



"""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 				UpperCamelCase__       :




      """simple docstring"""




      def __init__(				self    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				str = "cpu"   ,      SCREAMING_SNAKE_CASE_    :				str = "openai/clip-vit-large-patch14"	):
        lowerCAmelCase_       :    Any								=			device
        lowerCAmelCase_       :    Tuple								=			CLIPTokenizerFast.from_pretrained(UpperCamelCase_	)
        lowerCAmelCase_       :    List[Any]								=			[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
        lowerCAmelCase_       :    Union[str, Any]								=			[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]
        lowerCAmelCase_       :    List[str]								=			torchvision.transforms.Normalize(self.image_mean   ,      self.image_std	)
        lowerCAmelCase_       :    Optional[Any]								=			torchvision.transforms.Resize(2_2_4	)
        lowerCAmelCase_       :    List[str]								=			torchvision.transforms.CenterCrop(2_2_4	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				Tuple	):
        lowerCAmelCase_       :    Dict								=			self.resize(UpperCamelCase_	)
        lowerCAmelCase_       :    List[str]								=			self.center_crop(UpperCamelCase_	)
        lowerCAmelCase_       :    Optional[int]								=			self.normalize(UpperCamelCase_	)
        return images





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







class 				UpperCamelCase__       (     nn.Module  ):




      """simple docstring"""




      def __init__(				self    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]=1_0   ,      SCREAMING_SNAKE_CASE_    :				List[str]=0.01   ,      SCREAMING_SNAKE_CASE_    :				List[str]=None   ,      SCREAMING_SNAKE_CASE_    :				Dict=None   ,      SCREAMING_SNAKE_CASE_    :				str=None   ,      SCREAMING_SNAKE_CASE_    :				str=None   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=None   ,      SCREAMING_SNAKE_CASE_    :				str=None   ,      SCREAMING_SNAKE_CASE_    :				List[str]=False   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=True   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]="image"   ,      SCREAMING_SNAKE_CASE_    :				str=True   ,      SCREAMING_SNAKE_CASE_    :				str=False   ,      SCREAMING_SNAKE_CASE_    :				str=False   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]=False   ,      ):
        super().__init__()
        lowerCAmelCase_       :    List[str]								=			None
        lowerCAmelCase_       :    str								=			device if device else get_device()
        if vqgan:
          lowerCAmelCase_       :    List[str]								=			vqgan
        else:
          lowerCAmelCase_       :    List[Any]								=			load_vqgan(self.device   ,      conf_path=UpperCamelCase_   ,      ckpt_path=UpperCamelCase_	)
        self.vqgan.eval()
        if clip:
          lowerCAmelCase_       :    Tuple								=			clip
        else:
          lowerCAmelCase_       :    str								=			CLIPModel.from_pretrained('openai/clip-vit-base-patch32'	)
        self.clip.to(self.device	)
        lowerCAmelCase_       :    Union[str, Any]								=			ProcessorGradientFlow(device=self.device	)

        lowerCAmelCase_       :    List[Any]								=			iterations
        lowerCAmelCase_       :    str								=			lr
        lowerCAmelCase_       :    Tuple								=			log
        lowerCAmelCase_       :    str								=			make_grid
        lowerCAmelCase_       :    List[str]								=			return_val
        lowerCAmelCase_       :    List[str]								=			quantize
        lowerCAmelCase_       :    List[Any]								=			self.vqgan.decoder.z_shape



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[str]   ,      SCREAMING_SNAKE_CASE_    :				Tuple=None   ,      SCREAMING_SNAKE_CASE_    :				Any=None   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]=5   ,      SCREAMING_SNAKE_CASE_    :				int=True	):
        lowerCAmelCase_       :    Optional[int]								=			[]
        if output_path is None:
          lowerCAmelCase_       :    Optional[int]								=			'./animation.gif'
        if input_path is None:
          lowerCAmelCase_       :    Any								=			self.save_path
        lowerCAmelCase_       :    Optional[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?)'	)
        lowerCAmelCase_       :    int								=			total_duration / len(UpperCamelCase_	)
        lowerCAmelCase_       :    Tuple								=			[frame_duration] * len(UpperCamelCase_	)
        if extend_frames:
          lowerCAmelCase_       :    str								=			1.5
          lowerCAmelCase_       :    str								=			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 					SCREAMING_SNAKE_CASE__			(				self    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				Dict=None   ,      SCREAMING_SNAKE_CASE_    :				Dict=None	):
        if not (path or img):
          raise ValueError('Input either path or tensor'	)
        if img is not None:
          raise NotImplementedError
        lowerCAmelCase_       :    List[Any]								=			preprocess(Image.open(UpperCamelCase_	)   ,      target_image_size=2_5_6	).to(self.device	)
        lowerCAmelCase_       :    List[Any]								=			preprocess_vqgan(UpperCamelCase_	)
        lowerCAmelCase_						,*lowerCAmelCase_       :    List[str]								=			self.vqgan.encode(UpperCamelCase_	)
        return z



      def 					SCREAMING_SNAKE_CASE__			(				self    :				str   ,      SCREAMING_SNAKE_CASE_    :				List[Any]	):
        lowerCAmelCase_       :    Any								=			self.latent.detach().requires_grad_()
        lowerCAmelCase_       :    List[str]								=			base_latent + transform_vector
        if self.quantize:
          lowerCAmelCase_						,*lowerCAmelCase_       :    List[Any]								=			self.vqgan.quantize(UpperCamelCase_	)
        else:
          lowerCAmelCase_       :    List[Any]								=			trans_latent
        return self.vqgan.decode(UpperCamelCase_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[str]   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				List[str]=None	):
        lowerCAmelCase_       :    Optional[int]								=			self.clip_preprocessor(text=UpperCamelCase_   ,      images=UpperCamelCase_   ,      return_tensors='pt'   ,      padding=UpperCamelCase_	)
        lowerCAmelCase_       :    Any								=			self.clip(**UpperCamelCase_	)
        lowerCAmelCase_       :    List[Any]								=			clip_outputs.logits_per_image
        if weights is not None:
          lowerCAmelCase_       :    Optional[Any]								=			similarity_logits * weights
        return similarity_logits.sum()



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				int   ,      SCREAMING_SNAKE_CASE_    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				Dict	):
        lowerCAmelCase_       :    Any								=			self._get_clip_similarity(pos_prompts['prompts']   ,      UpperCamelCase_   ,      weights=(1 / pos_prompts['weights'])	)
        if neg_prompts:
          lowerCAmelCase_       :    Optional[Any]								=			self._get_clip_similarity(neg_prompts['prompts']   ,      UpperCamelCase_   ,      weights=neg_prompts['weights']	)
        else:
          lowerCAmelCase_       :    Tuple								=			torch.tensor([1]   ,      device=self.device	)
        lowerCAmelCase_       :    Optional[int]								=			-torch.log(UpperCamelCase_	) + torch.log(UpperCamelCase_	)
        return loss



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[str]   ,      SCREAMING_SNAKE_CASE_    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				int   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]	):
        lowerCAmelCase_       :    Tuple								=			torch.randn_like(self.latent   ,      requires_grad=UpperCamelCase_   ,      device=self.device	)
        lowerCAmelCase_       :    List[str]								=			torch.optim.Adam([vector]   ,      lr=self.lr	)

        for i in range(self.iterations	):
          optim.zero_grad()
          lowerCAmelCase_       :    int								=			self._add_vector(UpperCamelCase_	)
          lowerCAmelCase_       :    Any								=			loop_post_process(UpperCamelCase_	)
          lowerCAmelCase_       :    Dict								=			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 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				str   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]	):
        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:
          lowerCAmelCase_       :    int								=			Image.open(UpperCamelCase_	)
          lowerCAmelCase_       :    Dict								=			image.resize((2_5_6, 2_5_6)	)
          wandb.log('Original Image'   ,      wandb.Image(UpperCamelCase_	)	)



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





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

        lowerCAmelCase_       :    List[Any]								=			self.vqgan.decode(self.latent	)[0]
        if show_intermediate:
          print('Original Image'	)
          show_pil(custom_to_pil(UpperCamelCase_	)	)

        lowerCAmelCase_       :    Optional[int]								=			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"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase__		:    Optional[int]				   =     logging.get_logger(__name__)







class 				UpperCamelCase__       (     lowercase_, lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					"""maskformer-swin"""

      _SCREAMING_SNAKE_CASE      =					{
          """num_attention_heads""": """num_heads""",
          """num_hidden_layers""": """num_layers""",
      }



      def __init__(				self    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=2_2_4   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=4   ,      SCREAMING_SNAKE_CASE_    :				int=3   ,      SCREAMING_SNAKE_CASE_    :				Dict=9_6   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]=[2, 2, 6, 2]   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=[3, 6, 1_2, 2_4]   ,      SCREAMING_SNAKE_CASE_    :				List[str]=7   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=4.0   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=True   ,      SCREAMING_SNAKE_CASE_    :				Tuple=0.0   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=0.0   ,      SCREAMING_SNAKE_CASE_    :				int=0.1   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]="gelu"   ,      SCREAMING_SNAKE_CASE_    :				Dict=False   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=0.02   ,      SCREAMING_SNAKE_CASE_    :				int=1E-5   ,      SCREAMING_SNAKE_CASE_    :				Dict=None   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]=None   ,      **SCREAMING_SNAKE_CASE_    :				str   ,      ):
        super().__init__(**SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    Dict								=			image_size
        lowerCAmelCase_       :    Optional[Any]								=			patch_size
        lowerCAmelCase_       :    Optional[int]								=			num_channels
        lowerCAmelCase_       :    List[str]								=			embed_dim
        lowerCAmelCase_       :    Dict								=			depths
        lowerCAmelCase_       :    Optional[Any]								=			len(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Tuple								=			num_heads
        lowerCAmelCase_       :    List[str]								=			window_size
        lowerCAmelCase_       :    Any								=			mlp_ratio
        lowerCAmelCase_       :    Any								=			qkv_bias
        lowerCAmelCase_       :    List[Any]								=			hidden_dropout_prob
        lowerCAmelCase_       :    List[Any]								=			attention_probs_dropout_prob
        lowerCAmelCase_       :    Tuple								=			drop_path_rate
        lowerCAmelCase_       :    List[str]								=			hidden_act
        lowerCAmelCase_       :    Any								=			use_absolute_embeddings
        lowerCAmelCase_       :    Optional[Any]								=			layer_norm_eps
        lowerCAmelCase_       :    str								=			initializer_range
        # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
        # this indicates the channel dimension after the last stage of the model
        lowerCAmelCase_       :    List[str]								=			int(embed_dim * 2 ** (len(SCREAMING_SNAKE_CASE_	) - 1)	)
        lowerCAmelCase_       :    List[Any]								=			['stem'] + [F"stage{idx}" for idx in range(1   ,      len(SCREAMING_SNAKE_CASE_	) + 1	)]
        lowerCAmelCase_						,lowerCAmelCase_       :    Tuple								=			get_aligned_output_features_output_indices(
            out_features=SCREAMING_SNAKE_CASE_   ,      out_indices=SCREAMING_SNAKE_CASE_   ,      stage_names=self.stage_names	)





