






"""simple docstring"""




from __future__ import annotations

A_		:		List[str]		     =			[]

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    bool:
  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:
  if row >= len(UpperCAmelCase__  ):
    solution.append(UpperCAmelCase__  )
    printboard(UpperCAmelCase__  )
    print()
    return True
  for i in range(len(UpperCAmelCase__  )  ):
    if is_safe(UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  ):
      UpperCamelCase_: Tuple   				=    1
      solve(UpperCAmelCase__     ,   row + 1  )
      UpperCamelCase_: Union[str, Any]   				=    0
  return False

def   snake_case (UpperCAmelCase__  )	->    None:
  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_		:		int		     =			8
A_		:		Union[str, Any]		     =			[[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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A_		:		Tuple		     =			{
    'configuration_funnel': ['FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FunnelConfig'],
    'convert_funnel_original_tf_checkpoint_to_pytorch': [],
    'tokenization_funnel': ['FunnelTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_		:		List[str]		     =			['FunnelTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_		:		List[str]		     =			[
       'FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
       'FunnelBaseModel',
       'FunnelForMaskedLM',
       'FunnelForMultipleChoice',
       'FunnelForPreTraining',
       'FunnelForQuestionAnswering',
       'FunnelForSequenceClassification',
       'FunnelForTokenClassification',
       'FunnelModel',
       'FunnelPreTrainedModel',
       'load_tf_weights_in_funnel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_		:		str		     =			[
       'TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFFunnelBaseModel',
       'TFFunnelForMaskedLM',
       'TFFunnelForMultipleChoice',
       'TFFunnelForPreTraining',
       'TFFunnelForQuestionAnswering',
       'TFFunnelForSequenceClassification',
       'TFFunnelForTokenClassification',
       'TFFunnelModel',
       'TFFunnelPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
   from .tokenization_funnel import FunnelTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_funnel_fast import FunnelTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_funnel import (
          FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
          FunnelBaseModel,
          FunnelForMaskedLM,
          FunnelForMultipleChoice,
          FunnelForPreTraining,
          FunnelForQuestionAnswering,
          FunnelForSequenceClassification,
          FunnelForTokenClassification,
          FunnelModel,
          FunnelPreTrainedModel,
          load_tf_weights_in_funnel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_funnel import (
          TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFFunnelBaseModel,
          TFFunnelForMaskedLM,
          TFFunnelForMultipleChoice,
          TFFunnelForPreTraining,
          TFFunnelForQuestionAnswering,
          TFFunnelForSequenceClassification,
          TFFunnelForTokenClassification,
          TFFunnelModel,
          TFFunnelPreTrainedModel,
      )

else:
   import sys

   A_		:		Any		     =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)