






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  (    A__       ):
 _lowerCAmelCase       :       torch.FloatTensor







class 			__a  (    A__       ,   A__       ):







 @register_to_config
 def __init__(		self					:   str							,   SCREAMING_SNAKE_CASE					:   int = 3							,   SCREAMING_SNAKE_CASE					:   int = 3							,   SCREAMING_SNAKE_CASE					:   Tuple[str] = ("DownEncoderBlock2D",)							,   SCREAMING_SNAKE_CASE					:   Tuple[str] = ("UpDecoderBlock2D",)							,   SCREAMING_SNAKE_CASE					:   Tuple[int] = (64,)							,   SCREAMING_SNAKE_CASE					:   int = 1							,   SCREAMING_SNAKE_CASE					:   str = "silu"							,   SCREAMING_SNAKE_CASE					:   int = 3							,   SCREAMING_SNAKE_CASE					:   int = 32							,   SCREAMING_SNAKE_CASE					:   int = 2_56							,   SCREAMING_SNAKE_CASE					:   int = 32							,   SCREAMING_SNAKE_CASE					:   Optional[int] = None							,   SCREAMING_SNAKE_CASE					:   float = 0.1_8_2_1_5							,   SCREAMING_SNAKE_CASE					:   str = "group"							,   ):


      '''simple docstring'''






      super().__init__()

      # pass init params to Encoder
      UpperCamelCase__			:   Union[str, Any]       				=  Encoder(
          in_channels=SCREAMING_SNAKE_CASE							,   out_channels=SCREAMING_SNAKE_CASE							,   down_block_types=SCREAMING_SNAKE_CASE							,   block_out_channels=SCREAMING_SNAKE_CASE							,   layers_per_block=SCREAMING_SNAKE_CASE							,   act_fn=SCREAMING_SNAKE_CASE							,   norm_num_groups=SCREAMING_SNAKE_CASE							,   double_z=SCREAMING_SNAKE_CASE							,   )

      UpperCamelCase__			:   List[Any]       				=  vq_embed_dim if vq_embed_dim is not None else latent_channels

      UpperCamelCase__			:   Tuple       				=  nn.Convad(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   1						)
      UpperCamelCase__			:   List[Any]       				=  VectorQuantizer(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   beta=0.2_5							,   remap=SCREAMING_SNAKE_CASE							,   sane_index_shape=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Dict       				=  nn.Convad(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   1						)

      # pass init params to Decoder
      UpperCamelCase__			:   Optional[int]       				=  Decoder(
          in_channels=SCREAMING_SNAKE_CASE							,   out_channels=SCREAMING_SNAKE_CASE							,   up_block_types=SCREAMING_SNAKE_CASE							,   block_out_channels=SCREAMING_SNAKE_CASE							,   layers_per_block=SCREAMING_SNAKE_CASE							,   act_fn=SCREAMING_SNAKE_CASE							,   norm_num_groups=SCREAMING_SNAKE_CASE							,   norm_type=SCREAMING_SNAKE_CASE							,   )







 @apply_forward_hook
 def 					__lowercase					(		self					:   Tuple							,   SCREAMING_SNAKE_CASE					:   torch.FloatTensor							,   SCREAMING_SNAKE_CASE					:   bool = True						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  self.encoder(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Optional[int]       				=  self.quant_conv(SCREAMING_SNAKE_CASE						)

      if not return_dict:
           return (h,)

      return VQEncoderOutput(latents=SCREAMING_SNAKE_CASE						)







 @apply_forward_hook
 def 					__lowercase					(		self					:   Tuple							,   SCREAMING_SNAKE_CASE					:   torch.FloatTensor							,   SCREAMING_SNAKE_CASE					:   bool = False							,   SCREAMING_SNAKE_CASE					:   bool = True						):


      '''simple docstring'''






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

      if not return_dict:
           return (dec,)

      return DecoderOutput(sample=SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Dict							,   SCREAMING_SNAKE_CASE					:   torch.FloatTensor							,   SCREAMING_SNAKE_CASE					:   bool = True						):


      '''simple docstring'''






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

      if not return_dict:
           return (dec,)

      return DecoderOutput(sample=SCREAMING_SNAKE_CASE						)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase	:							Tuple				={
    '''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
    '''tokenization_luke''': ['''LukeTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase	:							Union[str, Any]				=[
     '''LUKE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''LukeForEntityClassification''',
     '''LukeForEntityPairClassification''',
     '''LukeForEntitySpanClassification''',
     '''LukeForMultipleChoice''',
     '''LukeForQuestionAnswering''',
     '''LukeForSequenceClassification''',
     '''LukeForTokenClassification''',
     '''LukeForMaskedLM''',
     '''LukeModel''',
     '''LukePreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
 from .tokenization_luke import LukeTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_luke import (
      LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
      LukeForEntityClassification,
      LukeForEntityPairClassification,
      LukeForEntitySpanClassification,
      LukeForMaskedLM,
      LukeForMultipleChoice,
      LukeForQuestionAnswering,
      LukeForSequenceClassification,
      LukeForTokenClassification,
      LukeModel,
      LukePreTrainedModel,
  )

else:
 import sys

 lowerCamelCase	:							Dict				=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)