'''simple docstring'''

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer


@dataclass
class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      torch.FloatTensor




class 				_lowercase	(      lowerCAmelCase		,lowerCAmelCase   ):
      '''simple docstring'''


      @register_to_config
      def __init__(				self					,lowerCamelCase_ = 3					,lowerCamelCase_ = 3					,lowerCamelCase_ = ("DownEncoderBlock2D",)					,lowerCamelCase_ = ("UpDecoderBlock2D",)					,lowerCamelCase_ = (64,)					,lowerCamelCase_ = 1					,lowerCamelCase_ = "silu"					,lowerCamelCase_ = 3					,lowerCamelCase_ = 32					,lowerCamelCase_ = 256					,lowerCamelCase_ = 32					,lowerCamelCase_ = None					,lowerCamelCase_ = 0.1_8215					,lowerCamelCase_ = "group"					,)			->			Optional[Any]:



         '''simple docstring'''



         super().__init__()

         # pass init params to Encoder
         UpperCAmelCase__							:				Optional[int]   				=  Encoder(
             in_channels=lowerCamelCase_					,out_channels=lowerCamelCase_					,down_block_types=lowerCamelCase_					,block_out_channels=lowerCamelCase_					,layers_per_block=lowerCamelCase_					,act_fn=lowerCamelCase_					,norm_num_groups=lowerCamelCase_					,double_z=lowerCamelCase_					,)

         UpperCAmelCase__							:				List[Any]   				=  vq_embed_dim if vq_embed_dim is not None else latent_channels

         UpperCAmelCase__							:				Any   				=  nn.Convad(lowerCamelCase_					,lowerCamelCase_					,1							)
         UpperCAmelCase__							:				Tuple   				=  VectorQuantizer(lowerCamelCase_					,lowerCamelCase_					,beta=0.25					,remap=lowerCamelCase_					,sane_index_shape=lowerCamelCase_							)
         UpperCAmelCase__							:				List[str]   				=  nn.Convad(lowerCamelCase_					,lowerCamelCase_					,1							)

         # pass init params to Decoder
         UpperCAmelCase__							:				Optional[Any]   				=  Decoder(
             in_channels=lowerCamelCase_					,out_channels=lowerCamelCase_					,up_block_types=lowerCamelCase_					,block_out_channels=lowerCamelCase_					,layers_per_block=lowerCamelCase_					,act_fn=lowerCamelCase_					,norm_num_groups=lowerCamelCase_					,norm_type=lowerCamelCase_					,)




      @apply_forward_hook
      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = True							)			->			VQEncoderOutput:



         '''simple docstring'''



         UpperCAmelCase__							:				List[str]   				=  self.encoder(lowerCamelCase_							)
         UpperCAmelCase__							:				Union[str, Any]   				=  self.quant_conv(lowerCamelCase_							)

         if not return_dict:
            return (h,)

         return VQEncoderOutput(latents=lowerCamelCase_							)




      @apply_forward_hook
      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = False					,lowerCamelCase_ = True							)			->			Union[DecoderOutput, torch.FloatTensor]:



         '''simple docstring'''



         if not force_not_quantize:
            UpperCAmelCase__				,      UpperCAmelCase__				,      UpperCAmelCase__							:				List[str]   				=  self.quantize(lowerCamelCase_							)
         else:
            UpperCAmelCase__							:				List[Any]   				=  h
         UpperCAmelCase__							:				List[str]   				=  self.post_quant_conv(lowerCamelCase_							)
         UpperCAmelCase__							:				Dict   				=  self.decoder(lowerCamelCase_					,quant if self.config.norm_type == '''spatial''' else None							)

         if not return_dict:
            return (dec,)

         return DecoderOutput(sample=lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = True							)			->			Union[DecoderOutput, torch.FloatTensor]:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  sample
         UpperCAmelCase__							:				List[Any]   				=  self.encode(lowerCamelCase_							).latents
         UpperCAmelCase__							:				Optional[int]   				=  self.decode(lowerCamelCase_							).sample

         if not return_dict:
            return (dec,)

         return DecoderOutput(sample=lowerCamelCase_							)





'''simple docstring'''

import unittest

import numpy as np







def __UpperCamelCase(   _A					:				np.ndarray  ,			_A					:				np.ndarray  ,			_A					:				np.ndarray  ,			_A					:				np.ndarray | None = None  ,			):
   '''simple docstring'''
   UpperCAmelCase__							:				Any   				=  np.shape(_A    )
   UpperCAmelCase__							:				List[Any]   				=  np.shape(_A    )
   UpperCAmelCase__							:				Tuple   				=  np.shape(_A    )

   if shape_a[0] != shape_b[0]:
      UpperCAmelCase__							:				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]:
      UpperCAmelCase__							:				List[str]   				=  (
          '''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    )

   UpperCAmelCase__							:				Optional[Any]   				=  pseudo_inv
   if a_inv is None:
      try:
         UpperCAmelCase__							:				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 				_lowercase	(      unittest.TestCase   ):
      '''simple docstring'''


      def 		lowerCAmelCase__					(				self							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				int   				=  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
         UpperCAmelCase__							:				List[Any]   				=  np.array([[0, 3], [3, 0], [2, 3]]							)
         UpperCAmelCase__							:				Optional[Any]   				=  np.array([[2, 1], [6, 3]]							)

         UpperCAmelCase__							:				Tuple   				=  schur_complement(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)

         UpperCAmelCase__							:				Tuple   				=  np.block([[a, b], [b.T, c]]							)

         UpperCAmelCase__							:				int   				=  np.linalg.det(lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[Any]   				=  np.linalg.det(lowerCamelCase_							)
         UpperCAmelCase__							:				Union[str, Any]   				=  np.linalg.det(lowerCamelCase_							)

         self.assertAlmostEqual(lowerCamelCase_					,det_a * det_s							)




      def 		lowerCAmelCase__					(				self							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
         UpperCAmelCase__							:				Tuple   				=  np.array([[0, 3], [3, 0], [2, 3]]							)
         UpperCAmelCase__							:				Tuple   				=  np.array([[2, 1], [6, 3]]							)

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




      def 		lowerCAmelCase__					(				self							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				Dict   				=  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
         UpperCAmelCase__							:				str   				=  np.array([[0, 3], [3, 0], [2, 3]]							)
         UpperCAmelCase__							:				Any   				=  np.array([[2, 1, 3], [6, 3, 5]]							)

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


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  unittest.main()




