






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


__lowerCamelCase :      int										=				logging.get_logger(__name__)

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



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		"switch_transformers"
					a_     =		["past_key_values"]
					a_     =		{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}



					def __init__( self					:  List[Any]							,		__A					:  Any=3_2_1_2_8							,		__A					:  Dict=7_6_8							,		__A					:  Tuple=6_4							,		__A					:  Any=2_0_4_8							,		__A					:  List[str]=6_4							,		__A					:  Union[str, Any]=1_2							,		__A					:  Optional[int]=3							,		__A					:  Optional[int]=1_2							,		__A					:  str=3							,		__A					:  int=1_2							,		__A					:  List[str]=8							,		__A					:  List[Any]=False							,		__A					:  Dict=0.0_1							,		__A					:  List[str]="float32"							,		__A					:  int=False							,		__A					:  Optional[Any]=3_2							,		__A					:  Dict=1_2_8							,		__A					:  str=0.1							,		__A					:  int=1e-6							,		__A					:  Union[str, Any]=0.0_0_1							,		__A					:  Any=0.0_0_1							,		__A					:  Dict=1.0							,		__A					:  Optional[Any]="relu"							,		__A					:  List[Any]=True							,		__A					:  List[str]=False							,		__A					:  int=True							,		__A					:  Dict=0							,		__A					:  str=1							,		**__A					:  str							,		):
						snake_case__			:				List[Any]       =  vocab_size
						snake_case__			:				Tuple       =  d_model
						snake_case__			:				List[str]       =  d_kv
						snake_case__			:				List[Any]       =  d_ff

						snake_case__			:				int       =  num_sparse_encoder_layers

						snake_case__			:				Optional[int]       =  num_layers
						snake_case__			:				List[str]       =  (
						    num_decoder_layers if num_decoder_layers is not None else self.num_layers
						)  # default = symmetry
						snake_case__			:				Tuple       =  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:
							snake_case__			:				str       =  self.num_layers // self.num_sparse_encoder_layers
						else:
							snake_case__			:				List[Any]       =  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:
							snake_case__			:				List[Any]       =  self.num_decoder_layers // self.num_sparse_decoder_layers
						else:
							snake_case__			:				Optional[Any]       =  self.num_decoder_layers  # HACK: this will create 0 sparse layers

						snake_case__			:				str       =  num_heads
						snake_case__			:				List[str]       =  num_experts
						snake_case__			:				Dict       =  expert_capacity
						snake_case__			:				Dict       =  router_bias
						snake_case__			:				Union[str, Any]       =  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}'''	)
						snake_case__			:				Union[str, Any]       =  router_dtype

						snake_case__			:				int       =  router_ignore_padding_tokens
						snake_case__			:				Optional[int]       =  relative_attention_num_buckets
						snake_case__			:				Optional[int]       =  relative_attention_max_distance

						snake_case__			:				Optional[Any]       =  dropout_rate
						snake_case__			:				List[Any]       =  layer_norm_epsilon
						snake_case__			:				List[Any]       =  initializer_factor
						snake_case__			:				int       =  feed_forward_proj
						snake_case__			:				List[Any]       =  use_cache
						snake_case__			:				str       =  add_router_probs

						snake_case__			:				List[Any]       =  router_z_loss_coef
						snake_case__			:				List[Any]       =  router_aux_loss_coef

						snake_case__			:				Union[str, Any]       =  self.feed_forward_proj.split("-"	)
						snake_case__			:				List[str]       =  act_info[-1]
						snake_case__			:				Optional[Any]       =  act_info[0] == "gated"

						if len(__A	) > 1 and act_info[0] != "gated" or len(__A	) > 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":
							snake_case__			:				Tuple       =  "gelu_new"

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











def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int					,  snake_case_ :							float					,  snake_case_ :							float       ):
	return round(float(moles / volume       ) * nfactor       )







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							float					,  snake_case_ :							float					,  snake_case_ :							float       ):
	return round(float((moles * 0.08_21 * temperature) / (volume)       )       )







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							float					,  snake_case_ :							float					,  snake_case_ :							float       ):
	return round(float((moles * 0.08_21 * temperature) / (pressure)       )       )







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							float					,  snake_case_ :							float					,  snake_case_ :							float       ):
	return round(float((pressure * volume) / (0.08_21 * moles)       )       )


if __name__ == "__main__":
	import doctest

	doctest.testmod()



