






'''simple docstring'''





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

import yaml


class    _lowerCAmelCase			(		yaml.SafeLoader      ):



				"""simple docstring"""



				def 					__magic_name__							(      self    :    List[Any]			,		UpperCamelCase__    :    List[str]):






											'''simple docstring'''







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



				def 					__magic_name__							(      self    :    Union[str, Any]			,		UpperCamelCase__    :    str			,		UpperCamelCase__    :    str=False):






											'''simple docstring'''







											snake_case__	  =       super().construct_mapping(UpperCamelCase__			,		deep=UpperCamelCase__)
											self._check_no_duplicates_on_constructed_node(UpperCamelCase__)
											return mapping


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

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


class    _lowerCAmelCase			(		lowercase_      ):



				"""simple docstring"""



				_lowercase				:   Optional[int]      =  {'''train_eval_index'''}  # train-eval-index in the YAML metadata



				@classmethod
				def 					__magic_name__							(      cls    :    List[Any]			,		UpperCamelCase__    :    Path):






											'''simple docstring'''







											with open(UpperCamelCase__			,		encoding="""utf-8""") as readme_file:
																		snake_case__  , snake_case__	  =       _split_yaml_from_readme(readme_file.read())
											if yaml_string is not None:
																		return cls.from_yaml_string(UpperCamelCase__)
											else:
																		return cls()



				def 					__magic_name__							(      self    :    Union[str, Any]			,		UpperCamelCase__    :    Path):






											'''simple docstring'''







											if path.exists():
																		with open(UpperCamelCase__			,		encoding="""utf-8""") as readme_file:
																									snake_case__	  =       readme_file.read()
											else:
																		snake_case__	  =       None
											snake_case__	  =       self._to_readme(UpperCamelCase__)
											with open(UpperCamelCase__			,		"""w"""			,		encoding="""utf-8""") as readme_file:
																		readme_file.write(UpperCamelCase__)



				def 					__magic_name__							(      self    :    Any			,		UpperCamelCase__    :    Optional[str] = None):






											'''simple docstring'''







											if readme_content is not None:
																		snake_case__  , snake_case__	  =       _split_yaml_from_readme(UpperCamelCase__)
																		snake_case__	  =       """---\n""" + self.to_yaml_string() + """---\n""" + content
											else:
																		snake_case__	  =       """---\n""" + self.to_yaml_string() + """---\n"""
											return full_content



				@classmethod
				def 					__magic_name__							(      cls    :    int			,		UpperCamelCase__    :    str):






											'''simple docstring'''







											snake_case__	  =       yaml.load(UpperCamelCase__			,		Loader=_NoDuplicateSafeLoader) or {}

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



				def 					__magic_name__							(      self    :    int):






											'''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=UpperCamelCase__			,		allow_unicode=UpperCamelCase__			,		encoding="""utf-8"""			,		).decode("""utf-8""")


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

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








def _UpperCAmelCase   (	a						:	int = 400_0000 ):
							snake_case__	  =       [0, 1]
							snake_case__	  =       0
							while fib[i] <= n:
														fib.append(fib[i] + fib[i + 1] )
														if fib[i + 2] > n:
																					break
														i += 1
							snake_case__	  =       0
							for j in range(len(a ) - 1 ):
														if fib[j] % 2 == 0:
																					total += fib[j]

							return total


if __name__ == "__main__":
	print(F'''{solution() = }''')
