





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


__lowerCamelCase			:	str					    =	logging.get_logger(__name__)

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



class    A__  (			__snake_case     ):
      _UpperCAmelCase      :int		       =  'switch_transformers'
      _UpperCAmelCase      :Union[str, Any]		       =  ['past_key_values']
      _UpperCAmelCase      :Optional[Any]		       =  {'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}





      def __init__(					self			,				A_=3_2128			,				A_=768			,				A_=64			,				A_=2048			,				A_=64			,				A_=12			,				A_=3			,				A_=12			,				A_=3			,				A_=12			,				A_=8			,				A_=False			,				A_=0.01			,				A_="float32"			,				A_=False			,				A_=32			,				A_=128			,				A_=0.1			,				A_=1e-6			,				A_=0.0_01			,				A_=0.0_01			,				A_=1.0			,				A_="relu"			,				A_=True			,				A_=False			,				A_=True			,				A_=0			,				A_=1			,				**A_			,				):
       '''simple docstring'''




       UpperCamelCase      :   List[Any]			      =  vocab_size
       UpperCamelCase      :   str			      =  d_model
       UpperCamelCase      :   int			      =  d_kv
       UpperCamelCase      :   int			      =  d_ff

       UpperCamelCase      :   Optional[Any]			      =  num_sparse_encoder_layers

       UpperCamelCase      :   Optional[Any]			      =  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      :   List[str]			      =  self.num_layers // self.num_sparse_encoder_layers
       else:
        UpperCamelCase      :   Optional[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      :   Optional[int]			      =  self.num_decoder_layers // self.num_sparse_decoder_layers
       else:
        UpperCamelCase      :   str			      =  self.num_decoder_layers  # HACK: this will create 0 sparse layers

       UpperCamelCase      :   Dict			      =  num_heads
       UpperCamelCase      :   Optional[int]			      =  num_experts
       UpperCamelCase      :   List[Any]			      =  expert_capacity
       UpperCamelCase      :   Dict			      =  router_bias
       UpperCamelCase      :   List[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      :   Optional[Any]			      =  router_dtype

       UpperCamelCase      :   str			      =  router_ignore_padding_tokens
       UpperCamelCase      :   Optional[int]			      =  relative_attention_num_buckets
       UpperCamelCase      :   str			      =  relative_attention_max_distance

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

       UpperCamelCase      :   List[str]			      =  router_z_loss_coef
       UpperCamelCase      :   List[Any]			      =  router_aux_loss_coef

       UpperCamelCase      :   Union[str, Any]			      =  self.feed_forward_proj.split("-"  )
       UpperCamelCase      :   Any			      =  act_info[-1]
       UpperCamelCase      :   List[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":
        UpperCamelCase      :   Tuple			      =  "gelu_new"

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







__lowerCamelCase			:	str					    =	6_5521



def 						A_							(	_lowerCAmelCase	)		->					int:
 UpperCamelCase      :   Any			      =  1
 UpperCamelCase      :   str			      =  0
 for plain_chr in plain_text:
  UpperCamelCase      :   List[Any]			      =  (a + ord(_lowerCAmelCase	)) % MOD_ADLER
  UpperCamelCase      :   List[Any]			      =  (b + a) % MOD_ADLER
 return (b << 16) | a
