
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      __A(    _A			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      42






class      __A(    _A    ,				_A			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      True







  @register_to_config
  def __init__(self	,      SCREAMING_SNAKE_CASE_ = 3	,      SCREAMING_SNAKE_CASE_ = 3	,      SCREAMING_SNAKE_CASE_ = ("DownEncoderBlock2D",)	,      SCREAMING_SNAKE_CASE_ = ("UpDecoderBlock2D",)	,      SCREAMING_SNAKE_CASE_ = (64,)	,      SCREAMING_SNAKE_CASE_ = 1	,      SCREAMING_SNAKE_CASE_ = "silu"	,      SCREAMING_SNAKE_CASE_ = 4	,      SCREAMING_SNAKE_CASE_ = 32	,      SCREAMING_SNAKE_CASE_ = 32	,      SCREAMING_SNAKE_CASE_ = 0.1_8215	,      ):
       super().__init__()

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

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

       UpperCamelCase__				=    nn.Convad(2 * latent_channels	,      2 * latent_channels	,      1						)
       UpperCamelCase__				=    nn.Convad(__SCREAMING_SNAKE_CASE	,      __SCREAMING_SNAKE_CASE	,      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 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=False						):
       if isinstance(__SCREAMING_SNAKE_CASE	,      (Encoder, Decoder)						):
            UpperCamelCase__				=    value







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_ = True						):
       UpperCamelCase__				=    use_tiling







  def 	UpperCAmelCase_ (self						):
       self.enable_tiling(__SCREAMING_SNAKE_CASE						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    True







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    False







  @property
  # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    {}

       def fn_recursive_add_processors(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
            if hasattr(__SCREAMING_SNAKE_CASE	,      """set_processor"""						):
                 UpperCamelCase__				=    module.processor

            for sub_name, child in module.named_children():
                 fn_recursive_add_processors(F"{name}.{sub_name}"	,      __SCREAMING_SNAKE_CASE	,      __SCREAMING_SNAKE_CASE						)

            return processors

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

       return processors







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    len(self.attn_processors.keys()						)

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

       def fn_recursive_attn_processor(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
            if hasattr(__SCREAMING_SNAKE_CASE	,      """set_processor"""						):
                 if not isinstance(__SCREAMING_SNAKE_CASE	,      __SCREAMING_SNAKE_CASE						):
                      module.set_processor(__SCREAMING_SNAKE_CASE						)
                 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}"	,      __SCREAMING_SNAKE_CASE	,      __SCREAMING_SNAKE_CASE						)

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







  def 	UpperCAmelCase_ (self						):
       self.set_attn_processor(AttnProcessor()						)







  @apply_forward_hook
  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_ = 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(__SCREAMING_SNAKE_CASE	,      return_dict=__SCREAMING_SNAKE_CASE						)

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

       UpperCamelCase__				=    self.quant_conv(__SCREAMING_SNAKE_CASE						)
       UpperCamelCase__				=    DiagonalGaussianDistribution(__SCREAMING_SNAKE_CASE						)

       if not return_dict:
            return (posterior,)

       return AutoencoderKLOutput(latent_dist=__SCREAMING_SNAKE_CASE						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_ = 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(__SCREAMING_SNAKE_CASE	,      return_dict=__SCREAMING_SNAKE_CASE						)

       UpperCamelCase__				=    self.post_quant_conv(__SCREAMING_SNAKE_CASE						)
       UpperCamelCase__				=    self.decoder(__SCREAMING_SNAKE_CASE						)

       if not return_dict:
            return (dec,)

       return DecoderOutput(sample=__SCREAMING_SNAKE_CASE						)







  @apply_forward_hook
  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_ = True						):
       if self.use_slicing and z.shape[0] > 1:
            UpperCamelCase__				=    [self._decode(__SCREAMING_SNAKE_CASE						).sample for z_slice in z.split(1						)]
            UpperCamelCase__				=    torch.cat(__SCREAMING_SNAKE_CASE						)
       else:
            UpperCamelCase__				=    self._decode(__SCREAMING_SNAKE_CASE						).sample

       if not return_dict:
            return (decoded,)

       return DecoderOutput(sample=__SCREAMING_SNAKE_CASE						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    min(a.shape[2]	,      b.shape[2]	,      __SCREAMING_SNAKE_CASE						)
       for y in range(__SCREAMING_SNAKE_CASE						):
            UpperCamelCase__				=    a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
       return b







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    min(a.shape[3]	,      b.shape[3]	,      __SCREAMING_SNAKE_CASE						)
       for x in range(__SCREAMING_SNAKE_CASE						):
            UpperCamelCase__				=    a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
       return b







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_ = 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]	,      __SCREAMING_SNAKE_CASE						):
            UpperCamelCase__				=    []
            for j in range(0	,      x.shape[3]	,      __SCREAMING_SNAKE_CASE						):
                 UpperCamelCase__				=    x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
                 UpperCamelCase__				=    self.encoder(__SCREAMING_SNAKE_CASE						)
                 UpperCamelCase__				=    self.quant_conv(__SCREAMING_SNAKE_CASE						)
                 row.append(__SCREAMING_SNAKE_CASE						)
            rows.append(__SCREAMING_SNAKE_CASE						)
       UpperCamelCase__				=    []
       for i, row in enumerate(__SCREAMING_SNAKE_CASE						):
            UpperCamelCase__				=    []
            for j, tile in enumerate(__SCREAMING_SNAKE_CASE						):
                 # 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]	,      __SCREAMING_SNAKE_CASE	,      __SCREAMING_SNAKE_CASE						)
                 if j > 0:
                      UpperCamelCase__				=    self.blend_h(row[j - 1]	,      __SCREAMING_SNAKE_CASE	,      __SCREAMING_SNAKE_CASE						)
                 result_row.append(tile[:, :, :row_limit, :row_limit]						)
            result_rows.append(torch.cat(__SCREAMING_SNAKE_CASE	,      dim=3						)						)

       UpperCamelCase__				=    torch.cat(__SCREAMING_SNAKE_CASE	,      dim=2						)
       UpperCamelCase__				=    DiagonalGaussianDistribution(__SCREAMING_SNAKE_CASE						)

       if not return_dict:
            return (posterior,)

       return AutoencoderKLOutput(latent_dist=__SCREAMING_SNAKE_CASE						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_ = 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]	,      __SCREAMING_SNAKE_CASE						):
            UpperCamelCase__				=    []
            for j in range(0	,      z.shape[3]	,      __SCREAMING_SNAKE_CASE						):
                 UpperCamelCase__				=    z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
                 UpperCamelCase__				=    self.post_quant_conv(__SCREAMING_SNAKE_CASE						)
                 UpperCamelCase__				=    self.decoder(__SCREAMING_SNAKE_CASE						)
                 row.append(__SCREAMING_SNAKE_CASE						)
            rows.append(__SCREAMING_SNAKE_CASE						)
       UpperCamelCase__				=    []
       for i, row in enumerate(__SCREAMING_SNAKE_CASE						):
            UpperCamelCase__				=    []
            for j, tile in enumerate(__SCREAMING_SNAKE_CASE						):
                 # 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]	,      __SCREAMING_SNAKE_CASE	,      __SCREAMING_SNAKE_CASE						)
                 if j > 0:
                      UpperCamelCase__				=    self.blend_h(row[j - 1]	,      __SCREAMING_SNAKE_CASE	,      __SCREAMING_SNAKE_CASE						)
                 result_row.append(tile[:, :, :row_limit, :row_limit]						)
            result_rows.append(torch.cat(__SCREAMING_SNAKE_CASE	,      dim=3						)						)

       UpperCamelCase__				=    torch.cat(__SCREAMING_SNAKE_CASE	,      dim=2						)
       if not return_dict:
            return (dec,)

       return DecoderOutput(sample=__SCREAMING_SNAKE_CASE						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_ = False	,      SCREAMING_SNAKE_CASE_ = True	,      SCREAMING_SNAKE_CASE_ = None	,      ):
       UpperCamelCase__				=    sample
       UpperCamelCase__				=    self.encode(__SCREAMING_SNAKE_CASE						).latent_dist
       if sample_posterior:
            UpperCamelCase__				=    posterior.sample(generator=__SCREAMING_SNAKE_CASE						)
       else:
            UpperCamelCase__				=    posterior.mode()
       UpperCamelCase__				=    self.decode(__SCREAMING_SNAKE_CASE						).sample

       if not return_dict:
            return (dec,)

       return DecoderOutput(sample=__SCREAMING_SNAKE_CASE						)







from PIL import Image






def 						__magic_name__			(   __a      :	Image						):



     '''simple docstring'''




     UpperCamelCase__		,	UpperCamelCase__				=    image.size
     UpperCamelCase__				=    0
     UpperCamelCase__				=    image.load()
     for i in range(__a						):
          for j in range(__a						):
               UpperCamelCase__				=    pixels[j, i]
               mean += pixel
     mean //= width * height

     for j in range(__a						):
          for i in range(__a						):
               UpperCamelCase__				=    255 if pixels[i, j] > mean else 0
     return image


if __name__ == "__main__":
    lowerCamelCase_						     =					mean_threshold(Image.open('''path_to_image''').convert('''L'''))
    image.save('''output_image_path''')





