

'''simple docstring'''



from typing import TYPE_CHECKING

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


UpperCAmelCase				: Optional[Any]         =  {
    'configuration_rwkv': ['RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RwkvConfig', 'RwkvOnnxConfig'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase				: List[Any]         =  [
      'RWKV_PRETRAINED_MODEL_ARCHIVE_LIST',
      'RwkvForCausalLM',
      'RwkvModel',
      'RwkvPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_rwkv import (
        RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
        RwkvForCausalLM,
        RwkvModel,
        RwkvPreTrainedModel,
    )
else:
  import sys

  UpperCAmelCase				: int         =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)









'''simple docstring'''



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


UpperCAmelCase				: Union[str, Any]         =  logging.get_logger(__name__)

UpperCAmelCase				: Optional[int]         =  {
    'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/config.json',
    # QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
}







class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 lowerCAmelCase__						      =   "qdqbert"
 def __init__(	self      : str      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=30_522      ,							__SCREAMING_SNAKE_CASE      : str=768      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=12      ,							__SCREAMING_SNAKE_CASE      : Any=12      ,							__SCREAMING_SNAKE_CASE      : Any=3_072      ,							__SCREAMING_SNAKE_CASE      : List[str]="gelu"      ,							__SCREAMING_SNAKE_CASE      : Any=0.1      ,							__SCREAMING_SNAKE_CASE      : Any=0.1      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=512      ,							__SCREAMING_SNAKE_CASE      : List[str]=2      ,							__SCREAMING_SNAKE_CASE      : Dict=0.02      ,							__SCREAMING_SNAKE_CASE      : List[str]=1E-12      ,							__SCREAMING_SNAKE_CASE      : str=True      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=1      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=0      ,							__SCREAMING_SNAKE_CASE      : Dict=2      ,							**__SCREAMING_SNAKE_CASE      : Union[str, Any]      ,							)						->				str:


      """simple docstring"""




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

      __SCREAMING_SNAKE_CASE						     =   vocab_size
      __SCREAMING_SNAKE_CASE						     =   max_position_embeddings
      __SCREAMING_SNAKE_CASE						     =   hidden_size
      __SCREAMING_SNAKE_CASE						     =   num_hidden_layers
      __SCREAMING_SNAKE_CASE						     =   num_attention_heads
      __SCREAMING_SNAKE_CASE						     =   intermediate_size
      __SCREAMING_SNAKE_CASE						     =   hidden_act
      __SCREAMING_SNAKE_CASE						     =   hidden_dropout_prob
      __SCREAMING_SNAKE_CASE						     =   attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE						     =   initializer_range
      __SCREAMING_SNAKE_CASE						     =   type_vocab_size
      __SCREAMING_SNAKE_CASE						     =   layer_norm_eps
      __SCREAMING_SNAKE_CASE						     =   use_cache






