





"""simple docstring"""
class       a							:



 def __init__(     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= val
  lowerCAmelCase    				= None
  lowerCAmelCase    				= None



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  if self.val:
   if val < self.val:
    if self.left is None:
     lowerCAmelCase    				= Node(_snake_case     )
    else:
     self.left.insert(_snake_case     )
   elif val > self.val:
    if self.right is None:
     lowerCAmelCase    				= Node(_snake_case     )
    else:
     self.right.insert(_snake_case     )
  else:
   lowerCAmelCase    				= val


def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    List[str]  ,     _UpperCAmelCase					:    str			):
 # Recursive traversal
 if root:
  inorder(root.left  ,     _UpperCAmelCase			)
  res.append(root.val			)
  inorder(root.right  ,     _UpperCAmelCase			)

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    int			):
 # Build BST
 if len(_UpperCAmelCase			) == 0:
  return arr
 lowerCAmelCase    				= Node(arr[0]			)
 for i in range(1  ,     len(_UpperCAmelCase			)			):
  root.insert(arr[i]			)
 # Traverse BST in order.
 lowerCAmelCase    				= []
 inorder(_UpperCAmelCase  ,     _UpperCAmelCase			)
 return res


if __name__ == "__main__":
       print(tree_sort([10, 1, 3, 2, 9, 14, 13]))












"""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       a							(   a__  ):
 snake_case__             =       42







class       a							(   a__   , a__  ):



 @register_to_config
 def __init__(     self				,       _snake_case = 3				,       _snake_case = 3				,       _snake_case = ("DownEncoderBlock2D",)				,       _snake_case = ("UpDecoderBlock2D",)				,       _snake_case = (64,)				,       _snake_case = 1				,       _snake_case = "silu"				,       _snake_case = 3				,       _snake_case = 32				,       _snake_case = 2_56				,       _snake_case = 32				,       _snake_case = None				,       _snake_case = 0.18_215				,       _snake_case = "group"				,       ):







  """simple docstring"""






  super().__init__()

  # pass init params to Encoder
  lowerCAmelCase    				= Encoder(
      in_channels=_snake_case				,       out_channels=_snake_case				,       down_block_types=_snake_case				,       block_out_channels=_snake_case				,       layers_per_block=_snake_case				,       act_fn=_snake_case				,       norm_num_groups=_snake_case				,       double_z=_snake_case				,       )

  lowerCAmelCase    				= vq_embed_dim if vq_embed_dim is not None else latent_channels

  lowerCAmelCase    				= nn.Convad(_snake_case				,       _snake_case				,       1     )
  lowerCAmelCase    				= VectorQuantizer(_snake_case				,       _snake_case				,       beta=0.25				,       remap=_snake_case				,       sane_index_shape=_snake_case     )
  lowerCAmelCase    				= nn.Convad(_snake_case				,       _snake_case				,       1     )

  # pass init params to Decoder
  lowerCAmelCase    				= Decoder(
      in_channels=_snake_case				,       out_channels=_snake_case				,       up_block_types=_snake_case				,       block_out_channels=_snake_case				,       layers_per_block=_snake_case				,       act_fn=_snake_case				,       norm_num_groups=_snake_case				,       norm_type=_snake_case				,       )



 @apply_forward_hook
 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case = True     ):







  """simple docstring"""






  lowerCAmelCase    				= self.encoder(_snake_case     )
  lowerCAmelCase    				= self.quant_conv(_snake_case     )

  if not return_dict:
   return (h,)

  return VQEncoderOutput(latents=_snake_case     )



 @apply_forward_hook
 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case = False				,       _snake_case = True     ):







  """simple docstring"""






  if not force_not_quantize:
   lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= self.quantize(_snake_case     )
  else:
   lowerCAmelCase    				= h
  lowerCAmelCase    				= self.post_quant_conv(_snake_case     )
  lowerCAmelCase    				= self.decoder(_snake_case				,       quant if self.config.norm_type == 'spatial' else None     )

  if not return_dict:
   return (dec,)

  return DecoderOutput(sample=_snake_case     )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case = True     ):







  """simple docstring"""






  lowerCAmelCase    				= sample
  lowerCAmelCase    				= self.encode(_snake_case     ).latents
  lowerCAmelCase    				= self.decode(_snake_case     ).sample

  if not return_dict:
   return (dec,)

  return DecoderOutput(sample=_snake_case     )





