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


UpperCAmelCase       =					logging.get_logger(__name__)

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







class 			A_  (     __lowerCamelCase  ):




	'''simple docstring'''


	_UpperCamelCase	:	List[Any]									=			"""switch_transformers"""
	_UpperCamelCase	:	int									=			["""past_key_values"""]
	_UpperCamelCase	:	Any									=			{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}





	def __init__(			self						,       snake_case=3_2128						,       snake_case=768						,       snake_case=64						,       snake_case=2048						,       snake_case=64						,       snake_case=12						,       snake_case=3						,       snake_case=12						,       snake_case=3						,       snake_case=12						,       snake_case=8						,       snake_case=False						,       snake_case=0.01						,       snake_case="float32"						,       snake_case=False						,       snake_case=32						,       snake_case=128						,       snake_case=0.1						,       snake_case=1E-6						,       snake_case=0.001						,       snake_case=0.001						,       snake_case=1.0						,       snake_case="relu"						,       snake_case=True						,       snake_case=False						,       snake_case=True						,       snake_case=0						,       snake_case=1						,       **snake_case						,       ):
		lowercase	    =       vocab_size
		lowercase	    =       d_model
		lowercase	    =       d_kv
		lowercase	    =       d_ff

		lowercase	    =       num_sparse_encoder_layers

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

		lowercase	    =       num_heads
		lowercase	    =       num_experts
		lowercase	    =       expert_capacity
		lowercase	    =       router_bias
		lowercase	    =       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}'''			)
		lowercase	    =       router_dtype

		lowercase	    =       router_ignore_padding_tokens
		lowercase	    =       relative_attention_num_buckets
		lowercase	    =       relative_attention_max_distance

		lowercase	    =       dropout_rate
		lowercase	    =       layer_norm_epsilon
		lowercase	    =       initializer_factor
		lowercase	    =       feed_forward_proj
		lowercase	    =       use_cache
		lowercase	    =       add_router_probs

		lowercase	    =       router_z_loss_coef
		lowercase	    =       router_aux_loss_coef

		lowercase	    =       self.feed_forward_proj.split('-'			)
		lowercase	    =       act_info[-1]
		lowercase	    =       act_info[0] == 'gated'

		if len(snake_case			) > 1 and act_info[0] != "gated" or len(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":
			lowercase	    =       'gelu_new'

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





"""simple docstring"""




def       a_ (	lowercase__ :str     ):
			__lowerCamelCase          =      [int(lowercase__     ) for i in ip_va_address.split("""."""     ) if i.isdigit()]
			return len(lowercase__     ) == 4 and all(0 <= int(lowercase__     ) <= 254 for octet in octets     )


if __name__ == "__main__":
					__magic_name__						: Tuple    =				input().strip()
					__magic_name__						: Optional[int]    =				'valid' if is_ip_va_address_valid(ip) else 'invalid'
					print(f"""{ip} is a {valid_or_invalid} IP v4 address.""")




