"""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_	:					torch.FloatTensor






class      _A	(						_UpperCAmelCase							, _UpperCAmelCase					):




    """simple docstring"""




    @register_to_config
    def __init__(		self							:     str	,      A_							:     int = 3	,      A_							:     int = 3	,      A_							:     Tuple[str] = ("DownEncoderBlock2D",)	,      A_							:     Tuple[str] = ("UpDecoderBlock2D",)	,      A_							:     Tuple[int] = (64,)	,      A_							:     int = 1	,      A_							:     str = "silu"	,      A_							:     int = 3	,      A_							:     int = 32	,      A_							:     int = 256	,      A_							:     int = 32	,      A_							:     Optional[int] = None	,      A_							:     float = 0.1_82_15	,      A_							:     str = "group"	,      )    ->       List[str]:
         super().__init__()

         # pass init params to Encoder
         __snake_case				=							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_	,      )

         __snake_case				=							vq_embed_dim if vq_embed_dim is not None else latent_channels

         __snake_case				=							nn.Convad(A_	,      A_	,      1    )
         __snake_case				=							VectorQuantizer(A_	,      A_	,      beta=0.25	,      remap=A_	,      sane_index_shape=A_    )
         __snake_case				=							nn.Convad(A_	,      A_	,      1    )

         # pass init params to Decoder
         __snake_case				=							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  lowercase					(		self							:     Dict	,      A_							:     torch.FloatTensor	,      A_							:     bool = True    )    ->       VQEncoderOutput:
         __snake_case				=							self.encoder(A_    )
         __snake_case				=							self.quant_conv(A_    )

         if not return_dict:
              return (h,)

         return VQEncoderOutput(latents=A_    )
    @apply_forward_hook
    def  lowercase					(		self							:     str	,      A_							:     torch.FloatTensor	,      A_							:     bool = False	,      A_							:     bool = True    )    ->       Union[DecoderOutput, torch.FloatTensor]:
         # also go through quantization layer
         if not force_not_quantize:
              __snake_case		,	__snake_case		,	__snake_case				=							self.quantize(A_    )
         else:
              __snake_case				=							h
         __snake_case				=							self.post_quant_conv(A_    )
         __snake_case				=							self.decoder(A_	,      quant if self.config.norm_type == '''spatial''' else None    )

         if not return_dict:
              return (dec,)

         return DecoderOutput(sample=A_    )







    def  lowercase					(		self							:     int	,      A_							:     torch.FloatTensor	,      A_							:     bool = True    )    ->       Union[DecoderOutput, torch.FloatTensor]:
         __snake_case				=							sample
         __snake_case				=							self.encode(A_    ).latents
         __snake_case				=							self.decode(A_    ).sample

         if not return_dict:
              return (dec,)

         return DecoderOutput(sample=A_    )
"""simple docstring"""



from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate



@dataclass(frozen=_UpperCAmelCase					)
class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					str							=					field(default='''summarization'''							, metadata={'''include_in_asdict_even_if_is_default''': True}					)
    UpperCamelCase_	:					ClassVar[Features]							=					Features({'''text''': Value('''string'''					)}					)
    UpperCamelCase_	:					ClassVar[Features]							=					Features({'''summary''': Value('''string'''					)}					)
    UpperCamelCase_	:					str							=					"text"
    UpperCamelCase_	:					str							=					"summary"
    @property
    def  lowercase					(		self							:     List[Any]    )    ->       Dict[str, str]:
         return {self.text_column: "text", self.summary_column: "summary"}