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,
  )
  
  
  __A   :     Optional[Any]      =			logging.getLogger(__name__)
  
  
  
  
  
  
  class    __A				(							lowerCAmelCase ):
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[Any]    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    List[Any]    ,				UpperCAmelCase_			:    Tuple=None    ,				UpperCAmelCase_			:    Dict=None    ):
          if current_layer == 0:
              lowerCAmelCase				:			List[str]    			=      self.embedding_hidden_mapping_in(UpperCamelCase_    )
          else:
              lowerCAmelCase				:			Union[str, Any]    			=      hidden_states[0]
  
          lowerCAmelCase				:			Tuple    			=      int(self.config.num_hidden_layers / self.config.num_hidden_groups    )
  
          # Index of the hidden group
          lowerCAmelCase				:			Union[str, Any]    			=      int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)    )
  
          lowerCAmelCase				:			Any    			=      self.albert_layer_groups[group_idx](
              UpperCamelCase_    ,				UpperCamelCase_    ,				head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]    ,				)
          lowerCAmelCase				:			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."						,						lowerCAmelCase						,						)
  class    __A				(							lowerCAmelCase ):
  
  
  
  
  
  
      def __init__(	self			:    Tuple    ,				UpperCAmelCase_			:    Union[str, Any]    ):
          super().__init__(UpperCamelCase_    )
  
          lowerCAmelCase				:			List[str]    			=      AlbertTransformerWithPabee(UpperCamelCase_    )
  
          self.init_weights()
          lowerCAmelCase				:			List[str]    			=      0
          lowerCAmelCase				:			Tuple    			=      0
          lowerCAmelCase				:			Union[str, Any]    			=      0
  
          lowerCAmelCase				:			Tuple    			=      0
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[Any]    ,				UpperCAmelCase_			:    Union[str, Any]    ):
          lowerCAmelCase				:			Tuple    			=      threshold
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Any    ,				UpperCAmelCase_			:    str    ):
          lowerCAmelCase				:			Any    			=      patience
  
  
  
  
  
  
      def 	lowercase__ (	self			:    int    ):
          lowerCAmelCase				:			Tuple    			=      0
          lowerCAmelCase				:			int    			=      0
  
  
  
  
  
  
      def 	lowercase__ (	self			:    str    ):
          lowerCAmelCase				:			str    			=      self.inference_layers_num / self.inference_instances_num
          lowerCAmelCase				:			List[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(UpperCamelCase_    )
  
  
  
  
  
  
      @add_start_docstrings_to_model_forward(UpperCamelCase_    )
      def 	lowercase__ (	self			:    int    ,				UpperCAmelCase_			:    List[Any]=None    ,				UpperCAmelCase_			:    List[str]=None    ,				UpperCAmelCase_			:    List[Any]=None    ,				UpperCAmelCase_			:    Dict=None    ,				UpperCAmelCase_			:    Optional[int]=None    ,				UpperCAmelCase_			:    str=None    ,				UpperCAmelCase_			:    str=None    ,				UpperCAmelCase_			:    int=None    ,				UpperCAmelCase_			:    Union[str, 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:
              lowerCAmelCase				:			Tuple    			=      input_ids.size()
          elif inputs_embeds is not None:
              lowerCAmelCase				:			str    			=      inputs_embeds.size()[:-1]
          else:
              raise ValueError('You have to specify either input_ids or inputs_embeds'    )
  
          lowerCAmelCase				:			Tuple    			=      input_ids.device if input_ids is not None else inputs_embeds.device
  
          if attention_mask is None:
              lowerCAmelCase				:			Optional[Any]    			=      torch.ones(UpperCamelCase_    ,				device=UpperCamelCase_    )
          if token_type_ids is None:
              lowerCAmelCase				:			Any    			=      torch.zeros(UpperCamelCase_    ,				dtype=torch.long    ,				device=UpperCamelCase_    )
  
          lowerCAmelCase				:			Dict    			=      attention_mask.unsqueeze(1    ).unsqueeze(2    )
          lowerCAmelCase				:			int    			=      extended_attention_mask.to(dtype=self.dtype    )  # fp16 compatibility
          lowerCAmelCase				:			Dict    			=      (1.0 - extended_attention_mask) * -10000.0
          lowerCAmelCase				:			str    			=      self.get_head_mask(UpperCamelCase_    ,				self.config.num_hidden_layers    )
  
          lowerCAmelCase				:			List[Any]    			=      self.embeddings(
              UpperCamelCase_    ,				position_ids=UpperCamelCase_    ,				token_type_ids=UpperCamelCase_    ,				inputs_embeds=UpperCamelCase_    )
          lowerCAmelCase				:			Any    			=      embedding_output
  
          if self.training:
              lowerCAmelCase				:			str    			=      []
              for i in range(self.config.num_hidden_layers    ):
                  lowerCAmelCase				:			Dict    			=      self.encoder.adaptive_forward(
                      UpperCamelCase_    ,				current_layer=UpperCamelCase_    ,				attention_mask=UpperCamelCase_    ,				head_mask=UpperCamelCase_    ,				)
  
                  lowerCAmelCase				:			Dict    			=      self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]    )    )
                  lowerCAmelCase				:			Union[str, Any]    			=      output_layers[i](output_dropout(UpperCamelCase_    )    )
                  res.append(UpperCamelCase_    )
          elif self.patience == 0:  # Use all layers for inference
              lowerCAmelCase				:			str    			=      self.encoder(UpperCamelCase_    ,				UpperCamelCase_    ,				head_mask=UpperCamelCase_    )
              lowerCAmelCase				:			List[Any]    			=      self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]    )    )
              lowerCAmelCase				:			int    			=      [output_layers[self.config.num_hidden_layers - 1](UpperCamelCase_    )]
          else:
              lowerCAmelCase				:			Tuple    			=      0
              lowerCAmelCase				:			List[Any]    			=      None
              lowerCAmelCase				:			Dict    			=      0
              for i in range(self.config.num_hidden_layers    ):
                  calculated_layer_num += 1
                  lowerCAmelCase				:			List[Any]    			=      self.encoder.adaptive_forward(
                      UpperCamelCase_    ,				current_layer=UpperCamelCase_    ,				attention_mask=UpperCamelCase_    ,				head_mask=UpperCamelCase_    ,				)
  
                  lowerCAmelCase				:			Optional[int]    			=      self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]    )    )
                  lowerCAmelCase				:			str    			=      output_layers[i](UpperCamelCase_    )
                  if regression:
                      lowerCAmelCase				:			Any    			=      logits.detach()
                      if patient_result is not None:
                          lowerCAmelCase				:			Union[str, Any]    			=      patient_result.detach()
                      if (patient_result is not None) and torch.abs(patient_result - labels    ) < self.regression_threshold:
                          patient_counter += 1
                      else:
                          lowerCAmelCase				:			List[Any]    			=      0
                  else:
                      lowerCAmelCase				:			int    			=      logits.detach().argmax(dim=1    )
                      if patient_result is not None:
                          lowerCAmelCase				:			Dict    			=      patient_result.detach().argmax(dim=1    )
                      if (patient_result is not None) and torch.all(labels.eq(UpperCamelCase_    )    ):
                          patient_counter += 1
                      else:
                          lowerCAmelCase				:			Tuple    			=      0
  
                  lowerCAmelCase				:			Optional[int]    			=      logits
                  if patient_counter == self.patience:
                      break
              lowerCAmelCase				:			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. "						,						lowerCAmelCase						,						)
  class    __A				(							lowerCAmelCase ):
  
  
  
  
  
  
      def __init__(	self			:    str    ,				UpperCAmelCase_			:    Any    ):
          super().__init__(UpperCamelCase_    )
          lowerCAmelCase				:			Optional[int]    			=      config.num_labels
  
          lowerCAmelCase				:			int    			=      AlbertModelWithPabee(UpperCamelCase_    )
          lowerCAmelCase				:			Union[str, Any]    			=      nn.Dropout(config.classifier_dropout_prob    )
          lowerCAmelCase				:			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(UpperCamelCase_    )
      def 	lowercase__ (	self			:    Optional[int]    ,				UpperCAmelCase_			:    Any=None    ,				UpperCAmelCase_			:    Tuple=None    ,				UpperCAmelCase_			:    Dict=None    ,				UpperCAmelCase_			:    int=None    ,				UpperCAmelCase_			:    List[str]=None    ,				UpperCAmelCase_			:    Optional[Any]=None    ,				UpperCAmelCase_			:    Optional[Any]=None    ,				):
          lowerCAmelCase				:			int    			=      self.albert(
              input_ids=UpperCamelCase_    ,				attention_mask=UpperCamelCase_    ,				token_type_ids=UpperCamelCase_    ,				position_ids=UpperCamelCase_    ,				head_mask=UpperCamelCase_    ,				inputs_embeds=UpperCamelCase_    ,				output_dropout=self.dropout    ,				output_layers=self.classifiers    ,				regression=self.num_labels == 1    ,				)
  
          lowerCAmelCase				:			Union[str, Any]    			=      (logits[-1],)
  
          if labels is not None:
              lowerCAmelCase				:			Union[str, Any]    			=      None
              lowerCAmelCase				:			List[str]    			=      0
              for ix, logits_item in enumerate(UpperCamelCase_    ):
                  if self.num_labels == 1:
                      #  We are doing regression
                      lowerCAmelCase				:			List[Any]    			=      MSELoss()
                      lowerCAmelCase				:			List[str]    			=      loss_fct(logits_item.view(-1    )    ,				labels.view(-1    )    )
                  else:
                      lowerCAmelCase				:			Union[str, Any]    			=      CrossEntropyLoss()
                      lowerCAmelCase				:			Optional[Any]    			=      loss_fct(logits_item.view(-1    ,				self.num_labels    )    ,				labels.view(-1    )    )
                  if total_loss is None:
                      lowerCAmelCase				:			Optional[Any]    			=      loss
                  else:
                      total_loss += loss * (ix + 1)
                  total_weights += ix + 1
              lowerCAmelCase				:			Union[str, Any]    			=      (total_loss / total_weights,) + outputs
  
          return outputs
  
  
  
  
  
  
style_context: 
  
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      )					-> tuple[list[list[int]], list[list[str]]]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			List[Any]    			=      list(_UpperCAmelCase				)
      lowerCAmelCase				:			Union[str, Any]    			=      list(_UpperCAmelCase				)
      lowerCAmelCase				:			Optional[int]    			=      len(_UpperCAmelCase				)
      lowerCAmelCase				:			List[Any]    			=      len(_UpperCAmelCase				)
  
      lowerCAmelCase				:			str    			=      [
          [0 for _ in range(len_destination_seq + 1				)] for _ in range(len_source_seq + 1				)
      ]
      lowerCAmelCase				:			str    			=      [
          ['0' for _ in range(len_destination_seq + 1				)] for _ in range(len_source_seq + 1				)
      ]
  
      for i in range(1,      len_source_seq + 1				):
          lowerCAmelCase				:			int    			=      i * delete_cost
          lowerCAmelCase				:			List[str]    			=      f"D{source_seq[i - 1]:c}"
  
      for i in range(1,      len_destination_seq + 1				):
          lowerCAmelCase				:			Tuple    			=      i * insert_cost
          lowerCAmelCase				:			Union[str, Any]    			=      f"I{destination_seq[i - 1]:c}"
  
      for i in range(1,      len_source_seq + 1				):
          for j in range(1,      len_destination_seq + 1				):
              if source_seq[i - 1] == destination_seq[j - 1]:
                  lowerCAmelCase				:			str    			=      costs[i - 1][j - 1] + copy_cost
                  lowerCAmelCase				:			Tuple    			=      f"C{source_seq[i - 1]:c}"
              else:
                  lowerCAmelCase				:			Optional[int]    			=      costs[i - 1][j - 1] + replace_cost
                  lowerCAmelCase				:			Optional[int]    			=      f"R{source_seq[i - 1]:c}" + str(destination_seq[j - 1]				)
  
              if costs[i - 1][j] + delete_cost < costs[i][j]:
                  lowerCAmelCase				:			List[str]    			=      costs[i - 1][j] + delete_cost
                  lowerCAmelCase				:			Optional[int]    			=      f"D{source_seq[i - 1]:c}"
  
              if costs[i][j - 1] + insert_cost < costs[i][j]:
                  lowerCAmelCase				:			Union[str, Any]    			=      costs[i][j - 1] + insert_cost
                  lowerCAmelCase				:			str    			=      f"I{destination_seq[j - 1]:c}"
  
      return costs, ops
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase				)					-> list[str]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      if i == 0 and j == 0:
          return []
      else:
          if ops[i][j][0] == "C" or ops[i][j][0] == "R":
              lowerCAmelCase				:			List[str]    			=      assemble_transformation(_UpperCAmelCase,      i - 1,      j - 1				)
              seq.append(ops[i][j]				)
              return seq
          elif ops[i][j][0] == "D":
              lowerCAmelCase				:			int    			=      assemble_transformation(_UpperCAmelCase,      i - 1,      _UpperCAmelCase				)
              seq.append(ops[i][j]				)
              return seq
          else:
              lowerCAmelCase				:			Tuple    			=      assemble_transformation(_UpperCAmelCase,      _UpperCAmelCase,      j - 1				)
              seq.append(ops[i][j]				)
              return seq
  
  
  if __name__ == "__main__":
   __A     ,      __A   :     List[str]      =			compute_transform_tables('''Python''', '''Algorithms''', -1, 1, 2, 2)
  
   __A   :     Optional[int]      =			len(operations)
   __A   :     Optional[Any]      =			len(operations[0])
   __A   :     Any      =			assemble_transformation(operations, m - 1, n - 1)
  
   __A   :     int      =			list('''Python''')
   __A   :     Tuple      =			0
   __A   :     Tuple      =			0
  
   with open('''min_cost.txt''', '''w''') as file:
    for op in sequence:
     print(''''''.join(string))
  
     if op[0] == "C":
      file.write('''%-16s''' % '''Copy %c''' % op[1])
      file.write('''\t\t\t''' + ''''''.join(string))
      file.write('''\r\n''')
  
      cost -= 1
     elif op[0] == "R":
      __A   :     Any      =			op[2]
  
      file.write('''%-16s''' % ('''Replace %c''' % op[1] + ''' with ''' + str(op[2])))
      file.write('''\t\t''' + ''''''.join(string))
      file.write('''\r\n''')
  
      cost += 1
     elif op[0] == "D":
      string.pop(i)
  
      file.write('''%-16s''' % '''Delete %c''' % op[1])
      file.write('''\t\t\t''' + ''''''.join(string))
      file.write('''\r\n''')
  
      cost += 2
     else:
      string.insert(i, op[1])
  
      file.write('''%-16s''' % '''Insert %c''' % op[1])
      file.write('''\t\t\t''' + ''''''.join(string))
      file.write('''\r\n''')
  
      cost += 2
  
     i += 1
  
    print(''''''.join(string))
    print('''Cost: ''', cost)
  
    file.write('''\r\nMinimum cost: ''' + str(cost))
  
  
  
  
  
  