






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     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		torch.FloatTensor







class     _lowerCAmelCase(	UpperCAmelCase_							,     UpperCAmelCase_ ):





       """simple docstring"""







       @register_to_config
       def __init__( self  ,      _lowerCamelCase = 3  ,      _lowerCamelCase = 3  ,      _lowerCamelCase = ("DownEncoderBlock2D",)  ,      _lowerCamelCase = ("UpDecoderBlock2D",)  ,      _lowerCamelCase = (6_4,)  ,      _lowerCamelCase = 1  ,      _lowerCamelCase = "silu"  ,      _lowerCamelCase = 3  ,      _lowerCamelCase = 3_2  ,      _lowerCamelCase = 2_5_6  ,      _lowerCamelCase = 3_2  ,      _lowerCamelCase = None  ,      _lowerCamelCase = 0.1_8_2_1_5  ,      _lowerCamelCase = "group"  ,      ):
         super().__init__()

         # pass init params to Encoder
         UpperCamelCase_: 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_: Any   				=    vq_embed_dim if vq_embed_dim is not None else latent_channels

         UpperCamelCase_: List[str]   				=    nn.Convad(_lowerCamelCase  ,      _lowerCamelCase  ,      1							)
         UpperCamelCase_: Union[str, Any]   				=    VectorQuantizer(_lowerCamelCase  ,      _lowerCamelCase  ,      beta=0.2_5  ,      remap=_lowerCamelCase  ,      sane_index_shape=_lowerCamelCase							)
         UpperCamelCase_: List[Any]   				=    nn.Convad(_lowerCamelCase  ,      _lowerCamelCase  ,      1							)

         # pass init params to Decoder
         UpperCamelCase_: str   				=    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       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase = True							):
         UpperCamelCase_: int   				=    self.encoder(_lowerCamelCase							)
         UpperCamelCase_: Tuple   				=    self.quant_conv(_lowerCamelCase							)

         if not return_dict:
           return (h,)

         return VQEncoderOutput(latents=_lowerCamelCase							)





       @apply_forward_hook
       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase = False  ,      _lowerCamelCase = True							):
         # also go through quantization layer
         if not force_not_quantize:
           UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_: Optional[int]   				=    self.quantize(_lowerCamelCase							)
         else:
           UpperCamelCase_: str   				=    h
         UpperCamelCase_: List[str]   				=    self.post_quant_conv(_lowerCamelCase							)
         UpperCamelCase_: Tuple   				=    self.decoder(_lowerCamelCase  ,      quant if self.config.norm_type == 'spatial' else None							)

         if not return_dict:
           return (dec,)

         return DecoderOutput(sample=_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase = True							):
         UpperCamelCase_: int   				=    sample
         UpperCamelCase_: int   				=    self.encode(_lowerCamelCase							).latents
         UpperCamelCase_: Dict   				=    self.decode(_lowerCamelCase							).sample

         if not return_dict:
           return (dec,)

         return DecoderOutput(sample=_lowerCamelCase							)







import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor

def   snake_case (UpperCAmelCase__  )	->    tuple:
  return (data["data"], data["target"])

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    np.ndarray:
  UpperCamelCase_: Dict   				=    XGBRegressor(verbosity=0     ,   random_state=4_2  )
  xgb.fit(UpperCAmelCase__     ,   UpperCAmelCase__  )
  # Predict target for test data
  UpperCamelCase_: int   				=    xgb.predict(UpperCAmelCase__  )
  UpperCamelCase_: Any   				=    predictions.reshape(len(UpperCAmelCase__  )     ,   1  )
  return predictions

def   snake_case ()	->    None:
  UpperCamelCase_: Union[str, Any]   				=    fetch_california_housing()
  UpperCamelCase_				,UpperCamelCase_: Tuple   				=    data_handling(UpperCAmelCase__  )
  UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_: Optional[Any]   				=    train_test_split(
      UpperCAmelCase__     ,   UpperCAmelCase__     ,   test_size=0.25     ,   random_state=1  )
  UpperCamelCase_: Union[str, Any]   				=    xgboost(UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )
  # Error printing
  print(F'''Mean Absolute Error : {mean_absolute_error(UpperCAmelCase__     ,   UpperCAmelCase__  )}'''  )
  print(F'''Mean Square Error  : {mean_squared_error(UpperCAmelCase__     ,   UpperCAmelCase__  )}'''  )


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   main()