
def 						lowercase_      (       A__		)							->							int:
    """simple docstring"""

    snake_case								= abs(A__		)
    snake_case								= 0
    while n > 0:
        res += n % 10
        n //= 10
    return res



def 						lowercase_      (       A__		)							->							int:
    """simple docstring"""

    snake_case								= abs(A__		)
    return n if n < 10 else n % 10 + sum_of_digits(n // 10		)



def 						lowercase_      (       A__		)							->							int:
    """simple docstring"""

    return sum(int(A__		) for c in str(abs(A__		)		)		)



def 						lowercase_      (       )							->							None:
    """simple docstring"""

    from collections.abc import Callable
    from timeit import timeit

    def benchmark_a_function(A__			,		A__		) -> None:
        snake_case								= F'{func.__name__}({value})'
        snake_case								= timeit(F'__main__.{call}'			,		setup="import __main__"		)
        print(F'{call:56} = {func(A__		)} -- {timing:.4f} seconds'		)

    for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
        for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
            benchmark_a_function(A__			,		A__		)
        print()


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   benchmark()



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A   =			logging.get_logger(__name__)

_A   =			{
    "google/realm-cc-news-pretrained-embedder": (
        "https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json"
    ),
    "google/realm-cc-news-pretrained-encoder": (
        "https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json"
    ),
    "google/realm-cc-news-pretrained-scorer": (
        "https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json"
    ),
    "google/realm-cc-news-pretrained-openqa": (
        "https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json"
    ),
    "google/realm-orqa-nq-openqa": "https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json",
    "google/realm-orqa-nq-reader": "https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json",
    "google/realm-orqa-wq-openqa": "https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json",
    "google/realm-orqa-wq-reader": "https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json",
    # See all REALM models at https://huggingface.co/models?filter=realm
}







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							str									=		"realm"





     def __init__(self				:   Optional[int]    ,   _A				:   Optional[Any]=3_0_5_2_2    ,   _A				:   Tuple=7_6_8    ,   _A				:   List[str]=1_2_8    ,   _A				:   Optional[Any]=1_2    ,   _A				:   Dict=1_2    ,   _A				:   Tuple=8    ,   _A				:   Dict=3_0_7_2    ,   _A				:   Union[str, Any]="gelu_new"    ,   _A				:   Any=0.1    ,   _A				:   int=0.1    ,   _A				:   Union[str, Any]=5_1_2    ,   _A				:   List[str]=2    ,   _A				:   Any=0.02    ,   _A				:   int=1E-12    ,   _A				:   Tuple=2_5_6    ,   _A				:   Optional[Any]=1_0    ,   _A				:   Any=1E-3    ,   _A				:   int=5    ,   _A				:   int=3_2_0    ,   _A				:   Dict=1_3_3_5_3_7_1_8    ,   _A				:   Any=5_0_0_0    ,   _A				:   Union[str, Any]=1    ,   _A				:   Dict=0    ,   _A				:   int=2    ,   **_A				:   Union[str, Any]    ,   )						->						Optional[Any]:
         super().__init__(pad_token_id=_A    ,   bos_token_id=_A    ,   eos_token_id=_A    ,   **_A       )

         # Common config
         snake_case								= vocab_size
         snake_case								= max_position_embeddings
         snake_case								= hidden_size
         snake_case								= retriever_proj_size
         snake_case								= num_hidden_layers
         snake_case								= num_attention_heads
         snake_case								= num_candidates
         snake_case								= intermediate_size
         snake_case								= hidden_act
         snake_case								= hidden_dropout_prob
         snake_case								= attention_probs_dropout_prob
         snake_case								= initializer_range
         snake_case								= type_vocab_size
         snake_case								= layer_norm_eps

         # Reader config
         snake_case								= span_hidden_size
         snake_case								= max_span_width
         snake_case								= reader_layer_norm_eps
         snake_case								= reader_beam_size
         snake_case								= reader_seq_len

         # Retrieval config
         snake_case								= num_block_records
         snake_case								= searcher_beam_size

