

from __future__ import annotations

lowercase_             =     []
def   a   (    A__			:     list[list[int]]   ,     A__			:     int   ,     A__			:     int  )					->   bool:





      """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__			:     list[list[int]]   ,     A__			:     int  )					->   bool:





      """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__  ):
                  _lowercase			 =1
                  solve(A__   ,     row + 1  )
                  _lowercase			 =0
      return False
def   a   (    A__			:     list[list[int]]  )					->   None:





      """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"))
lowercase_             =     8
lowercase_             =     [[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_torch_available,
    is_vision_available,
)


lowercase_             =     {'configuration_beit': ['BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BeitConfig', 'BeitOnnxConfig']}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_             =     ['BeitFeatureExtractor']
  lowercase_             =     ['BeitImageProcessor']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_             =     [
      'BEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'BeitForImageClassification',
      'BeitForMaskedImageModeling',
      'BeitForSemanticSegmentation',
      'BeitModel',
      'BeitPreTrainedModel',
  ]


try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_             =     [
      'FlaxBeitForImageClassification',
      'FlaxBeitForMaskedImageModeling',
      'FlaxBeitModel',
      'FlaxBeitPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_beit import BeitFeatureExtractor
    from .image_processing_beit import BeitImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_beit import (
        BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BeitForImageClassification,
        BeitForMaskedImageModeling,
        BeitForSemanticSegmentation,
        BeitModel,
        BeitPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_beit import (
        FlaxBeitForImageClassification,
        FlaxBeitForMaskedImageModeling,
        FlaxBeitModel,
        FlaxBeitPreTrainedModel,
    )


else:
  import sys

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


