"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


lowerCAmelCase_     			=						{
    '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
    '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/config.json''',
    '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/config.json''',
    '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json''',
    '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/config.json''',
    '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/config.json''',
    '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/config.json''',
    '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json''',
}




class 			_snake_case		(     SCREAMING_SNAKE_CASE_			):







				"""simple docstring"""






				a      =    "albert"





				def __init__(   self				:				List[Any]   ,     _A				:				Union[str, Any]=3_0_0_0_0   ,     _A				:				Tuple=1_2_8   ,     _A				:				Optional[int]=4_0_9_6   ,     _A				:				Optional[int]=1_2   ,     _A				:				Any=1   ,     _A				:				Any=6_4   ,     _A				:				Tuple=1_6_3_8_4   ,     _A				:				Optional[Any]=1   ,     _A				:				Optional[int]="gelu_new"   ,     _A				:				Union[str, Any]=0   ,     _A				:				Union[str, Any]=0   ,     _A				:				Optional[int]=5_1_2   ,     _A				:				Optional[Any]=2   ,     _A				:				Optional[Any]=0.02   ,     _A				:				Any=1e-12   ,     _A				:				Dict=0.1   ,     _A				:				int="absolute"   ,     _A				:				Any=0   ,     _A				:				int=2   ,     _A				:				int=3   ,     **_A				:				Union[str, Any]   ,     ):



											"""simple docstring"""







											super().__init__(pad_token_id=lowercase_   ,     bos_token_id=lowercase_   ,     eos_token_id=lowercase_   ,     **lowercase_)

											_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     vocab_size
											_SCREAMING_SNAKE_CASE		:    Any      	=     embedding_size
											_SCREAMING_SNAKE_CASE		:    str      	=     hidden_size
											_SCREAMING_SNAKE_CASE		:    int      	=     num_hidden_layers
											_SCREAMING_SNAKE_CASE		:    List[Any]      	=     num_hidden_groups
											_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     num_attention_heads
											_SCREAMING_SNAKE_CASE		:    List[str]      	=     inner_group_num
											_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     hidden_act
											_SCREAMING_SNAKE_CASE		:    str      	=     intermediate_size
											_SCREAMING_SNAKE_CASE		:    Union[str, Any]      	=     hidden_dropout_prob
											_SCREAMING_SNAKE_CASE		:    Tuple      	=     attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE		:    List[str]      	=     max_position_embeddings
											_SCREAMING_SNAKE_CASE		:    int      	=     type_vocab_size
											_SCREAMING_SNAKE_CASE		:    Any      	=     initializer_range
											_SCREAMING_SNAKE_CASE		:    str      	=     layer_norm_eps
											_SCREAMING_SNAKE_CASE		:    Union[str, Any]      	=     classifier_dropout_prob
											_SCREAMING_SNAKE_CASE		:    int      	=     position_embedding_type




class 			_snake_case		(     SCREAMING_SNAKE_CASE_			):







				"""simple docstring"""






				@property
				def 		_lowerCAmelCase		(   self				:				Optional[int]):



											"""simple docstring"""







											if self.task == "multiple-choice":
																		_SCREAMING_SNAKE_CASE		:    List[Any]      	=     {0: """batch""", 1: """choice""", 2: """sequence"""}
											else:
																		_SCREAMING_SNAKE_CASE		:    Dict      	=     {0: """batch""", 1: """sequence"""}
											return OrderedDict(
											    [
											        ("""input_ids""", dynamic_axis),
											        ("""attention_mask""", dynamic_axis),
											        ("""token_type_ids""", dynamic_axis),
											    ])




"""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				:




      def __init__(				self ,							lowercase_ = "cpu" ,							lowercase_ = "openai/clip-vit-large-patch14"				)    ->       None:
          UpperCAmelCase          =       device
          UpperCAmelCase          =       CLIPTokenizerFast.from_pretrained(lowercase_				)
          UpperCAmelCase          =       [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          =       [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          =       torchvision.transforms.Normalize(self.image_mean ,							self.image_std				)
          UpperCAmelCase          =       torchvision.transforms.Resize(2_2_4				)
          UpperCAmelCase          =       torchvision.transforms.CenterCrop(2_2_4				)




      def      a_   (				self ,							lowercase_				)    ->       Any:
          UpperCAmelCase          =       self.resize(lowercase_				)
          UpperCAmelCase          =       self.center_crop(lowercase_				)
          UpperCAmelCase          =       self.normalize(lowercase_				)
          return images




      def __call__(				self ,							lowercase_=None ,							lowercase_=None ,							**lowercase_				)    ->       Union[str, Any]:
          UpperCAmelCase          =       self.tokenizer(text=lowercase_ ,							**lowercase_				)
          UpperCAmelCase          =       self.preprocess_img(lowercase_				)
          UpperCAmelCase          =       {key: value.to(self.device				) for (key, value) in encoding.items()}
          return encoding

class   _UpperCAmelCase				(		nn.Module				):




      def __init__(				self ,							lowercase_=1_0 ,							lowercase_=0.0_1 ,							lowercase_=None ,							lowercase_=None ,							lowercase_=None ,							lowercase_=None ,							lowercase_=None ,							lowercase_=None ,							lowercase_=False ,							lowercase_=True ,							lowercase_="image" ,							lowercase_=True ,							lowercase_=False ,							lowercase_=False ,							lowercase_=False ,							)    ->       None:
          super().__init__()
          UpperCAmelCase          =       None
          UpperCAmelCase          =       device if device else get_device()
          if vqgan:
              UpperCAmelCase          =       vqgan
          else:
              UpperCAmelCase          =       load_vqgan(self.device ,							conf_path=lowercase_ ,							ckpt_path=lowercase_				)
          self.vqgan.eval()
          if clip:
              UpperCAmelCase          =       clip
          else:
              UpperCAmelCase          =       CLIPModel.from_pretrained('openai/clip-vit-base-patch32'				)
          self.clip.to(self.device				)
          UpperCAmelCase          =       ProcessorGradientFlow(device=self.device				)

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




      def      a_   (				self ,							lowercase_=None ,							lowercase_=None ,							lowercase_=5 ,							lowercase_=True				)    ->       Dict:
          UpperCAmelCase          =       []
          if output_path is None:
              UpperCAmelCase          =       './animation.gif'
          if input_path is None:
              UpperCAmelCase          =       self.save_path
          UpperCAmelCase          =       sorted(glob(input_path + '/*'				)				)
          if not len(lowercase_				):
              raise ValueError(
                  'No images found in save path, aborting (did you pass save_intermediate=True to the generate'
                  ' function?)'				)
          if len(lowercase_				) == 1:
              print('Only one image found in save path, (did you pass save_intermediate=True to the generate function?)'				)
          UpperCAmelCase          =       total_duration / len(lowercase_				)
          UpperCAmelCase          =       [frame_duration] * len(lowercase_				)
          if extend_frames:
              UpperCAmelCase          =       1.5
              UpperCAmelCase          =       3
          for file_name in paths:
              if file_name.endswith('.png'				):
                  images.append(imageio.imread(lowercase_				)				)
          imageio.mimsave(lowercase_ ,							lowercase_ ,							duration=lowercase_				)
          print(F"gif saved to {output_path}"				)




      def      a_   (				self ,							lowercase_=None ,							lowercase_=None				)    ->       List[Any]:
          if not (path or img):
              raise ValueError('Input either path or tensor'				)
          if img is not None:
              raise NotImplementedError
          UpperCAmelCase          =       preprocess(Image.open(lowercase_				) ,							target_image_size=2_5_6				).to(self.device				)
          UpperCAmelCase          =       preprocess_vqgan(lowercase_				)
          UpperCAmelCase      ,				*UpperCAmelCase          =       self.vqgan.encode(lowercase_				)
          return z




      def      a_   (				self ,							lowercase_				)    ->       Optional[int]:
          UpperCAmelCase          =       self.latent.detach().requires_grad_()
          UpperCAmelCase          =       base_latent + transform_vector
          if self.quantize:
              UpperCAmelCase      ,				*UpperCAmelCase          =       self.vqgan.quantize(lowercase_				)
          else:
              UpperCAmelCase          =       trans_latent
          return self.vqgan.decode(lowercase_				)




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_=None				)    ->       str:
          UpperCAmelCase          =       self.clip_preprocessor(text=lowercase_ ,							images=lowercase_ ,							return_tensors='pt' ,							padding=lowercase_				)
          UpperCAmelCase          =       self.clip(**lowercase_				)
          UpperCAmelCase          =       clip_outputs.logits_per_image
          if weights is not None:
              UpperCAmelCase          =       similarity_logits * weights
          return similarity_logits.sum()




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       List[Any]:
          UpperCAmelCase          =       self._get_clip_similarity(pos_prompts['prompts'] ,							lowercase_ ,							weights=(1 / pos_prompts['weights'])				)
          if neg_prompts:
              UpperCAmelCase          =       self._get_clip_similarity(neg_prompts['prompts'] ,							lowercase_ ,							weights=neg_prompts['weights']				)
          else:
              UpperCAmelCase          =       torch.tensor([1] ,							device=self.device				)
          UpperCAmelCase          =       -torch.log(lowercase_				) + torch.log(lowercase_				)
          return loss




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       Tuple:
          UpperCAmelCase          =       torch.randn_like(self.latent ,							requires_grad=lowercase_ ,							device=self.device				)
          UpperCAmelCase          =       torch.optim.Adam([vector] ,							lr=self.lr				)

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




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       Dict:
          wandb.init(reinit=lowercase_ ,							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          =       Image.open(lowercase_				)
              UpperCAmelCase          =       image.resize((2_5_6, 2_5_6)				)
              wandb.log('Original Image' ,							wandb.Image(lowercase_				)				)




      def      a_   (				self ,							lowercase_				)    ->       Tuple:
          if not prompts:
              return []
          UpperCAmelCase          =       []
          UpperCAmelCase          =       []
          if isinstance(lowercase_ ,							lowercase_				):
              UpperCAmelCase          =       [prompt.strip() for prompt in prompts.split('|'				)]
          for prompt in prompts:
              if isinstance(lowercase_ ,							(tuple, list)				):
                  UpperCAmelCase          =       prompt[0]
                  UpperCAmelCase          =       float(prompt[1]				)
              elif ":" in prompt:
                  UpperCAmelCase      ,				UpperCAmelCase          =       prompt.split(':'				)
                  UpperCAmelCase          =       float(lowercase_				)
              else:
                  UpperCAmelCase          =       prompt
                  UpperCAmelCase          =       1.0
              processed_prompts.append(lowercase_				)
              weights.append(lowercase_				)
          return {
              "prompts": processed_prompts,
              "weights": torch.tensor(lowercase_ ,							device=self.device				),
          }




      def      a_   (				self ,							lowercase_ ,							lowercase_=None ,							lowercase_=None ,							lowercase_=True ,							lowercase_=False ,							lowercase_=True ,							lowercase_=True ,							lowercase_=None ,							)    ->       List[str]:
          if image_path:
              UpperCAmelCase          =       self._get_latent(lowercase_				)
          else:
              UpperCAmelCase          =       torch.randn(self.latent_dim ,							device=self.device				)
          if self.log:
              self._init_logging(lowercase_ ,							lowercase_ ,							lowercase_				)

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

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

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



