


from __future__ import annotations

A	:		Any    =       []






def 			UpperCamelCase (     __magic_name__					:							list[list[int]]   ,   __magic_name__					:							int   ,   __magic_name__					:							int					)       ->  bool:
		"""simple docstring"""




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






def 			UpperCamelCase (     __magic_name__					:							list[list[int]]   ,   __magic_name__					:							int					)       ->  bool:
		"""simple docstring"""




		if row >= len(__magic_name__					):
				solution.append(__magic_name__					)
				printboard(__magic_name__					)
				print()
				return True
		for i in range(len(__magic_name__					)					):
				if is_safe(__magic_name__   ,   __magic_name__   ,   __magic_name__					):
						lowercase__					      =		1
						solve(__magic_name__   ,   row + 1					)
						lowercase__					      =		0
		return False






def 			UpperCamelCase (     __magic_name__					:							list[list[int]]					)       ->  None:
		"""simple docstring"""




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


# n=int(input("The no. of queens"))
A	:		Dict    =       8
A	:		List[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_torch_available, is_vision_available


A	:		Dict    =       {'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A	:		Tuple    =       ['GLPNFeatureExtractor']
	A	:		List[str]    =       ['GLPNImageProcessor']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A	:		Optional[int]    =       [
	    'GLPN_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'GLPNForDepthEstimation',
	    'GLPNLayer',
	    'GLPNModel',
	    'GLPNPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_glpn import GLPNFeatureExtractor
		from .image_processing_glpn import GLPNImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_glpn import (
		    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GLPNForDepthEstimation,
		    GLPNLayer,
		    GLPNModel,
		    GLPNPreTrainedModel,
		)


else:
	import sys

	A	:		Tuple    =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

