"""simple docstring"""


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

import yaml



class 			lowerCamelCase__						(   yaml.SafeLoader			):







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







						def __a						(							self   :    Optional[int]			,			_lowercase   :    Union[str, Any]			,			_lowercase   :    Dict=False		):
								A					       = super().construct_mapping(UpperCAmelCase_			,			deep=UpperCAmelCase_		)
								self._check_no_duplicates_on_constructed_node(UpperCAmelCase_		)
								return mapping







def  __snake_case       (   UpperCamelCase__	)      ->		str:







		"""simple docstring"""





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

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



class 			lowerCamelCase__						(   __UpperCAmelCase			):
						lowerCAmelCase       =     {"""train_eval_index"""}  # train-eval-index in the YAML metadata







						@classmethod
						def __a						(							cls   :    str			,			_lowercase   :    Dict		):
								with open(UpperCAmelCase_			,			encoding='utf-8'		) as readme_file:
										A       , A					       = _split_yaml_from_readme(readme_file.read()		)
								if yaml_string is not None:
										return cls.from_yaml_string(UpperCAmelCase_		)
								else:
										return cls()







						def __a						(							self   :    int			,			_lowercase   :    List[Any]		):
								if path.exists():
										with open(UpperCAmelCase_			,			encoding='utf-8'		) as readme_file:
												A					       = readme_file.read()
								else:
										A					       = None
								A					       = self._to_readme(UpperCAmelCase_		)
								with open(UpperCAmelCase_			,			'w'			,			encoding='utf-8'		) as readme_file:
										readme_file.write(UpperCAmelCase_		)







						def __a						(							self   :    str			,			_lowercase   :    Tuple = None		):
								if readme_content is not None:
										A       , A					       = _split_yaml_from_readme(UpperCAmelCase_		)
										A					       = '---\n' + self.to_yaml_string() + '---\n' + content
								else:
										A					       = '---\n' + self.to_yaml_string() + '---\n'
								return full_content







						@classmethod
						def __a						(							cls   :    List[Any]			,			_lowercase   :    List[str]		):
								A					       = yaml.load(UpperCAmelCase_			,			Loader=_NoDuplicateSafeLoader		) or {}

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







						def __a						(							self   :    Any		):
								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'		)

UpperCamelCase						:				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

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

	UpperCamelCase						:				Dict             =      Path(args.readme_filepath)
	UpperCamelCase						:				Tuple             =      DatasetMetadata.from_readme(readme_filepath)
	print(dataset_metadata)
	dataset_metadata.to_readme(readme_filepath)









"""simple docstring"""


from __future__ import annotations







def  __snake_case       (   UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		if not nums:
				return 0
		A					       = nums[0]
		A					       = 0
		for num in nums[1:]:
				A       , A					       = (
				    max_excluding + num,
				    max(UpperCamelCase__ ,			UpperCamelCase__	),
				)
		return max(UpperCamelCase__ ,			UpperCamelCase__	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()





