





'''simple docstring'''


import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.albert.modeling_albert import (
    ALBERT_INPUTS_DOCSTRING,
    ALBERT_START_DOCSTRING,
    AlbertModel,
    AlbertPreTrainedModel,
    AlbertTransformer,
)


_lowerCamelCase				:	int				    =       logging.getLogger(__name__)



class 							__UpperCAmelCase  (       A__						):







		'''simple docstring'''







		def        A     (self						:      Any      ,      _lowerCAmelCase						:      Any      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      Optional[Any]=None      ,      _lowerCAmelCase						:      str=None      ):
			if current_layer == 0:
				A							  =      self.embedding_hidden_mapping_in(_lowerCAmelCase      )
			else:
				A							  =      hidden_states[0]

			A							  =      int(self.config.num_hidden_layers / self.config.num_hidden_groups      )

			# Index of the hidden group
			A							  =      int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)      )

			A							  =      self.albert_layer_groups[group_idx](
			    _lowerCAmelCase      ,      _lowerCAmelCase      ,      head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]      ,      )
			A							  =      layer_group_output[0]

			return (hidden_states,)






@add_start_docstrings(
    '''The bare ALBERT Model transformer with PABEE outputting raw hidden-states without any specific head on top.'''		,					A__		,					)
class 							__UpperCAmelCase  (       A__						):







		'''simple docstring'''







		def __init__(self						:      Union[str, Any]      ,      _lowerCAmelCase						:      Optional[int]      ):
			super().__init__(_lowerCAmelCase      )

			A							  =      AlbertTransformerWithPabee(_lowerCAmelCase      )

			self.init_weights()
			A							  =      0
			A							  =      0
			A							  =      0

			A							  =      0







		def        A     (self						:      Any      ,      _lowerCAmelCase						:      Tuple      ):
			A							  =      threshold







		def        A     (self						:      Tuple      ,      _lowerCAmelCase						:      List[Any]      ):
			A							  =      patience







		def        A     (self						:      List[str]      ):
			A							  =      0
			A							  =      0







		def        A     (self						:      str      ):
			A							  =      self.inference_layers_num / self.inference_instances_num
			A							  =      (
			    F"""*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ="""
			    F""" {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"""
			)
			print(_lowerCAmelCase      )







		@add_start_docstrings_to_model_forward(_lowerCAmelCase      )
		def        A     (self						:      Dict      ,      _lowerCAmelCase						:      int=None      ,      _lowerCAmelCase						:      str=None      ,      _lowerCAmelCase						:      Optional[int]=None      ,      _lowerCAmelCase						:      Union[str, Any]=None      ,      _lowerCAmelCase						:      List[str]=None      ,      _lowerCAmelCase						:      Optional[int]=None      ,      _lowerCAmelCase						:      Union[str, Any]=None      ,      _lowerCAmelCase						:      Tuple=None      ,      _lowerCAmelCase						:      List[Any]=False      ,      ):

			if input_ids is not None and inputs_embeds is not None:
				raise ValueError("""You cannot specify both input_ids and inputs_embeds at the same time"""      )
			elif input_ids is not None:
				A							  =      input_ids.size()
			elif inputs_embeds is not None:
				A							  =      inputs_embeds.size()[:-1]
			else:
				raise ValueError("""You have to specify either input_ids or inputs_embeds"""      )

			A							  =      input_ids.device if input_ids is not None else inputs_embeds.device

			if attention_mask is None:
				A							  =      torch.ones(_lowerCAmelCase      ,      device=_lowerCAmelCase      )
			if token_type_ids is None:
				A							  =      torch.zeros(_lowerCAmelCase      ,      dtype=torch.long      ,      device=_lowerCAmelCase      )

			A							  =      attention_mask.unsqueeze(1      ).unsqueeze(2      )
			A							  =      extended_attention_mask.to(dtype=self.dtype      )  # fp16 compatibility
			A							  =      (1.0 - extended_attention_mask) * -10_000.0
			A							  =      self.get_head_mask(_lowerCAmelCase      ,      self.config.num_hidden_layers      )

			A							  =      self.embeddings(
			    _lowerCAmelCase      ,      position_ids=_lowerCAmelCase      ,      token_type_ids=_lowerCAmelCase      ,      inputs_embeds=_lowerCAmelCase      )
			A							  =      embedding_output

			if self.training:
				A							  =      []
				for i in range(self.config.num_hidden_layers      ):
					A							  =      self.encoder.adaptive_forward(
					    _lowerCAmelCase      ,      current_layer=_lowerCAmelCase      ,      attention_mask=_lowerCAmelCase      ,      head_mask=_lowerCAmelCase      ,      )

					A							  =      self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]      )      )
					A							  =      output_layers[i](output_dropout(_lowerCAmelCase      )      )
					res.append(_lowerCAmelCase      )
			elif self.patience == 0:  # Use all layers for inference
				A							  =      self.encoder(_lowerCAmelCase      ,      _lowerCAmelCase      ,      head_mask=_lowerCAmelCase      )
				A							  =      self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]      )      )
				A							  =      [output_layers[self.config.num_hidden_layers - 1](_lowerCAmelCase      )]
			else:
				A							  =      0
				A							  =      None
				A							  =      0
				for i in range(self.config.num_hidden_layers      ):
					calculated_layer_num += 1
					A							  =      self.encoder.adaptive_forward(
					    _lowerCAmelCase      ,      current_layer=_lowerCAmelCase      ,      attention_mask=_lowerCAmelCase      ,      head_mask=_lowerCAmelCase      ,      )

					A							  =      self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]      )      )
					A							  =      output_layers[i](_lowerCAmelCase      )
					if regression:
						A							  =      logits.detach()
						if patient_result is not None:
							A							  =      patient_result.detach()
						if (patient_result is not None) and torch.abs(patient_result - labels      ) < self.regression_threshold:
							patient_counter += 1
						else:
							A							  =      0
					else:
						A							  =      logits.detach().argmax(dim=1      )
						if patient_result is not None:
							A							  =      patient_result.detach().argmax(dim=1      )
						if (patient_result is not None) and torch.all(labels.eq(_lowerCAmelCase      )      ):
							patient_counter += 1
						else:
							A							  =      0

					A							  =      logits
					if patient_counter == self.patience:
						break
				A							  =      [patient_result]
				self.inference_layers_num += calculated_layer_num
				self.inference_instances_num += 1

			return res






@add_start_docstrings(
    '''Albert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. '''		,					A__		,					)
class 							__UpperCAmelCase  (       A__						):







		'''simple docstring'''







		def __init__(self						:      Optional[Any]      ,      _lowerCAmelCase						:      str      ):
			super().__init__(_lowerCAmelCase      )
			A							  =      config.num_labels

			A							  =      AlbertModelWithPabee(_lowerCAmelCase      )
			A							  =      nn.Dropout(config.classifier_dropout_prob      )
			A							  =      nn.ModuleList(
			    [nn.Linear(config.hidden_size      ,      self.config.num_labels      ) for _ in range(config.num_hidden_layers      )]      )

			self.init_weights()







		@add_start_docstrings_to_model_forward(_lowerCAmelCase      )
		def        A     (self						:      int      ,      _lowerCAmelCase						:      Any=None      ,      _lowerCAmelCase						:      Optional[int]=None      ,      _lowerCAmelCase						:      str=None      ,      _lowerCAmelCase						:      Optional[int]=None      ,      _lowerCAmelCase						:      int=None      ,      _lowerCAmelCase						:      Union[str, Any]=None      ,      _lowerCAmelCase						:      Tuple=None      ,      ):
			A							  =      self.albert(
			    input_ids=_lowerCAmelCase      ,      attention_mask=_lowerCAmelCase      ,      token_type_ids=_lowerCAmelCase      ,      position_ids=_lowerCAmelCase      ,      head_mask=_lowerCAmelCase      ,      inputs_embeds=_lowerCAmelCase      ,      output_dropout=self.dropout      ,      output_layers=self.classifiers      ,      regression=self.num_labels == 1      ,      )

			A							  =      (logits[-1],)

			if labels is not None:
				A							  =      None
				A							  =      0
				for ix, logits_item in enumerate(_lowerCAmelCase      ):
					if self.num_labels == 1:
						#  We are doing regression
						A							  =      MSELoss()
						A							  =      loss_fct(logits_item.view(-1      )      ,      labels.view(-1      )      )
					else:
						A							  =      CrossEntropyLoss()
						A							  =      loss_fct(logits_item.view(-1      ,      self.num_labels      )      ,      labels.view(-1      )      )
					if total_loss is None:
						A							  =      loss
					else:
						total_loss += loss * (ix + 1)
					total_weights += ix + 1
				A							  =      (total_loss / total_weights,) + outputs

			return outputs








'''simple docstring'''


_lowerCamelCase				:	List[str]				    =       [[0, 1, 1], [0, 0, 1], [1, 0, 0]]



class 							__UpperCAmelCase  :







		'''simple docstring'''







		def __init__(self						:      List[Any]      ,      _lowerCAmelCase						:      List[Any]      ):
			A							  =      name
			A							  =      []
			A							  =      []







		def        A     (self						:      Dict      ,      _lowerCAmelCase						:      str      ):
			self.inbound.append(_lowerCAmelCase      )







		def        A     (self						:      Dict      ,      _lowerCAmelCase						:      Any      ):
			self.outbound.append(_lowerCAmelCase      )







		def __repr__(self						:      Tuple      ):
			return F"""<node={self.name} inbound={self.inbound} outbound={self.outbound}>"""






def    __a (     UpperCAmelCase       ,			UpperCAmelCase=3       ,			UpperCAmelCase=0.85    )      ->Tuple:



	"""simple docstring"""







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

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

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




def    __a (     )      ->Any:



	"""simple docstring"""







	A							  =      list(input("""Enter Names of the Nodes: """    ).split()    )

	A							  =      [Node(UpperCAmelCase    ) for name in names]

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

	print("""======= Nodes ======="""    )
	for node in nodes:
		print(UpperCAmelCase    )

	page_rank(UpperCAmelCase    )


if __name__ == "__main__":
				main()

