




UpperCAmelCase_   							=      {
    '0': 1,
    '1': 1,
    '2': 2,
    '3': 6,
    '4': 24,
    '5': 120,
    '6': 720,
    '7': 5_040,
    '8': 40_320,
    '9': 362_880,
}

UpperCAmelCase_   							=      {145: 145}

UpperCAmelCase_   							=      {
    145: 0,
    169: 3,
    36_301: 3,
    1_454: 3,
    871: 2,
    45_361: 2,
    872: 2,
}




def    lowerCamelCase__			(		A__			:   int      ):





       '''simple docstring'''



       if n in CACHE_SUM_DIGIT_FACTORIALS:
              return CACHE_SUM_DIGIT_FACTORIALS[n]
       __lowerCamelCase  =		sum(DIGIT_FACTORIALS[let] for let in str(__SCREAMING_SNAKE_CASE      )      )
       __lowerCamelCase  =		ret
       return ret




def    lowerCamelCase__			(		A__			:   int    ,     A__			:   set | None = None      ):





       '''simple docstring'''



       __lowerCamelCase  =		previous or set()
       if n in CHAIN_LENGTH_CACHE:
              return CHAIN_LENGTH_CACHE[n]
       __lowerCamelCase  =		sum_digit_factorials(__SCREAMING_SNAKE_CASE      )
       if next_number in previous:
              __lowerCamelCase  =		0
              return 0
       else:
              previous.add(__SCREAMING_SNAKE_CASE      )
              __lowerCamelCase  =		1 + chain_length(__SCREAMING_SNAKE_CASE    ,     __SCREAMING_SNAKE_CASE      )
              __lowerCamelCase  =		ret
              return ret




def    lowerCamelCase__			(		A__			:   int = 60    ,     A__			:   int = 1000000      ):





       '''simple docstring'''



       return sum(1 for i in range(1    ,     __SCREAMING_SNAKE_CASE      ) if chain_length(__SCREAMING_SNAKE_CASE      ) == num_terms      )


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


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/mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"
}







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	List[Any]          =	MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
       A							:	int          =	"mobilebert"

       def __init__(							self					:     Any			,			_lowerCAmelCase					:     Union[str, Any]=3_05_22			,			_lowerCAmelCase					:     Tuple=5_12			,			_lowerCAmelCase					:     Dict=24			,			_lowerCAmelCase					:     int=4			,			_lowerCAmelCase					:     Optional[Any]=5_12			,			_lowerCAmelCase					:     Union[str, Any]="relu"			,			_lowerCAmelCase					:     List[str]=0.0			,			_lowerCAmelCase					:     str=0.1			,			_lowerCAmelCase					:     Tuple=5_12			,			_lowerCAmelCase					:     Optional[Any]=2			,			_lowerCAmelCase					:     Optional[int]=0.02			,			_lowerCAmelCase					:     List[str]=1e-12			,			_lowerCAmelCase					:     int=0			,			_lowerCAmelCase					:     Union[str, Any]=1_28			,			_lowerCAmelCase					:     Optional[Any]=True			,			_lowerCAmelCase					:     Dict=True			,			_lowerCAmelCase					:     Any=1_28			,			_lowerCAmelCase					:     Optional[int]=False			,			_lowerCAmelCase					:     str=True			,			_lowerCAmelCase					:     Tuple=4			,			_lowerCAmelCase					:     List[str]="no_norm"			,			_lowerCAmelCase					:     Union[str, Any]=True			,			_lowerCAmelCase					:     Optional[int]=None			,			**_lowerCAmelCase					:     int			,			):
           super().__init__(pad_token_id=_lowerCAmelCase			,			**_lowerCAmelCase			)

           __snake_case       :				Any 			=    vocab_size
           __snake_case       :				Tuple 			=    hidden_size
           __snake_case       :				int 			=    num_hidden_layers
           __snake_case       :				Dict 			=    num_attention_heads
           __snake_case       :				List[Any] 			=    hidden_act
           __snake_case       :				List[Any] 			=    intermediate_size
           __snake_case       :				Union[str, Any] 			=    hidden_dropout_prob
           __snake_case       :				Any 			=    attention_probs_dropout_prob
           __snake_case       :				Any 			=    max_position_embeddings
           __snake_case       :				List[Any] 			=    type_vocab_size
           __snake_case       :				List[Any] 			=    initializer_range
           __snake_case       :				Dict 			=    layer_norm_eps
           __snake_case       :				Any 			=    embedding_size
           __snake_case       :				List[str] 			=    trigram_input
           __snake_case       :				Union[str, Any] 			=    use_bottleneck
           __snake_case       :				List[Any] 			=    intra_bottleneck_size
           __snake_case       :				Tuple 			=    use_bottleneck_attention
           __snake_case       :				Optional[Any] 			=    key_query_shared_bottleneck
           __snake_case       :				Tuple 			=    num_feedforward_networks
           __snake_case       :				int 			=    normalization_type
           __snake_case       :				List[Any] 			=    classifier_activation

           if self.use_bottleneck:
               __snake_case       :				List[str] 			=    intra_bottleneck_size
           else:
               __snake_case       :				int 			=    hidden_size

           __snake_case       :				Optional[Any] 			=    classifier_dropout







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):

       @property
       def 			snake_case__    (							self					:     Optional[Any]			):
           if self.task == "multiple-choice":
               __snake_case       :				Any 			=    {0: """batch""", 1: """choice""", 2: """sequence"""}
           else:
               __snake_case       :				Optional[Any] 			=    {0: """batch""", 1: """sequence"""}
           return OrderedDict(
               [
                   ("""input_ids""", dynamic_axis),
                   ("""attention_mask""", dynamic_axis),
                   ("""token_type_ids""", dynamic_axis),
               ]			)



