Add BROS (#23190)

* add Bros boilerplate

* copy and pasted modeling_bros.py from official Bros repo

* update copyright of bros files

* copy tokenization_bros.py from official repo and update import path

* copy tokenization_bros_fast.py from official repo and update import path

* copy configuration_bros.py from official repo and update import path

* remove trailing period in copyright line

* copy and paste bros/__init__.py from official repo

* save formatting

* remove unused unnecessary pe_type argument - using only crel type

* resolve import issue

* remove unused model classes

* remove unnecessary tests

* remove unused classes

* fix original code's bug - layer_module's argument order

* clean up modeling auto

* add bbox to prepare_config_and_inputs

* set temporary value to hidden_size (32 is too low because of the of the
Bros' positional embedding)

* remove decoder test, update create_and_check* input arguemnts

* add missing variable to model tests

* do make fixup

* update bros.mdx

* add boilerate plate for no_head inference test

* update BROS_PRETRAINED_MODEL_ARCHIVE_LIST (add naver-clova-ocr prefix)

* add prepare_bros_batch_inputs function

* update modeling_common to add bbox inputs in Bros Model Test

* remove unnecessary model inference

* add test case

* add model_doc

* add test case for token_classification

* apply fixup

* update modeling code

* update BrosForTokenClassification loss calculation logic

* revert logits preprocessing logic to make sure logits have original shape

* - update class name

* - add BrosSpadeOutput
- update BrosConfig arguments

* add boilerate plate for no_head inference test

* add prepare_bros_batch_inputs function

* add test case

* add test case for token_classification

* update modeling code

* update BrosForTokenClassification loss calculation logic

* revert logits preprocessing logic to make sure logits have original shape

* apply masking on the fly

* add BrosSpadeForTokenLinking

* update class name
put docstring to the beginning of the file

* separate the logits calculation logic and loss calculation logic

* update logic for loss calculation so that logits shape doesn't change
when return

* update typo

* update prepare_config_and_inputs

* update dummy node initialization

* update last_hidden_states getting logic to consider when return_dict is False

* update box first token mask param

* bugfix: remove random attention mask generation

* update keys to ignore on load missing

* run make style and quality

* apply make style and quality of other codes

* update box_first_token_mask to bool type

* update index.md

* apply make style and quality

* apply make fix-copies

* pass check_repo

* update bros model doc

* docstring bugfix fix

* add checkpoint for doc, tokenizer for doc

* Update README.md

* Update docs/source/en/model_doc/bros.md
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update bros.md

* Update src/transformers/__init__.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update docs/source/en/model_doc/bros.md
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* apply suggestions from code review

* apply suggestions from code review

* revert test_processor_markuplm.py

* Update test_processor_markuplm.py

* apply suggestions from code review

* apply suggestions from code review

* apply suggestions from code review

* update BrosSpadeELForTokenClassification head name to entity linker

* add doc string for config params

* update class, var names to more explicit and apply suggestions from code review

* remove unnecessary keys to ignore

* update relation extractor to be initialized with config

* add bros processor

* apply make style and quality

* update bros.md

* remove bros tokenizer, add bros processor that wraps bert tokenizer

* revert change

* apply make fix-copies

* update processor code, update itc -> initial token, stc -> subsequent token

* add type hint

* remove unnecessary condition branches in embedding forward

* fix auto tokenizer fail

* update docstring for each classes

* update bbox input dimension as standard 2 points and convert them to 4
points in forward pass

* update bros docs

* apply suggestions from code review : update Bros -> BROS in bros.md

* 1. box prefix var -> bbox
2. update variable names to be more explicit

* replace einsum with torch matmul

* apply style and quality

* remove unused argument

* remove unused arguments

* update docstrings

* apply suggestions from code review: add BrosBboxEmbeddings, replace
einsum with classical matrix operations

* revert einsum update

* update bros processor

* apply suggestions from code review

* add conversion script for bros

* Apply suggestions from code review

* fix readme

* apply fix-copies

---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

src/transformers/models/bros/modeling_bros.py

+# 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 = "naver-clova-ocr/bros-base-uncased"
+_CONFIG_FOR_DOC = "BrosConfig"
+
+BROS_PRETRAINED_MODEL_ARCHIVE_LIST = [
+    "naver-clova-ocr/bros-base-uncased",
+    "naver-clova-ocr/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("naver-clova-ocr/bros-base-uncased")
+
+        >>> model = BrosModel.from_pretrained("naver-clova-ocr/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")
+
+        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,
+        )
+
+        bbox_position_embeddings = None
+        if bbox is not None:
+            # 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("naver-clova-ocr/bros-base-uncased")
+
+        >>> model = BrosForTokenClassification.from_pretrained("naver-clova-ocr/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("naver-clova-ocr/bros-base-uncased")
+
+        >>> model = BrosSpadeEEForTokenClassification.from_pretrained("naver-clova-ocr/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("naver-clova-ocr/bros-base-uncased")
+
+        >>> model = BrosSpadeELForTokenClassification.from_pretrained("naver-clova-ocr/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,
+        )

src/transformers/models/bros/processing_bros.py

+# coding=utf-8
+# Copyright 2023 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.
+"""
+Processor class for Bros.
+"""
+
+from typing import List, Optional, Union
+
+from ...processing_utils import ProcessorMixin
+from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
+from ...utils import TensorType
+
+
+class BrosProcessor(ProcessorMixin):
+    r"""
+    Constructs a Bros processor which wraps a BERT tokenizer.
+
+    [`BrosProcessor`] offers all the functionalities of [`BertTokenizerFast`]. See the docstring of
+    [`~BrosProcessor.__call__`] and [`~BrosProcessor.decode`] for more information.
+
+    Args:
+        tokenizer (`BertTokenizerFast`):
+            An instance of ['BertTokenizerFast`]. The tokenizer is a required input.
+    """
+    attributes = ["tokenizer"]
+    tokenizer_class = ("BertTokenizer", "BertTokenizerFast")
+
+    def __init__(self, tokenizer=None, **kwargs):
+        if tokenizer is None:
+            raise ValueError("You need to specify a `tokenizer`.")
+
+        super().__init__(tokenizer)
+
+    def __call__(
+        self,
+        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
+        add_special_tokens: bool = True,
+        padding: Union[bool, str, PaddingStrategy] = False,
+        truncation: Union[bool, str, TruncationStrategy] = None,
+        max_length: Optional[int] = None,
+        stride: int = 0,
+        pad_to_multiple_of: Optional[int] = None,
+        return_token_type_ids: Optional[bool] = None,
+        return_attention_mask: Optional[bool] = None,
+        return_overflowing_tokens: bool = False,
+        return_special_tokens_mask: bool = False,
+        return_offsets_mapping: bool = False,
+        return_length: bool = False,
+        verbose: bool = True,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        **kwargs,
+    ) -> BatchEncoding:
+        """
+        This method uses [`BertTokenizerFast.__call__`] to prepare text for the model.
+
+        Please refer to the docstring of the above two methods for more information.
+        """
+        encoding = self.tokenizer(
+            text=text,
+            add_special_tokens=add_special_tokens,
+            padding=padding,
+            truncation=truncation,
+            max_length=max_length,
+            stride=stride,
+            pad_to_multiple_of=pad_to_multiple_of,
+            return_token_type_ids=return_token_type_ids,
+            return_attention_mask=return_attention_mask,
+            return_overflowing_tokens=return_overflowing_tokens,
+            return_special_tokens_mask=return_special_tokens_mask,
+            return_offsets_mapping=return_offsets_mapping,
+            return_length=return_length,
+            verbose=verbose,
+            return_tensors=return_tensors,
+            **kwargs,
+        )
+
+        return encoding
+
+    def batch_decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to BertTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
+        refer to the docstring of this method for more information.
+        """
+        return self.tokenizer.batch_decode(*args, **kwargs)
+
+    def decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to BertTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
+        the docstring of this method for more information.
+        """
+        return self.tokenizer.decode(*args, **kwargs)
+
+    @property
+    def model_input_names(self):
+        tokenizer_input_names = self.tokenizer.model_input_names
+        return list(dict.fromkeys(tokenizer_input_names))

src/transformers/utils/dummy_pt_objects.py

@@ -1604,6 +1604,51 @@ class BridgeTowerPreTrainedModel(metaclass=DummyObject):
         requires_backends(self, ["torch"])
 
 
+BROS_PRETRAINED_MODEL_ARCHIVE_LIST = None
+
+
+class BrosForTokenClassification(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class BrosModel(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class BrosPreTrainedModel(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class BrosProcessor(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class BrosSpadeEEForTokenClassification(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class BrosSpadeELForTokenClassification(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
 
 

tests/models/bros/test_modeling_bros.py

+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" Testing suite for the PyTorch Bros model. """
+
+import copy
+import unittest
+
+from transformers import BrosProcessor
+from transformers.testing_utils import require_torch, slow, torch_device
+from transformers.utils import cached_property, is_torch_available, is_vision_available
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        BrosConfig,
+        BrosForTokenClassification,
+        BrosModel,
+        BrosSpadeEEForTokenClassification,
+        BrosSpadeELForTokenClassification,
+    )
+    from transformers.models.bros.modeling_bros import (
+        BROS_PRETRAINED_MODEL_ARCHIVE_LIST,
+    )
+
+
+class BrosModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=13,
+        seq_length=7,
+        is_training=True,
+        use_input_mask=True,
+        use_token_type_ids=True,
+        use_bbox_first_token_mask=True,
+        use_labels=True,
+        vocab_size=99,
+        hidden_size=64,
+        num_hidden_layers=5,
+        num_attention_heads=4,
+        intermediate_size=37,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.1,
+        max_position_embeddings=512,
+        type_vocab_size=16,
+        type_sequence_label_size=2,
+        initializer_range=0.02,
+        num_labels=3,
+        num_choices=4,
+        scope=None,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.seq_length = seq_length
+        self.is_training = is_training
+        self.use_input_mask = use_input_mask
+        self.use_bbox_first_token_mask = use_bbox_first_token_mask
+        self.use_token_type_ids = use_token_type_ids
+        self.use_labels = use_labels
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.type_vocab_size = type_vocab_size
+        self.type_sequence_label_size = type_sequence_label_size
+        self.initializer_range = initializer_range
+        self.num_labels = num_labels
+
+    def prepare_config_and_inputs(self):
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+
+        bbox = ids_tensor([self.batch_size, self.seq_length, 8], 1)
+        # Ensure that bbox is legal
+        for i in range(bbox.shape[0]):
+            for j in range(bbox.shape[1]):
+                if bbox[i, j, 3] < bbox[i, j, 1]:
+                    t = bbox[i, j, 3]
+                    bbox[i, j, 3] = bbox[i, j, 1]
+                    bbox[i, j, 1] = t
+                if bbox[i, j, 2] < bbox[i, j, 0]:
+                    t = bbox[i, j, 2]
+                    bbox[i, j, 2] = bbox[i, j, 0]
+                    bbox[i, j, 0] = t
+
+        input_mask = None
+        if self.use_input_mask:
+            input_mask = random_attention_mask([self.batch_size, self.seq_length])
+
+        bbox_first_token_mask = None
+        if self.use_bbox_first_token_mask:
+            bbox_first_token_mask = torch.ones([self.batch_size, self.seq_length], dtype=torch.bool).to(torch_device)
+
+        token_type_ids = None
+        if self.use_token_type_ids:
+            token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
+
+        token_labels = None
+        if self.use_labels:
+            token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
+            initial_token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
+            subsequent_token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
+
+        config = self.get_config()
+
+        return (
+            config,
+            input_ids,
+            bbox,
+            token_type_ids,
+            input_mask,
+            bbox_first_token_mask,
+            token_labels,
+            initial_token_labels,
+            subsequent_token_labels,
+        )
+
+    def get_config(self):
+        return BrosConfig(
+            vocab_size=self.vocab_size,
+            hidden_size=self.hidden_size,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            intermediate_size=self.intermediate_size,
+            hidden_act=self.hidden_act,
+            hidden_dropout_prob=self.hidden_dropout_prob,
+            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
+            max_position_embeddings=self.max_position_embeddings,
+            type_vocab_size=self.type_vocab_size,
+            is_decoder=False,
+            initializer_range=self.initializer_range,
+        )
+
+    def create_and_check_model(
+        self,
+        config,
+        input_ids,
+        bbox,
+        token_type_ids,
+        input_mask,
+        bbox_first_token_mask,
+        token_labels,
+        initial_token_labels,
+        subsequent_token_labels,
+    ):
+        model = BrosModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, bbox=bbox, attention_mask=input_mask, token_type_ids=token_type_ids)
+        result = model(input_ids, bbox=bbox, token_type_ids=token_type_ids)
+        result = model(input_ids, bbox=bbox)
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
+
+    def create_and_check_for_token_classification(
+        self,
+        config,
+        input_ids,
+        bbox,
+        token_type_ids,
+        input_mask,
+        bbox_first_token_mask,
+        token_labels,
+        initial_token_labels,
+        subsequent_token_labels,
+    ):
+        config.num_labels = self.num_labels
+        model = BrosForTokenClassification(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(
+            input_ids, bbox=bbox, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels
+        )
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
+
+    def create_and_check_for_spade_ee_token_classification(
+        self,
+        config,
+        input_ids,
+        bbox,
+        token_type_ids,
+        input_mask,
+        bbox_first_token_mask,
+        token_labels,
+        initial_token_labels,
+        subsequent_token_labels,
+    ):
+        config.num_labels = self.num_labels
+        model = BrosSpadeEEForTokenClassification(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(
+            input_ids,
+            bbox=bbox,
+            attention_mask=input_mask,
+            bbox_first_token_mask=bbox_first_token_mask,
+            token_type_ids=token_type_ids,
+            initial_token_labels=token_labels,
+            subsequent_token_labels=token_labels,
+        )
+        self.parent.assertEqual(result.initial_token_logits.shape, (self.batch_size, self.seq_length, self.num_labels))
+        self.parent.assertEqual(
+            result.subsequent_token_logits.shape, (self.batch_size, self.seq_length, self.seq_length + 1)
+        )
+
+    def create_and_check_for_spade_el_token_classification(
+        self,
+        config,
+        input_ids,
+        bbox,
+        token_type_ids,
+        input_mask,
+        bbox_first_token_mask,
+        token_labels,
+        initial_token_labels,
+        subsequent_token_labels,
+    ):
+        config.num_labels = self.num_labels
+        model = BrosSpadeELForTokenClassification(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(
+            input_ids,
+            bbox=bbox,
+            attention_mask=input_mask,
+            bbox_first_token_mask=bbox_first_token_mask,
+            token_type_ids=token_type_ids,
+            labels=token_labels,
+        )
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.seq_length + 1))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        (
+            config,
+            input_ids,
+            bbox,
+            token_type_ids,
+            input_mask,
+            bbox_first_token_mask,
+            token_labels,
+            initial_token_labels,
+            subsequent_token_labels,
+        ) = config_and_inputs
+        inputs_dict = {
+            "input_ids": input_ids,
+            "bbox": bbox,
+            "token_type_ids": token_type_ids,
+            "attention_mask": input_mask,
+        }
+        return config, inputs_dict
+
+
+@require_torch
+class BrosModelTest(ModelTesterMixin, unittest.TestCase):
+    test_pruning = False
+    test_torchscript = False
+    test_mismatched_shapes = False
+
+    all_model_classes = (
+        (
+            BrosForTokenClassification,
+            BrosSpadeEEForTokenClassification,
+            BrosSpadeELForTokenClassification,
+            BrosModel,
+        )
+        if is_torch_available()
+        else ()
+    )
+    all_generative_model_classes = () if is_torch_available() else ()
+
+    def setUp(self):
+        self.model_tester = BrosModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=BrosConfig, hidden_size=37)
+
+    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
+        inputs_dict = copy.deepcopy(inputs_dict)
+
+        if return_labels:
+            if model_class.__name__ in ["BrosForTokenClassification", "BrosSpadeELForTokenClassification"]:
+                inputs_dict["labels"] = torch.zeros(
+                    (self.model_tester.batch_size, self.model_tester.seq_length),
+                    dtype=torch.long,
+                    device=torch_device,
+                )
+                inputs_dict["bbox_first_token_mask"] = torch.ones(
+                    [self.model_tester.batch_size, self.model_tester.seq_length],
+                    dtype=torch.bool,
+                    device=torch_device,
+                )
+            elif model_class.__name__ in ["BrosSpadeEEForTokenClassification"]:
+                inputs_dict["initial_token_labels"] = torch.zeros(
+                    (self.model_tester.batch_size, self.model_tester.seq_length),
+                    dtype=torch.long,
+                    device=torch_device,
+                )
+                inputs_dict["subsequent_token_labels"] = torch.zeros(
+                    (self.model_tester.batch_size, self.model_tester.seq_length),
+                    dtype=torch.long,
+                    device=torch_device,
+                )
+                inputs_dict["bbox_first_token_mask"] = torch.ones(
+                    [self.model_tester.batch_size, self.model_tester.seq_length],
+                    dtype=torch.bool,
+                    device=torch_device,
+                )
+
+        return inputs_dict
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    def test_multi_gpu_data_parallel_forward(self):
+        super().test_multi_gpu_data_parallel_forward()
+
+    def test_model_various_embeddings(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        for type in ["absolute", "relative_key", "relative_key_query"]:
+            config_and_inputs[0].position_embedding_type = type
+            self.model_tester.create_and_check_model(*config_and_inputs)
+
+    def test_for_token_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
+
+    def test_for_spade_ee_token_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_spade_ee_token_classification(*config_and_inputs)
+
+    def test_for_spade_el_token_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_spade_el_token_classification(*config_and_inputs)
+
+    @slow
+    def test_model_from_pretrained(self):
+        for model_name in BROS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
+            model = BrosModel.from_pretrained(model_name)
+            self.assertIsNotNone(model)
+
+
+def prepare_bros_batch_inputs():
+    attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
+
+    bbox = torch.tensor(
+        [
+            [
+                [0.0000, 0.0000, 0.0000, 0.0000],
+                [0.5223, 0.5590, 0.5787, 0.5720],
+                [0.5853, 0.5590, 0.6864, 0.5720],
+                [0.5853, 0.5590, 0.6864, 0.5720],
+                [0.1234, 0.5700, 0.2192, 0.5840],
+                [0.2231, 0.5680, 0.2782, 0.5780],
+                [0.2874, 0.5670, 0.3333, 0.5780],
+                [0.3425, 0.5640, 0.4344, 0.5750],
+                [0.0866, 0.7770, 0.1181, 0.7870],
+                [0.1168, 0.7770, 0.1522, 0.7850],
+                [0.1535, 0.7750, 0.1864, 0.7850],
+                [0.1890, 0.7750, 0.2572, 0.7850],
+                [1.0000, 1.0000, 1.0000, 1.0000],
+            ],
+            [
+                [0.0000, 0.0000, 0.0000, 0.0000],
+                [0.4396, 0.6720, 0.4659, 0.6850],
+                [0.4698, 0.6720, 0.4843, 0.6850],
+                [0.1575, 0.6870, 0.2021, 0.6980],
+                [0.2047, 0.6870, 0.2730, 0.7000],
+                [0.1299, 0.7010, 0.1430, 0.7140],
+                [0.1299, 0.7010, 0.1430, 0.7140],
+                [0.1562, 0.7010, 0.2441, 0.7120],
+                [0.1562, 0.7010, 0.2441, 0.7120],
+                [0.2454, 0.7010, 0.3150, 0.7120],
+                [0.3176, 0.7010, 0.3320, 0.7110],
+                [0.3333, 0.7000, 0.4029, 0.7140],
+                [1.0000, 1.0000, 1.0000, 1.0000],
+            ],
+        ]
+    )
+    input_ids = torch.tensor(
+        [
+            [101, 1055, 8910, 1012, 5719, 3296, 5366, 3378, 2146, 2846, 10807, 13494, 102],
+            [101, 2112, 1997, 3671, 6364, 1019, 1012, 5057, 1011, 4646, 2030, 2974, 102],
+        ]
+    )
+
+    return input_ids, bbox, attention_mask
+
+
+@require_torch
+class BrosModelIntegrationTest(unittest.TestCase):
+    @cached_property
+    def default_processor(self):
+        return BrosProcessor.from_pretrained("naver-clova-ocr/bros-base-uncased") if is_vision_available() else None
+
+    @slow
+    def test_inference_no_head(self):
+        model = BrosModel.from_pretrained("naver-clova-ocr/bros-base-uncased").to(torch_device)
+        input_ids, bbox, attention_mask = prepare_bros_batch_inputs()
+
+        with torch.no_grad():
+            outputs = model(
+                input_ids.to(torch_device),
+                bbox.to(torch_device),
+                attention_mask=attention_mask.to(torch_device),
+                return_dict=True,
+            )
+
+        # verify the logits
+        expected_shape = torch.Size((2, 13, 768))
+        self.assertEqual(outputs.last_hidden_state.shape, expected_shape)
+
+        expected_slice = torch.tensor(
+            [[-0.4027, 0.0756, -0.0647], [-0.0192, -0.0065, 0.1042], [-0.0671, 0.0214, 0.0960]]
+        ).to(torch_device)
+
+        self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-4))

tests/test_modeling_common.py

@@ -728,6 +728,13 @@ class ModelTesterMixin:
                     traced_model = torch.jit.trace(
                         model, (input_ids, bbox, image), check_trace=False
                     )  # when traced model is checked, an error is produced due to name mangling
+                elif "bbox" in inputs:  # Bros requires additional inputs (bbox)
+                    input_ids = inputs["input_ids"]
+                    bbox = inputs["bbox"]
+                    model(input_ids, bbox)
+                    traced_model = torch.jit.trace(
+                        model, (input_ids, bbox), check_trace=False
+                    )  # when traced model is checked, an error is produced due to name mangling
                 else:
                     main_input = inputs[main_input_name]
                     model(main_input)

utils/check_repo.py

@@ -158,6 +158,8 @@ IGNORE_NON_AUTO_CONFIGURED = PRIVATE_MODELS.copy() + [
     "BlipVisionModel",
     "BlipTextLMHeadModel",
     "BlipTextModel",
+    "BrosSpadeEEForTokenClassification",
+    "BrosSpadeELForTokenClassification",
     "TFBlipForConditionalGeneration",
     "TFBlipForImageTextRetrieval",
     "TFBlipForQuestionAnswering",

utils/documentation_tests.txt

@@ -77,6 +77,7 @@ src/transformers/models/bloom/configuration_bloom.py
 src/transformers/models/bloom/tokenization_bloom_fast.py
 src/transformers/models/bridgetower/image_processing_bridgetower.py
 src/transformers/models/bridgetower/processing_bridgetower.py
+src/transformers/models/bros/processing_bros.py
 src/transformers/models/byt5/tokenization_byt5.py
 src/transformers/models/camembert/configuration_camembert.py
 src/transformers/models/camembert/tokenization_camembert.py