




"""simple docstring"""

def 						_snake_case					( _snake_case							: list    ,  _snake_case							: list    ,  _snake_case							: int							)   -> list:






      '''simple docstring'''


      _A        =       len(_snake_case							)
      _A        =       [[0] * n for i in range(_snake_case							)]
      for i in range(_snake_case							):
            _A        =       y_points[i]

      for i in range(2    ,  _snake_case							):
            for j in range(_snake_case    ,  _snake_case							):
                  _A        =       (
                      (xa - x_points[j - i + 1]) * q[j][i - 1]
                      - (xa - x_points[j]) * q[j - 1][i - 1]
                  ) / (x_points[j] - x_points[j - i + 1])

      return [q[n - 1][n - 1], q]


if __name__ == "__main__":
       import doctest

       doctest.testmod()







"""simple docstring"""

import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


a         =      logging.getLogger(__name__)





@dataclass
@add_start_docstrings(TrainingArguments.__doc__							)
class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					bool				  =     field(default=__lowerCAmelCase      ,     metadata={'''help''': '''Whether to use SortishSampler or not.'''}							)
 UpperCAmelCase   :					bool				  =     field(
     default=__lowerCAmelCase      ,     metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}							)
 UpperCAmelCase   :					Optional[int]				  =     field(
     default=__lowerCAmelCase      ,     metadata={
         '''help''': (
             '''The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
             '''to the `max_length` value of the model configuration.'''
         )
     }      ,     )
 UpperCAmelCase   :					Optional[int]				  =     field(
     default=__lowerCAmelCase      ,     metadata={
         '''help''': (
             '''The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
             '''to the `num_beams` value of the model configuration.'''
         )
     }      ,     )
 UpperCAmelCase   :					Optional[Union[str, Path, GenerationConfig]]				  =     field(
     default=__lowerCAmelCase      ,     metadata={
         '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
     }      ,     )

 def       lowerCAmelCase_							(			self	:	int		):
       _A        =       super().to_dict()
       for k, v in d.items():
             if isinstance(_UpperCAmelCase   ,							_UpperCAmelCase		):
                   _A        =       v.to_dict()
       return d

