
'''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 	snake_case__       :







		"""simple docstring"""

		def __init__(      self				:			Optional[Any]  ,	UpperCamelCase__				:			str = "cpu"  ,	UpperCamelCase__				:			str = "openai/clip-vit-large-patch14"      )						->			None:






				"""simple docstring"""







				snake_case  :       int     			=						device
				snake_case  :       List[str]     			=						CLIPTokenizerFast.from_pretrained(UpperCamelCase__      )
				snake_case  :       Any     			=						[0.48_145_466, 0.4_578_275, 0.40_821_073]
				snake_case  :       Dict     			=						[0.26_862_954, 0.26_130_258, 0.27_577_711]
				snake_case  :       Tuple     			=						torchvision.transforms.Normalize(self.image_mean  ,	self.image_std      )
				snake_case  :       int     			=						torchvision.transforms.Resize(224      )
				snake_case  :       int     			=						torchvision.transforms.CenterCrop(224      )



		def       lowerCAmelCase				(      self				:			List[Any]  ,	UpperCamelCase__				:			str      )						->			Tuple:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						self.resize(UpperCamelCase__      )
				snake_case  :       Optional[int]     			=						self.center_crop(UpperCamelCase__      )
				snake_case  :       Any     			=						self.normalize(UpperCamelCase__      )
				return images



		def __call__(      self				:			Union[str, Any]  ,	UpperCamelCase__				:			Any=None  ,	UpperCamelCase__				:			Optional[Any]=None  ,	**UpperCamelCase__				:			int      )						->			Dict:






				"""simple docstring"""







				snake_case  :       Tuple     			=						self.tokenizer(text=UpperCamelCase__  ,	**UpperCamelCase__      )
				snake_case  :       Optional[int]     			=						self.preprocess_img(UpperCamelCase__      )
				snake_case  :       Dict     			=						{key: value.to(self.device      ) for (key, value) in encoding.items()}
				return encoding







class 	snake_case__       (      nn.Module					):







		"""simple docstring"""

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






				"""simple docstring"""







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

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



		def       lowerCAmelCase				(      self				:			int  ,	UpperCamelCase__				:			Optional[Any]=None  ,	UpperCamelCase__				:			Dict=None  ,	UpperCamelCase__				:			Optional[int]=5  ,	UpperCamelCase__				:			Dict=True      )						->			Dict:






				"""simple docstring"""







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



		def       lowerCAmelCase				(      self				:			str  ,	UpperCamelCase__				:			Any=None  ,	UpperCamelCase__				:			Any=None      )						->			Dict:






				"""simple docstring"""







				if not (path or img):
						raise ValueError('''Input either path or tensor'''      )
				if img is not None:
						raise NotImplementedError
				snake_case  :       Tuple     			=						preprocess(Image.open(UpperCamelCase__      )  ,	target_image_size=256      ).to(self.device      )
				snake_case  :       Optional[int]     			=						preprocess_vqgan(UpperCamelCase__      )
				snake_case			,*snake_case  :       Union[str, Any]     			=						self.vqgan.encode(UpperCamelCase__      )
				return z



		def       lowerCAmelCase				(      self				:			Any  ,	UpperCamelCase__				:			Optional[int]      )						->			List[Any]:






				"""simple docstring"""







				snake_case  :       str     			=						self.latent.detach().requires_grad_()
				snake_case  :       Any     			=						base_latent + transform_vector
				if self.quantize:
						snake_case			,*snake_case  :       List[Any]     			=						self.vqgan.quantize(UpperCamelCase__      )
				else:
						snake_case  :       List[Any]     			=						trans_latent
				return self.vqgan.decode(UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			Optional[int]  ,	UpperCamelCase__				:			Optional[int]  ,	UpperCamelCase__				:			Optional[int]  ,	UpperCamelCase__				:			Union[str, Any]=None      )						->			Tuple:






				"""simple docstring"""







				snake_case  :       Optional[int]     			=						self.clip_preprocessor(text=UpperCamelCase__  ,	images=UpperCamelCase__  ,	return_tensors='''pt'''  ,	padding=UpperCamelCase__      )
				snake_case  :       Dict     			=						self.clip(**UpperCamelCase__      )
				snake_case  :       Optional[int]     			=						clip_outputs.logits_per_image
				if weights is not None:
						snake_case  :       Union[str, Any]     			=						similarity_logits * weights
				return similarity_logits.sum()



		def       lowerCAmelCase				(      self				:			Dict  ,	UpperCamelCase__				:			Optional[Any]  ,	UpperCamelCase__				:			Union[str, Any]  ,	UpperCamelCase__				:			str      )						->			Optional[Any]:






				"""simple docstring"""







				snake_case  :       str     			=						self._get_clip_similarity(pos_prompts['''prompts''']  ,	UpperCamelCase__  ,	weights=(1 / pos_prompts['''weights'''])      )
				if neg_prompts:
						snake_case  :       Any     			=						self._get_clip_similarity(neg_prompts['''prompts''']  ,	UpperCamelCase__  ,	weights=neg_prompts['''weights''']      )
				else:
						snake_case  :       Optional[int]     			=						torch.tensor([1]  ,	device=self.device      )
				snake_case  :       List[Any]     			=						-torch.log(UpperCamelCase__      ) + torch.log(UpperCamelCase__      )
				return loss



		def       lowerCAmelCase				(      self				:			Union[str, Any]  ,	UpperCamelCase__				:			Any  ,	UpperCamelCase__				:			List[str]  ,	UpperCamelCase__				:			str      )						->			Optional[Any]:






				"""simple docstring"""







				snake_case  :       str     			=						torch.randn_like(self.latent  ,	requires_grad=UpperCamelCase__  ,	device=self.device      )
				snake_case  :       List[Any]     			=						torch.optim.Adam([vector]  ,	lr=self.lr      )

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



		def       lowerCAmelCase				(      self				:			Optional[Any]  ,	UpperCamelCase__				:			Tuple  ,	UpperCamelCase__				:			Union[str, Any]  ,	UpperCamelCase__				:			Tuple      )						->			Any:






				"""simple docstring"""







				wandb.init(reinit=UpperCamelCase__  ,	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  :       Optional[Any]     			=						Image.open(UpperCamelCase__      )
						snake_case  :       int     			=						image.resize((256, 256)      )
						wandb.log('''Original Image'''  ,	wandb.Image(UpperCamelCase__      )      )



		def       lowerCAmelCase				(      self				:			Tuple  ,	UpperCamelCase__				:			Dict      )						->			Union[str, Any]:






				"""simple docstring"""







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



		def       lowerCAmelCase				(      self				:			str  ,	UpperCamelCase__				:			Any  ,	UpperCamelCase__				:			Optional[int]=None  ,	UpperCamelCase__				:			Optional[int]=None  ,	UpperCamelCase__				:			Optional[int]=True  ,	UpperCamelCase__				:			Optional[int]=False  ,	UpperCamelCase__				:			Dict=True  ,	UpperCamelCase__				:			Tuple=True  ,	UpperCamelCase__				:			Dict=None  ,	)						->			Optional[Any]:






				"""simple docstring"""







				if image_path:
						snake_case  :       str     			=						self._get_latent(UpperCamelCase__      )
				else:
						snake_case  :       Optional[int]     			=						torch.randn(self.latent_dim  ,	device=self.device      )
				if self.log:
						self._init_logging(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )

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

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

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




'''simple docstring'''
import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


lowercase__						       =      logging.get_logger(__name__)






class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		def __init__(      self				:			Optional[Any]  ,	*UpperCamelCase__				:			Optional[Any]  ,	**UpperCamelCase__				:			str      )						->			None:






				"""simple docstring"""







				warnings.warn(
				    '''The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
				    ''' use GLPNImageProcessor instead.'''  ,	UpperCamelCase__  ,	)
				super().__init__(*UpperCamelCase__  ,	**UpperCamelCase__      )


