






import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      def __init__(      self      :						Optional[int] ,					__UpperCAmelCase      :						NestedDataStructureLike[PathLike] ,					__UpperCAmelCase      :						Optional[NamedSplit] = None ,					__UpperCAmelCase      :						Optional[Features] = None ,					__UpperCAmelCase      :						str = None ,					__UpperCAmelCase      :						bool = False ,					__UpperCAmelCase      :						bool = False ,					__UpperCAmelCase      :						Optional[str] = None ,					__UpperCAmelCase      :						Optional[int] = None ,					**__UpperCAmelCase      :						Any ,					)    ->str:




       """simple docstring"""


       super().__init__(
           __UpperCAmelCase ,					split=__UpperCAmelCase ,					features=__UpperCAmelCase ,					cache_dir=__UpperCAmelCase ,					keep_in_memory=__UpperCAmelCase ,					streaming=__UpperCAmelCase ,					num_proc=__UpperCAmelCase ,					**__UpperCAmelCase ,					)
       a		  =      field
       a		  =      path_or_paths if isinstance(__UpperCAmelCase ,					__UpperCAmelCase  ) else {self.split: path_or_paths}
       a		  =      Json(
           cache_dir=__UpperCAmelCase ,					data_files=__UpperCAmelCase ,					features=__UpperCAmelCase ,					field=__UpperCAmelCase ,					**__UpperCAmelCase ,					)







      def       __lowerCAmelCase					(      self      :						Any  )    ->Union[str, Any]:




       """simple docstring"""


       if self.streaming:
        a		  =      self.builder.as_streaming_dataset(split=self.split  )
       # Build regular (map-style) dataset
       else:
        a		  =      None
        a		  =      None
        a		  =      None
        a		  =      None

        self.builder.download_and_prepare(
            download_config=__UpperCAmelCase ,					download_mode=__UpperCAmelCase ,					verification_mode=__UpperCAmelCase ,					base_path=__UpperCAmelCase ,					num_proc=self.num_proc ,					)
        a		  =      self.builder.as_dataset(
            split=self.split ,					verification_mode=__UpperCAmelCase ,					in_memory=self.keep_in_memory  )
       return dataset






class 		lowercase_					:






      '''simple docstring'''




      def __init__(      self      :						List[str] ,					__UpperCAmelCase      :						Dataset ,					__UpperCAmelCase      :						Union[PathLike, BinaryIO] ,					__UpperCAmelCase      :						Optional[int] = None ,					__UpperCAmelCase      :						Optional[int] = None ,					**__UpperCAmelCase      :						List[Any] ,					)    ->int:




       """simple docstring"""


       if num_proc is not None and num_proc <= 0:
        raise ValueError(F"""num_proc {num_proc} must be an integer > 0."""  )

       a		  =      dataset
       a		  =      path_or_buf
       a		  =      batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
       a		  =      num_proc
       a		  =      '''utf-8'''
       a		  =      to_json_kwargs







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




       """simple docstring"""


       a		  =      self.to_json_kwargs.pop('''path_or_buf''' ,					__UpperCAmelCase  )
       a		  =      self.to_json_kwargs.pop('''orient''' ,					'''records'''  )
       a		  =      self.to_json_kwargs.pop('''lines''' ,					True if orient == '''records''' else False  )
       a		  =      self.to_json_kwargs.pop('''index''' ,					False if orient in ['''split''', '''table'''] else True  )
       a		  =      self.to_json_kwargs.pop('''compression''' ,					__UpperCAmelCase  )

       if compression not in [None, "infer", "gzip", "bz2", "xz"]:
        raise NotImplementedError(F"""`datasets` currently does not support {compression} compression"""  )

       if isinstance(self.path_or_buf ,					(str, bytes, os.PathLike)  ):
        with fsspec.open(self.path_or_buf ,					'''wb''' ,					compression=__UpperCAmelCase  ) as buffer:
         a		  =      self._write(file_obj=__UpperCAmelCase ,					orient=__UpperCAmelCase ,					lines=__UpperCAmelCase ,					index=__UpperCAmelCase ,					**self.to_json_kwargs  )
       else:
        if compression:
         raise NotImplementedError(
             F"""The compression parameter is not supported when writing to a buffer, but compression={compression}"""
             ''' was passed. Please provide a local path instead.'''  )
        a		  =      self._write(
            file_obj=self.path_or_buf ,					orient=__UpperCAmelCase ,					lines=__UpperCAmelCase ,					index=__UpperCAmelCase ,					**self.to_json_kwargs  )
       return written







      def       __lowerCAmelCase					(      self      :						Any ,					__UpperCAmelCase      :						Optional[Any]  )    ->Dict:




       """simple docstring"""


       a  ,     a  ,     a  ,     a  ,     a		  =      args

       a		  =      query_table(
           table=self.dataset.data ,					key=slice(__UpperCAmelCase ,					offset + self.batch_size  ) ,					indices=self.dataset._indices ,					)
       a		  =      batch.to_pandas().to_json(
           path_or_buf=__UpperCAmelCase ,					orient=__UpperCAmelCase ,					lines=__UpperCAmelCase ,					index=__UpperCAmelCase ,					**__UpperCAmelCase  )
       if not json_str.endswith('''\n'''  ):
        json_str += "\n"
       return json_str.encode(self.encoding  )







      def       __lowerCAmelCase					(      self      :						Dict ,					__UpperCAmelCase      :						BinaryIO ,					__UpperCAmelCase      :						Tuple ,					__UpperCAmelCase      :						Optional[Any] ,					__UpperCAmelCase      :						Optional[int] ,					**__UpperCAmelCase      :						Optional[Any] ,					)    ->int:




       """simple docstring"""


       a		  =      0

       if self.num_proc is None or self.num_proc == 1:
        for offset in logging.tqdm(
            range(0 ,					len(self.dataset  ) ,					self.batch_size  ) ,					unit='''ba''' ,					disable=not logging.is_progress_bar_enabled() ,					desc='''Creating json from Arrow format''' ,					):
         a		  =      self._batch_json((offset, orient, lines, index, to_json_kwargs)  )
         written += file_obj.write(__UpperCAmelCase  )
       else:
        a  ,     a		  =      len(self.dataset  ), self.batch_size
        with multiprocessing.Pool(self.num_proc  ) as pool:
         for json_str in logging.tqdm(
             pool.imap(
                 self._batch_json ,					[(offset, orient, lines, index, to_json_kwargs) for offset in range(0 ,					__UpperCAmelCase ,					__UpperCAmelCase  )] ,					) ,					total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size ,					unit='''ba''' ,					disable=not logging.is_progress_bar_enabled() ,					desc='''Creating json from Arrow format''' ,					):
          written += file_obj.write(__UpperCAmelCase  )

       return written










from __future__ import annotations

UpperCAmelCase__				   =		list[list[int]]

# assigning initial values to the grid
UpperCAmelCase__				   =		[
    [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
UpperCAmelCase__				   =		[
    [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 						_a    (      a :Matrix	,							a :int	,							a :int	,							a :int )							->			bool:
 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 						_a    (      a :Matrix )							->			tuple[int, int] | None:
 for i in range(9 ):
  for j in range(9 ):
   if grid[i][j] == 0:
    return i, j
 return None
def 						_a    (      a :Matrix )							->			Matrix | None:
 if location := find_empty_location(a ):
  a  ,     a		  =      location
 else:
  # If the location is ``None``, then the grid is solved.
  return grid

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

   if sudoku(a ) is not None:
    return grid

   a		  =      0

 return None
def 						_a    (      a :Matrix )							->			None:
 for row in grid:
  for cell in row:
   print(a	,							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:")
          UpperCAmelCase__				   =		sudoku(example_grid)
          if solution is not None:
               print_solution(solution)
          else:
               print("Cannot find a solution.")


