def A__  (			lowercase:     list[int],		lowercase:     list[int]					)				->		tuple[float, float]:

       # Check if the input is valid
       if not len(lowercase					) == len(lowercase					) == 3:
              raise ValueError('Please enter a valid equation.'					)
       if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
              raise ValueError('Both a & b of two equations can\'t be zero.'					)

       # Extract the coefficients
       A  ,  A  ,  A						:     Dict         =equationa
       A  ,  A  ,  A						:     str         =equationa

       # Calculate the determinants of the matrices
       A						:     Tuple         =aa * ba - aa * ba
       A						:     List[str]         =ca * ba - ca * ba
       A						:     List[str]         =aa * ca - aa * ca

       # Check if the system of linear equations has a solution (using Cramer's rule)
       if determinant == 0:
              if determinant_x == determinant_y == 0:
                     raise ValueError('Infinite solutions. (Consistent system)'					)
              else:
                     raise ValueError('No solution. (Inconsistent system)'					)
       else:
              if determinant_x == determinant_y == 0:
                     # Trivial solution (Inconsistent system)
                     return (0.0, 0.0)
              else:
                     A						:     Optional[Any]         =determinant_x / determinant
                     A						:     Any         =determinant_y / determinant
                     # Non-Trivial Solution (Consistent system)
                     return (x, y)







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


_lowercase							:      Optional[int]      							=logging.get_logger(__name__)

_lowercase							:      List[str]      							={
    '''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  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     int            =				"deberta-v2"







  def __init__(				self    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					str=12_81_00      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=15_36      ,	SCREAMING_SNAKE_CASE__    :					Dict=24      ,	SCREAMING_SNAKE_CASE__    :					List[str]=24      ,	SCREAMING_SNAKE_CASE__    :					List[str]=61_44      ,	SCREAMING_SNAKE_CASE__    :					List[Any]="gelu"      ,	SCREAMING_SNAKE_CASE__    :					int=0.1      ,	SCREAMING_SNAKE_CASE__    :					Any=0.1      ,	SCREAMING_SNAKE_CASE__    :					List[str]=5_12      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=0      ,	SCREAMING_SNAKE_CASE__    :					Tuple=0.0_2      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=1e-7      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=False      ,	SCREAMING_SNAKE_CASE__    :					Tuple=-1      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0      ,	SCREAMING_SNAKE_CASE__    :					Dict=True      ,	SCREAMING_SNAKE_CASE__    :					List[str]=None      ,	SCREAMING_SNAKE_CASE__    :					List[str]=0      ,	SCREAMING_SNAKE_CASE__    :					List[str]="gelu"      ,	**SCREAMING_SNAKE_CASE__    :					Dict      ,	)    ->				Dict:
         super().__init__(**SCREAMING_SNAKE_CASE__     )

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

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

         A						:     Any         =pos_att_type
         A						:     Tuple         =vocab_size
         A						:     Any         =layer_norm_eps

         A						:     Optional[Any]         =kwargs.get('pooler_hidden_size'      ,	SCREAMING_SNAKE_CASE__     )
         A						:     str         =pooler_dropout
         A						:     Any         =pooler_hidden_act







class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


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







  def 		SCREAMING_SNAKE_CASE_		(				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      ,	)    ->				Mapping[str, Any]:
         A						:     str         =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






