from __future__ import annotations

import math


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase				):

      '''simple docstring'''





      SCREAMING_SNAKE_CASE_          =  u
      for i in range(1     ,      lowerCamelCase_				):
            SCREAMING_SNAKE_CASE_          =  temp * (u - i)
      return temp


def      a__ (		):

      '''simple docstring'''





      SCREAMING_SNAKE_CASE_          =  int(input("enter the numbers of values: "				)				)
      SCREAMING_SNAKE_CASE_          =  []
      for _ in range(lowerCamelCase_				):
            y.append([]				)
      for i in range(lowerCamelCase_				):
            for j in range(lowerCamelCase_				):
                  y[i].append(lowerCamelCase_				)
                  SCREAMING_SNAKE_CASE_          =  0

      print("enter the values of parameters in a list: "				)
      SCREAMING_SNAKE_CASE_          =  list(map(lowerCamelCase_     ,      input().split()				)				)

      print("enter the values of corresponding parameters: "				)
      for i in range(lowerCamelCase_				):
            SCREAMING_SNAKE_CASE_          =  float(input()				)

      SCREAMING_SNAKE_CASE_          =  int(input("enter the value to interpolate: "				)				)
      SCREAMING_SNAKE_CASE_          =  (value - x[0]) / (x[1] - x[0])

      # for calculating forward difference table

      for i in range(1     ,      lowerCamelCase_				):
            for j in range(n - i				):
                  SCREAMING_SNAKE_CASE_          =  y[j + 1][i - 1] - y[j][i - 1]

      SCREAMING_SNAKE_CASE_          =  y[0][0]
      for i in range(1     ,      lowerCamelCase_				):
            summ += (ucal(lowerCamelCase_     ,      lowerCamelCase_				) * y[0][i]) / math.factorial(lowerCamelCase_				)

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


if __name__ == "__main__":
      main()



import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A     :  str					     =       logging.getLogger(__name__)


@dataclass
class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  lowerCamelCase__     =						field(
      default=0.0  ,      metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}					)
  lowerCamelCase__     =						field(default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Whether to SortishSamler or not.'''}					)
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}					)
  lowerCamelCase__     =						field(default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''whether to use adafactor'''}					)
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}					)
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}					)
  lowerCamelCase__     =						field(default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Dropout probability. Goes into model.config.'''}					)
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}					)
  lowerCamelCase__     =						field(
      default='''linear'''  ,      metadata={'''help''': f"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()					)}"}  ,      )


