

'''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    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE:Optional[int]						      =					torch.exp(snake_case )
  SCREAMING_SNAKE_CASE:int						      =					torch.sum(snake_case      ,       dim=1 )  # sum of exp(x_i)
  SCREAMING_SNAKE_CASE:List[str]						      =					torch.sum(x * exp_x      ,       dim=1 )  # sum of x_i * exp(x_i)
  return torch.log(snake_case ) - B / A





class 							_snake_case (       nn.Module						):
   def __init__( self			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      str							):
     super().__init__()
     SCREAMING_SNAKE_CASE:Dict						      =					config.output_attentions
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					config.output_hidden_states
     SCREAMING_SNAKE_CASE:Any						      =					nn.ModuleList([BertLayer(SCREAMING_SNAKE_CASE__							) for _ in range(config.num_hidden_layers							)]							)
     SCREAMING_SNAKE_CASE:Optional[int]						      =					nn.ModuleList([BertHighway(SCREAMING_SNAKE_CASE__							) for _ in range(config.num_hidden_layers							)]							)

     SCREAMING_SNAKE_CASE:str						      =					[-1 for _ in range(config.num_hidden_layers							)]
   def     __UpperCamelCase						( self			:      Optional[Any]     ,SCREAMING_SNAKE_CASE__			:      List[Any]							):
     if (type(SCREAMING_SNAKE_CASE__							) is float) or (type(SCREAMING_SNAKE_CASE__							) is int):
       for i in range(len(self.early_exit_entropy							)							):
         SCREAMING_SNAKE_CASE:Optional[int]						      =					x
     else:
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					x
   def     __UpperCamelCase						( self			:      Dict     ,SCREAMING_SNAKE_CASE__			:      int							):
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					pooler.state_dict()
     for highway in self.highway:
       for name, param in highway.pooler.state_dict().items():
         param.copy_(loaded_model[name]							)







   def     __UpperCamelCase						( self			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]=None     ,SCREAMING_SNAKE_CASE__			:      str=None     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=None     ,SCREAMING_SNAKE_CASE__			:      int=None     ,):
     SCREAMING_SNAKE_CASE:str						      =					()
     SCREAMING_SNAKE_CASE:int						      =					()
     SCREAMING_SNAKE_CASE:Tuple						      =					()
     for i, layer_module in enumerate(self.layer							):
       if self.output_hidden_states:
         SCREAMING_SNAKE_CASE:List[str]						      =					all_hidden_states + (hidden_states,)

       SCREAMING_SNAKE_CASE:str						      =					layer_module(
           SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__     ,head_mask[i]     ,SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
       SCREAMING_SNAKE_CASE:Dict						      =					layer_outputs[0]

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

       SCREAMING_SNAKE_CASE:int						      =					(hidden_states,)
       if self.output_hidden_states:
         SCREAMING_SNAKE_CASE:List[str]						      =					current_outputs + (all_hidden_states,)
       if self.output_attentions:
         SCREAMING_SNAKE_CASE:str						      =					current_outputs + (all_attentions,)

       SCREAMING_SNAKE_CASE:str						      =					self.highway[i](SCREAMING_SNAKE_CASE__							)
       # logits, pooled_output

       if not self.training:
         SCREAMING_SNAKE_CASE:Optional[int]						      =					highway_exit[0]
         SCREAMING_SNAKE_CASE:Optional[Any]						      =					entropy(SCREAMING_SNAKE_CASE__							)
         SCREAMING_SNAKE_CASE:Union[str, Any]						      =					highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
         SCREAMING_SNAKE_CASE:int						      =					all_highway_exits + (highway_exit,)

         if highway_entropy < self.early_exit_entropy[i]:
           SCREAMING_SNAKE_CASE:Dict						      =					(highway_logits,) + current_outputs[1:] + (all_highway_exits,)
           raise HighwayException(SCREAMING_SNAKE_CASE__     ,i + 1							)
       else:
         SCREAMING_SNAKE_CASE:List[str]						      =					all_highway_exits + (highway_exit,)

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

     SCREAMING_SNAKE_CASE:List[str]						      =					(hidden_states,)
     if self.output_hidden_states:
       SCREAMING_SNAKE_CASE:Tuple						      =					outputs + (all_hidden_states,)
     if self.output_attentions:
       SCREAMING_SNAKE_CASE:List[str]						      =					outputs + (all_attentions,)

     SCREAMING_SNAKE_CASE:Dict						      =					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). '''	,  _a	,  )
class 							_snake_case (       _a						):
   def __init__( self			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      Tuple							):
     super().__init__(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:List[str]						      =					config

     SCREAMING_SNAKE_CASE:List[str]						      =					BertEmbeddings(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Dict						      =					DeeBertEncoder(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					BertPooler(SCREAMING_SNAKE_CASE__							)

     self.init_weights()
   def     __UpperCamelCase						( self			:      int							):
     self.encoder.init_highway_pooler(self.pooler							)
   def     __UpperCamelCase						( self			:      Dict							):
     return self.embeddings.word_embeddings
   def     __UpperCamelCase						( self			:      Optional[Any]     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]							):
     SCREAMING_SNAKE_CASE:Tuple						      =					value
   def     __UpperCamelCase						( self			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]							):
     for layer, heads in heads_to_prune.items():
       self.encoder.layer[layer].attention.prune_heads(SCREAMING_SNAKE_CASE__							)







   @add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      List[str]=None     ,SCREAMING_SNAKE_CASE__			:      List[str]=None     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=None     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=None     ,SCREAMING_SNAKE_CASE__			:      str=None     ,SCREAMING_SNAKE_CASE__			:      str=None     ,SCREAMING_SNAKE_CASE__			:      str=None     ,SCREAMING_SNAKE_CASE__			:      List[Any]=None     ,):
     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:
       SCREAMING_SNAKE_CASE:Tuple						      =					input_ids.size()
     elif inputs_embeds is not None:
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					inputs_embeds.size()[:-1]
     else:
       raise ValueError("You have to specify either input_ids or inputs_embeds"							)

     SCREAMING_SNAKE_CASE:str						      =					input_ids.device if input_ids is not None else inputs_embeds.device

     if attention_mask is None:
       SCREAMING_SNAKE_CASE:Any						      =					torch.ones(SCREAMING_SNAKE_CASE__     ,device=SCREAMING_SNAKE_CASE__							)
     if encoder_attention_mask is None:
       SCREAMING_SNAKE_CASE:Optional[Any]						      =					torch.ones(SCREAMING_SNAKE_CASE__     ,device=SCREAMING_SNAKE_CASE__							)
     if token_type_ids is None:
       SCREAMING_SNAKE_CASE:Tuple						      =					torch.zeros(SCREAMING_SNAKE_CASE__     ,dtype=torch.long     ,device=SCREAMING_SNAKE_CASE__							)

     # 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.
     SCREAMING_SNAKE_CASE:torch.Tensor						      =					self.get_extended_attention_mask(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)

     # 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:
       SCREAMING_SNAKE_CASE:Dict						      =					encoder_attention_mask[:, None, :, :]
     if encoder_attention_mask.dim() == 2:
       SCREAMING_SNAKE_CASE:Dict						      =					encoder_attention_mask[:, None, None, :]

     SCREAMING_SNAKE_CASE:int						      =					encoder_extended_attention_mask.to(
         dtype=next(self.parameters()							).dtype							)  # fp16 compatibility
     SCREAMING_SNAKE_CASE:str						      =					(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]
     SCREAMING_SNAKE_CASE:Any						      =					self.get_head_mask(SCREAMING_SNAKE_CASE__     ,self.config.num_hidden_layers							)

     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					self.embeddings(
         input_ids=SCREAMING_SNAKE_CASE__     ,position_ids=SCREAMING_SNAKE_CASE__     ,token_type_ids=SCREAMING_SNAKE_CASE__     ,inputs_embeds=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Optional[int]						      =					self.encoder(
         SCREAMING_SNAKE_CASE__     ,attention_mask=SCREAMING_SNAKE_CASE__     ,head_mask=SCREAMING_SNAKE_CASE__     ,encoder_hidden_states=SCREAMING_SNAKE_CASE__     ,encoder_attention_mask=SCREAMING_SNAKE_CASE__     ,)
     SCREAMING_SNAKE_CASE:List[str]						      =					encoder_outputs[0]
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					self.pooler(SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:List[Any]						      =					(
         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 (       _a						):
   def __init__( self			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      str     ,SCREAMING_SNAKE_CASE__			:      List[str]							):
     SCREAMING_SNAKE_CASE:Tuple						      =					message
     SCREAMING_SNAKE_CASE:Any						      =					exit_layer  # start from 1!





class 							_snake_case (       nn.Module						):
   def __init__( self			:      str     ,SCREAMING_SNAKE_CASE__			:      int							):
     super().__init__()
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					BertPooler(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Tuple						      =					nn.Dropout(config.hidden_dropout_prob							)
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					nn.Linear(config.hidden_size     ,config.num_labels							)







   def     __UpperCamelCase						( self			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      Any							):
     # Pooler
     SCREAMING_SNAKE_CASE:int						      =					encoder_outputs[0]
     SCREAMING_SNAKE_CASE:Any						      =					self.pooler(SCREAMING_SNAKE_CASE__							)
     # "return" pooler_output

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

     # Dropout and classification
     SCREAMING_SNAKE_CASE:Tuple						      =					bmodel_output[1]

     SCREAMING_SNAKE_CASE:List[str]						      =					self.dropout(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Optional[int]						      =					self.classifier(SCREAMING_SNAKE_CASE__							)

     return logits, pooled_output





@add_start_docstrings(
    '''Bert Model (with early exiting - DeeBERT) with a classifier on top,
    also takes care of multi-layer training. '''	,  _a	,  )
class 							_snake_case (       _a						):
   def __init__( self			:      Any     ,SCREAMING_SNAKE_CASE__			:      List[str]							):
     super().__init__(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:List[Any]						      =					config.num_labels
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					config.num_hidden_layers

     SCREAMING_SNAKE_CASE:int						      =					DeeBertModel(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Dict						      =					nn.Dropout(config.hidden_dropout_prob							)
     SCREAMING_SNAKE_CASE:str						      =					nn.Linear(config.hidden_size     ,self.config.num_labels							)

     self.init_weights()







   @add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Optional[Any]     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]=None     ,SCREAMING_SNAKE_CASE__			:      str=None     ,SCREAMING_SNAKE_CASE__			:      Any=None     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=None     ,SCREAMING_SNAKE_CASE__			:      int=None     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=None     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=None     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]=-1     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=False     ,):
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					self.num_layers
     try:
       SCREAMING_SNAKE_CASE:Optional[Any]						      =					self.bert(
           SCREAMING_SNAKE_CASE__     ,attention_mask=SCREAMING_SNAKE_CASE__     ,token_type_ids=SCREAMING_SNAKE_CASE__     ,position_ids=SCREAMING_SNAKE_CASE__     ,head_mask=SCREAMING_SNAKE_CASE__     ,inputs_embeds=SCREAMING_SNAKE_CASE__     ,)
       # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

       SCREAMING_SNAKE_CASE:List[str]						      =					outputs[1]

       SCREAMING_SNAKE_CASE:List[Any]						      =					self.dropout(SCREAMING_SNAKE_CASE__							)
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					self.classifier(SCREAMING_SNAKE_CASE__							)
       SCREAMING_SNAKE_CASE:Tuple						      =					(logits,) + outputs[2:]  # add hidden states and attention if they are here
     except HighwayException as e:
       SCREAMING_SNAKE_CASE:Optional[int]						      =					e.message
       SCREAMING_SNAKE_CASE:Dict						      =					e.exit_layer
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					outputs[0]

     if not self.training:
       SCREAMING_SNAKE_CASE:List[str]						      =					entropy(SCREAMING_SNAKE_CASE__							)
       SCREAMING_SNAKE_CASE:Any						      =					[]
       SCREAMING_SNAKE_CASE:str						      =					[]
     if labels is not None:
       if self.num_labels == 1:
         #  We are doing regression
         SCREAMING_SNAKE_CASE:Optional[int]						      =					MSELoss()
         SCREAMING_SNAKE_CASE:List[str]						      =					loss_fct(logits.view(-1							)     ,labels.view(-1							)							)
       else:
         SCREAMING_SNAKE_CASE:str						      =					CrossEntropyLoss()
         SCREAMING_SNAKE_CASE:int						      =					loss_fct(logits.view(-1     ,self.num_labels							)     ,labels.view(-1							)							)

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

       if train_highway:
         SCREAMING_SNAKE_CASE:Union[str, Any]						      =					(sum(highway_losses[:-1]							),) + outputs
         # exclude the final highway, of course
       else:
         SCREAMING_SNAKE_CASE:Optional[int]						      =					(loss,) + outputs
     if not self.training:
       SCREAMING_SNAKE_CASE:Tuple						      =					outputs + ((original_entropy, highway_entropy), exit_layer)
       if output_layer >= 0:
         SCREAMING_SNAKE_CASE:Optional[Any]						      =					(
             (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 ..utils import DummyObject, requires_backends





class 							_snake_case (       metaclass=_a						):
   _A       :     Any       =					['''torch''', '''torchsde''']
   def __init__( self			:      Any     ,*SCREAMING_SNAKE_CASE__			:      int     ,**SCREAMING_SNAKE_CASE__			:      Tuple							):
     requires_backends(self     ,["torch", "torchsde"]							)
   @classmethod
   def     __UpperCamelCase						( cls			:      Dict     ,*SCREAMING_SNAKE_CASE__			:      Dict     ,**SCREAMING_SNAKE_CASE__			:      Optional[Any]							):
     requires_backends(cls     ,["torch", "torchsde"]							)







   @classmethod
   def     __UpperCamelCase						( cls			:      List[str]     ,*SCREAMING_SNAKE_CASE__			:      int     ,**SCREAMING_SNAKE_CASE__			:      Optional[Any]							):
     requires_backends(cls     ,["torch", "torchsde"]							)






