





'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

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


_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)

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

class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							Optional[Any]         =					"roberta-prelayernorm"




 def __init__(	self			,     _lowerCAmelCase=50265			,     _lowerCAmelCase=768			,     _lowerCAmelCase=12			,     _lowerCAmelCase=12			,     _lowerCAmelCase=3072			,     _lowerCAmelCase="gelu"			,     _lowerCAmelCase=0.1			,     _lowerCAmelCase=0.1			,     _lowerCAmelCase=512			,     _lowerCAmelCase=2			,     _lowerCAmelCase=0.02			,     _lowerCAmelCase=1E-12			,     _lowerCAmelCase=1			,     _lowerCAmelCase=0			,     _lowerCAmelCase=2			,     _lowerCAmelCase="absolute"			,     _lowerCAmelCase=True			,     _lowerCAmelCase=None			,     **_lowerCAmelCase			,     )    ->							List[Any]:
     super().__init__(pad_token_id=_lowerCAmelCase			,     bos_token_id=_lowerCAmelCase			,     eos_token_id=_lowerCAmelCase			,     **_lowerCAmelCase		)

     _lowerCAmelCase				=      vocab_size
     _lowerCAmelCase				=      hidden_size
     _lowerCAmelCase				=      num_hidden_layers
     _lowerCAmelCase				=      num_attention_heads
     _lowerCAmelCase				=      hidden_act
     _lowerCAmelCase				=      intermediate_size
     _lowerCAmelCase				=      hidden_dropout_prob
     _lowerCAmelCase				=      attention_probs_dropout_prob
     _lowerCAmelCase				=      max_position_embeddings
     _lowerCAmelCase				=      type_vocab_size
     _lowerCAmelCase				=      initializer_range
     _lowerCAmelCase				=      layer_norm_eps
     _lowerCAmelCase				=      position_embedding_type
     _lowerCAmelCase				=      use_cache
     _lowerCAmelCase				=      classifier_dropout







class    lowerCAmelCase_		(		__magic_name__		):




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









'''simple docstring'''


from ...configuration_utils import PretrainedConfig

class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							List[str]         =					"bert-generation"




 def __init__(	self			,     _lowerCAmelCase=50358			,     _lowerCAmelCase=1024			,     _lowerCAmelCase=24			,     _lowerCAmelCase=16			,     _lowerCAmelCase=4096			,     _lowerCAmelCase="gelu"			,     _lowerCAmelCase=0.1			,     _lowerCAmelCase=0.1			,     _lowerCAmelCase=512			,     _lowerCAmelCase=0.02			,     _lowerCAmelCase=1E-12			,     _lowerCAmelCase=0			,     _lowerCAmelCase=2			,     _lowerCAmelCase=1			,     _lowerCAmelCase="absolute"			,     _lowerCAmelCase=True			,     **_lowerCAmelCase			,     )    ->							Tuple:
     super().__init__(pad_token_id=_lowerCAmelCase			,     bos_token_id=_lowerCAmelCase			,     eos_token_id=_lowerCAmelCase			,     **_lowerCAmelCase		)

     _lowerCAmelCase				=      vocab_size
     _lowerCAmelCase				=      hidden_size
     _lowerCAmelCase				=      num_hidden_layers
     _lowerCAmelCase				=      num_attention_heads
     _lowerCAmelCase				=      hidden_act
     _lowerCAmelCase				=      intermediate_size
     _lowerCAmelCase				=      hidden_dropout_prob
     _lowerCAmelCase				=      attention_probs_dropout_prob
     _lowerCAmelCase				=      max_position_embeddings
     _lowerCAmelCase				=      initializer_range
     _lowerCAmelCase				=      layer_norm_eps
     _lowerCAmelCase				=      position_embedding_type
     _lowerCAmelCase				=      use_cache


