

'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

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


__lowerCamelCase						    =     logging.get_logger(__name__)

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


class    A__ ( _a     ):
    lowercase		   =						"""data2vec-text"""



    def __init__(   self						,			UpperCamelCase__=30522						,			UpperCamelCase__=768						,			UpperCamelCase__=12						,			UpperCamelCase__=12						,			UpperCamelCase__=3072						,			UpperCamelCase__="gelu"						,			UpperCamelCase__=0.1						,			UpperCamelCase__=0.1						,			UpperCamelCase__=512						,			UpperCamelCase__=2						,			UpperCamelCase__=0.02						,			UpperCamelCase__=1e-1_2						,			UpperCamelCase__=1						,			UpperCamelCase__=0						,			UpperCamelCase__=2						,			UpperCamelCase__="absolute"						,			UpperCamelCase__=True						,			UpperCamelCase__=None						,			**UpperCamelCase__						,			)  -> List[str]:





           '''simple docstring'''
           super().__init__(pad_token_id=UpperCamelCase__						,			bos_token_id=UpperCamelCase__						,			eos_token_id=UpperCamelCase__						,			**UpperCamelCase__    )

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


class    A__ ( _a     ):



    @property
    def    snake_case_     (   self    )  -> Mapping[str, Mapping[int, str]]:





           '''simple docstring'''

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



'''simple docstring'''







def  UpperCAmelCase__      ( UpperCAmelCase__ = 10_00			)     ->     int:
       return sum(2 * a * ((a - 1) // 2) for a in range(3,    n + 1			)			)


if __name__ == "__main__":
      print(solution())
