





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


__lowerCamelCase			:	Optional[int]					    =	logging.get_logger(__name__)



class    A__  (			__snake_case     ):
      _UpperCAmelCase      :List[str]		       =  'upernet'





      def __init__(					self			,				A_=None			,				A_=512			,				A_=0.02			,				A_=[1, 2, 3, 6]			,				A_=True			,				A_=0.4			,				A_=384			,				A_=256			,				A_=1			,				A_=False			,				A_=255			,				**A_			,				):
       '''simple docstring'''




       super().__init__(**A_  )

       if backbone_config is None:
        logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."  )
        UpperCamelCase      :   Union[str, Any]			      =  CONFIG_MAPPING["resnet"](out_features=["stage1", "stage2", "stage3", "stage4"]  )
       elif isinstance(A_			,				A_  ):
        UpperCamelCase      :   str			      =  backbone_config.get("model_type"  )
        UpperCamelCase      :   str			      =  CONFIG_MAPPING[backbone_model_type]
        UpperCamelCase      :   Any			      =  config_class.from_dict(A_  )

       UpperCamelCase      :   int			      =  backbone_config
       UpperCamelCase      :   Tuple			      =  hidden_size
       UpperCamelCase      :   Any			      =  initializer_range
       UpperCamelCase      :   Optional[Any]			      =  pool_scales
       UpperCamelCase      :   int			      =  use_auxiliary_head
       UpperCamelCase      :   Any			      =  auxiliary_loss_weight
       UpperCamelCase      :   List[str]			      =  auxiliary_in_channels
       UpperCamelCase      :   Optional[Any]			      =  auxiliary_channels
       UpperCamelCase      :   Union[str, Any]			      =  auxiliary_num_convs
       UpperCamelCase      :   str			      =  auxiliary_concat_input
       UpperCamelCase      :   Any			      =  loss_ignore_index





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   List[Any]			      =  copy.deepcopy(self.__dict__  )
       UpperCamelCase      :   Any			      =  self.backbone_config.to_dict()
       UpperCamelCase      :   Optional[int]			      =  self.__class__.model_type
       return output







from __future__ import annotations

import math



def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase	)		->					float:
 UpperCamelCase      :   Tuple			      =  u
 for i in range(1				,  _lowerCAmelCase	):
  UpperCamelCase      :   Any			      =  temp * (u - i)
 return temp



def 						A_							(	)		->					None:
 UpperCamelCase      :   Union[str, Any]			      =  int(input("enter the numbers of values: "	)	)
 UpperCamelCase      :   list[list[float]]			      =  []
 for _ in range(_lowerCAmelCase	):
  y.append([]	)
 for i in range(_lowerCAmelCase	):
  for j in range(_lowerCAmelCase	):
   y[i].append(_lowerCAmelCase	)
   UpperCamelCase      :   List[str]			      =  0

 print("enter the values of parameters in a list: "	)
 UpperCamelCase      :   Optional[Any]			      =  list(map(_lowerCAmelCase				,  input().split()	)	)

 print("enter the values of corresponding parameters: "	)
 for i in range(_lowerCAmelCase	):
  UpperCamelCase      :   Tuple			      =  float(input()	)

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

 # for calculating forward difference table

 for i in range(1				,  _lowerCAmelCase	):
  for j in range(n - i	):
   UpperCamelCase      :   Optional[Any]			      =  y[j + 1][i - 1] - y[j][i - 1]

 UpperCamelCase      :   List[str]			      =  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()
