





'''simple docstring'''


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


_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	 =							{
    "google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json",
}

class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							List[Any]         =					"switch_transformers"
 __lowerCamelCase						:							str         =					["past_key_values"]
 __lowerCamelCase						:							Any         =					{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}




 def __init__(	self			,     _lowerCAmelCase=32128			,     _lowerCAmelCase=768			,     _lowerCAmelCase=64			,     _lowerCAmelCase=2048			,     _lowerCAmelCase=64			,     _lowerCAmelCase=12			,     _lowerCAmelCase=3			,     _lowerCAmelCase=12			,     _lowerCAmelCase=3			,     _lowerCAmelCase=12			,     _lowerCAmelCase=8			,     _lowerCAmelCase=False			,     _lowerCAmelCase=0.01			,     _lowerCAmelCase="float32"			,     _lowerCAmelCase=False			,     _lowerCAmelCase=32			,     _lowerCAmelCase=128			,     _lowerCAmelCase=0.1			,     _lowerCAmelCase=1E-6			,     _lowerCAmelCase=0.001			,     _lowerCAmelCase=0.001			,     _lowerCAmelCase=1.0			,     _lowerCAmelCase="relu"			,     _lowerCAmelCase=True			,     _lowerCAmelCase=False			,     _lowerCAmelCase=True			,     _lowerCAmelCase=0			,     _lowerCAmelCase=1			,     **_lowerCAmelCase			,     )    ->							Optional[int]:
     _lowerCAmelCase				=      vocab_size
     _lowerCAmelCase				=      d_model
     _lowerCAmelCase				=      d_kv
     _lowerCAmelCase				=      d_ff

     _lowerCAmelCase				=      num_sparse_encoder_layers

     _lowerCAmelCase				=      num_layers
     _lowerCAmelCase				=      (
         num_decoder_layers if num_decoder_layers is not None else self.num_layers
     )  # default = symmetry
     _lowerCAmelCase				=      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				=      self.num_layers // self.num_sparse_encoder_layers
     else:
         _lowerCAmelCase				=      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				=      self.num_decoder_layers // self.num_sparse_decoder_layers
     else:
         _lowerCAmelCase				=      self.num_decoder_layers  # HACK: this will create 0 sparse layers

     _lowerCAmelCase				=      num_heads
     _lowerCAmelCase				=      num_experts
     _lowerCAmelCase				=      expert_capacity
     _lowerCAmelCase				=      router_bias
     _lowerCAmelCase				=      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				=      router_dtype

     _lowerCAmelCase				=      router_ignore_padding_tokens
     _lowerCAmelCase				=      relative_attention_num_buckets
     _lowerCAmelCase				=      relative_attention_max_distance

     _lowerCAmelCase				=      dropout_rate
     _lowerCAmelCase				=      layer_norm_epsilon
     _lowerCAmelCase				=      initializer_factor
     _lowerCAmelCase				=      feed_forward_proj
     _lowerCAmelCase				=      use_cache
     _lowerCAmelCase				=      add_router_probs

     _lowerCAmelCase				=      router_z_loss_coef
     _lowerCAmelCase				=      router_aux_loss_coef

     _lowerCAmelCase				=      self.feed_forward_proj.split("-"		)
     _lowerCAmelCase				=      act_info[-1]
     _lowerCAmelCase				=      act_info[0] == "gated"

     if len(_lowerCAmelCase		) > 1 and act_info[0] != "gated" or len(_lowerCAmelCase		) > 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				=      "gelu_new"

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









'''simple docstring'''


def 	__a(SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





    if not isinstance(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
        raise ValueError("check_bouncy() accepts only integer arguments"    )
    _lowerCAmelCase				=      str(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      "".join(sorted(SCREAMING_SNAKE_CASE_    )    )
    return sorted_str_n != str_n and sorted_str_n[::-1] != str_n


def 	__a(SCREAMING_SNAKE_CASE_  :					float = 99    ):



    '''simple docstring'''





    if not 0 < percent < 100:
        raise ValueError("solution() only accepts values from 0 to 100"    )
    _lowerCAmelCase				=      0
    _lowerCAmelCase				=      1

    while True:
        if check_bouncy(SCREAMING_SNAKE_CASE_    ):
            bouncy_num += 1
        if (bouncy_num / num) * 100 >= percent:
            return num
        num += 1


if __name__ == "__main__":
       from doctest import testmod

       testmod()
       print(f'''{solution(99)}''')


