


"""simple docstring"""


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

import yaml
class snake_case      (  yaml.SafeLoader):







		def  a_ (					self      :       int    ,   a__      :       Union[str, Any]		)       ->						Union[str, Any]:





						'''simple docstring'''







						_A					  =			[self.constructed_objects[key_node] for key_node, _ in node.value]
						_A					  =			[tuple(a__		) if isinstance(a__    ,   a__		) else key for key in keys]
						_A					  =			Counter(a__		)
						_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      :       int    ,   a__      :       Union[str, Any]    ,   a__      :       Dict=False		)       ->						Dict:





						'''simple docstring'''







						_A					  =			super().construct_mapping(a__    ,   deep=a__		)
						self._check_no_duplicates_on_constructed_node(a__		)
						return mapping






def      a__ (				__lowercase   )	->	Tuple[Optional[str], str]:
				_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(__A   )
class snake_case      (  __A):
		__UpperCamelCase           =			{'train_eval_index'}  # train-eval-index in the YAML metadata







		@classmethod
		def  a_ (					cls      :       Optional[Any]    ,   a__      :       Path		)       ->						"DatasetMetadata":





						'''simple docstring'''







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







		def  a_ (					self      :       Dict    ,   a__      :       Path		)       ->						int:





						'''simple docstring'''







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







		def  a_ (					self      :       List[Any]    ,   a__      :       Optional[str] = None		)       ->						str:





						'''simple docstring'''







						if readme_content is not None:
										_A					  =			_split_yaml_from_readme(a__		)
										_A					  =			'''---\n''' + self.to_yaml_string() + '''---\n''' + content
						else:
										_A					  =			'''---\n''' + self.to_yaml_string() + '''---\n'''
						return full_content







		@classmethod
		def  a_ (					cls      :       List[str]    ,   a__      :       str		)       ->						"DatasetMetadata":





						'''simple docstring'''







						_A					  =			yaml.load(a__    ,   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(**a__		)







		def  a_ (					self      :       Tuple		)       ->						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=a__    ,   allow_unicode=a__    ,   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)






"""simple docstring"""


from __future__ import annotations






def      a__ (				__lowercase     ,			__lowercase   )	->	float:
				_A					  =			sorted(numsa + numsa   )
				_A  ,		_A					  =			divmod(len(__lowercase   )     ,			2   )
				if mod == 1:
								return all_numbers[div]
				else:
								return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		a_       							=    [float(x) for x in input("Enter the elements of first array: ").split()]
		a_       							=    [float(x) for x in input("Enter the elements of second array: ").split()]
		print(f'''The median of two arrays is: {median_of_two_arrays(array_a, array_a)}''')