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

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


lowercase__   			=      logging.get_logger(__name__)

lowercase__   			=      {
    'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/config.json',
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}




class   __snake_case			(	__lowerCAmelCase							):
      a__								=						"""rembert"""



      def __init__(		self       ,     lowercase=25_03_00       ,     lowercase=11_52       ,     lowercase=32       ,     lowercase=18       ,     lowercase=2_56       ,     lowercase=16_64       ,     lowercase=46_08       ,     lowercase="gelu"       ,     lowercase=0.0       ,     lowercase=0.0       ,     lowercase=0.1       ,     lowercase=5_12       ,     lowercase=2       ,     lowercase=0.02       ,     lowercase=1e-12       ,     lowercase=True       ,     lowercase=0       ,     lowercase=3_12       ,     lowercase=3_13       ,     **lowercase       ,     )      ->     Optional[int]:





        '''simple docstring'''





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

        a__:	str     				=      vocab_size
        a__:	List[str]     				=      input_embedding_size
        a__:	Optional[int]     				=      output_embedding_size
        a__:	List[str]     				=      max_position_embeddings
        a__:	int     				=      hidden_size
        a__:	Union[str, Any]     				=      num_hidden_layers
        a__:	List[str]     				=      num_attention_heads
        a__:	Optional[int]     				=      intermediate_size
        a__:	Optional[Any]     				=      hidden_act
        a__:	List[str]     				=      hidden_dropout_prob
        a__:	int     				=      attention_probs_dropout_prob
        a__:	Dict     				=      classifier_dropout_prob
        a__:	List[str]     				=      initializer_range
        a__:	int     				=      type_vocab_size
        a__:	Tuple     				=      layer_norm_eps
        a__:	Any     				=      use_cache
        a__:	List[str]     				=      False







class   __snake_case			(	__lowerCAmelCase							):



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





        '''simple docstring'''





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



      @property
      def     lowerCamelCase_  (		self)      ->     float:





        '''simple docstring'''





        return 1e-4



"""simple docstring"""
from __future__ import annotations

import sys







def 		__a  (      _SCREAMING_SNAKE_CASE  )      ->int:
  if n < 0:
    raise ValueError('Negative arguments are not supported'  )
  return _fib(_SCREAMING_SNAKE_CASE  )[0]







def 		__a  (      _SCREAMING_SNAKE_CASE  )      ->tuple[int, int]:
  if n == 0:  # (F(0), F(1))
    return (0, 1)

  # F(2n) = F(n)[2F(n+1) − F(n)]
  # F(2n+1) = F(n+1)^2+F(n)^2
  a__				,       a__:	Optional[int]     				=      _fib(n // 2  )
  a__:	List[str]     				=      a * (b * 2 - a)
  a__:	Dict     				=      a * a + b * b
  return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
 lowercase__   			=      int(sys.argv[1])
 print(f"fibonacci({n}) is {fibonacci(n)}")


