
"""simple docstring"""


import copy

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


_lowercase							   =				logging.get_logger(__name__)

_lowercase							   =				{
    '''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_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    Any      =    '''dpt'''


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

							A						   =   hidden_size
							A						   =   is_hybrid

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

								A						   =   backbone_featmap_shape
								A						   =   neck_ignore_stages

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

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




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]      )     ->   Dict:
							A						   =   copy.deepcopy(self.__dict__      )

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

							A						   =   self.__class__.model_type
							return output

"""simple docstring"""


def       _snake_case				(		snake_case__   :     list	,      snake_case__   :     list	,      snake_case__   :     int     ):
	A						   =   len(snake_case__     )
	A						   =   [[0] * n for i in range(snake_case__     )]
	for i in range(snake_case__     ):
		A						   =   y_points[i]

	for i in range(2	,      snake_case__     ):
		for j in range(snake_case__	,      snake_case__     ):
			A						   =   (
			    (xa - x_points[j - i + 1]) * q[j][i - 1]
			    - (xa - x_points[j]) * q[j - 1][i - 1]
			) / (x_points[j] - x_points[j - i + 1])

	return [q[n - 1][n - 1], q]


if __name__ == "__main__":
		import doctest

		doctest.testmod()