




"""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/xlm-roberta-xl''': '''https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json''',
    '''facebook/xlm-roberta-xxl''': '''https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json''',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}






class  lowercase(					__SCREAMING_SNAKE_CASE			):







  '''simple docstring'''





  lowercase__			 = 'xlm-roberta-xl'


  def __init__(    self:		int,				a_:		Union[str, Any]=250_880,				a_:		Optional[int]=2_560,				a_:		str=36,				a_:		List[Any]=32,				a_:		int=10_240,				a_:		Dict="gelu",				a_:		Dict=0.1,				a_:		int=0.1,				a_:		List[str]=514,				a_:		List[str]=1,				a_:		Tuple=0.02,				a_:		Optional[Any]=1E-05,				a_:		str=1,				a_:		Union[str, Any]=0,				a_:		int=2,				a_:		List[str]="absolute",				a_:		int=True,				a_:		Optional[int]=None,				**a_:		List[str],				):




    '''simple docstring'''



    super().__init__(pad_token_id=_a,				bos_token_id=_a,				eos_token_id=_a,				**_a		)
    _snake_case							:		int								=  vocab_size
    _snake_case							:		Optional[Any]								=  hidden_size
    _snake_case							:		Union[str, Any]								=  num_hidden_layers
    _snake_case							:		List[str]								=  num_attention_heads
    _snake_case							:		Union[str, Any]								=  hidden_act
    _snake_case							:		Optional[Any]								=  intermediate_size
    _snake_case							:		Optional[Any]								=  hidden_dropout_prob
    _snake_case							:		List[str]								=  attention_probs_dropout_prob
    _snake_case							:		Optional[Any]								=  max_position_embeddings
    _snake_case							:		Optional[int]								=  type_vocab_size
    _snake_case							:		Dict								=  initializer_range
    _snake_case							:		str								=  layer_norm_eps
    _snake_case							:		List[str]								=  position_embedding_type
    _snake_case							:		Optional[Any]								=  use_cache
    _snake_case							:		Dict								=  classifier_dropout







class  lowercase(					__SCREAMING_SNAKE_CASE			):







  '''simple docstring'''





  @property
  def 			UpperCamelCase_       (    self:		List[Any]		):




    '''simple docstring'''



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








"""simple docstring"""


def  UpperCAmelCase__	(snake_case__							: int = 10_00   ):
  """simple docstring"""

  _snake_case		, _snake_case							:		List[Any]								=  1, 1
  _snake_case							:		str								=  []
  for i in range(1		,       n + 1   ):
    _snake_case							:		Any								=  prev_numerator + 2 * prev_denominator
    _snake_case							:		Optional[Any]								=  prev_numerator + prev_denominator
    if len(str(snake_case__   )   ) > len(str(snake_case__   )   ):
      result.append(snake_case__   )
    _snake_case							:		int								=  numerator
    _snake_case							:		Any								=  denominator

  return len(snake_case__   )


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


