


'''simple docstring'''



from __future__ import absolute_import, division, print_function, unicode_literals

from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
    ROBERTA_INPUTS_DOCSTRING,
    ROBERTA_START_DOCSTRING,
    RobertaEmbeddings,
)

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy





@add_start_docstrings(
    'The RoBERTa Model transformer with early exiting (DeeRoBERTa). '   ,						__a   ,						)
class   UpperCAmelCase_    (__a    ):





    """simple docstring"""






    lowerCamelCase    :   List[Any]					=     RobertaConfig
    lowerCamelCase    :   Optional[Any]					=     '''roberta'''







    def __init__(				self     ,	SCREAMING_SNAKE_CASE_      )							->	str:
         super().__init__(_lowercase      )

         __lowerCamelCase  :      Optional[int]														=						RobertaEmbeddings(_lowercase      )
         self.init_weights()







@add_start_docstrings(
    'RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,\n    also takes care of multi-layer training. '   ,						__a   ,						)
class   UpperCAmelCase_    (__a    ):





    """simple docstring"""






    lowerCamelCase    :   Dict					=     RobertaConfig
    lowerCamelCase    :   Union[str, Any]					=     '''roberta'''







    def __init__(				self     ,	SCREAMING_SNAKE_CASE_      )							->	Any:
         super().__init__(_lowercase      )
         __lowerCamelCase  :      Union[str, Any]														=						config.num_labels
         __lowerCamelCase  :      Tuple														=						config.num_hidden_layers

         __lowerCamelCase  :      int														=						DeeRobertaModel(_lowercase      )
         __lowerCamelCase  :      str														=						nn.Dropout(config.hidden_dropout_prob      )
         __lowerCamelCase  :      Union[str, Any]														=						nn.Linear(config.hidden_size     ,	self.config.num_labels      )







    @add_start_docstrings_to_model_forward(_lowercase      )
    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=-1     ,	SCREAMING_SNAKE_CASE_=False     ,	)							->	Tuple:
         __lowerCamelCase  :      Optional[Any]														=						self.num_layers
         try:
              __lowerCamelCase  :      Optional[int]														=						self.roberta(
                  _lowercase     ,	attention_mask=_lowercase     ,	token_type_ids=_lowercase     ,	position_ids=_lowercase     ,	head_mask=_lowercase     ,	inputs_embeds=_lowercase     ,	)

              __lowerCamelCase  :      str														=						outputs[1]

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

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

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

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

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







import torch




def    lowerCAmelCase_  (		)     ->					int:







 '''simple docstring'''
 if torch.cuda.is_available():
  _UpperCamelCase:  Any    						=			torch.cuda.device_count()
 else:
  _UpperCamelCase:  Union[str, Any]    						=			0
 print(F"""Successfully ran on {num_gpus} GPUs"""		)


if __name__ == "__main__":
  main()