


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 			_lowercase (							lowercase__):


       """simple docstring"""




       A__											=			42







class 			_lowercase (							lowercase__							,  lowercase__):


       """simple docstring"""




       @register_to_config
       def __init__(   self							:			List[str]					,      __lowerCamelCase							:			int = 3					,      __lowerCamelCase							:			int = 3					,      __lowerCamelCase							:			Tuple[str] = ("DownEncoderBlock2D",)					,      __lowerCamelCase							:			Tuple[str] = ("UpDecoderBlock2D",)					,      __lowerCamelCase							:			Tuple[int] = (64,)					,      __lowerCamelCase							:			int = 1					,      __lowerCamelCase							:			str = "silu"					,      __lowerCamelCase							:			int = 3					,      __lowerCamelCase							:			int = 32					,      __lowerCamelCase							:			int = 256					,      __lowerCamelCase							:			int = 32					,      __lowerCamelCase							:			Optional[int] = None					,      __lowerCamelCase							:			float = 0.1_8_2_1_5					,      __lowerCamelCase							:			str = "group"					,      ):


             '''simple docstring'''



             super().__init__()

             # pass init params to Encoder
             lowerCamelCase__		:				Union[str, Any]    =  Encoder(
                 in_channels=__lowerCamelCase					,      out_channels=__lowerCamelCase					,      down_block_types=__lowerCamelCase					,      block_out_channels=__lowerCamelCase					,      layers_per_block=__lowerCamelCase					,      act_fn=__lowerCamelCase					,      norm_num_groups=__lowerCamelCase					,      double_z=__lowerCamelCase					,      )

             lowerCamelCase__		:				Optional[Any]    =  vq_embed_dim if vq_embed_dim is not None else latent_channels

             lowerCamelCase__		:				List[str]    =  nn.Convad(__lowerCamelCase					,      __lowerCamelCase					,      1       )
             lowerCamelCase__		:				Optional[Any]    =  VectorQuantizer(__lowerCamelCase					,      __lowerCamelCase					,      beta=0.2_5					,      remap=__lowerCamelCase					,      sane_index_shape=__lowerCamelCase       )
             lowerCamelCase__		:				Optional[int]    =  nn.Convad(__lowerCamelCase					,      __lowerCamelCase					,      1       )

             # pass init params to Decoder
             lowerCamelCase__		:				List[str]    =  Decoder(
                 in_channels=__lowerCamelCase					,      out_channels=__lowerCamelCase					,      up_block_types=__lowerCamelCase					,      block_out_channels=__lowerCamelCase					,      layers_per_block=__lowerCamelCase					,      act_fn=__lowerCamelCase					,      norm_num_groups=__lowerCamelCase					,      norm_type=__lowerCamelCase					,      )





       @apply_forward_hook
       def 						lowerCAmelCase     (   self							:			Optional[int]					,      __lowerCamelCase							:			torch.FloatTensor					,      __lowerCamelCase							:			bool = True       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  self.encoder(__lowerCamelCase       )
             lowerCamelCase__		:				int    =  self.quant_conv(__lowerCamelCase       )

             if not return_dict:
                   return (h,)

             return VQEncoderOutput(latents=__lowerCamelCase       )





       @apply_forward_hook
       def 						lowerCAmelCase     (   self							:			Optional[int]					,      __lowerCamelCase							:			torch.FloatTensor					,      __lowerCamelCase							:			bool = False					,      __lowerCamelCase							:			bool = True       ):


             '''simple docstring'''



             if not force_not_quantize:
                   lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__		:				Dict    =  self.quantize(__lowerCamelCase       )
             else:
                   lowerCamelCase__		:				Tuple    =  h
             lowerCamelCase__		:				Tuple    =  self.post_quant_conv(__lowerCamelCase       )
             lowerCamelCase__		:				Tuple    =  self.decoder(__lowerCamelCase					,      quant if self.config.norm_type == "spatial" else None       )

             if not return_dict:
                   return (dec,)

             return DecoderOutput(sample=__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Optional[Any]					,      __lowerCamelCase							:			torch.FloatTensor					,      __lowerCamelCase							:			bool = True       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  sample
             lowerCamelCase__		:				List[str]    =  self.encode(__lowerCamelCase       ).latents
             lowerCamelCase__		:				Dict    =  self.decode(__lowerCamelCase       ).sample

             if not return_dict:
                   return (dec,)

             return DecoderOutput(sample=__lowerCamelCase       )






from __future__ import annotations

from typing import Any

def       lowercase_		(		_A			:    list[Any]						):

      """simple docstring"""




      create_state_space_tree(_A    ,					[]    ,					0						)

def       lowercase_		(		_A			:    list[Any]    ,					_A			:    list[Any]    ,					_A			:    int						):

      """simple docstring"""




      if index == len(_A						):
            print(_A						)
            return

      create_state_space_tree(_A    ,					_A    ,					index + 1						)
      current_subsequence.append(sequence[index]						)
      create_state_space_tree(_A    ,					_A    ,					index + 1						)
      current_subsequence.pop()


if __name__ == "__main__":
    A      :    list[Any]		=					[3, 1, 2, 4]
    generate_all_subsequences(seq)

    seq.clear()
    seq.extend(["A", "B", "C"])
    generate_all_subsequences(seq)


