


'''simple docstring'''

import copy

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


lowerCAmelCase				:List[Any]        =      logging.get_logger(__name__)

lowerCAmelCase				:int        =      {
    '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 _lowerCamelCase	(						SCREAMING_SNAKE_CASE__							):



		'''simple docstring'''







		A_     :      Union[str, Any]				   =    """dpt"""







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

			__magic_name__	:   List[Any]					     =					hidden_size
			__magic_name__	:   Optional[Any]					     =					is_hybrid

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

				__magic_name__	:   Optional[int]					     =					backbone_featmap_shape
				__magic_name__	:   Any					     =					neck_ignore_stages

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

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







		def        __lowerCAmelCase		( self		:						Optional[int]      )  ->			List[str]:
			__magic_name__	:   Dict					     =					copy.deepcopy(self.__dict__      )

			if output["backbone_config"] is not None:
				__magic_name__	:   Union[str, Any]					     =					self.backbone_config.to_dict()

			__magic_name__	:   List[str]					     =					self.__class__.model_type
			return output



'''simple docstring'''

import math





def lowerCamelCase      (      lowerCAmelCase    :     float		,		lowerCAmelCase    :     float		):





	"""simple docstring"""







	return math.pow(lowerCAmelCase		,		2		) - a





def lowerCamelCase      (      lowerCAmelCase    :     float		):





	"""simple docstring"""







	return 2 * x





def lowerCamelCase      (      lowerCAmelCase    :     float		):





	"""simple docstring"""







	__magic_name__	:   List[Any]					     =					2.0

	while start <= a:
		__magic_name__	:   List[str]					     =					math.pow(lowerCAmelCase		,		2		)

	return start





def lowerCamelCase      (      lowerCAmelCase    :     float		,		lowerCAmelCase    :     int = 9999		,		lowerCAmelCase    :     float = 0.00_0000_0000_0001		):





	"""simple docstring"""







	if a < 0:
		raise ValueError('math domain error'		)

	__magic_name__	:   Any					     =					get_initial_point(lowerCAmelCase		)

	for _ in range(lowerCAmelCase		):
		__magic_name__	:   List[str]					     =					value
		__magic_name__	:   Optional[int]					     =					value - fx(lowerCAmelCase		,		lowerCAmelCase		) / fx_derivative(lowerCAmelCase		)
		if abs(prev_value - value		) < tolerance:
			return value

	return value


if __name__ == "__main__":
							from doctest import testmod

							testmod()