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
  
  
  a__				: Optional[int]									=    logging.get_logger(__name__)
  
  a__				: int									=    {
      '''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 							__snake_case    (				__magic_name__       ):
  	__lowerCAmelCase						=     '''layoutlm'''
  
  
  
  
  
  	def __init__(		self			,					UpperCamelCase_=3_0522			,					UpperCamelCase_=768			,					UpperCamelCase_=12			,					UpperCamelCase_=12			,					UpperCamelCase_=3072			,					UpperCamelCase_="gelu"			,					UpperCamelCase_=0.1			,					UpperCamelCase_=0.1			,					UpperCamelCase_=512			,					UpperCamelCase_=2			,					UpperCamelCase_=0.0_2			,					UpperCamelCase_=1E-1_2			,					UpperCamelCase_=0			,					UpperCamelCase_="absolute"			,					UpperCamelCase_=True			,					UpperCamelCase_=1024			,					**UpperCamelCase_			,					)			->		Optional[int]:
  		super().__init__(pad_token_id=UpperCamelCase_			,					**UpperCamelCase_		)
  		snake_case__  					=      vocab_size
  		snake_case__  					=      hidden_size
  		snake_case__  					=      num_hidden_layers
  		snake_case__  					=      num_attention_heads
  		snake_case__  					=      hidden_act
  		snake_case__  					=      intermediate_size
  		snake_case__  					=      hidden_dropout_prob
  		snake_case__  					=      attention_probs_dropout_prob
  		snake_case__  					=      max_position_embeddings
  		snake_case__  					=      type_vocab_size
  		snake_case__  					=      initializer_range
  		snake_case__  					=      layer_norm_eps
  		snake_case__  					=      position_embedding_type
  		snake_case__  					=      use_cache
  		snake_case__  					=      max_ad_position_embeddings
  
  
  
  
  
  
  class 							__snake_case    (				__magic_name__       ):
  
  
  
  
  
  	def __init__(		self			,					UpperCamelCase_			,					UpperCamelCase_ = "default"			,					UpperCamelCase_ = None			,					)			->		List[str]:
  		super().__init__(UpperCamelCase_			,					task=UpperCamelCase_			,					patching_specs=UpperCamelCase_		)
  		snake_case__  					=      config.max_ad_position_embeddings - 1
  
  
  
  
  
  	@property
  	def      _snake_case      (		self		)			->		Mapping[str, Mapping[int, str]]:
  		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      _snake_case      (		self			,					UpperCamelCase_			,					UpperCamelCase_ = -1			,					UpperCamelCase_ = -1			,					UpperCamelCase_ = False			,					UpperCamelCase_ = None			,					)			->		Mapping[str, Any]:
  		snake_case__  					=      super().generate_dummy_inputs(
  		    UpperCamelCase_			,					batch_size=UpperCamelCase_			,					seq_length=UpperCamelCase_			,					is_pair=UpperCamelCase_			,					framework=UpperCamelCase_		)
  
  		# Generate a dummy bbox
  		snake_case__  					=      [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
  
  		snake_case__					,     snake_case__  					=      input_dict['input_ids'].shape
  		snake_case__  					=      torch.tensor([*[box] * seq_length]		).tile(UpperCamelCase_			,					1			,					1		)
  		return input_dict
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  a__				: Any									=    {
      0: '''underwear''',
      1: '''pants''',
      2: '''belt''',
      3: '''suit''',
      4: '''shoe''',
      5: '''socks''',
      6: '''shirt''',
      7: '''tie''',
      8: '''watch''',
  }
  
  a__				: Optional[Any]									=    [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]
  
  a__				: List[str]									=    [0 for x in range(len(graph))]
  a__				: List[Any]									=    []
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_       )      ->Dict:
  	snake_case__  					=      1
  	while stack:
  		snake_case__  					=      stack.pop()
  		print(UpperCAmelCase_				,							clothes[current_clothing]       )
  		order += 1
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_       )      ->Dict:
  	snake_case__  					=      1
  	for v in graph[u]:
  		if not visited[v]:
  			depth_first_search(UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_       )
  
  	stack.append(UpperCAmelCase_       )
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_       )      ->Union[str, Any]:
  	for v in range(len(UpperCAmelCase_       )       ):
  		if not visited[v]:
  			depth_first_search(UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_       )
  
  
  if __name__ == "__main__":
  			topological_sort(graph, visited)
  			print(stack)
  			print_stack(stack, clothes)
  
  
  
  