




"""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


lowerCamelCase__			:       List[Any]         =							logging.get_logger(__name__)

lowerCamelCase__			:       Dict         =							{
    '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 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE ):


 '''simple docstring'''







 lowercase_       =   "deberta-v2"



 def __init__(							self  :		Union[str, Any]    ,    _lowerCAmelCase  :		Dict=128_100    ,    _lowerCAmelCase  :		List[str]=1_536    ,    _lowerCAmelCase  :		Optional[int]=24    ,    _lowerCAmelCase  :		List[str]=24    ,    _lowerCAmelCase  :		str=6_144    ,    _lowerCAmelCase  :		List[Any]="gelu"    ,    _lowerCAmelCase  :		str=0.1    ,    _lowerCAmelCase  :		List[Any]=0.1    ,    _lowerCAmelCase  :		Any=512    ,    _lowerCAmelCase  :		Optional[Any]=0    ,    _lowerCAmelCase  :		Optional[Any]=0.02    ,    _lowerCAmelCase  :		Any=1E-7    ,    _lowerCAmelCase  :		List[str]=False    ,    _lowerCAmelCase  :		int=-1    ,    _lowerCAmelCase  :		Tuple=0    ,    _lowerCAmelCase  :		Union[str, Any]=True    ,    _lowerCAmelCase  :		Tuple=None    ,    _lowerCAmelCase  :		List[str]=0    ,    _lowerCAmelCase  :		Optional[Any]="gelu"    ,    **_lowerCAmelCase  :		int    ,    ):
       super().__init__(**_lowerCAmelCase						)

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

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

       SCREAMING_SNAKE_CASE_     						=      pos_att_type
       SCREAMING_SNAKE_CASE_     						=      vocab_size
       SCREAMING_SNAKE_CASE_     						=      layer_norm_eps

       SCREAMING_SNAKE_CASE_     						=      kwargs.get('pooler_hidden_size'    ,    _lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      pooler_dropout
       SCREAMING_SNAKE_CASE_     						=      pooler_hidden_act





class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE ):


 '''simple docstring'''







 @property
 def 				lowerCAmelCase_  (							self  :		Any						):
       if self.task == "multiple-choice":
             SCREAMING_SNAKE_CASE_     						=      {0: 'batch', 1: 'choice', 2: 'sequence'}
       else:
             SCREAMING_SNAKE_CASE_     						=      {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  :		List[Any]						):
       return 12




 def 				lowerCAmelCase_  (							self  :		Any    ,    _lowerCAmelCase  :		Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]    ,    _lowerCAmelCase  :		int = -1    ,    _lowerCAmelCase  :		int = -1    ,    _lowerCAmelCase  :		int = -1    ,    _lowerCAmelCase  :		bool = False    ,    _lowerCAmelCase  :		Optional["TensorType"] = None    ,    _lowerCAmelCase  :		int = 3    ,    _lowerCAmelCase  :		int = 40    ,    _lowerCAmelCase  :		int = 40    ,    _lowerCAmelCase  :		"PreTrainedTokenizerBase" = None    ,    ):
       SCREAMING_SNAKE_CASE_     						=      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
from typing import Any

def  UpperCAmelCase_							(				__UpperCAmelCase						:     list							) ->			list[Any]:
      for _ in range(len(__UpperCAmelCase							)							):
            SCREAMING_SNAKE_CASE_     						=      random.randint(0      ,      len(__UpperCAmelCase							) - 1							)
            SCREAMING_SNAKE_CASE_     						=      random.randint(0      ,      len(__UpperCAmelCase							) - 1							)
            SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      data[b], data[a]
      return data


if __name__ == "__main__":
   lowerCamelCase__			:       Union[str, Any]         =							[0, 1, 2, 3, 4, 5, 6, 7]
   lowerCamelCase__			:       Optional[int]         =							['python', 'says', 'hello', '!']
   print('Fisher-Yates Shuffle:')
   print('List', integers, strings)
   print('FY Shuffle', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))