






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       __SCREAMING_SNAKE_CASE   (				__lowercase):
						_SCREAMING_SNAKE_CASE		:    torch.FloatTensor




class       __SCREAMING_SNAKE_CASE   (				__lowercase						,   __lowercase):






						@register_to_config
						def __init__(		self       ,  _UpperCamelCase = 3       ,  _UpperCamelCase = 3       ,  _UpperCamelCase = ("DownEncoderBlock2D",)       ,  _UpperCamelCase = ("UpDecoderBlock2D",)       ,  _UpperCamelCase = (64,)       ,  _UpperCamelCase = 1       ,  _UpperCamelCase = "silu"       ,  _UpperCamelCase = 3       ,  _UpperCamelCase = 32       ,  _UpperCamelCase = 2_56       ,  _UpperCamelCase = 32       ,  _UpperCamelCase = None       ,  _UpperCamelCase = 0.1_82_15       ,  _UpperCamelCase = "group"       ,  ):






												"""simple docstring"""
												super().__init__()

												# pass init params to Encoder
												lowerCAmelCase__        =  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       ,  )

												lowerCAmelCase__        =  vq_embed_dim if vq_embed_dim is not None else latent_channels

												lowerCAmelCase__        =  nn.Convad(_UpperCamelCase       ,  _UpperCamelCase       ,  1				)
												lowerCAmelCase__        =  VectorQuantizer(_UpperCamelCase       ,  _UpperCamelCase       ,  beta=0.25       ,  remap=_UpperCamelCase       ,  sane_index_shape=_UpperCamelCase				)
												lowerCAmelCase__        =  nn.Convad(_UpperCamelCase       ,  _UpperCamelCase       ,  1				)

												# pass init params to Decoder
												lowerCAmelCase__        =  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  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = True				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.encoder(_UpperCamelCase				)
												lowerCAmelCase__        =  self.quant_conv(_UpperCamelCase				)

												if not return_dict:
																		return (h,)

												return VQEncoderOutput(latents=_UpperCamelCase				)






						@apply_forward_hook
						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = False       ,  _UpperCamelCase = True				):






												"""simple docstring"""
												# also go through quantization layer
												if not force_not_quantize:
																		lowerCAmelCase__  ,    lowerCAmelCase__  ,    lowerCAmelCase__        =  self.quantize(_UpperCamelCase				)
												else:
																		lowerCAmelCase__        =  h
												lowerCAmelCase__        =  self.post_quant_conv(_UpperCamelCase				)
												lowerCAmelCase__        =  self.decoder(_UpperCamelCase       ,  quant if self.config.norm_type == 'spatial' else None				)

												if not return_dict:
																		return (dec,)

												return DecoderOutput(sample=_UpperCamelCase				)







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = True				):






												"""simple docstring"""
												lowerCAmelCase__        =  sample
												lowerCAmelCase__        =  self.encode(_UpperCamelCase				).latents
												lowerCAmelCase__        =  self.decode(_UpperCamelCase				).sample

												if not return_dict:
																		return (dec,)

												return DecoderOutput(sample=_UpperCamelCase				)













from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments





