



'''simple docstring'''

from typing import TYPE_CHECKING

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


UpperCAmelCase     		=   {
    'configuration_bert': ['BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BertConfig', 'BertOnnxConfig'],
    'tokenization_bert': ['BasicTokenizer', 'BertTokenizer', 'WordpieceTokenizer'],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase     		=   ['BertTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase     		=   [
				    'BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'BertForMaskedLM',
				    'BertForMultipleChoice',
				    'BertForNextSentencePrediction',
				    'BertForPreTraining',
				    'BertForQuestionAnswering',
				    'BertForSequenceClassification',
				    'BertForTokenClassification',
				    'BertLayer',
				    'BertLMHeadModel',
				    'BertModel',
				    'BertPreTrainedModel',
				    'load_tf_weights_in_bert',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase     		=   [
				    'TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFBertEmbeddings',
				    'TFBertForMaskedLM',
				    'TFBertForMultipleChoice',
				    'TFBertForNextSentencePrediction',
				    'TFBertForPreTraining',
				    'TFBertForQuestionAnswering',
				    'TFBertForSequenceClassification',
				    'TFBertForTokenClassification',
				    'TFBertLMHeadModel',
				    'TFBertMainLayer',
				    'TFBertModel',
				    'TFBertPreTrainedModel',
				]
try:
				if not is_tensorflow_text_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase     		=   ['TFBertTokenizer']

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase     		=   [
				    'FlaxBertForCausalLM',
				    'FlaxBertForMaskedLM',
				    'FlaxBertForMultipleChoice',
				    'FlaxBertForNextSentencePrediction',
				    'FlaxBertForPreTraining',
				    'FlaxBertForQuestionAnswering',
				    'FlaxBertForSequenceClassification',
				    'FlaxBertForTokenClassification',
				    'FlaxBertModel',
				    'FlaxBertPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
				from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_bert_fast import BertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_bert import (
								    BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BertForMaskedLM,
								    BertForMultipleChoice,
								    BertForNextSentencePrediction,
								    BertForPreTraining,
								    BertForQuestionAnswering,
								    BertForSequenceClassification,
								    BertForTokenClassification,
								    BertLayer,
								    BertLMHeadModel,
								    BertModel,
								    BertPreTrainedModel,
								    load_tf_weights_in_bert,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_bert import (
								    TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFBertEmbeddings,
								    TFBertForMaskedLM,
								    TFBertForMultipleChoice,
								    TFBertForNextSentencePrediction,
								    TFBertForPreTraining,
								    TFBertForQuestionAnswering,
								    TFBertForSequenceClassification,
								    TFBertForTokenClassification,
								    TFBertLMHeadModel,
								    TFBertMainLayer,
								    TFBertModel,
								    TFBertPreTrainedModel,
								)

				try:
								if not is_tensorflow_text_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_bert_tf import TFBertTokenizer

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_bert import (
								    FlaxBertForCausalLM,
								    FlaxBertForMaskedLM,
								    FlaxBertForMultipleChoice,
								    FlaxBertForNextSentencePrediction,
								    FlaxBertForPreTraining,
								    FlaxBertForQuestionAnswering,
								    FlaxBertForSequenceClassification,
								    FlaxBertForTokenClassification,
								    FlaxBertModel,
								    FlaxBertPreTrainedModel,
								)

else:
				import sys

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




'''simple docstring'''

from __future__ import annotations

UpperCAmelCase     		=   []






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list[list[int]]							,		_SCREAMING_SNAKE_CASE					:						int							,		_SCREAMING_SNAKE_CASE					:						int							)      ->						bool:







	"""simple docstring"""
	for i in range(len(_SCREAMING_SNAKE_CASE							)							):
		if board[row][i] == 1:
			return False
	for i in range(len(_SCREAMING_SNAKE_CASE							)							):
		if board[i][column] == 1:
			return False
	for i, j in zip(range(_SCREAMING_SNAKE_CASE							,		-1							,		-1							)							,		range(_SCREAMING_SNAKE_CASE							,		-1							,		-1							)							):
		if board[i][j] == 1:
			return False
	for i, j in zip(range(_SCREAMING_SNAKE_CASE							,		-1							,		-1							)							,		range(_SCREAMING_SNAKE_CASE							,		len(_SCREAMING_SNAKE_CASE							)							)							):
		if board[i][j] == 1:
			return False
	return True






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list[list[int]]							,		_SCREAMING_SNAKE_CASE					:						int							)      ->						bool:







	"""simple docstring"""
	if row >= len(_SCREAMING_SNAKE_CASE							):
		solution.append(_SCREAMING_SNAKE_CASE							)
		printboard(_SCREAMING_SNAKE_CASE							)
		print()
		return True
	for i in range(len(_SCREAMING_SNAKE_CASE							)							):
		if is_safe(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							):
			lowerCAmelCase   =     1
			solve(_SCREAMING_SNAKE_CASE							,		row + 1							)
			lowerCAmelCase   =     0
	return False






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list[list[int]]							)      ->						None:







	"""simple docstring"""
	for i in range(len(_SCREAMING_SNAKE_CASE							)							):
		for j in range(len(_SCREAMING_SNAKE_CASE							)							):
			if board[i][j] == 1:
				print("""Q"""							,		end=""" """							)
			else:
				print("""."""							,		end=""" """							)
		print()


# n=int(input("The no. of queens"))
UpperCAmelCase     		=   8
UpperCAmelCase     		=   [[0 for i in range(n)] for j in range(n)]
solve(board, 0)





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