





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase				)						->  int:
				while second != 0:
								lowerCamelCase	 =first & second
								first ^= second
								lowerCamelCase	 =c << 1
				return first


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							UpperCAmelCase__						:			Union[str, Any]									=int(input('''Enter the first number: ''').strip())
							UpperCAmelCase__						:			Union[str, Any]									=int(input('''Enter the second number: ''').strip())
							print(F"{add(first, second) = }")












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       __A     (						a    ):
				__A      =				42




class       __A     (						nn.Module    ):


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

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

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

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

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

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

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

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

								lowerCamelCase	 =False


				def _snake_case       (		self   , UpperCAmelCase_							):
								lowerCamelCase	 =x
								lowerCamelCase	 =self.conv_in(UpperCAmelCase_							)

								if self.training and self.gradient_checkpointing:

												def create_custom_forward(UpperCAmelCase_							):
																def custom_forward(*UpperCAmelCase_							):
																				return module(*UpperCAmelCase_							)

																return custom_forward

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

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

												# middle
												lowerCamelCase	 =self.mid_block(UpperCAmelCase_							)

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

								return sample




class       __A     (						nn.Module    ):


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

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

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

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

								# mid
								lowerCamelCase	 =UNetMidBlockaD(
								    in_channels=block_out_channels[-1]   , resnet_eps=1E-6   , resnet_act_fn=UpperCAmelCase_   , 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=UpperCAmelCase_   , temb_channels=UpperCAmelCase_   , )

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

												lowerCamelCase	 =i == len(UpperCAmelCase_							) - 1

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

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

								lowerCamelCase	 =False


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_=None							):
								lowerCamelCase	 =z
								lowerCamelCase	 =self.conv_in(UpperCAmelCase_							)

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

												def create_custom_forward(UpperCAmelCase_							):
																def custom_forward(*UpperCAmelCase_							):
																				return module(*UpperCAmelCase_							)

																return custom_forward

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

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

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

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

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

								return sample




class       __A     (						nn.Module    ):


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

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

								lowerCamelCase	 =remap
								if self.remap is not None:
												self.register_buffer("""used"""   , torch.tensor(np.load(self.remap							)							)							)
												lowerCamelCase	 =self.used.shape[0]
												lowerCamelCase	 =unknown_index  # "random" or "extra" or integer
												if self.unknown_index == "extra":
																lowerCamelCase	 =self.re_embed
																lowerCamelCase	 =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:
												lowerCamelCase	 =n_e

								lowerCamelCase	 =sane_index_shape


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


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


				def _snake_case       (		self   , UpperCAmelCase_							):
								# reshape z -> (batch, height, width, channel) and flatten
								lowerCamelCase	 =z.permute(0   , 2   , 3   , 1							).contiguous()
								lowerCamelCase	 =z.view(-1   , self.vq_embed_dim							)

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

								lowerCamelCase	 =self.embedding(UpperCAmelCase_							).view(z.shape							)
								lowerCamelCase	 =None
								lowerCamelCase	 =None

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

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

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

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

								if self.sane_index_shape:
												lowerCamelCase	 =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 _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_							):
								# shape specifying (batch, height, width, channel)
								if self.remap is not None:
												lowerCamelCase	 =indices.reshape(shape[0]   , -1							)  # add batch axis
												lowerCamelCase	 =self.unmap_to_all(UpperCAmelCase_							)
												lowerCamelCase	 =indices.reshape(-1							)  # flatten again

								# get quantized latent vectors
								lowerCamelCase	 =self.embedding(UpperCAmelCase_							)

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

								return z_q




class       __A     (						a    ):


				def __init__(		self   , UpperCAmelCase_   , UpperCAmelCase_=False							):
								lowerCamelCase	 =parameters
								lowerCamelCase ,  lowerCamelCase	 =torch.chunk(UpperCAmelCase_   , 2   , dim=1							)
								lowerCamelCase	 =torch.clamp(self.logvar   , -3_0.0   , 2_0.0							)
								lowerCamelCase	 =deterministic
								lowerCamelCase	 =torch.exp(0.5 * self.logvar							)
								lowerCamelCase	 =torch.exp(self.logvar							)
								if self.deterministic:
												lowerCamelCase	 =lowerCamelCase	 =torch.zeros_like(
												    self.mean   , device=self.parameters.device   , dtype=self.parameters.dtype							)


				def _snake_case       (		self   , UpperCAmelCase_ = None							):
								# make sure sample is on the same device as the parameters and has same dtype
								lowerCamelCase	 =randn_tensor(
								    self.mean.shape   , generator=UpperCAmelCase_   , device=self.parameters.device   , dtype=self.parameters.dtype							)
								lowerCamelCase	 =self.mean + self.std * sample
								return x


				def _snake_case       (		self   , UpperCAmelCase_=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 _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_=[1, 2, 3]							):
								if self.deterministic:
												return torch.Tensor([0.0]							)
								lowerCamelCase	 =np.log(2.0 * np.pi							)
								return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean   , 2							) / self.var   , dim=UpperCAmelCase_							)


				def _snake_case       (		self							):
								return self.mean





