



from __future__ import annotations

import math






def      __lowercase       (			a__							,       a__       )    ->			float:
							__SCREAMING_SNAKE_CASE									= u
							for i in range(1							,       a__       ):
														__SCREAMING_SNAKE_CASE									= temp * (u - i)
							return temp






def      __lowercase       (			)    ->			None:
							__SCREAMING_SNAKE_CASE									= int(input('enter the numbers of values: '       )       )
							__SCREAMING_SNAKE_CASE									= []
							for _ in range(a__       ):
														y.append([]       )
							for i in range(a__       ):
														for j in range(a__       ):
																					y[i].append(a__       )
																					__SCREAMING_SNAKE_CASE									= 0

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

							print('enter the values of corresponding parameters: '       )
							for i in range(a__       ):
														__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							,       a__       ):
														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							,       a__       ):
														summ += (ucal(a__							,       a__       ) * y[0][i]) / math.factorial(a__       )

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


if __name__ == "__main__":
			main()











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__		:					Optional[int]							={'''configuration_fnet''': ['''FNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FNetConfig''']}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					Optional[int]							=['''FNetTokenizer''']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					Optional[Any]							=['''FNetTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					Tuple							=[
			    '''FNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''FNetForMaskedLM''',
			    '''FNetForMultipleChoice''',
			    '''FNetForNextSentencePrediction''',
			    '''FNetForPreTraining''',
			    '''FNetForQuestionAnswering''',
			    '''FNetForSequenceClassification''',
			    '''FNetForTokenClassification''',
			    '''FNetLayer''',
			    '''FNetModel''',
			    '''FNetPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_fnet import FNetTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_fnet_fast import FNetTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_fnet import (
						    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FNetForMaskedLM,
						    FNetForMultipleChoice,
						    FNetForNextSentencePrediction,
						    FNetForPreTraining,
						    FNetForQuestionAnswering,
						    FNetForSequenceClassification,
						    FNetForTokenClassification,
						    FNetLayer,
						    FNetModel,
						    FNetPreTrainedModel,
						)


else:
			import sys

			lowerCAmelCase__		:					Any							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






