



'''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							,  lowerCAmelCase_ = "cpu"							,  lowerCAmelCase_ = "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_2_4)
      a_       		=torchvision.transforms.CenterCrop(2_2_4)




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      Optional[Any]:



      """simple docstring"""







      a_       		=self.resize(lowerCAmelCase_)
      a_       		=self.center_crop(lowerCAmelCase_)
      a_       		=self.normalize(lowerCAmelCase_)
      return images




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



      """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 	UpperCAmelCase	(		nn.Module):


 '''simple docstring'''




 def __init__(   self							,  lowerCAmelCase_=1_0							,  lowerCAmelCase_=0.0_1							,  lowerCAmelCase_=None							,  lowerCAmelCase_=None							,  lowerCAmelCase_=None							,  lowerCAmelCase_=None							,  lowerCAmelCase_=None							,  lowerCAmelCase_=None							,  lowerCAmelCase_=False							,  lowerCAmelCase_=True							,  lowerCAmelCase_="image"							,  lowerCAmelCase_=True							,  lowerCAmelCase_=False							,  lowerCAmelCase_=False							,  lowerCAmelCase_=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       lowercase_  (   self							,  lowerCAmelCase_=None							,  lowerCAmelCase_=None							,  lowerCAmelCase_=5							,  lowerCAmelCase_=True) ->      Any:



      """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       lowercase_  (   self							,  lowerCAmelCase_=None							,  lowerCAmelCase_=None) ->      str:



      """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_5_6).to(self.device)
      a_       		=preprocess_vqgan(lowerCAmelCase_)
      a_		,						*a_       		=self.vqgan.encode(lowerCAmelCase_)
      return z




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      Union[str, Any]:



      """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       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_=None) ->      Tuple:



      """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       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      int:



      """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       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      Any:



      """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       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      List[str]:



      """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_5_6, 2_5_6))
           wandb.log("Original Image"							,  wandb.Image(lowerCAmelCase_))




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      Union[str, Any]:



      """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       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_=None							,  lowerCAmelCase_=None							,  lowerCAmelCase_=True							,  lowerCAmelCase_=False							,  lowerCAmelCase_=True							,  lowerCAmelCase_=True							,  lowerCAmelCase_=None							,  ) ->      str:



      """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"""))







'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

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


lowercase							       =			{
    '''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 	UpperCAmelCase	(		__a):


 '''simple docstring'''




 __magic_name__					: int  =      "albert"




 def __init__(   self							,  lowerCAmelCase_=3_0_0_0_0							,  lowerCAmelCase_=1_2_8							,  lowerCAmelCase_=4_0_9_6							,  lowerCAmelCase_=1_2							,  lowerCAmelCase_=1							,  lowerCAmelCase_=6_4							,  lowerCAmelCase_=1_6_3_8_4							,  lowerCAmelCase_=1							,  lowerCAmelCase_="gelu_new"							,  lowerCAmelCase_=0							,  lowerCAmelCase_=0							,  lowerCAmelCase_=5_1_2							,  lowerCAmelCase_=2							,  lowerCAmelCase_=0.0_2							,  lowerCAmelCase_=1e-12							,  lowerCAmelCase_=0.1							,  lowerCAmelCase_="absolute"							,  lowerCAmelCase_=0							,  lowerCAmelCase_=2							,  lowerCAmelCase_=3							,  **lowerCAmelCase_							,  ) ->      Any:



      """simple docstring"""







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

      a_       		=vocab_size
      a_       		=embedding_size
      a_       		=hidden_size
      a_       		=num_hidden_layers
      a_       		=num_hidden_groups
      a_       		=num_attention_heads
      a_       		=inner_group_num
      a_       		=hidden_act
      a_       		=intermediate_size
      a_       		=hidden_dropout_prob
      a_       		=attention_probs_dropout_prob
      a_       		=max_position_embeddings
      a_       		=type_vocab_size
      a_       		=initializer_range
      a_       		=layer_norm_eps
      a_       		=classifier_dropout_prob
      a_       		=position_embedding_type







class 	UpperCAmelCase	(		__a):


 '''simple docstring'''




 @property
 def       lowercase_  (   self) ->      Mapping[str, Mapping[int, str]]:



      """simple docstring"""







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


