


'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

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


__UpperCAmelCase				       =     logging.get_logger(__name__)

__UpperCAmelCase				       =     {
    "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 			a       (	__UpperCAmelCase ):


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				List[str]							     =     "xlm-roberta-xl"

     def __init__(       self  :	List[str]							,				snake_case  :	Dict=25_0880							,				snake_case  :	int=2560							,				snake_case  :	Optional[int]=36							,				snake_case  :	Union[str, Any]=32							,				snake_case  :	str=1_0240							,				snake_case  :	str="gelu"							,				snake_case  :	Dict=0.1							,				snake_case  :	List[Any]=0.1							,				snake_case  :	Optional[Any]=514							,				snake_case  :	List[Any]=1							,				snake_case  :	int=0.02							,				snake_case  :	Optional[int]=1E-05							,				snake_case  :	Dict=1							,				snake_case  :	str=0							,				snake_case  :	Union[str, Any]=2							,				snake_case  :	List[str]="absolute"							,				snake_case  :	Dict=True							,				snake_case  :	Dict=None							,				**snake_case  :	str							,				)		->							List[Any]:
          super().__init__(pad_token_id=lowerCAmelCase_							,				bos_token_id=lowerCAmelCase_							,				eos_token_id=lowerCAmelCase_							,				**lowerCAmelCase_    )
          __UpperCAmelCase			:				Tuple													=    vocab_size
          __UpperCAmelCase			:				Dict													=    hidden_size
          __UpperCAmelCase			:				Optional[int]													=    num_hidden_layers
          __UpperCAmelCase			:				Any													=    num_attention_heads
          __UpperCAmelCase			:				Tuple													=    hidden_act
          __UpperCAmelCase			:				Tuple													=    intermediate_size
          __UpperCAmelCase			:				Optional[Any]													=    hidden_dropout_prob
          __UpperCAmelCase			:				List[Any]													=    attention_probs_dropout_prob
          __UpperCAmelCase			:				str													=    max_position_embeddings
          __UpperCAmelCase			:				str													=    type_vocab_size
          __UpperCAmelCase			:				Any													=    initializer_range
          __UpperCAmelCase			:				Dict													=    layer_norm_eps
          __UpperCAmelCase			:				int													=    position_embedding_type
          __UpperCAmelCase			:				str													=    use_cache
          __UpperCAmelCase			:				Union[str, Any]													=    classifier_dropout



class 			a       (	__UpperCAmelCase ):


     """simple docstring"""






     @property
     def 				lowerCamelCase__				(       self  :	Union[str, Any]    )		->							Union[str, Any]:







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



'''simple docstring'''



def 			_a			(		_lowercase      :			int = 50000000						):





     '''simple docstring'''






     __UpperCAmelCase			:				List[str]													=    set()
     __UpperCAmelCase			:				Tuple													=    int((limit - 24) ** (1 / 2)						)

     __UpperCAmelCase			:				Tuple													=    set(range(3    ,      prime_square_limit + 1    ,      2						)						)
     primes.add(2						)
     for p in range(3    ,      prime_square_limit + 1    ,      2						):
          if p not in primes:
               continue
          primes.difference_update(set(range(p * p    ,      prime_square_limit + 1    ,      _lowercase						)						)						)

     for primea in primes:
          __UpperCAmelCase			:				Any													=    primea * primea
          for primea in primes:
               __UpperCAmelCase			:				Any													=    primea * primea * primea
               if square + cube >= limit - 16:
                    break
               for primea in primes:
                    __UpperCAmelCase			:				Optional[Any]													=    primea * primea * primea * primea
                    __UpperCAmelCase			:				Union[str, Any]													=    square + cube + tetr
                    if total >= limit:
                         break
                    ret.add(_lowercase						)

     return len(_lowercase						)


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