

from __future__ import annotations

import math




def 					_lowercase     (				a_				:					float					,a_				:					int				)				->	float:




					'''simple docstring'''







					__magic_name__							=    u
					for i in range(1					,a_				):
										__magic_name__							=    temp * (u - i)
					return temp




def 					_lowercase     (				)				->	None:




					'''simple docstring'''







					__magic_name__							=    int(input('enter the numbers of values: '				)				)
					__magic_name__							=    []
					for _ in range(a_				):
										y.append([]				)
					for i in range(a_				):
										for j in range(a_				):
															y[i].append(a_				)
															__magic_name__							=    0

					print('enter the values of parameters in a list: '				)
					__magic_name__							=    list(map(a_					,input().split()				)				)

					print('enter the values of corresponding parameters: '				)
					for i in range(a_				):
										__magic_name__							=    float(input()				)

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

					# for calculating forward difference table

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

					__magic_name__							=    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()







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
						from transformers import AutoModelForSeqaSeqLM, AutoTokenizer

@require_torch
@require_sentencepiece
@require_tokenizers
class 				__UpperCamelCase				(					unittest.TestCase			):







	@slow
	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						__magic_name__							=    AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'  , return_dict=__UpperCamelCase      ).to(__UpperCamelCase      )
						__magic_name__							=    AutoTokenizer.from_pretrained('google/mt5-small'      )

						__magic_name__							=    tokenizer('Hello there'  , return_tensors='pt'      ).input_ids
						__magic_name__							=    tokenizer('Hi I am'  , return_tensors='pt'      ).input_ids

						__magic_name__							=    model(input_ids.to(__UpperCamelCase      )  , labels=labels.to(__UpperCamelCase      )      ).loss
						__magic_name__							=    -(labels.shape[-1] * loss.item())

						__magic_name__							=    -84.9127
						self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 1E-4      )




