





'''simple docstring'''
from dataclasses import dataclass
from typing import Dict, 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 .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder

@dataclass
class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					"DiagonalGaussianDistribution"






class      snake_case     (    __lowerCamelCase			,       __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					List[Any]					=True






 @register_to_config
 def __init__(       self       :							Tuple							,	__A       :							int = 3							,	__A       :							int = 3							,	__A       :							Tuple[str] = ("DownEncoderBlock2D",)							,	__A       :							Tuple[str] = ("UpDecoderBlock2D",)							,	__A       :							Tuple[int] = (6_4,)							,	__A       :							int = 1							,	__A       :							str = "silu"							,	__A       :							int = 4							,	__A       :							int = 3_2							,	__A       :							int = 3_2							,	__A       :							float = 0.1_8215							,	):
        super().__init__()

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

        # pass init params to Decoder
        __UpperCamelCase       =  Decoder(
            in_channels=__A							,	out_channels=__A							,	up_block_types=__A							,	block_out_channels=__A							,	layers_per_block=__A							,	norm_num_groups=__A							,	act_fn=__A							,	)

        __UpperCamelCase       =  nn.Convad(2 * latent_channels							,	2 * latent_channels							,	1	)
        __UpperCamelCase       =  nn.Convad(__A							,	__A							,	1	)

        __UpperCamelCase       =  False
        __UpperCamelCase       =  False

        # only relevant if vae tiling is enabled
        __UpperCamelCase       =  self.config.sample_size
        __UpperCamelCase       =  (
            self.config.sample_size[0]
            if isinstance(self.config.sample_size							,	(list, tuple)	)
            else self.config.sample_size
        )
        __UpperCamelCase       =  int(sample_size / (2 ** (len(self.config.block_out_channels	) - 1))	)
        __UpperCamelCase       =  0.25






 def        _lowerCamelCase					(       self       :							List[str]							,	__A       :							List[str]							,	__A       :							Dict=False	):
        if isinstance(__A							,	(Encoder, Decoder)	):
               __UpperCamelCase       =  value






 def        _lowerCamelCase					(       self       :							str							,	__A       :							bool = True	):
        __UpperCamelCase       =  use_tiling






 def        _lowerCamelCase					(       self       :							Union[str, Any]	):
        self.enable_tiling(__A	)






 def        _lowerCamelCase					(       self       :							Optional[Any]	):
        __UpperCamelCase       =  True






 def        _lowerCamelCase					(       self       :							Dict	):
        __UpperCamelCase       =  False






 @property
 # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
 def        _lowerCamelCase					(       self       :							int	):
        __UpperCamelCase       =  {}

        def fn_recursive_add_processors(__A       :							str							,	__A       :							torch.nn.Module							,	__A       :							Dict[str, AttentionProcessor]	):
               if hasattr(__A							,	'set_processor'	):
                      __UpperCamelCase       =  module.processor

               for sub_name, child in module.named_children():
                      fn_recursive_add_processors(f'''{name}.{sub_name}'''							,	__A							,	__A	)

               return processors

        for name, module in self.named_children():
               fn_recursive_add_processors(__A							,	__A							,	__A	)

        return processors






 def        _lowerCamelCase					(       self       :							List[str]							,	__A       :							Union[AttentionProcessor, Dict[str, AttentionProcessor]]	):
        __UpperCamelCase       =  len(self.attn_processors.keys()	)

        if isinstance(__A							,	__A	) and len(__A	) != count:
               raise ValueError(
                   f'''A dict of processors was passed, but the number of processors {len(__A	)} does not match the'''
                   f''' number of attention layers: {count}. Please make sure to pass {count} processor classes.'''	)

        def fn_recursive_attn_processor(__A       :							str							,	__A       :							torch.nn.Module							,	__A       :							Tuple	):
               if hasattr(__A							,	'set_processor'	):
                      if not isinstance(__A							,	__A	):
                             module.set_processor(__A	)
                      else:
                             module.set_processor(processor.pop(f'''{name}.processor'''	)	)

               for sub_name, child in module.named_children():
                      fn_recursive_attn_processor(f'''{name}.{sub_name}'''							,	__A							,	__A	)

        for name, module in self.named_children():
               fn_recursive_attn_processor(__A							,	__A							,	__A	)






 def        _lowerCamelCase					(       self       :							Tuple	):
        self.set_attn_processor(AttnProcessor()	)






 @apply_forward_hook
 def        _lowerCamelCase					(       self       :							Any							,	__A       :							torch.FloatTensor							,	__A       :							bool = True	):
        if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
               return self.tiled_encode(__A							,	return_dict=__A	)

        if self.use_slicing and x.shape[0] > 1:
               __UpperCamelCase       =  [self.encoder(__A	) for x_slice in x.split(1	)]
               __UpperCamelCase       =  torch.cat(__A	)
        else:
               __UpperCamelCase       =  self.encoder(__A	)

        __UpperCamelCase       =  self.quant_conv(__A	)
        __UpperCamelCase       =  DiagonalGaussianDistribution(__A	)

        if not return_dict:
               return (posterior,)

        return AutoencoderKLOutput(latent_dist=__A	)






 def        _lowerCamelCase					(       self       :							List[str]							,	__A       :							torch.FloatTensor							,	__A       :							bool = True	):
        if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
               return self.tiled_decode(__A							,	return_dict=__A	)

        __UpperCamelCase       =  self.post_quant_conv(__A	)
        __UpperCamelCase       =  self.decoder(__A	)

        if not return_dict:
               return (dec,)

        return DecoderOutput(sample=__A	)






 @apply_forward_hook
 def        _lowerCamelCase					(       self       :							List[Any]							,	__A       :							torch.FloatTensor							,	__A       :							bool = True	):
        if self.use_slicing and z.shape[0] > 1:
               __UpperCamelCase       =  [self._decode(__A	).sample for z_slice in z.split(1	)]
               __UpperCamelCase       =  torch.cat(__A	)
        else:
               __UpperCamelCase       =  self._decode(__A	).sample

        if not return_dict:
               return (decoded,)

        return DecoderOutput(sample=__A	)






 def        _lowerCamelCase					(       self       :							Any							,	__A       :							Dict							,	__A       :							Union[str, Any]							,	__A       :							Optional[int]	):
        __UpperCamelCase       =  min(a.shape[2]							,	b.shape[2]							,	__A	)
        for y in range(__A	):
               __UpperCamelCase       =  a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
        return b






 def        _lowerCamelCase					(       self       :							Any							,	__A       :							Union[str, Any]							,	__A       :							Optional[int]							,	__A       :							Optional[Any]	):
        __UpperCamelCase       =  min(a.shape[3]							,	b.shape[3]							,	__A	)
        for x in range(__A	):
               __UpperCamelCase       =  a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
        return b






 def        _lowerCamelCase					(       self       :							Union[str, Any]							,	__A       :							torch.FloatTensor							,	__A       :							bool = True	):
        __UpperCamelCase       =  int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)	)
        __UpperCamelCase       =  int(self.tile_latent_min_size * self.tile_overlap_factor	)
        __UpperCamelCase       =  self.tile_latent_min_size - blend_extent

        # Split the image into 512x512 tiles and encode them separately.
        __UpperCamelCase       =  []
        for i in range(0							,	x.shape[2]							,	__A	):
               __UpperCamelCase       =  []
               for j in range(0							,	x.shape[3]							,	__A	):
                      __UpperCamelCase       =  x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
                      __UpperCamelCase       =  self.encoder(__A	)
                      __UpperCamelCase       =  self.quant_conv(__A	)
                      row.append(__A	)
               rows.append(__A	)
        __UpperCamelCase       =  []
        for i, row in enumerate(__A	):
               __UpperCamelCase       =  []
               for j, tile in enumerate(__A	):
                      # blend the above tile and the left tile
                      # to the current tile and add the current tile to the result row
                      if i > 0:
                             __UpperCamelCase       =  self.blend_v(rows[i - 1][j]							,	__A							,	__A	)
                      if j > 0:
                             __UpperCamelCase       =  self.blend_h(row[j - 1]							,	__A							,	__A	)
                      result_row.append(tile[:, :, :row_limit, :row_limit]	)
               result_rows.append(torch.cat(__A							,	dim=3	)	)

        __UpperCamelCase       =  torch.cat(__A							,	dim=2	)
        __UpperCamelCase       =  DiagonalGaussianDistribution(__A	)

        if not return_dict:
               return (posterior,)

        return AutoencoderKLOutput(latent_dist=__A	)






 def        _lowerCamelCase					(       self       :							int							,	__A       :							torch.FloatTensor							,	__A       :							bool = True	):
        __UpperCamelCase       =  int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)	)
        __UpperCamelCase       =  int(self.tile_sample_min_size * self.tile_overlap_factor	)
        __UpperCamelCase       =  self.tile_sample_min_size - blend_extent

        # Split z into overlapping 64x64 tiles and decode them separately.
        # The tiles have an overlap to avoid seams between tiles.
        __UpperCamelCase       =  []
        for i in range(0							,	z.shape[2]							,	__A	):
               __UpperCamelCase       =  []
               for j in range(0							,	z.shape[3]							,	__A	):
                      __UpperCamelCase       =  z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
                      __UpperCamelCase       =  self.post_quant_conv(__A	)
                      __UpperCamelCase       =  self.decoder(__A	)
                      row.append(__A	)
               rows.append(__A	)
        __UpperCamelCase       =  []
        for i, row in enumerate(__A	):
               __UpperCamelCase       =  []
               for j, tile in enumerate(__A	):
                      # blend the above tile and the left tile
                      # to the current tile and add the current tile to the result row
                      if i > 0:
                             __UpperCamelCase       =  self.blend_v(rows[i - 1][j]							,	__A							,	__A	)
                      if j > 0:
                             __UpperCamelCase       =  self.blend_h(row[j - 1]							,	__A							,	__A	)
                      result_row.append(tile[:, :, :row_limit, :row_limit]	)
               result_rows.append(torch.cat(__A							,	dim=3	)	)

        __UpperCamelCase       =  torch.cat(__A							,	dim=2	)
        if not return_dict:
               return (dec,)

        return DecoderOutput(sample=__A	)






 def        _lowerCamelCase					(       self       :							List[str]							,	__A       :							torch.FloatTensor							,	__A       :							bool = False							,	__A       :							bool = True							,	__A       :							Optional[torch.Generator] = None							,	):
        __UpperCamelCase       =  sample
        __UpperCamelCase       =  self.encode(__A	).latent_dist
        if sample_posterior:
               __UpperCamelCase       =  posterior.sample(generator=__A	)
        else:
               __UpperCamelCase       =  posterior.mode()
        __UpperCamelCase       =  self.decode(__A	).sample

        if not return_dict:
               return (dec,)

        return DecoderOutput(sample=__A	)








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 						__UpperCAmelCase			(     a_):
							return (data["data"], data["target"])





def 						__UpperCAmelCase			(     a_							,     a_							,     a_):
							snake_case_				=							XGBRegressor(verbosity=0							,     random_state=42)
							xgb.fit(a_							,     a_)
							# Predict target for test data
							snake_case_				=							xgb.predict(a_)
							snake_case_				=							predictions.reshape(len(a_)							,     1)
							return predictions





def 						__UpperCAmelCase			(     ):
							snake_case_				=							fetch_california_housing()
							snake_case_			,		snake_case_				=							data_handling(a_)
							snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							train_test_split(
							    a_							,     a_							,     test_size=0.25							,     random_state=1)
							snake_case_				=							xgboost(a_							,     a_							,     a_)
							# Error printing
							print(f'''Mean Absolute Error : {mean_absolute_error(a_							,     a_)}''')
							print(f'''Mean Square Error  : {mean_squared_error(a_							,     a_)}''')


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)
	main()





