



'''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 				lowercase__      (				lowercase  ):
	lowercase__    							=     RobertaConfig
	lowercase__    							=     """roberta"""





	def __init__(					self   :							Tuple					,lowerCamelCase__   :							Union[str, Any]					):







							'''simple docstring'''
							super().__init__(lowerCamelCase__					)

							_UpperCamelCase   :						Optional[Any]												=  RobertaEmbeddings(lowerCamelCase__					)
							self.init_weights()






@add_start_docstrings(
    """RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
    also takes care of multi-layer training. """		,   lowercase		,   )
class 				lowercase__      (				lowercase  ):
	lowercase__    							=     RobertaConfig
	lowercase__    							=     """roberta"""





	def __init__(					self   :							int					,lowerCamelCase__   :							Optional[Any]					):







							'''simple docstring'''
							super().__init__(lowerCamelCase__					)
							_UpperCamelCase   :						Union[str, Any]												=  config.num_labels
							_UpperCamelCase   :						Tuple												=  config.num_hidden_layers

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





	@add_start_docstrings_to_model_forward(lowerCamelCase__					)
	def       UpperCamelCase_    (					self   :							Tuple					,lowerCamelCase__   :							List[Any]=None					,lowerCamelCase__   :							Dict=None					,lowerCamelCase__   :							Tuple=None					,lowerCamelCase__   :							Union[str, Any]=None					,lowerCamelCase__   :							Tuple=None					,lowerCamelCase__   :							Any=None					,lowerCamelCase__   :							Any=None					,lowerCamelCase__   :							List[str]=-1					,lowerCamelCase__   :							Dict=False					,):







							'''simple docstring'''
							_UpperCamelCase   :						Dict												=  self.num_layers
							try:
													_UpperCamelCase   :						List[str]												=  self.roberta(
													    lowerCamelCase__					,attention_mask=lowerCamelCase__					,token_type_ids=lowerCamelCase__					,position_ids=lowerCamelCase__					,head_mask=lowerCamelCase__					,inputs_embeds=lowerCamelCase__					,)

													_UpperCamelCase   :						str												=  outputs[1]

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

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

													# work with highway exits
													_UpperCamelCase   :						Dict												=  []
													for highway_exit in outputs[-1]:
																			_UpperCamelCase   :						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
																									_UpperCamelCase   :						Tuple												=  MSELoss()
																									_UpperCamelCase   :						Optional[int]												=  loss_fct(highway_logits.view(-1					)					,labels.view(-1					)					)
																			else:
																									_UpperCamelCase   :						int												=  CrossEntropyLoss()
																									_UpperCamelCase   :						Tuple												=  loss_fct(highway_logits.view(-1					,self.num_labels					)					,labels.view(-1					)					)
																			highway_losses.append(lowerCamelCase__					)

													if train_highway:
																			_UpperCamelCase   :						Optional[int]												=  (sum(highway_losses[:-1]					),) + outputs
																			# exclude the final highway, of course
													else:
																			_UpperCamelCase   :						Union[str, Any]												=  (loss,) + outputs
							if not self.training:
													_UpperCamelCase   :						Tuple												=  outputs + ((original_entropy, highway_entropy), exit_layer)
													if output_layer >= 0:
																			_UpperCamelCase   :						str												=  (
																			    (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 A__       (	UpperCAmelCase_ = 6_0_0_8_5_1_4_7_5_1_4_3				):

						try:
												_UpperCamelCase   :						str												=  int(UpperCAmelCase_				)
						except (TypeError, ValueError):
												raise TypeError('Parameter n must be int or castable to int.'				)
						if n <= 0:
												raise ValueError('Parameter n must be greater than or equal to one.'				)
						_UpperCamelCase   :						int												=  1
						_UpperCamelCase   :						Optional[Any]												=  2
						while i * i <= n:
												while n % i == 0:
																		_UpperCamelCase   :						Union[str, Any]												=  i
																		n //= i
												i += 1
						if n > 1:
												_UpperCamelCase   :						Dict												=  n
						return int(UpperCAmelCase_				)


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


