code: 
  
  
  def       a__						(		snake_case__				:       str	,      snake_case__				:       str	,      snake_case__				:       int	,      snake_case__				:       int	,      snake_case__				:       int	,      snake_case__				:       int	,      ):
    _UpperCAmelCase		:       List[str]       =    list(snake_case__    )
    _UpperCAmelCase		:       Any       =    list(snake_case__    )
    _UpperCAmelCase		:       Optional[int]       =    len(snake_case__    )
    _UpperCAmelCase		:       Dict       =    len(snake_case__    )
  
    _UpperCAmelCase		:       Dict       =    [
        [0 for _ in range(len_destination_seq + 1    )] for _ in range(len_source_seq + 1    )
    ]
    _UpperCAmelCase		:       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    ):
      _UpperCAmelCase		:       Union[str, Any]       =    i * delete_cost
      _UpperCAmelCase		:       str       =    f'''D{source_seq[i - 1]:c}'''
  
    for i in range(1	,      len_destination_seq + 1    ):
      _UpperCAmelCase		:       Any       =    i * insert_cost
      _UpperCAmelCase		:       List[str]       =    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]:
          _UpperCAmelCase		:       Tuple       =    costs[i - 1][j - 1] + copy_cost
          _UpperCAmelCase		:       Dict       =    f'''C{source_seq[i - 1]:c}'''
        else:
          _UpperCAmelCase		:       Optional[Any]       =    costs[i - 1][j - 1] + replace_cost
          _UpperCAmelCase		:       List[Any]       =    f'''R{source_seq[i - 1]:c}''' + str(destination_seq[j - 1]    )
  
        if costs[i - 1][j] + delete_cost < costs[i][j]:
          _UpperCAmelCase		:       Tuple       =    costs[i - 1][j] + delete_cost
          _UpperCAmelCase		:       List[Any]       =    f'''D{source_seq[i - 1]:c}'''
  
        if costs[i][j - 1] + insert_cost < costs[i][j]:
          _UpperCAmelCase		:       Dict       =    costs[i][j - 1] + insert_cost
          _UpperCAmelCase		:       str       =    f'''I{destination_seq[j - 1]:c}'''
  
    return costs, ops
  
  
  
  
  def       a__						(		snake_case__				:       list[list[str]]	,      snake_case__				:       int	,      snake_case__				:       int    ):
    if i == 0 and j == 0:
      return []
    else:
      if ops[i][j][0] == "C" or ops[i][j][0] == "R":
        _UpperCAmelCase		:       Optional[Any]       =    assemble_transformation(snake_case__	,      i - 1	,      j - 1    )
        seq.append(ops[i][j]    )
        return seq
      elif ops[i][j][0] == "D":
        _UpperCAmelCase		:       Union[str, Any]       =    assemble_transformation(snake_case__	,      i - 1	,      snake_case__    )
        seq.append(ops[i][j]    )
        return seq
      else:
        _UpperCAmelCase		:       List[str]       =    assemble_transformation(snake_case__	,      snake_case__	,      j - 1    )
        seq.append(ops[i][j]    )
        return seq
  
  
  if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__				:    Tuple         =    compute_transform_tables('Python', 'Algorithms', -1, 1, 2, 2)
  
   SCREAMING_SNAKE_CASE__				:    str         =    len(operations)
   SCREAMING_SNAKE_CASE__				:    List[str]         =    len(operations[0])
   SCREAMING_SNAKE_CASE__				:    Any         =    assemble_transformation(operations, m - 1, n - 1)
  
   SCREAMING_SNAKE_CASE__				:    str         =    list('Python')
   SCREAMING_SNAKE_CASE__				:    List[Any]         =    0
   SCREAMING_SNAKE_CASE__				:    Optional[int]         =    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":
      SCREAMING_SNAKE_CASE__				:    str         =    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))
  
  
  
  
  
  
style_context: 
  
  
  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,
  )
  
  
  SCREAMING_SNAKE_CASE__				:    Dict         =    logging.getLogger(__name__)
  
  
  class 		_SCREAMING_SNAKE_CASE    (	A    ):
  
  
       def 					__snake_case(     self       ,				A_       ,				A_       ,				A_=None       ,				A_=None						):
         if current_layer == 0:
           _UpperCAmelCase		:       Any       =    self.embedding_hidden_mapping_in(A_						)
         else:
           _UpperCAmelCase		:       Optional[int]       =    hidden_states[0]
  
         _UpperCAmelCase		:       Tuple       =    int(self.config.num_hidden_layers / self.config.num_hidden_groups						)
  
         # Index of the hidden group
         _UpperCAmelCase		:       Union[str, Any]       =    int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)						)
  
         _UpperCAmelCase		:       int       =    self.albert_layer_groups[group_idx](
             A_       ,				A_       ,				head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]       ,				)
         _UpperCAmelCase		:       Dict       =    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 		_SCREAMING_SNAKE_CASE    (	A    ):
  
  
       def __init__(     self       ,				A_						):
         super().__init__(A_						)
  
         _UpperCAmelCase		:       List[Any]       =    AlbertTransformerWithPabee(A_						)
  
         self.init_weights()
         _UpperCAmelCase		:       Tuple       =    0
         _UpperCAmelCase		:       Optional[Any]       =    0
         _UpperCAmelCase		:       int       =    0
  
         _UpperCAmelCase		:       Dict       =    0
  
  
       def 					__snake_case(     self       ,				A_						):
         _UpperCAmelCase		:       str       =    threshold
  
  
       def 					__snake_case(     self       ,				A_						):
         _UpperCAmelCase		:       Optional[Any]       =    patience
  
  
       def 					__snake_case(     self						):
         _UpperCAmelCase		:       Tuple       =    0
         _UpperCAmelCase		:       Tuple       =    0
  
  
       def 					__snake_case(     self						):
         _UpperCAmelCase		:       Dict       =    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(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:
           _UpperCAmelCase		:       List[str]       =    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		:       List[str]       =    input_ids.device if input_ids is not None else inputs_embeds.device
  
         if attention_mask is None:
           _UpperCAmelCase		:       Any       =    torch.ones(A_       ,				device=A_						)
         if token_type_ids is None:
           _UpperCAmelCase		:       List[str]       =    torch.zeros(A_       ,				dtype=torch.long       ,				device=A_						)
  
         _UpperCAmelCase		:       str       =    attention_mask.unsqueeze(1						).unsqueeze(2						)
         _UpperCAmelCase		:       Tuple       =    extended_attention_mask.to(dtype=self.dtype						)  # fp16 compatibility
         _UpperCAmelCase		:       Tuple       =    (1.0 - extended_attention_mask) * -1_0_0_0_0.0
         _UpperCAmelCase		:       Union[str, Any]       =    self.get_head_mask(A_       ,				self.config.num_hidden_layers						)
  
         _UpperCAmelCase		:       Dict       =    self.embeddings(
             A_       ,				position_ids=A_       ,				token_type_ids=A_       ,				inputs_embeds=A_						)
         _UpperCAmelCase		:       str       =    embedding_output
  
         if self.training:
           _UpperCAmelCase		:       Any       =    []
           for i in range(self.config.num_hidden_layers						):
             _UpperCAmelCase		:       Tuple       =    self.encoder.adaptive_forward(
                 A_       ,				current_layer=A_       ,				attention_mask=A_       ,				head_mask=A_       ,				)
  
             _UpperCAmelCase		:       List[Any]       =    self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]						)						)
             _UpperCAmelCase		:       List[str]       =    output_layers[i](output_dropout(A_						)						)
             res.append(A_						)
         elif self.patience == 0:  # Use all layers for inference
           _UpperCAmelCase		:       Optional[int]       =    self.encoder(A_       ,				A_       ,				head_mask=A_						)
           _UpperCAmelCase		:       Optional[int]       =    self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]						)						)
           _UpperCAmelCase		:       List[Any]       =    [output_layers[self.config.num_hidden_layers - 1](A_						)]
         else:
           _UpperCAmelCase		:       str       =    0
           _UpperCAmelCase		:       str       =    None
           _UpperCAmelCase		:       str       =    0
           for i in range(self.config.num_hidden_layers						):
             calculated_layer_num += 1
             _UpperCAmelCase		:       int       =    self.encoder.adaptive_forward(
                 A_       ,				current_layer=A_       ,				attention_mask=A_       ,				head_mask=A_       ,				)
  
             _UpperCAmelCase		:       Tuple       =    self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]						)						)
             _UpperCAmelCase		:       List[str]       =    output_layers[i](A_						)
             if regression:
               _UpperCAmelCase		:       Optional[Any]       =    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		:       Union[str, Any]       =    0
             else:
               _UpperCAmelCase		:       Dict       =    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		:       Dict       =    0
  
             _UpperCAmelCase		:       int       =    logits
             if patient_counter == self.patience:
               break
           _UpperCAmelCase		:       Tuple       =    [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 		_SCREAMING_SNAKE_CASE    (	A    ):
  
  
       def __init__(     self       ,				A_						):
         super().__init__(A_						)
         _UpperCAmelCase		:       List[str]       =    config.num_labels
  
         _UpperCAmelCase		:       List[Any]       =    AlbertModelWithPabee(A_						)
         _UpperCAmelCase		:       Optional[int]       =    nn.Dropout(config.classifier_dropout_prob						)
         _UpperCAmelCase		:       Tuple       =    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       ,				):
         _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		:       str       =    (logits[-1],)
  
         if labels is not None:
           _UpperCAmelCase		:       List[Any]       =    None
           _UpperCAmelCase		:       Optional[int]       =    0
           for ix, logits_item in enumerate(A_						):
             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		:       List[Any]       =    CrossEntropyLoss()
               _UpperCAmelCase		:       List[Any]       =    loss_fct(logits_item.view(-1       ,				self.num_labels						)       ,				labels.view(-1						)						)
             if total_loss is None:
               _UpperCAmelCase		:       List[str]       =    loss
             else:
               total_loss += loss * (ix + 1)
             total_weights += ix + 1
           _UpperCAmelCase		:       List[str]       =    (total_loss / total_weights,) + outputs
  
         return outputs
  
  
  
  
  
  