


'''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 						__lowerCAmelCase		(				__SCREAMING_SNAKE_CASE     ):


  '''simple docstring'''







  a_	       =				42







class 						__lowerCAmelCase		(				__SCREAMING_SNAKE_CASE	,			__SCREAMING_SNAKE_CASE     ):


  '''simple docstring'''







  @register_to_config
  def __init__(				self			:							Optional[int]						,_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 = 0.18215						,_a			:							str = "group"						,):

        '''simple docstring'''




        super().__init__()

        # pass init params to Encoder
        A_    :	int							 =					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_    :	Tuple							 =					vq_embed_dim if vq_embed_dim is not None else latent_channels

        A_    :	Any							 =					nn.Convad(UpperCamelCase__						,UpperCamelCase__						,1			)
        A_    :	List[Any]							 =					VectorQuantizer(UpperCamelCase__						,UpperCamelCase__						,beta=0.25						,remap=UpperCamelCase__						,sane_index_shape=UpperCamelCase__			)
        A_    :	Dict							 =					nn.Convad(UpperCamelCase__						,UpperCamelCase__						,1			)

        # pass init params to Decoder
        A_    :	Tuple							 =					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 							_a				(				self			:							Any						,_a			:							torch.FloatTensor						,_a			:							bool = True			):

        '''simple docstring'''




        A_    :	int							 =					self.encoder(UpperCamelCase__			)
        A_    :	Dict							 =					self.quant_conv(UpperCamelCase__			)

        if not return_dict:
              return (h,)

        return VQEncoderOutput(latents=UpperCamelCase__			)






  @apply_forward_hook
  def 							_a				(				self			:							str						,_a			:							torch.FloatTensor						,_a			:							bool = False						,_a			:							bool = True			):

        '''simple docstring'''




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

        if not return_dict:
              return (dec,)

        return DecoderOutput(sample=UpperCamelCase__			)






  def 							_a				(				self			:							List[str]						,_a			:							torch.FloatTensor						,_a			:							bool = True			):

        '''simple docstring'''




        A_    :	Any							 =					sample
        A_    :	int							 =					self.encode(UpperCamelCase__			).latents
        A_    :	List[str]							 =					self.decode(UpperCamelCase__			).sample

        if not return_dict:
              return (dec,)

        return DecoderOutput(sample=UpperCamelCase__			)


'''simple docstring'''
from .data_collator import (
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForSeqaSeq,
    DataCollatorForSOP,
    DataCollatorForTokenClassification,
    DataCollatorForWholeWordMask,
    DataCollatorWithPadding,
    DefaultDataCollator,
    default_data_collator,
)
from .metrics import glue_compute_metrics, xnli_compute_metrics
from .processors import (
    DataProcessor,
    InputExample,
    InputFeatures,
    SingleSentenceClassificationProcessor,
    SquadExample,
    SquadFeatures,
    SquadVaProcessor,
    SquadVaProcessor,
    glue_convert_examples_to_features,
    glue_output_modes,
    glue_processors,
    glue_tasks_num_labels,
    squad_convert_examples_to_features,
    xnli_output_modes,
    xnli_processors,
    xnli_tasks_num_labels,
)


