


'''simple docstring'''



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


A__						:   Dict				=    logging.get_logger(__name__)

A__						:   Union[str, Any]				=    {
    """weiweishi/roc-bert-base-zh""": """https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json""",
}





class   UpperCAmelCase_    (A__    ):





    """simple docstring"""






    lowerCamelCase    :   Optional[Any]					=     'roc_bert'







    def __init__(				self     ,	SCREAMING_SNAKE_CASE_=3_05_22     ,	SCREAMING_SNAKE_CASE_=7_68     ,	SCREAMING_SNAKE_CASE_=12     ,	SCREAMING_SNAKE_CASE_=12     ,	SCREAMING_SNAKE_CASE_=30_72     ,	SCREAMING_SNAKE_CASE_="gelu"     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=5_12     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	SCREAMING_SNAKE_CASE_=1E-12     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=0     ,	SCREAMING_SNAKE_CASE_="absolute"     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=7_68     ,	SCREAMING_SNAKE_CASE_=9_10     ,	SCREAMING_SNAKE_CASE_=5_12     ,	SCREAMING_SNAKE_CASE_=2_48_58     ,	SCREAMING_SNAKE_CASE_=True     ,	**SCREAMING_SNAKE_CASE_     ,	)							->	Optional[int]:
         __lowerCamelCase  :      str														=						vocab_size
         __lowerCamelCase  :      List[str]														=						max_position_embeddings
         __lowerCamelCase  :      List[Any]														=						hidden_size
         __lowerCamelCase  :      Optional[int]														=						num_hidden_layers
         __lowerCamelCase  :      List[str]														=						num_attention_heads
         __lowerCamelCase  :      Optional[Any]														=						intermediate_size
         __lowerCamelCase  :      Any														=						hidden_act
         __lowerCamelCase  :      Dict														=						hidden_dropout_prob
         __lowerCamelCase  :      Any														=						attention_probs_dropout_prob
         __lowerCamelCase  :      Tuple														=						initializer_range
         __lowerCamelCase  :      Tuple														=						type_vocab_size
         __lowerCamelCase  :      Optional[int]														=						layer_norm_eps
         __lowerCamelCase  :      Union[str, Any]														=						use_cache
         __lowerCamelCase  :      str														=						enable_pronunciation
         __lowerCamelCase  :      str														=						enable_shape
         __lowerCamelCase  :      Any														=						pronunciation_embed_dim
         __lowerCamelCase  :      List[Any]														=						pronunciation_vocab_size
         __lowerCamelCase  :      List[str]														=						shape_embed_dim
         __lowerCamelCase  :      List[Any]														=						shape_vocab_size
         __lowerCamelCase  :      Optional[Any]														=						concat_input
         __lowerCamelCase  :      Tuple														=						position_embedding_type
         __lowerCamelCase  :      List[str]														=						classifier_dropout
         super().__init__(pad_token_id=_lowerCamelCase     ,	**_lowerCamelCase      )








"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__SCREAMING_SNAKE_CASE							=     {
    'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __SCREAMING_SNAKE_CASE							=     [
     'MEGA_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MegaForCausalLM',
     'MegaForMaskedLM',
     'MegaForMultipleChoice',
     'MegaForQuestionAnswering',
     'MegaForSequenceClassification',
     'MegaForTokenClassification',
     'MegaModel',
     'MegaPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mega import (
      MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
      MegaForCausalLM,
      MegaForMaskedLM,
      MegaForMultipleChoice,
      MegaForQuestionAnswering,
      MegaForSequenceClassification,
      MegaForTokenClassification,
      MegaModel,
      MegaPreTrainedModel,
  )

else:
 import sys

 __SCREAMING_SNAKE_CASE							=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
