





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


_UpperCAmelCase							:			Tuple        =							logging.get_logger(__name__)

_UpperCAmelCase							:			Optional[Any]        =							{
    """google/switch-base-8""": """https://huggingface.co/google/switch-base-8/blob/main/config.json""",
}

class   lowerCAmelCase				(   __UpperCamelCase     ):
 UpperCAmelCase__						=						"""switch_transformers"""
 UpperCAmelCase__						=						["""past_key_values"""]
 UpperCAmelCase__						=						{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}
 def __init__( self    : int     ,	UpperCAmelCase    : int=32128     ,	UpperCAmelCase    : Any=768     ,	UpperCAmelCase    : Union[str, Any]=64     ,	UpperCAmelCase    : Optional[Any]=2048     ,	UpperCAmelCase    : List[str]=64     ,	UpperCAmelCase    : Optional[int]=12     ,	UpperCAmelCase    : Dict=3     ,	UpperCAmelCase    : List[str]=12     ,	UpperCAmelCase    : Tuple=3     ,	UpperCAmelCase    : Union[str, Any]=12     ,	UpperCAmelCase    : List[str]=8     ,	UpperCAmelCase    : Any=False     ,	UpperCAmelCase    : Any=0.0_1     ,	UpperCAmelCase    : str="float32"     ,	UpperCAmelCase    : List[Any]=False     ,	UpperCAmelCase    : int=32     ,	UpperCAmelCase    : Dict=128     ,	UpperCAmelCase    : Tuple=0.1     ,	UpperCAmelCase    : Union[str, Any]=1e-6     ,	UpperCAmelCase    : List[Any]=0.0_0_1     ,	UpperCAmelCase    : str=0.0_0_1     ,	UpperCAmelCase    : Dict=1.0     ,	UpperCAmelCase    : List[str]="relu"     ,	UpperCAmelCase    : Dict=True     ,	UpperCAmelCase    : Tuple=False     ,	UpperCAmelCase    : Union[str, Any]=True     ,	UpperCAmelCase    : Union[str, Any]=0     ,	UpperCAmelCase    : List[str]=1     ,	**UpperCAmelCase    : Any     ,	)     ->      Optional[Any]:
     lowerCamelCase__       : Optional[int]   					=						vocab_size
     lowerCamelCase__       : Optional[Any]   					=						d_model
     lowerCamelCase__       : Dict   					=						d_kv
     lowerCamelCase__       : Tuple   					=						d_ff

     lowerCamelCase__       : Optional[Any]   					=						num_sparse_encoder_layers

     lowerCamelCase__       : List[str]   					=						num_layers
     lowerCamelCase__       : str   					=						(
         num_decoder_layers if num_decoder_layers is not None else self.num_layers
     )  # default = symmetry
     lowerCamelCase__       : Dict   					=						num_sparse_decoder_layers

     # This tells us, each how many encoder layer we'll have to set a sparse layer.
     if self.num_sparse_encoder_layers > 0:
         lowerCamelCase__       : List[str]   					=						self.num_layers // self.num_sparse_encoder_layers
     else:
         lowerCamelCase__       : List[str]   					=						self.num_layers  # HACK: this will create 0 sparse layers

     # This tells us, each how many encoder layer we'll have to set a sparse layer.
     if self.num_sparse_decoder_layers > 0:
         lowerCamelCase__       : int   					=						self.num_decoder_layers // self.num_sparse_decoder_layers
     else:
         lowerCamelCase__       : List[str]   					=						self.num_decoder_layers  # HACK: this will create 0 sparse layers

     lowerCamelCase__       : Optional[int]   					=						num_heads
     lowerCamelCase__       : List[str]   					=						num_experts
     lowerCamelCase__       : Tuple   					=						expert_capacity
     lowerCamelCase__       : List[Any]   					=						router_bias
     lowerCamelCase__       : Optional[int]   					=						router_jitter_noise
     if router_dtype not in ["float32", "float16", "bfloat16"]:
         raise ValueError(F"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}"""      )
     lowerCamelCase__       : List[Any]   					=						router_dtype

     lowerCamelCase__       : List[str]   					=						router_ignore_padding_tokens
     lowerCamelCase__       : List[str]   					=						relative_attention_num_buckets
     lowerCamelCase__       : List[Any]   					=						relative_attention_max_distance

     lowerCamelCase__       : Dict   					=						dropout_rate
     lowerCamelCase__       : Optional[int]   					=						layer_norm_epsilon
     lowerCamelCase__       : Tuple   					=						initializer_factor
     lowerCamelCase__       : List[str]   					=						feed_forward_proj
     lowerCamelCase__       : str   					=						use_cache
     lowerCamelCase__       : int   					=						add_router_probs

     lowerCamelCase__       : Optional[Any]   					=						router_z_loss_coef
     lowerCamelCase__       : Optional[int]   					=						router_aux_loss_coef

     lowerCamelCase__       : List[str]   					=						self.feed_forward_proj.split('-'      )
     lowerCamelCase__       : Optional[int]   					=						act_info[-1]
     lowerCamelCase__       : List[Any]   					=						act_info[0] == 'gated'

     if len(UpperCAmelCase      ) > 1 and act_info[0] != "gated" or len(UpperCAmelCase      ) > 2:
         raise ValueError(
             F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
             'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
             '\'gated-gelu\' or \'relu\''      )

     # for backwards compatibility
     if feed_forward_proj == "gated-gelu":
         lowerCamelCase__       : List[Any]   					=						'gelu_new'

     super().__init__(
         pad_token_id=UpperCAmelCase     ,	eos_token_id=UpperCAmelCase     ,	is_encoder_decoder=UpperCAmelCase     ,	**UpperCAmelCase     ,	)












def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase = 200					)     ->	int:
    lowerCamelCase__       : Dict   					=						[1, 2, 5, 10, 20, 50, 100, 200]
    lowerCamelCase__       : Union[str, Any]   					=						[0] * (pence + 1)
    lowerCamelCase__       : List[str]   					=						1  # base case: 1 way to make 0 pence

    for coin in coins:
        for i in range(_UpperCAmelCase		,      pence + 1		,      1					):
            number_of_ways[i] += number_of_ways[i - coin]
    return number_of_ways[pence]


if __name__ == "__main__":
    assert solution(2_00) == 7_36_82





