



"""simple docstring"""






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


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


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

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


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

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


def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case      )		->  Tuple:

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

			# recursion
			__lowerCAmelCase :    Optional[int]				=			points_counts // 2
			__lowerCAmelCase :    str				=			closest_pair_of_points_sqr(
			    __snake_case   ,points_sorted_on_y[:mid]   ,__snake_case      )
			__lowerCAmelCase :    Any				=			closest_pair_of_points_sqr(
			    __snake_case   ,points_sorted_on_y[mid:]   ,points_counts - mid      )
			__lowerCAmelCase :    Optional[int]				=			min(__snake_case   ,__snake_case      )
			__lowerCAmelCase :    Tuple				=			[]
			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 :    Any				=			dis_between_closest_in_strip(
			    __snake_case   ,len(__snake_case      )   ,__snake_case      )
			return min(__snake_case   ,__snake_case      )


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




"""simple docstring"""






import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class      A__  (    __SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	@register_to_config
	def __init__(	self:						List[str]			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						float			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						str			,     _SCREAMING_SNAKE_CASE:						bool = False			,     )							->			Any:

				"""simple docstring"""

				super().__init__()

				__lowerCAmelCase :    str				=			nn.Embedding(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Dict				=			nn.Embedding(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    str				=			False

				__lowerCAmelCase :    Optional[Any]				=			nn.Dropout(p=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Dict				=			TaConfig(
				    vocab_size=_SCREAMING_SNAKE_CASE			,     d_model=_SCREAMING_SNAKE_CASE			,     num_heads=_SCREAMING_SNAKE_CASE			,     d_kv=_SCREAMING_SNAKE_CASE			,     d_ff=_SCREAMING_SNAKE_CASE			,     dropout_rate=_SCREAMING_SNAKE_CASE			,     feed_forward_proj=_SCREAMING_SNAKE_CASE			,     is_decoder=_SCREAMING_SNAKE_CASE			,     is_encoder_decoder=_SCREAMING_SNAKE_CASE			,     )

				__lowerCAmelCase :    str				=			nn.ModuleList()
				for lyr_num in range(_SCREAMING_SNAKE_CASE):
							__lowerCAmelCase :    int				=			TaBlock(_SCREAMING_SNAKE_CASE)
							self.encoders.append(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Dict				=			TaLayerNorm(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Any				=			nn.Dropout(p=_SCREAMING_SNAKE_CASE)







	def 				_SCREAMING_SNAKE_CASE  (	self:						Any			,     _SCREAMING_SNAKE_CASE:						Tuple			,     _SCREAMING_SNAKE_CASE:						str)							->			List[str]:

				"""simple docstring"""

				__lowerCAmelCase :    Union[str, Any]				=			self.token_embedder(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Optional[Any]				=			encoder_input_tokens.shape[1]
				__lowerCAmelCase :    List[Any]				=			torch.arange(_SCREAMING_SNAKE_CASE			,     device=encoder_input_tokens.device)
				x += self.position_encoding(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    List[Any]				=			self.dropout_pre(_SCREAMING_SNAKE_CASE)

				# inverted the attention mask
				__lowerCAmelCase :    List[Any]				=			encoder_input_tokens.size()
				__lowerCAmelCase :    Any				=			self.get_extended_attention_mask(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

				for lyr in self.encoders:
							__lowerCAmelCase :    Union[str, Any]				=			lyr(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)[0]
				__lowerCAmelCase :    int				=			self.layer_norm(_SCREAMING_SNAKE_CASE)

				return self.dropout_post(_SCREAMING_SNAKE_CASE), encoder_inputs_mask