code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
  
  
  UpperCAmelCase__      =							{
      '''configuration_realm''': ['''REALM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RealmConfig'''],
      '''tokenization_realm''': ['''RealmTokenizer'''],
  }
  
  try:
       if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       UpperCAmelCase__      =							['''RealmTokenizerFast''']
  
  try:
       if not is_torch_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       UpperCAmelCase__      =							[
           '''REALM_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''RealmEmbedder''',
           '''RealmForOpenQA''',
           '''RealmKnowledgeAugEncoder''',
           '''RealmPreTrainedModel''',
           '''RealmReader''',
           '''RealmScorer''',
           '''load_tf_weights_in_realm''',
       ]
       UpperCAmelCase__      =							['''RealmRetriever''']
  
  
  if TYPE_CHECKING:
       from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
       from .tokenization_realm import RealmTokenizer
  
       try:
            if not is_tokenizers_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .tokenization_realm import RealmTokenizerFast
  
       try:
            if not is_torch_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .modeling_realm import (
                REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
                RealmEmbedder,
                RealmForOpenQA,
                RealmKnowledgeAugEncoder,
                RealmPreTrainedModel,
                RealmReader,
                RealmScorer,
                load_tf_weights_in_realm,
            )
            from .retrieval_realm import RealmRetriever
  
  
  else:
       import sys
  
       UpperCAmelCase__      =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  UpperCAmelCase__      =							logging.get_logger(__name__)
  
  UpperCAmelCase__      =							{
      '''dandelin/vilt-b32-mlm''': '''https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json'''
  }
  class 	a__				(				a_      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							Dict							= '''vilt'''
  			def __init__(     self       : str ,						lowerCAmelCase_       : Tuple=30_522 ,						lowerCAmelCase_       : List[Any]=2 ,						lowerCAmelCase_       : Optional[Any]=2 ,						lowerCAmelCase_       : Union[str, Any]=40 ,						lowerCAmelCase_       : str=768 ,						lowerCAmelCase_       : Tuple=12 ,						lowerCAmelCase_       : List[Any]=12 ,						lowerCAmelCase_       : int=3_072 ,						lowerCAmelCase_       : List[Any]="gelu" ,						lowerCAmelCase_       : int=0.0 ,						lowerCAmelCase_       : str=0.0 ,						lowerCAmelCase_       : Optional[Any]=0.02 ,						lowerCAmelCase_       : Union[str, Any]=1E-12 ,						lowerCAmelCase_       : Dict=384 ,						lowerCAmelCase_       : Optional[int]=32 ,						lowerCAmelCase_       : Optional[Any]=3 ,						lowerCAmelCase_       : str=True ,						lowerCAmelCase_       : List[Any]=-1 ,						lowerCAmelCase_       : Union[str, Any]=False ,						lowerCAmelCase_       : str=-1 ,						**lowerCAmelCase_       : Any ,						)			->		Tuple:
  						super().__init__(tie_word_embeddings=lowerCAmelCase_ ,						**lowerCAmelCase_  )
  
  						__A=  vocab_size
  						__A=  type_vocab_size
  						__A=  modality_type_vocab_size
  						__A=  max_position_embeddings
  
  						__A=  hidden_size
  						__A=  num_hidden_layers
  						__A=  num_attention_heads
  						__A=  intermediate_size
  						__A=  hidden_act
  						__A=  hidden_dropout_prob
  						__A=  attention_probs_dropout_prob
  						__A=  initializer_range
  						__A=  layer_norm_eps
  
  						__A=  image_size
  						__A=  patch_size
  						__A=  num_channels
  						__A=  qkv_bias
  						__A=  max_image_length
  						__A=  num_images
  
  
  
  