






'''simple docstring'''
from __future__ import annotations

import math





def  _a(  UpperCamelCase__				:						float,	UpperCamelCase__				:						int							):




						'''simple docstring'''






						SCREAMING_SNAKE_CASE__     :				Any									=u
						for i in range(1,	UpperCamelCase__							):
												SCREAMING_SNAKE_CASE__     :				Optional[int]									=temp * (u - i)
						return temp





def  _a(  ):




						'''simple docstring'''






						SCREAMING_SNAKE_CASE__     :				Any									=int(input('''enter the numbers of values: '''							)							)
						SCREAMING_SNAKE_CASE__     :				list[list[float]]									=[]
						for _ in range(UpperCamelCase__							):
												y.append([]							)
						for i in range(UpperCamelCase__							):
												for j in range(UpperCamelCase__							):
																		y[i].append(UpperCamelCase__							)
																		SCREAMING_SNAKE_CASE__     :				int									=0

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

						print('''enter the values of corresponding parameters: '''							)
						for i in range(UpperCamelCase__							):
												SCREAMING_SNAKE_CASE__     :				Any									=float(input()							)

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

						# for calculating forward difference table

						for i in range(1,	UpperCamelCase__							):
												for j in range(n - i							):
																		SCREAMING_SNAKE_CASE__     :				List[str]									=y[j + 1][i - 1] - y[j][i - 1]

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

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


if __name__ == "__main__":
		main()







'''simple docstring'''
from typing import TYPE_CHECKING

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


a_					= {
    'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaConfig', 'DebertaOnnxConfig'],
    'tokenization_deberta': ['DebertaTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					= ['DebertaTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					= [
      'DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
      'DebertaForMaskedLM',
      'DebertaForQuestionAnswering',
      'DebertaForSequenceClassification',
      'DebertaForTokenClassification',
      'DebertaModel',
      'DebertaPreTrainedModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					= [
      'TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFDebertaForMaskedLM',
      'TFDebertaForQuestionAnswering',
      'TFDebertaForSequenceClassification',
      'TFDebertaForTokenClassification',
      'TFDebertaModel',
      'TFDebertaPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
  from .tokenization_deberta import DebertaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_deberta_fast import DebertaTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_deberta import (
        DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
        DebertaForMaskedLM,
        DebertaForQuestionAnswering,
        DebertaForSequenceClassification,
        DebertaForTokenClassification,
        DebertaModel,
        DebertaPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_deberta import (
        TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFDebertaForMaskedLM,
        TFDebertaForQuestionAnswering,
        TFDebertaForSequenceClassification,
        TFDebertaForTokenClassification,
        TFDebertaModel,
        TFDebertaPreTrainedModel,
    )


else:
  import sys

  a_					= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)