

'''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,
)


a       :      List[str]  	=			logging.getLogger(__name__)







class   UpperCamelCase__   (       lowercase__       ):


		"""simple docstring"""

		def 				A_	(			self   ,      snake_case   ,      snake_case   ,      snake_case=None   ,      snake_case=None      ):


							'''simple docstring'''

							if current_layer == 0:
												UpperCAmelCase		:							Any        =       self.embedding_hidden_mapping_in(snake_case      )
							else:
												UpperCAmelCase		:							Union[str, Any]        =       hidden_states[0]

							UpperCAmelCase		:							int        =       int(self.config.num_hidden_layers / self.config.num_hidden_groups      )

							# Index of the hidden group
							UpperCAmelCase		:							List[Any]        =       int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)      )

							UpperCAmelCase		:							str        =       self.albert_layer_groups[group_idx](
							    snake_case   ,      snake_case   ,      head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]   ,      )
							UpperCAmelCase		:							int        =       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."							,							lowercase__							,							)
class   UpperCamelCase__   (       lowercase__       ):


		"""simple docstring"""

		def __init__(			self   ,      snake_case      ):


							'''simple docstring'''

							super().__init__(snake_case      )

							UpperCAmelCase		:							List[str]        =       AlbertTransformerWithPabee(snake_case      )

							self.init_weights()
							UpperCAmelCase		:							Union[str, Any]        =       0
							UpperCAmelCase		:							Tuple        =       0
							UpperCAmelCase		:							int        =       0

							UpperCAmelCase		:							List[Any]        =       0







		def 				A_	(			self   ,      snake_case      ):


							'''simple docstring'''

							UpperCAmelCase		:							Optional[int]        =       threshold







		def 				A_	(			self   ,      snake_case      ):


							'''simple docstring'''

							UpperCAmelCase		:							List[Any]        =       patience







		def 				A_	(			self      ):


							'''simple docstring'''

							UpperCAmelCase		:							int        =       0
							UpperCAmelCase		:							Optional[int]        =       0







		def 				A_	(			self      ):


							'''simple docstring'''

							UpperCAmelCase		:							List[Any]        =       self.inference_layers_num / self.inference_instances_num
							UpperCAmelCase		:							Optional[int]        =       (
							    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(snake_case      )







		@add_start_docstrings_to_model_forward(snake_case      )
		def 				A_	(			self   ,      snake_case=None   ,      snake_case=None   ,      snake_case=None   ,      snake_case=None   ,      snake_case=None   ,      snake_case=None   ,      snake_case=None   ,      snake_case=None   ,      snake_case=False   ,      ):


							'''simple docstring'''

							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:
												UpperCAmelCase		:							Optional[int]        =       input_ids.size()
							elif inputs_embeds is not None:
												UpperCAmelCase		:							List[Any]        =       inputs_embeds.size()[:-1]
							else:
												raise ValueError("You have to specify either input_ids or inputs_embeds"      )

							UpperCAmelCase		:							List[Any]        =       input_ids.device if input_ids is not None else inputs_embeds.device

							if attention_mask is None:
												UpperCAmelCase		:							Any        =       torch.ones(snake_case   ,      device=snake_case      )
							if token_type_ids is None:
												UpperCAmelCase		:							Tuple        =       torch.zeros(snake_case   ,      dtype=torch.long   ,      device=snake_case      )

							UpperCAmelCase		:							List[str]        =       attention_mask.unsqueeze(1      ).unsqueeze(2      )
							UpperCAmelCase		:							List[str]        =       extended_attention_mask.to(dtype=self.dtype      )  # fp16 compatibility
							UpperCAmelCase		:							List[Any]        =       (1.0 - extended_attention_mask) * -1_0000.0
							UpperCAmelCase		:							List[str]        =       self.get_head_mask(snake_case   ,      self.config.num_hidden_layers      )

							UpperCAmelCase		:							List[str]        =       self.embeddings(
							    snake_case   ,      position_ids=snake_case   ,      token_type_ids=snake_case   ,      inputs_embeds=snake_case      )
							UpperCAmelCase		:							str        =       embedding_output

							if self.training:
												UpperCAmelCase		:							str        =       []
												for i in range(self.config.num_hidden_layers      ):
																	UpperCAmelCase		:							int        =       self.encoder.adaptive_forward(
																	    snake_case   ,      current_layer=snake_case   ,      attention_mask=snake_case   ,      head_mask=snake_case   ,      )

																	UpperCAmelCase		:							List[str]        =       self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]      )      )
																	UpperCAmelCase		:							Optional[int]        =       output_layers[i](output_dropout(snake_case      )      )
																	res.append(snake_case      )
							elif self.patience == 0:  # Use all layers for inference
												UpperCAmelCase		:							Optional[Any]        =       self.encoder(snake_case   ,      snake_case   ,      head_mask=snake_case      )
												UpperCAmelCase		:							Union[str, Any]        =       self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]      )      )
												UpperCAmelCase		:							Dict        =       [output_layers[self.config.num_hidden_layers - 1](snake_case      )]
							else:
												UpperCAmelCase		:							int        =       0
												UpperCAmelCase		:							Union[str, Any]        =       None
												UpperCAmelCase		:							Tuple        =       0
												for i in range(self.config.num_hidden_layers      ):
																	calculated_layer_num += 1
																	UpperCAmelCase		:							Dict        =       self.encoder.adaptive_forward(
																	    snake_case   ,      current_layer=snake_case   ,      attention_mask=snake_case   ,      head_mask=snake_case   ,      )

																	UpperCAmelCase		:							Union[str, Any]        =       self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]      )      )
																	UpperCAmelCase		:							int        =       output_layers[i](snake_case      )
																	if regression:
																						UpperCAmelCase		:							str        =       logits.detach()
																						if patient_result is not None:
																											UpperCAmelCase		:							Tuple        =       patient_result.detach()
																						if (patient_result is not None) and torch.abs(patient_result - labels      ) < self.regression_threshold:
																											patient_counter += 1
																						else:
																											UpperCAmelCase		:							List[str]        =       0
																	else:
																						UpperCAmelCase		:							Any        =       logits.detach().argmax(dim=1      )
																						if patient_result is not None:
																											UpperCAmelCase		:							Union[str, Any]        =       patient_result.detach().argmax(dim=1      )
																						if (patient_result is not None) and torch.all(labels.eq(snake_case      )      ):
																											patient_counter += 1
																						else:
																											UpperCAmelCase		:							Union[str, Any]        =       0

																	UpperCAmelCase		:							Any        =       logits
																	if patient_counter == self.patience:
																						break
												UpperCAmelCase		:							Optional[Any]        =       [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\n    the pooled output) e.g. for GLUE tasks. "							,							lowercase__							,							)
class   UpperCamelCase__   (       lowercase__       ):


		"""simple docstring"""

		def __init__(			self   ,      snake_case      ):


							'''simple docstring'''

							super().__init__(snake_case      )
							UpperCAmelCase		:							Optional[Any]        =       config.num_labels

							UpperCAmelCase		:							Tuple        =       AlbertModelWithPabee(snake_case      )
							UpperCAmelCase		:							List[Any]        =       nn.Dropout(config.classifier_dropout_prob      )
							UpperCAmelCase		:							Any        =       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(snake_case      )
		def 				A_	(			self   ,      snake_case=None   ,      snake_case=None   ,      snake_case=None   ,      snake_case=None   ,      snake_case=None   ,      snake_case=None   ,      snake_case=None   ,      ):


							'''simple docstring'''

							UpperCAmelCase		:							Dict        =       self.albert(
							    input_ids=snake_case   ,      attention_mask=snake_case   ,      token_type_ids=snake_case   ,      position_ids=snake_case   ,      head_mask=snake_case   ,      inputs_embeds=snake_case   ,      output_dropout=self.dropout   ,      output_layers=self.classifiers   ,      regression=self.num_labels == 1   ,      )

							UpperCAmelCase		:							Union[str, Any]        =       (logits[-1],)

							if labels is not None:
												UpperCAmelCase		:							str        =       None
												UpperCAmelCase		:							Union[str, Any]        =       0
												for ix, logits_item in enumerate(snake_case      ):
																	if self.num_labels == 1:
																						#  We are doing regression
																						UpperCAmelCase		:							int        =       MSELoss()
																						UpperCAmelCase		:							Union[str, Any]        =       loss_fct(logits_item.view(-1      )   ,      labels.view(-1      )      )
																	else:
																						UpperCAmelCase		:							Optional[Any]        =       CrossEntropyLoss()
																						UpperCAmelCase		:							Union[str, Any]        =       loss_fct(logits_item.view(-1   ,      self.num_labels      )   ,      labels.view(-1      )      )
																	if total_loss is None:
																						UpperCAmelCase		:							Tuple        =       loss
																	else:
																						total_loss += loss * (ix + 1)
																	total_weights += ix + 1
												UpperCAmelCase		:							List[str]        =       (total_loss / total_weights,) + outputs

							return outputs



'''simple docstring'''

a       :      str  	=			[[0, 1, 1], [0, 0, 1], [1, 0, 0]]







class   UpperCamelCase__   :


		"""simple docstring"""

		def __init__(			self   ,      snake_case      ):


							'''simple docstring'''

							UpperCAmelCase		:							Optional[int]        =       name
							UpperCAmelCase		:							Optional[Any]        =       []
							UpperCAmelCase		:							Tuple        =       []







		def 				A_	(			self   ,      snake_case      ):


							'''simple docstring'''

							self.inbound.append(snake_case      )







		def 				A_	(			self   ,      snake_case      ):


							'''simple docstring'''

							self.outbound.append(snake_case      )







		def __repr__(			self      ):


							'''simple docstring'''

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





def 				lowercase (					__magic_name__			,   __magic_name__=3			,   __magic_name__=0.8_5							):



					'''simple docstring'''







					UpperCAmelCase		:							str        =       {}
					for node in nodes:
										UpperCAmelCase		:							Tuple        =       1

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

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





def 				lowercase (					):



					'''simple docstring'''







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

					UpperCAmelCase		:							Any        =       [Node(__magic_name__							) for name in names]

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

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

					page_rank(__magic_name__							)


if __name__ == "__main__":
				main()
