

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


lowercase_			=  logging.get_logger(__name__)

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


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    lowerCamelCase		     =							'roc_bert'


    def __init__(				self			:	Union[str, Any],lowercase_			:	Dict=3_0_5_2_2,lowercase_			:	Any=7_6_8,lowercase_			:	Optional[int]=1_2,lowercase_			:	Tuple=1_2,lowercase_			:	Optional[Any]=3_0_7_2,lowercase_			:	Tuple="gelu",lowercase_			:	Dict=0.1,lowercase_			:	Tuple=0.1,lowercase_			:	Tuple=5_1_2,lowercase_			:	str=2,lowercase_			:	List[str]=0.02,lowercase_			:	List[str]=1E-12,lowercase_			:	Optional[Any]=True,lowercase_			:	List[str]=0,lowercase_			:	str="absolute",lowercase_			:	int=None,lowercase_			:	List[str]=True,lowercase_			:	List[Any]=True,lowercase_			:	Tuple=7_6_8,lowercase_			:	Union[str, Any]=9_1_0,lowercase_			:	Optional[Any]=5_1_2,lowercase_			:	Tuple=2_4_8_5_8,lowercase_			:	Dict=True,**lowercase_			:	Optional[Any],)->   Tuple:





        '''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=lowercase_,**lowercase_		)




from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
     if not (is_transformers_available() and is_torch_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_torch_and_transformers_objects import (
         ImageTextPipelineOutput,
         UniDiffuserPipeline,
     )
else:
     from .modeling_text_decoder import UniDiffuserTextDecoder
     from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
     from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline

