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Grounded-Segment-Anything / transformers_4_35_0 /models /bros /modeling_bros.py

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	# coding=utf-8
	# Copyright 2023-present NAVER Corp, The Microsoft Research Asia LayoutLM Team Authors 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 Bros model."""


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

	import torch
	import torch.utils.checkpoint
	from torch import nn
	from torch.nn import CrossEntropyLoss

	from ...activations import ACT2FN
	from ...modeling_outputs import (
	BaseModelOutputWithPastAndCrossAttentions,
	BaseModelOutputWithPoolingAndCrossAttentions,
	TokenClassifierOutput,
	)
	from ...modeling_utils import PreTrainedModel
	from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
	from ...utils import (
	ModelOutput,
	add_start_docstrings,
	add_start_docstrings_to_model_forward,
	logging,
	replace_return_docstrings,
	)
	from .configuration_bros import BrosConfig


	logger = logging.get_logger(__name__)

	_CHECKPOINT_FOR_DOC = "jinho8345/bros-base-uncased"
	_CONFIG_FOR_DOC = "BrosConfig"

	BROS_PRETRAINED_MODEL_ARCHIVE_LIST = [
	"jinho8345/bros-base-uncased",
	"jinho8345/bros-large-uncased",
	# See all Bros models at https://huggingface.co/models?filter=bros
	]

	BROS_START_DOCSTRING = r"""
	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 ([`BrosConfig`]): 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.
	"""

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

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

	[What are input IDs?](../glossary#input-ids)

	bbox ('torch.FloatTensor' of shape '(batch_size, num_boxes, 4)'):
	Bounding box coordinates for each token in the input sequence. Each bounding box is a list of four values
	(x1, y1, x2, y2), where (x1, y1) is the top left corner, and (x2, y2) is the bottom right corner of the
	bounding box.

	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)

	bbox_first_token_mask (`torch.FloatTensor` of shape `({0})`, optional):
	Mask to indicate the first token of each bounding box. Mask values selected in `[0, 1]`:

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

	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)

	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.

	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.

	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 [`~file_utils.ModelOutput`] instead of a plain tuple.
	"""


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

	Args:
	loss (`torch.FloatTensor` of shape `(1,)`, optional, returned when `labels` is provided) :
	Classification loss.
	initial_token_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`):
	Classification scores for entity initial tokens (before SoftMax).
	subsequent_token_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, sequence_length+1)`):
	Classification scores for entity sequence tokens (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.
	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
	initial_token_logits: torch.FloatTensor = None
	subsequent_token_logits: torch.FloatTensor = None
	hidden_states: Optional[Tuple[torch.FloatTensor]] = None
	attentions: Optional[Tuple[torch.FloatTensor]] = None


	class BrosPositionalEmbedding1D(nn.Module):
	# Reference: https://github.com/kimiyoung/transformer-xl/blob/master/pytorch/mem_transformer.py#L15

	def __init__(self, config):
	super(BrosPositionalEmbedding1D, self).__init__()

	self.dim_bbox_sinusoid_emb_1d = config.dim_bbox_sinusoid_emb_1d

	inv_freq = 1 / (
	10000 ** (torch.arange(0.0, self.dim_bbox_sinusoid_emb_1d, 2.0) / self.dim_bbox_sinusoid_emb_1d)
	)
	self.register_buffer("inv_freq", inv_freq)

	def forward(self, pos_seq: torch.Tensor) -> torch.Tensor:
	seq_size = pos_seq.size()
	b1, b2, b3 = seq_size
	sinusoid_inp = pos_seq.view(b1, b2, b3, 1) * self.inv_freq.view(1, 1, 1, self.dim_bbox_sinusoid_emb_1d // 2)
	pos_emb = torch.cat([sinusoid_inp.sin(), sinusoid_inp.cos()], dim=-1)
	return pos_emb


	class BrosPositionalEmbedding2D(nn.Module):
	def __init__(self, config):
	super(BrosPositionalEmbedding2D, self).__init__()

	self.dim_bbox = config.dim_bbox
	self.x_pos_emb = BrosPositionalEmbedding1D(config)
	self.y_pos_emb = BrosPositionalEmbedding1D(config)

	def forward(self, bbox: torch.Tensor) -> torch.Tensor:
	stack = []
	for i in range(self.dim_bbox):
	if i % 2 == 0:
	stack.append(self.x_pos_emb(bbox[..., i]))
	else:
	stack.append(self.y_pos_emb(bbox[..., i]))
	bbox_pos_emb = torch.cat(stack, dim=-1)
	return bbox_pos_emb


	class BrosBboxEmbeddings(nn.Module):
	def __init__(self, config):
	super(BrosBboxEmbeddings, self).__init__()
	self.bbox_sinusoid_emb = BrosPositionalEmbedding2D(config)
	self.bbox_projection = nn.Linear(config.dim_bbox_sinusoid_emb_2d, config.dim_bbox_projection, bias=False)

	def forward(self, bbox: torch.Tensor):
	bbox_t = bbox.transpose(0, 1)
	bbox_pos = bbox_t[None, :, :, :] - bbox_t[:, None, :, :]
	bbox_pos_emb = self.bbox_sinusoid_emb(bbox_pos)
	bbox_pos_emb = self.bbox_projection(bbox_pos_emb)

	return bbox_pos_emb


	class BrosTextEmbeddings(nn.Module):
	"""Construct the embeddings from word, position and token_type embeddings."""

	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)))
	self.register_buffer(
	"token_type_ids",
	torch.zeros(
	self.position_ids.size(),
	dtype=torch.long,
	device=self.position_ids.device,
	),
	persistent=False,
	)

	def forward(
	self,
	input_ids: Optional[torch.Tensor] = None,
	token_type_ids: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.Tensor] = None,
	inputs_embeds: Optional[torch.Tensor] = None,
	past_key_values_length: int = 0,
	) -> torch.Tensor:
	if input_ids is not None:
	input_shape = input_ids.size()
	else:
	input_shape = inputs_embeds.size()[:-1]

	seq_length = input_shape[1]

	if position_ids is None:
	position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]

	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


	class BrosSelfAttention(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: torch.Tensor):
	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: torch.Tensor,
	bbox_pos_emb: torch.Tensor,
	attention_mask: Optional[torch.Tensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	encoder_hidden_states: Optional[torch.Tensor] = None,
	encoder_attention_mask: Optional[torch.Tensor] = None,
	past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
	output_attentions: Optional[torch.Tensor] = False,
	) -> Tuple[torch.Tensor]:
	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

	# bbox positional encoding
	batch_size, n_head, seq_length, d_head = query_layer.shape
	bbox_pos_emb = bbox_pos_emb.view(seq_length, seq_length, batch_size, d_head)
	bbox_pos_emb = bbox_pos_emb.permute([2, 0, 1, 3])
	bbox_pos_scores = torch.einsum("bnid,bijd->bnij", (query_layer, bbox_pos_emb))

	attention_scores = attention_scores + bbox_pos_scores

	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 BrosModel 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.bert.modeling_bert.BertSelfOutput with Bert->Bros
	class BrosSelfOutput(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 BrosAttention(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.self = BrosSelfAttention(config)
	self.output = BrosSelfOutput(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: torch.Tensor,
	bbox_pos_emb: torch.Tensor,
	attention_mask: Optional[torch.Tensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	encoder_hidden_states: Optional[torch.Tensor] = None,
	encoder_attention_mask: Optional[torch.Tensor] = None,
	past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
	output_attentions: Optional[bool] = False,
	) -> Tuple[torch.Tensor]:
	self_outputs = self.self(
	hidden_states=hidden_states,
	bbox_pos_emb=bbox_pos_emb,
	attention_mask=attention_mask,
	head_mask=head_mask,
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_attention_mask,
	past_key_value=past_key_value,
	output_attentions=output_attentions,
	)
	attention_output = self.output(self_outputs[0], hidden_states)
	outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
	return outputs


	# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->Bros
	class BrosIntermediate(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


	class BrosOutput(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 BrosLayer(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 = BrosAttention(config)
	self.is_decoder = config.is_decoder
	self.add_cross_attention = config.add_cross_attention
	if self.add_cross_attention:
	if not self.is_decoder:
	raise Exception(f"{self} should be used as a decoder model if cross attention is added")
	self.crossattention = BrosAttention(config)
	self.intermediate = BrosIntermediate(config)
	self.output = BrosOutput(config)

	def forward(
	self,
	hidden_states: torch.Tensor,
	bbox_pos_emb: torch.Tensor,
	attention_mask: Optional[torch.FloatTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	encoder_hidden_states: Optional[torch.FloatTensor] = None,
	encoder_attention_mask: Optional[torch.FloatTensor] = None,
	past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
	output_attentions: Optional[bool] = False,
	) -> Tuple[torch.Tensor]:
	# 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,
	bbox_pos_emb=bbox_pos_emb,
	attention_mask=attention_mask,
	head_mask=head_mask,
	output_attentions=output_attentions,
	past_key_value=self_attn_past_key_value,
	)
	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:
	if hasattr(self, "crossattention"):
	raise Exception(
	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,
	)
	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):
	intermediate_output = self.intermediate(attention_output)
	layer_output = self.output(intermediate_output, attention_output)
	return layer_output


	class BrosEncoder(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.config = config
	self.layer = nn.ModuleList([BrosLayer(config) for _ in range(config.num_hidden_layers)])

	def forward(
	self,
	hidden_states: torch.Tensor,
	bbox_pos_emb: torch.Tensor,
	attention_mask: Optional[torch.FloatTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	encoder_hidden_states: Optional[torch.FloatTensor] = None,
	encoder_attention_mask: Optional[torch.FloatTensor] = None,
	past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = False,
	output_hidden_states: Optional[bool] = False,
	return_dict: Optional[bool] = True,
	) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
	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 getattr(self.config, "gradient_checkpointing", False) and self.training:
	if use_cache:
	logger.warning(
	"`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting "
	"`use_cache=False`..."
	)
	use_cache = False

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

	return custom_forward

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

	hidden_states = layer_outputs[0]
	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 with Bert->Bros
	class BrosPooler(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 BrosRelationExtractor(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.n_relations = config.n_relations
	self.backbone_hidden_size = config.hidden_size
	self.head_hidden_size = config.hidden_size
	self.classifier_dropout_prob = config.classifier_dropout_prob

	self.drop = nn.Dropout(self.classifier_dropout_prob)
	self.query = nn.Linear(self.backbone_hidden_size, self.n_relations * self.head_hidden_size)

	self.key = nn.Linear(self.backbone_hidden_size, self.n_relations * self.head_hidden_size)

	self.dummy_node = nn.Parameter(torch.zeros(1, self.backbone_hidden_size))

	def forward(self, query_layer: torch.Tensor, key_layer: torch.Tensor):
	query_layer = self.query(self.drop(query_layer))

	dummy_vec = self.dummy_node.unsqueeze(0).repeat(1, key_layer.size(1), 1)
	key_layer = torch.cat([key_layer, dummy_vec], axis=0)
	key_layer = self.key(self.drop(key_layer))

	query_layer = query_layer.view(
	query_layer.size(0), query_layer.size(1), self.n_relations, self.head_hidden_size
	)
	key_layer = key_layer.view(key_layer.size(0), key_layer.size(1), self.n_relations, self.head_hidden_size)

	relation_score = torch.matmul(
	query_layer.permute(2, 1, 0, 3), key_layer.permute(2, 1, 3, 0)
	) # equivalent to torch.einsum("ibnd,jbnd->nbij", (query_layer, key_layer))

	return relation_score


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

	config_class = BrosConfig
	base_model_prefix = "bros"

	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)


	@add_start_docstrings(
	"The bare Bros Model transformer outputting raw hidden-states without any specific head on top.",
	BROS_START_DOCSTRING,
	)
	class BrosModel(BrosPreTrainedModel):
	def __init__(self, config, add_pooling_layer=True):
	super().__init__(config)
	self.config = config

	self.embeddings = BrosTextEmbeddings(config)
	self.bbox_embeddings = BrosBboxEmbeddings(config)
	self.encoder = BrosEncoder(config)

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

	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(BROS_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@replace_return_docstrings(output_type=BaseModelOutputWithPoolingAndCrossAttentions, config_class=_CONFIG_FOR_DOC)
	def forward(
	self,
	input_ids: Optional[torch.Tensor] = None,
	bbox: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	token_type_ids: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.Tensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	inputs_embeds: Optional[torch.Tensor] = None,
	encoder_hidden_states: Optional[torch.Tensor] = None,
	encoder_attention_mask: Optional[torch.Tensor] = None,
	past_key_values: Optional[List[torch.FloatTensor]] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
	r"""
	Returns:

	Examples:

	```python
	>>> import torch
	>>> from transformers import BrosProcessor, BrosModel

	>>> processor = BrosProcessor.from_pretrained("jinho8345/bros-base-uncased")

	>>> model = BrosModel.from_pretrained("jinho8345/bros-base-uncased")

	>>> encoding = processor("Hello, my dog is cute", add_special_tokens=False, return_tensors="pt")
	>>> bbox = torch.tensor([[[0, 0, 1, 1]]]).repeat(1, encoding["input_ids"].shape[-1], 1)
	>>> encoding["bbox"] = bbox

	>>> outputs = model(**encoding)
	>>> last_hidden_states = outputs.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 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")

	if bbox is None:
	raise ValueError("You have to specify bbox")

	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(input_shape, 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,
	)

	# if bbox has 2 points (4 float tensors) per token, convert it to 4 points (8 float tensors) per token
	if bbox.shape[-1] == 4:
	bbox = bbox[:, :, [0, 1, 2, 1, 2, 3, 0, 3]]
	scaled_bbox = bbox * self.config.bbox_scale
	bbox_position_embeddings = self.bbox_embeddings(scaled_bbox)

	encoder_outputs = self.encoder(
	embedding_output,
	bbox_pos_emb=bbox_position_embeddings,
	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,
	)
	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(
	"""
	Bros 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.
	""",
	BROS_START_DOCSTRING,
	)
	class BrosForTokenClassification(BrosPreTrainedModel):
	_keys_to_ignore_on_load_unexpected = [r"pooler"]

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

	self.bros = BrosModel(config)
	classifier_dropout = (
	config.classifier_dropout if hasattr(config, "classifier_dropout") 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(BROS_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@replace_return_docstrings(output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC)
	def forward(
	self,
	input_ids: Optional[torch.Tensor] = None,
	bbox: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	bbox_first_token_mask: Optional[torch.Tensor] = None,
	token_type_ids: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.Tensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	inputs_embeds: Optional[torch.Tensor] = None,
	labels: Optional[torch.Tensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
	r"""

	Returns:

	Examples:

	```python
	>>> import torch
	>>> from transformers import BrosProcessor, BrosForTokenClassification

	>>> processor = BrosProcessor.from_pretrained("jinho8345/bros-base-uncased")

	>>> model = BrosForTokenClassification.from_pretrained("jinho8345/bros-base-uncased")

	>>> encoding = processor("Hello, my dog is cute", add_special_tokens=False, return_tensors="pt")
	>>> bbox = torch.tensor([[[0, 0, 1, 1]]]).repeat(1, encoding["input_ids"].shape[-1], 1)
	>>> encoding["bbox"] = bbox

	>>> outputs = model(**encoding)
	```"""

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

	outputs = self.bros(
	input_ids,
	bbox=bbox,
	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]

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

	loss = None
	if labels is not None:
	loss_fct = CrossEntropyLoss()
	if bbox_first_token_mask is not None:
	bbox_first_token_mask = bbox_first_token_mask.view(-1)
	loss = loss_fct(
	logits.view(-1, self.num_labels)[bbox_first_token_mask], labels.view(-1)[bbox_first_token_mask]
	)
	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,
	)


	@add_start_docstrings(
	"""
	Bros Model with a token classification head on top (initial_token_layers and subsequent_token_layer on top of the
	hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. The initial_token_classifier is used to
	predict the first token of each entity, and the subsequent_token_classifier is used to predict the subsequent
	tokens within an entity. Compared to BrosForTokenClassification, this model is more robust to serialization errors
	since it predicts next token from one token.
	""",
	BROS_START_DOCSTRING,
	)
	class BrosSpadeEEForTokenClassification(BrosPreTrainedModel):
	_keys_to_ignore_on_load_unexpected = [r"pooler"]

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

	self.bros = BrosModel(config)
	classifier_dropout = (
	config.classifier_dropout if hasattr(config, "classifier_dropout") else config.hidden_dropout_prob
	)

	# Initial token classification for Entity Extraction (NER)
	self.initial_token_classifier = nn.Sequential(
	nn.Dropout(classifier_dropout),
	nn.Linear(config.hidden_size, config.hidden_size),
	nn.Dropout(classifier_dropout),
	nn.Linear(config.hidden_size, config.num_labels),
	)

	# Subsequent token classification for Entity Extraction (NER)
	self.subsequent_token_classifier = BrosRelationExtractor(config)

	self.init_weights()

	@add_start_docstrings_to_model_forward(BROS_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@replace_return_docstrings(output_type=BrosSpadeOutput, config_class=_CONFIG_FOR_DOC)
	def forward(
	self,
	input_ids: Optional[torch.Tensor] = None,
	bbox: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	bbox_first_token_mask: Optional[torch.Tensor] = None,
	token_type_ids: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.Tensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	inputs_embeds: Optional[torch.Tensor] = None,
	initial_token_labels: Optional[torch.Tensor] = None,
	subsequent_token_labels: Optional[torch.Tensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple[torch.Tensor], BrosSpadeOutput]:
	r"""
	Returns:

	Examples:

	```python
	>>> import torch
	>>> from transformers import BrosProcessor, BrosSpadeEEForTokenClassification

	>>> processor = BrosProcessor.from_pretrained("jinho8345/bros-base-uncased")

	>>> model = BrosSpadeEEForTokenClassification.from_pretrained("jinho8345/bros-base-uncased")

	>>> encoding = processor("Hello, my dog is cute", add_special_tokens=False, return_tensors="pt")
	>>> bbox = torch.tensor([[[0, 0, 1, 1]]]).repeat(1, encoding["input_ids"].shape[-1], 1)
	>>> encoding["bbox"] = bbox

	>>> outputs = model(**encoding)
	```"""

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

	outputs = self.bros(
	input_ids=input_ids,
	bbox=bbox,
	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,
	)

	last_hidden_states = outputs[0]
	last_hidden_states = last_hidden_states.transpose(0, 1).contiguous()
	initial_token_logits = self.initial_token_classifier(last_hidden_states).transpose(0, 1).contiguous()
	subsequent_token_logits = self.subsequent_token_classifier(last_hidden_states, last_hidden_states).squeeze(0)

	# make subsequent token (sequence token classification) mask
	inv_attention_mask = 1 - attention_mask
	batch_size, max_seq_length = inv_attention_mask.shape
	device = inv_attention_mask.device
	invalid_token_mask = torch.cat([inv_attention_mask, torch.zeros([batch_size, 1]).to(device)], axis=1).bool()
	subsequent_token_logits = subsequent_token_logits.masked_fill(
	invalid_token_mask[:, None, :], torch.finfo(subsequent_token_logits.dtype).min
	)
	self_token_mask = torch.eye(max_seq_length, max_seq_length + 1).to(device).bool()
	subsequent_token_logits = subsequent_token_logits.masked_fill(
	self_token_mask[None, :, :], torch.finfo(subsequent_token_logits.dtype).min
	)
	subsequent_token_mask = attention_mask.view(-1).bool()

	loss = None
	if initial_token_labels is not None and subsequent_token_labels is not None:
	loss_fct = CrossEntropyLoss()

	# get initial token loss
	initial_token_labels = initial_token_labels.view(-1)
	if bbox_first_token_mask is not None:
	bbox_first_token_mask = bbox_first_token_mask.view(-1)
	initial_token_loss = loss_fct(
	initial_token_logits.view(-1, self.num_labels)[bbox_first_token_mask],
	initial_token_labels[bbox_first_token_mask],
	)
	else:
	initial_token_loss = loss_fct(initial_token_logits.view(-1, self.num_labels), initial_token_labels)

	subsequent_token_labels = subsequent_token_labels.view(-1)
	subsequent_token_loss = loss_fct(
	subsequent_token_logits.view(-1, max_seq_length + 1)[subsequent_token_mask],
	subsequent_token_labels[subsequent_token_mask],
	)

	loss = initial_token_loss + subsequent_token_loss

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

	return BrosSpadeOutput(
	loss=loss,
	initial_token_logits=initial_token_logits,
	subsequent_token_logits=subsequent_token_logits,
	hidden_states=outputs.hidden_states,
	attentions=outputs.attentions,
	)


	@add_start_docstrings(
	"""
	Bros Model with a token classification head on top (a entity_linker layer on top of the hidden-states output) e.g.
	for Entity-Linking. The entity_linker is used to predict intra-entity links (one entity to another entity).
	""",
	BROS_START_DOCSTRING,
	)
	class BrosSpadeELForTokenClassification(BrosPreTrainedModel):
	_keys_to_ignore_on_load_unexpected = [r"pooler"]

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

	self.bros = BrosModel(config)
	(config.classifier_dropout if hasattr(config, "classifier_dropout") else config.hidden_dropout_prob)

	self.entity_linker = BrosRelationExtractor(config)

	self.init_weights()

	@add_start_docstrings_to_model_forward(BROS_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@replace_return_docstrings(output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC)
	def forward(
	self,
	input_ids: Optional[torch.Tensor] = None,
	bbox: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	bbox_first_token_mask: Optional[torch.Tensor] = None,
	token_type_ids: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.Tensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	inputs_embeds: Optional[torch.Tensor] = None,
	labels: Optional[torch.Tensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
	r"""
	Returns:

	Examples:

	```python
	>>> import torch
	>>> from transformers import BrosProcessor, BrosSpadeELForTokenClassification

	>>> processor = BrosProcessor.from_pretrained("jinho8345/bros-base-uncased")

	>>> model = BrosSpadeELForTokenClassification.from_pretrained("jinho8345/bros-base-uncased")

	>>> encoding = processor("Hello, my dog is cute", add_special_tokens=False, return_tensors="pt")
	>>> bbox = torch.tensor([[[0, 0, 1, 1]]]).repeat(1, encoding["input_ids"].shape[-1], 1)
	>>> encoding["bbox"] = bbox

	>>> outputs = model(**encoding)
	```"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	outputs = self.bros(
	input_ids=input_ids,
	bbox=bbox,
	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,
	)

	last_hidden_states = outputs[0]
	last_hidden_states = last_hidden_states.transpose(0, 1).contiguous()

	logits = self.entity_linker(last_hidden_states, last_hidden_states).squeeze(0)

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

	batch_size, max_seq_length = attention_mask.shape
	device = attention_mask.device

	self_token_mask = torch.eye(max_seq_length, max_seq_length + 1).to(device).bool()

	mask = bbox_first_token_mask.view(-1)
	bbox_first_token_mask = torch.cat(
	[
	~bbox_first_token_mask,
	torch.zeros([batch_size, 1], dtype=torch.bool).to(device),
	],
	axis=1,
	)
	logits = logits.masked_fill(bbox_first_token_mask[:, None, :], torch.finfo(logits.dtype).min)
	logits = logits.masked_fill(self_token_mask[None, :, :], torch.finfo(logits.dtype).min)

	loss = loss_fct(logits.view(-1, max_seq_length + 1)[mask], labels.view(-1)[mask])

	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,
	)