





from collections import OrderedDict
from typing import Mapping

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


A   : Optional[int] =				logging.get_logger(__name__)

A   : Optional[int] =				{
    '''andreasmadsen/efficient_mlm_m0.40''': (
        '''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
    ),
}


class   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			__lowerCamelCase     :	List[str]          =   '''roberta-prelayernorm'''






			def __init__(      self       :   Union[str, Any]							,     __lowerCAmelCase       :   List[str]=5_02_65							,     __lowerCAmelCase       :   List[str]=7_68							,     __lowerCAmelCase       :   List[str]=12							,     __lowerCAmelCase       :   Optional[Any]=12							,     __lowerCAmelCase       :   Any=30_72							,     __lowerCAmelCase       :   int="gelu"							,     __lowerCAmelCase       :   List[Any]=0.1							,     __lowerCAmelCase       :   Dict=0.1							,     __lowerCAmelCase       :   Tuple=5_12							,     __lowerCAmelCase       :   List[str]=2							,     __lowerCAmelCase       :   List[str]=0.0_2							,     __lowerCAmelCase       :   List[str]=1e-12							,     __lowerCAmelCase       :   Union[str, Any]=1							,     __lowerCAmelCase       :   Optional[Any]=0							,     __lowerCAmelCase       :   Optional[int]=2							,     __lowerCAmelCase       :   Dict="absolute"							,     __lowerCAmelCase       :   Any=True							,     __lowerCAmelCase       :   Dict=None							,     **__lowerCAmelCase       :   Dict							,     )     ->     int:







					"""simple docstring"""






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

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


class   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			@property
			def        a_	(      self       :   Union[str, Any]       )     ->     Dict:







					"""simple docstring"""






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










from typing import TYPE_CHECKING

from ...utils import _LazyModule


A   : Optional[Any]			=				{'''processing_wav2vec2_with_lm''': ['''Wav2Vec2ProcessorWithLM''']}


if TYPE_CHECKING:
					from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
					import sys

					A   : List[str]			=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



