






"""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 							__SCREAMING_SNAKE_CASE						(				_UpperCamelCase		):
							snake_case_     =							42






class 							__SCREAMING_SNAKE_CASE						(				_UpperCamelCase       ,		_UpperCamelCase		):


							@register_to_config
							def __init__(			self      :						Any			,    snake_case      :						int = 3			,    snake_case      :						int = 3			,    snake_case      :						Tuple[str] = ("DownEncoderBlock2D",)			,    snake_case      :						Tuple[str] = ("UpDecoderBlock2D",)			,    snake_case      :						Tuple[int] = (64,)			,    snake_case      :						int = 1			,    snake_case      :						str = "silu"			,    snake_case      :						int = 3			,    snake_case      :						int = 32			,    snake_case      :						int = 256			,    snake_case      :						int = 32			,    snake_case      :						Optional[int] = None			,    snake_case      :						float = 0.18215			,    snake_case      :						str = "group"			,    ):
													'''simple docstring'''





													super().__init__()

													# pass init params to Encoder
													A__						:  List[str]		  =  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			,    )

													A__						:  Tuple		  =  vq_embed_dim if vq_embed_dim is not None else latent_channels

													A__						:  int		  =  nn.Convad(__a			,    __a			,    1							)
													A__						:  List[str]		  =  VectorQuantizer(__a			,    __a			,    beta=0.25			,    remap=__a			,    sane_index_shape=__a							)
													A__						:  List[str]		  =  nn.Convad(__a			,    __a			,    1							)

													# pass init params to Decoder
													A__						:  Optional[Any]		  =  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 						_UpperCamelCase       (			self      :						str			,    snake_case      :						torch.FloatTensor			,    snake_case      :						bool = True							):
													'''simple docstring'''





													A__						:  Optional[int]		  =  self.encoder(__a							)
													A__						:  Tuple		  =  self.quant_conv(__a							)

													if not return_dict:
																			return (h,)

													return VQEncoderOutput(latents=__a							)


							@apply_forward_hook
							def 						_UpperCamelCase       (			self      :						Tuple			,    snake_case      :						torch.FloatTensor			,    snake_case      :						bool = False			,    snake_case      :						bool = True							):
													'''simple docstring'''





													if not force_not_quantize:
																			A__						:  Any		  =  self.quantize(__a							)
													else:
																			A__						:  str		  =  h
													A__						:  Optional[Any]		  =  self.post_quant_conv(__a							)
													A__						:  int		  =  self.decoder(__a			,    quant if self.config.norm_type == """spatial""" else None							)

													if not return_dict:
																			return (dec,)

													return DecoderOutput(sample=__a							)


							def 						_UpperCamelCase       (			self      :						int			,    snake_case      :						torch.FloatTensor			,    snake_case      :						bool = True							):
													'''simple docstring'''





													A__						:  Tuple		  =  sample
													A__						:  int		  =  self.encode(__a							).latents
													A__						:  int		  =  self.decode(__a							).sample

													if not return_dict:
																			return (dec,)

													return DecoderOutput(sample=__a							)














"""simple docstring"""



def      _lowerCAmelCase		(							UpperCAmelCase__       :					str,						UpperCAmelCase__       :					List[Any]      )		->List[Any]:
						A__						:  Union[str, Any]		  =  [1]
						for i in range(2,						UpperCAmelCase__      ):
												factorials.append(factorials[-1] * i      )
						assert 0 <= k < factorials[-1] * n, "k out of bounds"

						A__						:  Optional[int]		  =  []
						A__						:  List[str]		  =  list(range(UpperCAmelCase__      )      )

						# Find permutation
						while factorials:
												A__						:  Optional[int]		  =  factorials.pop()
												A__  ,  A__						:  Optional[int]		  =  divmod(UpperCAmelCase__,						UpperCAmelCase__      )
												permutation.append(elements[number]      )
												elements.remove(elements[number]      )
						permutation.append(elements[0]      )

						return permutation


if __name__ == "__main__":
						import doctest

						doctest.testmod()






