

'''simple docstring'''

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


A_				  =							logging.get_logger(__name__)

A_				  =							{
    "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       (				A_  ):
   _A				:   List[str]			      =    '''deberta-v2'''
   def __init__(       self	:					Dict   ,SCREAMING_SNAKE_CASE__	:					Any=128_100   ,SCREAMING_SNAKE_CASE__	:					int=1_536   ,SCREAMING_SNAKE_CASE__	:					Any=24   ,SCREAMING_SNAKE_CASE__	:					str=24   ,SCREAMING_SNAKE_CASE__	:					Optional[Any]=6_144   ,SCREAMING_SNAKE_CASE__	:					Dict="gelu"   ,SCREAMING_SNAKE_CASE__	:					int=0.1   ,SCREAMING_SNAKE_CASE__	:					Dict=0.1   ,SCREAMING_SNAKE_CASE__	:					Union[str, Any]=512   ,SCREAMING_SNAKE_CASE__	:					Optional[Any]=0   ,SCREAMING_SNAKE_CASE__	:					int=0.02   ,SCREAMING_SNAKE_CASE__	:					List[str]=1e-7   ,SCREAMING_SNAKE_CASE__	:					Union[str, Any]=False   ,SCREAMING_SNAKE_CASE__	:					Dict=-1   ,SCREAMING_SNAKE_CASE__	:					Any=0   ,SCREAMING_SNAKE_CASE__	:					Optional[int]=True   ,SCREAMING_SNAKE_CASE__	:					Any=None   ,SCREAMING_SNAKE_CASE__	:					List[Any]=0   ,SCREAMING_SNAKE_CASE__	:					List[Any]="gelu"   ,**SCREAMING_SNAKE_CASE__	:					int   ,):
     super().__init__(**_lowerCamelCase							)

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

     # Backwards compatibility
     if type(_lowerCamelCase							) == str:
       SCREAMING_SNAKE_CASE:str  					=    [x.strip() for x in pos_att_type.lower().split("|"							)]

     SCREAMING_SNAKE_CASE:Tuple  					=    pos_att_type
     SCREAMING_SNAKE_CASE:Tuple  					=    vocab_size
     SCREAMING_SNAKE_CASE:List[str]  					=    layer_norm_eps

     SCREAMING_SNAKE_CASE:Union[str, Any]  					=    kwargs.get("pooler_hidden_size"   ,_lowerCamelCase							)
     SCREAMING_SNAKE_CASE:Optional[int]  					=    pooler_dropout
     SCREAMING_SNAKE_CASE:str  					=    pooler_hidden_act





class 							_snake_case       (				A_  ):
   @property
   def 			__UpperCamelCase  (       self	:					str							):
     if self.task == "multiple-choice":
       SCREAMING_SNAKE_CASE:str  					=    {0: "batch", 1: "choice", 2: "sequence"}
     else:
       SCREAMING_SNAKE_CASE:Optional[Any]  					=    {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	:					Any							):
     return 12







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








"""simple docstring"""



def 					_UpperCAmelCase      ( __lowerCamelCase   :       int = 1_00_00_00		)     ->					int:
  _snake_case					     =       limit + 1
  _snake_case					     =       [0] * limit
  for first_term in range(1 ,					__lowerCamelCase		):
    for n in range(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		):
      _snake_case					     =       first_term + n / first_term
      if common_difference % 4:  # d must be divisble by 4
        continue
      else:
        common_difference /= 4
        if (
            first_term > common_difference
            and first_term < 4 * common_difference
        ):  # since x,y,z are positive integers
          frequency[n] += 1  # so z>0 and a>d ,also 4d<a

  _snake_case					     =       sum(1 for x in frequency[1:limit] if x == 10		)

  return count


if __name__ == "__main__":
 print(F"{solution() = }")



