





'''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










'''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)



