





"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml






class 					_SCREAMING_SNAKE_CASE(   yaml.SafeLoader  ):




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__							)			->      Union[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :List[str]						=  [self.constructed_objects[key_node] for key_node, _ in node.value]
             __SCREAMING_SNAKE_CASE   :List[Any]						=  [tuple(SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							) else key for key in keys]
             __SCREAMING_SNAKE_CASE   :int						=  Counter(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :List[Any]						=  [key for key in counter if counter[key] > 1]
             if duplicate_keys:
                   raise TypeError(f'''Got duplicate yaml keys: {duplicate_keys}'''							)




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=False							)			->      int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :str						=  super().construct_mapping(SCREAMING_SNAKE_CASE__		,deep=SCREAMING_SNAKE_CASE__							)
             self._check_no_duplicates_on_constructed_node(SCREAMING_SNAKE_CASE__							)
             return mapping




def      __lowerCamelCase   (							a_     :		str							)			->      Tuple[Optional[str], str]:
      __SCREAMING_SNAKE_CASE   :Tuple						=  list(readme_content.splitlines()							)
      if full_content and full_content[0] == "---" and "---" in full_content[1:]:
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  full_content[1:].index('''---'''							) + 1
            __SCREAMING_SNAKE_CASE   :int						=  '''\n'''.join(full_content[1:sep_idx]							)
            return yamlblock, "\n".join(full_content[sep_idx + 1 :]							)

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






class 					_SCREAMING_SNAKE_CASE(   A  ):
       # class attributes
       SCREAMING_SNAKE_CASE_   :      Tuple							=  {'''train_eval_index'''}  # train-eval-index in the YAML metadata




       @classmethod
       def     _UpperCamelCase     (						cls		,SCREAMING_SNAKE_CASE__							)			->      "DatasetMetadata":





             """simple docstring"""






             with open(SCREAMING_SNAKE_CASE__		,encoding='''utf-8'''							) as readme_file:
                   __SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE   :int						=  _split_yaml_from_readme(readme_file.read()							)
             if yaml_string is not None:
                   return cls.from_yaml_string(SCREAMING_SNAKE_CASE__							)
             else:
                   return cls()




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__							)			->      List[str]:





             """simple docstring"""






             if path.exists():
                   with open(SCREAMING_SNAKE_CASE__		,encoding='''utf-8'''							) as readme_file:
                         __SCREAMING_SNAKE_CASE   :List[str]						=  readme_file.read()
             else:
                   __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  None
             __SCREAMING_SNAKE_CASE   :Any						=  self._to_readme(SCREAMING_SNAKE_CASE__							)
             with open(SCREAMING_SNAKE_CASE__		,'''w'''		,encoding='''utf-8'''							) as readme_file:
                   readme_file.write(SCREAMING_SNAKE_CASE__							)




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__ = None							)			->      str:





             """simple docstring"""






             if readme_content is not None:
                   __SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE   :Tuple						=  _split_yaml_from_readme(SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE   :Optional[Any]						=  '''---\n''' + self.to_yaml_string() + '''---\n''' + content
             else:
                   __SCREAMING_SNAKE_CASE   :Dict						=  '''---\n''' + self.to_yaml_string() + '''---\n'''
             return full_content




       @classmethod
       def     _UpperCamelCase     (						cls		,SCREAMING_SNAKE_CASE__							)			->      "DatasetMetadata":





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :int						=  yaml.load(SCREAMING_SNAKE_CASE__		,Loader=_NoDuplicateSafeLoader							) or {}

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




       def     _UpperCamelCase     (						self							)			->      str:





             """simple docstring"""






             return yaml.safe_dump(
                 {
                     (key.replace('''_'''		,'''-'''							) if key in self._FIELDS_WITH_DASHES else key): value
                     for key, value in self.items()
                 }		,sort_keys=SCREAMING_SNAKE_CASE__		,allow_unicode=SCREAMING_SNAKE_CASE__		,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"""
import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict



@pytest.mark.parametrize(
    '''split_dict''' ,   [
        SplitDict(),
        SplitDict({'''train''': SplitInfo(name='''train''' ,   num_bytes=13_37 ,   num_examples=42 ,   dataset_name='''my_dataset'''							)}							),
        SplitDict({'''train''': SplitInfo(name='''train''' ,   num_bytes=13_37 ,   num_examples=42							)}							),
        SplitDict({'''train''': SplitInfo()}							),
    ] ,   )
def      __lowerCamelCase   (							a_     :		SplitDict							)			->      Optional[int]:
      __SCREAMING_SNAKE_CASE   :Tuple						=  split_dict._to_yaml_list()
      assert len(a_							) == len(a_							)
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  SplitDict._from_yaml_list(a_							)
      for split_name, split_info in split_dict.items():
            # dataset_name field is deprecated, and is therefore not part of the YAML dump
            __SCREAMING_SNAKE_CASE   :int						=  None
            # the split name of split_dict takes over the name of the split info object
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  split_name
      assert split_dict == reloaded







@pytest.mark.parametrize(
    '''split_info''' ,   [SplitInfo(), SplitInfo(dataset_name=a_							), SplitInfo(dataset_name='''my_dataset'''							)]							)
def      __lowerCamelCase   (							a_     :		Dict							)			->      Any:
      # For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
      # field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  asdict(SplitDict({'''train''': split_info}							)							)
      assert "dataset_name" in split_dict_asdict["train"]
      assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name