


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). '					,	UpperCAmelCase					,	)
class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					str    =		RobertaConfig
			_UpperCamelCase		:					List[Any]    =		'roberta'




			def __init__(							self      ,						snake_case     ):

						'''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,\n    also takes care of multi-layer training. '					,	UpperCAmelCase					,	)
class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					Union[str, Any]    =		RobertaConfig
			_UpperCamelCase		:					str    =		'roberta'




			def __init__(							self      ,						snake_case     ):

						'''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 				snake_case__				(							self      ,						snake_case=None      ,						snake_case=None      ,						snake_case=None      ,						snake_case=None      ,						snake_case=None      ,						snake_case=None      ,						snake_case=None      ,						snake_case=-1      ,						snake_case=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






from typing import Any







class        lowerCamelCase__	:






			"""simple docstring"""






			def __init__(							self      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       data
						UpperCamelCase__					=       None







class        lowerCamelCase__	:






			"""simple docstring"""






			def __init__(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       None




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.head
						while temp is not None:
									print(temp.data      ,						end=" "     )
									UpperCamelCase__					=       temp.next
						print()




			def 				snake_case__				(							self      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       Node(snake_case     )
						UpperCamelCase__					=       self.head
						UpperCamelCase__					=       new_node







			def 				snake_case__				(							self      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						if node_data_a == node_data_a:
									return
						else:
									UpperCamelCase__					=       self.head
									while node_a is not None and node_a.data != node_data_a:
												UpperCamelCase__					=       node_a.next

									UpperCamelCase__					=       self.head
									while node_a is not None and node_a.data != node_data_a:
												UpperCamelCase__					=       node_a.next

									if node_a is None or node_a is None:
												return

									UpperCamelCase__,      UpperCamelCase__					=       node_a.data, node_a.data


if __name__ == "__main__":
						__UpperCamelCase    		=       LinkedList()
						for i in range(5, 0, -1):
												ll.push(i)

						ll.print_list()

						ll.swap_nodes(1, 4)
						print('After swapping')
						ll.print_list()


