


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


UpperCAmelCase_   :				Dict						      = logging.get_logger(__name__)

UpperCAmelCase_   :				str						      = {
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}
class 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      snake_case__       :  str       =		'''deta'''
      snake_case__       :  Optional[Any]       =		{
          '''hidden_size''': '''d_model''',
          '''num_attention_heads''': '''encoder_attention_heads''',
      }
      def __init__( self       : List[str]  ,							SCREAMING_SNAKE_CASE__       : List[Any]=None  ,							SCREAMING_SNAKE_CASE__       : List[str]=9_0_0  ,							SCREAMING_SNAKE_CASE__       : Any=2_0_4_8  ,							SCREAMING_SNAKE_CASE__       : List[str]=6  ,							SCREAMING_SNAKE_CASE__       : List[str]=2_0_4_8  ,							SCREAMING_SNAKE_CASE__       : List[str]=8  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=6  ,							SCREAMING_SNAKE_CASE__       : List[Any]=1_0_2_4  ,							SCREAMING_SNAKE_CASE__       : Any=8  ,							SCREAMING_SNAKE_CASE__       : List[str]=0.0  ,							SCREAMING_SNAKE_CASE__       : Dict=True  ,							SCREAMING_SNAKE_CASE__       : List[str]="relu"  ,							SCREAMING_SNAKE_CASE__       : List[str]=2_5_6  ,							SCREAMING_SNAKE_CASE__       : List[str]=0.1  ,							SCREAMING_SNAKE_CASE__       : Dict=0.0  ,							SCREAMING_SNAKE_CASE__       : str=0.0  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=0.02  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=1.0  ,							SCREAMING_SNAKE_CASE__       : List[str]=True  ,							SCREAMING_SNAKE_CASE__       : List[str]=False  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]="sine"  ,							SCREAMING_SNAKE_CASE__       : List[str]=5  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=4  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=4  ,							SCREAMING_SNAKE_CASE__       : Tuple=True  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=3_0_0  ,							SCREAMING_SNAKE_CASE__       : List[Any]=True  ,							SCREAMING_SNAKE_CASE__       : int=True  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=1  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=5  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=2  ,							SCREAMING_SNAKE_CASE__       : List[Any]=1  ,							SCREAMING_SNAKE_CASE__       : Dict=1  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=5  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=2  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=0.1  ,							SCREAMING_SNAKE_CASE__       : Any=0.25  ,							**SCREAMING_SNAKE_CASE__       : Union[str, Any]  ,							)	->    List[str]:
             if backbone_config is None:
                    logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'  )
                    a_      :	Dict							=						CONFIG_MAPPING['resnet'](out_features=['stage2', 'stage3', 'stage4']  )
             else:
                    if isinstance(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ):
                           a_      :	Union[str, Any]							=						backbone_config.pop('model_type'  )
                           a_      :	Tuple							=						CONFIG_MAPPING[backbone_model_type]
                           a_      :	List[str]							=						config_class.from_dict(SCREAMING_SNAKE_CASE__  )

             a_      :	Dict							=						backbone_config
             a_      :	Tuple							=						num_queries
             a_      :	Optional[int]							=						max_position_embeddings
             a_      :	List[Any]							=						d_model
             a_      :	int							=						encoder_ffn_dim
             a_      :	Optional[int]							=						encoder_layers
             a_      :	List[str]							=						encoder_attention_heads
             a_      :	Union[str, Any]							=						decoder_ffn_dim
             a_      :	Optional[Any]							=						decoder_layers
             a_      :	Dict							=						decoder_attention_heads
             a_      :	Union[str, Any]							=						dropout
             a_      :	Any							=						attention_dropout
             a_      :	str							=						activation_dropout
             a_      :	Dict							=						activation_function
             a_      :	Optional[int]							=						init_std
             a_      :	str							=						init_xavier_std
             a_      :	Any							=						encoder_layerdrop
             a_      :	Tuple							=						auxiliary_loss
             a_      :	Dict							=						position_embedding_type
             # deformable attributes
             a_      :	int							=						num_feature_levels
             a_      :	Union[str, Any]							=						encoder_n_points
             a_      :	Union[str, Any]							=						decoder_n_points
             a_      :	Optional[Any]							=						two_stage
             a_      :	List[str]							=						two_stage_num_proposals
             a_      :	List[str]							=						with_box_refine
             a_      :	Optional[Any]							=						assign_first_stage
             if two_stage is True and with_box_refine is False:
                    raise ValueError('If two_stage is True, with_box_refine must be True.'  )
             # Hungarian matcher
             a_      :	Optional[Any]							=						class_cost
             a_      :	Any							=						bbox_cost
             a_      :	Union[str, Any]							=						giou_cost
             # Loss coefficients
             a_      :	List[str]							=						mask_loss_coefficient
             a_      :	List[str]							=						dice_loss_coefficient
             a_      :	Dict							=						bbox_loss_coefficient
             a_      :	List[str]							=						giou_loss_coefficient
             a_      :	Optional[int]							=						eos_coefficient
             a_      :	Any							=						focal_alpha
             super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE__  ,							**SCREAMING_SNAKE_CASE__  )
      @property
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    int:
             return self.encoder_attention_heads
      @property
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    int:
             return self.d_model

      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    List[Any]:
             a_      :	Optional[int]							=						copy.deepcopy(self.__dict__  )
             a_      :	int							=						self.backbone_config.to_dict()
             a_      :	Optional[Any]							=						self.__class__.model_type
             return output






import unittest

import numpy as np







def  SCREAMING_SNAKE_CASE_		(    __A				:    np.ndarray						,					__A				:    np.ndarray						,					__A				:    np.ndarray						,					__A				:    np.ndarray | None = None						,					)			->	np.ndarray:

       """simple docstring"""

       a_      :	List[str]							=						np.shape(__A       )
       a_      :	Any							=						np.shape(__A       )
       a_      :	List[Any]							=						np.shape(__A       )

       if shape_a[0] != shape_b[0]:
              a_      :	List[Any]							=						(
                  'Expected the same number of rows for A and B. '
                  F"""Instead found A of size {shape_a} and B of size {shape_b}"""
              )
              raise ValueError(__A       )

       if shape_b[1] != shape_c[1]:
              a_      :	int							=						(
                  'Expected the same number of columns for B and C. '
                  F"""Instead found B of size {shape_b} and C of size {shape_c}"""
              )
              raise ValueError(__A       )

       a_      :	Any							=						pseudo_inv
       if a_inv is None:
              try:
                     a_      :	List[str]							=						np.linalg.inv(__A       )
              except np.linalg.LinAlgError:
                     raise ValueError(
                         'Input matrix A is not invertible. Cannot compute Schur complement.'       )

       return mat_c - mat_b.T @ a_inv @ mat_b
class 				SCREAMING_SNAKE_CASE__						(       unittest.TestCase    ):
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    None:
             a_      :	str							=						np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]  )
             a_      :	Any							=						np.array([[0, 3], [3, 0], [2, 3]]  )
             a_      :	List[str]							=						np.array([[2, 1], [6, 3]]  )

             a_      :	int							=						schur_complement(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

             a_      :	Optional[Any]							=						np.block([[a, b], [b.T, c]]  )

             a_      :	str							=						np.linalg.det(SCREAMING_SNAKE_CASE__  )
             a_      :	List[Any]							=						np.linalg.det(SCREAMING_SNAKE_CASE__  )
             a_      :	str							=						np.linalg.det(SCREAMING_SNAKE_CASE__  )

             self.assertAlmostEqual(SCREAMING_SNAKE_CASE__  ,							det_a * det_s  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    None:
             a_      :	Optional[int]							=						np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]  )
             a_      :	Dict							=						np.array([[0, 3], [3, 0], [2, 3]]  )
             a_      :	str							=						np.array([[2, 1], [6, 3]]  )

             with self.assertRaises(SCREAMING_SNAKE_CASE__  ):
                    schur_complement(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    None:
             a_      :	Tuple							=						np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]  )
             a_      :	Dict							=						np.array([[0, 3], [3, 0], [2, 3]]  )
             a_      :	List[str]							=						np.array([[2, 1, 3], [6, 3, 5]]  )

             with self.assertRaises(SCREAMING_SNAKE_CASE__  ):
                    schur_complement(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   unittest.main()


