




'''simple docstring'''

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

import yaml





class lowerCamelCase						(yaml.SafeLoader					):







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						Any:






        """simple docstring"""





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







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__=False		)		->						Tuple:






        """simple docstring"""





        _snake_case		:	Tuple					     =     super().construct_mapping(lowercase__     ,     deep=lowercase__		)
        self._check_no_duplicates_on_constructed_node(lowercase__		)
        return mapping




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



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

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





class lowerCamelCase						(a__					):
  # class attributes
  _lowercase  :	Tuple       					=				{"""train_eval_index"""}  # train-eval-index in the YAML metadata







  @classmethod
  def 				UpperCAmelCase_				(		cls     ,     lowercase__		)		->						"DatasetMetadata":






        """simple docstring"""





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







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						Dict:






        """simple docstring"""





        if path.exists():
              with open(lowercase__     ,     encoding='''utf-8'''		) as readme_file:
                    _snake_case		:	Any					     =     readme_file.read()
        else:
              _snake_case		:	int					     =     None
        _snake_case		:	Optional[int]					     =     self._to_readme(lowercase__		)
        with open(lowercase__     ,     '''w'''     ,     encoding='''utf-8'''		) as readme_file:
              readme_file.write(lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__ = None		)		->						str:






        """simple docstring"""





        if readme_content is not None:
              _snake_case		:	Any					     =     _split_yaml_from_readme(lowercase__		)
              _snake_case		:	Tuple					     =     '''---\n''' + self.to_yaml_string() + '''---\n''' + content
        else:
              _snake_case		:	int					     =     '''---\n''' + self.to_yaml_string() + '''---\n'''
        return full_content







  @classmethod
  def 				UpperCAmelCase_				(		cls     ,     lowercase__		)		->						"DatasetMetadata":






        """simple docstring"""





        _snake_case		:	Optional[int]					     =     yaml.load(lowercase__     ,     Loader=_NoDuplicateSafeLoader		) or {}

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







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



UpperCAmelCase					:		Optional[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					:		Optional[Any]		=							ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
   ap.add_argument('readme_filepath')
   UpperCAmelCase					:		Optional[int]		=							ap.parse_args()

   UpperCAmelCase					:		int		=							Path(args.readme_filepath)
   UpperCAmelCase					:		Optional[int]		=							DatasetMetadata.from_readme(readme_filepath)
   print(dataset_metadata)
   dataset_metadata.to_readme(readme_filepath)









'''simple docstring'''

import math

from numpy import inf
from scipy.integrate import quad




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      if num <= 0:
            raise ValueError('''math domain error'''	)

      return quad(lowerCAmelCase_			,      0			,      lowerCAmelCase_			,      args=(lowerCAmelCase_)	)[0]




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      return math.pow(lowerCAmelCase_			,      z - 1	) * math.exp(-x	)


if __name__ == "__main__":
   from doctest import testmod

   testmod()


