"""simple docstring"""



from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block






@dataclass
class  snake_case__      (       UpperCAmelCase_       ):
 _snake_case       :					Optional[Any]							  =						42






class  snake_case__      (       nn.Module       ):


 def __init__( self    ,		lowerCamelCase=3    ,		lowerCamelCase=3    ,		lowerCamelCase=("DownEncoderBlock2D",)    ,		lowerCamelCase=(64,)    ,		lowerCamelCase=2    ,		lowerCamelCase=32    ,		lowerCamelCase="silu"    ,		lowerCamelCase=True    ,		):
        super().__init__()
        __a       =    layers_per_block

        __a       =    torch.nn.Convad(
            __a    ,		block_out_channels[0]    ,		kernel_size=3    ,		stride=1    ,		padding=1    ,		)

        __a       =    None
        __a       =    nn.ModuleList([]						)

        # down
        __a       =    block_out_channels[0]
        for i, down_block_type in enumerate(__a						):
               __a       =    output_channel
               __a       =    block_out_channels[i]
               __a       =    i == len(__a						) - 1

               __a       =    get_down_block(
                   __a    ,		num_layers=self.layers_per_block    ,		in_channels=__a    ,		out_channels=__a    ,		add_downsample=not is_final_block    ,		resnet_eps=1E-6    ,		downsample_padding=0    ,		resnet_act_fn=__a    ,		resnet_groups=__a    ,		attention_head_dim=__a    ,		temb_channels=__a    ,		)
               self.down_blocks.append(__a						)

        # mid
        __a       =    UNetMidBlockaD(
            in_channels=block_out_channels[-1]    ,		resnet_eps=1E-6    ,		resnet_act_fn=__a    ,		output_scale_factor=1    ,		resnet_time_scale_shift="default"    ,		attention_head_dim=block_out_channels[-1]    ,		resnet_groups=__a    ,		temb_channels=__a    ,		)

        # out
        __a       =    nn.GroupNorm(num_channels=block_out_channels[-1]    ,		num_groups=__a    ,		eps=1E-6						)
        __a       =    nn.SiLU()

        __a       =    2 * out_channels if double_z else out_channels
        __a       =    nn.Convad(block_out_channels[-1]    ,		__a    ,		3    ,		padding=1						)

        __a       =    False






 def 						a__   ( self    ,		lowerCamelCase						):
        __a       =    x
        __a       =    self.conv_in(__a						)

        if self.training and self.gradient_checkpointing:

               def create_custom_forward(lowerCamelCase						):
                      def custom_forward(*lowerCamelCase						):
                             return module(*__a						)

                      return custom_forward

               # down
               if is_torch_version(">="    ,		"1.11.0"						):
                      for down_block in self.down_blocks:
                             __a       =    torch.utils.checkpoint.checkpoint(
                                 create_custom_forward(__a						)    ,		__a    ,		use_reentrant=__a						)
                      # middle
                      __a       =    torch.utils.checkpoint.checkpoint(
                          create_custom_forward(self.mid_block						)    ,		__a    ,		use_reentrant=__a						)
               else:
                      for down_block in self.down_blocks:
                             __a       =    torch.utils.checkpoint.checkpoint(create_custom_forward(__a						)    ,		__a						)
                      # middle
                      __a       =    torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block						)    ,		__a						)

        else:
               # down
               for down_block in self.down_blocks:
                      __a       =    down_block(__a						)

               # middle
               __a       =    self.mid_block(__a						)

        # post-process
        __a       =    self.conv_norm_out(__a						)
        __a       =    self.conv_act(__a						)
        __a       =    self.conv_out(__a						)

        return sample






class  snake_case__      (       nn.Module       ):


 def __init__( self    ,		lowerCamelCase=3    ,		lowerCamelCase=3    ,		lowerCamelCase=("UpDecoderBlock2D",)    ,		lowerCamelCase=(64,)    ,		lowerCamelCase=2    ,		lowerCamelCase=32    ,		lowerCamelCase="silu"    ,		lowerCamelCase="group"    ,		):
        super().__init__()
        __a       =    layers_per_block

        __a       =    nn.Convad(
            __a    ,		block_out_channels[-1]    ,		kernel_size=3    ,		stride=1    ,		padding=1    ,		)

        __a       =    None
        __a       =    nn.ModuleList([]						)

        __a       =    in_channels if norm_type == "spatial" else None

        # mid
        __a       =    UNetMidBlockaD(
            in_channels=block_out_channels[-1]    ,		resnet_eps=1E-6    ,		resnet_act_fn=__a    ,		output_scale_factor=1    ,		resnet_time_scale_shift="default" if norm_type == "group" else norm_type    ,		attention_head_dim=block_out_channels[-1]    ,		resnet_groups=__a    ,		temb_channels=__a    ,		)

        # up
        __a       =    list(reversed(__a						)						)
        __a       =    reversed_block_out_channels[0]
        for i, up_block_type in enumerate(__a						):
               __a       =    output_channel
               __a       =    reversed_block_out_channels[i]

               __a       =    i == len(__a						) - 1

               __a       =    get_up_block(
                   __a    ,		num_layers=self.layers_per_block + 1    ,		in_channels=__a    ,		out_channels=__a    ,		prev_output_channel=__a    ,		add_upsample=not is_final_block    ,		resnet_eps=1E-6    ,		resnet_act_fn=__a    ,		resnet_groups=__a    ,		attention_head_dim=__a    ,		temb_channels=__a    ,		resnet_time_scale_shift=__a    ,		)
               self.up_blocks.append(__a						)
               __a       =    output_channel

        # out
        if norm_type == "spatial":
               __a       =    SpatialNorm(block_out_channels[0]    ,		__a						)
        else:
               __a       =    nn.GroupNorm(num_channels=block_out_channels[0]    ,		num_groups=__a    ,		eps=1E-6						)
        __a       =    nn.SiLU()
        __a       =    nn.Convad(block_out_channels[0]    ,		__a    ,		3    ,		padding=1						)

        __a       =    False






 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase=None						):
        __a       =    z
        __a       =    self.conv_in(__a						)

        __a       =    next(iter(self.up_blocks.parameters()						)						).dtype
        if self.training and self.gradient_checkpointing:

               def create_custom_forward(lowerCamelCase						):
                      def custom_forward(*lowerCamelCase						):
                             return module(*__a						)

                      return custom_forward

               if is_torch_version(">="    ,		"1.11.0"						):
                      # middle
                      __a       =    torch.utils.checkpoint.checkpoint(
                          create_custom_forward(self.mid_block						)    ,		__a    ,		__a    ,		use_reentrant=__a						)
                      __a       =    sample.to(__a						)

                      # up
                      for up_block in self.up_blocks:
                             __a       =    torch.utils.checkpoint.checkpoint(
                                 create_custom_forward(__a						)    ,		__a    ,		__a    ,		use_reentrant=__a						)
               else:
                      # middle
                      __a       =    torch.utils.checkpoint.checkpoint(
                          create_custom_forward(self.mid_block						)    ,		__a    ,		__a						)
                      __a       =    sample.to(__a						)

                      # up
                      for up_block in self.up_blocks:
                             __a       =    torch.utils.checkpoint.checkpoint(create_custom_forward(__a						)    ,		__a    ,		__a						)
        else:
               # middle
               __a       =    self.mid_block(__a    ,		__a						)
               __a       =    sample.to(__a						)

               # up
               for up_block in self.up_blocks:
                      __a       =    up_block(__a    ,		__a						)

        # post-process
        if latent_embeds is None:
               __a       =    self.conv_norm_out(__a						)
        else:
               __a       =    self.conv_norm_out(__a    ,		__a						)
        __a       =    self.conv_act(__a						)
        __a       =    self.conv_out(__a						)

        return sample






class  snake_case__      (       nn.Module       ):


 def __init__( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase=None    ,		lowerCamelCase="random"    ,		lowerCamelCase=False    ,		lowerCamelCase=True						):
        super().__init__()
        __a       =    n_e
        __a       =    vq_embed_dim
        __a       =    beta
        __a       =    legacy

        __a       =    nn.Embedding(self.n_e    ,		self.vq_embed_dim						)
        self.embedding.weight.data.uniform_(-1.0 / self.n_e    ,		1.0 / self.n_e						)

        __a       =    remap
        if self.remap is not None:
               self.register_buffer("used"    ,		torch.tensor(np.load(self.remap						)						)						)
               __a       =    self.used.shape[0]
               __a       =    unknown_index  # "random" or "extra" or integer
               if self.unknown_index == "extra":
                      __a       =    self.re_embed
                      __a       =    self.re_embed + 1
               print(
                   F"Remapping {self.n_e} indices to {self.re_embed} indices. "
                   F"Using {self.unknown_index} for unknown indices."						)
        else:
               __a       =    n_e

        __a       =    sane_index_shape


 def 						a__   ( self    ,		lowerCamelCase						):
        __a       =    inds.shape
        assert len(__a						) > 1
        __a       =    inds.reshape(ishape[0]    ,		-1						)
        __a       =    self.used.to(__a						)
        __a       =    (inds[:, :, None] == used[None, None, ...]).long()
        __a       =    match.argmax(-1						)
        __a       =    match.sum(2						) < 1
        if self.unknown_index == "random":
               __a       =    torch.randint(0    ,		self.re_embed    ,		size=new[unknown].shape						).to(device=new.device						)
        else:
               __a       =    self.unknown_index
        return new.reshape(__a						)


 def 						a__   ( self    ,		lowerCamelCase						):
        __a       =    inds.shape
        assert len(__a						) > 1
        __a       =    inds.reshape(ishape[0]    ,		-1						)
        __a       =    self.used.to(__a						)
        if self.re_embed > self.used.shape[0]:  # extra token
               __a       =    0  # simply set to zero
        __a       =    torch.gather(used[None, :][inds.shape[0] * [0], :]    ,		1    ,		__a						)
        return back.reshape(__a						)


 def 						a__   ( self    ,		lowerCamelCase						):
        __a       =    z.permute(0    ,		2    ,		3    ,		1						).contiguous()
        __a       =    z.view(-1    ,		self.vq_embed_dim						)

        # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
        __a       =    torch.argmin(torch.cdist(__a    ,		self.embedding.weight						)    ,		dim=1						)

        __a       =    self.embedding(__a						).view(z.shape						)
        __a       =    None
        __a       =    None

        # compute loss for embedding
        if not self.legacy:
               __a       =    self.beta * torch.mean((z_q.detach() - z) ** 2						) + torch.mean((z_q - z.detach()) ** 2						)
        else:
               __a       =    torch.mean((z_q.detach() - z) ** 2						) + self.beta * torch.mean((z_q - z.detach()) ** 2						)

        # preserve gradients
        __a       =    z + (z_q - z).detach()

        # reshape back to match original input shape
        __a       =    z_q.permute(0    ,		3    ,		1    ,		2						).contiguous()

        if self.remap is not None:
               __a       =    min_encoding_indices.reshape(z.shape[0]    ,		-1						)  # add batch axis
               __a       =    self.remap_to_used(__a						)
               __a       =    min_encoding_indices.reshape(-1    ,		1						)  # flatten

        if self.sane_index_shape:
               __a       =    min_encoding_indices.reshape(z_q.shape[0]    ,		z_q.shape[2]    ,		z_q.shape[3]						)

        return z_q, loss, (perplexity, min_encodings, min_encoding_indices)






 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase						):



        if self.remap is not None:
               __a       =    indices.reshape(shape[0]    ,		-1						)  # add batch axis
               __a       =    self.unmap_to_all(__a						)
               __a       =    indices.reshape(-1						)  # flatten again

        # get quantized latent vectors
        __a       =    self.embedding(__a						)

        if shape is not None:
               __a       =    z_q.view(__a						)
               # reshape back to match original input shape
               __a       =    z_q.permute(0    ,		3    ,		1    ,		2						).contiguous()

        return z_q






class  snake_case__      (       UpperCAmelCase_       ):


 def __init__( self    ,		lowerCamelCase    ,		lowerCamelCase=False						):
        __a       =    parameters
        __a ,   __a       =    torch.chunk(__a    ,		2    ,		dim=1						)
        __a       =    torch.clamp(self.logvar    ,		-30.0    ,		20.0						)
        __a       =    deterministic
        __a       =    torch.exp(0.5 * self.logvar						)
        __a       =    torch.exp(self.logvar						)
        if self.deterministic:
               __a       =    __a       =    torch.zeros_like(
                   self.mean    ,		device=self.parameters.device    ,		dtype=self.parameters.dtype						)


 def 						a__   ( self    ,		lowerCamelCase = None						):
        __a       =    randn_tensor(
            self.mean.shape    ,		generator=__a    ,		device=self.parameters.device    ,		dtype=self.parameters.dtype						)
        __a       =    self.mean + self.std * sample
        return x


 def 						a__   ( self    ,		lowerCamelCase=None						):



        if self.deterministic:
               return torch.Tensor([0.0]						)
        else:
               if other is None:
                      return 0.5 * torch.sum(torch.pow(self.mean    ,		2						) + self.var - 1.0 - self.logvar    ,		dim=[1, 2, 3]						)
               else:
                      return 0.5 * torch.sum(
                          torch.pow(self.mean - other.mean    ,		2						) / other.var
                          + self.var / other.var
                          - 1.0
                          - self.logvar
                          + other.logvar    ,		dim=[1, 2, 3]    ,		)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase=[1, 2, 3]						):



        if self.deterministic:
               return torch.Tensor([0.0]						)
        __a       =    np.log(2.0 * np.pi						)
        return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean    ,		2						) / self.var    ,		dim=__a						)






 def 						a__   ( self						):
        return self.mean





import socket

def      __lowercase(						):


      """simple docstring"""



      lowerCamelCase             =		socket.socket(socket.AF_INET , socket.SOCK_STREAM			)
      lowerCamelCase             =		socket.gethostname()
      lowerCamelCase             =		12312

      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:
                  lowerCamelCase             =		sock.recv(1024			)
                  if not data:
                        break
                  out_file.write(UpperCAmelCase__			)

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


if __name__ == "__main__":
     main()