





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


_UpperCAmelCase :    Dict             =							logging.get_logger(__name__)

_UpperCAmelCase :    List[Any]             =							{
    '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_    (		__lowercase						):
  A_      							=   'deberta-v2'






  def __init__(							self			:					Optional[int]       ,  _snake_case			:					Tuple=128100       ,  _snake_case			:					List[str]=1536       ,  _snake_case			:					List[str]=24       ,  _snake_case			:					Tuple=24       ,  _snake_case			:					Tuple=6144       ,  _snake_case			:					Optional[Any]="gelu"       ,  _snake_case			:					Optional[int]=0.1       ,  _snake_case			:					Union[str, Any]=0.1       ,  _snake_case			:					Tuple=512       ,  _snake_case			:					Dict=0       ,  _snake_case			:					Optional[Any]=0.02       ,  _snake_case			:					int=1E-7       ,  _snake_case			:					Dict=False       ,  _snake_case			:					int=-1       ,  _snake_case			:					Dict=0       ,  _snake_case			:					int=True       ,  _snake_case			:					List[Any]=None       ,  _snake_case			:					Optional[Any]=0       ,  _snake_case			:					List[str]="gelu"       ,  **_snake_case			:					Union[str, Any]       ,  )->Optional[int]:






   '''simple docstring'''

   super().__init__(**_lowerCAmelCase      )

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

   # Backwards compatibility
   if type(_lowerCAmelCase      ) == str:
    __lowerCAmelCase					:	Optional[Any]      =       [x.strip() for x in pos_att_type.lower().split("""|"""      )]

   __lowerCAmelCase					:	Optional[int]      =       pos_att_type
   __lowerCAmelCase					:	Optional[Any]      =       vocab_size
   __lowerCAmelCase					:	Any      =       layer_norm_eps

   __lowerCAmelCase					:	Optional[Any]      =       kwargs.get("""pooler_hidden_size"""       ,  _lowerCAmelCase      )
   __lowerCAmelCase					:	Union[str, Any]      =       pooler_dropout
   __lowerCAmelCase					:	Optional[Any]      =       pooler_hidden_act






class        snake_case_    (		__lowercase						):






  @property
  def 				UpperCAmelCase__						(							self			:					List[Any]      )->Optional[int]:






   '''simple docstring'''

   if self.task == "multiple-choice":
    __lowerCAmelCase					:	List[Any]      =       {0: """batch""", 1: """choice""", 2: """sequence"""}
   else:
    __lowerCAmelCase					:	Dict      =       {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			:					Optional[Any]      )->Optional[int]:






   '''simple docstring'''

   return 12






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






   '''simple docstring'''

   __lowerCAmelCase					:	Tuple      =       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






import random






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list   ,   SCREAMING_SNAKE_CASE		:Dict   )      ->   tuple:
 __lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase					:	List[str]      =       [], [], []
 for element in data:
  if element < pivot:
   less.append(SCREAMING_SNAKE_CASE   )
  elif element > pivot:
   greater.append(SCREAMING_SNAKE_CASE   )
  else:
   equal.append(SCREAMING_SNAKE_CASE   )
 return less, equal, greater







def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list   ,   SCREAMING_SNAKE_CASE		:int   )      ->   Dict:
 # index = len(items) // 2 when trying to find the median
 #   (value of index when items is sorted)

 # invalid input
 if index >= len(SCREAMING_SNAKE_CASE   ) or index < 0:
  return None

 __lowerCAmelCase					:	Union[str, Any]      =       items[random.randint(0   ,   len(SCREAMING_SNAKE_CASE   ) - 1   )]
 __lowerCAmelCase					:	int      =       0
 __lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase					:	Tuple      =       _partition(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Optional[int]      =       len(SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	List[str]      =       len(SCREAMING_SNAKE_CASE   )

 # index is the pivot
 if m <= index < m + count:
  return pivot
 # must be in smaller
 elif m > index:
  return quick_select(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 # must be in larger
 else:
  return quick_select(SCREAMING_SNAKE_CASE   ,   index - (m + count)   )