






from __future__ import annotations

from collections.abc import Iterator
from typing import Generic, TypeVar

__a      :       List[str]				  =   TypeVar("T")


class       __lowercase      (	Generic[T]  ):







     '''simple docstring'''




     def __init__(				self       :			Dict ,	UpperCamelCase_       :			T				):


         """simple docstring"""



         __A         =	data
         __A         =	None





     def __str__(				self       :			Optional[int]				):


         """simple docstring"""



         return F"{self.data}"


class       __lowercase      (	Generic[T]  ):







     '''simple docstring'''




     def __init__(				self       :			int				):


         """simple docstring"""



         __A         =	None




     def __iter__(				self       :			Tuple				):


         """simple docstring"""



         __A         =	self.top
         while node:
             yield node.data
             __A         =	node.next




     def __str__(				self       :			List[Any]				):


         """simple docstring"""



         return "->".join([str(UpperCamelCase_				) for item in self]				)




     def __len__(				self       :			str				):


         """simple docstring"""



         return len(tuple(iter(self				)				)				)




     def 		lowerCAmelCase_     (				self       :			List[str]				):


         """simple docstring"""



         return self.top is None




     def 		lowerCAmelCase_     (				self       :			Dict ,	UpperCamelCase_       :			T				):


         """simple docstring"""



         __A         =	Node(UpperCamelCase_				)
         if not self.is_empty():
             __A         =	self.top
         __A         =	node




     def 		lowerCAmelCase_     (				self       :			str				):


         """simple docstring"""



         if self.is_empty():
             raise IndexError("""pop from empty stack"""				)
         assert isinstance(self.top ,	UpperCamelCase_				)
         __A         =	self.top
         __A         =	self.top.next
         return pop_node.data




     def 		lowerCAmelCase_     (				self       :			Tuple				):


         """simple docstring"""



         if self.is_empty():
             raise IndexError("""peek from empty stack"""				)

         assert self.top is not None
         return self.top.data





     def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


         """simple docstring"""



         __A         =	None


if __name__ == "__main__":
    from doctest import testmod

    testmod()












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'''
     SCREAMING_SNAKE_CASE   						=				42


class       __lowercase      (	lowercase_			,      lowercase_  ):







     '''simple docstring'''




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


         """simple docstring"""



         super().__init__()

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

         __A         =	vq_embed_dim if vq_embed_dim is not None else latent_channels

         __A         =	nn.Convad(UpperCamelCase_ ,	UpperCamelCase_ ,	1				)
         __A         =	VectorQuantizer(UpperCamelCase_ ,	UpperCamelCase_ ,	beta=0.25 ,	remap=UpperCamelCase_ ,	sane_index_shape=UpperCamelCase_				)
         __A         =	nn.Convad(UpperCamelCase_ ,	UpperCamelCase_ ,	1				)

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




     @apply_forward_hook
     def 		lowerCAmelCase_     (				self       :			List[str] ,	UpperCamelCase_       :			torch.FloatTensor ,	UpperCamelCase_       :			bool = True				):


         """simple docstring"""



         __A         =	self.encoder(UpperCamelCase_				)
         __A         =	self.quant_conv(UpperCamelCase_				)

         if not return_dict:
             return (h,)

         return VQEncoderOutput(latents=UpperCamelCase_				)




     @apply_forward_hook
     def 		lowerCAmelCase_     (				self       :			List[str] ,	UpperCamelCase_       :			torch.FloatTensor ,	UpperCamelCase_       :			bool = False ,	UpperCamelCase_       :			bool = True				):


         """simple docstring"""



         if not force_not_quantize:
             __A    ,	__A    ,	__A         =	self.quantize(UpperCamelCase_				)
         else:
             __A         =	h
         __A         =	self.post_quant_conv(UpperCamelCase_				)
         __A         =	self.decoder(UpperCamelCase_ ,	quant if self.config.norm_type == """spatial""" else None				)

         if not return_dict:
             return (dec,)

         return DecoderOutput(sample=UpperCamelCase_				)





     def 		lowerCAmelCase_     (				self       :			Any ,	UpperCamelCase_       :			torch.FloatTensor ,	UpperCamelCase_       :			bool = True				):


         """simple docstring"""



         __A         =	sample
         __A         =	self.encode(UpperCamelCase_				).latents
         __A         =	self.decode(UpperCamelCase_				).sample

         if not return_dict:
             return (dec,)

         return DecoderOutput(sample=UpperCamelCase_				)




