


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 	__lowercase					:





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






         """simple docstring"""







         UpperCAmelCase      					=  device
         UpperCAmelCase      					=  CLIPTokenizerFast.from_pretrained(__lowerCamelCase							)
         UpperCAmelCase      					=  [0.48_145_466, 0.4_578_275, 0.40_821_073]
         UpperCAmelCase      					=  [0.26_862_954, 0.26_130_258, 0.27_577_711]
         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 							_lowercase					(		self    :       Optional[Any]       ,							__lowerCamelCase    :       Dict							)     -> Dict:






         """simple docstring"""







         UpperCAmelCase      					=  self.resize(__lowerCamelCase							)
         UpperCAmelCase      					=  self.center_crop(__lowerCamelCase							)
         UpperCAmelCase      					=  self.normalize(__lowerCamelCase							)
         return images





      def __call__(		self    :       Dict       ,							__lowerCamelCase    :       Tuple=None       ,							__lowerCamelCase    :       Tuple=None       ,							**__lowerCamelCase    :       int							)     -> int:






         """simple docstring"""







         UpperCAmelCase      					=  self.tokenizer(text=__lowerCamelCase       ,							**__lowerCamelCase							)
         UpperCAmelCase      					=  self.preprocess_img(__lowerCamelCase							)
         UpperCAmelCase      					=  {key: value.to(self.device							) for (key, value) in encoding.items()}
         return encoding






class 	__lowercase					(	nn.Module	):





      def __init__(		self    :       str       ,							__lowerCamelCase    :       str=1_0       ,							__lowerCamelCase    :       Any=0.01       ,							__lowerCamelCase    :       List[Any]=None       ,							__lowerCamelCase    :       List[Any]=None       ,							__lowerCamelCase    :       Tuple=None       ,							__lowerCamelCase    :       Optional[int]=None       ,							__lowerCamelCase    :       Tuple=None       ,							__lowerCamelCase    :       Tuple=None       ,							__lowerCamelCase    :       List[str]=False       ,							__lowerCamelCase    :       Union[str, Any]=True       ,							__lowerCamelCase    :       Tuple="image"       ,							__lowerCamelCase    :       int=True       ,							__lowerCamelCase    :       Optional[Any]=False       ,							__lowerCamelCase    :       int=False       ,							__lowerCamelCase    :       List[str]=False       ,							)     -> None:






         """simple docstring"""







         super().__init__()
         UpperCAmelCase      					=  None
         UpperCAmelCase      					=  device if device else get_device()
         if vqgan:
            UpperCAmelCase      					=  vqgan
         else:
            UpperCAmelCase      					=  load_vqgan(self.device       ,							conf_path=__lowerCamelCase       ,							ckpt_path=__lowerCamelCase							)
         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 							_lowercase					(		self    :       List[Any]       ,							__lowerCamelCase    :       Any=None       ,							__lowerCamelCase    :       List[str]=None       ,							__lowerCamelCase    :       List[str]=5       ,							__lowerCamelCase    :       int=True							)     -> Tuple:






         """simple docstring"""







         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(__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?)"""							)
         UpperCAmelCase      					=  total_duration / len(__lowerCamelCase							)
         UpperCAmelCase      					=  [frame_duration] * len(__lowerCamelCase							)
         if extend_frames:
            UpperCAmelCase      					=  1.5
            UpperCAmelCase      					=  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    :       Dict       ,							__lowerCamelCase    :       List[Any]=None       ,							__lowerCamelCase    :       List[Any]=None							)     -> Dict:






         """simple docstring"""







         if not (path or img):
            raise ValueError("""Input either path or tensor"""							)
         if img is not None:
            raise NotImplementedError
         UpperCAmelCase      					=  preprocess(Image.open(__lowerCamelCase							)       ,							target_image_size=2_5_6							).to(self.device							)
         UpperCAmelCase      					=  preprocess_vqgan(__lowerCamelCase							)
         UpperCAmelCase					,	*UpperCAmelCase      					=  self.vqgan.encode(__lowerCamelCase							)
         return z





      def 							_lowercase					(		self    :       Any       ,							__lowerCamelCase    :       Dict							)     -> Optional[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  self.latent.detach().requires_grad_()
         UpperCAmelCase      					=  base_latent + transform_vector
         if self.quantize:
            UpperCAmelCase					,	*UpperCAmelCase      					=  self.vqgan.quantize(__lowerCamelCase							)
         else:
            UpperCAmelCase      					=  trans_latent
         return self.vqgan.decode(__lowerCamelCase							)





      def 							_lowercase					(		self    :       List[Any]       ,							__lowerCamelCase    :       Tuple       ,							__lowerCamelCase    :       Optional[Any]       ,							__lowerCamelCase    :       str=None							)     -> Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase      					=  self.clip_preprocessor(text=__lowerCamelCase       ,							images=__lowerCamelCase       ,							return_tensors="""pt"""       ,							padding=__lowerCamelCase							)
         UpperCAmelCase      					=  self.clip(**__lowerCamelCase							)
         UpperCAmelCase      					=  clip_outputs.logits_per_image
         if weights is not None:
            UpperCAmelCase      					=  similarity_logits * weights
         return similarity_logits.sum()





      def 							_lowercase					(		self    :       int       ,							__lowerCamelCase    :       Dict       ,							__lowerCamelCase    :       int       ,							__lowerCamelCase    :       List[Any]							)     -> Optional[int]:






         """simple docstring"""







         UpperCAmelCase      					=  self._get_clip_similarity(pos_prompts["""prompts"""]       ,							__lowerCamelCase       ,							weights=(1 / pos_prompts["""weights"""])							)
         if neg_prompts:
            UpperCAmelCase      					=  self._get_clip_similarity(neg_prompts["""prompts"""]       ,							__lowerCamelCase       ,							weights=neg_prompts["""weights"""]							)
         else:
            UpperCAmelCase      					=  torch.tensor([1]       ,							device=self.device							)
         UpperCAmelCase      					=  -torch.log(__lowerCamelCase							) + torch.log(__lowerCamelCase							)
         return loss





      def 							_lowercase					(		self    :       List[str]       ,							__lowerCamelCase    :       Optional[Any]       ,							__lowerCamelCase    :       List[Any]       ,							__lowerCamelCase    :       Union[str, Any]							)     -> Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase      					=  torch.randn_like(self.latent       ,							requires_grad=__lowerCamelCase       ,							device=self.device							)
         UpperCAmelCase      					=  torch.optim.Adam([vector]       ,							lr=self.lr							)

         for i in range(self.iterations							):
            optim.zero_grad()
            UpperCAmelCase      					=  self._add_vector(__lowerCamelCase							)
            UpperCAmelCase      					=  loop_post_process(__lowerCamelCase							)
            UpperCAmelCase      					=  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    :       Optional[Any]       ,							__lowerCamelCase    :       Any       ,							__lowerCamelCase    :       Union[str, Any]       ,							__lowerCamelCase    :       Union[str, Any]							)     -> List[Any]:






         """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:
            UpperCAmelCase      					=  Image.open(__lowerCamelCase							)
            UpperCAmelCase      					=  image.resize((2_5_6, 2_5_6)							)
            wandb.log("""Original Image"""       ,							wandb.Image(__lowerCamelCase							)							)





      def 							_lowercase					(		self    :       List[str]       ,							__lowerCamelCase    :       Any							)     -> int:






         """simple docstring"""







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





      def 							_lowercase					(		self    :       Any       ,							__lowerCamelCase    :       Tuple       ,							__lowerCamelCase    :       Optional[int]=None       ,							__lowerCamelCase    :       Optional[int]=None       ,							__lowerCamelCase    :       Any=True       ,							__lowerCamelCase    :       Optional[Any]=False       ,							__lowerCamelCase    :       List[str]=True       ,							__lowerCamelCase    :       Union[str, Any]=True       ,							__lowerCamelCase    :       Optional[Any]=None       ,							)     -> Optional[Any]:






         """simple docstring"""







         if image_path:
            UpperCAmelCase      					=  self._get_latent(__lowerCamelCase							)
         else:
            UpperCAmelCase      					=  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."
         UpperCAmelCase      					=  self.process_prompts(__lowerCamelCase							)
         UpperCAmelCase      					=  self.process_prompts(__lowerCamelCase							)
         if save_final and save_path is None:
            UpperCAmelCase      					=  os.path.join("""./outputs/"""       ,							"""_""".join(pos_prompts["""prompts"""]							)							)
         if not os.path.exists(__lowerCamelCase							):
            os.makedirs(__lowerCamelCase							)
         else:
            UpperCAmelCase      					=  save_path + """_""" + get_timestamp()
            os.makedirs(__lowerCamelCase							)
         UpperCAmelCase      					=  save_path

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

         UpperCAmelCase      					=  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"""							)							)







import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Optional[Any]:
   UpperCAmelCase      					=  ("""dense.weight""", """attention.self.query""", """attention.self.key""", """attention.self.value""")

   UpperCAmelCase      					=  (
       ("""layer.""", """layer_"""),
       ("""word_embeddings.weight""", """word_embeddings"""),
       ("""position_embeddings.weight""", """position_embeddings"""),
       ("""token_type_embeddings.weight""", """token_type_embeddings"""),
       (""".""", """/"""),
       ("""LayerNorm/weight""", """LayerNorm/gamma"""),
       ("""LayerNorm/bias""", """LayerNorm/beta"""),
       ("""weight""", """kernel"""),
   )

   if not os.path.isdir(lowerCAmelCase_ ):
      os.makedirs(lowerCAmelCase_ )

   UpperCAmelCase      					=  model.state_dict()

   def to_tf_var_name(lowerCAmelCase_ ):
      for patt, repl in iter(lowerCAmelCase_ ):
         UpperCAmelCase      					=  name.replace(lowerCAmelCase_	,    lowerCAmelCase_ )
      return F"""bert/{name}"""

   def create_tf_var(lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ ):
      UpperCAmelCase      					=  tf.dtypes.as_dtype(tensor.dtype )
      UpperCAmelCase      					=  tf.get_variable(dtype=lowerCAmelCase_	,    shape=tensor.shape	,    name=lowerCAmelCase_	,    initializer=tf.zeros_initializer() )
      session.run(tf.variables_initializer([tf_var] ) )
      session.run(lowerCAmelCase_ )
      return tf_var

   tf.reset_default_graph()
   with tf.Session() as session:
      for var_name in state_dict:
         UpperCAmelCase      					=  to_tf_var_name(lowerCAmelCase_ )
         UpperCAmelCase      					=  state_dict[var_name].numpy()
         if any(x in var_name for x in tensors_to_transpose ):
            UpperCAmelCase      					=  torch_tensor.T
         UpperCAmelCase      					=  create_tf_var(tensor=lowerCAmelCase_	,    name=lowerCAmelCase_	,    session=lowerCAmelCase_ )
         tf.keras.backend.set_value(lowerCAmelCase_	,    lowerCAmelCase_ )
         UpperCAmelCase      					=  session.run(lowerCAmelCase_ )
         print(F"""Successfully created {tf_name}: {np.allclose(lowerCAmelCase_	,    lowerCAmelCase_ )}""" )

      UpperCAmelCase      					=  tf.train.Saver(tf.trainable_variables() )
      saver.save(lowerCAmelCase_	,    os.path.join(lowerCAmelCase_	,    model_name.replace("""-"""	,    """_""" ) + """.ckpt""" ) )
def 	_UpperCamelCase					(    lowerCAmelCase_=None )    ->Union[str, Any]:
   UpperCAmelCase      					=  argparse.ArgumentParser()
   parser.add_argument("""--model_name"""	,    type=lowerCAmelCase_	,    required=lowerCAmelCase_	,    help="""model name e.g. bert-base-uncased""" )
   parser.add_argument(
       """--cache_dir"""	,    type=lowerCAmelCase_	,    default=lowerCAmelCase_	,    required=lowerCAmelCase_	,    help="""Directory containing pytorch model""" )
   parser.add_argument("""--pytorch_model_path"""	,    type=lowerCAmelCase_	,    required=lowerCAmelCase_	,    help="""/path/to/<pytorch-model-name>.bin""" )
   parser.add_argument("""--tf_cache_dir"""	,    type=lowerCAmelCase_	,    required=lowerCAmelCase_	,    help="""Directory in which to save tensorflow model""" )
   UpperCAmelCase      					=  parser.parse_args(lowerCAmelCase_ )

   UpperCAmelCase      					=  BertModel.from_pretrained(
       pretrained_model_name_or_path=args.model_name	,    state_dict=torch.load(args.pytorch_model_path )	,    cache_dir=args.cache_dir	,    )

   convert_pytorch_checkpoint_to_tf(model=lowerCAmelCase_	,    ckpt_dir=args.tf_cache_dir	,    model_name=args.model_name )


if __name__ == "__main__":
 main()



