


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


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

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

class       __magic_name__	(     __lowerCAmelCase):
     A:		Optional[Any]			=      "switch_transformers"
     A:		Optional[Any]			=      ["past_key_values"]
     A:		int			=      {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}


     def __init__(      self							:     Optional[Any]							,	lowerCamelCase__							:     int=32128							,	lowerCamelCase__							:     Optional[int]=768							,	lowerCamelCase__							:     str=64							,	lowerCamelCase__							:     Any=2048							,	lowerCamelCase__							:     Any=64							,	lowerCamelCase__							:     Optional[int]=12							,	lowerCamelCase__							:     Optional[int]=3							,	lowerCamelCase__							:     List[Any]=12							,	lowerCamelCase__							:     List[str]=3							,	lowerCamelCase__							:     Optional[Any]=12							,	lowerCamelCase__							:     str=8							,	lowerCamelCase__							:     List[Any]=False							,	lowerCamelCase__							:     Optional[int]=0.01							,	lowerCamelCase__							:     Union[str, Any]="float32"							,	lowerCamelCase__							:     Optional[Any]=False							,	lowerCamelCase__							:     Optional[int]=32							,	lowerCamelCase__							:     str=128							,	lowerCamelCase__							:     Dict=0.1							,	lowerCamelCase__							:     int=1E-6							,	lowerCamelCase__							:     Dict=0.001							,	lowerCamelCase__							:     str=0.001							,	lowerCamelCase__							:     List[str]=1.0							,	lowerCamelCase__							:     Union[str, Any]="relu"							,	lowerCamelCase__							:     int=True							,	lowerCamelCase__							:     Optional[int]=False							,	lowerCamelCase__							:     List[Any]=True							,	lowerCamelCase__							:     Any=0							,	lowerCamelCase__							:     List[Any]=1							,	**lowerCamelCase__							:     Dict							,	)  ->      Dict:





      '''simple docstring'''






      UpperCamelCase__					:    Any          =   vocab_size
      UpperCamelCase__					:    Tuple          =   d_model
      UpperCamelCase__					:    int          =   d_kv
      UpperCamelCase__					:    Optional[Any]          =   d_ff

      UpperCamelCase__					:    List[Any]          =   num_sparse_encoder_layers

      UpperCamelCase__					:    Optional[Any]          =   num_layers
      UpperCamelCase__					:    Any          =   (
          num_decoder_layers if num_decoder_layers is not None else self.num_layers
      )  # default = symmetry
      UpperCamelCase__					:    Tuple          =   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[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:
       UpperCamelCase__					:    Union[str, Any]          =   self.num_decoder_layers // self.num_sparse_decoder_layers
      else:
       UpperCamelCase__					:    int          =   self.num_decoder_layers  # HACK: this will create 0 sparse layers

      UpperCamelCase__					:    List[Any]          =   num_heads
      UpperCamelCase__					:    Any          =   num_experts
      UpperCamelCase__					:    List[str]          =   expert_capacity
      UpperCamelCase__					:    List[str]          =   router_bias
      UpperCamelCase__					:    Union[str, 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[int]          =   router_dtype

      UpperCamelCase__					:    str          =   router_ignore_padding_tokens
      UpperCamelCase__					:    Dict          =   relative_attention_num_buckets
      UpperCamelCase__					:    Union[str, Any]          =   relative_attention_max_distance

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

      UpperCamelCase__					:    Optional[Any]          =   router_z_loss_coef
      UpperCamelCase__					:    Optional[Any]          =   router_aux_loss_coef

      UpperCamelCase__					:    List[str]          =   self.feed_forward_proj.split('''-'''			)
      UpperCamelCase__					:    Optional[int]          =   act_info[-1]
      UpperCamelCase__					:    Union[str, Any]          =   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__					:    Tuple          =   '''gelu_new'''

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






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCamelCase       :       Any						       =				logging.get_logger(__name__)

__UpperCamelCase       :       Any						       =				{
    "andreasmadsen/efficient_mlm_m0.40": (
        "https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"
    ),
}

class       __magic_name__	(     __lowerCAmelCase):
     A:		List[Any]			=      "roberta-prelayernorm"


     def __init__(      self							:     Tuple							,	lowerCamelCase__							:     List[Any]=50265							,	lowerCamelCase__							:     Optional[Any]=768							,	lowerCamelCase__							:     str=12							,	lowerCamelCase__							:     Union[str, Any]=12							,	lowerCamelCase__							:     Dict=3072							,	lowerCamelCase__							:     int="gelu"							,	lowerCamelCase__							:     Tuple=0.1							,	lowerCamelCase__							:     Tuple=0.1							,	lowerCamelCase__							:     List[str]=512							,	lowerCamelCase__							:     int=2							,	lowerCamelCase__							:     Tuple=0.02							,	lowerCamelCase__							:     List[Any]=1E-1_2							,	lowerCamelCase__							:     str=1							,	lowerCamelCase__							:     int=0							,	lowerCamelCase__							:     int=2							,	lowerCamelCase__							:     Union[str, Any]="absolute"							,	lowerCamelCase__							:     Optional[Any]=True							,	lowerCamelCase__							:     Dict=None							,	**lowerCamelCase__							:     Any							,	)  ->      Optional[int]:





      '''simple docstring'''






      super().__init__(pad_token_id=lowerCamelCase__							,	bos_token_id=lowerCamelCase__							,	eos_token_id=lowerCamelCase__							,	**lowerCamelCase__			)

      UpperCamelCase__					:    Optional[int]          =   vocab_size
      UpperCamelCase__					:    Union[str, Any]          =   hidden_size
      UpperCamelCase__					:    List[str]          =   num_hidden_layers
      UpperCamelCase__					:    Optional[int]          =   num_attention_heads
      UpperCamelCase__					:    List[str]          =   hidden_act
      UpperCamelCase__					:    Optional[int]          =   intermediate_size
      UpperCamelCase__					:    Optional[int]          =   hidden_dropout_prob
      UpperCamelCase__					:    List[str]          =   attention_probs_dropout_prob
      UpperCamelCase__					:    Optional[int]          =   max_position_embeddings
      UpperCamelCase__					:    Optional[Any]          =   type_vocab_size
      UpperCamelCase__					:    Union[str, Any]          =   initializer_range
      UpperCamelCase__					:    Dict          =   layer_norm_eps
      UpperCamelCase__					:    Union[str, Any]          =   position_embedding_type
      UpperCamelCase__					:    Optional[int]          =   use_cache
      UpperCamelCase__					:    int          =   classifier_dropout



class       __magic_name__	(     __lowerCAmelCase):


     @property
     def      UpperCAmelCase__	(      self							:     int			)  ->      Mapping[str, Mapping[int, str]]:





      '''simple docstring'''






      if self.task == "multiple-choice":
       UpperCamelCase__					:    Dict          =   {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
      else:
       UpperCamelCase__					:    Any          =   {0: '''batch''', 1: '''sequence'''}
      return OrderedDict(
          [
              ('''input_ids''', dynamic_axis),
              ('''attention_mask''', dynamic_axis),
          ]			)


