
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"""



UpperCamelCase__          =	{
    'meter': 'm',
    'kilometer': 'km',
    'megametre': 'Mm',
    'gigametre': 'Gm',
    'terametre': 'Tm',
    'petametre': 'Pm',
    'exametre': 'Em',
    'zettametre': 'Zm',
    'yottametre': 'Ym',
}
# Exponent of the factor(meter)
UpperCamelCase__          =	{
    'm': 0,
    'km': 3,
    'Mm': 6,
    'Gm': 9,
    'Tm': 12,
    'Pm': 15,
    'Em': 18,
    'Zm': 21,
    'Ym': 24,
}
def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case ):
    UpperCAmelCase__	:      int     							=					from_type.lower().strip('s' )
    UpperCAmelCase__	:      List[Any]     							=					to_type.lower().strip('s' )

    UpperCAmelCase__	:      Optional[int]     							=					UNIT_SYMBOL.get(_snake_case    ,_snake_case )
    UpperCAmelCase__	:      Optional[Any]     							=					UNIT_SYMBOL.get(_snake_case    ,_snake_case )

    if from_sanitized not in METRIC_CONVERSION:
        UpperCAmelCase__	:      Optional[int]     							=					(
            F'''Invalid \'from_type\' value: {from_type!r}.\n'''
            F'''Conversion abbreviations are: {', '.join(_snake_case )}'''
        )
        raise ValueError(_snake_case )
    if to_sanitized not in METRIC_CONVERSION:
        UpperCAmelCase__	:      int     							=					(
            F'''Invalid \'to_type\' value: {to_type!r}.\n'''
            F'''Conversion abbreviations are: {', '.join(_snake_case )}'''
        )
        raise ValueError(_snake_case )
    UpperCAmelCase__	:      str     							=					METRIC_CONVERSION[from_sanitized]
    UpperCAmelCase__	:      Optional[int]     							=					METRIC_CONVERSION[to_sanitized]
    UpperCAmelCase__	:      int     							=					1

    if from_exponent > to_exponent:
        UpperCAmelCase__	:      List[str]     							=					from_exponent - to_exponent
    else:
        UpperCAmelCase__	:      int     							=					-(to_exponent - from_exponent)

    return value * pow(10    ,_snake_case )


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

    testmod()

