





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). '''			,  __UpperCAmelCase			,  )
class 			A__   (							__UpperCAmelCase     ):




   """simple docstring"""



   __A       :    List[str]	  =  RobertaConfig
   __A       :    Dict	  =  '''roberta'''

   def __init__( self ,  lowercase)      ->						str:







          '''simple docstring'''

          super().__init__(lowercase)

          a__						:     Dict			=    RobertaEmbeddings(lowercase)
          self.init_weights()





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




   """simple docstring"""



   __A       :    Optional[int]	  =  RobertaConfig
   __A       :    Tuple	  =  '''roberta'''

   def __init__( self ,  lowercase)      ->						Union[str, Any]:







          '''simple docstring'''

          super().__init__(lowercase)
          a__						:     int			=    config.num_labels
          a__						:     List[str]			=    config.num_hidden_layers

          a__						:     Any			=    DeeRobertaModel(lowercase)
          a__						:     Tuple			=    nn.Dropout(config.hidden_dropout_prob)
          a__						:     Union[str, Any]			=    nn.Linear(config.hidden_size ,  self.config.num_labels)

   @add_start_docstrings_to_model_forward(lowercase)
   def 							__lowercase		( self ,  lowercase=None ,  lowercase=None ,  lowercase=None ,  lowercase=None ,  lowercase=None ,  lowercase=None ,  lowercase=None ,  lowercase=-1 ,  lowercase=False ,  )      ->						Tuple:







          '''simple docstring'''

          a__						:     Optional[Any]			=    self.num_layers
          try:
                 a__						:     Any			=    self.roberta(
                     lowercase ,  attention_mask=lowercase ,  token_type_ids=lowercase ,  position_ids=lowercase ,  head_mask=lowercase ,  inputs_embeds=lowercase ,  )

                 a__						:     Dict			=    outputs[1]

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

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

                 # work with highway exits
                 a__						:     Optional[int]			=    []
                 for highway_exit in outputs[-1]:
                        a__						:     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
                               a__						:     Optional[int]			=    MSELoss()
                               a__						:     Tuple			=    loss_fct(highway_logits.view(-1) ,  labels.view(-1))
                        else:
                               a__						:     Any			=    CrossEntropyLoss()
                               a__						:     Tuple			=    loss_fct(highway_logits.view(-1 ,  self.num_labels) ,  labels.view(-1))
                        highway_losses.append(lowercase)

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

from filelock import FileLock


try:
    import nltk

    lowercase				:	List[str]      						=  True
except (ImportError, ModuleNotFoundError):
    lowercase				:	Tuple      						=  False

if NLTK_AVAILABLE:
    with FileLock(""".lock""") as lock:
        nltk.download("""punkt""", quiet=True)



def A_     (		A__    )  ->		str:
       re.sub('<n>'		,				''		,				A__    )  # remove pegasus newline char
       assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
       return "\n".join(nltk.sent_tokenize(A__    )    )


