


"""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


__lowerCamelCase      :Union[str, Any]	    =		logging.getLogger(__name__)


@dataclass
@add_start_docstrings(TrainingArguments.__doc__)
class 			A__	(						a__):


     """simple docstring"""
     snake_case__	:					Dict													=field(default=a__ ,     metadata={'''help''': '''Whether to use SortishSampler or not.'''})
     snake_case__	:					Union[str, Any]													=field(
         default=a__ ,     metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''})
     snake_case__	:					Any													=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.'''
             )
         } ,     )
     snake_case__	:					Tuple													=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.'''
             )
         } ,     )
     snake_case__	:					List[Any]													=field(
         default=a__ ,     metadata={
             '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
         } ,     )

     def 		a__						(	self:						Union[str, Any]	)->    Optional[Any]:
           lowerCamelCase					:					str        =	super().to_dict()
           for k, v in d.items():
                 if isinstance(_A     ,		_A	):
                       lowerCamelCase					:					Tuple        =	v.to_dict()
           return d










"""simple docstring"""


from __future__ import annotations

import math




def   snake_case		(     UpperCamelCase__       :						float			,				UpperCamelCase__       :						int    )   ->      float:
      lowerCamelCase					:					Dict        =	u
      for i in range(1			,				UpperCamelCase__    ):
            lowerCamelCase					:					List[str]        =	temp * (u - i)
      return temp




def   snake_case		(     )   ->      None:
      lowerCamelCase					:					List[Any]        =	int(input("""enter the numbers of values: """    )    )
      lowerCamelCase					:					list[list[float]]        =	[]
      for _ in range(UpperCamelCase__    ):
            y.append([]    )
      for i in range(UpperCamelCase__    ):
            for j in range(UpperCamelCase__    ):
                  y[i].append(UpperCamelCase__    )
                  lowerCamelCase					:					Union[str, Any]        =	0

      print("""enter the values of parameters in a list: """    )
      lowerCamelCase					:					Any        =	list(map(UpperCamelCase__			,				input().split()    )    )

      print("""enter the values of corresponding parameters: """    )
      for i in range(UpperCamelCase__    ):
            lowerCamelCase					:					int        =	float(input()    )

      lowerCamelCase					:					Dict        =	int(input("""enter the value to interpolate: """    )    )
      lowerCamelCase					:					List[Any]        =	(value - x[0]) / (x[1] - x[0])

      # for calculating forward difference table

      for i in range(1			,				UpperCamelCase__    ):
            for j in range(n - i    ):
                  lowerCamelCase					:					str        =	y[j + 1][i - 1] - y[j][i - 1]

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

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


if __name__ == "__main__":
    main()




