



"""simple docstring"""

def __lowercase    (							_a	,    _a					):
    return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2




def __lowercase    (							_a	,    _a=0					):
    return sorted(_a	,    key=lambda _a					: x[column]					)




def __lowercase    (							_a	,    _a	,    _a=float('''inf'''					)					):

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




def __lowercase    (							_a	,    _a	,    _a=float('''inf'''					)					):

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




def __lowercase    (							_a	,    _a	,    _a					):

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

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

    snake_case_				:  str       =			dis_between_closest_in_strip(
        _a	,    len(_a					)	,    _a					)
    return min(_a	,    _a					)




def __lowercase    (							_a	,    _a					):
    snake_case_				:  Any       =			column_based_sort(_a	,    column=0					)
    snake_case_				:  int       =			column_based_sort(_a	,    column=1					)
    return (
        closest_pair_of_points_sqr(
            _a	,    _a	,    _a					)
    ) ** 0.5


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










"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

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


lowercase__					:						Any												=			logging.get_logger(__name__)

lowercase__					:						Tuple												=			{
    '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
    '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
    '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
    '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
    '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
    '''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}




class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Dict	      =	"""roberta"""

 def __init__(  self	: int						,  lowercase_	: Any=50265						,  lowercase_	: Union[str, Any]=768						,  lowercase_	: Optional[int]=12						,  lowercase_	: List[str]=12						,  lowercase_	: Dict=3072						,  lowercase_	: Tuple="gelu"						,  lowercase_	: Optional[int]=0.1						,  lowercase_	: Tuple=0.1						,  lowercase_	: List[str]=512						,  lowercase_	: List[Any]=2						,  lowercase_	: Union[str, Any]=0.02						,  lowercase_	: List[Any]=1E-12						,  lowercase_	: Optional[int]=1						,  lowercase_	: Tuple=0						,  lowercase_	: str=2						,  lowercase_	: int="absolute"						,  lowercase_	: str=True						,  lowercase_	: Tuple=None						,  **lowercase_	: str						,  ):
     super().__init__(pad_token_id=lowercase_						,  bos_token_id=lowercase_						,  eos_token_id=lowercase_						,  **lowercase_       )

     snake_case_				:  str       =			vocab_size
     snake_case_				:  Optional[Any]       =			hidden_size
     snake_case_				:  Any       =			num_hidden_layers
     snake_case_				:  List[str]       =			num_attention_heads
     snake_case_				:  Dict       =			hidden_act
     snake_case_				:  List[Any]       =			intermediate_size
     snake_case_				:  Optional[int]       =			hidden_dropout_prob
     snake_case_				:  List[str]       =			attention_probs_dropout_prob
     snake_case_				:  Any       =			max_position_embeddings
     snake_case_				:  Optional[Any]       =			type_vocab_size
     snake_case_				:  Optional[int]       =			initializer_range
     snake_case_				:  Dict       =			layer_norm_eps
     snake_case_				:  Optional[int]       =			position_embedding_type
     snake_case_				:  str       =			use_cache
     snake_case_				:  Tuple       =			classifier_dropout





class  _UpperCAmelCase (    lowerCAmelCase__):

 @property
 def 		_snake_case      (  self	: Any       ):
     if self.task == "multiple-choice":
         snake_case_				:  List[Any]       =			{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
     else:
         snake_case_				:  Union[str, Any]       =			{0: '''batch''', 1: '''sequence'''}
     return OrderedDict(
         [
             ('''input_ids''', dynamic_axis),
             ('''attention_mask''', dynamic_axis),
         ]       )





