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 						lowercase    (     a     ):
							lowercase__						:       Optional[int]								=							RobertaConfig
							lowercase__						:       Optional[Any]								=							"""roberta"""




							def __init__(						self   :					int	,							_UpperCamelCase   :					Tuple      )					->				int:



												'''simple docstring'''

												super().__init__(_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       RobertaEmbeddings(_UpperCamelCase      )
												self.init_weights()



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




							def __init__(						self   :					Union[str, Any]	,							_UpperCamelCase   :					Tuple      )					->				Optional[Any]:



												'''simple docstring'''

												super().__init__(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       config.num_labels
												SCREAMING_SNAKE_CASE    			=       config.num_hidden_layers

												SCREAMING_SNAKE_CASE    			=       DeeRobertaModel(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       nn.Dropout(config.hidden_dropout_prob      )
												SCREAMING_SNAKE_CASE    			=       nn.Linear(config.hidden_size	,							self.config.num_labels      )





							@add_start_docstrings_to_model_forward(_UpperCamelCase      )
							def 	__snake_case(						self   :					List[str]	,							_UpperCamelCase   :					str=None	,							_UpperCamelCase   :					Dict=None	,							_UpperCamelCase   :					Any=None	,							_UpperCamelCase   :					Optional[Any]=None	,							_UpperCamelCase   :					Optional[Any]=None	,							_UpperCamelCase   :					Optional[Any]=None	,							_UpperCamelCase   :					Optional[int]=None	,							_UpperCamelCase   :					Tuple=-1	,							_UpperCamelCase   :					int=False	,							)					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.num_layers
												try:
																	SCREAMING_SNAKE_CASE    			=       self.roberta(
																	    _UpperCamelCase	,							attention_mask=_UpperCamelCase	,							token_type_ids=_UpperCamelCase	,							position_ids=_UpperCamelCase	,							head_mask=_UpperCamelCase	,							inputs_embeds=_UpperCamelCase	,							)

																	SCREAMING_SNAKE_CASE    			=       outputs[1]

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

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

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

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





from __future__ import annotations

from collections.abc import Iterator
from typing import Generic, TypeVar

_lowerCamelCase					:   Optional[Any]							=		TypeVar('''T''')


class 						lowercase    (     Generic[T]     ):




							def __init__(						self   :					Any	,							_UpperCamelCase   :					T      )					->				Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       data
												SCREAMING_SNAKE_CASE    			=       None





							def __str__(						self   :					Union[str, Any]      )					->				str:



												'''simple docstring'''

												return F"{self.data}"



class 						lowercase    (     Generic[T]     ):




							def __init__(						self   :					Optional[int]      )					->				None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       None




							def __iter__(						self   :					str      )					->				Iterator[T]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.top
												while node:
																	yield node.data
																	SCREAMING_SNAKE_CASE    			=       node.next




							def __str__(						self   :					int      )					->				str:



												'''simple docstring'''

												return "->".join([str(_UpperCamelCase      ) for item in self]      )




							def __len__(						self   :					Tuple      )					->				int:



												'''simple docstring'''

												return len(tuple(iter(self      )      )      )




							def 	__snake_case(						self   :					Union[str, Any]      )					->				bool:



												'''simple docstring'''

												return self.top is None




							def 	__snake_case(						self   :					str	,							_UpperCamelCase   :					T      )					->				None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       Node(_UpperCamelCase      )
												if not self.is_empty():
																	SCREAMING_SNAKE_CASE    			=       self.top
												SCREAMING_SNAKE_CASE    			=       node




							def 	__snake_case(						self   :					Union[str, Any]      )					->				T:



												'''simple docstring'''

												if self.is_empty():
																	raise IndexError("pop from empty stack"      )
												assert isinstance(self.top	,							_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       self.top
												SCREAMING_SNAKE_CASE    			=       self.top.next
												return pop_node.data




							def 	__snake_case(						self   :					Union[str, Any]      )					->				T:



												'''simple docstring'''

												if self.is_empty():
																	raise IndexError("peek from empty stack"      )

												assert self.top is not None
												return self.top.data





							def 	__snake_case(						self   :					Dict      )					->				None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       None


if __name__ == "__main__":
							from doctest import testmod

							testmod()




