# What does this PR do?

This PR fixes the last batch of models that are too big for common tests. After it, only two classes override the common test to skip it:
- timm-backbone
- layoutlmv2
In the first case, it's because there is no timm model small enough to work (I did switch to the smallest resnet though) and in the second detectron2 does not let us configure a smaller backbone (I did try with the constant exposed by they don't seem to have any effect).
Fix last models for common tests that are too big.. # What does this PR do?

This PR fixes the last batch of models that are too big for common tests. After it, only two classes override the common test to skip it:
- timm-backbone
- layoutlmv2
In the first case, it's because there is no timm model small enough to work (I did switch to the smallest resnet though) and in the second detectron2 does not let us configure a smaller backbone (I did try with the constant exposed by they don't seem to have any effect).
# coding=utf-8
# Copyright 2022 The Hugging Face 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.


import unittest

from transformers import MarkupLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        MarkupLMForQuestionAnswering,
        MarkupLMForSequenceClassification,
        MarkupLMForTokenClassification,
        MarkupLMModel,
    )

# TODO check dependencies
from transformers import MarkupLMFeatureExtractor, MarkupLMProcessor, MarkupLMTokenizer


class MarkupLMModelTester:
    """You can also import this e.g from .test_modeling_markuplm import MarkupLMModelTester"""

    def __init__(
        self,
        parent,
        batch_size=13,
        seq_length=7,
        is_training=True,
        use_input_mask=True,
        use_token_type_ids=True,
        use_labels=True,
        vocab_size=99,
        hidden_size=32,
        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,
        scope=None,
        max_xpath_tag_unit_embeddings=20,
        max_xpath_subs_unit_embeddings=30,
        tag_pad_id=2,
        subs_pad_id=2,
        max_depth=10,
    ):
        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_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
        self.scope = scope
        self.max_xpath_tag_unit_embeddings = max_xpath_tag_unit_embeddings
        self.max_xpath_subs_unit_embeddings = max_xpath_subs_unit_embeddings
        self.tag_pad_id = tag_pad_id
        self.subs_pad_id = subs_pad_id
        self.max_depth = max_depth

    def prepare_config_and_inputs(self):
        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)

        xpath_tags_seq = ids_tensor(
            [self.batch_size, self.seq_length, self.max_depth], self.max_xpath_tag_unit_embeddings
        )

        xpath_subs_seq = ids_tensor(
            [self.batch_size, self.seq_length, self.max_depth], self.max_xpath_subs_unit_embeddings
        )

        input_mask = None
        if self.use_input_mask:
            input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)

        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)

        sequence_labels = None
        token_labels = None
        if self.use_labels:
            sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
            token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)

        config = self.get_config()

        return (
            config,
            input_ids,
            xpath_tags_seq,
            xpath_subs_seq,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
        )

    def get_config(self):
        return MarkupLMConfig(
            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,
            initializer_range=self.initializer_range,
            max_xpath_tag_unit_embeddings=self.max_xpath_tag_unit_embeddings,
            max_xpath_subs_unit_embeddings=self.max_xpath_subs_unit_embeddings,
            tag_pad_id=self.tag_pad_id,
            subs_pad_id=self.subs_pad_id,
            max_depth=self.max_depth,
        )

    def create_and_check_model(
        self,
        config,
        input_ids,
        xpath_tags_seq,
        xpath_subs_seq,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
    ):
        model = MarkupLMModel(config=config)
        model.to(torch_device)
        model.eval()
        print("Configs:", model.config.tag_pad_id, model.config.subs_pad_id)
        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
        result = model(input_ids, token_type_ids=token_type_ids)
        result = model(input_ids)
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))

    def create_and_check_for_sequence_classification(
        self,
        config,
        input_ids,
        xpath_tags_seq,
        xpath_subs_seq,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
    ):
        config.num_labels = self.num_labels
        model = MarkupLMForSequenceClassification(config)
        model.to(torch_device)
        model.eval()
        result = model(
            input_ids,
            xpath_tags_seq=xpath_tags_seq,
            xpath_subs_seq=xpath_subs_seq,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
            labels=sequence_labels,
        )
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))

    def create_and_check_for_token_classification(
        self,
        config,
        input_ids,
        xpath_tags_seq,
        xpath_subs_seq,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
    ):
        config.num_labels = self.num_labels
        model = MarkupLMForTokenClassification(config=config)
        model.to(torch_device)
        model.eval()
        result = model(
            input_ids,
            xpath_tags_seq=xpath_tags_seq,
            xpath_subs_seq=xpath_subs_seq,
            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_question_answering(
        self,
        config,
        input_ids,
        xpath_tags_seq,
        xpath_subs_seq,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
    ):
        model = MarkupLMForQuestionAnswering(config=config)
        model.to(torch_device)
        model.eval()
        result = model(
            input_ids,
            xpath_tags_seq=xpath_tags_seq,
            xpath_subs_seq=xpath_subs_seq,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
            start_positions=sequence_labels,
            end_positions=sequence_labels,
        )
        self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
        self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        (
            config,
            input_ids,
            xpath_tags_seq,
            xpath_subs_seq,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
        ) = config_and_inputs
        inputs_dict = {
            "input_ids": input_ids,
            "xpath_tags_seq": xpath_tags_seq,
            "xpath_subs_seq": xpath_subs_seq,
            "token_type_ids": token_type_ids,
            "attention_mask": input_mask,
        }
        return config, inputs_dict


@require_torch
class MarkupLMModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (
        (
            MarkupLMModel,
            MarkupLMForSequenceClassification,
            MarkupLMForTokenClassification,
            MarkupLMForQuestionAnswering,
        )
        if is_torch_available()
        else None
    )
    pipeline_model_mapping = (
        {
            "feature-extraction": MarkupLMModel,
            "question-answering": MarkupLMForQuestionAnswering,
            "text-classification": MarkupLMForSequenceClassification,
            "token-classification": MarkupLMForTokenClassification,
            "zero-shot": MarkupLMForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )

    # TODO: Fix the failed tests
    def is_pipeline_test_to_skip(
        self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
    ):
        # ValueError: Nodes must be of type `List[str]` (single pretokenized example), or `List[List[str]]`
        # (batch of pretokenized examples).
        return True

    def setUp(self):
        self.model_tester = MarkupLMModelTester(self)
        self.config_tester = ConfigTester(self, config_class=MarkupLMConfig, hidden_size=37)

    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_for_sequence_classification(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*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_question_answering(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*config_and_inputs)


def prepare_html_string():
    html_string = """
    <!DOCTYPE html>
    <html>
    <head>
    <title>Page Title</title>
    </head>
    <body>

    <h1>This is a Heading</h1>
    <p>This is a paragraph.</p>

    </body>
    </html>
    """

    return html_string


@require_torch
class MarkupLMModelIntegrationTest(unittest.TestCase):
    @cached_property
    def default_processor(self):
        # TODO use from_pretrained here
        feature_extractor = MarkupLMFeatureExtractor()
        tokenizer = MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base")

        return MarkupLMProcessor(feature_extractor, tokenizer)

    @slow
    def test_forward_pass_no_head(self):
        model = MarkupLMModel.from_pretrained("microsoft/markuplm-base").to(torch_device)

        processor = self.default_processor

        inputs = processor(prepare_html_string(), return_tensors="pt")
        inputs = inputs.to(torch_device)

        # forward pass
        with torch.no_grad():
            outputs = model(**inputs)

        # verify the last hidden states
        expected_shape = torch.Size([1, 14, 768])
        self.assertEqual(outputs.last_hidden_state.shape, expected_shape)

        expected_slice = torch.tensor(
            [[0.0675, -0.0052, 0.5001], [-0.2281, 0.0802, 0.2192], [-0.0583, -0.3311, 0.1185]]
        ).to(torch_device)

        self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-4))

# coding=utf-8
# Copyright 2022 The Hugging Face 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.


import unittest

from transformers import MarkupLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        MarkupLMForQuestionAnswering,
        MarkupLMForSequenceClassification,
        MarkupLMForTokenClassification,
        MarkupLMModel,
    )

# TODO check dependencies
from transformers import MarkupLMFeatureExtractor, MarkupLMProcessor, MarkupLMTokenizer


class MarkupLMModelTester:
    """You can also import this e.g from .test_modeling_markuplm import MarkupLMModelTester"""

    def __init__(
        self,
        parent,
        batch_size=13,
        seq_length=7,
        is_training=True,
        use_input_mask=True,
        use_token_type_ids=True,
        use_labels=True,
        vocab_size=99,
        hidden_size=32,
        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,
        scope=None,
        max_xpath_tag_unit_embeddings=20,
        max_xpath_subs_unit_embeddings=30,
        tag_pad_id=2,
        subs_pad_id=2,
        max_depth=10,
    ):
        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_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
        self.scope = scope
        self.max_xpath_tag_unit_embeddings = max_xpath_tag_unit_embeddings
        self.max_xpath_subs_unit_embeddings = max_xpath_subs_unit_embeddings
        self.tag_pad_id = tag_pad_id
        self.subs_pad_id = subs_pad_id
        self.max_depth = max_depth

    def prepare_config_and_inputs(self):
        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)

        xpath_tags_seq = ids_tensor(
            [self.batch_size, self.seq_length, self.max_depth], self.max_xpath_tag_unit_embeddings
        )

        xpath_subs_seq = ids_tensor(
            [self.batch_size, self.seq_length, self.max_depth], self.max_xpath_subs_unit_embeddings
        )

        input_mask = None
        if self.use_input_mask:
            input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)

        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)

        sequence_labels = None
        token_labels = None
        if self.use_labels:
            sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
            token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)

        config = self.get_config()

        return (
            config,
            input_ids,
            xpath_tags_seq,
            xpath_subs_seq,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
        )

    def get_config(self):
        return MarkupLMConfig(
            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,
            initializer_range=self.initializer_range,
            max_xpath_tag_unit_embeddings=self.max_xpath_tag_unit_embeddings,
            max_xpath_subs_unit_embeddings=self.max_xpath_subs_unit_embeddings,
            tag_pad_id=self.tag_pad_id,
            subs_pad_id=self.subs_pad_id,
            max_depth=self.max_depth,
        )

    def create_and_check_model(
        self,
        config,
        input_ids,
        xpath_tags_seq,
        xpath_subs_seq,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
    ):
        model = MarkupLMModel(config=config)
        model.to(torch_device)
        model.eval()
        print("Configs:", model.config.tag_pad_id, model.config.subs_pad_id)
        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
        result = model(input_ids, token_type_ids=token_type_ids)
        result = model(input_ids)
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))

    def create_and_check_for_sequence_classification(
        self,
        config,
        input_ids,
        xpath_tags_seq,
        xpath_subs_seq,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
    ):
        config.num_labels = self.num_labels
        model = MarkupLMForSequenceClassification(config)
        model.to(torch_device)
        model.eval()
        result = model(
            input_ids,
            xpath_tags_seq=xpath_tags_seq,
            xpath_subs_seq=xpath_subs_seq,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
            labels=sequence_labels,
        )
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))

    def create_and_check_for_token_classification(
        self,
        config,
        input_ids,
        xpath_tags_seq,
        xpath_subs_seq,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
    ):
        config.num_labels = self.num_labels
        model = MarkupLMForTokenClassification(config=config)
        model.to(torch_device)
        model.eval()
        result = model(
            input_ids,
            xpath_tags_seq=xpath_tags_seq,
            xpath_subs_seq=xpath_subs_seq,
            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_question_answering(
        self,
        config,
        input_ids,
        xpath_tags_seq,
        xpath_subs_seq,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
    ):
        model = MarkupLMForQuestionAnswering(config=config)
        model.to(torch_device)
        model.eval()
        result = model(
            input_ids,
            xpath_tags_seq=xpath_tags_seq,
            xpath_subs_seq=xpath_subs_seq,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
            start_positions=sequence_labels,
            end_positions=sequence_labels,
        )
        self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
        self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        (
            config,
            input_ids,
            xpath_tags_seq,
            xpath_subs_seq,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
        ) = config_and_inputs
        inputs_dict = {
            "input_ids": input_ids,
            "xpath_tags_seq": xpath_tags_seq,
            "xpath_subs_seq": xpath_subs_seq,
            "token_type_ids": token_type_ids,
            "attention_mask": input_mask,
        }
        return config, inputs_dict


@require_torch
class MarkupLMModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (
        (
            MarkupLMModel,
            MarkupLMForSequenceClassification,
            MarkupLMForTokenClassification,
            MarkupLMForQuestionAnswering,
        )
        if is_torch_available()
        else None
    )
    pipeline_model_mapping = (
        {
            "feature-extraction": MarkupLMModel,
            "question-answering": MarkupLMForQuestionAnswering,
            "text-classification": MarkupLMForSequenceClassification,
            "token-classification": MarkupLMForTokenClassification,
            "zero-shot": MarkupLMForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )

    # TODO: Fix the failed tests
    def is_pipeline_test_to_skip(
        self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
    ):
        # ValueError: Nodes must be of type `List[str]` (single pretokenized example), or `List[List[str]]`
        # (batch of pretokenized examples).
        return True

    def setUp(self):
        self.model_tester = MarkupLMModelTester(self)
        self.config_tester = ConfigTester(self, config_class=MarkupLMConfig, hidden_size=37)

    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_for_sequence_classification(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*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_question_answering(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*config_and_inputs)


def prepare_html_string():
    html_string = """
    <!DOCTYPE html>
    <html>
    <head>
    <title>Page Title</title>
    </head>
    <body>

    <h1>This is a Heading</h1>
    <p>This is a paragraph.</p>

    </body>
    </html>
    """

    return html_string


@require_torch
class MarkupLMModelIntegrationTest(unittest.TestCase):
    @cached_property
    def default_processor(self):
        # TODO use from_pretrained here
        feature_extractor = MarkupLMFeatureExtractor()
        tokenizer = MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base")

        return MarkupLMProcessor(feature_extractor, tokenizer)

    @slow
    def test_forward_pass_no_head(self):
        model = MarkupLMModel.from_pretrained("microsoft/markuplm-base").to(torch_device)

        processor = self.default_processor

        inputs = processor(prepare_html_string(), return_tensors="pt")
        inputs = inputs.to(torch_device)

        # forward pass
        with torch.no_grad():
            outputs = model(**inputs)

        # verify the last hidden states
        expected_shape = torch.Size([1, 14, 768])
        self.assertEqual(outputs.last_hidden_state.shape, expected_shape)

        expected_slice = torch.tensor(
            [[0.0675, -0.0052, 0.5001], [-0.2281, 0.0802, 0.2192], [-0.0583, -0.3311, 0.1185]]
        ).to(torch_device)

        self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-4))
