






"""simple docstring"""
def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__=0					):
	return sorted(snake_case__   ,   key=lambda snake_case__					: x[column]					)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__=float("""inf"""					)					):

	for i in range(points_counts - 1					):
		for j in range(i + 1   ,   snake_case__					):
			A_		:						Optional[int]	    =   euclidean_distance_sqr(points[i]   ,   points[j]					)
			if current_dis < min_dis:
				A_		:						Optional[Any]	    =   current_dis
	return min_dis






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__=float("""inf"""					)					):

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






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):

	# base case
	if points_counts <= 3:
		return dis_between_closest_pair(snake_case__   ,   snake_case__					)

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

	A_		:						int	    =   dis_between_closest_in_strip(
	    snake_case__   ,   len(snake_case__					)   ,   snake_case__					)
	return min(snake_case__   ,   snake_case__					)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	A_		:						Dict	    =   column_based_sort(snake_case__   ,   column=0					)
	A_		:						Any	    =   column_based_sort(snake_case__   ,   column=1					)
	return (
	    closest_pair_of_points_sqr(
	        snake_case__   ,   snake_case__   ,   snake_case__					)
	) ** 0.5


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









"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				Optional[int]    =				["""image_processor""", """tokenizer"""]
	_A       :				str    =				"""AutoImageProcessor"""
	_A       :				Union[str, Any]    =				"""AutoTokenizer"""





	def __init__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		super().__init__(lowerCAmelCase_     ,     lowerCAmelCase_							)
		A_		:						Union[str, Any]	    =   self.image_processor





	def __call__(self     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     **lowerCAmelCase_							):

		if text is None and images is None:
			raise ValueError("""You have to specify either text or images. Both cannot be none."""							)

		if text is not None:
			A_		:						Optional[int]	    =   self.tokenizer(lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_     ,     **lowerCAmelCase_							)

		if images is not None:
			A_		:						List[Any]	    =   self.image_processor(lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_     ,     **lowerCAmelCase_							)

		if text is not None and images is not None:
			A_		:						int	    =   image_features.pixel_values
			return encoding
		elif text is not None:
			return encoding
		else:
			return BatchEncoding(data=dict(**lowerCAmelCase_							)     ,     tensor_type=lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     *lowerCAmelCase_     ,     **lowerCAmelCase_							):
		return self.tokenizer.batch_decode(*lowerCAmelCase_     ,     **lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     *lowerCAmelCase_     ,     **lowerCAmelCase_							):
		return self.tokenizer.decode(*lowerCAmelCase_     ,     **lowerCAmelCase_							)





	@property
	def    lowerCamelCase(self							):
		return ["input_ids", "attention_mask", "pixel_values"]

