



from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
 from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


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

__snake_case		:str 						=	{
    '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/config.json''',
    '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/config.json''',
    '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json''',
    '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json''',
    '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json''',
    '''microsoft/deberta-xlarge-mnli''': '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json''',
}

class        _A			(      __UpperCAmelCase       ):
       UpperCamelCase__    :       List[str]		   =		'''deberta'''



       def __init__(						self				:	Dict	,						__SCREAMING_SNAKE_CASE				:	Union[str, Any]=50_265	,						__SCREAMING_SNAKE_CASE				:	Any=768	,						__SCREAMING_SNAKE_CASE				:	Tuple=12	,						__SCREAMING_SNAKE_CASE				:	Any=12	,						__SCREAMING_SNAKE_CASE				:	int=3_072	,						__SCREAMING_SNAKE_CASE				:	str="gelu"	,						__SCREAMING_SNAKE_CASE				:	Optional[int]=0.1	,						__SCREAMING_SNAKE_CASE				:	str=0.1	,						__SCREAMING_SNAKE_CASE				:	List[Any]=512	,						__SCREAMING_SNAKE_CASE				:	Union[str, Any]=0	,						__SCREAMING_SNAKE_CASE				:	Tuple=0.02	,						__SCREAMING_SNAKE_CASE				:	Dict=1E-7	,						__SCREAMING_SNAKE_CASE				:	List[Any]=False	,						__SCREAMING_SNAKE_CASE				:	Optional[Any]=-1	,						__SCREAMING_SNAKE_CASE				:	str=0	,						__SCREAMING_SNAKE_CASE				:	List[Any]=True	,						__SCREAMING_SNAKE_CASE				:	int=None	,						__SCREAMING_SNAKE_CASE				:	Dict=0	,						__SCREAMING_SNAKE_CASE				:	Union[str, Any]="gelu"	,						**__SCREAMING_SNAKE_CASE				:	Dict	,						):


           '''simple docstring'''

           super().__init__(**__SCREAMING_SNAKE_CASE)

           __a					=							hidden_size
           __a					=							num_hidden_layers
           __a					=							num_attention_heads
           __a					=							intermediate_size
           __a					=							hidden_act
           __a					=							hidden_dropout_prob
           __a					=							attention_probs_dropout_prob
           __a					=							max_position_embeddings
           __a					=							type_vocab_size
           __a					=							initializer_range
           __a					=							relative_attention
           __a					=							max_relative_positions
           __a					=							pad_token_id
           __a					=							position_biased_input

           # Backwards compatibility
           if type(__SCREAMING_SNAKE_CASE) == str:
               __a					=							[x.strip() for x in pos_att_type.lower().split('''|''')]

           __a					=							pos_att_type
           __a					=							vocab_size
           __a					=							layer_norm_eps

           __a					=							kwargs.get('''pooler_hidden_size'''	,						__SCREAMING_SNAKE_CASE)
           __a					=							pooler_dropout
           __a					=							pooler_hidden_act


class        _A			(      __UpperCAmelCase       ):



       @property
       def 						_lowerCamelCase     (						self				:	Union[str, Any]):


           '''simple docstring'''

           if self.task == "multiple-choice":
               __a					=							{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
           else:
               __a					=							{0: '''batch''', 1: '''sequence'''}
           if self._config.type_vocab_size > 0:
               return OrderedDict(
                   [('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis)])
           else:
               return OrderedDict([('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis)])



       @property
       def 						_lowerCamelCase     (						self				:	Any):


           '''simple docstring'''

           return 12





       def 						_lowerCamelCase     (						self				:	str	,						__SCREAMING_SNAKE_CASE				:	Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]	,						__SCREAMING_SNAKE_CASE				:	int = -1	,						__SCREAMING_SNAKE_CASE				:	int = -1	,						__SCREAMING_SNAKE_CASE				:	int = -1	,						__SCREAMING_SNAKE_CASE				:	bool = False	,						__SCREAMING_SNAKE_CASE				:	Optional["TensorType"] = None	,						__SCREAMING_SNAKE_CASE				:	int = 3	,						__SCREAMING_SNAKE_CASE				:	int = 40	,						__SCREAMING_SNAKE_CASE				:	int = 40	,						__SCREAMING_SNAKE_CASE				:	"PreTrainedTokenizerBase" = None	,						):


           '''simple docstring'''

           __a					=							super().generate_dummy_inputs(preprocessor=__SCREAMING_SNAKE_CASE	,						framework=__SCREAMING_SNAKE_CASE)
           if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
               del dummy_inputs["token_type_ids"]
           return dummy_inputs










def 	__snake_case      (     _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase							):
    __a					=							[1 / first_term]
    __a					=							1 / first_term
    for _ in range(number_of_terms - 1							):
        first_term += common_difference
        arithmetic_progression.append(_UpperCAmelCase							)
    __a					=							[1 / step for step in arithmetic_progression]
    return sum(_UpperCAmelCase							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(sum_of_harmonic_progression(1 / 2, 2, 2))





