


from __future__ import annotations

from collections.abc import Callable

A   :					Dict	=      list[list[float | int]]

def    lowercase_	(	_A    :					Matrix					,      _A    :					Matrix		):

      """simple docstring"""




      lowerCamelCase__	:       int					=						len(_A		)
      lowerCamelCase__	:       Matrix					=						[[0 for _ in range(size + 1		)] for _ in range(_A		)]
      lowerCamelCase__	:       int
      lowerCamelCase__	:       int
      lowerCamelCase__	:       int
      lowerCamelCase__	:       int
      lowerCamelCase__	:       int
      lowerCamelCase__	:       float

      for row in range(_A		):
            for col in range(_A		):
                  lowerCamelCase__	:       Tuple					=						matrix[row][col]

            lowerCamelCase__	:       List[str]					=						vector[row][0]

      lowerCamelCase__	:       int					=						0
      lowerCamelCase__	:       List[Any]					=						0
      while row < size and col < size:
            # pivoting
            lowerCamelCase__	:       List[Any]					=						max((abs(augmented[rowa][col]		), rowa) for rowa in range(_A					,      _A		)		)[
                1
            ]
            if augmented[pivot_row][col] == 0:
                  col += 1
                  continue
            else:
                  lowerCamelCase__					,     lowerCamelCase__	:       List[Any]					=						augmented[pivot_row], augmented[row]

            for rowa in range(row + 1					,      _A		):
                  lowerCamelCase__	:       Union[str, Any]					=						augmented[rowa][col] / augmented[row][col]
                  lowerCamelCase__	:       int					=						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					,      _A		):
            for row in range(_A		):
                  lowerCamelCase__	:       Any					=						augmented[row][col] / augmented[col][col]
                  for cola in range(_A					,      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(_A		)
      ]

def    lowercase_	(	_A    :					list[int]		):

      """simple docstring"""




      lowerCamelCase__	:       int					=						len(_A		)
      lowerCamelCase__	:       Matrix					=						[[0 for _ in range(_A		)] for _ in range(_A		)]
      lowerCamelCase__	:       Matrix					=						[[0] for _ in range(_A		)]
      lowerCamelCase__	:       Matrix
      lowerCamelCase__	:       int
      lowerCamelCase__	:       int
      lowerCamelCase__	:       int

      for x_val, y_val in enumerate(_A		):
            for col in range(_A		):
                  lowerCamelCase__	:       List[Any]					=						(x_val + 1) ** (size - col - 1)
            lowerCamelCase__	:       str					=						y_val

      lowerCamelCase__	:       Optional[int]					=						solve(_A					,      _A		)

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

      return interpolated_func

def    lowercase_	(	_A    :					int		):

      """simple docstring"""




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

def    lowercase_	(	_A    :					Callable[[int], int] = question_function					,      _A    :					int = 10		):

      """simple docstring"""




      lowerCamelCase__	:       list[int]					=						[func(_A		) for x_val in range(1					,      order + 1		)]

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

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

      for poly in polynomials:
            lowerCamelCase__	:       Any					=						1
            while func(_A		) == poly(_A		):
                  x_val += 1

            ret += poly(_A		)

      return ret


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






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A   :					Dict	=      logging.get_logger(__name__)

A   :					Union[str, Any]	=      {
    "roberta-base": "https://huggingface.co/roberta-base/resolve/main/config.json",
    "roberta-large": "https://huggingface.co/roberta-large/resolve/main/config.json",
    "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/config.json",
    "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/config.json",
    "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json",
    "roberta-large-openai-detector": "https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json",
}







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    "roberta"





       def __init__(     self							:    int      ,   __lowerCamelCase							:    Dict=50265      ,   __lowerCamelCase							:    Optional[int]=768      ,   __lowerCamelCase							:    Optional[Any]=12      ,   __lowerCamelCase							:    Optional[int]=12      ,   __lowerCamelCase							:    int=3072      ,   __lowerCamelCase							:    Dict="gelu"      ,   __lowerCamelCase							:    Union[str, Any]=0.1      ,   __lowerCamelCase							:    int=0.1      ,   __lowerCamelCase							:    Tuple=512      ,   __lowerCamelCase							:    List[str]=2      ,   __lowerCamelCase							:    Any=0.0_2      ,   __lowerCamelCase							:    Optional[int]=1E-1_2      ,   __lowerCamelCase							:    List[Any]=1      ,   __lowerCamelCase							:    int=0      ,   __lowerCamelCase							:    Any=2      ,   __lowerCamelCase							:    Tuple="absolute"      ,   __lowerCamelCase							:    Tuple=True      ,   __lowerCamelCase							:    str=None      ,   **__lowerCamelCase							:    Optional[Any]      ,   ):


             '''simple docstring'''



             super().__init__(pad_token_id=__lowerCamelCase      ,   bos_token_id=__lowerCamelCase      ,   eos_token_id=__lowerCamelCase      ,   **__lowerCamelCase )

             lowerCamelCase__	:       List[Any]					=						vocab_size
             lowerCamelCase__	:       str					=						hidden_size
             lowerCamelCase__	:       int					=						num_hidden_layers
             lowerCamelCase__	:       Optional[Any]					=						num_attention_heads
             lowerCamelCase__	:       Optional[Any]					=						hidden_act
             lowerCamelCase__	:       Any					=						intermediate_size
             lowerCamelCase__	:       Tuple					=						hidden_dropout_prob
             lowerCamelCase__	:       Any					=						attention_probs_dropout_prob
             lowerCamelCase__	:       Tuple					=						max_position_embeddings
             lowerCamelCase__	:       int					=						type_vocab_size
             lowerCamelCase__	:       Any					=						initializer_range
             lowerCamelCase__	:       Dict					=						layer_norm_eps
             lowerCamelCase__	:       int					=						position_embedding_type
             lowerCamelCase__	:       Any					=						use_cache
             lowerCamelCase__	:       int					=						classifier_dropout







class     _lowercase				(							lowercase__):


       """simple docstring"""




       @property
       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



             if self.task == "multiple-choice":
                   lowerCamelCase__	:       int					=						{0: "batch", 1: "choice", 2: "sequence"}
             else:
                   lowerCamelCase__	:       Optional[Any]					=						{0: "batch", 1: "sequence"}
             return OrderedDict(
                 [
                     ("input_ids", dynamic_axis),
                     ("attention_mask", dynamic_axis),
                 ] )


