



'''simple docstring'''





def   a_   (		_UpperCAmelCase    :      Tuple ,_UpperCAmelCase    :      str				)    ->  Optional[Any]:
      return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2





def   a_   (		_UpperCAmelCase    :      int ,_UpperCAmelCase    :      str=0				)    ->  Tuple:
      return sorted(UpperCamelCase__ ,key=lambda _UpperCAmelCase				: x[column]				)





def   a_   (		_UpperCAmelCase    :      Optional[int] ,_UpperCAmelCase    :      Tuple ,_UpperCAmelCase    :      Optional[Any]=float('inf'				)				)    ->  Tuple:





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





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





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





def   a_   (		_UpperCAmelCase    :      Optional[Any] ,_UpperCAmelCase    :      int ,_UpperCAmelCase    :      Optional[Any]				)    ->  List[Any]:





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

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

      __snake_case :   Optional[Any]													=    dis_between_closest_in_strip(
          UpperCamelCase__ ,len(UpperCamelCase__				) ,UpperCamelCase__				)
      return min(UpperCamelCase__ ,UpperCamelCase__				)





def   a_   (		_UpperCAmelCase    :      int ,_UpperCAmelCase    :      List[Any]				)    ->  List[Any]:
      __snake_case :   int													=    column_based_sort(UpperCamelCase__ ,column=0				)
      __snake_case :   Dict													=    column_based_sort(UpperCamelCase__ ,column=1				)
      return (
          closest_pair_of_points_sqr(
              UpperCamelCase__ ,UpperCamelCase__ ,UpperCamelCase__				)
      ) ** 0.5


if __name__ == "__main__":
    A__    :   List[str]           =					[(2, 3), (1_2, 3_0), (4_0, 5_0), (5, 1), (1_2, 1_0), (3, 4)]
    print('''Distance:''', closest_pair_of_points(points, len(points)))










from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
							from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


lowerCAmelCase							:       Tuple					 =	logging.get_logger(__name__)

lowerCAmelCase							:       List[Any]					 =	{
    """microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"""
    ),
    """microsoft/deberta-v2-xxlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"""
    ),
}





class 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   str		      =     '''deberta-v2'''






				def __init__(  self			, _lowerCAmelCase=128100			, _lowerCAmelCase=1536			, _lowerCAmelCase=24			, _lowerCAmelCase=24			, _lowerCAmelCase=6144			, _lowerCAmelCase="gelu"			, _lowerCAmelCase=0.1			, _lowerCAmelCase=0.1			, _lowerCAmelCase=512			, _lowerCAmelCase=0			, _lowerCAmelCase=0.02			, _lowerCAmelCase=1e-7			, _lowerCAmelCase=False			, _lowerCAmelCase=-1			, _lowerCAmelCase=0			, _lowerCAmelCase=True			, _lowerCAmelCase=None			, _lowerCAmelCase=0			, _lowerCAmelCase="gelu"			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										super().__init__(**_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   hidden_size
										__SCREAMING_SNAKE_CASE:				Tuple           =   num_hidden_layers
										__SCREAMING_SNAKE_CASE:				str           =   num_attention_heads
										__SCREAMING_SNAKE_CASE:				List[Any]           =   intermediate_size
										__SCREAMING_SNAKE_CASE:				str           =   hidden_act
										__SCREAMING_SNAKE_CASE:				Dict           =   hidden_dropout_prob
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   attention_probs_dropout_prob
										__SCREAMING_SNAKE_CASE:				Dict           =   max_position_embeddings
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   type_vocab_size
										__SCREAMING_SNAKE_CASE:				str           =   initializer_range
										__SCREAMING_SNAKE_CASE:				Any           =   relative_attention
										__SCREAMING_SNAKE_CASE:				Tuple           =   max_relative_positions
										__SCREAMING_SNAKE_CASE:				List[str]           =   pad_token_id
										__SCREAMING_SNAKE_CASE:				List[str]           =   position_biased_input

										# Backwards compatibility
										if type(_lowerCAmelCase					) == str:
																__SCREAMING_SNAKE_CASE:				str           =   [x.strip() for x in pos_att_type.lower().split('''|'''					)]

										__SCREAMING_SNAKE_CASE:				int           =   pos_att_type
										__SCREAMING_SNAKE_CASE:				List[Any]           =   vocab_size
										__SCREAMING_SNAKE_CASE:				List[str]           =   layer_norm_eps

										__SCREAMING_SNAKE_CASE:				Optional[int]           =   kwargs.get('''pooler_hidden_size'''			, _lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Dict           =   pooler_dropout
										__SCREAMING_SNAKE_CASE:				int           =   pooler_hidden_act





class 				a			(      __lowercase			):






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										if self.task == "multiple-choice":
																__SCREAMING_SNAKE_CASE:				Any           =   {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
										else:
																__SCREAMING_SNAKE_CASE:				Any           =   {0: '''batch''', 1: '''sequence'''}
										if self._config.type_vocab_size > 0:
																return OrderedDict(
																    [('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis)]					)
										else:
																return OrderedDict([('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis)]					)






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										return 12






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase = -1			, _lowerCAmelCase = -1			, _lowerCAmelCase = -1			, _lowerCAmelCase = False			, _lowerCAmelCase = None			, _lowerCAmelCase = 3			, _lowerCAmelCase = 40			, _lowerCAmelCase = 40			, _lowerCAmelCase = None			, ):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				str           =   super().generate_dummy_inputs(preprocessor=_lowerCAmelCase			, framework=_lowerCAmelCase					)
										if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
																del dummy_inputs["token_type_ids"]
										return dummy_inputs



