'''simple docstring'''





# 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


SCREAMING_SNAKE_CASE :	List[Any]							     =		{"""configuration_mra""": ["""MRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MraConfig"""]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE :	Any							     =		[
      """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

  SCREAMING_SNAKE_CASE :	List[Any]							     =		_LazyModule(__name__, globals()["""__file__"""], _import_structure)



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))


