



'''simple docstring'''

def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase=0					)				->					Tuple:





			"""simple docstring"""







			return sorted(lowercase__				,							key=lambda __UpperCAmelCase					: x[column]					)







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase=float('inf'					)					)				->					Any:





			"""simple docstring"""







			for i in range(points_counts - 1					):
						for j in range(i + 1				,							lowercase__					):
									snake_case:    List[Any]      							=euclidean_distance_sqr(points[i]				,							points[j]					)
									if current_dis < min_dis:
												snake_case:    Union[str, Any]      							=current_dis
			return min_dis







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase=float('inf'					)					)				->					Union[str, Any]:





			"""simple docstring"""







			for i in range(min(6				,							points_counts - 1					)				,							lowercase__					):
						for j in range(max(0				,							i - 6					)				,							lowercase__					):
									snake_case:    List[Any]      							=euclidean_distance_sqr(points[i]				,							points[j]					)
									if current_dis < min_dis:
												snake_case:    Optional[Any]      							=current_dis
			return min_dis







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					List[str]:





			"""simple docstring"""







			if points_counts <= 3:
						return dis_between_closest_pair(lowercase__				,							lowercase__					)

			# recursion
			snake_case:    str      							=points_counts // 2
			snake_case:    Tuple      							=closest_pair_of_points_sqr(
			    lowercase__				,							points_sorted_on_y[:mid]				,							lowercase__					)
			snake_case:    Union[str, Any]      							=closest_pair_of_points_sqr(
			    lowercase__				,							points_sorted_on_y[mid:]				,							points_counts - mid					)
			snake_case:    Optional[Any]      							=min(lowercase__				,							lowercase__					)
			snake_case:    Union[str, Any]      							=[]
			for point in points_sorted_on_x:
						if abs(point[0] - points_sorted_on_x[mid][0]					) < closest_pair_dis:
									cross_strip.append(lowercase__					)

			snake_case:    Optional[Any]      							=dis_between_closest_in_strip(
			    lowercase__				,							len(lowercase__					)				,							lowercase__					)
			return min(lowercase__				,							lowercase__					)







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					Tuple:





			"""simple docstring"""







			snake_case:    int      							=column_based_sort(lowercase__				,							column=0					)
			snake_case:    List[Any]      							=column_based_sort(lowercase__				,							column=1					)
			return (
			    closest_pair_of_points_sqr(
			        lowercase__				,							lowercase__				,							lowercase__					)
			) ** 0.5


if __name__ == "__main__":
	a							   =				[(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
	print('Distance:', closest_pair_of_points(points, len(points)))








"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__magic_name__						: Any    =				{'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__						: Dict    =				['GLPNFeatureExtractor']
					__magic_name__						: Union[str, Any]    =				['GLPNImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__						: 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

					__magic_name__						: Optional[int]    =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




