






'''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 							_UpperCAmelCase    (		snake_case      ):
    __lowerCamelCase: torch.FloatTensor


class 							_UpperCAmelCase    (		snake_case					,       snake_case      ):




    @register_to_config
    def __init__(      self	:							List[str]	,  a	:							int = 3	,  a	:							int = 3	,  a	:							Tuple[str] = ("DownEncoderBlock2D",)	,  a	:							Tuple[str] = ("UpDecoderBlock2D",)	,  a	:							Tuple[int] = (6_4,)	,  a	:							int = 1	,  a	:							str = "silu"	,  a	:							int = 3	,  a	:							int = 3_2	,  a	:							int = 2_5_6	,  a	:							int = 3_2	,  a	:							Optional[int] = None	,  a	:							float = 0.1_8215	,  a	:							str = "group"	,  ):






       '''simple docstring'''






       super().__init__()

       # pass init params to Encoder
       lowercase_						:     int							     =  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	,  )

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

       lowercase_						:     List[Any]							     =  nn.Convad(a	,  a	,  1      )
       lowercase_						:     List[Any]							     =  VectorQuantizer(a	,  a	,  beta=0.25	,  remap=a	,  sane_index_shape=a      )
       lowercase_						:     Optional[Any]							     =  nn.Convad(a	,  a	,  1      )

       # pass init params to Decoder
       lowercase_						:     Union[str, 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 					lowerCAmelCase__					(      self	:							Optional[Any]	,  a	:							torch.FloatTensor	,  a	:							bool = True      ):






       '''simple docstring'''






       lowercase_						:     Union[str, Any]							     =  self.encoder(a      )
       lowercase_						:     List[str]							     =  self.quant_conv(a      )

       if not return_dict:
          return (h,)

       return VQEncoderOutput(latents=a      )




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






       '''simple docstring'''






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

       if not return_dict:
          return (dec,)

       return DecoderOutput(sample=a      )





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






       '''simple docstring'''






       lowercase_						:     Optional[Any]							     =  sample
       lowercase_						:     Union[str, Any]							     =  self.encode(a      ).latents
       lowercase_						:     Optional[int]							     =  self.decode(a      ).sample

       if not return_dict:
          return (dec,)

       return DecoderOutput(sample=a      )












'''simple docstring'''
UpperCamelCase__			=      {
    "joule": 1.0,
    "kilojoule": 1000,
    "megajoule": 1000000,
    "gigajoule": 1000000000,
    "wattsecond": 1.0,
    "watthour": 3600,
    "kilowatthour": 3600000,
    "newtonmeter": 1.0,
    "calorie_nutr": 4186.8,
    "kilocalorie_nutr": 4186800.00,
    "electronvolt": 1.602176634e-19,
    "britishthermalunit_it": 1055.05585,
    "footpound": 1.35_58_18,
}




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
      lowercase_						:     Union[str, Any]							     =  (
          F"""Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"""
          F"""Valid values are: {", ".join(_UpperCamelCase				)}"""
      )
      raise ValueError(_UpperCamelCase				)
   return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




