





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  __lowerCAmelCase	:






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


       '''simple docstring'''



       snake_case_						:   Any           =  device
       snake_case_						:   Dict           =  CLIPTokenizerFast.from_pretrained(__magic_name__   )
       snake_case_						:   Any           =  [0.48_145_466, 0.4_578_275, 0.40_821_073]
       snake_case_						:   Any           =  [0.26_862_954, 0.26_130_258, 0.27_577_711]
       snake_case_						:   str           =  torchvision.transforms.Normalize(self.image_mean ,							self.image_std   )
       snake_case_						:   Dict           =  torchvision.transforms.Resize(224   )
       snake_case_						:   List[Any]           =  torchvision.transforms.CenterCrop(224   )






   def       lowerCamelCase  (self ,							__magic_name__   )					->       Tuple:


       '''simple docstring'''



       snake_case_						:   Optional[int]           =  self.resize(__magic_name__   )
       snake_case_						:   Tuple           =  self.center_crop(__magic_name__   )
       snake_case_						:   Any           =  self.normalize(__magic_name__   )
       return images






   def __call__(self ,							__magic_name__=None ,							__magic_name__=None ,							**__magic_name__   )					->       Any:


       '''simple docstring'''



       snake_case_						:   Optional[Any]           =  self.tokenizer(text=__magic_name__ ,							**__magic_name__   )
       snake_case_						:   Dict           =  self.preprocess_img(__magic_name__   )
       snake_case_						:   str           =  {key: value.to(self.device   ) for (key, value) in encoding.items()}
       return encoding




class  __lowerCAmelCase	(      nn.Module					):






   def __init__(self ,							__magic_name__=10 ,							__magic_name__=0.01 ,							__magic_name__=None ,							__magic_name__=None ,							__magic_name__=None ,							__magic_name__=None ,							__magic_name__=None ,							__magic_name__=None ,							__magic_name__=False ,							__magic_name__=True ,							__magic_name__="image" ,							__magic_name__=True ,							__magic_name__=False ,							__magic_name__=False ,							__magic_name__=False ,							)					->       None:


       '''simple docstring'''



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

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






   def       lowerCamelCase  (self ,							__magic_name__=None ,							__magic_name__=None ,							__magic_name__=5 ,							__magic_name__=True   )					->       Tuple:


       '''simple docstring'''



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






   def       lowerCamelCase  (self ,							__magic_name__=None ,							__magic_name__=None   )					->       str:


       '''simple docstring'''



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






   def       lowerCamelCase  (self ,							__magic_name__   )					->       int:


       '''simple docstring'''



       snake_case_						:   Any           =  self.latent.detach().requires_grad_()
       snake_case_						:   Optional[int]           =  base_latent + transform_vector
       if self.quantize:
           snake_case_						:   Dict           =  self.vqgan.quantize(__magic_name__   )
       else:
           snake_case_						:   int           =  trans_latent
       return self.vqgan.decode(__magic_name__   )






   def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__=None   )					->       Tuple:


       '''simple docstring'''



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






   def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       Tuple:


       '''simple docstring'''



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






   def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       int:


       '''simple docstring'''



       snake_case_						:   str           =  torch.randn_like(self.latent ,							requires_grad=__magic_name__ ,							device=self.device   )
       snake_case_						:   Tuple           =  torch.optim.Adam([vector] ,							lr=self.lr   )

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






   def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       int:


       '''simple docstring'''



       wandb.init(reinit=__magic_name__ ,							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:
           snake_case_						:   Dict           =  Image.open(__magic_name__   )
           snake_case_						:   int           =  image.resize((256, 256)   )
           wandb.log('''Original Image''' ,							wandb.Image(__magic_name__   )   )






   def       lowerCamelCase  (self ,							__magic_name__   )					->       Tuple:


       '''simple docstring'''



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






   def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__=None ,							__magic_name__=None ,							__magic_name__=True ,							__magic_name__=False ,							__magic_name__=True ,							__magic_name__=True ,							__magic_name__=None ,							)					->       Any:


       '''simple docstring'''



       if image_path:
           snake_case_						:   Optional[int]           =  self._get_latent(__magic_name__   )
       else:
           snake_case_						:   List[Any]           =  torch.randn(self.latent_dim ,							device=self.device   )
       if self.log:
           self._init_logging(__magic_name__ ,							__magic_name__ ,							__magic_name__   )

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

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

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







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_    =    logging.get_logger(__name__)

lowerCAmelCase_    =    {
    '''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}



class  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					Any	      =				'''biogpt'''






			def __init__(self ,							__magic_name__=4_2384 ,							__magic_name__=1024 ,							__magic_name__=24 ,							__magic_name__=16 ,							__magic_name__=4096 ,							__magic_name__="gelu" ,							__magic_name__=0.1 ,							__magic_name__=0.1 ,							__magic_name__=1024 ,							__magic_name__=0.02 ,							__magic_name__=1e-12 ,							__magic_name__=True ,							__magic_name__=True ,							__magic_name__=0.0 ,							__magic_name__=0.0 ,							__magic_name__=1 ,							__magic_name__=0 ,							__magic_name__=2 ,							**__magic_name__ ,							)					->       List[str]:


							'''simple docstring'''



							snake_case_						:   List[str]           =  vocab_size
							snake_case_						:   Dict           =  max_position_embeddings
							snake_case_						:   Optional[int]           =  hidden_size
							snake_case_						:   List[Any]           =  num_hidden_layers
							snake_case_						:   List[str]           =  num_attention_heads
							snake_case_						:   int           =  intermediate_size
							snake_case_						:   List[Any]           =  hidden_act
							snake_case_						:   List[Any]           =  hidden_dropout_prob
							snake_case_						:   Optional[int]           =  attention_probs_dropout_prob
							snake_case_						:   Optional[int]           =  initializer_range
							snake_case_						:   Optional[int]           =  layer_norm_eps
							snake_case_						:   str           =  scale_embedding
							snake_case_						:   Optional[Any]           =  use_cache
							snake_case_						:   Optional[Any]           =  layerdrop
							snake_case_						:   Optional[Any]           =  activation_dropout
							super().__init__(pad_token_id=__magic_name__ ,							bos_token_id=__magic_name__ ,							eos_token_id=__magic_name__ ,							**__magic_name__   )
