





from __future__ import annotations

import math

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			int  						=   u
				for i in range(1	,			__lowerCAmelCase ):
								_snake_case :			Optional[int]  						=   temp * (u - i)
				return temp

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

				print('enter the values of parameters in a list: ' )
				_snake_case :			int  						=   list(map(__lowerCAmelCase	,			input().split() ) )

				print('enter the values of corresponding parameters: ' )
				for i in range(__lowerCAmelCase ):
								_snake_case :			List[str]  						=   float(input() )

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

				# for calculating forward difference table

				for i in range(1	,			__lowerCAmelCase ):
								for j in range(n - i ):
												_snake_case :			int  						=   y[j + 1][i - 1] - y[j][i - 1]

				_snake_case :			Union[str, Any]  						=   y[0][0]
				for i in range(1	,			__lowerCAmelCase ):
								summ += (ucal(__lowerCAmelCase	,			__lowerCAmelCase ) * y[0][i]) / math.factorial(__lowerCAmelCase )

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


if __name__ == "__main__":
		main()













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase_		:				Any				=						{
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_		:				Optional[Any]				=						[
		    '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''CanineForMultipleChoice''',
		    '''CanineForQuestionAnswering''',
		    '''CanineForSequenceClassification''',
		    '''CanineForTokenClassification''',
		    '''CanineLayer''',
		    '''CanineModel''',
		    '''CaninePreTrainedModel''',
		    '''load_tf_weights_in_canine''',
		]


if TYPE_CHECKING:
		from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
		from .tokenization_canine import CanineTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_canine import (
				    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CanineForMultipleChoice,
				    CanineForQuestionAnswering,
				    CanineForSequenceClassification,
				    CanineForTokenClassification,
				    CanineLayer,
				    CanineModel,
				    CaninePreTrainedModel,
				    load_tf_weights_in_canine,
				)


else:
		import sys

		lowercase_		:				str				=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






