

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 _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			lowerCamelCase__           ='dpt'
			def __init__(			self							: List[Any]      ,      a							: Union[str, Any]=768      ,      a							: Optional[Any]=12      ,      a							: Union[str, Any]=12      ,      a							: int=3072      ,      a							: Tuple="gelu"      ,      a							: Union[str, Any]=0.0      ,      a							: Union[str, Any]=0.0      ,      a							: Union[str, Any]=0.02      ,      a							: int=1e-12      ,      a							: int=384      ,      a							: List[str]=16      ,      a							: str=3      ,      a							: int=False      ,      a							: Union[str, Any]=True      ,      a							: Any=[2, 5, 8, 11]      ,      a							: Any="project"      ,      a							: Dict=[4, 2, 1, 0.5]      ,      a							: str=[96, 192, 384, 768]      ,      a							: Tuple=256      ,      a							: str=-1      ,      a							: str=False      ,      a							: List[Any]=True      ,      a							: List[str]=0.4      ,      a							: Tuple=255      ,      a							: int=0.1      ,      a							: Dict=[1, 1024, 24, 24]      ,      a							: Optional[Any]=[0, 1]      ,      a							: Dict=None      ,      **a							: Tuple      ,      )							->       str:

				"""simple docstring"""

				super().__init__(**_SCREAMING_SNAKE_CASE	)

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       hidden_size
				SCREAMING_SNAKE_CASE   :      str				     =       is_hybrid

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

					SCREAMING_SNAKE_CASE   :      int				     =       backbone_featmap_shape
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       neck_ignore_stages

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

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






			def 							__UpperCamelCase (			self							: Dict	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       copy.deepcopy(self.__dict__	)

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

				SCREAMING_SNAKE_CASE   :      str				     =       self.__class__.model_type
				return output









import math

def  lowerCamelCase__ (					_a							,			_a):
	if (
	    not isinstance(_a							,			(int, float))
	    or power_factor < -1
	    or power_factor > 1
	):
		raise ValueError("power_factor must be a valid float value between -1 and 1.")
	return apparent_power * power_factor

def  lowerCamelCase__ (					_a							,			_a):
	if (
	    not isinstance(_a							,			(int, float))
	    or power_factor < -1
	    or power_factor > 1
	):
		raise ValueError("power_factor must be a valid float value between -1 and 1.")
	return apparent_power * math.sqrt(1 - power_factor**2)


if __name__ == "__main__":
			import doctest

			doctest.testmod()