






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 					SCREAMING_SNAKE_CASE_  :
       """simple docstring"""


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



          """simple docstring"""

          _lowercase              =device
          _lowercase              =CLIPTokenizerFast.from_pretrained(snake_case)
          _lowercase              =[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]
          _lowercase              =[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]
          _lowercase              =torchvision.transforms.Normalize(self.image_mean,      self.image_std)
          _lowercase              =torchvision.transforms.Resize(224)
          _lowercase              =torchvision.transforms.CenterCrop(224)





       def 				UpperCamelCase__						(				self	:Any,      snake_case	:Any):



          """simple docstring"""

          _lowercase              =self.resize(snake_case)
          _lowercase              =self.center_crop(snake_case)
          _lowercase              =self.normalize(snake_case)
          return images





       def __call__(				self	:List[Any],      snake_case	:Optional[Any]=None,      snake_case	:Union[str, Any]=None,      **snake_case	:List[Any]):



          """simple docstring"""

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






class 					SCREAMING_SNAKE_CASE_  (       nn.Module ):
       """simple docstring"""


       def __init__(				self	:str,      snake_case	:Union[str, Any]=10,      snake_case	:Optional[Any]=0.0_1,      snake_case	:List[Any]=None,      snake_case	:Dict=None,      snake_case	:Optional[Any]=None,      snake_case	:str=None,      snake_case	:Optional[Any]=None,      snake_case	:Union[str, Any]=None,      snake_case	:List[str]=False,      snake_case	:str=True,      snake_case	:Dict="image",      snake_case	:int=True,      snake_case	:Optional[Any]=False,      snake_case	:Any=False,      snake_case	:int=False,      ):



          """simple docstring"""

          super().__init__()
          _lowercase              =None
          _lowercase              =device if device else get_device()
          if vqgan:
             _lowercase              =vqgan
          else:
             _lowercase              =load_vqgan(self.device,      conf_path=snake_case,      ckpt_path=snake_case)
          self.vqgan.eval()
          if clip:
             _lowercase              =clip
          else:
             _lowercase              =CLIPModel.from_pretrained('openai/clip-vit-base-patch32')
          self.clip.to(self.device)
          _lowercase              =ProcessorGradientFlow(device=self.device)

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





       def 				UpperCamelCase__						(				self	:Dict,      snake_case	:Union[str, Any]=None,      snake_case	:Dict=None,      snake_case	:str=5,      snake_case	:List[str]=True):



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:List[str],      snake_case	:Dict=None,      snake_case	:Tuple=None):



          """simple docstring"""

          if not (path or img):
             raise ValueError('Input either path or tensor')
          if img is not None:
             raise NotImplementedError
          _lowercase              =preprocess(Image.open(snake_case),      target_image_size=256).to(self.device)
          _lowercase              =preprocess_vqgan(snake_case)
          _lowercase       ,       *_lowercase              =self.vqgan.encode(snake_case)
          return z





       def 				UpperCamelCase__						(				self	:List[str],      snake_case	:Union[str, Any]):



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:List[Any],      snake_case	:Optional[Any],      snake_case	:Tuple,      snake_case	:Optional[Any]=None):



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:Dict,      snake_case	:Union[str, Any],      snake_case	:List[Any],      snake_case	:Optional[Any]):



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:Dict,      snake_case	:Tuple,      snake_case	:Any,      snake_case	:List[str]):



          """simple docstring"""

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

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





       def 				UpperCamelCase__						(				self	:str,      snake_case	:Optional[Any],      snake_case	:Union[str, Any],      snake_case	:Optional[int]):



          """simple docstring"""

          wandb.init(reinit=snake_case,      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:
             _lowercase              =Image.open(snake_case)
             _lowercase              =image.resize((256, 256))
             wandb.log('Original Image',      wandb.Image(snake_case))





       def 				UpperCamelCase__						(				self	:List[Any],      snake_case	:Optional[int]):



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:Optional[int],      snake_case	:List[str],      snake_case	:Union[str, Any]=None,      snake_case	:str=None,      snake_case	:Any=True,      snake_case	:List[Any]=False,      snake_case	:Any=True,      snake_case	:Tuple=True,      snake_case	:Tuple=None,      ):



          """simple docstring"""

          if image_path:
             _lowercase              =self._get_latent(snake_case)
          else:
             _lowercase              =torch.randn(self.latent_dim,      device=self.device)
          if self.log:
             self._init_logging(snake_case,      snake_case,      snake_case)

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

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

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










import warnings

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


_SCREAMING_SNAKE_CASE   =  logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   =  {
    "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		List[str]					='''mvp'''
       __lowerCAmelCase    :		List[Any]					=['''past_key_values''']
       __lowerCAmelCase    :		Optional[Any]					={'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}





       def __init__(				self	:Any,      snake_case	:Any=5_0267,      snake_case	:List[str]=1024,      snake_case	:List[str]=12,      snake_case	:List[Any]=4096,      snake_case	:List[Any]=16,      snake_case	:Optional[int]=12,      snake_case	:Optional[int]=4096,      snake_case	:List[str]=16,      snake_case	:Optional[Any]=0.0,      snake_case	:Tuple=0.0,      snake_case	:str="gelu",      snake_case	:int=1024,      snake_case	:Any=0.1,      snake_case	:Dict=0.0,      snake_case	:Optional[Any]=0.0,      snake_case	:int=0.0_2,      snake_case	:Union[str, Any]=0.0,      snake_case	:Tuple=False,      snake_case	:Optional[int]=True,      snake_case	:Any=1,      snake_case	:Union[str, Any]=0,      snake_case	:Optional[Any]=2,      snake_case	:Union[str, Any]=True,      snake_case	:Tuple=2,      snake_case	:Any=2,      snake_case	:List[Any]=False,      snake_case	:Optional[int]=100,      snake_case	:Union[str, Any]=800,      **snake_case	:Tuple,      ):



          """simple docstring"""

          _lowercase              =vocab_size
          _lowercase              =max_position_embeddings
          _lowercase              =d_model
          _lowercase              =encoder_ffn_dim
          _lowercase              =encoder_layers
          _lowercase              =encoder_attention_heads
          _lowercase              =decoder_ffn_dim
          _lowercase              =decoder_layers
          _lowercase              =decoder_attention_heads
          _lowercase              =dropout
          _lowercase              =attention_dropout
          _lowercase              =activation_dropout
          _lowercase              =activation_function
          _lowercase              =init_std
          _lowercase              =encoder_layerdrop
          _lowercase              =decoder_layerdrop
          _lowercase              =classifier_dropout
          _lowercase              =use_cache
          _lowercase              =encoder_layers
          _lowercase              =scale_embedding  # scale factor will be sqrt(d_model) if True
          _lowercase              =use_prompt
          _lowercase              =prompt_length
          _lowercase              =prompt_mid_dim

          super().__init__(
              pad_token_id=snake_case,      bos_token_id=snake_case,      eos_token_id=snake_case,      is_encoder_decoder=snake_case,      decoder_start_token_id=snake_case,      forced_eos_token_id=snake_case,      **snake_case,      )

          if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated',      snake_case):
             _lowercase              =self.bos_token_id
             warnings.warn(
                 f'''Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '''
                 'The config can simply be saved and uploaded again to be fixed.')


