





from __future__ import annotations





class 				A_				:







       """simple docstring"""





       def __init__(    self	:	int						,__A	:	list[list[int]]  )						->					str:
         _lowercase							= TypeError(
             'Matrices must be formed from a list of zero or more lists containing at '
             'least one and the same number of values, each of which must be of type '
             'int or float.'  )
         if len(__A  ) != 0:
           _lowercase							= len(rows[0]  )
           if cols == 0:
             raise error
           for row in rows:
             if len(__A  ) != cols:
               raise error
             for value in row:
               if not isinstance(__A						,(int, float)  ):
                 raise error
           _lowercase							= rows
         else:
           _lowercase							= []







       def    __UpperCAmelCase (    self	:	Dict  )						->					list[list[int]]:
         return [[row[i] for row in self.rows] for i in range(len(self.rows[0]  )  )]







       @property
       def    __UpperCAmelCase (    self	:	List[Any]  )						->					int:
         return len(self.rows  )







       @property
       def    __UpperCAmelCase (    self	:	Tuple  )						->					int:
         return len(self.rows[0]  )







       @property
       def    __UpperCAmelCase (    self	:	Tuple  )						->					tuple[int, int]:
         return (self.num_rows, self.num_columns)







       @property
       def    __UpperCAmelCase (    self	:	Any  )						->					bool:
         return self.order[0] == self.order[1]







       def    __UpperCAmelCase (    self	:	Any  )						->					Matrix:
         _lowercase							= [
             [0 if column_num != row_num else 1 for column_num in range(self.num_rows  )]
             for row_num in range(self.num_rows  )
         ]
         return Matrix(__A  )







       def    __UpperCAmelCase (    self	:	List[Any]  )						->					int:
         if not self.is_square:
           return 0
         if self.order == (0, 0):
           return 1
         if self.order == (1, 1):
           return int(self.rows[0][0]  )
         if self.order == (2, 2):
           return int(
               (self.rows[0][0] * self.rows[1][1])
               - (self.rows[0][1] * self.rows[1][0])  )
         else:
           return sum(
               self.rows[0][column] * self.cofactors().rows[0][column]
               for column in range(self.num_columns  )  )







       def    __UpperCAmelCase (    self	:	int  )						->					bool:
         return bool(self.determinant()  )







       def    __UpperCAmelCase (    self	:	List[Any]						,__A	:	int						,__A	:	int  )						->					int:
         _lowercase							= [
             [
                 self.rows[other_row][other_column]
                 for other_column in range(self.num_columns  )
                 if other_column != column
             ]
             for other_row in range(self.num_rows  )
             if other_row != row
         ]
         return Matrix(__A  ).determinant()







       def    __UpperCAmelCase (    self	:	List[Any]						,__A	:	int						,__A	:	int  )						->					int:
         if (row + column) % 2 == 0:
           return self.get_minor(__A						,__A  )
         return -1 * self.get_minor(__A						,__A  )







       def    __UpperCAmelCase (    self	:	Tuple  )						->					Matrix:
         return Matrix(
             [
                 [self.get_minor(__A						,__A  ) for column in range(self.num_columns  )]
                 for row in range(self.num_rows  )
             ]  )







       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					Matrix:
         return Matrix(
             [
                 [
                     self.minors().rows[row][column]
                     if (row + column) % 2 == 0
                     else self.minors().rows[row][column] * -1
                     for column in range(self.minors().num_columns  )
                 ]
                 for row in range(self.minors().num_rows  )
             ]  )







       def    __UpperCAmelCase (    self	:	List[Any]  )						->					Matrix:
         _lowercase							= [
             [self.cofactors().rows[column][row] for column in range(self.num_columns  )]
             for row in range(self.num_rows  )
         ]
         return Matrix(__A  )







       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					Matrix:
         _lowercase							= self.determinant()
         if not determinant:
           raise TypeError('Only matrices with a non-zero determinant have an inverse'  )
         return self.adjugate() * (1 / determinant)







       def __repr__(    self	:	int  )						->					str:
         return str(self.rows  )







       def __str__(    self	:	List[str]  )						->					str:
         if self.num_rows == 0:
           return "[]"
         if self.num_rows == 1:
           return "[[" + ". ".join(str(self.rows[0]  )  ) + "]]"
         return (
             "["
             + "\n ".join(
                 [
                     '[' + '. '.join([str(__A  ) for value in row]  ) + '.]'
                     for row in self.rows
                 ]  )
             + "]"
         )







       def    __UpperCAmelCase (    self	:	Tuple						,__A	:	list[int]						,__A	:	int | None = None  )						->					None:
         _lowercase							= TypeError('Row must be a list containing all ints and/or floats'  )
         if not isinstance(__A						,__A  ):
           raise type_error
         for value in row:
           if not isinstance(__A						,(int, float)  ):
             raise type_error
         if len(__A  ) != self.num_columns:
           raise ValueError(
               'Row must be equal in length to the other rows in the matrix'  )
         if position is None:
           self.rows.append(__A  )
         else:
           _lowercase							= self.rows[0:position] + [row] + self.rows[position:]







       def    __UpperCAmelCase (    self	:	Dict						,__A	:	list[int]						,__A	:	int | None = None  )						->					None:
         _lowercase							= TypeError(
             'Column must be a list containing all ints and/or floats'  )
         if not isinstance(__A						,__A  ):
           raise type_error
         for value in column:
           if not isinstance(__A						,(int, float)  ):
             raise type_error
         if len(__A  ) != self.num_rows:
           raise ValueError(
               'Column must be equal in length to the other columns in the matrix'  )
         if position is None:
           _lowercase							= [self.rows[i] + [column[i]] for i in range(self.num_rows  )]
         else:
           _lowercase							= [
               self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
               for i in range(self.num_rows  )
           ]







       def __eq__(    self	:	Dict						,__A	:	object  )						->					bool:
         if not isinstance(__A						,__A  ):
           return NotImplemented
         return self.rows == other.rows







       def __ne__(    self	:	Optional[int]						,__A	:	object  )						->					bool:
         return not self == other







       def __neg__(    self	:	int  )						->					Matrix:
         return self * -1







       def __add__(    self	:	Union[str, Any]						,__A	:	Matrix  )						->					Matrix:
         if self.order != other.order:
           raise ValueError('Addition requires matrices of the same order'  )
         return Matrix(
             [
                 [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns  )]
                 for i in range(self.num_rows  )
             ]  )







       def __sub__(    self	:	Optional[Any]						,__A	:	Matrix  )						->					Matrix:
         if self.order != other.order:
           raise ValueError('Subtraction requires matrices of the same order'  )
         return Matrix(
             [
                 [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns  )]
                 for i in range(self.num_rows  )
             ]  )







       def __mul__(    self	:	Any						,__A	:	Matrix | int | float  )						->					Matrix:
         if isinstance(__A						,(int, float)  ):
           return Matrix(
               [[int(element * other  ) for element in row] for row in self.rows]  )
         elif isinstance(__A						,__A  ):
           if self.num_columns != other.num_rows:
             raise ValueError(
                 'The number of columns in the first matrix must '
                 'be equal to the number of rows in the second'  )
           return Matrix(
               [
                   [Matrix.dot_product(__A						,__A  ) for column in other.columns()]
                   for row in self.rows
               ]  )
         else:
           raise TypeError(
               'A Matrix can only be multiplied by an int, float, or another matrix'  )







       def __pow__(    self	:	Optional[int]						,__A	:	int  )						->					Matrix:
         if not isinstance(__A						,__A  ):
           raise TypeError('A Matrix can only be raised to the power of an int'  )
         if not self.is_square:
           raise ValueError('Only square matrices can be raised to a power'  )
         if other == 0:
           return self.identity()
         if other < 0:
           if self.is_invertable():
             return self.inverse() ** (-other)
           raise ValueError(
               'Only invertable matrices can be raised to a negative power'  )
         _lowercase							= self
         for _ in range(other - 1  ):
           result *= self
         return result







       @classmethod
       def    __UpperCAmelCase (    cls	:	Dict						,__A	:	list[int]						,__A	:	list[int]  )						->					int:
         return sum(row[i] * column[i] for i in range(len(__A  )  )  )


if __name__ == "__main__":
      import doctest

      doctest.testmod()






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

import yaml





class 				A_				(      yaml.SafeLoader	):







       """simple docstring"""





       def    __UpperCAmelCase (    self	:	Dict						,__A	:	List[Any]  )						->					Any:
         _lowercase							= [self.constructed_objects[key_node] for key_node, _ in node.value]
         _lowercase							= [tuple(__A  ) if isinstance(__A						,__A  ) else key for key in keys]
         _lowercase							= Counter(__A  )
         _lowercase							= [key for key in counter if counter[key] > 1]
         if duplicate_keys:
           raise TypeError(F"""Got duplicate yaml keys: {duplicate_keys}"""  )







       def    __UpperCAmelCase (    self	:	Any						,__A	:	int						,__A	:	Dict=False  )						->					Union[str, Any]:
         _lowercase							= super().construct_mapping(__A						,deep=__A  )
         self._check_no_duplicates_on_constructed_node(__A  )
         return mapping

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :str     )      ->						Tuple[Optional[str], str]:
  _lowercase							= list(readme_content.splitlines()     )
  if full_content and full_content[0] == "---" and "---" in full_content[1:]:
    _lowercase							= full_content[1:].index('---'     ) + 1
    _lowercase							= '\n'.join(full_content[1:sep_idx]     )
    return yamlblock, "\n".join(full_content[sep_idx + 1 :]     )

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





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : Any      =  {'''train_eval_index'''}  # train-eval-index in the YAML metadata







       @classmethod
       def    __UpperCAmelCase (    cls	:	List[Any]						,__A	:	Path  )						->					"DatasetMetadata":
         with open(__A						,encoding='utf-8'  ) as readme_file:
           _lowercase						,		_lowercase							= _split_yaml_from_readme(readme_file.read()  )
         if yaml_string is not None:
           return cls.from_yaml_string(__A  )
         else:
           return cls()







       def    __UpperCAmelCase (    self	:	Union[str, Any]						,__A	:	Path  )						->					Optional[Any]:
         if path.exists():
           with open(__A						,encoding='utf-8'  ) as readme_file:
             _lowercase							= readme_file.read()
         else:
           _lowercase							= None
         _lowercase							= self._to_readme(__A  )
         with open(__A						,'w'						,encoding='utf-8'  ) as readme_file:
           readme_file.write(__A  )







       def    __UpperCAmelCase (    self	:	Tuple						,__A	:	Optional[str] = None  )						->					str:
         if readme_content is not None:
           _lowercase						,		_lowercase							= _split_yaml_from_readme(__A  )
           _lowercase							= '---\n' + self.to_yaml_string() + '---\n' + content
         else:
           _lowercase							= '---\n' + self.to_yaml_string() + '---\n'
         return full_content







       @classmethod
       def    __UpperCAmelCase (    cls	:	List[Any]						,__A	:	str  )						->					"DatasetMetadata":
         _lowercase							= yaml.load(__A						,Loader=_NoDuplicateSafeLoader  ) or {}

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







       def    __UpperCAmelCase (    self	:	Optional[int]  )						->					str:
         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'  )
snake_case 				=       {
    """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

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

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