




"""simple docstring"""






from __future__ import annotations

import math





def 							UpperCAmelCase		(		lowercase_			:					float      ,       lowercase_			:					int    ):
					_lowerCAmelCase:str									=  u
					for i in range(1      ,       __A    ):
										_lowerCAmelCase:Any									=  temp * (u - i)
					return temp





def 							UpperCAmelCase		(		):
					_lowerCAmelCase:List[str]									=  int(input('''enter the numbers of values: '''    )    )
					_lowerCAmelCase:list[list[float]]									=  []
					for _ in range(__A    ):
										y.append([]    )
					for i in range(__A    ):
										for j in range(__A    ):
															y[i].append(__A    )
															_lowerCAmelCase:List[Any]									=  0

					print('''enter the values of parameters in a list: '''    )
					_lowerCAmelCase:Optional[int]									=  list(map(__A      ,       input().split()    )    )

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

					_lowerCAmelCase:int									=  int(input('''enter the value to interpolate: '''    )    )
					_lowerCAmelCase:Union[str, Any]									=  (value - x[0]) / (x[1] - x[0])

					# for calculating forward difference table

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

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


if __name__ == "__main__":
				main()












"""simple docstring"""






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import torch

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available




@dataclass
class   a__			(       UpperCamelCase_  ):
						snake_case__             = 42


try:
				if not (is_transformers_available() and is_torch_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
				from .pipeline_text_to_video_synth import TextToVideoSDPipeline
				from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline  # noqa: F401
				from .pipeline_text_to_video_zero import TextToVideoZeroPipeline






