



'''simple docstring'''






import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin

class     SCREAMING_SNAKE_CASE(							A__							,		A__							,		A__      ):







	"""simple docstring"""






	@register_to_config
	def __init__(						self      :					Any		,      __snake_case      :					int		,      __snake_case      :					int		,      __snake_case      :					int		,      __snake_case      :					float		,      __snake_case      :					int		,      __snake_case      :					int		,      __snake_case      :					int		,      __snake_case      :					int		,      __snake_case      :					str		,      __snake_case      :					bool = False		,      )      ->		str:
								super().__init__()

								UpperCAmelCase			:	Any										=		nn.Linear(__snake_case		,      __snake_case		,      bias=__snake_case		)

								UpperCAmelCase			:	Union[str, Any]										=		nn.Embedding(__snake_case		,      __snake_case		)
								UpperCAmelCase			:	Dict										=		False

								UpperCAmelCase			:	Any										=		nn.Dropout(p=__snake_case		)

								UpperCAmelCase			:	Dict										=		TaConfig(
								    d_model=__snake_case		,      num_heads=__snake_case		,      d_kv=__snake_case		,      d_ff=__snake_case		,      feed_forward_proj=__snake_case		,      dropout_rate=__snake_case		,      is_decoder=__snake_case		,      is_encoder_decoder=__snake_case		,      )
								UpperCAmelCase			:	List[str]										=		nn.ModuleList()
								for lyr_num in range(__snake_case		):
															UpperCAmelCase			:	Optional[int]										=		TaBlock(__snake_case		)
															self.encoders.append(__snake_case		)

								UpperCAmelCase			:	Optional[int]										=		TaLayerNorm(__snake_case		)
								UpperCAmelCase			:	Optional[Any]										=		nn.Dropout(p=__snake_case		)







	def 					A     (						self      :					List[Any]		,      __snake_case      :					int		,      __snake_case      :					Optional[Any]		)      ->		str:
								UpperCAmelCase			:	Dict										=		self.input_proj(__snake_case		)

								# terminal relative positional encodings
								UpperCAmelCase			:	str										=		encoder_inputs.shape[1]
								UpperCAmelCase			:	int										=		torch.arange(__snake_case		,      device=encoder_inputs.device		)

								UpperCAmelCase			:	Union[str, Any]										=		encoder_inputs_mask.sum(-1		)
								UpperCAmelCase			:	Optional[int]										=		torch.roll(input_positions.unsqueeze(0		)		,      tuple(seq_lens.tolist()		)		,      dims=0		)
								x += self.position_encoding(__snake_case		)

								UpperCAmelCase			:	Optional[int]										=		self.dropout_pre(__snake_case		)

								# inverted the attention mask
								UpperCAmelCase			:	Any										=		encoder_inputs.size()
								UpperCAmelCase			:	str										=		self.get_extended_attention_mask(__snake_case		,      __snake_case		)

								for lyr in self.encoders:
															UpperCAmelCase			:	str										=		lyr(__snake_case		,      __snake_case		)[0]
								UpperCAmelCase			:	str										=		self.layer_norm(__snake_case		)

								return self.dropout_post(__snake_case		), encoder_inputs_mask










'''simple docstring'''






UpperCamelCase__:       Any							     =      [[0, 1, 1], [0, 0, 1], [1, 0, 0]]

class     SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	def __init__(						self      :					List[str]		,      __snake_case      :					int		)      ->		int:
								UpperCAmelCase			:	Any										=		name
								UpperCAmelCase			:	Optional[Any]										=		[]
								UpperCAmelCase			:	Any										=		[]







	def 					A     (						self      :					Dict		,      __snake_case      :					str		)      ->		Tuple:
								self.inbound.append(__snake_case		)







	def 					A     (						self      :					Union[str, Any]		,      __snake_case      :					str		)      ->		Optional[int]:
								self.outbound.append(__snake_case		)







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


def 			snake_case_					( _lowerCAmelCase  :						Tuple       ,				_lowerCAmelCase  :						Dict=3       ,				_lowerCAmelCase  :						Tuple=0.8_5  )				->       Optional[Any]:
							UpperCAmelCase			:	Tuple										=		{}
							for node in nodes:
														UpperCAmelCase			:	List[str]										=		1

							UpperCAmelCase			:	List[Any]										=		{}
							for node in nodes:
														UpperCAmelCase			:	str										=		len(node.outbound  )

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


def 			snake_case_					( )				->       List[str]:
							UpperCAmelCase			:	str										=		list(input('''Enter Names of the Nodes: '''  ).split()  )

							UpperCAmelCase			:	Dict										=		[Node(_lowerCAmelCase  ) for name in names]

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

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

							page_rank(_lowerCAmelCase  )


if __name__ == "__main__":
						main()





