

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_torch_available,
    is_vision_available,
)


lowercase_									=   {'configuration_beit': ['BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BeitConfig', 'BeitOnnxConfig']}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=   ['BeitFeatureExtractor']
  lowercase_									=   ['BeitImageProcessor']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=   [
      'BEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'BeitForImageClassification',
      'BeitForMaskedImageModeling',
      'BeitForSemanticSegmentation',
      'BeitModel',
      'BeitPreTrainedModel',
  ]


try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=   [
      'FlaxBeitForImageClassification',
      'FlaxBeitForMaskedImageModeling',
      'FlaxBeitModel',
      'FlaxBeitPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_beit import BeitFeatureExtractor
    from .image_processing_beit import BeitImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_beit import (
        BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BeitForImageClassification,
        BeitForMaskedImageModeling,
        BeitForSemanticSegmentation,
        BeitModel,
        BeitPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_beit import (
        FlaxBeitForImageClassification,
        FlaxBeitForMaskedImageModeling,
        FlaxBeitModel,
        FlaxBeitPreTrainedModel,
    )


else:
  import sys

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







'''simple docstring'''





from __future__ import annotations

import math




def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



     _a      =  u
     for i in range(1  ,	__A):
          _a      =  temp * (u - i)
     return temp




def lowerCAmelCase  ():



     """simple docstring"""



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

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

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

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

     # for calculating forward difference table

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

     _a      =  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()



