





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


A   : Dict =				logging.get_logger(__name__)

A   : int =				{
    'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json',
}


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




			__lowerCamelCase     :	List[str]          =   '''roc_bert'''






			def __init__(      self       :   Tuple							,     __lowerCAmelCase       :   int=3_05_22							,     __lowerCAmelCase       :   Optional[int]=7_68							,     __lowerCAmelCase       :   int=12							,     __lowerCAmelCase       :   Tuple=12							,     __lowerCAmelCase       :   Tuple=30_72							,     __lowerCAmelCase       :   Any="gelu"							,     __lowerCAmelCase       :   List[Any]=0.1							,     __lowerCAmelCase       :   Optional[int]=0.1							,     __lowerCAmelCase       :   Optional[int]=5_12							,     __lowerCAmelCase       :   str=2							,     __lowerCAmelCase       :   int=0.0_2							,     __lowerCAmelCase       :   int=1e-12							,     __lowerCAmelCase       :   Optional[int]=True							,     __lowerCAmelCase       :   List[str]=0							,     __lowerCAmelCase       :   List[str]="absolute"							,     __lowerCAmelCase       :   Any=None							,     __lowerCAmelCase       :   Optional[Any]=True							,     __lowerCAmelCase       :   Any=True							,     __lowerCAmelCase       :   Optional[Any]=7_68							,     __lowerCAmelCase       :   Optional[int]=9_10							,     __lowerCAmelCase       :   List[Any]=5_12							,     __lowerCAmelCase       :   Optional[Any]=2_48_58							,     __lowerCAmelCase       :   Dict=True							,     **__lowerCAmelCase       :   str							,     )     ->     int:







					"""simple docstring"""






					A__  				=       vocab_size
					A__  				=       max_position_embeddings
					A__  				=       hidden_size
					A__  				=       num_hidden_layers
					A__  				=       num_attention_heads
					A__  				=       intermediate_size
					A__  				=       hidden_act
					A__  				=       hidden_dropout_prob
					A__  				=       attention_probs_dropout_prob
					A__  				=       initializer_range
					A__  				=       type_vocab_size
					A__  				=       layer_norm_eps
					A__  				=       use_cache
					A__  				=       enable_pronunciation
					A__  				=       enable_shape
					A__  				=       pronunciation_embed_dim
					A__  				=       pronunciation_vocab_size
					A__  				=       shape_embed_dim
					A__  				=       shape_vocab_size
					A__  				=       concat_input
					A__  				=       position_embedding_type
					A__  				=       classifier_dropout
					super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE							,     **__SCREAMING_SNAKE_CASE       )










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__)



