



from typing import TYPE_CHECKING

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


__snake_case		:str   	=  {
    '''configuration_mobilebert''': [
        '''MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileBertConfig''',
        '''MobileBertOnnxConfig''',
    ],
    '''tokenization_mobilebert''': ['''MobileBertTokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case		:Tuple   	=  ['''MobileBertTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case		:List[Any]   	=  [
     '''MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''MobileBertForMaskedLM''',
     '''MobileBertForMultipleChoice''',
     '''MobileBertForNextSentencePrediction''',
     '''MobileBertForPreTraining''',
     '''MobileBertForQuestionAnswering''',
     '''MobileBertForSequenceClassification''',
     '''MobileBertForTokenClassification''',
     '''MobileBertLayer''',
     '''MobileBertModel''',
     '''MobileBertPreTrainedModel''',
     '''load_tf_weights_in_mobilebert''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case		:Union[str, Any]   	=  [
     '''TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFMobileBertForMaskedLM''',
     '''TFMobileBertForMultipleChoice''',
     '''TFMobileBertForNextSentencePrediction''',
     '''TFMobileBertForPreTraining''',
     '''TFMobileBertForQuestionAnswering''',
     '''TFMobileBertForSequenceClassification''',
     '''TFMobileBertForTokenClassification''',
     '''TFMobileBertMainLayer''',
     '''TFMobileBertModel''',
     '''TFMobileBertPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_mobilebert import (
     MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     MobileBertConfig,
     MobileBertOnnxConfig,
 )
 from .tokenization_mobilebert import MobileBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_mobilebert_fast import MobileBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mobilebert import (
      MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      MobileBertForMaskedLM,
      MobileBertForMultipleChoice,
      MobileBertForNextSentencePrediction,
      MobileBertForPreTraining,
      MobileBertForQuestionAnswering,
      MobileBertForSequenceClassification,
      MobileBertForTokenClassification,
      MobileBertLayer,
      MobileBertModel,
      MobileBertPreTrainedModel,
      load_tf_weights_in_mobilebert,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_mobilebert import (
      TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFMobileBertForMaskedLM,
      TFMobileBertForMultipleChoice,
      TFMobileBertForNextSentencePrediction,
      TFMobileBertForPreTraining,
      TFMobileBertForQuestionAnswering,
      TFMobileBertForSequenceClassification,
      TFMobileBertForTokenClassification,
      TFMobileBertMainLayer,
      TFMobileBertModel,
      TFMobileBertPreTrainedModel,
  )

else:
 import sys

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










from __future__ import annotations

__snake_case		:Optional[Any]   	=  []





def   __snake_case			(    _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   __snake_case			(    _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       ):
            __a		 =       1
            solve(_UpperCAmelCase     ,				row + 1       )
            __a		 =       0
    return False





def   __snake_case			(    _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"))
__snake_case		:Optional[Any]   	=  8
__snake_case		:Tuple   	=  [[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print('''The total no. of solutions are :''', len(solution))





