





'''simple docstring'''




from __future__ import annotations

import math






def 			_lowerCAmelCase     (    __snake_case    :					float					,				__snake_case    :					int     )	->	float:
     __A		:					int						=			u
     for i in range(1					,				__snake_case     ):
          __A		:					Optional[int]						=			temp * (u - i)
     return temp






def 			_lowerCAmelCase     (    )	->	None:
     __A		:					Dict						=			int(input('enter the numbers of values: '     )     )
     __A		:					list[list[float]]						=			[]
     for _ in range(__snake_case     ):
          y.append([]     )
     for i in range(__snake_case     ):
          for j in range(__snake_case     ):
               y[i].append(__snake_case     )
               __A		:					int						=			0

     print('enter the values of parameters in a list: '     )
     __A		:					List[str]						=			list(map(__snake_case					,				input().split()     )     )

     print('enter the values of corresponding parameters: '     )
     for i in range(__snake_case     ):
          __A		:					Tuple						=			float(input()     )

     __A		:					Tuple						=			int(input('enter the value to interpolate: '     )     )
     __A		:					Dict						=			(value - x[0]) / (x[1] - x[0])

     # for calculating forward difference table

     for i in range(1					,				__snake_case     ):
          for j in range(n - i     ):
               __A		:					Dict						=			y[j + 1][i - 1] - y[j][i - 1]

     __A		:					List[Any]						=			y[0][0]
     for i in range(1					,				__snake_case     ):
          summ += (ucal(__snake_case					,				__snake_case     ) * y[0][i]) / math.factorial(__snake_case     )

     print(f'the value at {value} is {summ}'     )


if __name__ == "__main__":
  main()






'''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


lowercase__       : Dict         =       logging.getLogger(__name__)


@dataclass
@add_start_docstrings(TrainingArguments.__doc__				)
class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			field(default=a__   ,						metadata={'''help''': '''Whether to use SortishSampler or not.'''}				)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}				)
     lowerCAmelCase						     =			field(
         default=a__   ,						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.'''
             )
         }   ,						)
     lowerCAmelCase						     =			field(
         default=a__   ,						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.'''
             )
         }   ,						)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={
             '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
         }   ,						)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					List[Any]						=			super().to_dict()
          for k, v in d.items():
               if isinstance(_UpperCAmelCase		,      _UpperCAmelCase):
                    __A		:					List[Any]						=			v.to_dict()
          return d