



from typing import TYPE_CHECKING

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


__snake_case		:Optional[Any]   	=  {
    '''configuration_albert''': ['''ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''AlbertConfig''', '''AlbertOnnxConfig'''],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case		:Optional[int]   	=  ['''AlbertTokenizer''']

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case		:Optional[int]   	=  ['''AlbertTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case		:Tuple   	=  [
     '''ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''AlbertForMaskedLM''',
     '''AlbertForMultipleChoice''',
     '''AlbertForPreTraining''',
     '''AlbertForQuestionAnswering''',
     '''AlbertForSequenceClassification''',
     '''AlbertForTokenClassification''',
     '''AlbertModel''',
     '''AlbertPreTrainedModel''',
     '''load_tf_weights_in_albert''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case		:Dict   	=  [
     '''TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFAlbertForMaskedLM''',
     '''TFAlbertForMultipleChoice''',
     '''TFAlbertForPreTraining''',
     '''TFAlbertForQuestionAnswering''',
     '''TFAlbertForSequenceClassification''',
     '''TFAlbertForTokenClassification''',
     '''TFAlbertMainLayer''',
     '''TFAlbertModel''',
     '''TFAlbertPreTrainedModel''',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case		:List[str]   	=  [
     '''FlaxAlbertForMaskedLM''',
     '''FlaxAlbertForMultipleChoice''',
     '''FlaxAlbertForPreTraining''',
     '''FlaxAlbertForQuestionAnswering''',
     '''FlaxAlbertForSequenceClassification''',
     '''FlaxAlbertForTokenClassification''',
     '''FlaxAlbertModel''',
     '''FlaxAlbertPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_albert import AlbertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_albert_fast import AlbertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_albert import (
      ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      AlbertForMaskedLM,
      AlbertForMultipleChoice,
      AlbertForPreTraining,
      AlbertForQuestionAnswering,
      AlbertForSequenceClassification,
      AlbertForTokenClassification,
      AlbertModel,
      AlbertPreTrainedModel,
      load_tf_weights_in_albert,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_albert import (
      TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFAlbertForMaskedLM,
      TFAlbertForMultipleChoice,
      TFAlbertForPreTraining,
      TFAlbertForQuestionAnswering,
      TFAlbertForSequenceClassification,
      TFAlbertForTokenClassification,
      TFAlbertMainLayer,
      TFAlbertModel,
      TFAlbertPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_albert import (
      FlaxAlbertForMaskedLM,
      FlaxAlbertForMultipleChoice,
      FlaxAlbertForPreTraining,
      FlaxAlbertForQuestionAnswering,
      FlaxAlbertForSequenceClassification,
      FlaxAlbertForTokenClassification,
      FlaxAlbertModel,
      FlaxAlbertPreTrainedModel,
  )
else:
 import sys

 __snake_case		:Union[str, 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))





