


from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_   :				Optional[Any]						      = logging.get_logger(__name__)

UpperCAmelCase_   :				List[Any]						      = {
    """microsoft/cvt-13""": """https://huggingface.co/microsoft/cvt-13/resolve/main/config.json""",
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}
class 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      snake_case__       :  Tuple       =		"cvt"
      def __init__( self       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=3  ,							SCREAMING_SNAKE_CASE__       : int=[7, 3, 3]  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=[4, 2, 2]  ,							SCREAMING_SNAKE_CASE__       : int=[2, 1, 1]  ,							SCREAMING_SNAKE_CASE__       : str=[6_4, 1_9_2, 3_8_4]  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=[1, 3, 6]  ,							SCREAMING_SNAKE_CASE__       : Dict=[1, 2, 1_0]  ,							SCREAMING_SNAKE_CASE__       : List[str]=[4.0, 4.0, 4.0]  ,							SCREAMING_SNAKE_CASE__       : int=[0.0, 0.0, 0.0]  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=[0.0, 0.0, 0.0]  ,							SCREAMING_SNAKE_CASE__       : Any=[0.0, 0.0, 0.1]  ,							SCREAMING_SNAKE_CASE__       : int=[True, True, True]  ,							SCREAMING_SNAKE_CASE__       : Any=[False, False, True]  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=["dw_bn", "dw_bn", "dw_bn"]  ,							SCREAMING_SNAKE_CASE__       : int=[3, 3, 3]  ,							SCREAMING_SNAKE_CASE__       : List[Any]=[1, 1, 1]  ,							SCREAMING_SNAKE_CASE__       : List[Any]=[2, 2, 2]  ,							SCREAMING_SNAKE_CASE__       : Any=[1, 1, 1]  ,							SCREAMING_SNAKE_CASE__       : List[str]=[1, 1, 1]  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=0.02  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=1E-12  ,							**SCREAMING_SNAKE_CASE__       : str  ,							)	->    Optional[int]:
             super().__init__(**SCREAMING_SNAKE_CASE__  )
             a_      :	Tuple							=						num_channels
             a_      :	Any							=						patch_sizes
             a_      :	Any							=						patch_stride
             a_      :	Dict							=						patch_padding
             a_      :	List[Any]							=						embed_dim
             a_      :	int							=						num_heads
             a_      :	Optional[int]							=						depth
             a_      :	List[Any]							=						mlp_ratio
             a_      :	Any							=						attention_drop_rate
             a_      :	Dict							=						drop_rate
             a_      :	int							=						drop_path_rate
             a_      :	int							=						qkv_bias
             a_      :	List[str]							=						cls_token
             a_      :	Any							=						qkv_projection_method
             a_      :	Optional[Any]							=						kernel_qkv
             a_      :	List[Any]							=						padding_kv
             a_      :	List[Any]							=						stride_kv
             a_      :	str							=						padding_q
             a_      :	List[str]							=						stride_q
             a_      :	Dict							=						initializer_range
             a_      :	Union[str, Any]							=						layer_norm_eps






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()


