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	# coding=utf-8
	# Copyright Studio Ousia and The HuggingFace Inc. team.
	#
	# 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 LUKE model."""

	import math
	from dataclasses import dataclass
	from typing import Optional, Tuple, Union

	import torch
	import torch.utils.checkpoint
	from torch import nn
	from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

	from ...activations import ACT2FN, gelu
	from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
	from ...modeling_utils import PreTrainedModel
	from ...pytorch_utils import apply_chunking_to_forward
	from ...utils import (
	ModelOutput,
	add_code_sample_docstrings,
	add_start_docstrings,
	add_start_docstrings_to_model_forward,
	logging,
	replace_return_docstrings,
	)
	from .configuration_luke import LukeConfig


	logger = logging.get_logger(__name__)

	_CONFIG_FOR_DOC = "LukeConfig"
	_CHECKPOINT_FOR_DOC = "studio-ousia/luke-base"


	@dataclass
	class BaseLukeModelOutputWithPooling(BaseModelOutputWithPooling):
	"""
	Base class for outputs of the LUKE model.

	Args:
	last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
	Sequence of hidden-states at the output of the last layer of the model.
	entity_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, entity_length, hidden_size)`):
	Sequence of entity hidden-states at the output of the last layer of the model.
	pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
	Last layer hidden-state of the first token of the sequence (classification token) further processed by a
	Linear layer and a Tanh activation function.
	hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
	Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
	shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
	plus the initial embedding outputs.
	entity_hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
	Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
	shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
	layer plus the initial entity embedding outputs.
	attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
	Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length +
	entity_length, sequence_length + entity_length)`. Attentions weights after the attention softmax, used to
	compute the weighted average in the self-attention heads.
	"""

	entity_last_hidden_state: torch.FloatTensor = None
	entity_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None


	@dataclass
	class BaseLukeModelOutput(BaseModelOutput):
	"""
	Base class for model's outputs, with potential hidden states and attentions.

	Args:
	last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
	Sequence of hidden-states at the output of the last layer of the model.
	entity_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, entity_length, hidden_size)`):
	Sequence of entity hidden-states at the output of the last layer of the model.
	hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
	Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
	shape `(batch_size, sequence_length, hidden_size)`.

	Hidden-states of the model at the output of each layer plus the initial embedding outputs.
	entity_hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
	Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
	shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
	layer plus the initial entity embedding outputs.
	attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
	Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
	sequence_length)`.

	Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
	heads.
	"""

	entity_last_hidden_state: torch.FloatTensor = None
	entity_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None


	@dataclass
	class LukeMaskedLMOutput(ModelOutput):
	"""
	Base class for model's outputs, with potential hidden states and attentions.

	Args:
	loss (`torch.FloatTensor` of shape `(1,)`, optional, returned when `labels` is provided):
	The sum of masked language modeling (MLM) loss and entity prediction loss.
	mlm_loss (`torch.FloatTensor` of shape `(1,)`, optional, returned when `labels` is provided):
	Masked language modeling (MLM) loss.
	mep_loss (`torch.FloatTensor` of shape `(1,)`, optional, returned when `labels` is provided):
	Masked entity prediction (MEP) loss.
	logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
	Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
	entity_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
	Prediction scores of the entity prediction head (scores for each entity vocabulary token before SoftMax).
	hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
	Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
	shape `(batch_size, sequence_length, hidden_size)`.

	Hidden-states of the model at the output of each layer plus the initial embedding outputs.
	entity_hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
	Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
	shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
	layer plus the initial entity embedding outputs.
	attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
	Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
	sequence_length)`.

	Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
	heads.
	"""

	loss: Optional[torch.FloatTensor] = None
	mlm_loss: Optional[torch.FloatTensor] = None
	mep_loss: Optional[torch.FloatTensor] = None
	logits: torch.FloatTensor = None
	entity_logits: torch.FloatTensor = None
	hidden_states: Optional[Tuple[torch.FloatTensor]] = None
	entity_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
	attentions: Optional[Tuple[torch.FloatTensor, ...]] = None


	@dataclass
	class EntityClassificationOutput(ModelOutput):
	"""
	Outputs of entity classification models.

	Args:
	loss (`torch.FloatTensor` of shape `(1,)`, optional, returned when `labels` is provided):
	Classification loss.
	logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
	Classification scores (before SoftMax).
	hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
	Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
	shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
	plus the initial embedding outputs.
	entity_hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
	Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
	shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
	layer plus the initial entity embedding outputs.
	attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
	Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
	sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
	the self-attention heads.
	"""

	loss: Optional[torch.FloatTensor] = None
	logits: torch.FloatTensor = None
	hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
	entity_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
	attentions: Optional[Tuple[torch.FloatTensor, ...]] = None


	@dataclass
	class EntityPairClassificationOutput(ModelOutput):
	"""
	Outputs of entity pair classification models.

	Args:
	loss (`torch.FloatTensor` of shape `(1,)`, optional, returned when `labels` is provided):
	Classification loss.
	logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
	Classification scores (before SoftMax).
	hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
	Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
	shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
	plus the initial embedding outputs.
	entity_hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
	Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
	shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
	layer plus the initial entity embedding outputs.
	attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
	Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
	sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
	the self-attention heads.
	"""

	loss: Optional[torch.FloatTensor] = None
	logits: torch.FloatTensor = None
	hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
	entity_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
	attentions: Optional[Tuple[torch.FloatTensor, ...]] = None


	@dataclass
	class EntitySpanClassificationOutput(ModelOutput):
	"""
	Outputs of entity span classification models.

	Args:
	loss (`torch.FloatTensor` of shape `(1,)`, optional, returned when `labels` is provided):
	Classification loss.
	logits (`torch.FloatTensor` of shape `(batch_size, entity_length, config.num_labels)`):
	Classification scores (before SoftMax).
	hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
	Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
	shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
	plus the initial embedding outputs.
	entity_hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
	Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
	shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
	layer plus the initial entity embedding outputs.
	attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
	Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
	sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
	the self-attention heads.
	"""

	loss: Optional[torch.FloatTensor] = None
	logits: torch.FloatTensor = None
	hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
	entity_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
	attentions: Optional[Tuple[torch.FloatTensor, ...]] = None


	@dataclass
	class LukeSequenceClassifierOutput(ModelOutput):
	"""
	Outputs of sentence classification models.

	Args:
	loss (`torch.FloatTensor` of shape `(1,)`, optional, returned when `labels` is provided):
	Classification (or regression if config.num_labels==1) loss.
	logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
	Classification (or regression if config.num_labels==1) scores (before SoftMax).
	hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
	Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
	one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

	Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
	entity_hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
	Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
	shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
	layer plus the initial entity embedding outputs.
	attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
	Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
	sequence_length)`.

	Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
	heads.
	"""

	loss: Optional[torch.FloatTensor] = None
	logits: torch.FloatTensor = None
	hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
	entity_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
	attentions: Optional[Tuple[torch.FloatTensor, ...]] = None


	@dataclass
	class LukeTokenClassifierOutput(ModelOutput):
	"""
	Base class for outputs of token classification models.

	Args:
	loss (`torch.FloatTensor` of shape `(1,)`, optional, returned when `labels` is provided) :
	Classification loss.
	logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`):
	Classification scores (before SoftMax).
	hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
	Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
	one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

	Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
	entity_hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
	Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
	shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
	layer plus the initial entity embedding outputs.
	attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
	Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
	sequence_length)`.

	Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
	heads.
	"""

	loss: Optional[torch.FloatTensor] = None
	logits: torch.FloatTensor = None
	hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
	entity_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
	attentions: Optional[Tuple[torch.FloatTensor, ...]] = None


	@dataclass
	class LukeQuestionAnsweringModelOutput(ModelOutput):
	"""
	Outputs of question answering models.

	Args:
	loss (`torch.FloatTensor` of shape `(1,)`, optional, returned when `labels` is provided):
	Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
	start_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
	Span-start scores (before SoftMax).
	end_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
	Span-end scores (before SoftMax).
	hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
	Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
	one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

	Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
	entity_hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
	Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
	shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
	layer plus the initial entity embedding outputs.
	attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
	Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
	sequence_length)`.

	Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
	heads.
	"""

	loss: Optional[torch.FloatTensor] = None
	start_logits: torch.FloatTensor = None
	end_logits: torch.FloatTensor = None
	hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
	entity_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
	attentions: Optional[Tuple[torch.FloatTensor, ...]] = None


	@dataclass
	class LukeMultipleChoiceModelOutput(ModelOutput):
	"""
	Outputs of multiple choice models.

	Args:
	loss (`torch.FloatTensor` of shape (1,), optional, returned when `labels` is provided):
	Classification loss.
	logits (`torch.FloatTensor` of shape `(batch_size, num_choices)`):
	num_choices is the second dimension of the input tensors. (see input_ids above).

	Classification scores (before SoftMax).
	hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
	Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
	one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

	Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
	entity_hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
	Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
	shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
	layer plus the initial entity embedding outputs.
	attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
	Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
	sequence_length)`.

	Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
	heads.
	"""

	loss: Optional[torch.FloatTensor] = None
	logits: torch.FloatTensor = None
	hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
	entity_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
	attentions: Optional[Tuple[torch.FloatTensor, ...]] = None


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

	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)

	# 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,
	):
	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).to(input_ids.device)
	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]

	if token_type_ids is None:
	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)

	position_embeddings = self.position_embeddings(position_ids)
	token_type_embeddings = self.token_type_embeddings(token_type_ids)

	embeddings = inputs_embeds + position_embeddings + token_type_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)


	class LukeEntityEmbeddings(nn.Module):
	def __init__(self, config: LukeConfig):
	super().__init__()
	self.config = config

	self.entity_embeddings = nn.Embedding(config.entity_vocab_size, config.entity_emb_size, padding_idx=0)
	if config.entity_emb_size != config.hidden_size:
	self.entity_embedding_dense = nn.Linear(config.entity_emb_size, config.hidden_size, bias=False)

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

	def forward(
	self, entity_ids: torch.LongTensor, position_ids: torch.LongTensor, token_type_ids: torch.LongTensor = None
	):
	if token_type_ids is None:
	token_type_ids = torch.zeros_like(entity_ids)

	entity_embeddings = self.entity_embeddings(entity_ids)
	if self.config.entity_emb_size != self.config.hidden_size:
	entity_embeddings = self.entity_embedding_dense(entity_embeddings)

	position_embeddings = self.position_embeddings(position_ids.clamp(min=0))
	position_embedding_mask = (position_ids != -1).type_as(position_embeddings).unsqueeze(-1)
	position_embeddings = position_embeddings * position_embedding_mask
	position_embeddings = torch.sum(position_embeddings, dim=-2)
	position_embeddings = position_embeddings / position_embedding_mask.sum(dim=-2).clamp(min=1e-7)

	token_type_embeddings = self.token_type_embeddings(token_type_ids)

	embeddings = entity_embeddings + position_embeddings + token_type_embeddings
	embeddings = self.LayerNorm(embeddings)
	embeddings = self.dropout(embeddings)

	return embeddings


	class LukeSelfAttention(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.use_entity_aware_attention = config.use_entity_aware_attention

	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)

	if self.use_entity_aware_attention:
	self.w2e_query = nn.Linear(config.hidden_size, self.all_head_size)
	self.e2w_query = nn.Linear(config.hidden_size, self.all_head_size)
	self.e2e_query = nn.Linear(config.hidden_size, self.all_head_size)

	self.dropout = nn.Dropout(config.attention_probs_dropout_prob)

	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,
	word_hidden_states,
	entity_hidden_states,
	attention_mask=None,
	head_mask=None,
	output_attentions=False,
	):
	word_size = word_hidden_states.size(1)

	if entity_hidden_states is None:
	concat_hidden_states = word_hidden_states
	else:
	concat_hidden_states = torch.cat([word_hidden_states, entity_hidden_states], dim=1)

	key_layer = self.transpose_for_scores(self.key(concat_hidden_states))
	value_layer = self.transpose_for_scores(self.value(concat_hidden_states))

	if self.use_entity_aware_attention and entity_hidden_states is not None:
	# compute query vectors using word-word (w2w), word-entity (w2e), entity-word (e2w), entity-entity (e2e)
	# query layers
	w2w_query_layer = self.transpose_for_scores(self.query(word_hidden_states))
	w2e_query_layer = self.transpose_for_scores(self.w2e_query(word_hidden_states))
	e2w_query_layer = self.transpose_for_scores(self.e2w_query(entity_hidden_states))
	e2e_query_layer = self.transpose_for_scores(self.e2e_query(entity_hidden_states))

	# compute w2w, w2e, e2w, and e2e key vectors used with the query vectors computed above
	w2w_key_layer = key_layer[:, :, :word_size, :]
	e2w_key_layer = key_layer[:, :, :word_size, :]
	w2e_key_layer = key_layer[:, :, word_size:, :]
	e2e_key_layer = key_layer[:, :, word_size:, :]

	# compute attention scores based on the dot product between the query and key vectors
	w2w_attention_scores = torch.matmul(w2w_query_layer, w2w_key_layer.transpose(-1, -2))
	w2e_attention_scores = torch.matmul(w2e_query_layer, w2e_key_layer.transpose(-1, -2))
	e2w_attention_scores = torch.matmul(e2w_query_layer, e2w_key_layer.transpose(-1, -2))
	e2e_attention_scores = torch.matmul(e2e_query_layer, e2e_key_layer.transpose(-1, -2))

	# combine attention scores to create the final attention score matrix
	word_attention_scores = torch.cat([w2w_attention_scores, w2e_attention_scores], dim=3)
	entity_attention_scores = torch.cat([e2w_attention_scores, e2e_attention_scores], dim=3)
	attention_scores = torch.cat([word_attention_scores, entity_attention_scores], dim=2)

	else:
	query_layer = self.transpose_for_scores(self.query(concat_hidden_states))
	attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))

	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 LukeModel forward() function)
	attention_scores = attention_scores + attention_mask

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

	# 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)

	output_word_hidden_states = context_layer[:, :word_size, :]
	if entity_hidden_states is None:
	output_entity_hidden_states = None
	else:
	output_entity_hidden_states = context_layer[:, word_size:, :]

	if output_attentions:
	outputs = (output_word_hidden_states, output_entity_hidden_states, attention_probs)
	else:
	outputs = (output_word_hidden_states, output_entity_hidden_states)

	return outputs


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

	def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
	hidden_states = self.dense(hidden_states)
	hidden_states = self.dropout(hidden_states)
	hidden_states = self.LayerNorm(hidden_states + input_tensor)
	return hidden_states


	class LukeAttention(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.self = LukeSelfAttention(config)
	self.output = LukeSelfOutput(config)
	self.pruned_heads = set()

	def prune_heads(self, heads):
	raise NotImplementedError("LUKE does not support the pruning of attention heads")

	def forward(
	self,
	word_hidden_states,
	entity_hidden_states,
	attention_mask=None,
	head_mask=None,
	output_attentions=False,
	):
	word_size = word_hidden_states.size(1)
	self_outputs = self.self(
	word_hidden_states,
	entity_hidden_states,
	attention_mask,
	head_mask,
	output_attentions,
	)
	if entity_hidden_states is None:
	concat_self_outputs = self_outputs[0]
	concat_hidden_states = word_hidden_states
	else:
	concat_self_outputs = torch.cat(self_outputs[:2], dim=1)
	concat_hidden_states = torch.cat([word_hidden_states, entity_hidden_states], dim=1)

	attention_output = self.output(concat_self_outputs, concat_hidden_states)

	word_attention_output = attention_output[:, :word_size, :]
	if entity_hidden_states is None:
	entity_attention_output = None
	else:
	entity_attention_output = attention_output[:, word_size:, :]

	# add attentions if we output them
	outputs = (word_attention_output, entity_attention_output) + self_outputs[2:]

	return outputs


	# Copied from transformers.models.bert.modeling_bert.BertIntermediate
	class LukeIntermediate(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: torch.Tensor) -> torch.Tensor:
	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 LukeOutput(nn.Module):
	def __init__(self, config):
	super().__init__()
	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)

	def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
	hidden_states = self.dense(hidden_states)
	hidden_states = self.dropout(hidden_states)
	hidden_states = self.LayerNorm(hidden_states + input_tensor)
	return hidden_states


	class LukeLayer(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 = LukeAttention(config)
	self.intermediate = LukeIntermediate(config)
	self.output = LukeOutput(config)

	def forward(
	self,
	word_hidden_states,
	entity_hidden_states,
	attention_mask=None,
	head_mask=None,
	output_attentions=False,
	):
	word_size = word_hidden_states.size(1)

	self_attention_outputs = self.attention(
	word_hidden_states,
	entity_hidden_states,
	attention_mask,
	head_mask,
	output_attentions=output_attentions,
	)
	if entity_hidden_states is None:
	concat_attention_output = self_attention_outputs[0]
	else:
	concat_attention_output = torch.cat(self_attention_outputs[:2], dim=1)

	outputs = self_attention_outputs[2:] # add self attentions if we output attention weights

	layer_output = apply_chunking_to_forward(
	self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, concat_attention_output
	)
	word_layer_output = layer_output[:, :word_size, :]
	if entity_hidden_states is None:
	entity_layer_output = None
	else:
	entity_layer_output = layer_output[:, word_size:, :]

	outputs = (word_layer_output, entity_layer_output) + outputs

	return outputs

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


	class LukeEncoder(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.config = config
	self.layer = nn.ModuleList([LukeLayer(config) for _ in range(config.num_hidden_layers)])
	self.gradient_checkpointing = False

	def forward(
	self,
	word_hidden_states,
	entity_hidden_states,
	attention_mask=None,
	head_mask=None,
	output_attentions=False,
	output_hidden_states=False,
	return_dict=True,
	):
	all_word_hidden_states = () if output_hidden_states else None
	all_entity_hidden_states = () if output_hidden_states else None
	all_self_attentions = () if output_attentions else None

	for i, layer_module in enumerate(self.layer):
	if output_hidden_states:
	all_word_hidden_states = all_word_hidden_states + (word_hidden_states,)
	all_entity_hidden_states = all_entity_hidden_states + (entity_hidden_states,)

	layer_head_mask = head_mask[i] if head_mask is not None else None
	if self.gradient_checkpointing and self.training:
	layer_outputs = self._gradient_checkpointing_func(
	layer_module.__call__,
	word_hidden_states,
	entity_hidden_states,
	attention_mask,
	layer_head_mask,
	output_attentions,
	)
	else:
	layer_outputs = layer_module(
	word_hidden_states,
	entity_hidden_states,
	attention_mask,
	layer_head_mask,
	output_attentions,
	)

	word_hidden_states = layer_outputs[0]

	if entity_hidden_states is not None:
	entity_hidden_states = layer_outputs[1]

	if output_attentions:
	all_self_attentions = all_self_attentions + (layer_outputs[2],)

	if output_hidden_states:
	all_word_hidden_states = all_word_hidden_states + (word_hidden_states,)
	all_entity_hidden_states = all_entity_hidden_states + (entity_hidden_states,)

	if not return_dict:
	return tuple(
	v
	for v in [
	word_hidden_states,
	all_word_hidden_states,
	all_self_attentions,
	entity_hidden_states,
	all_entity_hidden_states,
	]
	if v is not None
	)
	return BaseLukeModelOutput(
	last_hidden_state=word_hidden_states,
	hidden_states=all_word_hidden_states,
	attentions=all_self_attentions,
	entity_last_hidden_state=entity_hidden_states,
	entity_hidden_states=all_entity_hidden_states,
	)


	# Copied from transformers.models.bert.modeling_bert.BertPooler
	class LukePooler(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: torch.Tensor) -> torch.Tensor:
	# 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 EntityPredictionHeadTransform(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.dense = nn.Linear(config.hidden_size, config.entity_emb_size)
	if isinstance(config.hidden_act, str):
	self.transform_act_fn = ACT2FN[config.hidden_act]
	else:
	self.transform_act_fn = config.hidden_act
	self.LayerNorm = nn.LayerNorm(config.entity_emb_size, eps=config.layer_norm_eps)

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


	class EntityPredictionHead(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.config = config
	self.transform = EntityPredictionHeadTransform(config)
	self.decoder = nn.Linear(config.entity_emb_size, config.entity_vocab_size, bias=False)
	self.bias = nn.Parameter(torch.zeros(config.entity_vocab_size))

	def forward(self, hidden_states):
	hidden_states = self.transform(hidden_states)
	hidden_states = self.decoder(hidden_states) + self.bias

	return hidden_states


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

	config_class = LukeConfig
	base_model_prefix = "luke"
	supports_gradient_checkpointing = True
	_no_split_modules = ["LukeAttention", "LukeEntityEmbeddings"]

	def _init_weights(self, module: nn.Module):
	"""Initialize the weights"""
	if isinstance(module, nn.Linear):
	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):
	if module.embedding_dim == 1: # embedding for bias parameters
	module.weight.data.zero_()
	else:
	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)


	LUKE_START_DOCSTRING = r"""

	This model inherits from [`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 ([`LukeConfig`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
	"""

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

	Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
	[`PreTrainedTokenizer.__call__`] for details.

	[What are input IDs?](../glossary#input-ids)
	attention_mask (`torch.FloatTensor` of shape `({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#attention-mask)
	token_type_ids (`torch.LongTensor` of shape `({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#token-type-ids)
	position_ids (`torch.LongTensor` of shape `({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#position-ids)

	entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`):
	Indices of entity tokens in the entity vocabulary.

	Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
	[`PreTrainedTokenizer.__call__`] for details.

	entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, optional):
	Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`:

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

	entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, optional):
	Segment token indices to indicate first and second portions of the entity token inputs. Indices are
	selected in `[0, 1]`:

	- 0 corresponds to a portion A entity token,
	- 1 corresponds to a portion B entity token.

	entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, optional):
	Indices of positions of each input entity in the position embeddings. Selected in the range `[0,
	config.max_position_embeddings - 1]`.

	inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, optional):
	Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
	is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
	model's internal embedding lookup matrix.

	head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(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.

	output_attentions (`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 (`bool`, optional):
	Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
	more detail.
	return_dict (`bool`, optional):
	Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
	"""


	@add_start_docstrings(
	"The bare LUKE model transformer outputting raw hidden-states for both word tokens and entities without any"
	" specific head on top.",
	LUKE_START_DOCSTRING,
	)
	class LukeModel(LukePreTrainedModel):
	def __init__(self, config: LukeConfig, add_pooling_layer: bool = True):
	super().__init__(config)
	self.config = config

	self.embeddings = LukeEmbeddings(config)
	self.entity_embeddings = LukeEntityEmbeddings(config)
	self.encoder = LukeEncoder(config)

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

	# Initialize weights and apply final processing
	self.post_init()

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

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

	def get_entity_embeddings(self):
	return self.entity_embeddings.entity_embeddings

	def set_entity_embeddings(self, value):
	self.entity_embeddings.entity_embeddings = value

	def _prune_heads(self, heads_to_prune):
	raise NotImplementedError("LUKE does not support the pruning of attention heads")

	@add_start_docstrings_to_model_forward(LUKE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@replace_return_docstrings(output_type=BaseLukeModelOutputWithPooling, config_class=_CONFIG_FOR_DOC)
	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	attention_mask: Optional[torch.FloatTensor] = None,
	token_type_ids: Optional[torch.LongTensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	entity_ids: Optional[torch.LongTensor] = None,
	entity_attention_mask: Optional[torch.FloatTensor] = None,
	entity_token_type_ids: Optional[torch.LongTensor] = None,
	entity_position_ids: Optional[torch.LongTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, BaseLukeModelOutputWithPooling]:
	r"""

	Returns:

	Examples:

	```python
	>>> from transformers import AutoTokenizer, LukeModel

	>>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-base")
	>>> model = LukeModel.from_pretrained("studio-ousia/luke-base")
	# Compute the contextualized entity representation corresponding to the entity mention "Beyoncé"

	>>> text = "Beyoncé lives in Los Angeles."
	>>> entity_spans = [(0, 7)] # character-based entity span corresponding to "Beyoncé"

	>>> encoding = tokenizer(text, entity_spans=entity_spans, add_prefix_space=True, return_tensors="pt")
	>>> outputs = model(**encoding)
	>>> word_last_hidden_state = outputs.last_hidden_state
	>>> entity_last_hidden_state = outputs.entity_last_hidden_state
	# Input Wikipedia entities to obtain enriched contextualized representations of word tokens

	>>> text = "Beyoncé lives in Los Angeles."
	>>> entities = [
	... "Beyoncé",
	... "Los Angeles",
	... ] # Wikipedia entity titles corresponding to the entity mentions "Beyoncé" and "Los Angeles"
	>>> entity_spans = [
	... (0, 7),
	... (17, 28),
	... ] # character-based entity spans corresponding to "Beyoncé" and "Los Angeles"

	>>> encoding = tokenizer(
	... text, entities=entities, entity_spans=entity_spans, add_prefix_space=True, return_tensors="pt"
	... )
	>>> outputs = model(**encoding)
	>>> word_last_hidden_state = outputs.last_hidden_state
	>>> entity_last_hidden_state = outputs.entity_last_hidden_state
	```"""
	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

	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:
	self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
	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

	if attention_mask is None:
	attention_mask = torch.ones((batch_size, seq_length), device=device)
	if token_type_ids is None:
	token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
	if entity_ids is not None:
	entity_seq_length = entity_ids.size(1)
	if entity_attention_mask is None:
	entity_attention_mask = torch.ones((batch_size, entity_seq_length), device=device)
	if entity_token_type_ids is None:
	entity_token_type_ids = torch.zeros((batch_size, entity_seq_length), dtype=torch.long, device=device)

	# 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)

	# First, compute word embeddings
	word_embedding_output = self.embeddings(
	input_ids=input_ids,
	position_ids=position_ids,
	token_type_ids=token_type_ids,
	inputs_embeds=inputs_embeds,
	)

	# Second, compute extended attention mask
	extended_attention_mask = self.get_extended_attention_mask(attention_mask, entity_attention_mask)

	# Third, compute entity embeddings and concatenate with word embeddings
	if entity_ids is None:
	entity_embedding_output = None
	else:
	entity_embedding_output = self.entity_embeddings(entity_ids, entity_position_ids, entity_token_type_ids)

	# Fourth, send embeddings through the model
	encoder_outputs = self.encoder(
	word_embedding_output,
	entity_embedding_output,
	attention_mask=extended_attention_mask,
	head_mask=head_mask,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	# Fifth, get the output. LukeModel outputs the same as BertModel, namely sequence_output of shape (batch_size, seq_len, hidden_size)
	sequence_output = encoder_outputs[0]

	# Sixth, we compute the pooled_output, word_sequence_output and entity_sequence_output based on the sequence_output
	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 BaseLukeModelOutputWithPooling(
	last_hidden_state=sequence_output,
	pooler_output=pooled_output,
	hidden_states=encoder_outputs.hidden_states,
	attentions=encoder_outputs.attentions,
	entity_last_hidden_state=encoder_outputs.entity_last_hidden_state,
	entity_hidden_states=encoder_outputs.entity_hidden_states,
	)

	def get_extended_attention_mask(
	self, word_attention_mask: torch.LongTensor, entity_attention_mask: Optional[torch.LongTensor]
	):
	"""
	Makes broadcastable attention and causal masks so that future and masked tokens are ignored.

	Arguments:
	word_attention_mask (`torch.LongTensor`):
	Attention mask for word tokens with ones indicating tokens to attend to, zeros for tokens to ignore.
	entity_attention_mask (`torch.LongTensor`, optional):
	Attention mask for entity tokens with ones indicating tokens to attend to, zeros for tokens to ignore.

	Returns:
	`torch.Tensor` The extended attention mask, with a the same dtype as `attention_mask.dtype`.
	"""
	attention_mask = word_attention_mask
	if entity_attention_mask is not None:
	attention_mask = torch.cat([attention_mask, entity_attention_mask], dim=-1)

	if attention_mask.dim() == 3:
	extended_attention_mask = attention_mask[:, None, :, :]
	elif attention_mask.dim() == 2:
	extended_attention_mask = attention_mask[:, None, None, :]
	else:
	raise ValueError(f"Wrong shape for attention_mask (shape {attention_mask.shape})")

	extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility
	extended_attention_mask = (1.0 - extended_attention_mask) * torch.finfo(self.dtype).min
	return extended_attention_mask


	def create_position_ids_from_input_ids(input_ids, padding_idx):
	"""
	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)) * mask
	return incremental_indices.long() + padding_idx


	# Copied from transformers.models.roberta.modeling_roberta.RobertaLMHead
	class LukeLMHead(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, **kwargs):
	x = self.dense(features)
	x = gelu(x)
	x = self.layer_norm(x)

	# 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)
	# For accelerate compatibility and to not break backward compatibility
	if self.decoder.bias.device.type == "meta":
	self.decoder.bias = self.bias
	else:
	self.bias = self.decoder.bias


	@add_start_docstrings(
	"""
	The LUKE model with a language modeling head and entity prediction head on top for masked language modeling and
	masked entity prediction.
	""",
	LUKE_START_DOCSTRING,
	)
	class LukeForMaskedLM(LukePreTrainedModel):
	_tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias", "entity_predictions.decoder.weight"]

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

	self.luke = LukeModel(config)

	self.lm_head = LukeLMHead(config)
	self.entity_predictions = EntityPredictionHead(config)

	self.loss_fn = nn.CrossEntropyLoss()

	# Initialize weights and apply final processing
	self.post_init()

	def tie_weights(self):
	super().tie_weights()
	self._tie_or_clone_weights(self.entity_predictions.decoder, self.luke.entity_embeddings.entity_embeddings)

	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(LUKE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@replace_return_docstrings(output_type=LukeMaskedLMOutput, config_class=_CONFIG_FOR_DOC)
	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	attention_mask: Optional[torch.FloatTensor] = None,
	token_type_ids: Optional[torch.LongTensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	entity_ids: Optional[torch.LongTensor] = None,
	entity_attention_mask: Optional[torch.LongTensor] = None,
	entity_token_type_ids: Optional[torch.LongTensor] = None,
	entity_position_ids: Optional[torch.LongTensor] = None,
	labels: Optional[torch.LongTensor] = None,
	entity_labels: Optional[torch.LongTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, LukeMaskedLMOutput]:
	r"""
	labels (`torch.LongTensor` of shape `(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]`
	entity_labels (`torch.LongTensor` of shape `(batch_size, entity_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]`

	Returns:

	"""

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

	outputs = self.luke(
	input_ids=input_ids,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	entity_ids=entity_ids,
	entity_attention_mask=entity_attention_mask,
	entity_token_type_ids=entity_token_type_ids,
	entity_position_ids=entity_position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=True,
	)

	loss = None

	mlm_loss = None
	logits = self.lm_head(outputs.last_hidden_state)
	if labels is not None:
	# move labels to correct device to enable model parallelism
	labels = labels.to(logits.device)
	mlm_loss = self.loss_fn(logits.view(-1, self.config.vocab_size), labels.view(-1))
	if loss is None:
	loss = mlm_loss

	mep_loss = None
	entity_logits = None
	if outputs.entity_last_hidden_state is not None:
	entity_logits = self.entity_predictions(outputs.entity_last_hidden_state)
	if entity_labels is not None:
	mep_loss = self.loss_fn(entity_logits.view(-1, self.config.entity_vocab_size), entity_labels.view(-1))
	if loss is None:
	loss = mep_loss
	else:
	loss = loss + mep_loss

	if not return_dict:
	return tuple(
	v
	for v in [
	loss,
	mlm_loss,
	mep_loss,
	logits,
	entity_logits,
	outputs.hidden_states,
	outputs.entity_hidden_states,
	outputs.attentions,
	]
	if v is not None
	)

	return LukeMaskedLMOutput(
	loss=loss,
	mlm_loss=mlm_loss,
	mep_loss=mep_loss,
	logits=logits,
	entity_logits=entity_logits,
	hidden_states=outputs.hidden_states,
	entity_hidden_states=outputs.entity_hidden_states,
	attentions=outputs.attentions,
	)


	@add_start_docstrings(
	"""
	The LUKE model with a classification head on top (a linear layer on top of the hidden state of the first entity
	token) for entity classification tasks, such as Open Entity.
	""",
	LUKE_START_DOCSTRING,
	)
	class LukeForEntityClassification(LukePreTrainedModel):
	def __init__(self, config):
	super().__init__(config)

	self.luke = LukeModel(config)

	self.num_labels = config.num_labels
	self.dropout = nn.Dropout(config.hidden_dropout_prob)
	self.classifier = nn.Linear(config.hidden_size, config.num_labels)

	# Initialize weights and apply final processing
	self.post_init()

	@add_start_docstrings_to_model_forward(LUKE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@replace_return_docstrings(output_type=EntityClassificationOutput, config_class=_CONFIG_FOR_DOC)
	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	attention_mask: Optional[torch.FloatTensor] = None,
	token_type_ids: Optional[torch.LongTensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	entity_ids: Optional[torch.LongTensor] = None,
	entity_attention_mask: Optional[torch.FloatTensor] = None,
	entity_token_type_ids: Optional[torch.LongTensor] = None,
	entity_position_ids: Optional[torch.LongTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	labels: Optional[torch.FloatTensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, EntityClassificationOutput]:
	r"""
	labels (`torch.LongTensor` of shape `(batch_size,)` or `(batch_size, num_labels)`, optional):
	Labels for computing the classification loss. If the shape is `(batch_size,)`, the cross entropy loss is
	used for the single-label classification. In this case, labels should contain the indices that should be in
	`[0, ..., config.num_labels - 1]`. If the shape is `(batch_size, num_labels)`, the binary cross entropy
	loss is used for the multi-label classification. In this case, labels should only contain `[0, 1]`, where 0
	and 1 indicate false and true, respectively.

	Returns:

	Examples:

	```python
	>>> from transformers import AutoTokenizer, LukeForEntityClassification

	>>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-large-finetuned-open-entity")
	>>> model = LukeForEntityClassification.from_pretrained("studio-ousia/luke-large-finetuned-open-entity")

	>>> text = "Beyoncé lives in Los Angeles."
	>>> entity_spans = [(0, 7)] # character-based entity span corresponding to "Beyoncé"
	>>> inputs = tokenizer(text, entity_spans=entity_spans, return_tensors="pt")
	>>> outputs = model(**inputs)
	>>> logits = outputs.logits
	>>> predicted_class_idx = logits.argmax(-1).item()
	>>> print("Predicted class:", model.config.id2label[predicted_class_idx])
	Predicted class: person
	```"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	outputs = self.luke(
	input_ids=input_ids,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	entity_ids=entity_ids,
	entity_attention_mask=entity_attention_mask,
	entity_token_type_ids=entity_token_type_ids,
	entity_position_ids=entity_position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=True,
	)

	feature_vector = outputs.entity_last_hidden_state[:, 0, :]
	feature_vector = self.dropout(feature_vector)
	logits = self.classifier(feature_vector)

	loss = None
	if labels is not None:
	# When the number of dimension of `labels` is 1, cross entropy is used as the loss function. The binary
	# cross entropy is used otherwise.
	# move labels to correct device to enable model parallelism
	labels = labels.to(logits.device)
	if labels.ndim == 1:
	loss = nn.functional.cross_entropy(logits, labels)
	else:
	loss = nn.functional.binary_cross_entropy_with_logits(logits.view(-1), labels.view(-1).type_as(logits))

	if not return_dict:
	return tuple(
	v
	for v in [loss, logits, outputs.hidden_states, outputs.entity_hidden_states, outputs.attentions]
	if v is not None
	)

	return EntityClassificationOutput(
	loss=loss,
	logits=logits,
	hidden_states=outputs.hidden_states,
	entity_hidden_states=outputs.entity_hidden_states,
	attentions=outputs.attentions,
	)


	@add_start_docstrings(
	"""
	The LUKE model with a classification head on top (a linear layer on top of the hidden states of the two entity
	tokens) for entity pair classification tasks, such as TACRED.
	""",
	LUKE_START_DOCSTRING,
	)
	class LukeForEntityPairClassification(LukePreTrainedModel):
	def __init__(self, config):
	super().__init__(config)

	self.luke = LukeModel(config)

	self.num_labels = config.num_labels
	self.dropout = nn.Dropout(config.hidden_dropout_prob)
	self.classifier = nn.Linear(config.hidden_size * 2, config.num_labels, False)

	# Initialize weights and apply final processing
	self.post_init()

	@add_start_docstrings_to_model_forward(LUKE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@replace_return_docstrings(output_type=EntityPairClassificationOutput, config_class=_CONFIG_FOR_DOC)
	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	attention_mask: Optional[torch.FloatTensor] = None,
	token_type_ids: Optional[torch.LongTensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	entity_ids: Optional[torch.LongTensor] = None,
	entity_attention_mask: Optional[torch.FloatTensor] = None,
	entity_token_type_ids: Optional[torch.LongTensor] = None,
	entity_position_ids: Optional[torch.LongTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	labels: Optional[torch.LongTensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, EntityPairClassificationOutput]:
	r"""
	labels (`torch.LongTensor` of shape `(batch_size,)` or `(batch_size, num_labels)`, optional):
	Labels for computing the classification loss. If the shape is `(batch_size,)`, the cross entropy loss is
	used for the single-label classification. In this case, labels should contain the indices that should be in
	`[0, ..., config.num_labels - 1]`. If the shape is `(batch_size, num_labels)`, the binary cross entropy
	loss is used for the multi-label classification. In this case, labels should only contain `[0, 1]`, where 0
	and 1 indicate false and true, respectively.

	Returns:

	Examples:

	```python
	>>> from transformers import AutoTokenizer, LukeForEntityPairClassification

	>>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-large-finetuned-tacred")
	>>> model = LukeForEntityPairClassification.from_pretrained("studio-ousia/luke-large-finetuned-tacred")

	>>> text = "Beyoncé lives in Los Angeles."
	>>> entity_spans = [
	... (0, 7),
	... (17, 28),
	... ] # character-based entity spans corresponding to "Beyoncé" and "Los Angeles"
	>>> inputs = tokenizer(text, entity_spans=entity_spans, return_tensors="pt")
	>>> outputs = model(**inputs)
	>>> logits = outputs.logits
	>>> predicted_class_idx = logits.argmax(-1).item()
	>>> print("Predicted class:", model.config.id2label[predicted_class_idx])
	Predicted class: per:cities_of_residence
	```"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	outputs = self.luke(
	input_ids=input_ids,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	entity_ids=entity_ids,
	entity_attention_mask=entity_attention_mask,
	entity_token_type_ids=entity_token_type_ids,
	entity_position_ids=entity_position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=True,
	)

	feature_vector = torch.cat(
	[outputs.entity_last_hidden_state[:, 0, :], outputs.entity_last_hidden_state[:, 1, :]], dim=1
	)
	feature_vector = self.dropout(feature_vector)
	logits = self.classifier(feature_vector)

	loss = None
	if labels is not None:
	# When the number of dimension of `labels` is 1, cross entropy is used as the loss function. The binary
	# cross entropy is used otherwise.
	# move labels to correct device to enable model parallelism
	labels = labels.to(logits.device)
	if labels.ndim == 1:
	loss = nn.functional.cross_entropy(logits, labels)
	else:
	loss = nn.functional.binary_cross_entropy_with_logits(logits.view(-1), labels.view(-1).type_as(logits))

	if not return_dict:
	return tuple(
	v
	for v in [loss, logits, outputs.hidden_states, outputs.entity_hidden_states, outputs.attentions]
	if v is not None
	)

	return EntityPairClassificationOutput(
	loss=loss,
	logits=logits,
	hidden_states=outputs.hidden_states,
	entity_hidden_states=outputs.entity_hidden_states,
	attentions=outputs.attentions,
	)


	@add_start_docstrings(
	"""
	The LUKE model with a span classification head on top (a linear layer on top of the hidden states output) for tasks
	such as named entity recognition.
	""",
	LUKE_START_DOCSTRING,
	)
	class LukeForEntitySpanClassification(LukePreTrainedModel):
	def __init__(self, config):
	super().__init__(config)

	self.luke = LukeModel(config)

	self.num_labels = config.num_labels
	self.dropout = nn.Dropout(config.hidden_dropout_prob)
	self.classifier = nn.Linear(config.hidden_size * 3, config.num_labels)

	# Initialize weights and apply final processing
	self.post_init()

	@add_start_docstrings_to_model_forward(LUKE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@replace_return_docstrings(output_type=EntitySpanClassificationOutput, config_class=_CONFIG_FOR_DOC)
	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	attention_mask: Optional[torch.FloatTensor] = None,
	token_type_ids: Optional[torch.LongTensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	entity_ids: Optional[torch.LongTensor] = None,
	entity_attention_mask: Optional[torch.LongTensor] = None,
	entity_token_type_ids: Optional[torch.LongTensor] = None,
	entity_position_ids: Optional[torch.LongTensor] = None,
	entity_start_positions: Optional[torch.LongTensor] = None,
	entity_end_positions: Optional[torch.LongTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	labels: Optional[torch.LongTensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, EntitySpanClassificationOutput]:
	r"""
	entity_start_positions (`torch.LongTensor`):
	The start positions of entities in the word token sequence.

	entity_end_positions (`torch.LongTensor`):
	The end positions of entities in the word token sequence.

	labels (`torch.LongTensor` of shape `(batch_size, entity_length)` or `(batch_size, entity_length, num_labels)`, optional):
	Labels for computing the classification loss. If the shape is `(batch_size, entity_length)`, the cross
	entropy loss is used for the single-label classification. In this case, labels should contain the indices
	that should be in `[0, ..., config.num_labels - 1]`. If the shape is `(batch_size, entity_length,
	num_labels)`, the binary cross entropy loss is used for the multi-label classification. In this case,
	labels should only contain `[0, 1]`, where 0 and 1 indicate false and true, respectively.

	Returns:

	Examples:

	```python
	>>> from transformers import AutoTokenizer, LukeForEntitySpanClassification

	>>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-large-finetuned-conll-2003")
	>>> model = LukeForEntitySpanClassification.from_pretrained("studio-ousia/luke-large-finetuned-conll-2003")

	>>> text = "Beyoncé lives in Los Angeles"
	# List all possible entity spans in the text

	>>> word_start_positions = [0, 8, 14, 17, 21] # character-based start positions of word tokens
	>>> word_end_positions = [7, 13, 16, 20, 28] # character-based end positions of word tokens
	>>> entity_spans = []
	>>> for i, start_pos in enumerate(word_start_positions):
	... for end_pos in word_end_positions[i:]:
	... entity_spans.append((start_pos, end_pos))

	>>> inputs = tokenizer(text, entity_spans=entity_spans, return_tensors="pt")
	>>> outputs = model(**inputs)
	>>> logits = outputs.logits
	>>> predicted_class_indices = logits.argmax(-1).squeeze().tolist()
	>>> for span, predicted_class_idx in zip(entity_spans, predicted_class_indices):
	... if predicted_class_idx != 0:
	... print(text[span[0] : span[1]], model.config.id2label[predicted_class_idx])
	Beyoncé PER
	Los Angeles LOC
	```"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	outputs = self.luke(
	input_ids=input_ids,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	entity_ids=entity_ids,
	entity_attention_mask=entity_attention_mask,
	entity_token_type_ids=entity_token_type_ids,
	entity_position_ids=entity_position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=True,
	)
	hidden_size = outputs.last_hidden_state.size(-1)

	entity_start_positions = entity_start_positions.unsqueeze(-1).expand(-1, -1, hidden_size)
	if entity_start_positions.device != outputs.last_hidden_state.device:
	entity_start_positions = entity_start_positions.to(outputs.last_hidden_state.device)
	start_states = torch.gather(outputs.last_hidden_state, -2, entity_start_positions)

	entity_end_positions = entity_end_positions.unsqueeze(-1).expand(-1, -1, hidden_size)
	if entity_end_positions.device != outputs.last_hidden_state.device:
	entity_end_positions = entity_end_positions.to(outputs.last_hidden_state.device)
	end_states = torch.gather(outputs.last_hidden_state, -2, entity_end_positions)

	feature_vector = torch.cat([start_states, end_states, outputs.entity_last_hidden_state], dim=2)

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

	loss = None
	if labels is not None:
	# move labels to correct device to enable model parallelism
	labels = labels.to(logits.device)
	# When the number of dimension of `labels` is 2, cross entropy is used as the loss function. The binary
	# cross entropy is used otherwise.
	if labels.ndim == 2:
	loss = nn.functional.cross_entropy(logits.view(-1, self.num_labels), labels.view(-1))
	else:
	loss = nn.functional.binary_cross_entropy_with_logits(logits.view(-1), labels.view(-1).type_as(logits))

	if not return_dict:
	return tuple(
	v
	for v in [loss, logits, outputs.hidden_states, outputs.entity_hidden_states, outputs.attentions]
	if v is not None
	)

	return EntitySpanClassificationOutput(
	loss=loss,
	logits=logits,
	hidden_states=outputs.hidden_states,
	entity_hidden_states=outputs.entity_hidden_states,
	attentions=outputs.attentions,
	)


	@add_start_docstrings(
	"""
	The LUKE Model transformer with a sequence classification/regression head on top (a linear layer on top of the
	pooled output) e.g. for GLUE tasks.
	""",
	LUKE_START_DOCSTRING,
	)
	class LukeForSequenceClassification(LukePreTrainedModel):
	def __init__(self, config):
	super().__init__(config)
	self.num_labels = config.num_labels
	self.luke = LukeModel(config)
	self.dropout = nn.Dropout(
	config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
	)
	self.classifier = nn.Linear(config.hidden_size, config.num_labels)

	# Initialize weights and apply final processing
	self.post_init()

	@add_start_docstrings_to_model_forward(LUKE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@add_code_sample_docstrings(
	checkpoint=_CHECKPOINT_FOR_DOC,
	output_type=LukeSequenceClassifierOutput,
	config_class=_CONFIG_FOR_DOC,
	)
	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	attention_mask: Optional[torch.FloatTensor] = None,
	token_type_ids: Optional[torch.LongTensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	entity_ids: Optional[torch.LongTensor] = None,
	entity_attention_mask: Optional[torch.FloatTensor] = None,
	entity_token_type_ids: Optional[torch.LongTensor] = None,
	entity_position_ids: Optional[torch.LongTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	labels: Optional[torch.FloatTensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, LukeSequenceClassifierOutput]:
	r"""
	labels (`torch.LongTensor` of shape `(batch_size,)`, optional):
	Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
	config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
	`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.luke(
	input_ids=input_ids,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	entity_ids=entity_ids,
	entity_attention_mask=entity_attention_mask,
	entity_token_type_ids=entity_token_type_ids,
	entity_position_ids=entity_position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=True,
	)

	pooled_output = outputs.pooler_output

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

	loss = None
	if labels is not None:
	# move labels to correct device to enable model parallelism
	labels = labels.to(logits.device)
	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:
	return tuple(
	v
	for v in [loss, logits, outputs.hidden_states, outputs.entity_hidden_states, outputs.attentions]
	if v is not None
	)

	return LukeSequenceClassifierOutput(
	loss=loss,
	logits=logits,
	hidden_states=outputs.hidden_states,
	entity_hidden_states=outputs.entity_hidden_states,
	attentions=outputs.attentions,
	)


	@add_start_docstrings(
	"""
	The LUKE Model with a token classification head on top (a linear layer on top of the hidden-states output). To
	solve Named-Entity Recognition (NER) task using LUKE, `LukeForEntitySpanClassification` is more suitable than this
	class.
	""",
	LUKE_START_DOCSTRING,
	)
	class LukeForTokenClassification(LukePreTrainedModel):
	def __init__(self, config):
	super().__init__(config)
	self.num_labels = config.num_labels

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

	# Initialize weights and apply final processing
	self.post_init()

	@add_start_docstrings_to_model_forward(LUKE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@add_code_sample_docstrings(
	checkpoint=_CHECKPOINT_FOR_DOC,
	output_type=LukeTokenClassifierOutput,
	config_class=_CONFIG_FOR_DOC,
	)
	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	attention_mask: Optional[torch.FloatTensor] = None,
	token_type_ids: Optional[torch.LongTensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	entity_ids: Optional[torch.LongTensor] = None,
	entity_attention_mask: Optional[torch.FloatTensor] = None,
	entity_token_type_ids: Optional[torch.LongTensor] = None,
	entity_position_ids: Optional[torch.LongTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	labels: Optional[torch.FloatTensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, LukeTokenClassifierOutput]:
	r"""
	labels (`torch.LongTensor` of shape `(batch_size,)`, optional):
	Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
	num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
	`input_ids` above)
	"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	outputs = self.luke(
	input_ids=input_ids,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	entity_ids=entity_ids,
	entity_attention_mask=entity_attention_mask,
	entity_token_type_ids=entity_token_type_ids,
	entity_position_ids=entity_position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=True,
	)

	sequence_output = outputs.last_hidden_state

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

	loss = None
	if labels is not None:
	# move labels to correct device to enable model parallelism
	labels = labels.to(logits.device)
	loss_fct = CrossEntropyLoss()
	loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))

	if not return_dict:
	return tuple(
	v
	for v in [loss, logits, outputs.hidden_states, outputs.entity_hidden_states, outputs.attentions]
	if v is not None
	)

	return LukeTokenClassifierOutput(
	loss=loss,
	logits=logits,
	hidden_states=outputs.hidden_states,
	entity_hidden_states=outputs.entity_hidden_states,
	attentions=outputs.attentions,
	)


	@add_start_docstrings(
	"""
	The LUKE 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`).
	""",
	LUKE_START_DOCSTRING,
	)
	class LukeForQuestionAnswering(LukePreTrainedModel):
	def __init__(self, config):
	super().__init__(config)

	self.num_labels = config.num_labels

	self.luke = LukeModel(config, add_pooling_layer=False)
	self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)

	# Initialize weights and apply final processing
	self.post_init()

	@add_start_docstrings_to_model_forward(LUKE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@add_code_sample_docstrings(
	checkpoint=_CHECKPOINT_FOR_DOC,
	output_type=LukeQuestionAnsweringModelOutput,
	config_class=_CONFIG_FOR_DOC,
	)
	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	attention_mask: Optional[torch.FloatTensor] = None,
	token_type_ids: Optional[torch.LongTensor] = None,
	position_ids: Optional[torch.FloatTensor] = None,
	entity_ids: Optional[torch.LongTensor] = None,
	entity_attention_mask: Optional[torch.FloatTensor] = None,
	entity_token_type_ids: Optional[torch.LongTensor] = None,
	entity_position_ids: Optional[torch.LongTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	start_positions: Optional[torch.LongTensor] = None,
	end_positions: Optional[torch.LongTensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, LukeQuestionAnsweringModelOutput]:
	r"""
	start_positions (`torch.LongTensor` of shape `(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 (`sequence_length`). Position outside of the sequence
	are not taken into account for computing the loss.
	end_positions (`torch.LongTensor` of shape `(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 (`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.luke(
	input_ids=input_ids,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	entity_ids=entity_ids,
	entity_attention_mask=entity_attention_mask,
	entity_token_type_ids=entity_token_type_ids,
	entity_position_ids=entity_position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=True,
	)

	sequence_output = outputs.last_hidden_state

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

	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.clamp_(0, ignored_index)
	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:
	return tuple(
	v
	for v in [
	total_loss,
	start_logits,
	end_logits,
	outputs.hidden_states,
	outputs.entity_hidden_states,
	outputs.attentions,
	]
	if v is not None
	)

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


	@add_start_docstrings(
	"""
	The LUKE 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.
	""",
	LUKE_START_DOCSTRING,
	)
	class LukeForMultipleChoice(LukePreTrainedModel):
	def __init__(self, config):
	super().__init__(config)

	self.luke = LukeModel(config)
	self.dropout = nn.Dropout(
	config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
	)
	self.classifier = nn.Linear(config.hidden_size, 1)

	# Initialize weights and apply final processing
	self.post_init()

	@add_start_docstrings_to_model_forward(LUKE_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
	@add_code_sample_docstrings(
	checkpoint=_CHECKPOINT_FOR_DOC,
	output_type=LukeMultipleChoiceModelOutput,
	config_class=_CONFIG_FOR_DOC,
	)
	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	attention_mask: Optional[torch.FloatTensor] = None,
	token_type_ids: Optional[torch.LongTensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	entity_ids: Optional[torch.LongTensor] = None,
	entity_attention_mask: Optional[torch.FloatTensor] = None,
	entity_token_type_ids: Optional[torch.LongTensor] = None,
	entity_position_ids: Optional[torch.LongTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	labels: Optional[torch.FloatTensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, LukeMultipleChoiceModelOutput]:
	r"""
	labels (`torch.LongTensor` of shape `(batch_size,)`, optional):
	Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
	num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
	`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]

	input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
	attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask 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
	position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids 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
	)

	entity_ids = entity_ids.view(-1, entity_ids.size(-1)) if entity_ids is not None else None
	entity_attention_mask = (
	entity_attention_mask.view(-1, entity_attention_mask.size(-1))
	if entity_attention_mask is not None
	else None
	)
	entity_token_type_ids = (
	entity_token_type_ids.view(-1, entity_token_type_ids.size(-1))
	if entity_token_type_ids is not None
	else None
	)
	entity_position_ids = (
	entity_position_ids.view(-1, entity_position_ids.size(-2), entity_position_ids.size(-1))
	if entity_position_ids is not None
	else None
	)

	outputs = self.luke(
	input_ids=input_ids,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	entity_ids=entity_ids,
	entity_attention_mask=entity_attention_mask,
	entity_token_type_ids=entity_token_type_ids,
	entity_position_ids=entity_position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=True,
	)

	pooled_output = outputs.pooler_output

	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:
	# move labels to correct device to enable model parallelism
	labels = labels.to(reshaped_logits.device)
	loss_fct = CrossEntropyLoss()
	loss = loss_fct(reshaped_logits, labels)

	if not return_dict:
	return tuple(
	v
	for v in [
	loss,
	reshaped_logits,
	outputs.hidden_states,
	outputs.entity_hidden_states,
	outputs.attentions,
	]
	if v is not None
	)

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