

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        A_		(       SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase    :  torch.FloatTensor






class        A_		(       SCREAMING_SNAKE_CASE			,							SCREAMING_SNAKE_CASE				):
							@register_to_config
							def __init__(		self		:			Dict   ,SCREAMING_SNAKE_CASE__		:			int = 3   ,SCREAMING_SNAKE_CASE__		:			int = 3   ,SCREAMING_SNAKE_CASE__		:			Tuple[str] = ("DownEncoderBlock2D",)   ,SCREAMING_SNAKE_CASE__		:			Tuple[str] = ("UpDecoderBlock2D",)   ,SCREAMING_SNAKE_CASE__		:			Tuple[int] = (6_4,)   ,SCREAMING_SNAKE_CASE__		:			int = 1   ,SCREAMING_SNAKE_CASE__		:			str = "silu"   ,SCREAMING_SNAKE_CASE__		:			int = 3   ,SCREAMING_SNAKE_CASE__		:			int = 3_2   ,SCREAMING_SNAKE_CASE__		:			int = 2_5_6   ,SCREAMING_SNAKE_CASE__		:			int = 3_2   ,SCREAMING_SNAKE_CASE__		:			Optional[int] = None   ,SCREAMING_SNAKE_CASE__		:			float = 0.18215   ,SCREAMING_SNAKE_CASE__		:			str = "group"   ,):
								super().__init__()

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

								__lowerCamelCase				:				Any					       = vq_embed_dim if vq_embed_dim is not None else latent_channels

								__lowerCamelCase				:				Any					       = nn.Convad(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,1)
								__lowerCamelCase				:				Optional[Any]					       = VectorQuantizer(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,beta=0.25   ,remap=SCREAMING_SNAKE_CASE__   ,sane_index_shape=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Optional[Any]					       = nn.Convad(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,1)

								# pass init params to Decoder
								__lowerCamelCase				:				Dict					       = Decoder(
								    in_channels=SCREAMING_SNAKE_CASE__   ,out_channels=SCREAMING_SNAKE_CASE__   ,up_block_types=SCREAMING_SNAKE_CASE__   ,block_out_channels=SCREAMING_SNAKE_CASE__   ,layers_per_block=SCREAMING_SNAKE_CASE__   ,act_fn=SCREAMING_SNAKE_CASE__   ,norm_num_groups=SCREAMING_SNAKE_CASE__   ,norm_type=SCREAMING_SNAKE_CASE__   ,)
							@apply_forward_hook
							def     lowerCAmelCase     (		self		:			Any   ,SCREAMING_SNAKE_CASE__		:			torch.FloatTensor   ,SCREAMING_SNAKE_CASE__		:			bool = True):
								__lowerCamelCase				:				int					       = self.encoder(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				List[Any]					       = self.quant_conv(SCREAMING_SNAKE_CASE__)

								if not return_dict:
									return (h,)

								return VQEncoderOutput(latents=SCREAMING_SNAKE_CASE__)
							@apply_forward_hook
							def     lowerCAmelCase     (		self		:			List[str]   ,SCREAMING_SNAKE_CASE__		:			torch.FloatTensor   ,SCREAMING_SNAKE_CASE__		:			bool = False   ,SCREAMING_SNAKE_CASE__		:			bool = True):
								# also go through quantization layer
								if not force_not_quantize:
									__lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase				:				Any					       = self.quantize(SCREAMING_SNAKE_CASE__)
								else:
									__lowerCamelCase				:				Optional[Any]					       = h
								__lowerCamelCase				:				Optional[Any]					       = self.post_quant_conv(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Tuple					       = self.decoder(SCREAMING_SNAKE_CASE__   ,quant if self.config.norm_type == 'spatial' else None)

								if not return_dict:
									return (dec,)

								return DecoderOutput(sample=SCREAMING_SNAKE_CASE__)


							def     lowerCAmelCase     (		self		:			List[str]   ,SCREAMING_SNAKE_CASE__		:			torch.FloatTensor   ,SCREAMING_SNAKE_CASE__		:			bool = True):
								__lowerCamelCase				:				Union[str, Any]					       = sample
								__lowerCamelCase				:				Dict					       = self.encode(SCREAMING_SNAKE_CASE__).latents
								__lowerCamelCase				:				Union[str, Any]					       = self.decode(SCREAMING_SNAKE_CASE__).sample

								if not return_dict:
									return (dec,)

								return DecoderOutput(sample=SCREAMING_SNAKE_CASE__)







def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	[]
						if len(SCREAMING_SNAKE_CASE__       ) == 1:
												return [nums.copy()]
						for _ in range(len(SCREAMING_SNAKE_CASE__       )       ):
												snake_case_   =	nums.pop(0       )
												snake_case_   =	permute(SCREAMING_SNAKE_CASE__       )
												for perm in permutations:
																		perm.append(SCREAMING_SNAKE_CASE__       )
												result.extend(SCREAMING_SNAKE_CASE__       )
												nums.append(SCREAMING_SNAKE_CASE__       )
						return result


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):

						def backtrack(SCREAMING_SNAKE_CASE__       ):
												if start == len(SCREAMING_SNAKE_CASE__       ) - 1:
																		output.append(nums[:]       )
												else:
																		for i in range(SCREAMING_SNAKE_CASE__  ,		len(SCREAMING_SNAKE_CASE__       )       ):
																								snake_case_,    snake_case_   =	nums[i], nums[start]
																								backtrack(start + 1       )
																								snake_case_,    snake_case_   =	nums[i], nums[start]  # backtrack

						snake_case_   =	[]
						backtrack(0       )
						return output


if __name__ == "__main__":
					import doctest

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