__":
					import doctest

					doctest.testmod()



