__a  :					Union[str, Any]          =  [[0, 1, 1], [0, 0, 1], [1, 0, 0]]




class        _UpperCamelCase				:


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__						)					-> List[str]:

							'''simple docstring'''


							__lowercase			      =							name
							__lowercase			      =							[]
							__lowercase			      =							[]





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> Union[str, Any]:

							'''simple docstring'''


							self.inbound.append(lowerCAmelCase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> Union[str, Any]:

							'''simple docstring'''


							self.outbound.append(lowerCAmelCase__						)





			def __repr__(       self						)					-> Tuple:

							'''simple docstring'''


							return F"<node={self.name} inbound={self.inbound} outbound={self.outbound}>"





def     UpperCAmelCase						(   lowercase							,      lowercase=3							,      lowercase=0.85							):







				"""simple docstring"""



				__lowercase			      =							{}
				for node in nodes:
								__lowercase			      =							1

				__lowercase			      =							{}
				for node in nodes:
								__lowercase			      =							len(node.outbound							)

				for i in range(lowercase							):
								print(F"======= Iteration {i + 1} ======="							)
								for _, node in enumerate(lowercase							):
												__lowercase			      =							(1 - d) + d * sum(
												    ranks[ib] / outbounds[ib] for ib in node.inbound							)
								print(lowercase							)





def     UpperCAmelCase						(   ):







				"""simple docstring"""



				__lowercase			      =							list(input('''Enter Names of the Nodes: '''							).split()							)

				__lowercase			      =							[Node(lowercase							) for name in names]

				for ri, row in enumerate(lowercase							):
								for ci, col in enumerate(lowercase							):
												if col == 1:
																nodes[ci].add_inbound(names[ri]							)
																nodes[ri].add_outbound(names[ci]							)

				print('''======= Nodes ======='''							)
				for node in nodes:
								print(lowercase							)

				page_rank(lowercase							)


if __name__ == "__main__":
		main()
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  :					int          =  logging.get_logger(__name__)

__a  :					Optional[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				(						_UpperCAmelCase			):


			"""simple docstring"""






			__a  :			List[Any]      			=						'''layoutlm'''





			def __init__(       self				,       lowerCAmelCase__=3_05_22				,       lowerCAmelCase__=7_68				,       lowerCAmelCase__=12				,       lowerCAmelCase__=12				,       lowerCAmelCase__=30_72				,       lowerCAmelCase__="gelu"				,       lowerCAmelCase__=0.1				,       lowerCAmelCase__=0.1				,       lowerCAmelCase__=5_12				,       lowerCAmelCase__=2				,       lowerCAmelCase__=0.02				,       lowerCAmelCase__=1E-12				,       lowerCAmelCase__=0				,       lowerCAmelCase__="absolute"				,       lowerCAmelCase__=True				,       lowerCAmelCase__=10_24				,       **lowerCAmelCase__				,       )					-> Tuple:

							'''simple docstring'''


							super().__init__(pad_token_id=lowerCAmelCase__				,       **lowerCAmelCase__						)
							__lowercase			      =							vocab_size
							__lowercase			      =							hidden_size
							__lowercase			      =							num_hidden_layers
							__lowercase			      =							num_attention_heads
							__lowercase			      =							hidden_act
							__lowercase			      =							intermediate_size
							__lowercase			      =							hidden_dropout_prob
							__lowercase			      =							attention_probs_dropout_prob
							__lowercase			      =							max_position_embeddings
							__lowercase			      =							type_vocab_size
							__lowercase			      =							initializer_range
							__lowercase			      =							layer_norm_eps
							__lowercase			      =							position_embedding_type
							__lowercase			      =							use_cache
							__lowercase			      =							max_ad_position_embeddings




class        _UpperCamelCase				(						_UpperCAmelCase			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__				,       lowerCAmelCase__ = "default"				,       lowerCAmelCase__ = None				,       )					-> str:

							'''simple docstring'''


							super().__init__(lowerCAmelCase__				,       task=lowerCAmelCase__				,       patching_specs=lowerCAmelCase__						)
							__lowercase			      =							config.max_ad_position_embeddings - 1





			@property
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Mapping[str, Mapping[int, str]]:

							'''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 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__ = -1				,       lowerCAmelCase__ = -1				,       lowerCAmelCase__ = False				,       lowerCAmelCase__ = None				,       )					-> Mapping[str, Any]:

							'''simple docstring'''


							__lowercase			      =							super().generate_dummy_inputs(
							    lowerCAmelCase__				,       batch_size=lowerCAmelCase__				,       seq_length=lowerCAmelCase__				,       is_pair=lowerCAmelCase__				,       framework=lowerCAmelCase__						)

							# Generate a dummy bbox
							__lowercase			      =							[48, 84, 73, 1_28]

							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

							__lowercase      ,					__lowercase			      =							input_dict['''input_ids'''].shape
							__lowercase			      =							torch.tensor([*[box] * seq_length]						).tile(lowerCAmelCase__				,       1				,       1						)
							return input_dict