code: 
  
  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,
  )
  
  
  snake_case_    						=				logging.getLogger(__name__)
  
  
  class    SCREAMING_SNAKE_CASE__      (__snake_case  ):
  
  
  
  
  
      def    snake_case_       (   self		,     a		,     a		,     a=None		,     a=None):
           if current_layer == 0:
                lowercase__					:     Optional[Any]   = self.embedding_hidden_mapping_in(a)
           else:
                lowercase__					:     Union[str, Any]   = hidden_states[0]
  
           lowercase__					:     Dict   = int(self.config.num_hidden_layers / self.config.num_hidden_groups)
  
           # Index of the hidden group
           lowercase__					:     Dict   = int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups))
  
           lowercase__					:     List[Any]   = self.albert_layer_groups[group_idx](
               a		,     a		,     head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]		,     )
           lowercase__					:     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."""	, __snake_case	, )
  class    SCREAMING_SNAKE_CASE__      (__snake_case  ):
  
  
  
  
  
      def __init__(   self		,     a):
           super().__init__(a)
  
           lowercase__					:     Dict   = AlbertTransformerWithPabee(a)
  
           self.init_weights()
           lowercase__					:     str   = 0
           lowercase__					:     Union[str, Any]   = 0
           lowercase__					:     Dict   = 0
  
           lowercase__					:     str   = 0
  
  
  
  
  
      def    snake_case_       (   self		,     a):
           lowercase__					:     List[Any]   = threshold
  
  
  
  
  
      def    snake_case_       (   self		,     a):
           lowercase__					:     int   = patience
  
  
  
  
  
      def    snake_case_       (   self):
           lowercase__					:     Dict   = 0
           lowercase__					:     List[str]   = 0
  
  
  
  
  
      def    snake_case_       (   self):
           lowercase__					:     Union[str, Any]   = self.inference_layers_num / self.inference_instances_num
           lowercase__					:     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    snake_case_       (   self		,     a=None		,     a=None		,     a=None		,     a=None		,     a=None		,     a=None		,     a=None		,     a=None		,     a=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:
                lowercase__					:     Optional[Any]   = input_ids.size()
           elif inputs_embeds is not None:
                lowercase__					:     Optional[Any]   = inputs_embeds.size()[:-1]
           else:
                raise ValueError('You have to specify either input_ids or inputs_embeds')
  
           lowercase__					:     Tuple   = input_ids.device if input_ids is not None else inputs_embeds.device
  
           if attention_mask is None:
                lowercase__					:     Union[str, Any]   = torch.ones(a		,     device=a)
           if token_type_ids is None:
                lowercase__					:     Tuple   = torch.zeros(a		,     dtype=torch.long		,     device=a)
  
           lowercase__					:     Optional[Any]   = attention_mask.unsqueeze(1).unsqueeze(2)
           lowercase__					:     Union[str, Any]   = extended_attention_mask.to(dtype=self.dtype)  # fp16 compatibility
           lowercase__					:     Dict   = (1.0 - extended_attention_mask) * -1_0000.0
           lowercase__					:     Any   = self.get_head_mask(a		,     self.config.num_hidden_layers)
  
           lowercase__					:     int   = self.embeddings(
               a		,     position_ids=a		,     token_type_ids=a		,     inputs_embeds=a)
           lowercase__					:     Any   = embedding_output
  
           if self.training:
                lowercase__					:     Optional[Any]   = []
                for i in range(self.config.num_hidden_layers):
                     lowercase__					:     Union[str, Any]   = self.encoder.adaptive_forward(
                         a		,     current_layer=a		,     attention_mask=a		,     head_mask=a		,     )
  
                     lowercase__					:     List[str]   = self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]))
                     lowercase__					:     Dict   = output_layers[i](output_dropout(a))
                     res.append(a)
           elif self.patience == 0:  # Use all layers for inference
                lowercase__					:     Dict   = self.encoder(a		,     a		,     head_mask=a)
                lowercase__					:     Tuple   = self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]))
                lowercase__					:     Optional[int]   = [output_layers[self.config.num_hidden_layers - 1](a)]
           else:
                lowercase__					:     List[Any]   = 0
                lowercase__					:     int   = None
                lowercase__					:     int   = 0
                for i in range(self.config.num_hidden_layers):
                     calculated_layer_num += 1
                     lowercase__					:     Tuple   = self.encoder.adaptive_forward(
                         a		,     current_layer=a		,     attention_mask=a		,     head_mask=a		,     )
  
                     lowercase__					:     Any   = self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]))
                     lowercase__					:     Optional[Any]   = output_layers[i](a)
                     if regression:
                          lowercase__					:     str   = logits.detach()
                          if patient_result is not None:
                               lowercase__					:     str   = patient_result.detach()
                          if (patient_result is not None) and torch.abs(patient_result - labels) < self.regression_threshold:
                               patient_counter += 1
                          else:
                               lowercase__					:     List[Any]   = 0
                     else:
                          lowercase__					:     List[str]   = logits.detach().argmax(dim=1)
                          if patient_result is not None:
                               lowercase__					:     Optional[Any]   = patient_result.detach().argmax(dim=1)
                          if (patient_result is not None) and torch.all(labels.eq(a)):
                               patient_counter += 1
                          else:
                               lowercase__					:     Tuple   = 0
  
                     lowercase__					:     Any   = logits
                     if patient_counter == self.patience:
                          break
                lowercase__					:     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. """	, __snake_case	, )
  class    SCREAMING_SNAKE_CASE__      (__snake_case  ):
  
  
  
  
  
      def __init__(   self		,     a):
           super().__init__(a)
           lowercase__					:     List[Any]   = config.num_labels
  
           lowercase__					:     Optional[int]   = AlbertModelWithPabee(a)
           lowercase__					:     List[str]   = nn.Dropout(config.classifier_dropout_prob)
           lowercase__					:     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    snake_case_       (   self		,     a=None		,     a=None		,     a=None		,     a=None		,     a=None		,     a=None		,     a=None		,     ):
           lowercase__					:     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		,     )
  
           lowercase__					:     int   = (logits[-1],)
  
           if labels is not None:
                lowercase__					:     int   = None
                lowercase__					:     Tuple   = 0
                for ix, logits_item in enumerate(a):
                     if self.num_labels == 1:
                          #  We are doing regression
                          lowercase__					:     Dict   = MSELoss()
                          lowercase__					:     Optional[int]   = loss_fct(logits_item.view(-1)		,     labels.view(-1))
                     else:
                          lowercase__					:     List[str]   = CrossEntropyLoss()
                          lowercase__					:     Any   = loss_fct(logits_item.view(-1		,     self.num_labels)		,     labels.view(-1))
                     if total_loss is None:
                          lowercase__					:     Optional[Any]   = loss
                     else:
                          total_loss += loss * (ix + 1)
                     total_weights += ix + 1
                lowercase__					:     Any   = (total_loss / total_weights,) + outputs
  
           return outputs
  
  
style_context: 
  
  from __future__ import annotations
  
  
  class    SCREAMING_SNAKE_CASE__      :
  
  
  
  
  
      def __init__(   self		,     a = 0):
           lowercase__					:     int   = value
           lowercase__					:     Node | None   = None
           lowercase__					:     Node | None   = None
  
  
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Node | None			,  SCREAMING_SNAKE_CASE_	:					Node | None    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       if treea is None:
            return treea
       if treea is None:
            return treea
  
       lowercase__					:     Optional[Any]   = treea.value + treea.value
       lowercase__					:     Union[str, Any]   = merge_two_binary_trees(treea.left			,  treea.left    )
       lowercase__					:     Optional[Any]   = merge_two_binary_trees(treea.right			,  treea.right    )
       return treea
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Node | None    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       if root:
            print(root.value    )
            print_preorder(root.left    )
            print_preorder(root.right    )
  
  
  if __name__ == "__main__":
         snake_case_    						=				Node(1)
         snake_case_    						=				Node(2)
         snake_case_    						=				Node(3)
         snake_case_    						=				Node(4)
  
         snake_case_    						=				Node(2)
         snake_case_    						=				Node(4)
         snake_case_    						=				Node(6)
         snake_case_    						=				Node(9)
         snake_case_    						=				Node(5)
  
         print('''Tree1 is: ''')
         print_preorder(treea)
         print('''Tree2 is: ''')
         print_preorder(treea)
         snake_case_    						=				merge_two_binary_trees(treea, treea)
         print('''Merged Tree is: ''')
         print_preorder(merged_tree)
  
  