
"""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 				a__					:



     def __init__(							self :List[str]      ,  _lowerCamelCase :str = "cpu"      ,  _lowerCamelCase :str = "openai/clip-vit-large-patch14"   ):







      '''simple docstring'''



      UpperCamelCase_     :					Any   	=device
      UpperCamelCase_     :					Any   	=CLIPTokenizerFast.from_pretrained(_lowerCamelCase   )
      UpperCamelCase_     :					Optional[Any]   	=[0.48145466, 0.4578275, 0.40821073]
      UpperCamelCase_     :					Optional[Any]   	=[0.26862954, 0.26130258, 0.27577711]
      UpperCamelCase_     :					int   	=torchvision.transforms.Normalize(self.image_mean      ,  self.image_std   )
      UpperCamelCase_     :					Tuple   	=torchvision.transforms.Resize(224   )
      UpperCamelCase_     :					Dict   	=torchvision.transforms.CenterCrop(224   )



     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :Optional[int]   ):







      '''simple docstring'''



      UpperCamelCase_     :					Dict   	=self.resize(_lowerCamelCase   )
      UpperCamelCase_     :					Dict   	=self.center_crop(_lowerCamelCase   )
      UpperCamelCase_     :					Tuple   	=self.normalize(_lowerCamelCase   )
      return images




     def __call__(							self :Any      ,  _lowerCamelCase :int=None      ,  _lowerCamelCase :str=None      ,  **_lowerCamelCase :List[str]   ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[int]   	=self.tokenizer(text=_lowerCamelCase      ,  **_lowerCamelCase   )
      UpperCamelCase_     :					int   	=self.preprocess_img(_lowerCamelCase   )
      UpperCamelCase_     :					str   	={key: value.to(self.device   ) for (key, value) in encoding.items()}
      return encoding





class 				a__					(						nn.Module       ):



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







      '''simple docstring'''



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

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



     def  lowerCamelCase_						(							self :Tuple      ,  _lowerCamelCase :Dict=None      ,  _lowerCamelCase :str=None      ,  _lowerCamelCase :str=5      ,  _lowerCamelCase :Optional[Any]=True   ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[Any]   	=[]
      if output_path is None:
       UpperCamelCase_     :					Any   	='./animation.gif'
      if input_path is None:
       UpperCamelCase_     :					Dict   	=self.save_path
      UpperCamelCase_     :					Dict   	=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_     :					List[str]   	=total_duration / len(_lowerCamelCase   )
      UpperCamelCase_     :					str   	=[frame_duration] * len(_lowerCamelCase   )
      if extend_frames:
       UpperCamelCase_     :					int   	=1.5
       UpperCamelCase_     :					Dict   	=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  lowerCamelCase_						(							self :int      ,  _lowerCamelCase :str=None      ,  _lowerCamelCase :int=None   ):







      '''simple docstring'''



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



     def  lowerCamelCase_						(							self :Dict      ,  _lowerCamelCase :List[Any]   ):







      '''simple docstring'''



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



     def  lowerCamelCase_						(							self :int      ,  _lowerCamelCase :Any      ,  _lowerCamelCase :Optional[Any]      ,  _lowerCamelCase :Any=None   ):







      '''simple docstring'''



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



     def  lowerCamelCase_						(							self :Tuple      ,  _lowerCamelCase :Dict      ,  _lowerCamelCase :List[Any]      ,  _lowerCamelCase :Optional[Any]   ):







      '''simple docstring'''



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



     def  lowerCamelCase_						(							self :str      ,  _lowerCamelCase :Optional[Any]      ,  _lowerCamelCase :Dict      ,  _lowerCamelCase :Any   ):







      '''simple docstring'''



      UpperCamelCase_     :					str   	=torch.randn_like(self.latent      ,  requires_grad=_lowerCamelCase      ,  device=self.device   )
      UpperCamelCase_     :					str   	=torch.optim.Adam([vector]      ,  lr=self.lr   )

      for i in range(self.iterations   ):
       optim.zero_grad()
       UpperCamelCase_     :					str   	=self._add_vector(_lowerCamelCase   )
       UpperCamelCase_     :					int   	=loop_post_process(_lowerCamelCase   )
       UpperCamelCase_     :					Union[str, Any]   	=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  lowerCamelCase_						(							self :Dict      ,  _lowerCamelCase :Dict      ,  _lowerCamelCase :Tuple      ,  _lowerCamelCase :Tuple   ):







      '''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_     :					Tuple   	=Image.open(_lowerCamelCase   )
       UpperCamelCase_     :					List[str]   	=image.resize((256, 256)   )
       wandb.log('Original Image'      ,  wandb.Image(_lowerCamelCase   )   )



     def  lowerCamelCase_						(							self :int      ,  _lowerCamelCase :Union[str, Any]   ):







      '''simple docstring'''



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




     def  lowerCamelCase_						(							self :Tuple      ,  _lowerCamelCase :int      ,  _lowerCamelCase :int=None      ,  _lowerCamelCase :str=None      ,  _lowerCamelCase :Dict=True      ,  _lowerCamelCase :Union[str, Any]=False      ,  _lowerCamelCase :int=True      ,  _lowerCamelCase :List[str]=True      ,  _lowerCamelCase :Union[str, Any]=None      ,  ):







      '''simple docstring'''



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

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

      UpperCamelCase_     :					int   	=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"""






import math

import flax.linen as nn
import jax.numpy as jnp




def       A_  (				__lowercase						,	__lowercase						,	__lowercase = 1						,	__lowercase = 1						,	__lowercase = 1.0e4						,	__lowercase = False						,	__lowercase = 1.0						,	):
 assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
 assert embedding_dim % 2 == 0, F'''Embedding dimension {embedding_dim} should be even'''
 UpperCamelCase_     :					Optional[int]   	=float(embedding_dim // 2	)
 UpperCamelCase_     :					Optional[Any]   	=math.log(max_timescale / min_timescale	) / (num_timescales - freq_shift)
 UpperCamelCase_     :					List[Any]   	=min_timescale * jnp.exp(jnp.arange(__lowercase						,	dtype=jnp.floataa	) * -log_timescale_increment	)
 UpperCamelCase_     :					int   	=jnp.expand_dims(__lowercase						,	1	) * jnp.expand_dims(__lowercase						,	0	)

 # scale embeddings
 UpperCamelCase_     :					List[str]   	=scale * emb

 if flip_sin_to_cos:
  UpperCamelCase_     :					Tuple   	=jnp.concatenate([jnp.cos(__lowercase	), jnp.sin(__lowercase	)]						,	axis=1	)
 else:
  UpperCamelCase_     :					Tuple   	=jnp.concatenate([jnp.sin(__lowercase	), jnp.cos(__lowercase	)]						,	axis=1	)
 UpperCamelCase_     :					List[Any]   	=jnp.reshape(__lowercase						,	[jnp.shape(__lowercase	)[0], embedding_dim]	)
 return signal





class 				a__					(						nn.Module       ):
     UpperCAmelCase__         =      32
     UpperCAmelCase__         =      jnp.floataa



     @nn.compact
     def __call__(							self :Optional[Any]      ,  _lowerCamelCase :List[str]   ):







      '''simple docstring'''



      UpperCamelCase_     :					Dict   	=nn.Dense(self.time_embed_dim      ,  dtype=self.dtype      ,  name='linear_1'   )(_lowerCamelCase   )
      UpperCamelCase_     :					Any   	=nn.silu(_lowerCamelCase   )
      UpperCamelCase_     :					Tuple   	=nn.Dense(self.time_embed_dim      ,  dtype=self.dtype      ,  name='linear_2'   )(_lowerCamelCase   )
      return temb





class 				a__					(						nn.Module       ):
     UpperCAmelCase__         =      32
     UpperCAmelCase__         =      False
     UpperCAmelCase__         =      1



     @nn.compact
     def __call__(							self :Union[str, Any]      ,  _lowerCamelCase :Dict   ):







      '''simple docstring'''



      return get_sinusoidal_embeddings(
          _lowerCamelCase      ,  embedding_dim=self.dim      ,  flip_sin_to_cos=self.flip_sin_to_cos      ,  freq_shift=self.freq_shift   )
