
"""simple docstring"""





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


_a		       =							logging.get_logger(__name__)

_a		       =							{
    'Intel/dpt-large': 'https://huggingface.co/Intel/dpt-large/resolve/main/config.json',
    # See all DPT models at https://huggingface.co/models?filter=dpt
}


class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						str       =		"""dpt"""







  def __init__(		self				,   lowercase_=768				,   lowercase_=12				,   lowercase_=12				,   lowercase_=3072				,   lowercase_="gelu"				,   lowercase_=0.0				,   lowercase_=0.0				,   lowercase_=0.02				,   lowercase_=1E-1_2				,   lowercase_=384				,   lowercase_=16				,   lowercase_=3				,   lowercase_=False				,   lowercase_=True				,   lowercase_=[2, 5, 8, 11]				,   lowercase_="project"				,   lowercase_=[4, 2, 1, 0.5]				,   lowercase_=[96, 192, 384, 768]				,   lowercase_=256				,   lowercase_=-1				,   lowercase_=False				,   lowercase_=True				,   lowercase_=0.4				,   lowercase_=255				,   lowercase_=0.1				,   lowercase_=[1, 1024, 24, 24]				,   lowercase_=[0, 1]				,   lowercase_=None				,   **lowercase_				,   ):

     """simple docstring"""
     super().__init__(**lowercase_  )

     UpperCAmelCase_					:					int			     =    hidden_size
     UpperCAmelCase_					:					Optional[int]			     =    is_hybrid

     if self.is_hybrid:
        if backbone_config is None:
           logger.info("Initializing the config with a `BiT` backbone."  )
           UpperCAmelCase_					:					List[Any]			     =    {
               "global_padding": "same",
               "layer_type": "bottleneck",
               "depths": [3, 4, 9],
               "out_features": ["stage1", "stage2", "stage3"],
               "embedding_dynamic_padding": True,
           }
           UpperCAmelCase_					:					Tuple			     =    BitConfig(**lowercase_  )
        elif isinstance(lowercase_				,   lowercase_  ):
           logger.info("Initializing the config with a `BiT` backbone."  )
           UpperCAmelCase_					:					int			     =    BitConfig(**lowercase_  )
        elif isinstance(lowercase_				,   lowercase_  ):
           UpperCAmelCase_					:					Union[str, Any]			     =    backbone_config
        else:
           raise ValueError(
               F"""backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}."""  )

        UpperCAmelCase_					:					List[Any]			     =    backbone_featmap_shape
        UpperCAmelCase_					:					str			     =    neck_ignore_stages

        if readout_type != "project":
           raise ValueError("Readout type must be 'project' when using `DPT-hybrid` mode."  )
     else:
        UpperCAmelCase_					:					Any			     =    None
        UpperCAmelCase_					:					Optional[Any]			     =    None
        UpperCAmelCase_					:					List[str]			     =    []

     UpperCAmelCase_					:					int			     =    num_hidden_layers
     UpperCAmelCase_					:					List[Any]			     =    num_attention_heads
     UpperCAmelCase_					:					Dict			     =    intermediate_size
     UpperCAmelCase_					:					List[Any]			     =    hidden_act
     UpperCAmelCase_					:					Dict			     =    hidden_dropout_prob
     UpperCAmelCase_					:					Any			     =    attention_probs_dropout_prob
     UpperCAmelCase_					:					Optional[Any]			     =    initializer_range
     UpperCAmelCase_					:					int			     =    layer_norm_eps
     UpperCAmelCase_					:					Optional[int]			     =    image_size
     UpperCAmelCase_					:					int			     =    patch_size
     UpperCAmelCase_					:					str			     =    num_channels
     UpperCAmelCase_					:					Union[str, Any]			     =    qkv_bias
     UpperCAmelCase_					:					Optional[Any]			     =    backbone_out_indices
     if readout_type not in ["ignore", "add", "project"]:
        raise ValueError("Readout_type must be one of ['ignore', 'add', 'project']"  )
     UpperCAmelCase_					:					Any			     =    readout_type
     UpperCAmelCase_					:					str			     =    reassemble_factors
     UpperCAmelCase_					:					str			     =    neck_hidden_sizes
     UpperCAmelCase_					:					Optional[int]			     =    fusion_hidden_size
     UpperCAmelCase_					:					str			     =    head_in_index
     UpperCAmelCase_					:					Optional[Any]			     =    use_batch_norm_in_fusion_residual
     # auxiliary head attributes (semantic segmentation)
     UpperCAmelCase_					:					Optional[Any]			     =    use_auxiliary_head
     UpperCAmelCase_					:					Any			     =    auxiliary_loss_weight
     UpperCAmelCase_					:					List[str]			     =    semantic_loss_ignore_index
     UpperCAmelCase_					:					Union[str, Any]			     =    semantic_classifier_dropout







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    copy.deepcopy(self.__dict__  )

     if output["backbone_config"] is not None:
        UpperCAmelCase_					:					int			     =    self.backbone_config.to_dict()

     UpperCAmelCase_					:					str			     =    self.__class__.model_type
     return output




"""simple docstring"""





from __future__ import annotations

import math







def 	__a     (		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					Any			     =    u
   for i in range(1,		__lowerCamelCase  ):
      UpperCAmelCase_					:					int			     =    temp * (u - i)
   return temp







def 	__a     (		):
   UpperCAmelCase_					:					str			     =    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_					:					Tuple			     =    0

   print("enter the values of parameters in a list: "  )
   UpperCAmelCase_					:					Union[str, Any]			     =    list(map(__lowerCamelCase,		input().split()  )  )

   print("enter the values of corresponding parameters: "  )
   for i in range(__lowerCamelCase  ):
      UpperCAmelCase_					:					int			     =    float(input()  )

   UpperCAmelCase_					:					Tuple			     =    int(input("enter the value to interpolate: "  )  )
   UpperCAmelCase_					:					Tuple			     =    (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_					:					Union[str, Any]			     =    y[j + 1][i - 1] - y[j][i - 1]

   UpperCAmelCase_					:					Optional[int]			     =    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()


