


'''simple docstring'''

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.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)





def __snake_case					(      UpperCAmelCase_						:						Union[str, Any]		):
 lowerCamelCase_      =  torch.exp(UpperCAmelCase_		)
 lowerCamelCase_      =  torch.sum(UpperCAmelCase_					,				dim=1		)  # sum of exp(x_i)
 lowerCamelCase_      =  torch.sum(x * exp_x					,				dim=1		)  # sum of x_i * exp(x_i)
 return torch.log(UpperCAmelCase_		) - B / A





class      snake_case    (    nn.Module			):





  """simple docstring"""




  def __init__(    self			,  UpperCamelCase     ):







   """simple docstring"""
   super().__init__()
   lowerCamelCase_      =  config.output_attentions
   lowerCamelCase_      =  config.output_hidden_states
   lowerCamelCase_      =  nn.ModuleList([BertLayer(UpperCamelCase     ) for _ in range(config.num_hidden_layers     )]     )
   lowerCamelCase_      =  nn.ModuleList([BertHighway(UpperCamelCase     ) for _ in range(config.num_hidden_layers     )]     )

   lowerCamelCase_      =  [-1 for _ in range(config.num_hidden_layers     )]






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   if (type(UpperCamelCase     ) is float) or (type(UpperCamelCase     ) is int):
    for i in range(len(self.early_exit_entropy     )     ):
     lowerCamelCase_      =  x
   else:
    lowerCamelCase_      =  x






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  pooler.state_dict()
   for highway in self.highway:
    for name, param in highway.pooler.state_dict().items():
     param.copy_(loaded_model[name]     )







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







   """simple docstring"""
   lowerCamelCase_      =  ()
   lowerCamelCase_      =  ()
   lowerCamelCase_      =  ()
   for i, layer_module in enumerate(self.layer     ):
    if self.output_hidden_states:
     lowerCamelCase_      =  all_hidden_states + (hidden_states,)

    lowerCamelCase_      =  layer_module(
        UpperCamelCase			,  UpperCamelCase			,  head_mask[i]			,  UpperCamelCase			,  UpperCamelCase     )
    lowerCamelCase_      =  layer_outputs[0]

    if self.output_attentions:
     lowerCamelCase_      =  all_attentions + (layer_outputs[1],)

    lowerCamelCase_      =  (hidden_states,)
    if self.output_hidden_states:
     lowerCamelCase_      =  current_outputs + (all_hidden_states,)
    if self.output_attentions:
     lowerCamelCase_      =  current_outputs + (all_attentions,)

    lowerCamelCase_      =  self.highway[i](UpperCamelCase     )
    # logits, pooled_output

    if not self.training:
     lowerCamelCase_      =  highway_exit[0]
     lowerCamelCase_      =  entropy(UpperCamelCase     )
     lowerCamelCase_      =  highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
     lowerCamelCase_      =  all_highway_exits + (highway_exit,)

     if highway_entropy < self.early_exit_entropy[i]:
      lowerCamelCase_      =  (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
      raise HighwayException(UpperCamelCase			,  i + 1     )
    else:
     lowerCamelCase_      =  all_highway_exits + (highway_exit,)

        # Add last layer
   if self.output_hidden_states:
    lowerCamelCase_      =  all_hidden_states + (hidden_states,)

   lowerCamelCase_      =  (hidden_states,)
   if self.output_hidden_states:
    lowerCamelCase_      =  outputs + (all_hidden_states,)
   if self.output_attentions:
    lowerCamelCase_      =  outputs + (all_attentions,)

   lowerCamelCase_      =  outputs + (all_highway_exits,)
   return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits





@add_start_docstrings(
    "The Bert Model transformer with early exiting (DeeBERT). "			,						lowercase			,						)
class      snake_case    (    lowercase			):





  """simple docstring"""




  def __init__(    self			,  UpperCamelCase     ):







   """simple docstring"""
   super().__init__(UpperCamelCase     )
   lowerCamelCase_      =  config

   lowerCamelCase_      =  BertEmbeddings(UpperCamelCase     )
   lowerCamelCase_      =  DeeBertEncoder(UpperCamelCase     )
   lowerCamelCase_      =  BertPooler(UpperCamelCase     )

   self.init_weights()






  def   snake_case		(    self     ):







   """simple docstring"""
   self.encoder.init_highway_pooler(self.pooler     )






  def   snake_case		(    self     ):







   """simple docstring"""
   return self.embeddings.word_embeddings






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  value






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   for layer, heads in heads_to_prune.items():
    self.encoder.layer[layer].attention.prune_heads(UpperCamelCase     )







  @add_start_docstrings_to_model_forward(UpperCamelCase     )
  def   snake_case		(    self			,  UpperCamelCase=None			,  UpperCamelCase=None			,  UpperCamelCase=None			,  UpperCamelCase=None			,  UpperCamelCase=None			,  UpperCamelCase=None			,  UpperCamelCase=None			,  UpperCamelCase=None			,  ):







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

   lowerCamelCase_      =  input_ids.device if input_ids is not None else inputs_embeds.device

   if attention_mask is None:
    lowerCamelCase_      =  torch.ones(UpperCamelCase			,  device=UpperCamelCase     )
   if encoder_attention_mask is None:
    lowerCamelCase_      =  torch.ones(UpperCamelCase			,  device=UpperCamelCase     )
   if token_type_ids is None:
    lowerCamelCase_      =  torch.zeros(UpperCamelCase			,  dtype=torch.long			,  device=UpperCamelCase     )

   # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
   # ourselves in which case we just need to make it broadcastable to all heads.
   lowerCamelCase_      =  self.get_extended_attention_mask(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     )

   # If a 2D ou 3D attention mask is provided for the cross-attention
   # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
   if encoder_attention_mask.dim() == 3:
    lowerCamelCase_      =  encoder_attention_mask[:, None, :, :]
   if encoder_attention_mask.dim() == 2:
    lowerCamelCase_      =  encoder_attention_mask[:, None, None, :]

   lowerCamelCase_      =  encoder_extended_attention_mask.to(
       dtype=next(self.parameters()     ).dtype     )  # fp16 compatibility
   lowerCamelCase_      =  (1.0 - encoder_extended_attention_mask) * -10_000.0

   # Prepare head mask if needed
   # 1.0 in head_mask indicate we keep the head
   # attention_probs has shape bsz x n_heads x N x N
   # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
   # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
   lowerCamelCase_      =  self.get_head_mask(UpperCamelCase			,  self.config.num_hidden_layers     )

   lowerCamelCase_      =  self.embeddings(
       input_ids=UpperCamelCase			,  position_ids=UpperCamelCase			,  token_type_ids=UpperCamelCase			,  inputs_embeds=UpperCamelCase     )
   lowerCamelCase_      =  self.encoder(
       UpperCamelCase			,  attention_mask=UpperCamelCase			,  head_mask=UpperCamelCase			,  encoder_hidden_states=UpperCamelCase			,  encoder_attention_mask=UpperCamelCase			,  )
   lowerCamelCase_      =  encoder_outputs[0]
   lowerCamelCase_      =  self.pooler(UpperCamelCase     )

   lowerCamelCase_      =  (
       sequence_output,
       pooled_output,
   ) + encoder_outputs[
       1:
   ]  # add hidden_states and attentions if they are here
   return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits





class      snake_case    (    lowercase			):





  """simple docstring"""




  def __init__(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  message
   lowerCamelCase_      =  exit_layer  # start from 1!





class      snake_case    (    nn.Module			):





  """simple docstring"""




  def __init__(    self			,  UpperCamelCase     ):







   """simple docstring"""
   super().__init__()
   lowerCamelCase_      =  BertPooler(UpperCamelCase     )
   lowerCamelCase_      =  nn.Dropout(config.hidden_dropout_prob     )
   lowerCamelCase_      =  nn.Linear(config.hidden_size			,  config.num_labels     )







  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   # Pooler
   lowerCamelCase_      =  encoder_outputs[0]
   lowerCamelCase_      =  self.pooler(UpperCamelCase     )
   # "return" pooler_output

   # BertModel
   lowerCamelCase_      =  (pooler_input, pooler_output) + encoder_outputs[1:]
   # "return" bmodel_output

   # Dropout and classification
   lowerCamelCase_      =  bmodel_output[1]

   lowerCamelCase_      =  self.dropout(UpperCamelCase     )
   lowerCamelCase_      =  self.classifier(UpperCamelCase     )

   return logits, pooled_output





@add_start_docstrings(
    "Bert Model (with early exiting - DeeBERT) with a classifier on top,\n    also takes care of multi-layer training. "			,						lowercase			,						)
class      snake_case    (    lowercase			):





  """simple docstring"""




  def __init__(    self			,  UpperCamelCase     ):







   """simple docstring"""
   super().__init__(UpperCamelCase     )
   lowerCamelCase_      =  config.num_labels
   lowerCamelCase_      =  config.num_hidden_layers

   lowerCamelCase_      =  DeeBertModel(UpperCamelCase     )
   lowerCamelCase_      =  nn.Dropout(config.hidden_dropout_prob     )
   lowerCamelCase_      =  nn.Linear(config.hidden_size			,  self.config.num_labels     )

   self.init_weights()







  @add_start_docstrings_to_model_forward(UpperCamelCase     )
  def   snake_case		(    self			,  UpperCamelCase=None			,  UpperCamelCase=None			,  UpperCamelCase=None			,  UpperCamelCase=None			,  UpperCamelCase=None			,  UpperCamelCase=None			,  UpperCamelCase=None			,  UpperCamelCase=-1			,  UpperCamelCase=False			,  ):







   """simple docstring"""
   lowerCamelCase_      =  self.num_layers
   try:
    lowerCamelCase_      =  self.bert(
        UpperCamelCase			,  attention_mask=UpperCamelCase			,  token_type_ids=UpperCamelCase			,  position_ids=UpperCamelCase			,  head_mask=UpperCamelCase			,  inputs_embeds=UpperCamelCase			,  )
    # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

    lowerCamelCase_      =  outputs[1]

    lowerCamelCase_      =  self.dropout(UpperCamelCase     )
    lowerCamelCase_      =  self.classifier(UpperCamelCase     )
    lowerCamelCase_      =  (logits,) + outputs[2:]  # add hidden states and attention if they are here
   except HighwayException as e:
    lowerCamelCase_      =  e.message
    lowerCamelCase_      =  e.exit_layer
    lowerCamelCase_      =  outputs[0]

   if not self.training:
    lowerCamelCase_      =  entropy(UpperCamelCase     )
    lowerCamelCase_      =  []
    lowerCamelCase_      =  []
   if labels is not None:
    if self.num_labels == 1:
     #  We are doing regression
     lowerCamelCase_      =  MSELoss()
     lowerCamelCase_      =  loss_fct(logits.view(-1     )			,  labels.view(-1     )     )
    else:
     lowerCamelCase_      =  CrossEntropyLoss()
     lowerCamelCase_      =  loss_fct(logits.view(-1			,  self.num_labels     )			,  labels.view(-1     )     )

    # work with highway exits
    lowerCamelCase_      =  []
    for highway_exit in outputs[-1]:
     lowerCamelCase_      =  highway_exit[0]
     if not self.training:
      highway_logits_all.append(UpperCamelCase     )
      highway_entropy.append(highway_exit[2]     )
     if self.num_labels == 1:
      #  We are doing regression
      lowerCamelCase_      =  MSELoss()
      lowerCamelCase_      =  loss_fct(highway_logits.view(-1     )			,  labels.view(-1     )     )
     else:
      lowerCamelCase_      =  CrossEntropyLoss()
      lowerCamelCase_      =  loss_fct(highway_logits.view(-1			,  self.num_labels     )			,  labels.view(-1     )     )
     highway_losses.append(UpperCamelCase     )

    if train_highway:
     lowerCamelCase_      =  (sum(highway_losses[:-1]     ),) + outputs
     # exclude the final highway, of course
    else:
     lowerCamelCase_      =  (loss,) + outputs
   if not self.training:
    lowerCamelCase_      =  outputs + ((original_entropy, highway_entropy), exit_layer)
    if output_layer >= 0:
     lowerCamelCase_      =  (
         (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
     )  # use the highway of the last layer

   return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)







'''simple docstring'''

from __future__ import annotations





def __snake_case					(      UpperCAmelCase_						:						int		):
 lowerCamelCase_      =  2
 lowerCamelCase_      =  []
 while i * i <= n:
  if n % i:
   i += 1
  else:
   n //= i
   factors.append(UpperCAmelCase_		)
 if n > 1:
  factors.append(UpperCAmelCase_		)
 return factors


if __name__ == "__main__":
     import doctest

     doctest.testmod()



