


'''simple docstring'''





UpperCAmelCase__      =							[[0, 1, 1], [0, 0, 1], [1, 0, 0]]
class 	a__				:





			'''simple docstring'''

			def __init__(     self       : Any ,						lowerCAmelCase_       : Optional[int]  )			->		Tuple:
						__A=  name
						__A=  []
						__A=  []
			def 				lowerCAmelCase    (     self       : Any ,						lowerCAmelCase_       : Dict  )			->		Any:
						self.inbound.append(lowerCAmelCase_  )
			def 				lowerCAmelCase    (     self       : str ,						lowerCAmelCase_       : Any  )			->		List[Any]:
						self.outbound.append(lowerCAmelCase_  )

			def __repr__(     self       : int  )			->		Union[str, Any]:
						return F"""<node={self.name} inbound={self.inbound} outbound={self.outbound}>"""






def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Any,_SCREAMING_SNAKE_CASE				:   Optional[int]=3,_SCREAMING_SNAKE_CASE				:   Dict=0.85   ):






			"""simple docstring"""




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

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

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






def 	UpperCAmelCase__(							):






			"""simple docstring"""




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

			__A=  [Node(_SCREAMING_SNAKE_CASE   ) for name in names]

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

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

			page_rank(_SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
					main()







'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase__      =							logging.get_logger(__name__)

UpperCAmelCase__      =							{
    '''google/mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json'''
}
class 	a__				(				a_      ):





			'''simple docstring'''

			A	:							int							= MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
			A	:							Optional[int]							= '''mobilebert'''
			def __init__(     self       : Optional[int] ,						lowerCAmelCase_       : str=30_522 ,						lowerCAmelCase_       : List[Any]=512 ,						lowerCAmelCase_       : Optional[int]=24 ,						lowerCAmelCase_       : str=4 ,						lowerCAmelCase_       : List[str]=512 ,						lowerCAmelCase_       : List[str]="relu" ,						lowerCAmelCase_       : Dict=0.0 ,						lowerCAmelCase_       : List[Any]=0.1 ,						lowerCAmelCase_       : Optional[Any]=512 ,						lowerCAmelCase_       : str=2 ,						lowerCAmelCase_       : Dict=0.02 ,						lowerCAmelCase_       : Any=1E-12 ,						lowerCAmelCase_       : List[Any]=0 ,						lowerCAmelCase_       : Union[str, Any]=128 ,						lowerCAmelCase_       : Any=True ,						lowerCAmelCase_       : Optional[Any]=True ,						lowerCAmelCase_       : List[Any]=128 ,						lowerCAmelCase_       : Dict=False ,						lowerCAmelCase_       : Tuple=True ,						lowerCAmelCase_       : Any=4 ,						lowerCAmelCase_       : Optional[Any]="no_norm" ,						lowerCAmelCase_       : Optional[Any]=True ,						lowerCAmelCase_       : Union[str, Any]=None ,						**lowerCAmelCase_       : Union[str, Any] ,						)			->		List[Any]:
						super().__init__(pad_token_id=lowerCAmelCase_ ,						**lowerCAmelCase_  )

						__A=  vocab_size
						__A=  hidden_size
						__A=  num_hidden_layers
						__A=  num_attention_heads
						__A=  hidden_act
						__A=  intermediate_size
						__A=  hidden_dropout_prob
						__A=  attention_probs_dropout_prob
						__A=  max_position_embeddings
						__A=  type_vocab_size
						__A=  initializer_range
						__A=  layer_norm_eps
						__A=  embedding_size
						__A=  trigram_input
						__A=  use_bottleneck
						__A=  intra_bottleneck_size
						__A=  use_bottleneck_attention
						__A=  key_query_shared_bottleneck
						__A=  num_feedforward_networks
						__A=  normalization_type
						__A=  classifier_activation

						if self.use_bottleneck:
									__A=  intra_bottleneck_size
						else:
									__A=  hidden_size

						__A=  classifier_dropout


class 	a__				(				a_      ):





			'''simple docstring'''

			@property
			def 				lowerCAmelCase    (     self       : List[str]  )			->		Mapping[str, Mapping[int, str]]:
						if self.task == "multiple-choice":
									__A=  {0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
									__A=  {0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						        ('token_type_ids', dynamic_axis),
						    ]  )



