


from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase_   :				List[str]						      = logging.get_logger(__name__)

UpperCAmelCase_   :				Tuple						      = {
    'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/config.json',
    'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/config.json',
    'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/config.json',
    'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/config.json',
    'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json',
    'roberta-large-openai-detector': 'https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json',
}
class 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      snake_case__       :  Optional[Any]       =		'''roberta'''
      def __init__( self       : int  ,							SCREAMING_SNAKE_CASE__       : Dict=5_0_2_6_5  ,							SCREAMING_SNAKE_CASE__       : Dict=7_6_8  ,							SCREAMING_SNAKE_CASE__       : List[Any]=1_2  ,							SCREAMING_SNAKE_CASE__       : Any=1_2  ,							SCREAMING_SNAKE_CASE__       : str=3_0_7_2  ,							SCREAMING_SNAKE_CASE__       : Any="gelu"  ,							SCREAMING_SNAKE_CASE__       : Dict=0.1  ,							SCREAMING_SNAKE_CASE__       : List[str]=0.1  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=5_1_2  ,							SCREAMING_SNAKE_CASE__       : Any=2  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=0.02  ,							SCREAMING_SNAKE_CASE__       : int=1E-12  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=1  ,							SCREAMING_SNAKE_CASE__       : Dict=0  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=2  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]="absolute"  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=True  ,							SCREAMING_SNAKE_CASE__       : List[Any]=None  ,							**SCREAMING_SNAKE_CASE__       : List[Any]  ,							)	->    List[Any]:
             super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__  ,							bos_token_id=SCREAMING_SNAKE_CASE__  ,							eos_token_id=SCREAMING_SNAKE_CASE__  ,							**SCREAMING_SNAKE_CASE__  )

             a_      :	List[str]							=						vocab_size
             a_      :	str							=						hidden_size
             a_      :	List[Any]							=						num_hidden_layers
             a_      :	Optional[Any]							=						num_attention_heads
             a_      :	str							=						hidden_act
             a_      :	str							=						intermediate_size
             a_      :	List[Any]							=						hidden_dropout_prob
             a_      :	int							=						attention_probs_dropout_prob
             a_      :	Dict							=						max_position_embeddings
             a_      :	Dict							=						type_vocab_size
             a_      :	List[str]							=						initializer_range
             a_      :	Union[str, Any]							=						layer_norm_eps
             a_      :	Optional[Any]							=						position_embedding_type
             a_      :	Tuple							=						use_cache
             a_      :	int							=						classifier_dropout
class 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      @property
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    Mapping[str, Mapping[int, str]]:
             if self.task == "multiple-choice":
                    a_      :	List[Any]							=						{0: 'batch', 1: 'choice', 2: 'sequence'}
             else:
                    a_      :	Dict							=						{0: 'batch', 1: 'sequence'}
             return OrderedDict(
                 [
                     ('input_ids', dynamic_axis),
                     ('attention_mask', dynamic_axis),
                 ]  )






def  SCREAMING_SNAKE_CASE_		(    __A				:    int						,					__A				:    int						,					__A				:    int       )			->	float:

       """simple docstring"""

       a_      :	Optional[int]							=						(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
       # formula for sum of series
       return total







def  SCREAMING_SNAKE_CASE_		(    )			->	Optional[Any]:

       """simple docstring"""

       print(sum_of_series(1						,					1						,					10       )       )


if __name__ == "__main__":
   import doctest

   doctest.testmod()


