

'''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        A_ (				_UpperCAmelCase							):






     '''simple docstring'''



     UpperCAmelCase_						:	Optional[int]  		=  42




class        A_ (				_UpperCAmelCase       ,   _UpperCAmelCase							):






     '''simple docstring'''



     @register_to_config
     def __init__(		self :     Any     ,			lowercase_ :     Optional[int] = 3     ,			lowercase_ :     Dict = 3     ,			lowercase_ :     Optional[Any] = ("DownEncoderBlock2D",)     ,			lowercase_ :     Any = ("UpDecoderBlock2D",)     ,			lowercase_ :     List[str] = (64,)     ,			lowercase_ :     List[str] = 1     ,			lowercase_ :     Any = "silu"     ,			lowercase_ :     Optional[int] = 3     ,			lowercase_ :     int = 32     ,			lowercase_ :     Dict = 256     ,			lowercase_ :     Optional[int] = 32     ,			lowercase_ :     List[Any] = None     ,			lowercase_ :     Dict = 0.1_8215     ,			lowercase_ :     List[str] = "group"     ,			)						->	Union[str, Any]:
      super().__init__()

      # pass init params to Encoder
      UpperCAmelCase       :	Union[str, Any]											= 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_     ,			)

      UpperCAmelCase       :	Union[str, Any]											= vq_embed_dim if vq_embed_dim is not None else latent_channels

      UpperCAmelCase       :	Any											= nn.Convad(lowercase_     ,			lowercase_     ,			1       )
      UpperCAmelCase       :	Optional[int]											= VectorQuantizer(lowercase_     ,			lowercase_     ,			beta=0.25     ,			remap=lowercase_     ,			sane_index_shape=lowercase_       )
      UpperCAmelCase       :	Tuple											= nn.Convad(lowercase_     ,			lowercase_     ,			1       )

      # pass init params to Decoder
      UpperCAmelCase       :	Any											= 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    UpperCAmelCase_       (		self :     List[str]     ,			lowercase_ :     Union[str, Any]     ,			lowercase_ :     Dict = True       )						->	VQEncoderOutput:
      UpperCAmelCase       :	Tuple											= self.encoder(lowercase_       )
      UpperCAmelCase       :	Dict											= self.quant_conv(lowercase_       )

      if not return_dict:
       return (h,)

      return VQEncoderOutput(latents=lowercase_       )


     @apply_forward_hook
     def    UpperCAmelCase_       (		self :     List[Any]     ,			lowercase_ :     List[str]     ,			lowercase_ :     List[Any] = False     ,			lowercase_ :     str = True       )						->	Union[DecoderOutput, torch.FloatTensor]:
      # also go through quantization layer
      if not force_not_quantize:
       UpperCAmelCase					, UpperCAmelCase					, UpperCAmelCase       :	Optional[Any]											= self.quantize(lowercase_       )
      else:
       UpperCAmelCase       :	str											= h
      UpperCAmelCase       :	Any											= self.post_quant_conv(lowercase_       )
      UpperCAmelCase       :	List[Any]											= self.decoder(lowercase_     ,			quant if self.config.norm_type == 'spatial' else None       )

      if not return_dict:
       return (dec,)

      return DecoderOutput(sample=lowercase_       )




     def    UpperCAmelCase_       (		self :     Optional[Any]     ,			lowercase_ :     Dict     ,			lowercase_ :     Optional[int] = True       )						->	Union[DecoderOutput, torch.FloatTensor]:
      UpperCAmelCase       :	Dict											= sample
      UpperCAmelCase       :	Any											= self.encode(lowercase_       ).latents
      UpperCAmelCase       :	Optional[Any]											= self.decode(lowercase_       ).sample

      if not return_dict:
       return (dec,)

      return DecoderOutput(sample=lowercase_       )











"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


SCREAMING_SNAKE_CASE__					=   {}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__					=   ["BartphoTokenizer"]

if TYPE_CHECKING:
       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_bartpho import BartphoTokenizer

else:
       import sys

       SCREAMING_SNAKE_CASE__					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






