'''simple docstring'''





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    A_ (       a_ ):

  def __init__(		self :      Any     ,							__SCREAMING_SNAKE_CASE :      NestedDataStructureLike[PathLike]     ,							__SCREAMING_SNAKE_CASE :      Optional[NamedSplit] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[Features] = None     ,							__SCREAMING_SNAKE_CASE :      str = None     ,							__SCREAMING_SNAKE_CASE :      bool = False     ,							__SCREAMING_SNAKE_CASE :      bool = False     ,							__SCREAMING_SNAKE_CASE :      Optional[str] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[int] = None     ,							**__SCREAMING_SNAKE_CASE :      Union[str, Any]     ,							):
   super().__init__(
       __SCREAMING_SNAKE_CASE     ,							split=__SCREAMING_SNAKE_CASE     ,							features=__SCREAMING_SNAKE_CASE     ,							cache_dir=__SCREAMING_SNAKE_CASE     ,							keep_in_memory=__SCREAMING_SNAKE_CASE     ,							streaming=__SCREAMING_SNAKE_CASE     ,							num_proc=__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE     ,							)
   __a       =  field
   __a       =  path_or_paths if isinstance(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ) else {self.split: path_or_paths}
   __a       =  Json(
       cache_dir=__SCREAMING_SNAKE_CASE     ,							data_files=__SCREAMING_SNAKE_CASE     ,							features=__SCREAMING_SNAKE_CASE     ,							field=__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE     ,							)

  def 		_UpperCAmelCase   (		self :      List[Any]       ):
   # Build iterable dataset
   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=__SCREAMING_SNAKE_CASE     ,							download_mode=__SCREAMING_SNAKE_CASE     ,							verification_mode=__SCREAMING_SNAKE_CASE     ,							base_path=__SCREAMING_SNAKE_CASE     ,							num_proc=self.num_proc     ,							)
    __a       =  self.builder.as_dataset(
        split=self.split     ,							verification_mode=__SCREAMING_SNAKE_CASE     ,							in_memory=self.keep_in_memory       )
   return dataset
class    A_ :

  def __init__(		self :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Dataset     ,							__SCREAMING_SNAKE_CASE :      Union[PathLike, BinaryIO]     ,							__SCREAMING_SNAKE_CASE :      Optional[int] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[int] = None     ,							**__SCREAMING_SNAKE_CASE :      Any     ,							):
   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 		_UpperCAmelCase   (		self :      List[Any]       ):
   __a       =  self.to_json_kwargs.pop("path_or_buf"     ,							__SCREAMING_SNAKE_CASE       )
   __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"     ,							__SCREAMING_SNAKE_CASE       )

   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=__SCREAMING_SNAKE_CASE       ) as buffer:
     __a       =  self._write(file_obj=__SCREAMING_SNAKE_CASE     ,							orient=__SCREAMING_SNAKE_CASE     ,							lines=__SCREAMING_SNAKE_CASE     ,							index=__SCREAMING_SNAKE_CASE     ,							**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=__SCREAMING_SNAKE_CASE     ,							lines=__SCREAMING_SNAKE_CASE     ,							index=__SCREAMING_SNAKE_CASE     ,							**self.to_json_kwargs       )
   return written

  def 		_UpperCAmelCase   (		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      Tuple       ):
   __a       ,     __a       ,     __a       ,     __a       ,     __a       =  args

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

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      BinaryIO     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      List[str]     ,							**__SCREAMING_SNAKE_CASE :      Dict     ,							):
   __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(__SCREAMING_SNAKE_CASE       )
   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     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )]     ,							)     ,							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(__SCREAMING_SNAKE_CASE       )

   return written



from __future__ import annotations

SCREAMING_SNAKE_CASE :	Optional[int]							     =		[]



def   __A		(			_A							,				_A							,				_A					):



 """simple docstring"""


 for i in range(len(_A					)					):
  if board[row][i] == 1:
   return False
 for i in range(len(_A					)					):
  if board[i][column] == 1:
   return False
 for i, j in zip(range(_A							,				-1							,				-1					)							,				range(_A							,				-1							,				-1					)					):
  if board[i][j] == 1:
   return False
 for i, j in zip(range(_A							,				-1							,				-1					)							,				range(_A							,				len(_A					)					)					):
  if board[i][j] == 1:
   return False
 return True



def   __A		(			_A							,				_A					):



 """simple docstring"""


 if row >= len(_A					):
  solution.append(_A					)
  printboard(_A					)
  print()
  return True
 for i in range(len(_A					)					):
  if is_safe(_A							,				_A							,				_A					):
   __a       =  1
   solve(_A							,				row + 1					)
   __a       =  0
 return False



def   __A		(			_A					):



 """simple docstring"""


 for i in range(len(_A					)					):
  for j in range(len(_A					)					):
   if board[i][j] == 1:
    print("Q"							,				end=" "					)
   else:
    print("."							,				end=" "					)
  print()


# n=int(input("The no. of queens"))
SCREAMING_SNAKE_CASE :	List[str]							     =		8
SCREAMING_SNAKE_CASE :	str							     =		[[0 for i in range(n)] for j in range(n)]
solve(board, 0)






print("""The total no. of solutions are :""", len(solution))


