"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

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


a				=logging.get_logger(__name__)

a				={
    'facebook/data2vec-text-base': 'https://huggingface.co/data2vec/resolve/main/config.json',
}






class   __UpperCAmelCase		(      _a      ):
				A__ :  List[Any]    				=			'''data2vec-text'''

				def __init__( self      ,					_lowerCamelCase=30522      ,					_lowerCamelCase=768      ,					_lowerCamelCase=12      ,					_lowerCamelCase=12      ,					_lowerCamelCase=3072      ,					_lowerCamelCase="gelu"      ,					_lowerCamelCase=0.1      ,					_lowerCamelCase=0.1      ,					_lowerCamelCase=512      ,					_lowerCamelCase=2      ,					_lowerCamelCase=0.0_2      ,					_lowerCamelCase=1E-12      ,					_lowerCamelCase=1      ,					_lowerCamelCase=0      ,					_lowerCamelCase=2      ,					_lowerCamelCase="absolute"      ,					_lowerCamelCase=True      ,					_lowerCamelCase=None      ,					**_lowerCamelCase      ,					):
										super().__init__(pad_token_id=A_      ,					bos_token_id=A_      ,					eos_token_id=A_      ,					**A_ )

										lowerCamelCase__   =vocab_size
										lowerCamelCase__   =hidden_size
										lowerCamelCase__   =num_hidden_layers
										lowerCamelCase__   =num_attention_heads
										lowerCamelCase__   =hidden_act
										lowerCamelCase__   =intermediate_size
										lowerCamelCase__   =hidden_dropout_prob
										lowerCamelCase__   =attention_probs_dropout_prob
										lowerCamelCase__   =max_position_embeddings
										lowerCamelCase__   =type_vocab_size
										lowerCamelCase__   =initializer_range
										lowerCamelCase__   =layer_norm_eps
										lowerCamelCase__   =position_embedding_type
										lowerCamelCase__   =use_cache
										lowerCamelCase__   =classifier_dropout






class   __UpperCAmelCase		(      _a      ):

				@property
				def 						_a      ( self ):




										if self.task == "multiple-choice":
																lowerCamelCase__   ={0: "batch", 1: "choice", 2: "sequence"}
										else:
																lowerCamelCase__   ={0: "batch", 1: "sequence"}
										return OrderedDict(
										    [
										        ("input_ids", dynamic_axis),
										        ("attention_mask", dynamic_axis),
										    ] )






def 		_A		(		_lowercase					,  _lowercase )					-> int:


     """simple docstring"""


     return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2






def 		_A		(		_lowercase					,  _lowercase=0 )					-> Dict:


     """simple docstring"""


     return sorted(_lowercase					,  key=lambda _lowercase : x[column] )






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase=float('inf' ) )					-> List[Any]:


     """simple docstring"""


     for i in range(points_counts - 1 ):
          for j in range(i + 1					,  _lowercase ):
               __UpperCamelCase =			euclidean_distance_sqr(points[i]					,  points[j] )
               if current_dis < min_dis:
                    __UpperCamelCase =			current_dis
     return min_dis






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase=float('inf' ) )					-> Tuple:


     """simple docstring"""


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






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


     """simple docstring"""


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

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

     __UpperCamelCase =			dis_between_closest_in_strip(
         _lowercase					,  len(_lowercase )					,  _lowercase )
     return min(_lowercase					,  _lowercase )






def 		_A		(		_lowercase					,  _lowercase )					-> Optional[int]:


     """simple docstring"""


     __UpperCamelCase =			column_based_sort(_lowercase					,  column=0 )
     __UpperCamelCase =			column_based_sort(_lowercase					,  column=1 )
     return (
         closest_pair_of_points_sqr(
             _lowercase					,  _lowercase					,  _lowercase )
     ) ** 0.5


if __name__ == "__main__":
    __snake_case   							=						[(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)))


