

from __future__ import annotations

from typing import Any



class       __snake_case  (    _A       ):
  pass






class       __snake_case  :

  def __init__(					self						,		_A):
      SCREAMING_SNAKE_CASE_									=						data
      SCREAMING_SNAKE_CASE_									=						None

  def __iter__(					self):
      SCREAMING_SNAKE_CASE_									=						self
      SCREAMING_SNAKE_CASE_									=						[]
      while node:
          if node in visited:
              raise ContainsLoopError
          visited.append(__lowerCamelCase)
          yield node.data
          SCREAMING_SNAKE_CASE_									=						node.next_node


  @property
  def 						lowerCAmelCase__     (					self):
      try:
          list(self)
          return False
      except ContainsLoopError:
          return True


if __name__ == "__main__":
       UpperCamelCase__  :      Tuple			=    Node(1)
       UpperCamelCase__  :      Optional[Any]			=    Node(2)
       UpperCamelCase__  :      Dict			=    Node(3)
       UpperCamelCase__  :      str			=    Node(4)
       print(root_node.has_loop)  # False
       UpperCamelCase__  :      Optional[int]			=    root_node.next_node
       print(root_node.has_loop)  # True

       UpperCamelCase__  :      Union[str, Any]			=    Node(5)
       UpperCamelCase__  :      Dict			=    Node(6)
       UpperCamelCase__  :      Optional[int]			=    Node(5)
       UpperCamelCase__  :      Dict			=    Node(6)
       print(root_node.has_loop)  # False

       UpperCamelCase__  :      Any			=    Node(1)
       print(root_node.has_loop)  # False








from dataclasses import dataclass
from typing import 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 .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer



@dataclass
class       __snake_case  (    lowerCAmelCase__       ):
  __lowerCAmelCase     :	torch.FloatTensor






class       __snake_case  (    lowerCAmelCase__     , lowerCAmelCase__       ):

  @register_to_config
  def __init__(					self						,		_A = 3						,		_A = 3						,		_A = ("DownEncoderBlock2D",)						,		_A = ("UpDecoderBlock2D",)						,		_A = (64,)						,		_A = 1						,		_A = "silu"						,		_A = 3						,		_A = 32						,		_A = 256						,		_A = 32						,		_A = None						,		_A = 0.1_8_2_1_5						,		_A = "group"						,		):
      super().__init__()

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

      SCREAMING_SNAKE_CASE_									=						vq_embed_dim if vq_embed_dim is not None else latent_channels

      SCREAMING_SNAKE_CASE_									=						nn.Convad(_A						,		_A						,		1)
      SCREAMING_SNAKE_CASE_									=						VectorQuantizer(_A						,		_A						,		beta=0.2_5						,		remap=_A						,		sane_index_shape=_A)
      SCREAMING_SNAKE_CASE_									=						nn.Convad(_A						,		_A						,		1)

      # pass init params to Decoder
      SCREAMING_SNAKE_CASE_									=						Decoder(
          in_channels=_A						,		out_channels=_A						,		up_block_types=_A						,		block_out_channels=_A						,		layers_per_block=_A						,		act_fn=_A						,		norm_num_groups=_A						,		norm_type=_A						,		)

  @apply_forward_hook
  def 						lowerCAmelCase__     (					self						,		_A						,		_A = True):
      SCREAMING_SNAKE_CASE_									=						self.encoder(_A)
      SCREAMING_SNAKE_CASE_									=						self.quant_conv(_A)

      if not return_dict:
          return (h,)

      return VQEncoderOutput(latents=_A)

  @apply_forward_hook
  def 						lowerCAmelCase__     (					self						,		_A						,		_A = False						,		_A = True):
      # also go through quantization layer
      if not force_not_quantize:
          SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						self.quantize(_A)
      else:
          SCREAMING_SNAKE_CASE_									=						h
      SCREAMING_SNAKE_CASE_									=						self.post_quant_conv(_A)
      SCREAMING_SNAKE_CASE_									=						self.decoder(_A						,		quant if self.config.norm_type == 'spatial' else None)

      if not return_dict:
          return (dec,)

      return DecoderOutput(sample=_A)


  def 						lowerCAmelCase__     (					self						,		_A						,		_A = True):
      SCREAMING_SNAKE_CASE_									=						sample
      SCREAMING_SNAKE_CASE_									=						self.encode(_A).latents
      SCREAMING_SNAKE_CASE_									=						self.decode(_A).sample

      if not return_dict:
          return (dec,)

      return DecoderOutput(sample=_A)


