






from __future__ import annotations

a_		:    str   							=     []






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






def 					lowerCamelCase__					(_UpperCAmelCase):
			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_		:    Tuple   							=     8
a_		:    int   							=     [[0 for i in range(n)] for j in range(n)]
solve(board, 0)







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





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase_           =				{'''configuration_encoder_decoder''': ['''EncoderDecoderConfig''']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_           =				['''EncoderDecoderModel''']

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_           =				['''TFEncoderDecoderModel''']

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_           =				['''FlaxEncoderDecoderModel''']

if TYPE_CHECKING:
  from .configuration_encoder_decoder import EncoderDecoderConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_encoder_decoder import EncoderDecoderModel

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_encoder_decoder import TFEncoderDecoderModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel

else:
  import sys

  UpperCAmelCase_           =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)