

from __future__ import annotations

__A      		=     []



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     bool:



  """simple docstring"""






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



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     bool:



  """simple docstring"""






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



def 				snake_case_(_UpperCamelCase					)							->     None:



  """simple docstring"""






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


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

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




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A      		=     {'''configuration_mra''': ['''MRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MraConfig''']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A      		=     [
     '''MRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''MraForMaskedLM''',
     '''MraForMultipleChoice''',
     '''MraForQuestionAnswering''',
     '''MraForSequenceClassification''',
     '''MraForTokenClassification''',
     '''MraLayer''',
     '''MraModel''',
     '''MraPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mra import (
      MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
      MraForMaskedLM,
      MraForMultipleChoice,
      MraForQuestionAnswering,
      MraForSequenceClassification,
      MraForTokenClassification,
      MraLayer,
      MraModel,
      MraPreTrainedModel,
  )
else:
 import sys

 __A      		=     _LazyModule(__name__, globals()['''__file__'''], _import_structure)

