





"""simple docstring"""




import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)





def        _lowercase   (  _UpperCAmelCase			)  ->					int:
				lowerCamelCase			=torch.exp(_UpperCAmelCase			)
				lowerCamelCase			=torch.sum(_UpperCAmelCase     , dim=1			)  # sum of exp(x_i)
				lowerCamelCase			=torch.sum(x * exp_x     , dim=1			)  # sum of x_i * exp(x_i)
				return torch.log(_UpperCAmelCase			) - B / A

class  __A       (  nn.Module      ):


				def __init__(						self				,	UpperCAmelCase_							):
								super().__init__()
								lowerCamelCase			=config.output_attentions
								lowerCamelCase			=config.output_hidden_states
								lowerCamelCase			=nn.ModuleList([BertLayer(UpperCAmelCase__							) for _ in range(config.num_hidden_layers							)]							)
								lowerCamelCase			=nn.ModuleList([BertHighway(UpperCAmelCase__							) for _ in range(config.num_hidden_layers							)]							)

								lowerCamelCase			=[-1 for _ in range(config.num_hidden_layers							)]


				def _snake_case    (						self				,	UpperCAmelCase_							):
								if (type(UpperCAmelCase__							) is float) or (type(UpperCAmelCase__							) is int):
												for i in range(len(self.early_exit_entropy							)							):
																lowerCamelCase			=x
								else:
												lowerCamelCase			=x


				def _snake_case    (						self				,	UpperCAmelCase_							):
								lowerCamelCase			=pooler.state_dict()
								for highway in self.highway:
												for name, param in highway.pooler.state_dict().items():
																param.copy_(loaded_model[name]							)


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_=None				,	UpperCAmelCase_=None				,	UpperCAmelCase_=None				,	UpperCAmelCase_=None				,	):
								lowerCamelCase			=()
								lowerCamelCase			=()
								lowerCamelCase			=()
								for i, layer_module in enumerate(self.layer							):
												if self.output_hidden_states:
																lowerCamelCase			=all_hidden_states + (hidden_states,)

												lowerCamelCase			=layer_module(
												    UpperCAmelCase__				,	UpperCAmelCase__				,	head_mask[i]				,	UpperCAmelCase__				,	UpperCAmelCase__							)
												lowerCamelCase			=layer_outputs[0]

												if self.output_attentions:
																lowerCamelCase			=all_attentions + (layer_outputs[1],)

												lowerCamelCase			=(hidden_states,)
												if self.output_hidden_states:
																lowerCamelCase			=current_outputs + (all_hidden_states,)
												if self.output_attentions:
																lowerCamelCase			=current_outputs + (all_attentions,)

												lowerCamelCase			=self.highway[i](UpperCAmelCase__							)
												# logits, pooled_output

												if not self.training:
																lowerCamelCase			=highway_exit[0]
																lowerCamelCase			=entropy(UpperCAmelCase__							)
																lowerCamelCase			=highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
																lowerCamelCase			=all_highway_exits + (highway_exit,)

																if highway_entropy < self.early_exit_entropy[i]:
																				lowerCamelCase			=(highway_logits,) + current_outputs[1:] + (all_highway_exits,)
																				raise HighwayException(UpperCAmelCase__				,	i + 1							)
												else:
																lowerCamelCase			=all_highway_exits + (highway_exit,)

        # Add last layer
								if self.output_hidden_states:
												lowerCamelCase			=all_hidden_states + (hidden_states,)

								lowerCamelCase			=(hidden_states,)
								if self.output_hidden_states:
												lowerCamelCase			=outputs + (all_hidden_states,)
								if self.output_attentions:
												lowerCamelCase			=outputs + (all_attentions,)

								lowerCamelCase			=outputs + (all_highway_exits,)
								return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits




@add_start_docstrings(
    """The Bert Model transformer with early exiting (DeeBERT). """      ,   __lowercase      ,   )
class  __A       (  __lowercase      ):


				def __init__(						self				,	UpperCAmelCase_							):
								super().__init__(UpperCAmelCase__							)
								lowerCamelCase			=config

								lowerCamelCase			=BertEmbeddings(UpperCAmelCase__							)
								lowerCamelCase			=DeeBertEncoder(UpperCAmelCase__							)
								lowerCamelCase			=BertPooler(UpperCAmelCase__							)

								self.init_weights()


				def _snake_case    (						self							):
								self.encoder.init_highway_pooler(self.pooler							)


				def _snake_case    (						self							):
								return self.embeddings.word_embeddings


				def _snake_case    (						self				,	UpperCAmelCase_							):
								lowerCamelCase			=value


				def _snake_case    (						self				,	UpperCAmelCase_							):
								for layer, heads in heads_to_prune.items():
												self.encoder.layer[layer].attention.prune_heads(UpperCAmelCase__							)


				@add_start_docstrings_to_model_forward(UpperCAmelCase__							)
				def _snake_case    (						self				,	UpperCAmelCase_=None				,	UpperCAmelCase_=None				,	UpperCAmelCase_=None				,	UpperCAmelCase_=None				,	UpperCAmelCase_=None				,	UpperCAmelCase_=None				,	UpperCAmelCase_=None				,	UpperCAmelCase_=None				,	):
								if input_ids is not None and inputs_embeds is not None:
												raise ValueError("""You cannot specify both input_ids and inputs_embeds at the same time"""							)
								elif input_ids is not None:
												lowerCamelCase			=input_ids.size()
								elif inputs_embeds is not None:
												lowerCamelCase			=inputs_embeds.size()[:-1]
								else:
												raise ValueError("""You have to specify either input_ids or inputs_embeds"""							)

								lowerCamelCase			=input_ids.device if input_ids is not None else inputs_embeds.device

								if attention_mask is None:
												lowerCamelCase			=torch.ones(UpperCAmelCase__				,	device=UpperCAmelCase__							)
								if encoder_attention_mask is None:
												lowerCamelCase			=torch.ones(UpperCAmelCase__				,	device=UpperCAmelCase__							)
								if token_type_ids is None:
												lowerCamelCase			=torch.zeros(UpperCAmelCase__				,	dtype=torch.long				,	device=UpperCAmelCase__							)

								# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
								# ourselves in which case we just need to make it broadcastable to all heads.
								lowerCamelCase			=self.get_extended_attention_mask(UpperCAmelCase__				,	UpperCAmelCase__				,	UpperCAmelCase__							)

								# If a 2D ou 3D attention mask is provided for the cross-attention
								# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
								if encoder_attention_mask.dim() == 3:
												lowerCamelCase			=encoder_attention_mask[:, None, :, :]
								if encoder_attention_mask.dim() == 2:
												lowerCamelCase			=encoder_attention_mask[:, None, None, :]

								lowerCamelCase			=encoder_extended_attention_mask.to(
								    dtype=next(self.parameters()							).dtype							)  # fp16 compatibility
								lowerCamelCase			=(1.0 - encoder_extended_attention_mask) * -10000.0

								# Prepare head mask if needed
								# 1.0 in head_mask indicate we keep the head
								# attention_probs has shape bsz x n_heads x N x N
								# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
								# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
								lowerCamelCase			=self.get_head_mask(UpperCAmelCase__				,	self.config.num_hidden_layers							)

								lowerCamelCase			=self.embeddings(
								    input_ids=UpperCAmelCase__				,	position_ids=UpperCAmelCase__				,	token_type_ids=UpperCAmelCase__				,	inputs_embeds=UpperCAmelCase__							)
								lowerCamelCase			=self.encoder(
								    UpperCAmelCase__				,	attention_mask=UpperCAmelCase__				,	head_mask=UpperCAmelCase__				,	encoder_hidden_states=UpperCAmelCase__				,	encoder_attention_mask=UpperCAmelCase__				,	)
								lowerCamelCase			=encoder_outputs[0]
								lowerCamelCase			=self.pooler(UpperCAmelCase__							)

								lowerCamelCase			=(
								    sequence_output,
								    pooled_output,
								) + encoder_outputs[
								    1:
								]  # add hidden_states and attentions if they are here
								return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits




class  __A       (  __lowercase      ):


				def __init__(						self				,	UpperCAmelCase_				,	UpperCAmelCase_							):
								lowerCamelCase			=message
								lowerCamelCase			=exit_layer  # start from 1!




class  __A       (  nn.Module      ):


				def __init__(						self				,	UpperCAmelCase_							):
								super().__init__()
								lowerCamelCase			=BertPooler(UpperCAmelCase__							)
								lowerCamelCase			=nn.Dropout(config.hidden_dropout_prob							)
								lowerCamelCase			=nn.Linear(config.hidden_size				,	config.num_labels							)


				def _snake_case    (						self				,	UpperCAmelCase_							):
								# Pooler
								lowerCamelCase			=encoder_outputs[0]
								lowerCamelCase			=self.pooler(UpperCAmelCase__							)
								# "return" pooler_output

								# BertModel
								lowerCamelCase			=(pooler_input, pooler_output) + encoder_outputs[1:]
								# "return" bmodel_output

								# Dropout and classification
								lowerCamelCase			=bmodel_output[1]

								lowerCamelCase			=self.dropout(UpperCAmelCase__							)
								lowerCamelCase			=self.classifier(UpperCAmelCase__							)

								return logits, pooled_output




@add_start_docstrings(
    """Bert Model (with early exiting - DeeBERT) with a classifier on top,
    also takes care of multi-layer training. """      ,   __lowercase      ,   )
class  __A       (  __lowercase      ):


				def __init__(						self				,	UpperCAmelCase_							):
								super().__init__(UpperCAmelCase__							)
								lowerCamelCase			=config.num_labels
								lowerCamelCase			=config.num_hidden_layers

								lowerCamelCase			=DeeBertModel(UpperCAmelCase__							)
								lowerCamelCase			=nn.Dropout(config.hidden_dropout_prob							)
								lowerCamelCase			=nn.Linear(config.hidden_size				,	self.config.num_labels							)

								self.init_weights()


				@add_start_docstrings_to_model_forward(UpperCAmelCase__							)
				def _snake_case    (						self				,	UpperCAmelCase_=None				,	UpperCAmelCase_=None				,	UpperCAmelCase_=None				,	UpperCAmelCase_=None				,	UpperCAmelCase_=None				,	UpperCAmelCase_=None				,	UpperCAmelCase_=None				,	UpperCAmelCase_=-1				,	UpperCAmelCase_=False				,	):
								lowerCamelCase			=self.num_layers
								try:
												lowerCamelCase			=self.bert(
												    UpperCAmelCase__				,	attention_mask=UpperCAmelCase__				,	token_type_ids=UpperCAmelCase__				,	position_ids=UpperCAmelCase__				,	head_mask=UpperCAmelCase__				,	inputs_embeds=UpperCAmelCase__				,	)
												# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

												lowerCamelCase			=outputs[1]

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

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

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

												if train_highway:
																lowerCamelCase			=(sum(highway_losses[:-1]							),) + outputs
																# exclude the final highway, of course
												else:
																lowerCamelCase			=(loss,) + outputs
								if not self.training:
												lowerCamelCase			=outputs + ((original_entropy, highway_entropy), exit_layer)
												if output_layer >= 0:
																lowerCamelCase			=(
																    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
																)  # use the highway of the last layer

								return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)












from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels





