






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       :	str					, UpperCAmelCase_       :	Tuple = "cpu"					, UpperCAmelCase_       :	Optional[Any] = "openai/clip-vit-large-patch14"      ):
       SCREAMING_SNAKE_CASE       :      List[Any]							=    device
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    CLIPTokenizerFast.from_pretrained(UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Any							=    [0.48_145_466, 0.4_578_275, 0.40_821_073]
       SCREAMING_SNAKE_CASE       :      List[Any]							=    [0.26_862_954, 0.26_130_258, 0.27_577_711]
       SCREAMING_SNAKE_CASE       :      str							=    torchvision.transforms.Normalize(self.image_mean					, self.image_std      )
       SCREAMING_SNAKE_CASE       :      Dict							=    torchvision.transforms.Resize(224      )
       SCREAMING_SNAKE_CASE       :      Any							=    torchvision.transforms.CenterCrop(224      )


     def 				_A       (				self       :	Tuple					, UpperCAmelCase_       :	str      ):
       SCREAMING_SNAKE_CASE       :      List[str]							=    self.resize(UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.center_crop(UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.normalize(UpperCAmelCase_      )
       return images






     def __call__(				self       :	str					, UpperCAmelCase_       :	Optional[Any]=None					, UpperCAmelCase_       :	List[Any]=None					, **UpperCAmelCase_       :	str      ):
       SCREAMING_SNAKE_CASE       :      Tuple							=    self.tokenizer(text=UpperCAmelCase_					, **UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Tuple							=    self.preprocess_img(UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      List[str]							=    {key: value.to(self.device      ) for (key, value) in encoding.items()}
       return encoding





class  SCREAMING_SNAKE_CASE			(			nn.Module					):
     '''simple docstring'''


     def __init__(				self       :	Any					, UpperCAmelCase_       :	str=10					, UpperCAmelCase_       :	int=0.01					, UpperCAmelCase_       :	List[str]=None					, UpperCAmelCase_       :	int=None					, UpperCAmelCase_       :	Optional[Any]=None					, UpperCAmelCase_       :	str=None					, UpperCAmelCase_       :	Optional[int]=None					, UpperCAmelCase_       :	Tuple=None					, UpperCAmelCase_       :	Any=False					, UpperCAmelCase_       :	List[str]=True					, UpperCAmelCase_       :	Any="image"					, UpperCAmelCase_       :	Dict=True					, UpperCAmelCase_       :	Optional[int]=False					, UpperCAmelCase_       :	Any=False					, UpperCAmelCase_       :	List[str]=False					, ):
       super().__init__()
       SCREAMING_SNAKE_CASE       :      Dict							=    None
       SCREAMING_SNAKE_CASE       :      str							=    device if device else get_device()
       if vqgan:
         SCREAMING_SNAKE_CASE       :      Any							=    vqgan
       else:
         SCREAMING_SNAKE_CASE       :      List[Any]							=    load_vqgan(self.device					, conf_path=UpperCAmelCase_					, ckpt_path=UpperCAmelCase_      )
       self.vqgan.eval()
       if clip:
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    clip
       else:
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    CLIPModel.from_pretrained("openai/clip-vit-base-patch32"      )
       self.clip.to(self.device      )
       SCREAMING_SNAKE_CASE       :      List[str]							=    ProcessorGradientFlow(device=self.device      )

       SCREAMING_SNAKE_CASE       :      Tuple							=    iterations
       SCREAMING_SNAKE_CASE       :      int							=    lr
       SCREAMING_SNAKE_CASE       :      Dict							=    log
       SCREAMING_SNAKE_CASE       :      Tuple							=    make_grid
       SCREAMING_SNAKE_CASE       :      List[Any]							=    return_val
       SCREAMING_SNAKE_CASE       :      List[str]							=    quantize
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.vqgan.decoder.z_shape


     def 				_A       (				self       :	int					, UpperCAmelCase_       :	Tuple=None					, UpperCAmelCase_       :	str=None					, UpperCAmelCase_       :	List[Any]=5					, UpperCAmelCase_       :	Union[str, Any]=True      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    []
       if output_path is None:
         SCREAMING_SNAKE_CASE       :      Tuple							=    "./animation.gif"
       if input_path is None:
         SCREAMING_SNAKE_CASE       :      str							=    self.save_path
       SCREAMING_SNAKE_CASE       :      str							=    sorted(glob(input_path + "/*"      )      )
       if not len(UpperCAmelCase_      ):
         raise ValueError(
             "No images found in save path, aborting (did you pass save_intermediate=True to the generate"
             " function?)"      )
       if len(UpperCAmelCase_      ) == 1:
         print("Only one image found in save path, (did you pass save_intermediate=True to the generate function?)"      )
       SCREAMING_SNAKE_CASE       :      List[str]							=    total_duration / len(UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      int							=    [frame_duration] * len(UpperCAmelCase_      )
       if extend_frames:
         SCREAMING_SNAKE_CASE       :      str							=    1.5
         SCREAMING_SNAKE_CASE       :      int							=    3
       for file_name in paths:
         if file_name.endswith(".png"      ):
           images.append(imageio.imread(UpperCAmelCase_      )      )
       imageio.mimsave(UpperCAmelCase_					, UpperCAmelCase_					, duration=UpperCAmelCase_      )
       print(f'''gif saved to {output_path}'''      )


     def 				_A       (				self       :	Tuple					, UpperCAmelCase_       :	Tuple=None					, UpperCAmelCase_       :	Dict=None      ):
       if not (path or img):
         raise ValueError("Input either path or tensor"      )
       if img is not None:
         raise NotImplementedError
       SCREAMING_SNAKE_CASE       :      Any							=    preprocess(Image.open(UpperCAmelCase_      )					, target_image_size=256      ).to(self.device      )
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    preprocess_vqgan(UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE						,  *SCREAMING_SNAKE_CASE       :      Tuple							=    self.vqgan.encode(UpperCAmelCase_      )
       return z


     def 				_A       (				self       :	List[str]					, UpperCAmelCase_       :	Union[str, Any]      ):
       SCREAMING_SNAKE_CASE       :      int							=    self.latent.detach().requires_grad_()
       SCREAMING_SNAKE_CASE       :      int							=    base_latent + transform_vector
       if self.quantize:
         SCREAMING_SNAKE_CASE						,  *SCREAMING_SNAKE_CASE       :      List[str]							=    self.vqgan.quantize(UpperCAmelCase_      )
       else:
         SCREAMING_SNAKE_CASE       :      Tuple							=    trans_latent
       return self.vqgan.decode(UpperCAmelCase_      )


     def 				_A       (				self       :	Any					, UpperCAmelCase_       :	Dict					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	List[Any]=None      ):
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.clip_preprocessor(text=UpperCAmelCase_					, images=UpperCAmelCase_					, return_tensors="pt"					, padding=UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.clip(**UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Tuple							=    clip_outputs.logits_per_image
       if weights is not None:
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    similarity_logits * weights
       return similarity_logits.sum()


     def 				_A       (				self       :	Optional[Any]					, UpperCAmelCase_       :	Tuple					, UpperCAmelCase_       :	Tuple					, UpperCAmelCase_       :	List[str]      ):
       SCREAMING_SNAKE_CASE       :      List[str]							=    self._get_clip_similarity(pos_prompts["prompts"]					, UpperCAmelCase_					, weights=(1 / pos_prompts["weights"])      )
       if neg_prompts:
         SCREAMING_SNAKE_CASE       :      List[Any]							=    self._get_clip_similarity(neg_prompts["prompts"]					, UpperCAmelCase_					, weights=neg_prompts["weights"]      )
       else:
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    torch.tensor([1]					, device=self.device      )
       SCREAMING_SNAKE_CASE       :      int							=    -torch.log(UpperCAmelCase_      ) + torch.log(UpperCAmelCase_      )
       return loss


     def 				_A       (				self       :	Optional[int]					, UpperCAmelCase_       :	Optional[int]					, UpperCAmelCase_       :	str					, UpperCAmelCase_       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      Dict							=    torch.randn_like(self.latent					, requires_grad=UpperCAmelCase_					, device=self.device      )
       SCREAMING_SNAKE_CASE       :      List[Any]							=    torch.optim.Adam([vector]					, lr=self.lr      )

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


     def 				_A       (				self       :	Dict					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	Optional[int]					, UpperCAmelCase_       :	Optional[Any]      ):
       wandb.init(reinit=UpperCAmelCase_					, 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:
         SCREAMING_SNAKE_CASE       :      str							=    Image.open(UpperCAmelCase_      )
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    image.resize((256, 256)      )
         wandb.log("Original Image"					, wandb.Image(UpperCAmelCase_      )      )


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






     def 				_A       (				self       :	int					, UpperCAmelCase_       :	Optional[Any]					, UpperCAmelCase_       :	Any=None					, UpperCAmelCase_       :	str=None					, UpperCAmelCase_       :	Any=True					, UpperCAmelCase_       :	List[Any]=False					, UpperCAmelCase_       :	Any=True					, UpperCAmelCase_       :	str=True					, UpperCAmelCase_       :	Optional[int]=None					, ):
       if image_path:
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self._get_latent(UpperCAmelCase_      )
       else:
         SCREAMING_SNAKE_CASE       :      str							=    torch.randn(self.latent_dim					, device=self.device      )
       if self.log:
         self._init_logging(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_      )

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

       SCREAMING_SNAKE_CASE       :      Optional[int]							=    self.vqgan.decode(self.latent      )[0]
       if show_intermediate:
         print("Original Image"      )
         show_pil(custom_to_pil(UpperCAmelCase_      )      )

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














from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case    				=		logging.get_logger(__name__)

snake_case    				=		{
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Dict			=	'''camembert'''


     def __init__(				self       :	str					, UpperCAmelCase_       :	Optional[Any]=3_0522					, UpperCAmelCase_       :	Optional[int]=768					, UpperCAmelCase_       :	List[Any]=12					, UpperCAmelCase_       :	Union[str, Any]=12					, UpperCAmelCase_       :	Optional[Any]=3072					, UpperCAmelCase_       :	Optional[int]="gelu"					, UpperCAmelCase_       :	List[Any]=0.1					, UpperCAmelCase_       :	Dict=0.1					, UpperCAmelCase_       :	List[str]=512					, UpperCAmelCase_       :	List[Any]=2					, UpperCAmelCase_       :	Optional[int]=0.02					, UpperCAmelCase_       :	List[str]=1E-12					, UpperCAmelCase_       :	int=1					, UpperCAmelCase_       :	Union[str, Any]=0					, UpperCAmelCase_       :	List[str]=2					, UpperCAmelCase_       :	int="absolute"					, UpperCAmelCase_       :	Tuple=True					, UpperCAmelCase_       :	Optional[Any]=None					, **UpperCAmelCase_       :	Optional[int]					, ):
       super().__init__(pad_token_id=UpperCAmelCase_					, bos_token_id=UpperCAmelCase_					, eos_token_id=UpperCAmelCase_					, **UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      int							=    vocab_size
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    hidden_size
       SCREAMING_SNAKE_CASE       :      Any							=    num_hidden_layers
       SCREAMING_SNAKE_CASE       :      Dict							=    num_attention_heads
       SCREAMING_SNAKE_CASE       :      Dict							=    hidden_act
       SCREAMING_SNAKE_CASE       :      Tuple							=    intermediate_size
       SCREAMING_SNAKE_CASE       :      Any							=    hidden_dropout_prob
       SCREAMING_SNAKE_CASE       :      int							=    attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE       :      Dict							=    max_position_embeddings
       SCREAMING_SNAKE_CASE       :      List[Any]							=    type_vocab_size
       SCREAMING_SNAKE_CASE       :      List[Any]							=    initializer_range
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    layer_norm_eps
       SCREAMING_SNAKE_CASE       :      Dict							=    position_embedding_type
       SCREAMING_SNAKE_CASE       :      List[Any]							=    use_cache
       SCREAMING_SNAKE_CASE       :      str							=    classifier_dropout





class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     @property
     def 				_A       (				self       :	Optional[int]      ):
       if self.task == "multiple-choice":
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    {0: "batch", 1: "choice", 2: "sequence"}
       else:
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    {0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
           ]      )






