code: 
  
  
  
  """simple docstring"""
  import os
  
  
  
  
  
  def 				lowerCamelCase_		():
      _UpperCAmelCase							:						Dict       							=					os.path.dirname(os.path.realpath(UpperCamelCase__							)							)
      _UpperCAmelCase							:						str       							=					os.path.join(UpperCamelCase__  , '''triangle.txt'''							)
  
      with open(UpperCamelCase__							) as f:
          _UpperCAmelCase							:						Optional[Any]       							=					f.readlines()
  
      _UpperCAmelCase							:						List[str]       							=					[[int(UpperCamelCase__							) for y in x.rstrip('''\r\n'''							).split(''' '''							)] for x in triangle]
  
      for i in range(1  , len(UpperCamelCase__							)							):
          for j in range(len(a[i]							)							):
              _UpperCAmelCase							:						str       							=					a[i - 1][j] if j != len(a[i - 1]							) else 0
              _UpperCAmelCase							:						Any       							=					a[i - 1][j - 1] if j > 0 else 0
              a[i][j] += max(UpperCamelCase__  , UpperCamelCase__							)
      return max(a[-1]							)
  
  
  if __name__ == "__main__":
    print(solution())
  
  
  
style_context: 
  
  
  
  """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					:Union[str, Any]  =      logging.getLogger(__name__)
  class 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A ,			A=None ,			A=None	)							->      Tuple:
           if current_layer == 0:
               _UpperCAmelCase							:						Optional[Any]       							=					self.embedding_hidden_mapping_in(A	)
           else:
               _UpperCAmelCase							:						Union[str, Any]       							=					hidden_states[0]
  
           _UpperCAmelCase							:						Dict       							=					int(self.config.num_hidden_layers / self.config.num_hidden_groups	)
  
           # Index of the hidden group
           _UpperCAmelCase							:						Dict       							=					int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)	)
  
           _UpperCAmelCase							:						List[Any]       							=					self.albert_layer_groups[group_idx](
               A ,			A ,			head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group] ,			)
           _UpperCAmelCase							:						str       							=					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 ,			A	)							->      str:
           super().__init__(A	)
  
           _UpperCAmelCase							:						Dict       							=					AlbertTransformerWithPabee(A	)
  
           self.init_weights()
           _UpperCAmelCase							:						str       							=					0
           _UpperCAmelCase							:						Union[str, Any]       							=					0
           _UpperCAmelCase							:						Dict       							=					0
  
           _UpperCAmelCase							:						str       							=					0
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Optional[int]:
           _UpperCAmelCase							:						List[Any]       							=					threshold
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Optional[Any]:
           _UpperCAmelCase							:						int       							=					patience
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      Any:
           _UpperCAmelCase							:						Dict       							=					0
           _UpperCAmelCase							:						List[str]       							=					0
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      List[Any]:
           _UpperCAmelCase							:						Union[str, Any]       							=					self.inference_layers_num / self.inference_instances_num
           _UpperCAmelCase							:						str       							=					(
               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(A	)
  
  
  
  
  
       @add_start_docstrings_to_model_forward(A	)
       def 				__lowerCAmelCase			(      self ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			A=False ,			)							->      int:
  
           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[Any]       							=					input_ids.size()
           elif inputs_embeds is not None:
               _UpperCAmelCase							:						Optional[Any]       							=					inputs_embeds.size()[:-1]
           else:
               raise ValueError('''You have to specify either input_ids or inputs_embeds'''	)
  
           _UpperCAmelCase							:						Tuple       							=					input_ids.device if input_ids is not None else inputs_embeds.device
  
           if attention_mask is None:
               _UpperCAmelCase							:						Union[str, Any]       							=					torch.ones(A ,			device=A	)
           if token_type_ids is None:
               _UpperCAmelCase							:						Tuple       							=					torch.zeros(A ,			dtype=torch.long ,			device=A	)
  
           _UpperCAmelCase							:						Optional[Any]       							=					attention_mask.unsqueeze(1	).unsqueeze(2	)
           _UpperCAmelCase							:						Union[str, Any]       							=					extended_attention_mask.to(dtype=self.dtype	)  # fp16 compatibility
           _UpperCAmelCase							:						Dict       							=					(1.0 - extended_attention_mask) * -10_000.0
           _UpperCAmelCase							:						Any       							=					self.get_head_mask(A ,			self.config.num_hidden_layers	)
  
           _UpperCAmelCase							:						int       							=					self.embeddings(
               A ,			position_ids=A ,			token_type_ids=A ,			inputs_embeds=A	)
           _UpperCAmelCase							:						Any       							=					embedding_output
  
           if self.training:
               _UpperCAmelCase							:						Optional[Any]       							=					[]
               for i in range(self.config.num_hidden_layers	):
                   _UpperCAmelCase							:						Union[str, Any]       							=					self.encoder.adaptive_forward(
                       A ,			current_layer=A ,			attention_mask=A ,			head_mask=A ,			)
  
                   _UpperCAmelCase							:						List[str]       							=					self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]	)	)
                   _UpperCAmelCase							:						Dict       							=					output_layers[i](output_dropout(A	)	)
                   res.append(A	)
           elif self.patience == 0:  # Use all layers for inference
               _UpperCAmelCase							:						Dict       							=					self.encoder(A ,			A ,			head_mask=A	)
               _UpperCAmelCase							:						Tuple       							=					self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]	)	)
               _UpperCAmelCase							:						Optional[int]       							=					[output_layers[self.config.num_hidden_layers - 1](A	)]
           else:
               _UpperCAmelCase							:						List[Any]       							=					0
               _UpperCAmelCase							:						int       							=					None
               _UpperCAmelCase							:						int       							=					0
               for i in range(self.config.num_hidden_layers	):
                   calculated_layer_num += 1
                   _UpperCAmelCase							:						Tuple       							=					self.encoder.adaptive_forward(
                       A ,			current_layer=A ,			attention_mask=A ,			head_mask=A ,			)
  
                   _UpperCAmelCase							:						Any       							=					self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]	)	)
                   _UpperCAmelCase							:						Optional[Any]       							=					output_layers[i](A	)
                   if regression:
                       _UpperCAmelCase							:						str       							=					logits.detach()
                       if patient_result is not None:
                           _UpperCAmelCase							:						str       							=					patient_result.detach()
                       if (patient_result is not None) and torch.abs(patient_result - labels	) < self.regression_threshold:
                           patient_counter += 1
                       else:
                           _UpperCAmelCase							:						List[Any]       							=					0
                   else:
                       _UpperCAmelCase							:						List[str]       							=					logits.detach().argmax(dim=1	)
                       if patient_result is not None:
                           _UpperCAmelCase							:						Optional[Any]       							=					patient_result.detach().argmax(dim=1	)
                       if (patient_result is not None) and torch.all(labels.eq(A	)	):
                           patient_counter += 1
                       else:
                           _UpperCAmelCase							:						Tuple       							=					0
  
                   _UpperCAmelCase							:						Any       							=					logits
                   if patient_counter == self.patience:
                       break
               _UpperCAmelCase							:						List[str]       							=					[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 ,			A	)							->      Optional[int]:
           super().__init__(A	)
           _UpperCAmelCase							:						List[Any]       							=					config.num_labels
  
           _UpperCAmelCase							:						Optional[int]       							=					AlbertModelWithPabee(A	)
           _UpperCAmelCase							:						List[str]       							=					nn.Dropout(config.classifier_dropout_prob	)
           _UpperCAmelCase							:						Optional[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(A	)
       def 				__lowerCAmelCase			(      self ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			)							->      List[str]:
           _UpperCAmelCase							:						Optional[Any]       							=					self.albert(
               input_ids=A ,			attention_mask=A ,			token_type_ids=A ,			position_ids=A ,			head_mask=A ,			inputs_embeds=A ,			output_dropout=self.dropout ,			output_layers=self.classifiers ,			regression=self.num_labels == 1 ,			)
  
           _UpperCAmelCase							:						int       							=					(logits[-1],)
  
           if labels is not None:
               _UpperCAmelCase							:						int       							=					None
               _UpperCAmelCase							:						Tuple       							=					0
               for ix, logits_item in enumerate(A	):
                   if self.num_labels == 1:
                       #  We are doing regression
                       _UpperCAmelCase							:						Dict       							=					MSELoss()
                       _UpperCAmelCase							:						Optional[int]       							=					loss_fct(logits_item.view(-1	) ,			labels.view(-1	)	)
                   else:
                       _UpperCAmelCase							:						List[str]       							=					CrossEntropyLoss()
                       _UpperCAmelCase							:						Any       							=					loss_fct(logits_item.view(-1 ,			self.num_labels	) ,			labels.view(-1	)	)
                   if total_loss is None:
                       _UpperCAmelCase							:						Optional[Any]       							=					loss
                   else:
                       total_loss += loss * (ix + 1)
                   total_weights += ix + 1
               _UpperCAmelCase							:						Any       							=					(total_loss / total_weights,) + outputs
  
           return outputs
  
  
  