CPT / roberta_modeling.py

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	# coding=utf-8
	# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
	# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	"""PyTorch RoBERTa model. Modify the transformers implementation to accept **kwargs."""

	import math

	import torch
	import torch.utils.checkpoint
	from packaging import version
	from torch import nn
	from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
	from transformers import RobertaConfig
	from transformers.activations import ACT2FN, gelu
	from transformers.adapters.model_mixin import ModelWithHeadsAdaptersMixin
	from transformers.adapters.models.bert import (
	BertEncoderAdaptersMixin,
	BertLayerAdaptersMixin,
	BertModelAdaptersMixin,
	BertModelHeadsMixin,
	BertOutputAdaptersMixin,
	BertSelfOutputAdaptersMixin,
	)
	from transformers.file_utils import (
	ModelOutput,
	add_code_sample_docstrings,
	add_start_docstrings,
	add_start_docstrings_to_model_forward,
	replace_return_docstrings,
	)
	from transformers.modeling_outputs import (
	BaseModelOutputWithPastAndCrossAttentions,
	BaseModelOutputWithPoolingAndCrossAttentions,
	CausalLMOutputWithCrossAttentions,
	MaskedLMOutput,
	MultipleChoiceModelOutput,
	QuestionAnsweringModelOutput,
	SequenceClassifierOutput,
	TokenClassifierOutput,
	)
	from transformers.modeling_utils import (
	PreTrainedModel,
	apply_chunking_to_forward,
	find_pruneable_heads_and_indices,
	prune_linear_layer,
	)
	from transformers.utils import logging

	logger = logging.get_logger(__name__)

	_CHECKPOINT_FOR_DOC = "roberta-base"
	_CONFIG_FOR_DOC = "RobertaConfig"
	_TOKENIZER_FOR_DOC = "RobertaTokenizer"

	ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = [
	"roberta-base",
	"roberta-large",
	"roberta-large-mnli",
	"distilroberta-base",
	"roberta-base-openai-detector",
	"roberta-large-openai-detector",
	# See all RoBERTa models at https://huggingface.co/models?filter=roberta
	]


	class RobertaEmbeddings(nn.Module):
	"""
	Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
	"""

	# Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__
	def __init__(self, config):
	super().__init__()
	self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
	self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
	self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)

	# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
	# any TensorFlow checkpoint file
	self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
	self.dropout = nn.Dropout(config.hidden_dropout_prob)
	# position_ids (1, len position emb) is contiguous in memory and exported when serialized
	self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
	self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
	if version.parse(torch.__version__) > version.parse("1.6.0"):
	self.register_buffer(
	"token_type_ids",
	torch.zeros(self.position_ids.size(), dtype=torch.long, device=self.position_ids.device),
	persistent=False,
	)

	# End copy
	self.padding_idx = config.pad_token_id
	self.position_embeddings = nn.Embedding(
	config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
	)

	def forward(
	self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
	):
	if position_ids is None:
	if input_ids is not None:
	# Create the position ids from the input token ids. Any padded tokens remain padded.
	position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length)
	else:
	position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)

	if input_ids is not None:
	input_shape = input_ids.size()
	else:
	input_shape = inputs_embeds.size()[:-1]

	seq_length = input_shape[1]

	# Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
	# when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
	# issue #5664
	if token_type_ids is None:
	if hasattr(self, "token_type_ids"):
	buffered_token_type_ids = self.token_type_ids[:, :seq_length]
	buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
	token_type_ids = buffered_token_type_ids_expanded
	else:
	token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)

	if inputs_embeds is None:
	inputs_embeds = self.word_embeddings(input_ids)
	token_type_embeddings = self.token_type_embeddings(token_type_ids)

	embeddings = inputs_embeds + token_type_embeddings
	if self.position_embedding_type == "absolute":
	position_embeddings = self.position_embeddings(position_ids)
	embeddings += position_embeddings
	embeddings = self.LayerNorm(embeddings)
	embeddings = self.dropout(embeddings)
	return embeddings

	def create_position_ids_from_inputs_embeds(self, inputs_embeds):
	"""
	We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.

	Args:
	inputs_embeds: torch.Tensor

	Returns: torch.Tensor
	"""
	input_shape = inputs_embeds.size()[:-1]
	sequence_length = input_shape[1]

	position_ids = torch.arange(
	self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
	)
	return position_ids.unsqueeze(0).expand(input_shape)


	# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Roberta
	class RobertaSelfAttention(nn.Module):
	def __init__(self, config):
	super().__init__()
	if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
	raise ValueError(
	f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
	f"heads ({config.num_attention_heads})"
	)

	self.num_attention_heads = config.num_attention_heads
	self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
	self.all_head_size = self.num_attention_heads * self.attention_head_size

	self.query = nn.Linear(config.hidden_size, self.all_head_size)
	self.key = nn.Linear(config.hidden_size, self.all_head_size)
	self.value = nn.Linear(config.hidden_size, self.all_head_size)

	self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
	self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
	if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
	self.max_position_embeddings = config.max_position_embeddings
	self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)

	self.is_decoder = config.is_decoder

	def transpose_for_scores(self, x):
	new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
	x = x.view(*new_x_shape)
	return x.permute(0, 2, 1, 3)

	def forward(
	self,
	hidden_states,
	attention_mask=None,
	head_mask=None,
	encoder_hidden_states=None,
	encoder_attention_mask=None,
	past_key_value=None,
	output_attentions=False,
	**kwargs,
	):
	mixed_query_layer = self.query(hidden_states)

	# If this is instantiated as a cross-attention module, the keys
	# and values come from an encoder; the attention mask needs to be
	# such that the encoder's padding tokens are not attended to.
	is_cross_attention = encoder_hidden_states is not None

	if is_cross_attention and past_key_value is not None:
	# reuse k,v, cross_attentions
	key_layer = past_key_value[0]
	value_layer = past_key_value[1]
	attention_mask = encoder_attention_mask
	elif is_cross_attention:
	key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
	value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
	attention_mask = encoder_attention_mask
	elif past_key_value is not None:
	key_layer = self.transpose_for_scores(self.key(hidden_states))
	value_layer = self.transpose_for_scores(self.value(hidden_states))
	key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
	value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
	else:
	key_layer = self.transpose_for_scores(self.key(hidden_states))
	value_layer = self.transpose_for_scores(self.value(hidden_states))

	query_layer = self.transpose_for_scores(mixed_query_layer)

	if self.is_decoder:
	# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
	# Further calls to cross_attention layer can then reuse all cross-attention
	# key/value_states (first "if" case)
	# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
	# all previous decoder key/value_states. Further calls to uni-directional self-attention
	# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
	# if encoder bi-directional self-attention `past_key_value` is always `None`
	past_key_value = (key_layer, value_layer)

	# Take the dot product between "query" and "key" to get the raw attention scores.
	attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))

	if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
	seq_length = hidden_states.size()[1]
	position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
	position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
	distance = position_ids_l - position_ids_r
	positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
	positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility

	if self.position_embedding_type == "relative_key":
	relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
	attention_scores = attention_scores + relative_position_scores
	elif self.position_embedding_type == "relative_key_query":
	relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
	relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
	attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key

	attention_scores = attention_scores / math.sqrt(self.attention_head_size)
	if attention_mask is not None:
	# Apply the attention mask is (precomputed for all layers in RobertaModel forward() function)
	attention_scores = attention_scores + attention_mask

	# Normalize the attention scores to probabilities.
	attention_probs = nn.Softmax(dim=-1)(attention_scores)

	# This is actually dropping out entire tokens to attend to, which might
	# seem a bit unusual, but is taken from the original Transformer paper.
	attention_probs = self.dropout(attention_probs)

	# Mask heads if we want to
	if head_mask is not None:
	attention_probs = attention_probs * head_mask

	context_layer = torch.matmul(attention_probs, value_layer)

	context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
	new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
	context_layer = context_layer.view(*new_context_layer_shape)

	outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)

	if self.is_decoder:
	outputs = outputs + (past_key_value,)
	return outputs


	# Copied from transformers.models.modeling_bert.BertSelfOutput
	class RobertaSelfOutput(BertSelfOutputAdaptersMixin, nn.Module):
	def __init__(self, config):
	super().__init__()
	self.config = config

	self.dense = nn.Linear(config.hidden_size, config.hidden_size)
	self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
	self.dropout = nn.Dropout(config.hidden_dropout_prob)
	self._init_adapter_modules()

	def forward(self, hidden_states, input_tensor, **kwargs):
	hidden_states = self.dense(hidden_states)
	hidden_states = self.dropout(hidden_states)
	hidden_states = self.adapters_forward(hidden_states, input_tensor, **kwargs)
	return hidden_states


	# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Roberta
	class RobertaAttention(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.self = RobertaSelfAttention(config)
	self.output = RobertaSelfOutput(config)
	self.pruned_heads = set()

	def prune_heads(self, heads):
	if len(heads) == 0:
	return
	heads, index = find_pruneable_heads_and_indices(
	heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
	)

	# Prune linear layers
	self.self.query = prune_linear_layer(self.self.query, index)
	self.self.key = prune_linear_layer(self.self.key, index)
	self.self.value = prune_linear_layer(self.self.value, index)
	self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)

	# Update hyper params and store pruned heads
	self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
	self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
	self.pruned_heads = self.pruned_heads.union(heads)

	def forward(
	self,
	hidden_states,
	attention_mask=None,
	head_mask=None,
	encoder_hidden_states=None,
	encoder_attention_mask=None,
	past_key_value=None,
	output_attentions=False,
	**kwargs
	):
	self_outputs = self.self(
	hidden_states,
	attention_mask,
	head_mask,
	encoder_hidden_states,
	encoder_attention_mask,
	past_key_value,
	output_attentions,
	**kwargs,
	)
	attention_output = self.output(self_outputs[0], hidden_states, **kwargs)
	outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
	return outputs


	# Copied from transformers.models.bert.modeling_bert.BertIntermediate
	class RobertaIntermediate(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
	if isinstance(config.hidden_act, str):
	self.intermediate_act_fn = ACT2FN[config.hidden_act]
	else:
	self.intermediate_act_fn = config.hidden_act

	def forward(self, hidden_states):
	hidden_states = self.dense(hidden_states)
	hidden_states = self.intermediate_act_fn(hidden_states)
	return hidden_states


	# Copied from transformers.models.bert.modeling_bert.BertOutput
	class RobertaOutput(BertOutputAdaptersMixin, nn.Module):
	def __init__(self, config):
	super().__init__()
	self.config = config

	self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
	self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
	self.dropout = nn.Dropout(config.hidden_dropout_prob)
	self._init_adapter_modules()

	def forward(self, hidden_states, input_tensor, **kwargs):
	hidden_states = self.dense(hidden_states)
	hidden_states = self.dropout(hidden_states)
	hidden_states = self.adapters_forward(hidden_states, input_tensor, **kwargs)
	return hidden_states


	# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Roberta
	class RobertaLayer(BertLayerAdaptersMixin, nn.Module):
	def __init__(self, config):
	super().__init__()
	self.chunk_size_feed_forward = config.chunk_size_feed_forward
	self.seq_len_dim = 1
	self.attention = RobertaAttention(config)
	self.is_decoder = config.is_decoder
	self.add_cross_attention = config.add_cross_attention
	if self.add_cross_attention:
	assert self.is_decoder, f"{self} should be used as a decoder model if cross attention is added"
	self.crossattention = RobertaAttention(config)
	self.intermediate = RobertaIntermediate(config)
	self.output = RobertaOutput(config)

	def forward(
	self,
	hidden_states,
	attention_mask=None,
	head_mask=None,
	encoder_hidden_states=None,
	encoder_attention_mask=None,
	past_key_value=None,
	output_attentions=False,
	**kwargs
	):
	# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
	self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
	self_attention_outputs = self.attention(
	hidden_states,
	attention_mask,
	head_mask,
	output_attentions=output_attentions,
	past_key_value=self_attn_past_key_value,
	**kwargs,
	)
	attention_output = self_attention_outputs[0]

	# if decoder, the last output is tuple of self-attn cache
	if self.is_decoder:
	outputs = self_attention_outputs[1:-1]
	present_key_value = self_attention_outputs[-1]
	else:
	outputs = self_attention_outputs[1:] # add self attentions if we output attention weights

	cross_attn_present_key_value = None
	if self.is_decoder and encoder_hidden_states is not None:
	assert hasattr(
	self, "crossattention"
	), f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`"

	# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
	cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
	cross_attention_outputs = self.crossattention(
	attention_output,
	attention_mask,
	head_mask,
	encoder_hidden_states,
	encoder_attention_mask,
	cross_attn_past_key_value,
	output_attentions,
	)
	attention_output = cross_attention_outputs[0]
	outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights

	# add cross-attn cache to positions 3,4 of present_key_value tuple
	cross_attn_present_key_value = cross_attention_outputs[-1]
	present_key_value = present_key_value + cross_attn_present_key_value

	layer_output = apply_chunking_to_forward(
	self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output, **kwargs
	)
	outputs = (layer_output,) + outputs

	# if decoder, return the attn key/values as the last output
	if self.is_decoder:
	outputs = outputs + (present_key_value,)

	return outputs

	def feed_forward_chunk(self, attention_output, **kwargs):
	intermediate_output = self.intermediate(attention_output)
	layer_output = self.output(intermediate_output, attention_output, **kwargs)
	return layer_output


	# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Roberta
	class RobertaEncoder(BertEncoderAdaptersMixin, nn.Module):
	def __init__(self, config):
	super().__init__()
	self.config = config
	self.layer = nn.ModuleList([RobertaLayer(config) for _ in range(config.num_hidden_layers)])
	self.gradient_checkpointing = False

	def forward(
	self,
	hidden_states,
	attention_mask=None,
	head_mask=None,
	encoder_hidden_states=None,
	encoder_attention_mask=None,
	past_key_values=None,
	use_cache=None,
	output_attentions=False,
	output_hidden_states=False,
	return_dict=True,
	**kwargs
	):
	all_hidden_states = () if output_hidden_states else None
	all_self_attentions = () if output_attentions else None
	all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None

	next_decoder_cache = () if use_cache else None
	for i, layer_module in enumerate(self.layer):
	if output_hidden_states:
	all_hidden_states = all_hidden_states + (hidden_states,)

	layer_head_mask = head_mask[i] if head_mask is not None else None
	past_key_value = past_key_values[i] if past_key_values is not None else None

	if self.gradient_checkpointing and self.training:

	if use_cache:
	logger.warning(
	"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
	)
	use_cache = False

	def create_custom_forward(module):
	def custom_forward(*inputs):
	return module(*inputs, past_key_value, output_attentions)

	return custom_forward

	layer_outputs = torch.utils.checkpoint.checkpoint(
	create_custom_forward(layer_module),
	hidden_states,
	attention_mask,
	layer_head_mask,
	encoder_hidden_states,
	encoder_attention_mask,
	)
	else:
	layer_outputs = layer_module(
	hidden_states,
	attention_mask,
	layer_head_mask,
	encoder_hidden_states,
	encoder_attention_mask,
	past_key_value,
	output_attentions,
	**kwargs,
	)

	hidden_states = layer_outputs[0]
	attention_mask = self.adjust_attention_mask_for_parallel(hidden_states, attention_mask)

	if use_cache:
	next_decoder_cache += (layer_outputs[-1],)
	if output_attentions:
	all_self_attentions = all_self_attentions + (layer_outputs[1],)
	if self.config.add_cross_attention:
	all_cross_attentions = all_cross_attentions + (layer_outputs[2],)

	if output_hidden_states:
	all_hidden_states = all_hidden_states + (hidden_states,)

	if not return_dict:
	return tuple(
	v
	for v in [
	hidden_states,
	next_decoder_cache,
	all_hidden_states,
	all_self_attentions,
	all_cross_attentions,
	]
	if v is not None
	)
	return BaseModelOutputWithPastAndCrossAttentions(
	last_hidden_state=hidden_states,
	past_key_values=next_decoder_cache,
	hidden_states=all_hidden_states,
	attentions=all_self_attentions,
	cross_attentions=all_cross_attentions,
	)


	# Copied from transformers.models.bert.modeling_bert.BertPooler
	class RobertaPooler(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.dense = nn.Linear(config.hidden_size, config.hidden_size)
	self.activation = nn.Tanh()

	def forward(self, hidden_states):
	# We "pool" the model by simply taking the hidden state corresponding
	# to the first token.
	first_token_tensor = hidden_states[:, 0]
	pooled_output = self.dense(first_token_tensor)
	pooled_output = self.activation(pooled_output)
	return pooled_output


	class RobertaPreTrainedModel(PreTrainedModel):
	"""
	An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
	models.
	"""

	config_class = RobertaConfig
	base_model_prefix = "roberta"
	supports_gradient_checkpointing = True

	# Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights
	def _init_weights(self, module):
	"""Initialize the weights"""
	if isinstance(module, nn.Linear):
	# Slightly different from the TF version which uses truncated_normal for initialization
	# cf https://github.com/pytorch/pytorch/pull/5617
	module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
	if module.bias is not None:
	module.bias.data.zero_()
	elif isinstance(module, nn.Embedding):
	module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
	if module.padding_idx is not None:
	module.weight.data[module.padding_idx].zero_()
	elif isinstance(module, nn.LayerNorm):
	module.bias.data.zero_()
	module.weight.data.fill_(1.0)

	def _set_gradient_checkpointing(self, module, value=False):
	if isinstance(module, RobertaEncoder):
	module.gradient_checkpointing = value

	def update_keys_to_ignore(self, config, del_keys_to_ignore):
	"""Remove some keys from ignore list"""
	if not config.tie_word_embeddings:
	# must make a new list, or the class variable gets modified!
	self._keys_to_ignore_on_save = [k for k in self._keys_to_ignore_on_save if k not in del_keys_to_ignore]
	self._keys_to_ignore_on_load_missing = [
	k for k in self._keys_to_ignore_on_load_missing if k not in del_keys_to_ignore
	]


	ROBERTA_START_DOCSTRING = r"""

	This model inherits from :class:`~transformers.PreTrainedModel`. Check the superclass documentation for the generic
	methods the library implements for all its model (such as downloading or saving, resizing the input embeddings,
	pruning heads etc.)

	This model is also a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__
	subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to
	general usage and behavior.

	Parameters:
	config (:class:`~transformers.RobertaConfig`): Model configuration class with all the parameters of the
	model. Initializing with a config file does not load the weights associated with the model, only the
	configuration. Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model
	weights.
	"""

	ROBERTA_INPUTS_DOCSTRING = r"""
	Args:
	input_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`):
	Indices of input sequence tokens in the vocabulary.

	Indices can be obtained using :class:`~transformers.RobertaTokenizer`. See
	:meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for
	details.

	`What are input IDs? <../glossary.html#input-ids>`__
	attention_mask (:obj:`torch.FloatTensor` of shape :obj:`({0})`, `optional`):
	Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.

	`What are attention masks? <../glossary.html#attention-mask>`__
	token_type_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`, `optional`):
	Segment token indices to indicate first and second portions of the inputs. Indices are selected in ``[0,
	1]``:

	- 0 corresponds to a `sentence A` token,
	- 1 corresponds to a `sentence B` token.

	`What are token type IDs? <../glossary.html#token-type-ids>`_
	position_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`, `optional`):
	Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
	config.max_position_embeddings - 1]``.

	`What are position IDs? <../glossary.html#position-ids>`_
	head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
	Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``:

	- 1 indicates the head is not masked,
	- 0 indicates the head is masked.

	inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`({0}, hidden_size)`, `optional`):
	Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
	This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
	vectors than the model's internal embedding lookup matrix.
	output_attentions (:obj:`bool`, `optional`):
	Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
	tensors for more detail.
	output_hidden_states (:obj:`bool`, `optional`):
	Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
	more detail.
	return_dict (:obj:`bool`, `optional`):
	Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
	"""


	@add_start_docstrings(
	"The bare RoBERTa Model transformer outputting raw hidden-states without any specific head on top.",
	ROBERTA_START_DOCSTRING,
	)
	class RobertaModel(BertModelAdaptersMixin, RobertaPreTrainedModel):
	"""

	The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
	cross-attention is added between the self-attention layers, following the architecture described in `Attention is
	all you need`_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz
	Kaiser and Illia Polosukhin.

	To behave as an decoder the model needs to be initialized with the :obj:`is_decoder` argument of the configuration
	set to :obj:`True`. To be used in a Seq2Seq model, the model needs to initialized with both :obj:`is_decoder`
	argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an
	input to the forward pass.

	.. _`Attention is all you need`: https://arxiv.org/abs/1706.03762

	"""

	_keys_to_ignore_on_load_missing = [r"position_ids"]

	# Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Roberta
	def __init__(self, config, add_pooling_layer=True):
	super().__init__(config)
	self.config = config

	self.embeddings = RobertaEmbeddings(config)
	self.encoder = RobertaEncoder(config)

	self.pooler = RobertaPooler(config) if add_pooling_layer else None

	self._init_adapter_modules()

	self.init_weights()

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

	def set_input_embeddings(self, value):
	self.embeddings.word_embeddings = value

	def _prune_heads(self, heads_to_prune):
	"""
	Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
	class PreTrainedModel
	"""
	for layer, heads in heads_to_prune.items():
	self.encoder.layer[layer].attention.prune_heads(heads)

	@add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@add_code_sample_docstrings(
	tokenizer_class=_TOKENIZER_FOR_DOC,
	checkpoint=_CHECKPOINT_FOR_DOC,
	output_type=BaseModelOutputWithPoolingAndCrossAttentions,
	config_class=_CONFIG_FOR_DOC,
	)
	# Copied from transformers.models.bert.modeling_bert.BertModel.forward
	def forward(
	self,
	input_ids=None,
	attention_mask=None,
	token_type_ids=None,
	position_ids=None,
	head_mask=None,
	inputs_embeds=None,
	encoder_hidden_states=None,
	encoder_attention_mask=None,
	past_key_values=None,
	use_cache=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	**kwargs
	):
	r"""
	encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
	Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
	the model is configured as a decoder.
	encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
	Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
	the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.
	past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
	Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.

	If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
	(those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
	instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
	use_cache (:obj:`bool`, `optional`):
	If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
	decoding (see :obj:`past_key_values`).
	"""
	output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
	output_hidden_states = (
	output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
	)
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict
	self.pre_transformer_forward(**kwargs)

	if self.config.is_decoder:
	use_cache = use_cache if use_cache is not None else self.config.use_cache
	else:
	use_cache = False

	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:
	input_shape = input_ids.size()
	elif inputs_embeds is not None:
	input_shape = inputs_embeds.size()[:-1]
	else:
	raise ValueError("You have to specify either input_ids or inputs_embeds")

	batch_size, seq_length = input_shape
	device = input_ids.device if input_ids is not None else inputs_embeds.device

	# past_key_values_length
	past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0

	if attention_mask is None:
	attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)

	if token_type_ids is None:
	if hasattr(self.embeddings, "token_type_ids"):
	buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
	buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
	token_type_ids = buffered_token_type_ids_expanded
	else:
	token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)

	# 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.
	extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, device)

	# If a 2D or 3D attention mask is provided for the cross-attention
	# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
	if self.config.is_decoder and encoder_hidden_states is not None:
	encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
	encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
	if encoder_attention_mask is None:
	encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
	encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
	else:
	encoder_extended_attention_mask = None

	# 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]
	head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)

	embedding_output = self.embeddings(
	input_ids=input_ids,
	position_ids=position_ids,
	token_type_ids=token_type_ids,
	inputs_embeds=inputs_embeds,
	past_key_values_length=past_key_values_length,
	)
	embedding_output = self.invertible_adapters_forward(embedding_output)

	encoder_outputs = self.encoder(
	embedding_output,
	attention_mask=extended_attention_mask,
	head_mask=head_mask,
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_extended_attention_mask,
	past_key_values=past_key_values,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	**kwargs,
	)
	sequence_output = encoder_outputs[0]
	pooled_output = self.pooler(sequence_output) if self.pooler is not None else None

	if not return_dict:
	return (sequence_output, pooled_output) + encoder_outputs[1:]

	return BaseModelOutputWithPoolingAndCrossAttentions(
	last_hidden_state=sequence_output,
	pooler_output=pooled_output,
	past_key_values=encoder_outputs.past_key_values,
	hidden_states=encoder_outputs.hidden_states,
	attentions=encoder_outputs.attentions,
	cross_attentions=encoder_outputs.cross_attentions,
	)


	@add_start_docstrings(
	"""Roberta Model transformer with the option to add multiple flexible heads on top.""",
	ROBERTA_START_DOCSTRING,
	)
	class RobertaModelWithHeads(BertModelHeadsMixin, RobertaPreTrainedModel):
	def __init__(self, config):
	super().__init__(config)

	self.roberta = RobertaModel(config)

	self._init_head_modules()

	self.init_weights()

	@add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@add_code_sample_docstrings(
	tokenizer_class=_TOKENIZER_FOR_DOC,
	checkpoint="roberta-base",
	output_type=ModelOutput,
	config_class=_CONFIG_FOR_DOC,
	)
	def forward(
	self,
	input_ids=None,
	attention_mask=None,
	token_type_ids=None,
	position_ids=None,
	head_mask=None,
	inputs_embeds=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	adapter_names=None,
	head=None,
	**kwargs
	):
	input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
	position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
	token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
	attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
	inputs_embeds = (
	inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
	if inputs_embeds is not None
	else None
	)

	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	outputs = self.roberta(
	input_ids,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	adapter_names=adapter_names,
	)
	# BERT & RoBERTa return the pooled output as second item, we don't need that in these heads
	if not return_dict:
	head_inputs = (outputs[0],) + outputs[2:]
	else:
	head_inputs = outputs
	pooled_output = outputs[1]

	if head or self.active_head:
	head_outputs = self.forward_head(
	head_inputs,
	head_name=head,
	attention_mask=attention_mask,
	return_dict=return_dict,
	pooled_output=pooled_output,
	**kwargs,
	)
	return head_outputs
	else:
	# in case no head is used just return the output of the base model (including pooler output)
	return outputs


	@add_start_docstrings(
	"""RoBERTa Model with a `language modeling` head on top for CLM fine-tuning. """, ROBERTA_START_DOCSTRING
	)
	class RobertaForCausalLM(ModelWithHeadsAdaptersMixin, RobertaPreTrainedModel):
	_keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"]
	_keys_to_ignore_on_load_missing = [r"position_ids", r"lm_head.decoder.weight", r"lm_head.decoder.bias"]
	_keys_to_ignore_on_load_unexpected = [r"pooler"]

	def __init__(self, config):
	super().__init__(config)

	if not config.is_decoder:
	logger.warning("If you want to use `RobertaLMHeadModel` as a standalone, add `is_decoder=True.`")

	self.roberta = RobertaModel(config, add_pooling_layer=False)
	self.lm_head = RobertaLMHead(config)

	# The LM head weights require special treatment only when they are tied with the word embeddings
	self.update_keys_to_ignore(config, ["lm_head.decoder.weight"])

	self.init_weights()

	def get_output_embeddings(self):
	return self.lm_head.decoder

	def set_output_embeddings(self, new_embeddings):
	self.lm_head.decoder = new_embeddings

	@add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
	def forward(
	self,
	input_ids=None,
	attention_mask=None,
	token_type_ids=None,
	position_ids=None,
	head_mask=None,
	inputs_embeds=None,
	encoder_hidden_states=None,
	encoder_attention_mask=None,
	labels=None,
	past_key_values=None,
	use_cache=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	adapter_names=None,
	):
	r"""
	encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
	Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
	the model is configured as a decoder.
	encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
	Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
	the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.

	labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
	Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
	``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are
	ignored (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``
	past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
	Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.

	If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
	(those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
	instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
	use_cache (:obj:`bool`, `optional`):
	If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
	decoding (see :obj:`past_key_values`).

	Returns:

	Example::

	>>> from transformers import RobertaTokenizer, RobertaForCausalLM, RobertaConfig
	>>> import torch

	>>> tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
	>>> config = RobertaConfig.from_pretrained("roberta-base")
	>>> config.is_decoder = True
	>>> model = RobertaForCausalLM.from_pretrained('roberta-base', config=config)

	>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
	>>> outputs = model(**inputs)

	>>> prediction_logits = outputs.logits
	"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict
	if labels is not None:
	use_cache = False

	outputs = self.roberta(
	input_ids,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_attention_mask,
	past_key_values=past_key_values,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	adapter_names=adapter_names,
	)

	sequence_output = outputs[0]
	prediction_scores = self.lm_head(
	sequence_output,
	inv_lang_adapter=self.roberta.get_invertible_adapter(),
	)

	lm_loss = None
	if labels is not None:
	# we are doing next-token prediction; shift prediction scores and input ids by one
	shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
	labels = labels[:, 1:].contiguous()
	loss_fct = CrossEntropyLoss()
	lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))

	if not return_dict:
	output = (prediction_scores,) + outputs[2:]
	return ((lm_loss,) + output) if lm_loss is not None else output

	return CausalLMOutputWithCrossAttentions(
	loss=lm_loss,
	logits=prediction_scores,
	past_key_values=outputs.past_key_values,
	hidden_states=outputs.hidden_states,
	attentions=outputs.attentions,
	cross_attentions=outputs.cross_attentions,
	)

	def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs):
	input_shape = input_ids.shape
	# if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
	if attention_mask is None:
	attention_mask = input_ids.new_ones(input_shape)

	# cut decoder_input_ids if past is used
	if past is not None:
	input_ids = input_ids[:, -1:]

	return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past}

	def _reorder_cache(self, past, beam_idx):
	reordered_past = ()
	for layer_past in past:
	reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
	return reordered_past


	@add_start_docstrings("""RoBERTa Model with a `language modeling` head on top. """, ROBERTA_START_DOCSTRING)
	class RobertaForMaskedLM(ModelWithHeadsAdaptersMixin, RobertaPreTrainedModel):
	_keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"]
	_keys_to_ignore_on_load_missing = [r"position_ids", r"lm_head.decoder.weight", r"lm_head.decoder.bias"]
	_keys_to_ignore_on_load_unexpected = [r"pooler"]

	def __init__(self, config):
	super().__init__(config)

	if config.is_decoder:
	logger.warning(
	"If you want to use `RobertaForMaskedLM` make sure `config.is_decoder=False` for "
	"bi-directional self-attention."
	)

	self.roberta = RobertaModel(config, add_pooling_layer=False)
	self.lm_head = RobertaLMHead(config)

	# The LM head weights require special treatment only when they are tied with the word embeddings
	self.update_keys_to_ignore(config, ["lm_head.decoder.weight"])

	self.init_weights()

	def get_output_embeddings(self):
	return self.lm_head.decoder

	def set_output_embeddings(self, new_embeddings):
	self.lm_head.decoder = new_embeddings

	@add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@add_code_sample_docstrings(
	tokenizer_class=_TOKENIZER_FOR_DOC,
	checkpoint=_CHECKPOINT_FOR_DOC,
	output_type=MaskedLMOutput,
	config_class=_CONFIG_FOR_DOC,
	mask="<mask>",
	)
	def forward(
	self,
	input_ids=None,
	attention_mask=None,
	token_type_ids=None,
	position_ids=None,
	head_mask=None,
	inputs_embeds=None,
	encoder_hidden_states=None,
	encoder_attention_mask=None,
	labels=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	adapter_names=None,
	**kwargs,
	):
	r"""
	labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
	Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
	config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
	(masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``
	kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`):
	Used to hide legacy arguments that have been deprecated.
	"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	outputs = self.roberta(
	input_ids,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_attention_mask,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	**kwargs,
	)
	sequence_output = outputs[0]
	prediction_scores = self.lm_head(
	sequence_output,
	inv_lang_adapter=self.roberta.get_invertible_adapter(),
	)

	masked_lm_loss = None
	if labels is not None:
	loss_fct = CrossEntropyLoss()
	masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))

	if not return_dict:
	output = (prediction_scores,) + outputs[2:]
	return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output

	return MaskedLMOutput(
	loss=masked_lm_loss,
	logits=prediction_scores,
	hidden_states=outputs.hidden_states,
	attentions=outputs.attentions,
	)


	class RobertaLMHead(nn.Module):
	"""Roberta Head for masked language modeling."""

	def __init__(self, config):
	super().__init__()
	self.dense = nn.Linear(config.hidden_size, config.hidden_size)
	self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

	self.decoder = nn.Linear(config.hidden_size, config.vocab_size)
	self.bias = nn.Parameter(torch.zeros(config.vocab_size))
	self.decoder.bias = self.bias

	def forward(self, features, inv_lang_adapter=None, **kwargs):
	x = self.dense(features)
	x = gelu(x)
	x = self.layer_norm(x)

	if inv_lang_adapter:
	x = inv_lang_adapter(x, rev=True)

	# project back to size of vocabulary with bias
	x = self.decoder(x)

	return x

	def _tie_weights(self):
	# To tie those two weights if they get disconnected (on TPU or when the bias is resized)
	self.bias = self.decoder.bias


	@add_start_docstrings(
	"""
	RoBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the
	pooled output) e.g. for GLUE tasks.
	""",
	ROBERTA_START_DOCSTRING,
	)
	class RobertaForSequenceClassification(ModelWithHeadsAdaptersMixin, RobertaPreTrainedModel):
	_keys_to_ignore_on_load_missing = [r"position_ids"]

	def __init__(self, config):
	super().__init__(config)
	self.num_labels = config.num_labels
	self.config = config

	self.roberta = RobertaModel(config, add_pooling_layer=False)
	self.classifier = RobertaClassificationHead(config)

	self.init_weights()

	@add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@add_code_sample_docstrings(
	tokenizer_class=_TOKENIZER_FOR_DOC,
	checkpoint=_CHECKPOINT_FOR_DOC,
	output_type=SequenceClassifierOutput,
	config_class=_CONFIG_FOR_DOC,
	)
	def forward(
	self,
	input_ids=None,
	attention_mask=None,
	token_type_ids=None,
	position_ids=None,
	head_mask=None,
	inputs_embeds=None,
	labels=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	adapter_names=None,
	**kwargs,
	):
	r"""
	labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
	Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
	config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
	If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
	"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	outputs = self.roberta(
	input_ids,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	adapter_names=adapter_names,
	**kwargs,
	)
	sequence_output = outputs[0]
	logits = self.classifier(sequence_output)

	loss = None
	if labels is not None:
	if self.config.problem_type is None:
	if self.num_labels == 1:
	self.config.problem_type = "regression"
	elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
	self.config.problem_type = "single_label_classification"
	else:
	self.config.problem_type = "multi_label_classification"

	if self.config.problem_type == "regression":
	loss_fct = MSELoss()
	if self.num_labels == 1:
	loss = loss_fct(logits.squeeze(), labels.squeeze())
	else:
	loss = loss_fct(logits, labels)
	elif self.config.problem_type == "single_label_classification":
	loss_fct = CrossEntropyLoss()
	loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
	elif self.config.problem_type == "multi_label_classification":
	loss_fct = BCEWithLogitsLoss()
	loss = loss_fct(logits, labels)

	if not return_dict:
	output = (logits,) + outputs[2:]
	return ((loss,) + output) if loss is not None else output

	return SequenceClassifierOutput(
	loss=loss,
	logits=logits,
	hidden_states=outputs.hidden_states,
	attentions=outputs.attentions,
	)


	@add_start_docstrings(
	"""
	Roberta Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
	softmax) e.g. for RocStories/SWAG tasks.
	""",
	ROBERTA_START_DOCSTRING,
	)
	class RobertaForMultipleChoice(ModelWithHeadsAdaptersMixin, RobertaPreTrainedModel):
	_keys_to_ignore_on_load_missing = [r"position_ids"]

	def __init__(self, config):
	super().__init__(config)

	self.roberta = RobertaModel(config)
	self.dropout = nn.Dropout(config.hidden_dropout_prob)
	self.classifier = nn.Linear(config.hidden_size, 1)

	self.init_weights()

	@add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
	@add_code_sample_docstrings(
	tokenizer_class=_TOKENIZER_FOR_DOC,
	checkpoint=_CHECKPOINT_FOR_DOC,
	output_type=MultipleChoiceModelOutput,
	config_class=_CONFIG_FOR_DOC,
	)
	def forward(
	self,
	input_ids=None,
	token_type_ids=None,
	attention_mask=None,
	labels=None,
	position_ids=None,
	head_mask=None,
	inputs_embeds=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	adapter_names=None,
	):
	r"""
	labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
	Labels for computing the multiple choice classification loss. Indices should be in ``[0, ...,
	num_choices-1]`` where :obj:`num_choices` is the size of the second dimension of the input tensors. (See
	:obj:`input_ids` above)
	"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict
	num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]

	flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
	flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
	flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
	flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
	flat_inputs_embeds = (
	inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
	if inputs_embeds is not None
	else None
	)

	outputs = self.roberta(
	flat_input_ids,
	position_ids=flat_position_ids,
	token_type_ids=flat_token_type_ids,
	attention_mask=flat_attention_mask,
	head_mask=head_mask,
	inputs_embeds=flat_inputs_embeds,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	adapter_names=adapter_names,
	)
	pooled_output = outputs[1]

	pooled_output = self.dropout(pooled_output)
	logits = self.classifier(pooled_output)
	reshaped_logits = logits.view(-1, num_choices)

	loss = None
	if labels is not None:
	loss_fct = CrossEntropyLoss()
	loss = loss_fct(reshaped_logits, labels)

	if not return_dict:
	output = (reshaped_logits,) + outputs[2:]
	return ((loss,) + output) if loss is not None else output

	return MultipleChoiceModelOutput(
	loss=loss,
	logits=reshaped_logits,
	hidden_states=outputs.hidden_states,
	attentions=outputs.attentions,
	)


	@add_start_docstrings(
	"""
	Roberta Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
	Named-Entity-Recognition (NER) tasks.
	""",
	ROBERTA_START_DOCSTRING,
	)
	class RobertaForTokenClassification(ModelWithHeadsAdaptersMixin, RobertaPreTrainedModel):
	_keys_to_ignore_on_load_unexpected = [r"pooler"]
	_keys_to_ignore_on_load_missing = [r"position_ids"]

	def __init__(self, config):
	super().__init__(config)
	self.num_labels = config.num_labels

	self.roberta = RobertaModel(config, add_pooling_layer=False)
	classifier_dropout = (
	config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
	)
	self.dropout = nn.Dropout(classifier_dropout)
	self.classifier = nn.Linear(config.hidden_size, config.num_labels)

	self.init_weights()

	@add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@add_code_sample_docstrings(
	tokenizer_class=_TOKENIZER_FOR_DOC,
	checkpoint=_CHECKPOINT_FOR_DOC,
	output_type=TokenClassifierOutput,
	config_class=_CONFIG_FOR_DOC,
	)
	def forward(
	self,
	input_ids=None,
	attention_mask=None,
	token_type_ids=None,
	position_ids=None,
	head_mask=None,
	inputs_embeds=None,
	labels=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	adapter_names=None,
	):
	r"""
	labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
	Labels for computing the token classification loss. Indices should be in ``[0, ..., config.num_labels -
	1]``.
	"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	outputs = self.roberta(
	input_ids,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	adapter_names=adapter_names,
	)

	sequence_output = outputs[0]

	sequence_output = self.dropout(sequence_output)
	logits = self.classifier(sequence_output)

	loss = None
	if labels is not None:
	loss_fct = CrossEntropyLoss()
	# Only keep active parts of the loss
	if attention_mask is not None:
	active_loss = attention_mask.view(-1) == 1
	active_logits = logits.view(-1, self.num_labels)
	active_labels = torch.where(
	active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels)
	)
	loss = loss_fct(active_logits, active_labels)
	else:
	loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))

	if not return_dict:
	output = (logits,) + outputs[2:]
	return ((loss,) + output) if loss is not None else output

	return TokenClassifierOutput(
	loss=loss,
	logits=logits,
	hidden_states=outputs.hidden_states,
	attentions=outputs.attentions,
	)


	class RobertaClassificationHead(nn.Module):
	"""Head for sentence-level classification tasks."""

	def __init__(self, config):
	super().__init__()
	self.dense = nn.Linear(config.hidden_size, config.hidden_size)
	classifier_dropout = (
	config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
	)
	self.dropout = nn.Dropout(classifier_dropout)
	self.out_proj = nn.Linear(config.hidden_size, config.num_labels)

	def forward(self, features, **kwargs):
	x = features[:, 0, :] # take <s> token (equiv. to [CLS])
	x = self.dropout(x)
	x = self.dense(x)
	x = torch.tanh(x)
	x = self.dropout(x)
	x = self.out_proj(x)
	return x


	@add_start_docstrings(
	"""
	Roberta Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
	layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
	""",
	ROBERTA_START_DOCSTRING,
	)
	class RobertaForQuestionAnswering(ModelWithHeadsAdaptersMixin, RobertaPreTrainedModel):
	_keys_to_ignore_on_load_unexpected = [r"pooler"]
	_keys_to_ignore_on_load_missing = [r"position_ids"]

	def __init__(self, config):
	super().__init__(config)
	self.num_labels = config.num_labels

	self.roberta = RobertaModel(config, add_pooling_layer=False)
	self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)

	self.init_weights()

	@add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@add_code_sample_docstrings(
	tokenizer_class=_TOKENIZER_FOR_DOC,
	checkpoint=_CHECKPOINT_FOR_DOC,
	output_type=QuestionAnsweringModelOutput,
	config_class=_CONFIG_FOR_DOC,
	)
	def forward(
	self,
	input_ids=None,
	attention_mask=None,
	token_type_ids=None,
	position_ids=None,
	head_mask=None,
	inputs_embeds=None,
	start_positions=None,
	end_positions=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	adapter_names=None,
	):
	r"""
	start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
	Labels for position (index) of the start of the labelled span for computing the token classification loss.
	Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside of the
	sequence are not taken into account for computing the loss.
	end_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
	Labels for position (index) of the end of the labelled span for computing the token classification loss.
	Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside of the
	sequence are not taken into account for computing the loss.
	"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	outputs = self.roberta(
	input_ids,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	sequence_output = outputs[0]

	logits = self.qa_outputs(sequence_output)
	start_logits, end_logits = logits.split(1, dim=-1)
	start_logits = start_logits.squeeze(-1).contiguous()
	end_logits = end_logits.squeeze(-1).contiguous()

	total_loss = None
	if start_positions is not None and end_positions is not None:
	# If we are on multi-GPU, split add a dimension
	if len(start_positions.size()) > 1:
	start_positions = start_positions.squeeze(-1)
	if len(end_positions.size()) > 1:
	end_positions = end_positions.squeeze(-1)
	# sometimes the start/end positions are outside our model inputs, we ignore these terms
	ignored_index = start_logits.size(1)
	start_positions = start_positions.clamp(0, ignored_index)
	end_positions = end_positions.clamp(0, ignored_index)

	loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
	start_loss = loss_fct(start_logits, start_positions)
	end_loss = loss_fct(end_logits, end_positions)
	total_loss = (start_loss + end_loss) / 2

	if not return_dict:
	output = (start_logits, end_logits) + outputs[2:]
	return ((total_loss,) + output) if total_loss is not None else output

	return QuestionAnsweringModelOutput(
	loss=total_loss,
	start_logits=start_logits,
	end_logits=end_logits,
	hidden_states=outputs.hidden_states,
	attentions=outputs.attentions,
	)


	def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0):
	"""
	Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
	are ignored. This is modified from fairseq's `utils.make_positions`.

	Args:
	x: torch.Tensor x:

	Returns: torch.Tensor
	"""
	# The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
	mask = input_ids.ne(padding_idx).int()
	incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask
	return incremental_indices.long() + padding_idx

	from dataclasses import dataclass
	from typing import Union, Callable

	import torch.nn as nn


	@dataclass
	class AdapterMaskConfig:
	hidden_size: int
	adapter_size: int
	ffn_adapter_size: int
	attn_adapter_size: int
	adapter_act: Union[str, Callable]
	adapter_initializer_range: float
	ntasks: int
	smax: int
	mode: str = "sequential" # "sequential" / "parallel"

	def __post_init__(self):
	if self.mode not in ("sequential", "parallel"):
	raise NotImplementedError(f"The current mode {self.mode} is not supported!")


	def freeze_all_parameters(model: nn.Module) -> nn.Module:
	for param in model.parameters():
	param.requires_grad = False
	return model

	"""Roberta model with CPT CL-plugins."""
	import math
	from copy import deepcopy

	import torch
	import torch.nn as nn
	from transformers import BertModel
	from transformers.models.bert.modeling_bert import BertSelfOutput
	from transformers.models.roberta.modeling_roberta import RobertaSelfAttention


	class RobertaAdapter(nn.Module):
	def __init__(self, config: AdapterMaskConfig):
	super().__init__()
	self.fc1 = torch.nn.Linear(config.hidden_size, config.adapter_size)
	self.fc2 = torch.nn.Linear(config.adapter_size, config.hidden_size)
	self.activation = torch.nn.ReLU()

	def forward(self, x):
	h = self.activation(self.fc1(x))
	h = self.activation(self.fc2(h))
	return x + h
	# return h


	class RobertaAdapterMask(RobertaAdapter):
	def __init__(self, config: AdapterMaskConfig):
	super().__init__(config)
	self.efc1 = torch.nn.Embedding(config.ntasks, config.adapter_size)
	self.efc2 = torch.nn.Embedding(config.ntasks, config.hidden_size)
	self.gate = torch.nn.Sigmoid()
	self.config = config
	self.smax = config.smax

	def forward(self, x, t, s, smax=400, add_residual=True, residual=None):
	residual = x if residual is None else residual
	gfc1, gfc2 = self.mask(t=t, s=s)
	h = self.get_feature(gfc1, gfc2, x)
	if add_residual:
	output = residual + h
	else:
	output = h

	return output

	def get_feature(self, gfc1, gfc2, x):
	h = self.activation(self.fc1(x))
	h = h * gfc1.expand_as(h)

	h = self.activation(self.fc2(h))
	h = h * gfc2.expand_as(h)

	return h

	def mask(self, t: torch.LongTensor, s: int = None):

	efc1 = self.efc1(t)
	efc2 = self.efc2(t)

	gfc1 = self.gate(s * efc1)
	gfc2 = self.gate(s * efc2)

	if s == self.smax: # you want to use it for evluation
	gfc1 = (gfc1 > 0.5).float()
	gfc2 = (gfc2 > 0.5).float()

	return [gfc1, gfc2]


	class RobertaAdaptedSelfOutput(nn.Module):
	def __init__(self,
	self_output: BertSelfOutput,
	config: AdapterMaskConfig):
	super(RobertaAdaptedSelfOutput, self).__init__()
	self.self_output = self_output
	self.adapter_mask = RobertaAdapterMask(config)
	self.mode = config.mode

	def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor, t, s, **kwargs):
	if self.mode == "sequential":
	hidden_states = self.self_output.dense(hidden_states)
	hidden_states = self.self_output.dropout(hidden_states)
	hidden_states = self.adapter_mask(hidden_states, t, s)
	elif self.mode == "parallel":
	adapter_change = self.adapter_mask(input_tensor, t, s)
	hidden_states = self.self_output.dense(hidden_states)
	hidden_states = self.self_output.dropout(hidden_states)
	hidden_states = hidden_states + adapter_change
	hidden_states = self.self_output.LayerNorm(hidden_states + input_tensor)
	return hidden_states


	class RobertaAdaptedSelfAttention(nn.Module):
	"""For parallel adapter."""

	def __init__(self,
	self_attn: RobertaSelfAttention,
	config: AdapterMaskConfig):
	super(RobertaAdaptedSelfAttention, self).__init__()
	if config.mode != "parallel":
	raise ValueError("This class is tailored for parallel adapter!")
	self.self_attn = self_attn
	self.adapter_mask = RobertaAdapterMask(config)

	def forward(
	self,
	hidden_states,
	attention_mask=None,
	head_mask=None,
	encoder_hidden_states=None,
	encoder_attention_mask=None,
	past_key_value=None,
	output_attentions=False,
	t=None,
	s=None,
	**kwargs,
	):
	mixed_query_layer = self.self_attn.query(hidden_states)

	# If this is instantiated as a cross-attention module, the keys
	# and values come from an encoder; the attention mask needs to be
	# such that the encoder's padding tokens are not attended to.
	is_cross_attention = encoder_hidden_states is not None

	if is_cross_attention and past_key_value is not None:
	# reuse k,v, cross_attentions
	key_layer = past_key_value[0]
	value_layer = past_key_value[1]
	attention_mask = encoder_attention_mask
	elif is_cross_attention:
	key_layer = self.self_attn.transpose_for_scores(self.self_attn.key(encoder_hidden_states))
	value_layer = self.self_attn.transpose_for_scores(self.self_attn.value(encoder_hidden_states))
	attention_mask = encoder_attention_mask
	elif past_key_value is not None:
	key_layer = self.self_attn.transpose_for_scores(self.self_attn.key(hidden_states))
	value_layer = self.self_attn.transpose_for_scores(self.self_attn.value(hidden_states))
	key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
	value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
	else:
	key_layer = self.self_attn.transpose_for_scores(self.self_attn.key(hidden_states))
	value_layer = self.self_attn.transpose_for_scores(self.self_attn.value(hidden_states))

	query_layer = self.self_attn.transpose_for_scores(mixed_query_layer)

	if self.self_attn.is_decoder:
	# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
	# Further calls to cross_attention layer can then reuse all cross-attention
	# key/value_states (first "if" case)
	# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
	# all previous decoder key/value_states. Further calls to uni-directional self-attention
	# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
	# if encoder bi-directional self-attention `past_key_value` is always `None`
	past_key_value = (key_layer, value_layer)

	cross_attn_output = self.adapter_mask(hidden_states, t=t, s=s, add_residual=False) # For parallel adapter.

	# Take the dot product between "query" and "key" to get the raw attention scores.
	attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))

	if self.self_attn.position_embedding_type == "relative_key" or self.self_attn.position_embedding_type == "relative_key_query":
	seq_length = hidden_states.size()[1]
	position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
	position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
	distance = position_ids_l - position_ids_r
	positional_embedding = self.self_attn.distance_embedding(
	distance + self.self_attn.max_position_embeddings - 1)
	positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility

	if self.self_attn.position_embedding_type == "relative_key":
	relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
	attention_scores = attention_scores + relative_position_scores
	elif self.self_attn.position_embedding_type == "relative_key_query":
	relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
	relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
	attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key

	attention_scores = attention_scores / math.sqrt(self.self_attn.attention_head_size)
	if attention_mask is not None:
	# Apply the attention mask is (precomputed for all layers in RobertaModel forward() function)
	attention_scores = attention_scores + attention_mask

	# Normalize the attention scores to probabilities.
	attention_probs = nn.Softmax(dim=-1)(attention_scores)

	# This is actually dropping out entire tokens to attend to, which might
	# seem a bit unusual, but is taken from the original Transformer paper.
	attention_probs = self.self_attn.dropout(attention_probs)

	# Mask heads if we want to
	if head_mask is not None:
	attention_probs = attention_probs * head_mask

	context_layer = torch.matmul(attention_probs, value_layer)

	context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
	new_context_layer_shape = context_layer.size()[:-2] + (self.self_attn.all_head_size,)
	context_layer = context_layer.view(*new_context_layer_shape)

	context_layer = context_layer + cross_attn_output # For parallel adapter.

	outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)

	if self.self_attn.is_decoder:
	outputs = outputs + (past_key_value,)
	return outputs


	def adapt_roberta_self_output(config: AdapterMaskConfig):
	return lambda self_output: RobertaAdaptedSelfOutput(self_output, config=config)


	def adapt_roberta_self_attn(config: AdapterMaskConfig):
	return lambda self_attn: RobertaAdaptedSelfAttention(self_attn, config=config)


	def add_roberta_adapters(roberta_model: BertModel, config: AdapterMaskConfig) -> BertModel:
	attn_config, ffn_config = deepcopy(config), deepcopy(config)
	attn_config.adapter_size = attn_config.attn_adapter_size
	ffn_config.adapter_size = ffn_config.ffn_adapter_size

	if config.mode == "sequential":
	for layer in roberta_model.encoder.layer:
	layer.attention.output = adapt_roberta_self_output(
	attn_config)(layer.attention.output)
	layer.output = adapt_roberta_self_output(ffn_config)(layer.output)
	elif config.mode == "parallel":
	for layer in roberta_model.encoder.layer:
	layer.attention.self = adapt_roberta_self_attn(attn_config)(layer.attention.self)
	layer.output = adapt_roberta_self_output(ffn_config)(layer.output)
	return roberta_model


	def unfreeze_roberta_adapters(roberta_model: nn.Module) -> nn.Module:
	# Unfreeze trainable parts — layer norms and adapters
	for name, sub_module in roberta_model.named_modules():
	if isinstance(sub_module, (RobertaAdapter, nn.LayerNorm)):
	for param_name, param in sub_module.named_parameters():
	param.requires_grad = True
	return roberta_model


	def load_roberta_adapter_model(
	roberta_model: nn.Module,
	checkpoint: str = None,
	mode: str = "sequential",
	attn_adapter_size: int = 200,
	ffn_adapter_size: int = 512,
	ntasks: int = 5):
	adapter_config = AdapterMaskConfig(
	hidden_size=768,
	adapter_size=-1, # Deprecated.
	adapter_act='relu',
	adapter_initializer_range=1e-2,
	ntasks=ntasks,
	smax=400,
	mode=mode,
	attn_adapter_size=attn_adapter_size,
	ffn_adapter_size=ffn_adapter_size,
	)
	roberta_model.roberta = add_roberta_adapters(
	roberta_model.roberta, adapter_config)

	# freeze the bert model, unfreeze adapter
	roberta_model.roberta = freeze_all_parameters(roberta_model.roberta)
	roberta_model.roberta = unfreeze_roberta_adapters(roberta_model.roberta)

	if checkpoint is not None and checkpoint != 'None':
	print("loading checkpoint...")
	model_dict = roberta_model.state_dict()
	pretrained_dict = torch.load(checkpoint, map_location='cpu')
	new_dict = {k: v for k, v in pretrained_dict.items()
	if k in model_dict.keys()}
	model_dict.update(new_dict)
	print('Total : {} params are loaded.'.format(len(pretrained_dict)))
	roberta_model.load_state_dict(model_dict)
	print("loaded finished!")
	else:
	print('No checkpoint is included')
	return roberta_model


	def save_roberta_adapter_model(roberta_model: nn.Module, save_path: str, accelerator=None):
	model_dict = {k: v for k, v in roberta_model.state_dict().items()
	if 'adapter' in k}
	if accelerator is not None:
	accelerator.save(model_dict, save_path)
	else:
	torch.save(model_dict, save_path)


	def forward(self, t, input_ids, segment_ids, input_mask, s=None):
	output_dict = {}

	sequence_output, pooled_output = \
	self.bert(input_ids=input_ids, token_type_ids=segment_ids,
	attention_mask=input_mask, t=t, s=s)
	masks = self.mask(t, s)
	pooled_output = self.dropout(pooled_output)

	y = self.last(sequence_output)
	output_dict['y'] = y
	output_dict['masks'] = masks
	return output_dict


	def forward_cls(self, t, input_ids, segment_ids, input_mask, start_mixup=None, s=None, l=None, idx=None, mix_type=None):
	output_dict = {}

	sequence_output, pooled_output = \
	self.bert(input_ids=input_ids, token_type_ids=segment_ids,
	attention_mask=input_mask, t=t, s=s)
	masks = self.mask(t, s)
	pooled_output = self.dropout(pooled_output)

	y = self.last_cls(pooled_output)
	output_dict['y'] = y
	output_dict['masks'] = masks
	return output_dict


	def mask(roberta_model, t, s, adapter_type="sequential"):
	masks = {}
	for layer_id in range(len(roberta_model.roberta.encoder.layer)):
	if adapter_type == "sequential":
	fc1_key = 'roberta.encoder.layer.' + \
	str(layer_id) + '.attention.output.adapter_mask.fc1' # gfc1
	fc2_key = 'roberta.encoder.layer.' + \
	str(layer_id) + '.attention.output.adapter_mask.fc2' # gfc2

	masks[fc1_key], masks[fc2_key] = roberta_model.roberta.encoder.layer[
	layer_id].attention.output.adapter_mask.mask(
	t, s)
	else:
	fc1_key = 'roberta.encoder.layer.' + \
	str(layer_id) + '.attention.self.adapter_mask.fc1' # gfc1
	fc2_key = 'roberta.encoder.layer.' + \
	str(layer_id) + '.attention.self.adapter_mask.fc2' # gfc2

	masks[fc1_key], masks[fc2_key] = roberta_model.roberta.encoder.layer[
	layer_id].attention.self.adapter_mask.mask(
	t, s)

	fc1_key = 'roberta.encoder.layer.' + \
	str(layer_id) + '.output.adapter_mask.fc1' # gfc1
	fc2_key = 'roberta.encoder.layer.' + \
	str(layer_id) + '.output.adapter_mask.fc2' # gfc2

	masks[fc1_key], masks[fc2_key] = roberta_model.roberta.encoder.layer[layer_id].output.adapter_mask.mask(
	t, s)

	return masks


	def get_view_for(model, n, p, masks):
	for layer_id in range(12):
	if n == 'roberta.encoder.layer.' + str(layer_id) + '.attention.output.adapter_mask.fc1.weight':
	return masks[n.replace('.weight', '')].data.view(-1, 1).expand_as(p)
	elif n == 'roberta.encoder.layer.' + str(layer_id) + '.attention.output.adapter_mask.fc1.bias':
	return masks[n.replace('.bias', '')].data.view(-1)
	elif n == 'roberta.encoder.layer.' + str(layer_id) + '.attention.output.adapter_mask.fc2.weight':
	post = masks[n.replace('.weight', '')].data.view(-1, 1).expand_as(p)
	pre = masks[n.replace('.weight', '').replace('fc2', 'fc1')].data.view(1, -1).expand_as(p)
	return torch.min(post, pre)
	elif n == 'roberta.encoder.layer.' + str(layer_id) + '.attention.output.adapter_mask.fc2.bias':
	return masks[n.replace('.bias', '')].data.view(-1)
	elif n == 'roberta.encoder.layer.' + str(layer_id) + '.output.adapter_mask.fc1.weight':
	# print('not nont')
	return masks[n.replace('.weight', '')].data.view(-1, 1).expand_as(p)
	elif n == 'roberta.encoder.layer.' + str(layer_id) + '.output.adapter_mask.fc1.bias':
	return masks[n.replace('.bias', '')].data.view(-1)
	elif n == 'roberta.encoder.layer.' + str(layer_id) + '.output.adapter_mask.fc2.weight':
	post = masks[n.replace('.weight', '')].data.view(-1, 1).expand_as(p)
	pre = masks[n.replace('.weight', '').replace('fc2', 'fc1')].data.view(1, -1).expand_as(p)
	return torch.min(post, pre)
	elif n == 'roberta.encoder.layer.' + str(layer_id) + '.output.adapter_mask.fc2.bias':
	return masks[n.replace('.bias', '')].data.view(-1)
	elif n == 'roberta.encoder.layer.' + str(layer_id) + '.attention.self.adapter_mask.fc1.weight':
	return masks[n.replace('.weight', '')].data.view(-1, 1).expand_as(p)
	elif n == 'roberta.encoder.layer.' + str(layer_id) + '.attention.self.adapter_mask.fc1.bias':
	return masks[n.replace('.bias', '')].data.view(-1)
	elif n == 'roberta.encoder.layer.' + str(layer_id) + '.attention.self.adapter_mask.fc2.weight':
	post = masks[n.replace('.weight', '')].data.view(-1, 1).expand_as(p)
	pre = masks[n.replace('.weight', '').replace('fc2', 'fc1')].data.view(1, -1).expand_as(p)
	return torch.min(post, pre)
	elif n == 'roberta.encoder.layer.' + str(layer_id) + '.attention.self.adapter_mask.fc2.bias':
	return masks[n.replace('.bias', '')].data.view(-1)
	return None

	import os
	import pdb
	from pathlib import Path

	import torch
	import torch.nn as nn
	import sys

	class RobertaMaskBasedModel:

	def forward(
	self,
	input_ids=None,
	past_key_values=None,
	attention_mask=None,
	token_type_ids=None,
	position_ids=None,
	head_mask=None,
	inputs_embeds=None,
	encoder_hidden_states=None,
	encoder_attention_mask=None,
	labels=None,
	use_cache=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	for_end_task=False,
	use_prompt=True,
	**kwargs
	):
	# Drop most of the args for now
	outputs = super().forward(
	attention_mask=attention_mask,
	input_ids=input_ids,
	labels=labels,
	return_dict=return_dict,
	**kwargs
	)
	return outputs


	class RobertaMaskForMaskedLM(RobertaMaskBasedModel, RobertaForMaskedLM):
	def __init__(self, config):
	super().__init__(config)
	adapter_config = AdapterMaskConfig(
	hidden_size=768,
	adapter_size=-1, # Deprecated.
	adapter_act='relu',
	adapter_initializer_range=1e-2,
	ntasks=config.adapter_task,
	smax=config.smax,
	mode=config.adapter_mode,
	attn_adapter_size=config.attn_adapter_size,
	ffn_adapter_size=config.ffn_adapter_size,
	)
	self.roberta = add_roberta_adapters(self.roberta, adapter_config)

	class RobertaMaskForSequenceClassification(RobertaMaskBasedModel, RobertaForSequenceClassification):
	def __init__(self, config):
	super().__init__(config)
	adapter_config = AdapterMaskConfig(
	hidden_size=768,
	adapter_size=-1, # Deprecated.
	adapter_act='relu',
	adapter_initializer_range=1e-2,
	ntasks=config.adapter_task,
	smax=config.smax,
	mode=config.adapter_mode,
	attn_adapter_size=config.attn_adapter_size,
	ffn_adapter_size=config.ffn_adapter_size,
	)
	self.roberta = add_roberta_adapters(self.roberta, adapter_config)