


'''simple docstring'''



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


__A		=logging.get_logger(__name__)

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


class 						_snake_case   (       a__		):
					lowerCAmelCase :Dict						=   '''switch_transformers'''
					lowerCAmelCase :int						=   ['''past_key_values''']
					lowerCAmelCase :List[str]						=   {'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}




					def __init__( self							,							_lowerCamelCase=3_2128							,							_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							,							):
								UpperCAmelCase__					:   Tuple       				=       vocab_size
								UpperCAmelCase__					:   Optional[Any]       				=       d_model
								UpperCAmelCase__					:   List[Any]       				=       d_kv
								UpperCAmelCase__					:   int       				=       d_ff

								UpperCAmelCase__					:   Dict       				=       num_sparse_encoder_layers

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

								UpperCAmelCase__					:   Optional[Any]       				=       num_heads
								UpperCAmelCase__					:   Dict       				=       num_experts
								UpperCAmelCase__					:   Tuple       				=       expert_capacity
								UpperCAmelCase__					:   List[str]       				=       router_bias
								UpperCAmelCase__					:   Optional[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}''')
								UpperCAmelCase__					:   List[Any]       				=       router_dtype

								UpperCAmelCase__					:   Union[str, Any]       				=       router_ignore_padding_tokens
								UpperCAmelCase__					:   Optional[int]       				=       relative_attention_num_buckets
								UpperCAmelCase__					:   Tuple       				=       relative_attention_max_distance

								UpperCAmelCase__					:   List[str]       				=       dropout_rate
								UpperCAmelCase__					:   Optional[int]       				=       layer_norm_epsilon
								UpperCAmelCase__					:   List[str]       				=       initializer_factor
								UpperCAmelCase__					:   List[Any]       				=       feed_forward_proj
								UpperCAmelCase__					:   Tuple       				=       use_cache
								UpperCAmelCase__					:   Union[str, Any]       				=       add_router_probs

								UpperCAmelCase__					:   Dict       				=       router_z_loss_coef
								UpperCAmelCase__					:   int       				=       router_aux_loss_coef

								UpperCAmelCase__					:   List[str]       				=       self.feed_forward_proj.split("""-""")
								UpperCAmelCase__					:   int       				=       act_info[-1]
								UpperCAmelCase__					:   Optional[int]       				=       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":
											UpperCAmelCase__					:   Any       				=       """gelu_new"""

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



'''simple docstring'''



def       _UpperCamelCase (						UpperCamelCase__ = 1_0	, UpperCamelCase__ = 2_2   ):
			UpperCAmelCase__					:   List[str]       				=       range(1	, UpperCamelCase__   )
			UpperCAmelCase__					:   int       				=       range(1	, UpperCamelCase__   )
			return sum(
			    1 for power in powers for base in bases if len(str(base**power   )   ) == power   )


if __name__ == "__main__":
		print(f"""{solution(10, 22) = }""")