"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_a   :	str 					=     logging.get_logger(__name__)

_a   :	Union[str, Any] 					=     {
    'facebook/data2vec-text-base': 'https://huggingface.co/data2vec/resolve/main/config.json',
}
class 		__A							(				SCREAMING_SNAKE_CASE_	):
     _UpperCamelCase							:    Tuple	      =	"data2vec-text"






     def __init__(	self       ,  a__=30522       ,  a__=768       ,  a__=12       ,  a__=12       ,  a__=3072       ,  a__="gelu"       ,  a__=0.1       ,  a__=0.1       ,  a__=512       ,  a__=2       ,  a__=0.0_2       ,  a__=1e-12       ,  a__=1       ,  a__=0       ,  a__=2       ,  a__="absolute"       ,  a__=True       ,  a__=None       ,  **a__       ,  ):
       super().__init__(pad_token_id=a__       ,  bos_token_id=a__       ,  eos_token_id=a__       ,  **a__						)

       _lowerCAmelCase		: Optional[int]						    =  vocab_size
       _lowerCAmelCase		: List[str]						    =  hidden_size
       _lowerCAmelCase		: Any						    =  num_hidden_layers
       _lowerCAmelCase		: List[str]						    =  num_attention_heads
       _lowerCAmelCase		: Any						    =  hidden_act
       _lowerCAmelCase		: List[str]						    =  intermediate_size
       _lowerCAmelCase		: Union[str, Any]						    =  hidden_dropout_prob
       _lowerCAmelCase		: List[str]						    =  attention_probs_dropout_prob
       _lowerCAmelCase		: Optional[int]						    =  max_position_embeddings
       _lowerCAmelCase		: Tuple						    =  type_vocab_size
       _lowerCAmelCase		: int						    =  initializer_range
       _lowerCAmelCase		: List[str]						    =  layer_norm_eps
       _lowerCAmelCase		: Optional[Any]						    =  position_embedding_type
       _lowerCAmelCase		: Dict						    =  use_cache
       _lowerCAmelCase		: int						    =  classifier_dropout







class 		__A							(				SCREAMING_SNAKE_CASE_	):






     @property
     def 					__A						(	self						):
       if self.task == "multiple-choice":
         _lowerCAmelCase		: int						    =  {0: """batch""", 1: """choice""", 2: """sequence"""}
       else:
         _lowerCAmelCase		: str						    =  {0: """batch""", 1: """sequence"""}
       return OrderedDict(
           [
               ("""input_ids""", dynamic_axis),
               ("""attention_mask""", dynamic_axis),
           ]						)





"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


_a   :	Union[str, Any] 					=     {}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _a   :	Tuple 					=     ['GPTSw3Tokenizer']


if TYPE_CHECKING:
     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_gpt_swa import GPTSwaTokenizer

else:
     import sys

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




