

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 	UpperCAmelCase__		(	__lowerCAmelCase	):






		"""simple docstring"""


		a									= RobertaConfig
		a									= "roberta"






		def __init__(							self						:		Optional[int]     ,       __lowerCamelCase						:		str )		->							Dict:
							super().__init__(lowerCamelCase__ )

							SCREAMING_SNAKE_CASE__   =   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 	UpperCAmelCase__		(	__lowerCAmelCase	):






		"""simple docstring"""


		a									= RobertaConfig
		a									= "roberta"






		def __init__(							self						:		Dict     ,       __lowerCamelCase						:		Tuple )		->							Union[str, Any]:
							super().__init__(lowerCamelCase__ )
							SCREAMING_SNAKE_CASE__   =   config.num_labels
							SCREAMING_SNAKE_CASE__   =   config.num_hidden_layers

							SCREAMING_SNAKE_CASE__   =   DeeRobertaModel(lowerCamelCase__ )
							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(lowerCamelCase__ )
		def 				lowercase_		(							self						:		int     ,       __lowerCamelCase						:		List[Any]=None     ,       __lowerCamelCase						:		Optional[Any]=None     ,       __lowerCamelCase						:		Any=None     ,       __lowerCamelCase						:		Any=None     ,       __lowerCamelCase						:		Any=None     ,       __lowerCamelCase						:		Optional[int]=None     ,       __lowerCamelCase						:		str=None     ,       __lowerCamelCase						:		List[str]=-1     ,       __lowerCamelCase						:		int=False     ,       )		->							List[Any]:
							SCREAMING_SNAKE_CASE__   =   self.num_layers
							try:
												SCREAMING_SNAKE_CASE__   =   self.roberta(
												    lowerCamelCase__     ,       attention_mask=lowerCamelCase__     ,       token_type_ids=lowerCamelCase__     ,       position_ids=lowerCamelCase__     ,       head_mask=lowerCamelCase__     ,       inputs_embeds=lowerCamelCase__     ,       )

												SCREAMING_SNAKE_CASE__   =   outputs[1]

												SCREAMING_SNAKE_CASE__   =   self.dropout(lowerCamelCase__ )
												SCREAMING_SNAKE_CASE__   =   self.classifier(lowerCamelCase__ )
												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(lowerCamelCase__ )
												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(lowerCamelCase__ )
																						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(lowerCamelCase__ )

												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 ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
       from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline


