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						=			logging.get_logger(__name__)

__snake_case						=			{
    'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'
    ),
    'microsoft/deberta-v2-xxlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'
    ),
}







class 	__snake_case      (   lowerCAmelCase__      ):
 __lowerCamelCase			:      Any           =					"""deberta-v2"""
 def __init__(     self						,      snake_case__=12_8100						,      snake_case__=1536						,      snake_case__=24						,      snake_case__=24						,      snake_case__=6144						,      snake_case__="gelu"						,      snake_case__=0.1						,      snake_case__=0.1						,      snake_case__=512						,      snake_case__=0						,      snake_case__=0.02						,      snake_case__=1e-7						,      snake_case__=False						,      snake_case__=-1						,      snake_case__=0						,      snake_case__=True						,      snake_case__=None						,      snake_case__=0						,      snake_case__="gelu"						,      **snake_case__						,      )		->							Optional[int]:
       '''simple docstring'''





       super().__init__(**UpperCamelCase__  )

       UpperCAmelCase			:       List[str]			     =hidden_size
       UpperCAmelCase			:       Optional[int]			     =num_hidden_layers
       UpperCAmelCase			:       Optional[Any]			     =num_attention_heads
       UpperCAmelCase			:       Union[str, Any]			     =intermediate_size
       UpperCAmelCase			:       Any			     =hidden_act
       UpperCAmelCase			:       Dict			     =hidden_dropout_prob
       UpperCAmelCase			:       Dict			     =attention_probs_dropout_prob
       UpperCAmelCase			:       Tuple			     =max_position_embeddings
       UpperCAmelCase			:       Optional[Any]			     =type_vocab_size
       UpperCAmelCase			:       List[str]			     =initializer_range
       UpperCAmelCase			:       Tuple			     =relative_attention
       UpperCAmelCase			:       List[str]			     =max_relative_positions
       UpperCAmelCase			:       Optional[int]			     =pad_token_id
       UpperCAmelCase			:       int			     =position_biased_input

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

       UpperCAmelCase			:       List[Any]			     =pos_att_type
       UpperCAmelCase			:       Dict			     =vocab_size
       UpperCAmelCase			:       Dict			     =layer_norm_eps

       UpperCAmelCase			:       Any			     =kwargs.get('''pooler_hidden_size'''						,      UpperCamelCase__  )
       UpperCAmelCase			:       Dict			     =pooler_dropout
       UpperCAmelCase			:       int			     =pooler_hidden_act







class 	__snake_case      (   lowerCAmelCase__      ):
 @property
 def 		UpperCAmelCase__				(     self  )		->							Mapping[str, Mapping[int, str]]:
       '''simple docstring'''





       if self.task == "multiple-choice":
             UpperCAmelCase			:       str			     ={0: "batch", 1: "choice", 2: "sequence"}
       else:
             UpperCAmelCase			:       int			     ={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 		UpperCAmelCase__				(     self  )		->							int:
       '''simple docstring'''





       return 12






 def 		UpperCAmelCase__				(     self						,      snake_case__						,      snake_case__ = -1						,      snake_case__ = -1						,      snake_case__ = -1						,      snake_case__ = False						,      snake_case__ = None						,      snake_case__ = 3						,      snake_case__ = 40						,      snake_case__ = 40						,      snake_case__ = None						,      )		->							Mapping[str, Any]:
       '''simple docstring'''





       UpperCAmelCase			:       Union[str, Any]			     =super().generate_dummy_inputs(preprocessor=UpperCamelCase__						,      framework=UpperCamelCase__  )
       if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
             del dummy_inputs["token_type_ids"]
       return dummy_inputs






def      A      (      _SCREAMING_SNAKE_CASE    )	-> list:
       if n_term == "":
              return []
       lowerCamelCase   :      list			 =					[]
       for temp in range(int(_SCREAMING_SNAKE_CASE    )    ):
              series.append(f'''1/{temp + 1}''' if series else "1"    )
       return series


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      Dict  =   input('Enter the last number (nth term) of the Harmonic Series')
       print('Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n')
       print(harmonic_series(nth_term))






