



"""simple docstring"""






def     _lowercase       (						__snake_case      ,__snake_case			)		->     Optional[Any]:
			return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2


def     _lowercase       (						__snake_case      ,__snake_case=0			)		->     Optional[int]:
			return sorted(__snake_case      ,key=lambda __snake_case			: x[column]			)


def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case=float("inf"			)			)		->     Union[str, Any]:

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


def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case=float("inf"			)			)		->     int:

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


def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Optional[Any]:

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

			# recursion
			__lowerCAmelCase   :						Union[str, Any]				= points_counts // 2
			__lowerCAmelCase   :						Tuple				= closest_pair_of_points_sqr(
			    __snake_case      ,points_sorted_on_y[:mid]      ,__snake_case			)
			__lowerCAmelCase   :						Tuple				= closest_pair_of_points_sqr(
			    __snake_case      ,points_sorted_on_y[mid:]      ,points_counts - mid			)
			__lowerCAmelCase   :						Any				= min(__snake_case      ,__snake_case			)
			__lowerCAmelCase   :						List[Any]				= []
			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			)

			__lowerCAmelCase   :						int				= dis_between_closest_in_strip(
			    __snake_case      ,len(__snake_case			)      ,__snake_case			)
			return min(__snake_case      ,__snake_case			)


def     _lowercase       (						__snake_case      ,__snake_case			)		->     Optional[Any]:
			__lowerCAmelCase   :						Any				= column_based_sort(__snake_case      ,column=0			)
			__lowerCAmelCase   :						List[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__":
	__snake_case							:     Tuple   =     [(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
	print('Distance:', closest_pair_of_points(points, len(points)))




"""simple docstring"""






import logging

from transformers.configuration_utils import PretrainedConfig


__snake_case							:     Tuple   =					logging.getLogger(__name__)



class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			'masked_bert'

	def __init__(  self:	str ,    _SCREAMING_SNAKE_CASE:	Tuple=3_0522 ,    _SCREAMING_SNAKE_CASE:	Optional[int]=768 ,    _SCREAMING_SNAKE_CASE:	List[str]=12 ,    _SCREAMING_SNAKE_CASE:	Any=12 ,    _SCREAMING_SNAKE_CASE:	Optional[int]=3072 ,    _SCREAMING_SNAKE_CASE:	List[str]="gelu" ,    _SCREAMING_SNAKE_CASE:	List[str]=0.1 ,    _SCREAMING_SNAKE_CASE:	str=0.1 ,    _SCREAMING_SNAKE_CASE:	Dict=512 ,    _SCREAMING_SNAKE_CASE:	List[Any]=2 ,    _SCREAMING_SNAKE_CASE:	Tuple=0.02 ,    _SCREAMING_SNAKE_CASE:	int=1e-12 ,    _SCREAMING_SNAKE_CASE:	int=0 ,    _SCREAMING_SNAKE_CASE:	Tuple="topK" ,    _SCREAMING_SNAKE_CASE:	Any="constant" ,    _SCREAMING_SNAKE_CASE:	int=0.0 ,    **_SCREAMING_SNAKE_CASE:	Any ,    )       -> List[str]:

				"""simple docstring"""

				super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Union[str, Any]				= vocab_size
				__lowerCAmelCase   :						List[str]				= hidden_size
				__lowerCAmelCase   :						Any				= num_hidden_layers
				__lowerCAmelCase   :						Dict				= num_attention_heads
				__lowerCAmelCase   :						Union[str, Any]				= hidden_act
				__lowerCAmelCase   :						Optional[Any]				= intermediate_size
				__lowerCAmelCase   :						int				= hidden_dropout_prob
				__lowerCAmelCase   :						List[Any]				= attention_probs_dropout_prob
				__lowerCAmelCase   :						int				= max_position_embeddings
				__lowerCAmelCase   :						str				= type_vocab_size
				__lowerCAmelCase   :						Any				= initializer_range
				__lowerCAmelCase   :						Optional[int]				= layer_norm_eps
				__lowerCAmelCase   :						Tuple				= pruning_method
				__lowerCAmelCase   :						Union[str, Any]				= mask_init
				__lowerCAmelCase   :						str				= mask_scale