


'''simple docstring'''



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


A__		:					List[Any]												=  logging.get_logger(__name__)

A__		:					List[str]												=  {
    """google/switch-base-8""": """https://huggingface.co/google/switch-base-8/blob/main/config.json""",
}





class    UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  :					List[str]							     =			'switch_transformers'
    lowerCamelCase  :					Union[str, Any]							     =			['past_key_values']
    lowerCamelCase  :					int							     =			{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}







    def __init__(							self			,     SCREAMING_SNAKE_CASE_=3_21_28			,     SCREAMING_SNAKE_CASE_=7_68			,     SCREAMING_SNAKE_CASE_=64			,     SCREAMING_SNAKE_CASE_=20_48			,     SCREAMING_SNAKE_CASE_=64			,     SCREAMING_SNAKE_CASE_=12			,     SCREAMING_SNAKE_CASE_=3			,     SCREAMING_SNAKE_CASE_=12			,     SCREAMING_SNAKE_CASE_=3			,     SCREAMING_SNAKE_CASE_=12			,     SCREAMING_SNAKE_CASE_=8			,     SCREAMING_SNAKE_CASE_=False			,     SCREAMING_SNAKE_CASE_=0.0_1			,     SCREAMING_SNAKE_CASE_="float32"			,     SCREAMING_SNAKE_CASE_=False			,     SCREAMING_SNAKE_CASE_=32			,     SCREAMING_SNAKE_CASE_=1_28			,     SCREAMING_SNAKE_CASE_=0.1			,     SCREAMING_SNAKE_CASE_=1E-6			,     SCREAMING_SNAKE_CASE_=0.0_0_1			,     SCREAMING_SNAKE_CASE_=0.0_0_1			,     SCREAMING_SNAKE_CASE_=1.0			,     SCREAMING_SNAKE_CASE_="relu"			,     SCREAMING_SNAKE_CASE_=True			,     SCREAMING_SNAKE_CASE_=False			,     SCREAMING_SNAKE_CASE_=True			,     SCREAMING_SNAKE_CASE_=0			,     SCREAMING_SNAKE_CASE_=1			,     **SCREAMING_SNAKE_CASE_			,     )				->     int:
         __lowerCamelCase   :	str								=		vocab_size
         __lowerCamelCase   :	Dict								=		d_model
         __lowerCamelCase   :	List[str]								=		d_kv
         __lowerCamelCase   :	Dict								=		d_ff

         __lowerCamelCase   :	Union[str, Any]								=		num_sparse_encoder_layers

         __lowerCamelCase   :	Optional[int]								=		num_layers
         __lowerCamelCase   :	List[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[Any]								=		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   :	Union[str, Any]								=		self.num_decoder_layers // self.num_sparse_decoder_layers
         else:
              __lowerCamelCase   :	Optional[int]								=		self.num_decoder_layers  # HACK: this will create 0 sparse layers

         __lowerCamelCase   :	str								=		num_heads
         __lowerCamelCase   :	Tuple								=		num_experts
         __lowerCamelCase   :	str								=		expert_capacity
         __lowerCamelCase   :	Optional[Any]								=		router_bias
         __lowerCamelCase   :	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   :	int								=		router_dtype

         __lowerCamelCase   :	int								=		router_ignore_padding_tokens
         __lowerCamelCase   :	Tuple								=		relative_attention_num_buckets
         __lowerCamelCase   :	Dict								=		relative_attention_max_distance

         __lowerCamelCase   :	Optional[Any]								=		dropout_rate
         __lowerCamelCase   :	List[str]								=		layer_norm_epsilon
         __lowerCamelCase   :	List[str]								=		initializer_factor
         __lowerCamelCase   :	Any								=		feed_forward_proj
         __lowerCamelCase   :	Any								=		use_cache
         __lowerCamelCase   :	Tuple								=		add_router_probs

         __lowerCamelCase   :	int								=		router_z_loss_coef
         __lowerCamelCase   :	Optional[int]								=		router_aux_loss_coef

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

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

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










'''simple docstring'''



from numpy import exp, pi, sqrt



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Optional[int]      ,    UpperCAmelCase_    :				float = 0.0      ,    UpperCAmelCase_    :				float = 1.0							)     ->		int:
     return 1 / sqrt(2 * pi * sigma**2							) * exp(-((x - mu) ** 2) / (2 * sigma**2)							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()






