






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							(  __A						):





		'''simple docstring'''



		UpperCAmelCase__      :				Optional[Any]				    =						"""dpt"""


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

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   hidden_size
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   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_   :	List[str]													=   BitConfig(**UpperCamelCase_       )
										elif isinstance(UpperCamelCase_ ,    UpperCamelCase_       ):
														logger.info('Initializing the config with a `BiT` backbone.'       )
														SCREAMING_SNAKE_CASE_   :	List[Any]													=   BitConfig(**UpperCamelCase_       )
										elif isinstance(UpperCamelCase_ ,    UpperCamelCase_       ):
														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_   :	Optional[int]													=   backbone_featmap_shape
										SCREAMING_SNAKE_CASE_   :	str													=   neck_ignore_stages

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

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   num_hidden_layers
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   num_attention_heads
						SCREAMING_SNAKE_CASE_   :	Any													=   intermediate_size
						SCREAMING_SNAKE_CASE_   :	Tuple													=   hidden_act
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   hidden_dropout_prob
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE_   :	List[str]													=   initializer_range
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   layer_norm_eps
						SCREAMING_SNAKE_CASE_   :	Tuple													=   image_size
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   patch_size
						SCREAMING_SNAKE_CASE_   :	int													=   num_channels
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   qkv_bias
						SCREAMING_SNAKE_CASE_   :	int													=   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_   :	Any													=   readout_type
						SCREAMING_SNAKE_CASE_   :	str													=   reassemble_factors
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   neck_hidden_sizes
						SCREAMING_SNAKE_CASE_   :	Any													=   fusion_hidden_size
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   head_in_index
						SCREAMING_SNAKE_CASE_   :	str													=   use_batch_norm_in_fusion_residual
						# auxiliary head attributes (semantic segmentation)
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   use_auxiliary_head
						SCREAMING_SNAKE_CASE_   :	Any													=   auxiliary_loss_weight
						SCREAMING_SNAKE_CASE_   :	Tuple													=   semantic_loss_ignore_index
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   semantic_classifier_dropout






		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Tuple													=   copy.deepcopy(self.__dict__       )

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

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.__class__.model_type
						return output










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'''







				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   2.0

				while start <= a:
								SCREAMING_SNAKE_CASE_   :	str													=   math.pow(lowerCAmelCase__    ,    2      )

				return start



def 					_lowerCamelCase(			lowerCAmelCase__				:		float    ,    lowerCAmelCase__				:		int = 9999    ,    lowerCAmelCase__				:		float = 0.00_000_000_000_001      ):





				'''simple docstring'''







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

				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   get_initial_point(lowerCAmelCase__      )

				for _ in range(lowerCAmelCase__      ):
								SCREAMING_SNAKE_CASE_   :	Tuple													=   value
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   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()