





from __future__ import annotations

from decimal import Decimal

from numpy import array


def _A       (   __magic_name__	):
   lowercase__			       =  Decimal

   # Check if the provided matrix has 2 rows and 2 columns
   # since this implementation only works for 2x2 matrices
   if len(__magic_name__	) == 2 and len(matrix[0]	) == 2 and len(matrix[1]	) == 2:
      # Calculate the determinant of the matrix
      lowercase__			       =  float(
          d(matrix[0][0]	) * d(matrix[1][1]	) - d(matrix[1][0]	) * d(matrix[0][1]	)	)
      if determinant == 0:
         raise ValueError("This matrix has no inverse."	)

      # Creates a copy of the matrix with swapped positions of the elements
      lowercase__			       =  [[0.0, 0.0], [0.0, 0.0]]
      lowercase__       ,							lowercase__			       =  matrix[1][1], matrix[0][0]
      lowercase__       ,							lowercase__			       =  -matrix[1][0], -matrix[0][1]

      # Calculate the inverse of the matrix
      return [
          [(float(d(__magic_name__	)	) / determinant) or 0.0 for n in row] for row in swapped_matrix
      ]
   elif (
       len(__magic_name__	) == 3
       and len(matrix[0]	) == 3
       and len(matrix[1]	) == 3
       and len(matrix[2]	) == 3
   ):
      # Calculate the determinant of the matrix using Sarrus rule
      lowercase__			       =  float(
          (
              (d(matrix[0][0]	) * d(matrix[1][1]	) * d(matrix[2][2]	))
              + (d(matrix[0][1]	) * d(matrix[1][2]	) * d(matrix[2][0]	))
              + (d(matrix[0][2]	) * d(matrix[1][0]	) * d(matrix[2][1]	))
          )
          - (
              (d(matrix[0][2]	) * d(matrix[1][1]	) * d(matrix[2][0]	))
              + (d(matrix[0][1]	) * d(matrix[1][0]	) * d(matrix[2][2]	))
              + (d(matrix[0][0]	) * d(matrix[1][2]	) * d(matrix[2][1]	))
          )	)
      if determinant == 0:
         raise ValueError("This matrix has no inverse."	)

      # Creating cofactor matrix
      lowercase__			       =  [
          [d(0.0	), d(0.0	), d(0.0	)],
          [d(0.0	), d(0.0	), d(0.0	)],
          [d(0.0	), d(0.0	), d(0.0	)],
      ]
      lowercase__			       =  (d(matrix[1][1]	) * d(matrix[2][2]	)) - (
          d(matrix[1][2]	) * d(matrix[2][1]	)
      )
      lowercase__			       =  -(
          (d(matrix[1][0]	) * d(matrix[2][2]	)) - (d(matrix[1][2]	) * d(matrix[2][0]	))
      )
      lowercase__			       =  (d(matrix[1][0]	) * d(matrix[2][1]	)) - (
          d(matrix[1][1]	) * d(matrix[2][0]	)
      )
      lowercase__			       =  -(
          (d(matrix[0][1]	) * d(matrix[2][2]	)) - (d(matrix[0][2]	) * d(matrix[2][1]	))
      )
      lowercase__			       =  (d(matrix[0][0]	) * d(matrix[2][2]	)) - (
          d(matrix[0][2]	) * d(matrix[2][0]	)
      )
      lowercase__			       =  -(
          (d(matrix[0][0]	) * d(matrix[2][1]	)) - (d(matrix[0][1]	) * d(matrix[2][0]	))
      )
      lowercase__			       =  (d(matrix[0][1]	) * d(matrix[1][2]	)) - (
          d(matrix[0][2]	) * d(matrix[1][1]	)
      )
      lowercase__			       =  -(
          (d(matrix[0][0]	) * d(matrix[1][2]	)) - (d(matrix[0][2]	) * d(matrix[1][0]	))
      )
      lowercase__			       =  (d(matrix[0][0]	) * d(matrix[1][1]	)) - (
          d(matrix[0][1]	) * d(matrix[1][0]	)
      )

      # Transpose the cofactor matrix (Adjoint matrix)
      lowercase__			       =  array(__magic_name__	)
      for i in range(3	):
         for j in range(3	):
            lowercase__			       =  cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
      lowercase__			       =  array(__magic_name__	)
      for i in range(3	):
         for j in range(3	):
            inverse_matrix[i][j] /= d(__magic_name__	)

        # Calculate the inverse of the matrix
      return [[float(d(__magic_name__	)	) or 0.0 for n in row] for row in inverse_matrix]
   raise ValueError("Please provide a matrix of size 2x2 or 3x3."	)













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

import yaml





class  lowerCAmelCase							(       yaml.SafeLoader ):







  def   UpperCAmelCase					(      self  :Optional[Any]   ,							_lowercase  :Any ):






     '''simple docstring'''





     lowercase__			       =  [self.constructed_objects[key_node] for key_node, _ in node.value]
     lowercase__			       =  [tuple(_lowercase ) if isinstance(_lowercase   ,							_lowercase ) else key for key in keys]
     lowercase__			       =  Counter(_lowercase )
     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   ,							_lowercase  :str   ,							_lowercase  :Dict=False ):






     '''simple docstring'''





     lowercase__			       =  super().construct_mapping(_lowercase   ,							deep=_lowercase )
     self._check_no_duplicates_on_constructed_node(_lowercase )
     return mapping


def _A       (   __magic_name__	):
   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(__magic_name__	)





class  lowerCAmelCase							(       lowercase_ ):
  # class attributes
  __lowerCamelCase		=      {'train_eval_index'}  # train-eval-index in the YAML metadata







  @classmethod
  def   UpperCAmelCase					(      cls  :Dict   ,							_lowercase  :Path ):






     '''simple docstring'''





     with open(_lowercase   ,							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(_lowercase )
     else:
        return cls()







  def   UpperCAmelCase					(      self  :Any   ,							_lowercase  :Path ):






     '''simple docstring'''





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







  def   UpperCAmelCase					(      self  :List[str]   ,							_lowercase  :Optional[str] = None ):






     '''simple docstring'''





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







  @classmethod
  def   UpperCAmelCase					(      cls  :Any   ,							_lowercase  :str ):






     '''simple docstring'''





     lowercase__			       =  yaml.load(_lowercase   ,							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(**_lowercase )







  def   UpperCAmelCase					(      self  :Union[str, Any] ):






     '''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" )
_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)






