


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 		_snake_case  (				__lowerCAmelCase							):
	SCREAMING_SNAKE_CASE__				=	RobertaConfig
	SCREAMING_SNAKE_CASE__				=	"roberta"




	def __init__(		self ,  _lowerCamelCase   ):
							super().__init__(lowerCamelCase__   )

							a						:Dict										=	RobertaEmbeddings(lowerCamelCase__   )
							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. '						,   __lowerCAmelCase						,   )
class 		_snake_case  (				__lowerCAmelCase							):
	SCREAMING_SNAKE_CASE__				=	RobertaConfig
	SCREAMING_SNAKE_CASE__				=	"roberta"




	def __init__(		self ,  _lowerCamelCase   ):
							super().__init__(lowerCamelCase__   )
							a						:int										=	config.num_labels
							a						:Optional[Any]										=	config.num_hidden_layers

							a						:Optional[Any]										=	DeeRobertaModel(lowerCamelCase__   )
							a						:Any										=	nn.Dropout(config.hidden_dropout_prob   )
							a						:str										=	nn.Linear(config.hidden_size ,  self.config.num_labels   )




	@add_start_docstrings_to_model_forward(lowerCamelCase__   )
	def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase=None ,  _lowerCamelCase=None ,  _lowerCamelCase=None ,  _lowerCamelCase=None ,  _lowerCamelCase=None ,  _lowerCamelCase=None ,  _lowerCamelCase=None ,  _lowerCamelCase=-1 ,  _lowerCamelCase=False ,  ):
							a						:Optional[int]										=	self.num_layers
							try:
													a						:Tuple										=	self.roberta(
													    lowerCamelCase__ ,  attention_mask=lowerCamelCase__ ,  token_type_ids=lowerCamelCase__ ,  position_ids=lowerCamelCase__ ,  head_mask=lowerCamelCase__ ,  inputs_embeds=lowerCamelCase__ ,  )

													a						:Optional[int]										=	outputs[1]

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

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

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

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

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









def     __lowerCamelCase						(			UpperCAmelCase_						:		int = 100						):




      """simple docstring"""





      a						:List[Any]										=	0
      a						:List[Any]										=	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() = }""")





