


import flax.linen as nn
import jax
import jax.numpy as jnp
class 				SCREAMING_SNAKE_CASE__						(       nn.Module    ):
      snake_case__       :  int
      snake_case__       :  jnp.dtype       =		jnp.floataa
      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    int:
             a_      :	str							=						nn.Conv(
                 self.out_channels  ,							kernel_size=(3, 3)  ,							strides=(1, 1)  ,							padding=((1, 1), (1, 1))  ,							dtype=self.dtype  ,							)

      def __call__( self       : Tuple  ,							SCREAMING_SNAKE_CASE__       : Any  )	->    Optional[int]:
             a_							,	a_							,	a_							,	a_      :	List[Any]							=						hidden_states.shape
             a_      :	List[str]							=						jax.image.resize(
                 SCREAMING_SNAKE_CASE__  ,							shape=(batch, height * 2, width * 2, channels)  ,							method='nearest'  ,							)
             a_      :	Any							=						self.conv(SCREAMING_SNAKE_CASE__  )
             return hidden_states
class 				SCREAMING_SNAKE_CASE__						(       nn.Module    ):
      snake_case__       :  int
      snake_case__       :  jnp.dtype       =		jnp.floataa
      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    Tuple:
             a_      :	List[Any]							=						nn.Conv(
                 self.out_channels  ,							kernel_size=(3, 3)  ,							strides=(2, 2)  ,							padding=((1, 1), (1, 1))  ,							dtype=self.dtype  ,							)

      def __call__( self       : Dict  ,							SCREAMING_SNAKE_CASE__       : int  )	->    List[Any]:
             # pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
             # hidden_states = jnp.pad(hidden_states, pad_width=pad)
             a_      :	int							=						self.conv(SCREAMING_SNAKE_CASE__  )
             return hidden_states
class 				SCREAMING_SNAKE_CASE__						(       nn.Module    ):
      snake_case__       :  int
      snake_case__       :  int       =		None
      snake_case__       :  float       =		0.0
      snake_case__       :  bool       =		None
      snake_case__       :  jnp.dtype       =		jnp.floataa
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    List[Any]:
             a_      :	Union[str, Any]							=						self.in_channels if self.out_channels is None else self.out_channels

             a_      :	Optional[Any]							=						nn.GroupNorm(num_groups=3_2  ,							epsilon=1E-5  )
             a_      :	Tuple							=						nn.Conv(
                 SCREAMING_SNAKE_CASE__  ,							kernel_size=(3, 3)  ,							strides=(1, 1)  ,							padding=((1, 1), (1, 1))  ,							dtype=self.dtype  ,							)

             a_      :	List[str]							=						nn.Dense(SCREAMING_SNAKE_CASE__  ,							dtype=self.dtype  )

             a_      :	Any							=						nn.GroupNorm(num_groups=3_2  ,							epsilon=1E-5  )
             a_      :	List[Any]							=						nn.Dropout(self.dropout_prob  )
             a_      :	Union[str, Any]							=						nn.Conv(
                 SCREAMING_SNAKE_CASE__  ,							kernel_size=(3, 3)  ,							strides=(1, 1)  ,							padding=((1, 1), (1, 1))  ,							dtype=self.dtype  ,							)

             a_      :	Optional[Any]							=						self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

             a_      :	Optional[Any]							=						None
             if use_nin_shortcut:
                    a_      :	Tuple							=						nn.Conv(
                        SCREAMING_SNAKE_CASE__  ,							kernel_size=(1, 1)  ,							strides=(1, 1)  ,							padding='VALID'  ,							dtype=self.dtype  ,							)

      def __call__( self       : Dict  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : List[str]  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=True  )	->    Union[str, Any]:
             a_      :	int							=						hidden_states
             a_      :	int							=						self.norma(SCREAMING_SNAKE_CASE__  )
             a_      :	List[Any]							=						nn.swish(SCREAMING_SNAKE_CASE__  )
             a_      :	List[Any]							=						self.conva(SCREAMING_SNAKE_CASE__  )

             a_      :	Dict							=						self.time_emb_proj(nn.swish(SCREAMING_SNAKE_CASE__  )  )
             a_      :	Optional[Any]							=						jnp.expand_dims(jnp.expand_dims(SCREAMING_SNAKE_CASE__  ,							1  )  ,							1  )
             a_      :	Tuple							=						hidden_states + temb

             a_      :	Dict							=						self.norma(SCREAMING_SNAKE_CASE__  )
             a_      :	str							=						nn.swish(SCREAMING_SNAKE_CASE__  )
             a_      :	Union[str, Any]							=						self.dropout(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
             a_      :	Any							=						self.conva(SCREAMING_SNAKE_CASE__  )

             if self.conv_shortcut is not None:
                    a_      :	Any							=						self.conv_shortcut(SCREAMING_SNAKE_CASE__  )

             return hidden_states + residual






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()


