


'''simple docstring'''


from __future__ import annotations

from collections.abc import Callable

__magic_name__			=      list[list[float | int]]




def      lowerCamelCase					(      lowerCamelCase  :      Matrix							,						lowerCamelCase  :      Matrix):
      A_						:  int          =				len(lowerCamelCase)
      A_						:  Matrix          =				[[0 for _ in range(size + 1)] for _ in range(lowerCamelCase)]
      A_						:  int
      A_						:  int
      A_						:  int
      A_						:  int
      A_						:  int
      A_						:  float

      for row in range(lowerCamelCase):
            for col in range(lowerCamelCase):
                  A_						:  List[Any]          =				matrix[row][col]

            A_						:  Union[str, Any]          =				vector[row][0]

      A_						:  str          =				0
      A_						:  Dict          =				0
      while row < size and col < size:
            # pivoting
            A_						:  Tuple          =				max((abs(augmented[rowa][col]), rowa) for rowa in range(lowerCamelCase							,						lowerCamelCase))[
                1
            ]
            if augmented[pivot_row][col] == 0:
                  col += 1
                  continue
            else:
                  A_						:  List[str]          =				augmented[pivot_row], augmented[row]

            for rowa in range(row + 1							,						lowerCamelCase):
                  A_						:  List[str]          =				augmented[rowa][col] / augmented[row][col]
                  A_						:  str          =				0
                  for cola in range(col + 1							,						size + 1):
                        augmented[rowa][cola] -= augmented[row][cola] * ratio

            row += 1
            col += 1

      # back substitution
      for col in range(1							,						lowerCamelCase):
            for row in range(lowerCamelCase):
                  A_						:  Union[str, Any]          =				augmented[row][col] / augmented[col][col]
                  for cola in range(lowerCamelCase							,						size + 1):
                        augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
      return [
          [round(augmented[row][size] / augmented[row][row]							,						10)] for row in range(lowerCamelCase)
      ]




def      lowerCamelCase					(      lowerCamelCase  :      list[int]):
      A_						:  int          =				len(lowerCamelCase)
      A_						:  Matrix          =				[[0 for _ in range(lowerCamelCase)] for _ in range(lowerCamelCase)]
      A_						:  Matrix          =				[[0] for _ in range(lowerCamelCase)]
      A_						:  Matrix
      A_						:  int
      A_						:  int
      A_						:  int

      for x_val, y_val in enumerate(lowerCamelCase):
            for col in range(lowerCamelCase):
                  A_						:  str          =				(x_val + 1) ** (size - col - 1)
            A_						:  Tuple          =				y_val

      A_						:  Tuple          =				solve(lowerCamelCase							,						lowerCamelCase)

      def interpolated_func(lowerCamelCase  :      int) -> int:
            return sum(
                round(coeffs[x_val][0]) * (var ** (size - x_val - 1))
                for x_val in range(lowerCamelCase))

      return interpolated_func




def      lowerCamelCase					(      lowerCamelCase  :      int):
      return (
          1
          - variable
          + variable**2
          - variable**3
          + variable**4
          - variable**5
          + variable**6
          - variable**7
          + variable**8
          - variable**9
          + variable**10
      )




def      lowerCamelCase					(      lowerCamelCase  :      Callable[[int], int] = question_function							,						lowerCamelCase  :      int = 10):
      A_						:  list[int]          =				[func(lowerCamelCase) for x_val in range(1							,						order + 1)]

      A_						:  list[Callable[[int], int]]          =				[
          interpolate(data_points[:max_coeff]) for max_coeff in range(1							,						order + 1)
      ]

      A_						:  int          =				0
      A_						:  Callable[[int], int]
      A_						:  int

      for poly in polynomials:
            A_						:  int          =				1
            while func(lowerCamelCase) == poly(lowerCamelCase):
                  x_val += 1

            ret += poly(lowerCamelCase)

      return ret


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




'''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


__magic_name__			=      logging.get_logger(__name__)

__magic_name__			=      {
    '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'''







  a_  					=							"""deberta-v2"""






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

        '''simple docstring'''




        super().__init__(**_a )

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

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

        A_						:  Any          =				pos_att_type
        A_						:  Optional[int]          =				vocab_size
        A_						:  Tuple          =				layer_norm_eps

        A_						:  Any          =				kwargs.get("""pooler_hidden_size"""	,_a )
        A_						:  Union[str, Any]          =				pooler_dropout
        A_						:  List[Any]          =				pooler_hidden_act







class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  @property
  def        _a							(							self :       Any ):

        '''simple docstring'''




        if self.task == "multiple-choice":
              A_						:  Any          =				{0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
              A_						:  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        _a							(							self :       Optional[int] ):

        '''simple docstring'''




        return 12






  def        _a							(							self :       int	,_a :       Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]	,_a :       int = -1	,_a :       int = -1	,_a :       int = -1	,_a :       bool = False	,_a :       Optional["TensorType"] = None	,_a :       int = 3	,_a :       int = 40	,_a :       int = 40	,_a :       "PreTrainedTokenizerBase" = None	,):

        '''simple docstring'''




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