





"""simple docstring"""


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

import yaml



class      __SCREAMING_SNAKE_CASE    (						yaml.SafeLoader   ):

  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict					,   __a   :       Tuple	)		->				int:
        _UpperCamelCase      :	Dict						=       [self.constructed_objects[key_node] for key_node, _ in node.value]
        _UpperCamelCase      :	Optional[Any]						=       [tuple(UpperCAmelCase_	) if isinstance(UpperCAmelCase_					,   UpperCAmelCase_	) else key for key in keys]
        _UpperCamelCase      :	Optional[int]						=       Counter(UpperCAmelCase_	)
        _UpperCamelCase      :	Optional[Any]						=       [key for key in counter if counter[key] > 1]
        if duplicate_keys:
              raise TypeError(F'''Got duplicate yaml keys: {duplicate_keys}'''	)


  def    __SCREAMING_SNAKE_CASE				(		self   :       str					,   __a   :       Optional[int]					,   __a   :       List[str]=False	)		->				Tuple:
        _UpperCamelCase      :	str						=       super().construct_mapping(UpperCAmelCase_					,   deep=UpperCAmelCase_	)
        self._check_no_duplicates_on_constructed_node(UpperCAmelCase_	)
        return mapping





def 					lowercase__    (					lowercase_			)					->		int:




      """simple docstring"""

      _UpperCamelCase      :	Tuple						=       list(readme_content.splitlines()			)
      if full_content and full_content[0] == "---" and "---" in full_content[1:]:
            _UpperCamelCase      :	Union[str, Any]						=       full_content[1:].index("---"			) + 1
            _UpperCamelCase      :	Dict						=       "\n".join(full_content[1:sep_idx]			)
            return yamlblock, "\n".join(full_content[sep_idx + 1 :]			)

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



class      __SCREAMING_SNAKE_CASE    (						__UpperCAmelCase   ):
  SCREAMING_SNAKE_CASE__							:Any	     =					{"""train_eval_index"""}  # train-eval-index in the YAML metadata

  @classmethod
  def    __SCREAMING_SNAKE_CASE				(		cls   :       Tuple					,   __a   :       Optional[int]	)		->				Union[str, Any]:
        with open(UpperCAmelCase_					,   encoding="utf-8"	) as readme_file:
              _UpperCamelCase,  _UpperCamelCase      :	List[Any]						=       _split_yaml_from_readme(readme_file.read()	)
        if yaml_string is not None:
              return cls.from_yaml_string(UpperCAmelCase_	)
        else:
              return cls()

  def    __SCREAMING_SNAKE_CASE				(		self   :       str					,   __a   :       Any	)		->				Dict:
        if path.exists():
              with open(UpperCAmelCase_					,   encoding="utf-8"	) as readme_file:
                    _UpperCamelCase      :	List[str]						=       readme_file.read()
        else:
              _UpperCamelCase      :	Dict						=       None
        _UpperCamelCase      :	str						=       self._to_readme(UpperCAmelCase_	)
        with open(UpperCAmelCase_					,   "w"					,   encoding="utf-8"	) as readme_file:
              readme_file.write(UpperCAmelCase_	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]					,   __a   :       List[Any] = None	)		->				Optional[int]:
        if readme_content is not None:
              _UpperCamelCase,  _UpperCamelCase      :	Optional[int]						=       _split_yaml_from_readme(UpperCAmelCase_	)
              _UpperCamelCase      :	Optional[Any]						=       "---\n" + self.to_yaml_string() + "---\n" + content
        else:
              _UpperCamelCase      :	Union[str, Any]						=       "---\n" + self.to_yaml_string() + "---\n"
        return full_content

  @classmethod
  def    __SCREAMING_SNAKE_CASE				(		cls   :       List[Any]					,   __a   :       List[Any]	)		->				int:
        _UpperCamelCase      :	List[str]						=       yaml.load(UpperCAmelCase_					,   Loader=_NoDuplicateSafeLoader	) or {}

        # Convert the YAML keys to DatasetMetadata fields
        _UpperCamelCase      :	Union[str, Any]						=       {
            (key.replace("-"					,   "_"	) if key.replace("-"					,   "_"	) in cls._FIELDS_WITH_DASHES else key): value
            for key, value in metadata_dict.items()
        }
        return cls(**UpperCAmelCase_	)


  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]	)		->				List[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=UpperCAmelCase_					,   allow_unicode=UpperCAmelCase_					,   encoding="utf-8"					,   ).decode("utf-8"	)





lowerCamelCase__				  =   {
    "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

  lowerCamelCase__				  =   ArgumentParser(usage="Validate the yaml metadata block of a README.md file.")
  ap.add_argument("readme_filepath")
  lowerCamelCase__				  =   ap.parse_args()

  lowerCamelCase__				  =   Path(args.readme_filepath)
  lowerCamelCase__				  =   DatasetMetadata.from_readme(readme_filepath)
  print(dataset_metadata)
  dataset_metadata.to_readme(readme_filepath)







"""simple docstring"""


from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate



@dataclass(frozen=_UpperCamelCase   )
class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:str	     =					field(default="summarization"		,  metadata={"include_in_asdict_even_if_is_default": True}   )
  SCREAMING_SNAKE_CASE__							:ClassVar[Features]	     =					Features({"text": Value("string"   )}   )
  SCREAMING_SNAKE_CASE__							:ClassVar[Features]	     =					Features({"summary": Value("string"   )}   )
  SCREAMING_SNAKE_CASE__							:str	     =					"text"
  SCREAMING_SNAKE_CASE__							:str	     =					"summary"

  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]	)		->				Dict[str, str]:
        return {self.text_column: "text", self.summary_column: "summary"}
