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					:
  """simple docstring"""







  def __init__(							self :       int  ,     __magic_name__ :       str = "cpu"  ,     __magic_name__ :       str = "openai/clip-vit-large-patch14"   )  ->	None:
        SCREAMING_SNAKE_CASE_          =  device
        SCREAMING_SNAKE_CASE_          =  CLIPTokenizerFast.from_pretrained(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  [0.4814_5466, 0.457_8275, 0.4082_1073]
        SCREAMING_SNAKE_CASE_          =  [0.2686_2954, 0.2613_0258, 0.2757_7711]
        SCREAMING_SNAKE_CASE_          =  torchvision.transforms.Normalize(self.image_mean  ,     self.image_std   )
        SCREAMING_SNAKE_CASE_          =  torchvision.transforms.Resize(224   )
        SCREAMING_SNAKE_CASE_          =  torchvision.transforms.CenterCrop(224   )







  def    __A     (							self :       int  ,     __magic_name__ :       Tuple   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  self.resize(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.center_crop(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.normalize(__magic_name__   )
        return images







  def __call__(							self :       Union[str, Any]  ,     __magic_name__ :       List[str]=None  ,     __magic_name__ :       Dict=None  ,     **__magic_name__ :       Optional[int]   )  ->	int:
        SCREAMING_SNAKE_CASE_          =  self.tokenizer(text=__magic_name__  ,     **__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.preprocess_img(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  {key: value.to(self.device   ) for (key, value) in encoding.items()}
        return encoding



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







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

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







  def    __A     (							self :       Union[str, Any]  ,     __magic_name__ :       Optional[Any]=None  ,     __magic_name__ :       Dict=None  ,     __magic_name__ :       str=5  ,     __magic_name__ :       int=True   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  []
        if output_path is None:
              SCREAMING_SNAKE_CASE_          =  "./animation.gif"
        if input_path is None:
              SCREAMING_SNAKE_CASE_          =  self.save_path
        SCREAMING_SNAKE_CASE_          =  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?)"   )
        SCREAMING_SNAKE_CASE_          =  total_duration / len(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  [frame_duration] * len(__magic_name__   )
        if extend_frames:
              SCREAMING_SNAKE_CASE_          =  1.5
              SCREAMING_SNAKE_CASE_          =  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    __A     (							self :       Optional[int]  ,     __magic_name__ :       Dict=None  ,     __magic_name__ :       Union[str, Any]=None   )  ->	str:
        if not (path or img):
              raise ValueError("Input either path or tensor"   )
        if img is not None:
              raise NotImplementedError
        SCREAMING_SNAKE_CASE_          =  preprocess(Image.open(__magic_name__   )  ,     target_image_size=256   ).to(self.device   )
        SCREAMING_SNAKE_CASE_          =  preprocess_vqgan(__magic_name__   )
        SCREAMING_SNAKE_CASE_     , *SCREAMING_SNAKE_CASE_          =  self.vqgan.encode(__magic_name__   )
        return z







  def    __A     (							self :       Union[str, Any]  ,     __magic_name__ :       int   )  ->	Union[str, Any]:
        SCREAMING_SNAKE_CASE_          =  self.latent.detach().requires_grad_()
        SCREAMING_SNAKE_CASE_          =  base_latent + transform_vector
        if self.quantize:
              SCREAMING_SNAKE_CASE_     , *SCREAMING_SNAKE_CASE_          =  self.vqgan.quantize(__magic_name__   )
        else:
              SCREAMING_SNAKE_CASE_          =  trans_latent
        return self.vqgan.decode(__magic_name__   )







  def    __A     (							self :       Tuple  ,     __magic_name__ :       int  ,     __magic_name__ :       Any  ,     __magic_name__ :       List[Any]=None   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  self.clip_preprocessor(text=__magic_name__  ,     images=__magic_name__  ,     return_tensors="pt"  ,     padding=__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.clip(**__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  clip_outputs.logits_per_image
        if weights is not None:
              SCREAMING_SNAKE_CASE_          =  similarity_logits * weights
        return similarity_logits.sum()







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







  def    __A     (							self :       str  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       Any   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  torch.randn_like(self.latent  ,     requires_grad=__magic_name__  ,     device=self.device   )
        SCREAMING_SNAKE_CASE_          =  torch.optim.Adam([vector]  ,     lr=self.lr   )

        for i in range(self.iterations   ):
              optim.zero_grad()
              SCREAMING_SNAKE_CASE_          =  self._add_vector(__magic_name__   )
              SCREAMING_SNAKE_CASE_          =  loop_post_process(__magic_name__   )
              SCREAMING_SNAKE_CASE_          =  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    __A     (							self :       str  ,     __magic_name__ :       Optional[Any]  ,     __magic_name__ :       Optional[int]  ,     __magic_name__ :       Any   )  ->	Optional[int]:
        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:
              SCREAMING_SNAKE_CASE_          =  Image.open(__magic_name__   )
              SCREAMING_SNAKE_CASE_          =  image.resize((256, 256)   )
              wandb.log("Original Image"  ,     wandb.Image(__magic_name__   )   )







  def    __A     (							self :       List[str]  ,     __magic_name__ :       str   )  ->	Optional[Any]:
        if not prompts:
              return []
        SCREAMING_SNAKE_CASE_          =  []
        SCREAMING_SNAKE_CASE_          =  []
        if isinstance(__magic_name__  ,     __magic_name__   ):
              SCREAMING_SNAKE_CASE_          =  [prompt.strip() for prompt in prompts.split("|"   )]
        for prompt in prompts:
              if isinstance(__magic_name__  ,     (tuple, list)   ):
                    SCREAMING_SNAKE_CASE_          =  prompt[0]
                    SCREAMING_SNAKE_CASE_          =  float(prompt[1]   )
              elif ":" in prompt:
                    SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  prompt.split(":"   )
                    SCREAMING_SNAKE_CASE_          =  float(__magic_name__   )
              else:
                    SCREAMING_SNAKE_CASE_          =  prompt
                    SCREAMING_SNAKE_CASE_          =  1.0
              processed_prompts.append(__magic_name__   )
              weights.append(__magic_name__   )
        return {
            "prompts": processed_prompts,
            "weights": torch.tensor(__magic_name__  ,     device=self.device   ),
        }







  def    __A     (							self :       int  ,     __magic_name__ :       List[str]  ,     __magic_name__ :       Union[str, Any]=None  ,     __magic_name__ :       Union[str, Any]=None  ,     __magic_name__ :       str=True  ,     __magic_name__ :       Optional[int]=False  ,     __magic_name__ :       List[str]=True  ,     __magic_name__ :       Dict=True  ,     __magic_name__ :       Any=None  ,     )  ->	Dict:
        if image_path:
              SCREAMING_SNAKE_CASE_          =  self._get_latent(__magic_name__   )
        else:
              SCREAMING_SNAKE_CASE_          =  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."
        SCREAMING_SNAKE_CASE_          =  self.process_prompts(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.process_prompts(__magic_name__   )
        if save_final and save_path is None:
              SCREAMING_SNAKE_CASE_          =  os.path.join("./outputs/"  ,     "_".join(pos_prompts["prompts"]   )   )
        if not os.path.exists(__magic_name__   ):
              os.makedirs(__magic_name__   )
        else:
              SCREAMING_SNAKE_CASE_          =  save_path + "_" + get_timestamp()
              os.makedirs(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  save_path

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

        SCREAMING_SNAKE_CASE_          =  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 collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml


class   lowerCamelCase					(yaml.SafeLoader					):
  """simple docstring"""







  def    __A     (							self :       str  ,     __magic_name__ :       str   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  [self.constructed_objects[key_node] for key_node, _ in node.value]
        SCREAMING_SNAKE_CASE_          =  [tuple(__magic_name__   ) if isinstance(__magic_name__  ,     __magic_name__   ) else key for key in keys]
        SCREAMING_SNAKE_CASE_          =  Counter(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  [key for key in counter if counter[key] > 1]
        if duplicate_keys:
              raise TypeError(F'''Got duplicate yaml keys: {duplicate_keys}'''   )







  def    __A     (							self :       int  ,     __magic_name__ :       int  ,     __magic_name__ :       List[str]=False   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  super().construct_mapping(__magic_name__  ,     deep=__magic_name__   )
        self._check_no_duplicates_on_constructed_node(__magic_name__   )
        return mapping



def      a__ (		__UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  list(readme_content.splitlines()				)
      if full_content and full_content[0] == "---" and "---" in full_content[1:]:
            SCREAMING_SNAKE_CASE_          =  full_content[1:].index("---"				) + 1
            SCREAMING_SNAKE_CASE_          =  "\n".join(full_content[1:sep_idx]				)
            return yamlblock, "\n".join(full_content[sep_idx + 1 :]				)

      return None, "\n".join(__UpperCamelCase				)


class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  lowerCamelCase__     =						{'''train_eval_index'''}  # train-eval-index in the YAML metadata







  @classmethod
  def    __A     (							cls :       Dict  ,     __magic_name__ :       Path   )  ->	"DatasetMetadata":
        with open(__magic_name__  ,     encoding="utf-8"   ) as readme_file:
              SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  _split_yaml_from_readme(readme_file.read()   )
        if yaml_string is not None:
              return cls.from_yaml_string(__magic_name__   )
        else:
              return cls()







  def    __A     (							self :       str  ,     __magic_name__ :       Path   )  ->	List[str]:
        if path.exists():
              with open(__magic_name__  ,     encoding="utf-8"   ) as readme_file:
                    SCREAMING_SNAKE_CASE_          =  readme_file.read()
        else:
              SCREAMING_SNAKE_CASE_          =  None
        SCREAMING_SNAKE_CASE_          =  self._to_readme(__magic_name__   )
        with open(__magic_name__  ,     "w"  ,     encoding="utf-8"   ) as readme_file:
              readme_file.write(__magic_name__   )







  def    __A     (							self :       Any  ,     __magic_name__ :       Optional[str] = None   )  ->	str:
        if readme_content is not None:
              SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  _split_yaml_from_readme(__magic_name__   )
              SCREAMING_SNAKE_CASE_          =  "---\n" + self.to_yaml_string() + "---\n" + content
        else:
              SCREAMING_SNAKE_CASE_          =  "---\n" + self.to_yaml_string() + "---\n"
        return full_content







  @classmethod
  def    __A     (							cls :       List[Any]  ,     __magic_name__ :       str   )  ->	"DatasetMetadata":
        SCREAMING_SNAKE_CASE_          =  yaml.load(__magic_name__  ,     Loader=_NoDuplicateSafeLoader   ) or {}

        # Convert the YAML keys to DatasetMetadata fields
        SCREAMING_SNAKE_CASE_          =  {
            (key.replace("-"  ,     "_"   ) if key.replace("-"  ,     "_"   ) in cls._FIELDS_WITH_DASHES else key): value
            for key, value in metadata_dict.items()
        }
        return cls(**__magic_name__   )







  def    __A     (							self :       Optional[Any]   )  ->	str:
        return yaml.safe_dump(
            {
                (key.replace("_"  ,     "-"   ) if key in self._FIELDS_WITH_DASHES else key): value
                for key, value in self.items()
            }  ,     sort_keys=__magic_name__  ,     allow_unicode=__magic_name__  ,     encoding="utf-8"  ,     ).decode("utf-8"   )



A     :  List[Any]					     =       {
    "image-classification": [],
    "translation": [],
    "image-segmentation": [],
    "fill-mask": [],
    "automatic-speech-recognition": [],
    "token-classification": [],
    "sentence-similarity": [],
    "audio-classification": [],
    "question-answering": [],
    "summarization": [],
    "zero-shot-classification": [],
    "table-to-text": [],
    "feature-extraction": [],
    "other": [],
    "multiple-choice": [],
    "text-classification": [],
    "text-to-image": [],
    "text2text-generation": [],
    "zero-shot-image-classification": [],
    "tabular-classification": [],
    "tabular-regression": [],
    "image-to-image": [],
    "tabular-to-text": [],
    "unconditional-image-generation": [],
    "text-retrieval": [],
    "text-to-speech": [],
    "object-detection": [],
    "audio-to-audio": [],
    "text-generation": [],
    "conversational": [],
    "table-question-answering": [],
    "visual-question-answering": [],
    "image-to-text": [],
    "reinforcement-learning": [],
    "voice-activity-detection": [],
    "time-series-forecasting": [],
    "document-question-answering": [],
}


if __name__ == "__main__":
      from argparse import ArgumentParser

      A     :  Optional[Any]					     =       ArgumentParser(usage="Validate the yaml metadata block of a README.md file.")
      ap.add_argument("readme_filepath")
      A     :  Union[str, Any]					     =       ap.parse_args()

      A     :  Union[str, Any]					     =       Path(args.readme_filepath)
      A     :  List[Any]					     =       DatasetMetadata.from_readme(readme_filepath)
      print(dataset_metadata)
      dataset_metadata.to_readme(readme_filepath)


