



from __future__ import annotations

import math







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      float ,  lowercase_    :      int						):
						lowercase        =							u
						for i in range(1 ,  SCREAMING_SNAKE_CASE_						):
												lowercase        =							temp * (u - i)
						return temp







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

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

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

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

						# for calculating forward difference table

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

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

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


if __name__ == "__main__":
						main()







'''simple docstring'''


from ... import PretrainedConfig


lowercase_      :				int   					=    {
    '''sijunhe/nezha-cn-base''': '''https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json''',
}




class      __UpperCamelCase       (_UpperCAmelCase			):
	__A			  =					NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
	__A			  =					'''nezha'''







	def __init__(    self ,				_lowerCAmelCase=2_1128 ,				_lowerCAmelCase=768 ,				_lowerCAmelCase=12 ,				_lowerCAmelCase=12 ,				_lowerCAmelCase=3072 ,				_lowerCAmelCase="gelu" ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=512 ,				_lowerCAmelCase=64 ,				_lowerCAmelCase=2 ,				_lowerCAmelCase=0.02 ,				_lowerCAmelCase=1E-12 ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=0 ,				_lowerCAmelCase=2 ,				_lowerCAmelCase=3 ,				_lowerCAmelCase=True ,				**_lowerCAmelCase ,				)    ->    int:
							'''simple docstring'''






							super().__init__(pad_token_id=_lowerCAmelCase ,				bos_token_id=_lowerCAmelCase ,				eos_token_id=_lowerCAmelCase ,				**_lowerCAmelCase						)

							lowercase        =							vocab_size
							lowercase        =							hidden_size
							lowercase        =							num_hidden_layers
							lowercase        =							num_attention_heads
							lowercase        =							hidden_act
							lowercase        =							intermediate_size
							lowercase        =							hidden_dropout_prob
							lowercase        =							attention_probs_dropout_prob
							lowercase        =							max_position_embeddings
							lowercase        =							max_relative_position
							lowercase        =							type_vocab_size
							lowercase        =							initializer_range
							lowercase        =							layer_norm_eps
							lowercase        =							classifier_dropout
							lowercase        =							use_cache


