


'''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). "    ,		lowercase    ,		)
class   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						RobertaConfig
  _lowerCamelCase						=						"roberta"






  def __init__(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   super().__init__(UpperCamelCase  )

   lowerCamelCase_  				=						RobertaEmbeddings(UpperCamelCase  )
   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. "    ,		lowercase    ,		)
class   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						RobertaConfig
  _lowerCamelCase						=						"roberta"






  def __init__(					self    ,						UpperCamelCase  ):







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

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







  @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.roberta(
        UpperCamelCase    ,						attention_mask=UpperCamelCase    ,						token_type_ids=UpperCamelCase    ,						position_ids=UpperCamelCase    ,						head_mask=UpperCamelCase    ,						inputs_embeds=UpperCamelCase    ,						)

    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), entropy







'''simple docstring'''

def __snake_case      (   UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		int	):
 return "\n".join(
     F'''{number} * {i} = {number * i}''' for i in range(1      ,       number_of_terms + 1	)	)


if __name__ == "__main__":
     print(multiplication_table(number=5, number_of_terms=10))



