



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


__SCREAMING_SNAKE_CASE     =	logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     =	{
    """tau/splinter-base""": """https://huggingface.co/tau/splinter-base/resolve/main/config.json""",
    """tau/splinter-base-qass""": """https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json""",
    """tau/splinter-large""": """https://huggingface.co/tau/splinter-large/resolve/main/config.json""",
    """tau/splinter-large-qass""": """https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json""",
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}

class 						lowerCamelCase_     (			__a    ):






						'''simple docstring'''
						a__ =	"""splinter"""







						def __init__(						self   :      Optional[Any]						,						__lowerCamelCase   :      Dict=3_05_22						,						__lowerCamelCase   :      str=7_68						,						__lowerCamelCase   :      List[str]=12						,						__lowerCamelCase   :      List[Any]=12						,						__lowerCamelCase   :      str=30_72						,						__lowerCamelCase   :      Optional[Any]="gelu"						,						__lowerCamelCase   :      Tuple=0.1						,						__lowerCamelCase   :      Dict=0.1						,						__lowerCamelCase   :      Optional[Any]=5_12						,						__lowerCamelCase   :      int=2						,						__lowerCamelCase   :      Tuple=0.02						,						__lowerCamelCase   :      Any=1e-12						,						__lowerCamelCase   :      Tuple=True						,						__lowerCamelCase   :      Union[str, Any]=0						,						__lowerCamelCase   :      Optional[Any]=1_04						,						**__lowerCamelCase   :      Any						,						)   ->					Dict:
									super().__init__(pad_token_id=__lowerCamelCase						,						**__lowerCamelCase			)

									A				:       Union[str, Any]														=    vocab_size
									A				:       Union[str, Any]														=    max_position_embeddings
									A				:       Dict														=    hidden_size
									A				:       int														=    num_hidden_layers
									A				:       Dict														=    num_attention_heads
									A				:       str														=    intermediate_size
									A				:       Dict														=    hidden_act
									A				:       Dict														=    hidden_dropout_prob
									A				:       Union[str, Any]														=    attention_probs_dropout_prob
									A				:       Dict														=    initializer_range
									A				:       List[str]														=    type_vocab_size
									A				:       Union[str, Any]														=    layer_norm_eps
									A				:       int														=    use_cache
									A				:       Dict														=    question_token_id





'''simple docstring'''







def 		snake_case_				(_a      :				Any       ,  _a      :				Union[str, Any]	):
    if n < 0:
        return 0
    # table[i] represents the number of ways to get to amount i
    UpperCAmelCase     			=						[0] * (n + 1)

    # There is exactly 1 way to get to zero(You pick no coins).
    UpperCAmelCase     			=						1

    # Pick all coins one by one and update table[] values
    # after the index greater than or equal to the value of the
    # picked coin
    for coin_val in s:
        for j in range(_a       ,  n + 1	):
            table[j] += table[j - coin_val]

    return table[n]


if __name__ == "__main__":
   import doctest

   doctest.testmod()




