




"""simple docstring"""


def 					UpperCAmelCase__       (snake_case__							:					list[int]    ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  []
  if len(snake_case__    ) == 1:
    return [nums.copy()]
  for _ in range(len(snake_case__    )    ):
    _snake_case							:		Union[str, Any]								=  nums.pop(0    )
    _snake_case							:		Any								=  permute(snake_case__    )
    for perm in permutations:
      perm.append(snake_case__    )
    result.extend(snake_case__    )
    nums.append(snake_case__    )
  return result


def 					UpperCAmelCase__       (snake_case__							:					Union[str, Any]    ):
  """simple docstring"""

  def backtrack(snake_case__							:					List[str]    ):
    if start == len(snake_case__    ) - 1:
      output.append(nums[:]    )
    else:
      for i in range(snake_case__		,   len(snake_case__    )    ):
        _snake_case		,					_snake_case							:		Any								=  nums[i], nums[start]
        backtrack(start + 1    )
        _snake_case		,					_snake_case							:		Optional[int]								=  nums[i], nums[start]  # backtrack

  _snake_case							:		Any								=  []
  backtrack(0    )
  return output


if __name__ == "__main__":
     import doctest

     # use res to print the data in permute2 function
     A_						=				permutea([1, 2, 3])
     print(res)
     doctest.testmod()









"""simple docstring"""





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					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      42







class 		lowercase					(					_UpperCAmelCase     ,  _UpperCAmelCase					):





      @register_to_config
      def __init__(       self      ,						lowercase = 3      ,						lowercase = 3      ,						lowercase = ("DownEncoderBlock2D",)      ,						lowercase = ("UpDecoderBlock2D",)      ,						lowercase = (64,)      ,						lowercase = 1      ,						lowercase = "silu"      ,						lowercase = 3      ,						lowercase = 32      ,						lowercase = 256      ,						lowercase = 32      ,						lowercase = None      ,						lowercase = 0.18_215      ,						lowercase = "group"      ,						)		->     Union[str, Any]:
            super().__init__()

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

            lowerCAmelCase  						=       vq_embed_dim if vq_embed_dim is not None else latent_channels

            lowerCAmelCase  						=       nn.Convad(lowercase      ,						lowercase      ,						1 )
            lowerCAmelCase  						=       VectorQuantizer(lowercase      ,						lowercase      ,						beta=0.25      ,						remap=lowercase      ,						sane_index_shape=lowercase )
            lowerCAmelCase  						=       nn.Convad(lowercase      ,						lowercase      ,						1 )

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





      @apply_forward_hook
      def     _snake_case			(       self      ,						lowercase      ,						lowercase = True )		->     VQEncoderOutput:
            lowerCAmelCase  						=       self.encoder(lowercase )
            lowerCAmelCase  						=       self.quant_conv(lowercase )

            if not return_dict:
                  return (h,)

            return VQEncoderOutput(latents=lowercase )





      @apply_forward_hook
      def     _snake_case			(       self      ,						lowercase      ,						lowercase = False      ,						lowercase = True )		->     Union[DecoderOutput, torch.FloatTensor]:
            # also go through quantization layer
            if not force_not_quantize:
                  lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  						=       self.quantize(lowercase )
            else:
                  lowerCAmelCase  						=       h
            lowerCAmelCase  						=       self.post_quant_conv(lowercase )
            lowerCAmelCase  						=       self.decoder(lowercase      ,						quant if self.config.norm_type == """spatial""" else None )

            if not return_dict:
                  return (dec,)

            return DecoderOutput(sample=lowercase )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = True )		->     Union[DecoderOutput, torch.FloatTensor]:
            lowerCAmelCase  						=       sample
            lowerCAmelCase  						=       self.encode(lowercase ).latents
            lowerCAmelCase  						=       self.decode(lowercase ).sample

            if not return_dict:
                  return (dec,)

            return DecoderOutput(sample=lowercase )






