


import baseaa
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		return baseaa.baaencode(string.encode('''utf-8'''     )     )
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		return baseaa.baadecode(SCREAMING_SNAKE_CASE     ).decode('''utf-8'''     )


if __name__ == "__main__":
			lowerCAmelCase       =							'Hello World!'
			lowerCAmelCase       =							baseaa_encode(test)
			print(encoded)

			lowerCAmelCase       =							baseaa_decode(encoded)
			print(decoded)






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							(			UpperCamelCase__			):
							_lowercase	:			torch.FloatTensor


class        _a							(			nn.Module			):

							def __init__(							self:	int    ,						UpperCamelCase_:	int=3    ,						UpperCamelCase_:	Optional[Any]=3    ,						UpperCamelCase_:	Union[str, Any]=("DownEncoderBlock2D",)    ,						UpperCamelCase_:	Optional[int]=(64,)    ,						UpperCamelCase_:	Tuple=2    ,						UpperCamelCase_:	Dict=32    ,						UpperCamelCase_:	Any="silu"    ,						UpperCamelCase_:	Optional[int]=True    ,						)			->   Union[str, Any]:





									"""simple docstring"""







									super().__init__()
									lowercase__            =      layers_per_block

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

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

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

											lowercase__            =      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
									lowercase__            =      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
									lowercase__            =      nn.GroupNorm(num_channels=block_out_channels[-1]    ,						num_groups=UpperCamelCase_    ,						eps=1E-6							)
									lowercase__            =      nn.SiLU()

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

									lowercase__            =      False


							def       lowerCamelCase_ (							self:	Optional[Any]    ,						UpperCamelCase_:	str							)			->   str:





									"""simple docstring"""







									lowercase__            =      x
									lowercase__            =      self.conv_in(UpperCamelCase_							)

									if self.training and self.gradient_checkpointing:

											def create_custom_forward(UpperCamelCase_:	Dict							):
													def custom_forward(*UpperCamelCase_:	List[str]							):
															return module(*UpperCamelCase_							)

													return custom_forward

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

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

											# middle
											lowercase__            =      self.mid_block(UpperCamelCase_							)

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

									return sample


class        _a							(			nn.Module			):

							def __init__(							self:	Dict    ,						UpperCamelCase_:	int=3    ,						UpperCamelCase_:	Optional[Any]=3    ,						UpperCamelCase_:	int=("UpDecoderBlock2D",)    ,						UpperCamelCase_:	Any=(64,)    ,						UpperCamelCase_:	Optional[Any]=2    ,						UpperCamelCase_:	List[Any]=32    ,						UpperCamelCase_:	Optional[Any]="silu"    ,						UpperCamelCase_:	Dict="group"    ,						)			->   Any:





									"""simple docstring"""







									super().__init__()
									lowercase__            =      layers_per_block

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

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

									lowercase__            =      in_channels if norm_type == '''spatial''' else None

									# mid
									lowercase__            =      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
									lowercase__            =      list(reversed(UpperCamelCase_							)							)
									lowercase__            =      reversed_block_out_channels[0]
									for i, up_block_type in enumerate(UpperCamelCase_							):
											lowercase__            =      output_channel
											lowercase__            =      reversed_block_out_channels[i]

											lowercase__            =      i == len(UpperCamelCase_							) - 1

											lowercase__            =      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_							)
											lowercase__            =      output_channel

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

									lowercase__            =      False


							def       lowerCamelCase_ (							self:	Optional[Any]    ,						UpperCamelCase_:	Union[str, Any]    ,						UpperCamelCase_:	List[str]=None							)			->   int:





									"""simple docstring"""







									lowercase__            =      z
									lowercase__            =      self.conv_in(UpperCamelCase_							)

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

											def create_custom_forward(UpperCamelCase_:	List[str]							):
													def custom_forward(*UpperCamelCase_:	int							):
															return module(*UpperCamelCase_							)

													return custom_forward

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

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

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

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

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

									return sample


class        _a							(			nn.Module			):

							def __init__(							self:	Tuple    ,						UpperCamelCase_:	List[str]    ,						UpperCamelCase_:	Optional[Any]    ,						UpperCamelCase_:	Optional[Any]    ,						UpperCamelCase_:	Optional[int]=None    ,						UpperCamelCase_:	List[str]="random"    ,						UpperCamelCase_:	Union[str, Any]=False    ,						UpperCamelCase_:	Dict=True							)			->   List[Any]:





									"""simple docstring"""







									super().__init__()
									lowercase__            =      n_e
									lowercase__            =      vq_embed_dim
									lowercase__            =      beta
									lowercase__            =      legacy

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

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

									lowercase__            =      sane_index_shape

							def       lowerCamelCase_ (							self:	Optional[int]    ,						UpperCamelCase_:	Any							)			->   Dict:





									"""simple docstring"""







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

							def       lowerCamelCase_ (							self:	Union[str, Any]    ,						UpperCamelCase_:	Tuple							)			->   str:





									"""simple docstring"""







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

							def       lowerCamelCase_ (							self:	List[Any]    ,						UpperCamelCase_:	Any							)			->   Tuple:





									"""simple docstring"""







									lowercase__            =      z.permute(0    ,						2    ,						3    ,						1							).contiguous()
									lowercase__            =      z.view(-1    ,						self.vq_embed_dim							)

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

									lowercase__            =      self.embedding(UpperCamelCase_							).view(z.shape							)
									lowercase__            =      None
									lowercase__            =      None

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

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

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

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

									if self.sane_index_shape:
											lowercase__            =      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       lowerCamelCase_ (							self:	List[Any]    ,						UpperCamelCase_:	Tuple    ,						UpperCamelCase_:	List[Any]							)			->   List[str]:





									"""simple docstring"""







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

									# get quantized latent vectors
									lowercase__            =      self.embedding(UpperCamelCase_							)

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

									return z_q


class        _a							(			UpperCamelCase__			):

							def __init__(							self:	Union[str, Any]    ,						UpperCamelCase_:	Any    ,						UpperCamelCase_:	List[Any]=False							)			->   str:





									"""simple docstring"""







									lowercase__            =      parameters
									lowercase__     ,		lowercase__            =      torch.chunk(UpperCamelCase_    ,						2    ,						dim=1							)
									lowercase__            =      torch.clamp(self.logvar    ,						-30.0    ,						20.0							)
									lowercase__            =      deterministic
									lowercase__            =      torch.exp(0.5 * self.logvar							)
									lowercase__            =      torch.exp(self.logvar							)
									if self.deterministic:
											lowercase__            =      lowercase__            =      torch.zeros_like(
											    self.mean    ,						device=self.parameters.device    ,						dtype=self.parameters.dtype							)

							def       lowerCamelCase_ (							self:	Any    ,						UpperCamelCase_:	Optional[torch.Generator] = None							)			->   torch.FloatTensor:





									"""simple docstring"""







									lowercase__            =      randn_tensor(
									    self.mean.shape    ,						generator=UpperCamelCase_    ,						device=self.parameters.device    ,						dtype=self.parameters.dtype							)
									lowercase__            =      self.mean + self.std * sample
									return x

							def       lowerCamelCase_ (							self:	List[str]    ,						UpperCamelCase_:	str=None							)			->   Tuple:





									"""simple docstring"""







									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       lowerCamelCase_ (							self:	int    ,						UpperCamelCase_:	Any    ,						UpperCamelCase_:	Any=[1, 2, 3]							)			->   Tuple:





									"""simple docstring"""







									if self.deterministic:
											return torch.Tensor([0.0]							)
									lowercase__            =      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       lowerCamelCase_ (							self:	str							)			->   Any:





									"""simple docstring"""







									return self.mean


