



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


__UpperCAmelCase			  =			logging.get_logger(__name__)

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



class 							lowerCAmelCase_						(    A_				):
       UpperCAmelCase__							:   int  	=    "switch_transformers"
       UpperCAmelCase__							:   Optional[int]  	=    ["past_key_values"]
       UpperCAmelCase__							:   Any  	=    {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
       def __init__(     self,     SCREAMING_SNAKE_CASE_=3_2128,     SCREAMING_SNAKE_CASE_=768,     SCREAMING_SNAKE_CASE_=64,     SCREAMING_SNAKE_CASE_=2048,     SCREAMING_SNAKE_CASE_=64,     SCREAMING_SNAKE_CASE_=12,     SCREAMING_SNAKE_CASE_=3,     SCREAMING_SNAKE_CASE_=12,     SCREAMING_SNAKE_CASE_=3,     SCREAMING_SNAKE_CASE_=12,     SCREAMING_SNAKE_CASE_=8,     SCREAMING_SNAKE_CASE_=False,     SCREAMING_SNAKE_CASE_=0.01,     SCREAMING_SNAKE_CASE_="float32",     SCREAMING_SNAKE_CASE_=False,     SCREAMING_SNAKE_CASE_=32,     SCREAMING_SNAKE_CASE_=128,     SCREAMING_SNAKE_CASE_=0.1,     SCREAMING_SNAKE_CASE_=1e-6,     SCREAMING_SNAKE_CASE_=0.0_01,     SCREAMING_SNAKE_CASE_=0.0_01,     SCREAMING_SNAKE_CASE_=1.0,     SCREAMING_SNAKE_CASE_="relu",     SCREAMING_SNAKE_CASE_=True,     SCREAMING_SNAKE_CASE_=False,     SCREAMING_SNAKE_CASE_=True,     SCREAMING_SNAKE_CASE_=0,     SCREAMING_SNAKE_CASE_=1,     **SCREAMING_SNAKE_CASE_,     )	->						Any:
          UpperCamelCase       :    Optional[int]		      =      vocab_size
          UpperCamelCase       :    str		      =      d_model
          UpperCamelCase       :    Tuple		      =      d_kv
          UpperCamelCase       :    Any		      =      d_ff

          UpperCamelCase       :    str		      =      num_sparse_encoder_layers

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

          UpperCamelCase       :    Dict		      =      num_heads
          UpperCamelCase       :    List[Any]		      =      num_experts
          UpperCamelCase       :    Dict		      =      expert_capacity
          UpperCamelCase       :    Tuple		      =      router_bias
          UpperCamelCase       :    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       :    List[Any]		      =      router_ignore_padding_tokens
          UpperCamelCase       :    Optional[int]		      =      relative_attention_num_buckets
          UpperCamelCase       :    List[str]		      =      relative_attention_max_distance

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

          UpperCamelCase       :    Optional[Any]		      =      router_z_loss_coef
          UpperCamelCase       :    int		      =      router_aux_loss_coef

          UpperCamelCase       :    Union[str, Any]		      =      self.feed_forward_proj.split('-'			)
          UpperCamelCase       :    Any		      =      act_info[-1]
          UpperCamelCase       :    Any		      =      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":
             UpperCamelCase       :    Dict		      =      """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_,     )







"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase				=						logging.get_logger(__name__)

UpperCAmelCase				=						{
    """kssteven/ibert-roberta-base""": """https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json""",
    """kssteven/ibert-roberta-large""": """https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json""",
    """kssteven/ibert-roberta-large-mnli""": (
        """https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"""
    ),
}




class 	lowercase__				(					A_				):
   __UpperCAmelCase							       =					'''ibert'''





   def __init__(							self      ,   SCREAMING_SNAKE_CASE=3_0522      ,   SCREAMING_SNAKE_CASE=768      ,   SCREAMING_SNAKE_CASE=12      ,   SCREAMING_SNAKE_CASE=12      ,   SCREAMING_SNAKE_CASE=3072      ,   SCREAMING_SNAKE_CASE="gelu"      ,   SCREAMING_SNAKE_CASE=0.1      ,   SCREAMING_SNAKE_CASE=0.1      ,   SCREAMING_SNAKE_CASE=512      ,   SCREAMING_SNAKE_CASE=2      ,   SCREAMING_SNAKE_CASE=0.02      ,   SCREAMING_SNAKE_CASE=1e-1_2      ,   SCREAMING_SNAKE_CASE=1      ,   SCREAMING_SNAKE_CASE=0      ,   SCREAMING_SNAKE_CASE=2      ,   SCREAMING_SNAKE_CASE="absolute"      ,   SCREAMING_SNAKE_CASE=False      ,   SCREAMING_SNAKE_CASE="none"      ,   **SCREAMING_SNAKE_CASE      ,   )  ->  Any:
      super().__init__(pad_token_id=SCREAMING_SNAKE_CASE      ,   bos_token_id=SCREAMING_SNAKE_CASE      ,   eos_token_id=SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Optional[int]   =		vocab_size
      _lowerCamelCase	:					Dict   =		hidden_size
      _lowerCamelCase	:					List[str]   =		num_hidden_layers
      _lowerCamelCase	:					int   =		num_attention_heads
      _lowerCamelCase	:					Tuple   =		hidden_act
      _lowerCamelCase	:					str   =		intermediate_size
      _lowerCamelCase	:					Union[str, Any]   =		hidden_dropout_prob
      _lowerCamelCase	:					Tuple   =		attention_probs_dropout_prob
      _lowerCamelCase	:					Any   =		max_position_embeddings
      _lowerCamelCase	:					Dict   =		type_vocab_size
      _lowerCamelCase	:					List[Any]   =		initializer_range
      _lowerCamelCase	:					Dict   =		layer_norm_eps
      _lowerCamelCase	:					List[Any]   =		position_embedding_type
      _lowerCamelCase	:					Any   =		quant_mode
      _lowerCamelCase	:					List[str]   =		force_dequant







class 	lowercase__				(					A_				):





   @property
   def 		UpperCamelCase_  (							self)  ->  Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
         _lowerCamelCase	:					Dict   =		{0: """batch""", 1: """choice""", 2: """sequence"""}
      else:
         _lowerCamelCase	:					Optional[int]   =		{0: """batch""", 1: """sequence"""}
      return OrderedDict(
          [
              ("""input_ids""", dynamic_axis),
              ("""attention_mask""", dynamic_axis),
          ])

