code: 
  
  
  '''simple docstring'''
  
  
  
  from collections import OrderedDict
  from typing import Any, List, Mapping, Optional
  
  from ... import PretrainedConfig, PreTrainedTokenizer
  from ...onnx import OnnxConfig, PatchingSpec
  from ...utils import TensorType, is_torch_available, logging
  
  
  __lowercase			:		int =			logging.get_logger(__name__)
  
  __lowercase			:		List[Any] =			{
      '''microsoft/layoutlm-base-uncased''': (
          '''https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json'''
      ),
      '''microsoft/layoutlm-large-uncased''': (
          '''https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json'''
      ),
  }
  
  
  
  
  
  class 							__UpperCamelCase       (	lowercase__      ):
  	A_			    =					"""layoutlm"""
  
  
  
  
  	def __init__(     self		,       __a=3_0522		,       __a=768		,       __a=12		,       __a=12		,       __a=3072		,       __a="gelu"		,       __a=0.1		,       __a=0.1		,       __a=512		,       __a=2		,       __a=0.02		,       __a=1E-1_2		,       __a=0		,       __a="absolute"		,       __a=True		,       __a=1024		,       **__a		,       ):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().__init__(pad_token_id=_A		,       **_A							)
  						__a    : str      					=						vocab_size
  						__a    : Optional[int]      					=						hidden_size
  						__a    : Optional[Any]      					=						num_hidden_layers
  						__a    : List[Any]      					=						num_attention_heads
  						__a    : Any      					=						hidden_act
  						__a    : List[str]      					=						intermediate_size
  						__a    : Optional[int]      					=						hidden_dropout_prob
  						__a    : Optional[int]      					=						attention_probs_dropout_prob
  						__a    : Union[str, Any]      					=						max_position_embeddings
  						__a    : Union[str, Any]      					=						type_vocab_size
  						__a    : Dict      					=						initializer_range
  						__a    : List[Any]      					=						layer_norm_eps
  						__a    : Tuple      					=						position_embedding_type
  						__a    : Union[str, Any]      					=						use_cache
  						__a    : List[str]      					=						max_ad_position_embeddings
  
  
  
  
  
  class 							__UpperCamelCase       (	lowercase__      ):
  
  
  
  
  	def __init__(     self		,       __a		,       __a = "default"		,       __a = None		,       ):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().__init__(_A		,       task=_A		,       patching_specs=_A							)
  						__a    : int      					=						config.max_ad_position_embeddings - 1
  
  
  
  
  	@property
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						return OrderedDict(
  						    [
  						        ('input_ids', {0: 'batch', 1: 'sequence'}),
  						        ('bbox', {0: 'batch', 1: 'sequence'}),
  						        ('attention_mask', {0: 'batch', 1: 'sequence'}),
  						        ('token_type_ids', {0: 'batch', 1: 'sequence'}),
  						    ]							)
  
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a = -1		,       __a = -1		,       __a = False		,       __a = None		,       ):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : List[str]      					=						super().generate_dummy_inputs(
  						    _A		,       batch_size=_A		,       seq_length=_A		,       is_pair=_A		,       framework=_A							)
  
  						# Generate a dummy bbox
  						__a    : Tuple      					=						[48, 84, 73, 128]
  
  						if not framework == TensorType.PYTORCH:
  											raise NotImplementedError('Exporting LayoutLM to ONNX is currently only supported for PyTorch.'							)
  
  						if not is_torch_available():
  											raise ValueError('Cannot generate dummy inputs without PyTorch installed.'							)
  						import torch
  
  						__a    : Dict      					=						input_dict['input_ids'].shape
  						__a    : List[Any]      					=						torch.tensor([*[box] * seq_length]							).tile(_A		,       1		,       1							)
  						return input_dict
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  __lowercase			:		Any =			{'configuration_visual_bert': ['VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VisualBertConfig']}
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	__lowercase			:		Optional[int] =			[
  	    'VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
  	    'VisualBertForMultipleChoice',
  	    'VisualBertForPreTraining',
  	    'VisualBertForQuestionAnswering',
  	    'VisualBertForRegionToPhraseAlignment',
  	    'VisualBertForVisualReasoning',
  	    'VisualBertLayer',
  	    'VisualBertModel',
  	    'VisualBertPreTrainedModel',
  	]
  
  
  if TYPE_CHECKING:
  	from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_visual_bert import (
  		    VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    VisualBertForMultipleChoice,
  		    VisualBertForPreTraining,
  		    VisualBertForQuestionAnswering,
  		    VisualBertForRegionToPhraseAlignment,
  		    VisualBertForVisualReasoning,
  		    VisualBertLayer,
  		    VisualBertModel,
  		    VisualBertPreTrainedModel,
  		)
  
  
  else:
  	import sys
  
  	__lowercase			:		Tuple =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  