
'''simple docstring'''






from __future__ import annotations

A         =   []





def  UpperCAmelCase				(	UpperCAmelCase__				:				list[list[int]]							,				UpperCAmelCase__				:				int							,				UpperCAmelCase__				:				int):




				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  UpperCAmelCase				(	UpperCAmelCase__				:				list[list[int]]							,				UpperCAmelCase__				:				int):




				if row >= len(UpperCAmelCase__):
								solution.append(UpperCAmelCase__)
								printboard(UpperCAmelCase__)
								print()
								return True
				for i in range(len(UpperCAmelCase__)):
								if is_safe(UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__):
												lowerCamelCase       :      Optional[Any]          =			1
												solve(UpperCAmelCase__							,				row + 1)
												lowerCamelCase       :      Any          =			0
				return False





def  UpperCAmelCase				(	UpperCAmelCase__				:				list[list[int]]):




				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         =   8
A         =   [[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_torch_available,
    is_vision_available,
)


A			:						Union[str, Any]        =  {
    'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvNextConfig', 'ConvNextOnnxConfig']
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A			:						str        =  ['ConvNextFeatureExtractor']
	A			:						Optional[Any]        =  ['ConvNextImageProcessor']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A			:						str        =  [
	    'CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'ConvNextForImageClassification',
	    'ConvNextModel',
	    'ConvNextPreTrainedModel',
	    'ConvNextBackbone',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A			:						List[str]        =  [
	    'TFConvNextForImageClassification',
	    'TFConvNextModel',
	    'TFConvNextPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_convnext import ConvNextFeatureExtractor
		from .image_processing_convnext import ConvNextImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_convnext import (
		    CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ConvNextBackbone,
		    ConvNextForImageClassification,
		    ConvNextModel,
		    ConvNextPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
	import sys

	A			:						int        =  _LazyModule(__name__, globals()['__file__'], _import_structure)

