


"""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:

		"""simple docstring"""





		def __init__( self					,				SCREAMING_SNAKE_CASE__ = "cpu"					,				SCREAMING_SNAKE_CASE__ = "openai/clip-vit-large-patch14"   )      ->	None:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	device
					_UpperCamelCase					:Tuple					     =	CLIPTokenizerFast.from_pretrained(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:List[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]
					_UpperCamelCase					:Union[str, Any]					     =	[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]
					_UpperCamelCase					:Tuple					     =	torchvision.transforms.Normalize(self.image_mean					,				self.image_std   )
					_UpperCamelCase					:Tuple					     =	torchvision.transforms.Resize(2_24   )
					_UpperCamelCase					:Any					     =	torchvision.transforms.CenterCrop(2_24   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	List[str]:

					"""simple docstring"""



					_UpperCamelCase					:str					     =	self.resize(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:int					     =	self.center_crop(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:str					     =	self.normalize(SCREAMING_SNAKE_CASE__   )
					return images





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

					"""simple docstring"""



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






class      A(  nn.Module		):

		"""simple docstring"""





		def __init__( self					,				SCREAMING_SNAKE_CASE__=10					,				SCREAMING_SNAKE_CASE__=0.0_1					,				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					,				)      ->	None:

					"""simple docstring"""



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

					_UpperCamelCase					:Union[str, Any]					     =	iterations
					_UpperCamelCase					:Tuple					     =	lr
					_UpperCamelCase					:List[Any]					     =	log
					_UpperCamelCase					:Dict					     =	make_grid
					_UpperCamelCase					:Union[str, Any]					     =	return_val
					_UpperCamelCase					:Any					     =	quantize
					_UpperCamelCase					:int					     =	self.vqgan.decoder.z_shape
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=5					,				SCREAMING_SNAKE_CASE__=True   )      ->	str:

					"""simple docstring"""



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

					"""simple docstring"""



					if not (path or img):
								raise ValueError('''Input either path or tensor'''   )
					if img is not None:
								raise NotImplementedError
					_UpperCamelCase					:Any					     =	preprocess(Image.open(SCREAMING_SNAKE_CASE__   )					,				target_image_size=2_56   ).to(self.device   )
					_UpperCamelCase					:int					     =	preprocess_vqgan(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase     ,      *_UpperCamelCase					:str					     =	self.vqgan.encode(SCREAMING_SNAKE_CASE__   )
					return z
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:str					     =	self.latent.detach().requires_grad_()
					_UpperCamelCase					:Optional[int]					     =	base_latent + transform_vector
					if self.quantize:
								_UpperCamelCase     ,      *_UpperCamelCase					:Optional[Any]					     =	self.vqgan.quantize(SCREAMING_SNAKE_CASE__   )
					else:
								_UpperCamelCase					:Optional[Any]					     =	trans_latent
					return self.vqgan.decode(SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__=None   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:Any					     =	self.clip_preprocessor(text=SCREAMING_SNAKE_CASE__					,				images=SCREAMING_SNAKE_CASE__					,				return_tensors='''pt'''					,				padding=SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Union[str, Any]					     =	self.clip(**SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Union[str, Any]					     =	clip_outputs.logits_per_image
					if weights is not None:
								_UpperCamelCase					:Union[str, Any]					     =	similarity_logits * weights
					return similarity_logits.sum()
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	self._get_clip_similarity(pos_prompts['''prompts''']					,				SCREAMING_SNAKE_CASE__					,				weights=(1 / pos_prompts['''weights'''])   )
					if neg_prompts:
								_UpperCamelCase					:str					     =	self._get_clip_similarity(neg_prompts['''prompts''']					,				SCREAMING_SNAKE_CASE__					,				weights=neg_prompts['''weights''']   )
					else:
								_UpperCamelCase					:Union[str, Any]					     =	torch.tensor([1]					,				device=self.device   )
					_UpperCamelCase					:int					     =	-torch.log(SCREAMING_SNAKE_CASE__   ) + torch.log(SCREAMING_SNAKE_CASE__   )
					return loss
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	torch.randn_like(self.latent					,				requires_grad=SCREAMING_SNAKE_CASE__					,				device=self.device   )
					_UpperCamelCase					:int					     =	torch.optim.Adam([vector]					,				lr=self.lr   )

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

					"""simple docstring"""



					wandb.init(reinit=SCREAMING_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:
								_UpperCamelCase					:Union[str, Any]					     =	Image.open(SCREAMING_SNAKE_CASE__   )
								_UpperCamelCase					:Tuple					     =	image.resize((2_56, 2_56)   )
								wandb.log('''Original Image'''					,				wandb.Image(SCREAMING_SNAKE_CASE__   )   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	Optional[int]:

					"""simple docstring"""



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





		def   _UpperCamelCase( 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					,				)      ->	Any:

					"""simple docstring"""



					if image_path:
								_UpperCamelCase					:Dict					     =	self._get_latent(SCREAMING_SNAKE_CASE__   )
					else:
								_UpperCamelCase					:Any					     =	torch.randn(self.latent_dim					,				device=self.device   )
					if self.log:
								self._init_logging(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )

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

					_UpperCamelCase					:str					     =	self.vqgan.decode(self.latent   )[0]
					if show_intermediate:
								print('''Original Image'''   )
								show_pil(custom_to_pil(SCREAMING_SNAKE_CASE__   )   )

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





"""simple docstring"""






UpperCamelCase__				:Tuple			   =	frozenset(
    [
        """prompt""",
        """height""",
        """width""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
        """cross_attention_kwargs""",
    ]
)

UpperCamelCase__				:List[str]			   =	frozenset(["""prompt""", """negative_prompt"""])

UpperCamelCase__				:Tuple			   =	frozenset([])

UpperCamelCase__				:Union[str, Any]			   =	frozenset(["""image"""])

UpperCamelCase__				:str			   =	frozenset(
    [
        """image""",
        """height""",
        """width""",
        """guidance_scale""",
    ]
)

UpperCamelCase__				:Any			   =	frozenset(["""image"""])

UpperCamelCase__				:Optional[int]			   =	frozenset(
    [
        """prompt""",
        """image""",
        """height""",
        """width""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
    ]
)

UpperCamelCase__				:int			   =	frozenset(["""prompt""", """image""", """negative_prompt"""])

UpperCamelCase__				:Tuple			   =	frozenset(
    [
        # Text guided image variation with an image mask
        """prompt""",
        """image""",
        """mask_image""",
        """height""",
        """width""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
    ]
)

UpperCamelCase__				:Any			   =	frozenset(["""prompt""", """image""", """mask_image""", """negative_prompt"""])

UpperCamelCase__				:Dict			   =	frozenset(
    [
        # image variation with an image mask
        """image""",
        """mask_image""",
        """height""",
        """width""",
        """guidance_scale""",
    ]
)

UpperCamelCase__				:Dict			   =	frozenset(["""image""", """mask_image"""])

UpperCamelCase__				:Tuple			   =	frozenset(
    [
        """example_image""",
        """image""",
        """mask_image""",
        """height""",
        """width""",
        """guidance_scale""",
    ]
)

UpperCamelCase__				:List[str]			   =	frozenset(["""example_image""", """image""", """mask_image"""])

UpperCamelCase__				:List[str]			   =	frozenset(["""class_labels"""])

UpperCamelCase__				:Union[str, Any]			   =	frozenset(["""class_labels"""])

UpperCamelCase__				:Any			   =	frozenset(["""batch_size"""])

UpperCamelCase__				:Optional[Any]			   =	frozenset([])

UpperCamelCase__				:Optional[Any]			   =	frozenset(["""batch_size"""])

UpperCamelCase__				:Optional[int]			   =	frozenset([])

UpperCamelCase__				:str			   =	frozenset(
    [
        """prompt""",
        """audio_length_in_s""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
        """cross_attention_kwargs""",
    ]
)

UpperCamelCase__				:Union[str, Any]			   =	frozenset(["""prompt""", """negative_prompt"""])
UpperCamelCase__				:Tuple			   =	frozenset(["""input_tokens"""])

UpperCamelCase__				:int			   =	frozenset(["""input_tokens"""])

