


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


A				:       List[str]      =  logging.get_logger(__name__)

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






class      UpperCamelCase(	_a ):
					snake_case_		:					Union[str, Any]    					=						"""switch_transformers"""
					snake_case_		:					Optional[int]    					=						["""past_key_values"""]
					snake_case_		:					Dict    					=						{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}


					def __init__(    self						:		List[str]  ,     SCREAMING_SNAKE_CASE						:		Tuple=3_2_1_2_8  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]=7_6_8  ,     SCREAMING_SNAKE_CASE						:		str=6_4  ,     SCREAMING_SNAKE_CASE						:		Dict=2_0_4_8  ,     SCREAMING_SNAKE_CASE						:		Dict=6_4  ,     SCREAMING_SNAKE_CASE						:		Any=1_2  ,     SCREAMING_SNAKE_CASE						:		List[str]=3  ,     SCREAMING_SNAKE_CASE						:		str=1_2  ,     SCREAMING_SNAKE_CASE						:		str=3  ,     SCREAMING_SNAKE_CASE						:		Dict=1_2  ,     SCREAMING_SNAKE_CASE						:		List[Any]=8  ,     SCREAMING_SNAKE_CASE						:		Dict=False  ,     SCREAMING_SNAKE_CASE						:		Optional[int]=0.01  ,     SCREAMING_SNAKE_CASE						:		Dict="float32"  ,     SCREAMING_SNAKE_CASE						:		Dict=False  ,     SCREAMING_SNAKE_CASE						:		Dict=3_2  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]=1_2_8  ,     SCREAMING_SNAKE_CASE						:		str=0.1  ,     SCREAMING_SNAKE_CASE						:		Dict=1e-6  ,     SCREAMING_SNAKE_CASE						:		List[str]=0.001  ,     SCREAMING_SNAKE_CASE						:		str=0.001  ,     SCREAMING_SNAKE_CASE						:		List[str]=1.0  ,     SCREAMING_SNAKE_CASE						:		Any="relu"  ,     SCREAMING_SNAKE_CASE						:		List[Any]=True  ,     SCREAMING_SNAKE_CASE						:		List[Any]=False  ,     SCREAMING_SNAKE_CASE						:		List[Any]=True  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]=0  ,     SCREAMING_SNAKE_CASE						:		Optional[int]=1  ,     **SCREAMING_SNAKE_CASE						:		List[Any]  ,     )      ->						Optional[int]:




											'''simple docstring'''

											__snake_case				=		vocab_size
											__snake_case				=		d_model
											__snake_case				=		d_kv
											__snake_case				=		d_ff

											__snake_case				=		num_sparse_encoder_layers

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

											__snake_case				=		num_heads
											__snake_case				=		num_experts
											__snake_case				=		expert_capacity
											__snake_case				=		router_bias
											__snake_case				=		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				=		router_dtype

											__snake_case				=		router_ignore_padding_tokens
											__snake_case				=		relative_attention_num_buckets
											__snake_case				=		relative_attention_max_distance

											__snake_case				=		dropout_rate
											__snake_case				=		layer_norm_epsilon
											__snake_case				=		initializer_factor
											__snake_case				=		feed_forward_proj
											__snake_case				=		use_cache
											__snake_case				=		add_router_probs

											__snake_case				=		router_z_loss_coef
											__snake_case				=		router_aux_loss_coef

											__snake_case				=		self.feed_forward_proj.split("-"    )
											__snake_case				=		act_info[-1]
											__snake_case				=		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":
																	__snake_case				=		"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  ,     )









import baseaa





def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						bytes:


						'''simple docstring'''






						return baseaa.baaencode(string.encode("utf-8"			)			)





def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						str:


						'''simple docstring'''






						return baseaa.baadecode(_lowerCAmelCase			).decode("utf-8"			)


if __name__ == "__main__":
	A				:       Dict      =  'Hello World!'
	A				:       Any      =  baseaa_encode(test)
	print(encoded)

	A				:       Optional[int]      =  baseaa_decode(encoded)
	print(decoded)





