



"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

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


A:   str								=		logging.get_logger(__name__)

A:   List[str]								=		{
    "kssteven/ibert-roberta-base": "https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json",
    "kssteven/ibert-roberta-large": "https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json",
    "kssteven/ibert-roberta-large-mnli": (
        "https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"
    ),
}



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):
   __lowerCAmelCase  :   Dict						=						'ibert'

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE=30522      ,     _SCREAMING_SNAKE_CASE=768      ,     _SCREAMING_SNAKE_CASE=12      ,     _SCREAMING_SNAKE_CASE=12      ,     _SCREAMING_SNAKE_CASE=3072      ,     _SCREAMING_SNAKE_CASE="gelu"      ,     _SCREAMING_SNAKE_CASE=0.1      ,     _SCREAMING_SNAKE_CASE=0.1      ,     _SCREAMING_SNAKE_CASE=512      ,     _SCREAMING_SNAKE_CASE=2      ,     _SCREAMING_SNAKE_CASE=0.02      ,     _SCREAMING_SNAKE_CASE=1E-12      ,     _SCREAMING_SNAKE_CASE=1      ,     _SCREAMING_SNAKE_CASE=0      ,     _SCREAMING_SNAKE_CASE=2      ,     _SCREAMING_SNAKE_CASE="absolute"      ,     _SCREAMING_SNAKE_CASE=False      ,     _SCREAMING_SNAKE_CASE="none"      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      List[str]:





    '''simple docstring'''




    super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE      ,     bos_token_id=_SCREAMING_SNAKE_CASE      ,     eos_token_id=_SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Tuple	=	vocab_size
    UpperCAmelCase	:					Union[str, Any]	=	hidden_size
    UpperCAmelCase	:					Optional[int]	=	num_hidden_layers
    UpperCAmelCase	:					Union[str, Any]	=	num_attention_heads
    UpperCAmelCase	:					Dict	=	hidden_act
    UpperCAmelCase	:					List[str]	=	intermediate_size
    UpperCAmelCase	:					Any	=	hidden_dropout_prob
    UpperCAmelCase	:					Dict	=	attention_probs_dropout_prob
    UpperCAmelCase	:					Union[str, Any]	=	max_position_embeddings
    UpperCAmelCase	:					Union[str, Any]	=	type_vocab_size
    UpperCAmelCase	:					List[str]	=	initializer_range
    UpperCAmelCase	:					int	=	layer_norm_eps
    UpperCAmelCase	:					Any	=	position_embedding_type
    UpperCAmelCase	:					Optional[int]	=	quant_mode
    UpperCAmelCase	:					Tuple	=	force_dequant



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):

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





    '''simple docstring'''




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





"""simple docstring"""
from collections import defaultdict
from math import ceil, sqrt

def       _snake_case						(						UpperCamelCase						:		int = 1000000			,		UpperCamelCase						:		int = 10						):
 UpperCAmelCase	:					defaultdict	=	defaultdict(UpperCamelCase						)

 for outer_width in range(3			,		(t_limit // 4) + 2						):
  if outer_width * outer_width > t_limit:
   UpperCAmelCase	:					str	=	max(
       ceil(sqrt(outer_width * outer_width - t_limit						)						)			,		1						)
  else:
   UpperCAmelCase	:					Optional[Any]	=	1

  hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

  for hole_width in range(UpperCamelCase			,		outer_width - 1			,		2						):
   count[outer_width * outer_width - hole_width * hole_width] += 1

 return sum(1 for n in count.values() if 1 <= n <= 10						)


if __name__ == "__main__":
      print(f"""{solution() = }""")
