


'''simple docstring'''

from __future__ import annotations

a_  :				Tuple						= list[list[int]]

# assigning initial values to the grid
a_  :				Matrix						= [
    [3, 0, 6, 5, 0, 8, 4, 0, 0],
    [5, 2, 0, 0, 0, 0, 0, 0, 0],
    [0, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]

# a grid with no solution
a_  :				Matrix						= [
    [5, 0, 6, 5, 0, 8, 4, 0, 3],
    [5, 2, 0, 0, 0, 0, 0, 0, 2],
    [1, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]





def __snake_case					(      UpperCAmelCase_						:						Matrix					,				UpperCAmelCase_						:						int					,				UpperCAmelCase_						:						int					,				UpperCAmelCase_						:						int		):
 for i in range(9		):
  if grid[row][i] == n or grid[i][column] == n:
   return False

 for i in range(3		):
  for j in range(3		):
   if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
    return False

 return True





def __snake_case					(      UpperCAmelCase_						:						Matrix		):
 for i in range(9		):
  for j in range(9		):
   if grid[i][j] == 0:
    return i, j
 return None





def __snake_case					(      UpperCAmelCase_						:						Matrix		):
 if location := find_empty_location(UpperCAmelCase_		):
  lowerCamelCase_ ,lowerCamelCase_      =  location
 else:
  # If the location is ``None``, then the grid is solved.
  return grid

 for digit in range(1					,				10		):
  if is_safe(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_		):
   lowerCamelCase_      =  digit

   if sudoku(UpperCAmelCase_		) is not None:
    return grid

   lowerCamelCase_      =  0

 return None





def __snake_case					(      UpperCAmelCase_						:						Matrix		):
 for row in grid:
  for cell in row:
   print(UpperCAmelCase_					,				end=" "		)
  print()


if __name__ == "__main__":
     # make a copy of grid so that you can compare with the unmodified grid
     for example_grid in (initial_grid, no_solution):
          print("""\nExample grid:\n""" + """=""" * 20)
          print_solution(example_grid)
          print("""\nExample grid solution:""")
          a_  :				int						= sudoku(example_grid)
          if solution is not None:
               print_solution(solution)
          else:
               print("""Cannot find a solution.""")




"""simple docstring"""







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

import yaml







class        UpperCamelCase		(			yaml.SafeLoader  ):
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_	:				str													=	[self.constructed_objects[key_node] for key_node, _ in node.value]
        lowercase_	:				str													=	[tuple(__UpperCamelCase			) if isinstance(__UpperCamelCase     ,__UpperCamelCase			) else key for key in keys]
        lowercase_	:				List[Any]													=	Counter(__UpperCamelCase			)
        lowercase_	:				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     ,__UpperCamelCase     ,__UpperCamelCase=False			)	->      List[Any]:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	super().construct_mapping(__UpperCamelCase     ,deep=__UpperCamelCase			)
        self._check_no_duplicates_on_constructed_node(__UpperCamelCase			)
        return mapping






def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     str      ):
     lowercase_	:				Tuple													=	list(readme_content.splitlines()      )
     if full_content and full_content[0] == "---" and "---" in full_content[1:]:
          lowercase_	:				Dict													=	full_content[1:].index('---'      ) + 1
          lowercase_	:				Optional[int]													=	'\n'.join(full_content[1:sep_idx]      )
          return yamlblock, "\n".join(full_content[sep_idx + 1 :]      )

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







class        UpperCamelCase		(			lowercase_  ):
   # class attributes
   lowercase							=  {'train_eval_index'}  # train-eval-index in the YAML metadata
   @classmethod
   def 			_UpperCAmelCase   (			cls     ,__UpperCamelCase			)	->      "DatasetMetadata":


        '''simple docstring'''


        with open(__UpperCamelCase     ,encoding='utf-8'			) as readme_file:
             lowercase_							,  lowercase_	:				Optional[int]													=	_split_yaml_from_readme(readme_file.read()			)
        if yaml_string is not None:
             return cls.from_yaml_string(__UpperCamelCase			)
        else:
             return cls()
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      List[Any]:


        '''simple docstring'''


        if path.exists():
             with open(__UpperCamelCase     ,encoding='utf-8'			) as readme_file:
                  lowercase_	:				Dict													=	readme_file.read()
        else:
             lowercase_	:				int													=	None
        lowercase_	:				Any													=	self._to_readme(__UpperCamelCase			)
        with open(__UpperCamelCase     ,'w'     ,encoding='utf-8'			) as readme_file:
             readme_file.write(__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase = None			)	->      str:


        '''simple docstring'''


        if readme_content is not None:
             lowercase_							,  lowercase_	:				Optional[Any]													=	_split_yaml_from_readme(__UpperCamelCase			)
             lowercase_	:				Optional[Any]													=	'---\n' + self.to_yaml_string() + '---\n' + content
        else:
             lowercase_	:				Tuple													=	'---\n' + self.to_yaml_string() + '---\n'
        return full_content
   @classmethod
   def 			_UpperCAmelCase   (			cls     ,__UpperCamelCase			)	->      "DatasetMetadata":


        '''simple docstring'''


        lowercase_	:				List[str]													=	yaml.load(__UpperCamelCase     ,Loader=_NoDuplicateSafeLoader			) or {}

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







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






__SCREAMING_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

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

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

