





from collections import OrderedDict
from typing import Mapping

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


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

a__:      List[str]									=		{
    'facebook/data2vec-text-base': 'https://huggingface.co/data2vec/resolve/main/config.json',
}




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	'''data2vec-text'''



 def __init__(							self,__lowerCamelCase=3_0522,__lowerCamelCase=768,__lowerCamelCase=12,__lowerCamelCase=12,__lowerCamelCase=3072,__lowerCamelCase="gelu",__lowerCamelCase=0.1,__lowerCamelCase=0.1,__lowerCamelCase=512,__lowerCamelCase=2,__lowerCamelCase=0.02,__lowerCamelCase=1E-12,__lowerCamelCase=1,__lowerCamelCase=0,__lowerCamelCase=2,__lowerCamelCase="absolute",__lowerCamelCase=True,__lowerCamelCase=None,**__lowerCamelCase,):
        super().__init__(pad_token_id=__lowerCamelCase,bos_token_id=__lowerCamelCase,eos_token_id=__lowerCamelCase,**__lowerCamelCase )

        A__											=						vocab_size
        A__											=						hidden_size
        A__											=						num_hidden_layers
        A__											=						num_attention_heads
        A__											=						hidden_act
        A__											=						intermediate_size
        A__											=						hidden_dropout_prob
        A__											=						attention_probs_dropout_prob
        A__											=						max_position_embeddings
        A__											=						type_vocab_size
        A__											=						initializer_range
        A__											=						layer_norm_eps
        A__											=						position_embedding_type
        A__											=						use_cache
        A__											=						classifier_dropout




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):



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









def 							_UpperCAmelCase				(UpperCamelCase__     :  str				,							UpperCamelCase__     :  bool = False						):

       if not isinstance(UpperCamelCase__				,							UpperCamelCase__						):
              _A      :       Optional[Any]				    = f"Expected string as input, found {type(UpperCamelCase__						)}"
              raise ValueError(UpperCamelCase__						)
       if not isinstance(UpperCamelCase__				,							UpperCamelCase__						):
              _A      :       Union[str, Any]				    = f"Expected boolean as use_pascal parameter, found {type(UpperCamelCase__						)}"
              raise ValueError(UpperCamelCase__						)

       _A      :       int				    = input_str.split("_"						)

       _A      :       str				    = 0 if use_pascal else 1

       _A      :       str				    = words[start_index:]

       _A      :       Optional[Any]				    = [word[0].upper() + word[1:] for word in words_to_capitalize]

       _A      :       Any				    = "" if use_pascal else words[0]

       return "".join([initial_word, *capitalized_words]						)


if __name__ == "__main__":
    from doctest import testmod

    testmod()
