






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 ):
	_A          =   RobertaConfig
	_A          =   'roberta'






	def __init__(		self					,UpperCamelCase       )						->    Optional[int]:
					super().__init__(UpperCamelCase       )

					snake_case__      :List[str]         =   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. '       ,    __A       ,    )
class 					_snake_case						(			__A ):
	_A          =   RobertaConfig
	_A          =   'roberta'






	def __init__(		self					,UpperCamelCase       )						->    Union[str, Any]:
					super().__init__(UpperCamelCase       )
					snake_case__      :Optional[int]         =   config.num_labels
					snake_case__      :List[Any]         =   config.num_hidden_layers

					snake_case__      :Any         =   DeeRobertaModel(UpperCamelCase       )
					snake_case__      :str         =   nn.Dropout(config.hidden_dropout_prob       )
					snake_case__      :int         =   nn.Linear(config.hidden_size					,self.config.num_labels       )






	@add_start_docstrings_to_model_forward(UpperCamelCase       )
	def    lowerCAmelCase_							(		self					,UpperCamelCase=None					,UpperCamelCase=None					,UpperCamelCase=None					,UpperCamelCase=None					,UpperCamelCase=None					,UpperCamelCase=None					,UpperCamelCase=None					,UpperCamelCase=-1					,UpperCamelCase=False					,)						->    Dict:
					snake_case__      :List[str]         =   self.num_layers
					try:
									snake_case__      :Tuple         =   self.roberta(
									    UpperCamelCase					,attention_mask=UpperCamelCase					,token_type_ids=UpperCamelCase					,position_ids=UpperCamelCase					,head_mask=UpperCamelCase					,inputs_embeds=UpperCamelCase					,)

									snake_case__      :Optional[Any]         =   outputs[1]

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

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

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

									if train_highway:
													snake_case__      :int         =   (sum(highway_losses[:-1]       ),) + outputs
													# exclude the final highway, of course
									else:
													snake_case__      :Any         =   (loss,) + outputs
					if not self.training:
									snake_case__      :Union[str, Any]         =   outputs + ((original_entropy, highway_entropy), exit_layer)
									if output_layer >= 0:
													snake_case__      :Tuple         =   (
													    (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 					lowercase_ (    __snake_case     :							int = 10_00      )     -> int:





				'''simple docstring'''


				snake_case__      :int         =   3
				snake_case__      :int         =   0
				while a < n:
								if a % 3 == 0 or a % 5 == 0:
												result += a
								elif a % 15 == 0:
												result -= a
								a += 1
				return result


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