





'''simple docstring'''






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


snake_case_     =	logging.get_logger(__name__)

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


class   _lowercase						(							a				):
   _UpperCamelCase       		=   """roc_bert"""


   def __init__( self    ,   _UpperCAmelCase=30_522    ,   _UpperCAmelCase=768    ,   _UpperCAmelCase=12    ,   _UpperCAmelCase=12    ,   _UpperCAmelCase=3_072    ,   _UpperCAmelCase="gelu"    ,   _UpperCAmelCase=0.1    ,   _UpperCAmelCase=0.1    ,   _UpperCAmelCase=512    ,   _UpperCAmelCase=2    ,   _UpperCAmelCase=0.02    ,   _UpperCAmelCase=1E-1_2    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=0    ,   _UpperCAmelCase="absolute"    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=768    ,   _UpperCAmelCase=910    ,   _UpperCAmelCase=512    ,   _UpperCAmelCase=24_858    ,   _UpperCAmelCase=True    ,   **_UpperCAmelCase    ,   ):
       A       :       Tuple							=						vocab_size
       A       :       Dict							=						max_position_embeddings
       A       :       int							=						hidden_size
       A       :       int							=						num_hidden_layers
       A       :       int							=						num_attention_heads
       A       :       Any							=						intermediate_size
       A       :       str							=						hidden_act
       A       :       Any							=						hidden_dropout_prob
       A       :       str							=						attention_probs_dropout_prob
       A       :       str							=						initializer_range
       A       :       Union[str, Any]							=						type_vocab_size
       A       :       str							=						layer_norm_eps
       A       :       Tuple							=						use_cache
       A       :       Dict							=						enable_pronunciation
       A       :       Any							=						enable_shape
       A       :       Any							=						pronunciation_embed_dim
       A       :       List[Any]							=						pronunciation_vocab_size
       A       :       Any							=						shape_embed_dim
       A       :       Tuple							=						shape_vocab_size
       A       :       List[Any]							=						concat_input
       A       :       Optional[int]							=						position_embedding_type
       A       :       Tuple							=						classifier_dropout
       super().__init__(pad_token_id=_UpperCAmelCase    ,   **_UpperCAmelCase					)







'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case_     =	{
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case_     =	[
         """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """BioGptForCausalLM""",
         """BioGptForTokenClassification""",
         """BioGptForSequenceClassification""",
         """BioGptModel""",
         """BioGptPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
     from .tokenization_biogpt import BioGptTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_biogpt import (
              BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BioGptForCausalLM,
              BioGptForSequenceClassification,
              BioGptForTokenClassification,
              BioGptModel,
              BioGptPreTrainedModel,
          )


else:
     import sys

     snake_case_     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
