


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). '    ,    snake_case__    ,    )
class  lowerCAmelCase__(    snake_case__						):






    '''simple docstring'''





    A_  :     List[Any]			= RobertaConfig
    A_  :     List[Any]			= 'roberta'





    def __init__(	self				:   str       ,   __snake_case				:   Tuple	):





       '''simple docstring'''
       super().__init__(__snake_case	)

       UpperCAmelCase_							:      Tuple						=   RobertaEmbeddings(__snake_case	)
       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. '    ,    snake_case__    ,    )
class  lowerCAmelCase__(    snake_case__						):






    '''simple docstring'''





    A_  :     Optional[int]			= RobertaConfig
    A_  :     int			= 'roberta'





    def __init__(	self				:   str       ,   __snake_case				:   Optional[int]	):





       '''simple docstring'''
       super().__init__(__snake_case	)
       UpperCAmelCase_							:      int						=   config.num_labels
       UpperCAmelCase_							:      Dict						=   config.num_hidden_layers

       UpperCAmelCase_							:      Dict						=   DeeRobertaModel(__snake_case	)
       UpperCAmelCase_							:      Optional[int]						=   nn.Dropout(config.hidden_dropout_prob	)
       UpperCAmelCase_							:      str						=   nn.Linear(config.hidden_size       ,   self.config.num_labels	)





    @add_start_docstrings_to_model_forward(__snake_case	)
    def 					_lowerCamelCase (	self				:   List[Any]       ,   __snake_case				:   int=None       ,   __snake_case				:   Optional[Any]=None       ,   __snake_case				:   int=None       ,   __snake_case				:   Union[str, Any]=None       ,   __snake_case				:   List[str]=None       ,   __snake_case				:   Optional[int]=None       ,   __snake_case				:   List[Any]=None       ,   __snake_case				:   Optional[int]=-1       ,   __snake_case				:   List[str]=False       ,   ):





       '''simple docstring'''
       UpperCAmelCase_							:      List[str]						=   self.num_layers
       try:
          UpperCAmelCase_							:      List[Any]						=   self.roberta(
              __snake_case       ,   attention_mask=__snake_case       ,   token_type_ids=__snake_case       ,   position_ids=__snake_case       ,   head_mask=__snake_case       ,   inputs_embeds=__snake_case       ,   )

          UpperCAmelCase_							:      Optional[int]						=   outputs[1]

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

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

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

          if train_highway:
             UpperCAmelCase_							:      Optional[int]						=   (sum(highway_losses[:-1]	),) + outputs
             # exclude the final highway, of course
          else:
             UpperCAmelCase_							:      Optional[Any]						=   (loss,) + outputs
       if not self.training:
          UpperCAmelCase_							:      Optional[int]						=   outputs + ((original_entropy, highway_entropy), exit_layer)
          if output_layer >= 0:
             UpperCAmelCase_							:      List[Any]						=   (
                 (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 fire

from utils import calculate_rouge, save_json
def      snake_case_						( __lowercase			,       __lowercase			,       __lowercase=None			,       **__lowercase       ):
   UpperCAmelCase_							:      Tuple						=   [x.strip() for x in open(__lowercase       ).readlines()]
   UpperCAmelCase_							:      Dict						=   [x.strip() for x in open(__lowercase       ).readlines()][: len(__lowercase       )]
   UpperCAmelCase_							:      int						=   calculate_rouge(__lowercase			,       __lowercase			,       **__lowercase       )
   if save_path is not None:
      save_json(__lowercase			,       __lowercase			,       indent=__lowercase       )
   return metrics  # these print nicely


if __name__ == "__main__":
       fire.Fire(calculate_rouge_path)