


'''simple docstring'''







def     _UpperCamelCase				(			UpperCamelCase__ = 100		):






				"""simple docstring"""



				__magic_name__					:       Optional[int]    							=							n * (n + 1) * (2 * n + 1) / 6
				__magic_name__					:       List[str]    							=							(n * (n + 1) / 2) ** 2
				return int(square_of_sum - sum_of_squares		)


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





'''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). """   ,   __lowerCAmelCase   ,   )
class   _a							(		__lowerCAmelCase				):
     SCREAMING_SNAKE_CASE_				:       str       							=		RobertaConfig
     SCREAMING_SNAKE_CASE_				:       Dict       							=		"""roberta"""






     def __init__(					self   ,_SCREAMING_SNAKE_CASE       )				->     Optional[Any]:
          super().__init__(_SCREAMING_SNAKE_CASE       )

          _snake_case				= RobertaEmbeddings(_SCREAMING_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. """   ,   __lowerCAmelCase   ,   )
class   _a							(		__lowerCAmelCase				):
     SCREAMING_SNAKE_CASE_				:       List[Any]       							=		RobertaConfig
     SCREAMING_SNAKE_CASE_				:       List[str]       							=		"""roberta"""






     def __init__(					self   ,_SCREAMING_SNAKE_CASE       )				->     str:
          super().__init__(_SCREAMING_SNAKE_CASE       )
          _snake_case				= config.num_labels
          _snake_case				= config.num_hidden_layers

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






     @add_start_docstrings_to_model_forward(_SCREAMING_SNAKE_CASE       )
     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   ,)				->     List[str]:
          _snake_case				= self.num_layers
          try:
               _snake_case				= self.roberta(
                   _SCREAMING_SNAKE_CASE   ,attention_mask=_SCREAMING_SNAKE_CASE   ,token_type_ids=_SCREAMING_SNAKE_CASE   ,position_ids=_SCREAMING_SNAKE_CASE   ,head_mask=_SCREAMING_SNAKE_CASE   ,inputs_embeds=_SCREAMING_SNAKE_CASE   ,)

               _snake_case				= outputs[1]

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

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

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

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

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


