




'''simple docstring'''





from math import ceil, sqrt





def       __a      (  _UpperCamelCase:       int = 1_000_000       )		->					int:

     """simple docstring"""




     _snake_case				= 0

     for outer_width in range(3   ,		(limit // 4) + 2       ):
          if outer_width**2 > limit:
               _snake_case				= max(ceil(sqrt(outer_width**2 - limit       )       )   ,		1       )
          else:
               _snake_case				= 1
          if (outer_width - hole_width_lower_bound) % 2:
               hole_width_lower_bound += 1

          answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

     return answer


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








'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

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


UpperCamelCase_		:     Optional[int]          =		logging.get_logger(__name__)

UpperCamelCase_		:     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   _a							(		__lowerCAmelCase				):
     SCREAMING_SNAKE_CASE_				:       List[Any]       							=		"""ibert"""






     def __init__(					self   ,_SCREAMING_SNAKE_CASE=30_522   ,_SCREAMING_SNAKE_CASE=768   ,_SCREAMING_SNAKE_CASE=12   ,_SCREAMING_SNAKE_CASE=12   ,_SCREAMING_SNAKE_CASE=3_072   ,_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=0.0_2   ,_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   ,)				->     Dict:
          super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE   ,bos_token_id=_SCREAMING_SNAKE_CASE   ,eos_token_id=_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )

          _snake_case				= vocab_size
          _snake_case				= hidden_size
          _snake_case				= num_hidden_layers
          _snake_case				= num_attention_heads
          _snake_case				= hidden_act
          _snake_case				= intermediate_size
          _snake_case				= hidden_dropout_prob
          _snake_case				= attention_probs_dropout_prob
          _snake_case				= max_position_embeddings
          _snake_case				= type_vocab_size
          _snake_case				= initializer_range
          _snake_case				= layer_norm_eps
          _snake_case				= position_embedding_type
          _snake_case				= quant_mode
          _snake_case				= force_dequant







class   _a							(		__lowerCAmelCase				):






     @property
     def 							_lowercase      (					self       )				->     Mapping[str, Mapping[int, str]]:
          if self.task == "multiple-choice":
               _snake_case				= {0: "batch", 1: "choice", 2: "sequence"}
          else:
               _snake_case				= {0: "batch", 1: "sequence"}
          return OrderedDict(
              [
                  ("input_ids", dynamic_axis),
                  ("attention_mask", dynamic_axis),
              ]       )


