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_		):
   lowerCAmelCase_														=							42







class __snake_case				(						lowerCamelCase_   ,		lowerCamelCase_		):
   lowerCAmelCase_														=							True




   @register_to_config
   def __init__(    self			:				Optional[int]       ,	_lowercase			:				int = 3       ,	_lowercase			:				int = 3       ,	_lowercase			:				Tuple[str] = ("DownEncoderBlock2D",)       ,	_lowercase			:				Tuple[str] = ("UpDecoderBlock2D",)       ,	_lowercase			:				Tuple[int] = (64,)       ,	_lowercase			:				int = 1       ,	_lowercase			:				str = "silu"       ,	_lowercase			:				int = 4       ,	_lowercase			:				int = 32       ,	_lowercase			:				int = 32       ,	_lowercase			:				float = 0.1_82_15       ,	):



     """simple docstring"""






     super().__init__()

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

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

     SCREAMING_SNAKE_CASE__										=      nn.Convad(2 * latent_channels       ,	2 * latent_channels       ,	1      )
     SCREAMING_SNAKE_CASE__										=      nn.Convad(_lowercase       ,	_lowercase       ,	1      )

     SCREAMING_SNAKE_CASE__										=      False
     SCREAMING_SNAKE_CASE__										=      False

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




   def 	__a						(    self			:				Tuple       ,	_lowercase			:				List[Any]       ,	_lowercase			:				Tuple=False      ):



     """simple docstring"""






     if isinstance(_lowercase       ,	(Encoder, Decoder)      ):
       SCREAMING_SNAKE_CASE__										=      value




   def 	__a						(    self			:				Tuple       ,	_lowercase			:				bool = True      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      use_tiling




   def 	__a						(    self			:				int      ):



     """simple docstring"""






     self.enable_tiling(_lowercase      )




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      True




   def 	__a						(    self			:				Union[str, Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      False




   @property
   # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      {}

     def fn_recursive_add_processors(_lowercase			:				str       ,	_lowercase			:				torch.nn.Module       ,	_lowercase			:				Dict[str, AttentionProcessor]      ):
       if hasattr(_lowercase       ,	"""set_processor"""      ):
         SCREAMING_SNAKE_CASE__										=      module.processor

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

       return processors

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

     return processors




   def 	__a						(    self			:				Tuple       ,	_lowercase			:				Union[AttentionProcessor, Dict[str, AttentionProcessor]]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      len(self.attn_processors.keys()      )

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

     def fn_recursive_attn_processor(_lowercase			:				str       ,	_lowercase			:				torch.nn.Module       ,	_lowercase			:				List[str]      ):
       if hasattr(_lowercase       ,	"""set_processor"""      ):
         if not isinstance(_lowercase       ,	_lowercase      ):
           module.set_processor(_lowercase      )
         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}"""       ,	_lowercase       ,	_lowercase      )

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




   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     self.set_attn_processor(AttnProcessor()      )




   @apply_forward_hook
   def 	__a						(    self			:				Optional[int]       ,	_lowercase			:				torch.FloatTensor       ,	_lowercase			:				bool = True      ):



     """simple docstring"""






     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(_lowercase       ,	return_dict=_lowercase      )

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

     SCREAMING_SNAKE_CASE__										=      self.quant_conv(_lowercase      )
     SCREAMING_SNAKE_CASE__										=      DiagonalGaussianDistribution(_lowercase      )

     if not return_dict:
       return (posterior,)

     return AutoencoderKLOutput(latent_dist=_lowercase      )




   def 	__a						(    self			:				Optional[Any]       ,	_lowercase			:				torch.FloatTensor       ,	_lowercase			:				bool = True      ):



     """simple docstring"""






     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(_lowercase       ,	return_dict=_lowercase      )

     SCREAMING_SNAKE_CASE__										=      self.post_quant_conv(_lowercase      )
     SCREAMING_SNAKE_CASE__										=      self.decoder(_lowercase      )

     if not return_dict:
       return (dec,)

     return DecoderOutput(sample=_lowercase      )




   @apply_forward_hook
   def 	__a						(    self			:				int       ,	_lowercase			:				torch.FloatTensor       ,	_lowercase			:				bool = True      ):



     """simple docstring"""






     if self.use_slicing and z.shape[0] > 1:
       SCREAMING_SNAKE_CASE__										=      [self._decode(_lowercase      ).sample for z_slice in z.split(1      )]
       SCREAMING_SNAKE_CASE__										=      torch.cat(_lowercase      )
     else:
       SCREAMING_SNAKE_CASE__										=      self._decode(_lowercase      ).sample

     if not return_dict:
       return (decoded,)

     return DecoderOutput(sample=_lowercase      )




   def 	__a						(    self			:				List[str]       ,	_lowercase			:				Any       ,	_lowercase			:				int       ,	_lowercase			:				List[str]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      min(a.shape[2]       ,	b.shape[2]       ,	_lowercase      )
     for y in range(_lowercase      ):
       SCREAMING_SNAKE_CASE__										=      a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
     return b




   def 	__a						(    self			:				Dict       ,	_lowercase			:				str       ,	_lowercase			:				str       ,	_lowercase			:				Optional[int]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      min(a.shape[3]       ,	b.shape[3]       ,	_lowercase      )
     for x in range(_lowercase      ):
       SCREAMING_SNAKE_CASE__										=      a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
     return b




   def 	__a						(    self			:				List[str]       ,	_lowercase			:				torch.FloatTensor       ,	_lowercase			:				bool = True      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)      )
     SCREAMING_SNAKE_CASE__										=      int(self.tile_latent_min_size * self.tile_overlap_factor      )
     SCREAMING_SNAKE_CASE__										=      self.tile_latent_min_size - blend_extent

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

     SCREAMING_SNAKE_CASE__										=      torch.cat(_lowercase       ,	dim=2      )
     SCREAMING_SNAKE_CASE__										=      DiagonalGaussianDistribution(_lowercase      )

     if not return_dict:
       return (posterior,)

     return AutoencoderKLOutput(latent_dist=_lowercase      )




   def 	__a						(    self			:				Optional[Any]       ,	_lowercase			:				torch.FloatTensor       ,	_lowercase			:				bool = True      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)      )
     SCREAMING_SNAKE_CASE__										=      int(self.tile_sample_min_size * self.tile_overlap_factor      )
     SCREAMING_SNAKE_CASE__										=      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.
     SCREAMING_SNAKE_CASE__										=      []
     for i in range(0       ,	z.shape[2]       ,	_lowercase      ):
       SCREAMING_SNAKE_CASE__										=      []
       for j in range(0       ,	z.shape[3]       ,	_lowercase      ):
         SCREAMING_SNAKE_CASE__										=      z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
         SCREAMING_SNAKE_CASE__										=      self.post_quant_conv(_lowercase      )
         SCREAMING_SNAKE_CASE__										=      self.decoder(_lowercase      )
         row.append(_lowercase      )
       rows.append(_lowercase      )
     SCREAMING_SNAKE_CASE__										=      []
     for i, row in enumerate(_lowercase      ):
       SCREAMING_SNAKE_CASE__										=      []
       for j, tile in enumerate(_lowercase      ):
         # blend the above tile and the left tile
         # to the current tile and add the current tile to the result row
         if i > 0:
           SCREAMING_SNAKE_CASE__										=      self.blend_v(rows[i - 1][j]       ,	_lowercase       ,	_lowercase      )
         if j > 0:
           SCREAMING_SNAKE_CASE__										=      self.blend_h(row[j - 1]       ,	_lowercase       ,	_lowercase      )
         result_row.append(tile[:, :, :row_limit, :row_limit]      )
       result_rows.append(torch.cat(_lowercase       ,	dim=3      )      )

     SCREAMING_SNAKE_CASE__										=      torch.cat(_lowercase       ,	dim=2      )
     if not return_dict:
       return (dec,)

     return DecoderOutput(sample=_lowercase      )




   def 	__a						(    self			:				Tuple       ,	_lowercase			:				torch.FloatTensor       ,	_lowercase			:				bool = False       ,	_lowercase			:				bool = True       ,	_lowercase			:				Optional[torch.Generator] = None       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      sample
     SCREAMING_SNAKE_CASE__										=      self.encode(_lowercase      ).latent_dist
     if sample_posterior:
       SCREAMING_SNAKE_CASE__										=      posterior.sample(generator=_lowercase      )
     else:
       SCREAMING_SNAKE_CASE__										=      posterior.mode()
     SCREAMING_SNAKE_CASE__										=      self.decode(_lowercase      ).sample

     if not return_dict:
       return (dec,)

     return DecoderOutput(sample=_lowercase      )

import socket



def 	__SCREAMING_SNAKE_CASE  (					)							->					Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      socket.socket(socket.AF_INET ,     socket.SOCK_STREAM	)
  SCREAMING_SNAKE_CASE__										=      socket.gethostname()
  SCREAMING_SNAKE_CASE__										=      1_23_12

  sock.connect((host, port)	)
  sock.send(B"""Hello server!"""	)

  with open("""Received_file""" ,     """wb"""	) as out_file:
    print("""File opened"""	)
    print("""Receiving data..."""	)
    while True:
      SCREAMING_SNAKE_CASE__										=      sock.recv(10_24	)
      if not data:
        break
      out_file.write(__UpperCamelCase	)

  print("""Successfully received the file"""	)
  sock.close()
  print("""Connection closed"""	)


if __name__ == "__main__":
   main()
