






"""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    lowerCamelCase__   (			snake_case					):
    SCREAMING_SNAKE_CASE						=     42







class    lowerCamelCase__   (			nn.Module					):






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

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

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

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

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

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

          # out
          UpperCAmelCase							     =			nn.GroupNorm(num_channels=block_out_channels[-1]      ,num_groups=snake_case__      ,eps=1e-6   )
          UpperCAmelCase							     =			nn.SiLU()

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

          UpperCAmelCase							     =			False






    def 							_UpperCamelCase				(					self      ,A   ):
          UpperCAmelCase							     =			x
          UpperCAmelCase							     =			self.conv_in(snake_case__   )

          if self.training and self.gradient_checkpointing:

                def create_custom_forward(A   ):
                      def custom_forward(*A   ):
                            return module(*snake_case__   )

                      return custom_forward

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

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

                # middle
                UpperCAmelCase							     =			self.mid_block(snake_case__   )

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

          return sample







class    lowerCamelCase__   (			nn.Module					):






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

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

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

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

          # mid
          UpperCAmelCase							     =			UNetMidBlockaD(
              in_channels=block_out_channels[-1]      ,resnet_eps=1e-6      ,resnet_act_fn=snake_case__      ,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=snake_case__      ,temb_channels=snake_case__      ,)

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

                UpperCAmelCase							     =			i == len(snake_case__   ) - 1

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

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

          UpperCAmelCase							     =			False






    def 							_UpperCamelCase				(					self      ,A      ,A=None   ):
          UpperCAmelCase							     =			z
          UpperCAmelCase							     =			self.conv_in(snake_case__   )

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

                def create_custom_forward(A   ):
                      def custom_forward(*A   ):
                            return module(*snake_case__   )

                      return custom_forward

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

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

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

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

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

          return sample







class    lowerCamelCase__   (			nn.Module					):






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

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

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

          UpperCAmelCase							     =			sane_index_shape






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






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






    def 							_UpperCamelCase				(					self      ,A   ):
          UpperCAmelCase							     =			z.permute(0      ,2      ,3      ,1   ).contiguous()
          UpperCAmelCase							     =			z.view(-1      ,self.vq_embed_dim   )

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

          UpperCAmelCase							     =			self.embedding(snake_case__   ).view(z.shape   )
          UpperCAmelCase							     =			None
          UpperCAmelCase							     =			None

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

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

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

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

          if self.sane_index_shape:
                UpperCAmelCase							     =			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 							_UpperCamelCase				(					self      ,A      ,A   ):




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

          # get quantized latent vectors
          UpperCAmelCase							     =			self.embedding(snake_case__   )

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

          return z_q







class    lowerCamelCase__   (			snake_case					):






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






    def 							_UpperCamelCase				(					self      ,A = None   ):
          UpperCAmelCase							     =			randn_tensor(
              self.mean.shape      ,generator=snake_case__      ,device=self.parameters.device      ,dtype=self.parameters.dtype   )
          UpperCAmelCase							     =			self.mean + self.std * sample
          return x






    def 							_UpperCamelCase				(					self      ,A=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 							_UpperCamelCase				(					self      ,A      ,A=[1, 2, 3]   ):




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






    def 							_UpperCamelCase				(					self   ):
          return self.mean





'''simple docstring'''





def snake_case_     (					lowercase__		):
  return sum(i for i in range(1   ,       number // 2 + 1		) if number % i == 0		) == number


if __name__ == "__main__":
      print("""Program to check whether a number is a Perfect number or not...""")
      SCREAMING_SNAKE_CASE     =       int(input("""Enter number: """).strip())
      print(F'{number} is {"" if perfect(number) else "not "}a Perfect Number.')



