




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


__a    :						Union[str, Any]						      =       logging.get_logger(__name__)

__a    :						Optional[int]						      =       {
    '''google/switch-base-8''': '''https://huggingface.co/google/switch-base-8/blob/main/config.json''',
}







class  UpperCAmelCase(	lowercase_	):



	"""simple docstring"""






	a      :   Optional[int]			    =	"switch_transformers"
	a      :   int			    =	["past_key_values"]
	a      :   str			    =	{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}

	def __init__(	self			, lowerCamelCase=32128			, 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=0.0_01			, lowerCamelCase=0.0_01			, lowerCamelCase=1.0			, lowerCamelCase="relu"			, lowerCamelCase=True			, lowerCamelCase=False			, lowerCamelCase=True			, lowerCamelCase=0			, lowerCamelCase=1			, **lowerCamelCase			, )   ->     List[str]:





								"""simple docstring"""


								lowercase__    :			List[Any]     =				vocab_size
								lowercase__    :			int     =				d_model
								lowercase__    :			str     =				d_kv
								lowercase__    :			Union[str, Any]     =				d_ff

								lowercase__    :			Tuple     =				num_sparse_encoder_layers

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

								lowercase__    :			Optional[int]     =				num_heads
								lowercase__    :			List[str]     =				num_experts
								lowercase__    :			Tuple     =				expert_capacity
								lowercase__    :			List[str]     =				router_bias
								lowercase__    :			Optional[int]     =				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__    :			int     =				router_dtype

								lowercase__    :			Union[str, Any]     =				router_ignore_padding_tokens
								lowercase__    :			List[Any]     =				relative_attention_num_buckets
								lowercase__    :			int     =				relative_attention_max_distance

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

								lowercase__    :			Tuple     =				router_z_loss_coef
								lowercase__    :			List[str]     =				router_aux_loss_coef

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

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







from __future__ import annotations




class       snake_case__ :

       '''simple docstring'''



       def __init__(				self      ,    a__     )			->   None:






            '''simple docstring'''





            __snake_case      :str 							=    order

            # a_{0} ... a_{k}
            __snake_case      :int 							=    [1.0] + [0.0] * order
            # b_{0} ... b_{k}
            __snake_case      :Any 							=    [1.0] + [0.0] * order

            # x[n-1] ... x[n-k]
            __snake_case      :List[Any] 							=    [0.0] * self.order
            # y[n-1] ... y[n-k]
            __snake_case      :int 							=    [0.0] * self.order



       def        __lowercase    (				self      ,    a__      ,    a__     )			->   None:






            '''simple docstring'''





            if len(a__     ) < self.order:
                 __snake_case      :Optional[int] 							=    [1.0, *a_coeffs]

            if len(a__     ) != self.order + 1:
                 __snake_case      :Dict 							=    (
                     F'''Expected a_coeffs to have {self.order + 1} elements '''
                     F'''for {self.order}-order filter, got {len(a__     )}'''
                 )
                 raise ValueError(a__     )

            if len(a__     ) != self.order + 1:
                 __snake_case      :Optional[Any] 							=    (
                     F'''Expected b_coeffs to have {self.order + 1} elements '''
                     F'''for {self.order}-order filter, got {len(a__     )}'''
                 )
                 raise ValueError(a__     )

            __snake_case      :str 							=    a_coeffs
            __snake_case      :str 							=    b_coeffs



       def        __lowercase    (				self      ,    a__     )			->   float:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    0.0

            # Start at index 1 and do index 0 at the end.
            for i in range(1      ,    self.order + 1     ):
                 result += (
                     self.b_coeffs[i] * self.input_history[i - 1]
                     - self.a_coeffs[i] * self.output_history[i - 1]
                 )

            __snake_case      :Dict 							=    (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

            __snake_case      :Optional[int] 							=    self.input_history[:-1]
            __snake_case      :Optional[Any] 							=    self.output_history[:-1]

            __snake_case      :Union[str, Any] 							=    sample
            __snake_case      :Optional[Any] 							=    result

            return result


