




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 __snake_case			(			a		):
   UpperCAmelCase__  :    List[str]          =  RobertaConfig
   UpperCAmelCase__  :    str          =  '''roberta'''







   def __init__(      self		:						Dict					,						_snake_case		:						Union[str, Any]):

        """simple docstring"""


        super().__init__(_snake_case)

        UpperCAmelCase_          =							RobertaEmbeddings(_snake_case)
        self.init_weights()







@add_start_docstrings(
    '''RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
    also takes care of multi-layer training. ''' ,  a ,  )
class __snake_case			(			a		):
   UpperCAmelCase__  :    Any          =  RobertaConfig
   UpperCAmelCase__  :    Dict          =  '''roberta'''







   def __init__(      self		:						int					,						_snake_case		:						Tuple):

        """simple docstring"""


        super().__init__(_snake_case)
        UpperCAmelCase_          =							config.num_labels
        UpperCAmelCase_          =							config.num_hidden_layers

        UpperCAmelCase_          =							DeeRobertaModel(_snake_case)
        UpperCAmelCase_          =							nn.Dropout(config.hidden_dropout_prob)
        UpperCAmelCase_          =							nn.Linear(config.hidden_size					,						self.config.num_labels)







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

        """simple docstring"""


        UpperCAmelCase_          =							self.num_layers
        try:
             UpperCAmelCase_          =							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_          =							outputs[1]

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

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

             # work with highway exits
             UpperCAmelCase_          =							[]
             for highway_exit in outputs[-1]:
                  UpperCAmelCase_          =							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_          =							MSELoss()
                       UpperCAmelCase_          =							loss_fct(highway_logits.view(-1)					,						labels.view(-1))
                  else:
                       UpperCAmelCase_          =							CrossEntropyLoss()
                       UpperCAmelCase_          =							loss_fct(highway_logits.view(-1					,						self.num_labels)					,						labels.view(-1))
                  highway_losses.append(_snake_case)

             if train_highway:
                  UpperCAmelCase_          =							(sum(highway_losses[:-1]),) + outputs
                  # exclude the final highway, of course
             else:
                  UpperCAmelCase_          =							(loss,) + outputs
        if not self.training:
             UpperCAmelCase_          =							outputs + ((original_entropy, highway_entropy), exit_layer)
             if output_layer >= 0:
                  UpperCAmelCase_          =							(
                      (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 						_SCREAMING_SNAKE_CASE (    __snake_case			:  int = 1_00    ):





  '''simple docstring'''





  lowercase							    =       0
  lowercase							    =       0
  for i in range(1		,					n + 1    ):
    sum_of_squares += i**2
    sum_of_ints += i
  return sum_of_ints**2 - sum_of_squares


if __name__ == "__main__":
    print(F'''{solution() = }''')


