# 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 2018 The Microsoft Research Asia LayoutLM Team Authors, 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 LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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 (
        LayoutLMForMaskedLM,
        LayoutLMForQuestionAnswering,
        LayoutLMForSequenceClassification,
        LayoutLMForTokenClassification,
        LayoutLMModel,
    )


class LayoutLMModelTester:
    """You can also import this e.g from .test_modeling_layoutlm import LayoutLMModelTester"""

    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,
        num_choices=4,
        scope=None,
        range_bbox=1000,
    ):
        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.num_choices = num_choices
        self.scope = scope
        self.range_bbox = range_bbox

    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, 4], self.range_bbox)
        # 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 = 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
        choice_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)
            choice_labels = ids_tensor([self.batch_size], self.num_choices)

        config = self.get_config()

        return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

    def get_config(self):
        return LayoutLMConfig(
            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,
        )

    def create_and_check_model(
        self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        model = LayoutLMModel(config=config)
        model.to(torch_device)
        model.eval()
        result = model(input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids)
        result = model(input_ids, bbox, token_type_ids=token_type_ids)
        result = model(input_ids, bbox)
        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_masked_lm(
        self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        model = LayoutLMForMaskedLM(config=config)
        model.to(torch_device)
        model.eval()
        result = model(input_ids, 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.vocab_size))

    def create_and_check_for_sequence_classification(
        self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        config.num_labels = self.num_labels
        model = LayoutLMForSequenceClassification(config)
        model.to(torch_device)
        model.eval()
        result = model(
            input_ids, bbox, 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, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        config.num_labels = self.num_labels
        model = LayoutLMForTokenClassification(config=config)
        model.to(torch_device)
        model.eval()
        result = model(input_ids, 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_question_answering(
        self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        model = LayoutLMForQuestionAnswering(config=config)
        model.to(torch_device)
        model.eval()
        result = model(
            input_ids,
            bbox=bbox,
            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,
            bbox,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_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 LayoutLMModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (
        (
            LayoutLMModel,
            LayoutLMForMaskedLM,
            LayoutLMForSequenceClassification,
            LayoutLMForTokenClassification,
            LayoutLMForQuestionAnswering,
        )
        if is_torch_available()
        else None
    )
    pipeline_model_mapping = (
        {
            "document-question-answering": LayoutLMForQuestionAnswering,
            "feature-extraction": LayoutLMModel,
            "fill-mask": LayoutLMForMaskedLM,
            "text-classification": LayoutLMForSequenceClassification,
            "token-classification": LayoutLMForTokenClassification,
            "zero-shot": LayoutLMForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    fx_compatible = True

    def setUp(self):
        self.model_tester = LayoutLMModelTester(self)
        self.config_tester = ConfigTester(self, config_class=LayoutLMConfig, 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_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_masked_lm(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*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)

    @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
    def test_model_is_small(self):
        pass


def prepare_layoutlm_batch_inputs():
    # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
    # fmt: off
    input_ids = torch.tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]],device=torch_device)  # noqa: E231
    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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],],device=torch_device)  # noqa: E231
    bbox = torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]],device=torch_device)  # noqa: E231
    token_type_ids = torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]],device=torch_device)  # noqa: E231
    # these are sequence labels (i.e. at the token level)
    labels = torch.tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]],device=torch_device)  # noqa: E231
    # fmt: on

    return input_ids, attention_mask, bbox, token_type_ids, labels


@require_torch
class LayoutLMModelIntegrationTest(unittest.TestCase):
    @slow
    def test_forward_pass_no_head(self):
        model = LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased").to(torch_device)

        input_ids, attention_mask, bbox, token_type_ids, labels = prepare_layoutlm_batch_inputs()

        # forward pass
        outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids)

        # test the sequence output on [0, :3, :3]
        expected_slice = torch.tensor(
            [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]],
            device=torch_device,
        )

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

        # test the pooled output on [1, :3]
        expected_slice = torch.tensor([-0.6580, -0.0214, 0.8552], device=torch_device)

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

    @slow
    def test_forward_pass_sequence_classification(self):
        # initialize model with randomly initialized sequence classification head
        model = LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased", num_labels=2).to(
            torch_device
        )

        input_ids, attention_mask, bbox, token_type_ids, _ = prepare_layoutlm_batch_inputs()

        # forward pass
        outputs = model(
            input_ids=input_ids,
            bbox=bbox,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            labels=torch.tensor([1, 1], device=torch_device),
        )

        # test whether we get a loss as a scalar
        loss = outputs.loss
        expected_shape = torch.Size([])
        self.assertEqual(loss.shape, expected_shape)

        # test the shape of the logits
        logits = outputs.logits
        expected_shape = torch.Size((2, 2))
        self.assertEqual(logits.shape, expected_shape)

    @slow
    def test_forward_pass_token_classification(self):
        # initialize model with randomly initialized token classification head
        model = LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased", num_labels=13).to(
            torch_device
        )

        input_ids, attention_mask, bbox, token_type_ids, labels = prepare_layoutlm_batch_inputs()

        # forward pass
        outputs = model(
            input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids, labels=labels
        )

        # test the loss calculation to be around 2.65
        # expected_loss = torch.tensor(2.65, device=torch_device)

        # The loss is currently somewhat random and can vary between 0.1-0.3 atol.
        # self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

        # test the shape of the logits
        logits = outputs.logits
        expected_shape = torch.Size((2, 25, 13))
        self.assertEqual(logits.shape, expected_shape)

    @slow
    def test_forward_pass_question_answering(self):
        # initialize model with randomly initialized token classification head
        model = LayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased").to(torch_device)

        input_ids, attention_mask, bbox, token_type_ids, labels = prepare_layoutlm_batch_inputs()

        # forward pass
        outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids)

        # test the shape of the logits
        expected_shape = torch.Size((2, 25))
        self.assertEqual(outputs.start_logits.shape, expected_shape)
        self.assertEqual(outputs.end_logits.shape, expected_shape)

# coding=utf-8
# Copyright 2018 The Microsoft Research Asia LayoutLM Team Authors, 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 LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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 (
        LayoutLMForMaskedLM,
        LayoutLMForQuestionAnswering,
        LayoutLMForSequenceClassification,
        LayoutLMForTokenClassification,
        LayoutLMModel,
    )


class LayoutLMModelTester:
    """You can also import this e.g from .test_modeling_layoutlm import LayoutLMModelTester"""

    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,
        num_choices=4,
        scope=None,
        range_bbox=1000,
    ):
        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.num_choices = num_choices
        self.scope = scope
        self.range_bbox = range_bbox

    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, 4], self.range_bbox)
        # 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 = 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
        choice_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)
            choice_labels = ids_tensor([self.batch_size], self.num_choices)

        config = self.get_config()

        return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

    def get_config(self):
        return LayoutLMConfig(
            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,
        )

    def create_and_check_model(
        self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        model = LayoutLMModel(config=config)
        model.to(torch_device)
        model.eval()
        result = model(input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids)
        result = model(input_ids, bbox, token_type_ids=token_type_ids)
        result = model(input_ids, bbox)
        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_masked_lm(
        self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        model = LayoutLMForMaskedLM(config=config)
        model.to(torch_device)
        model.eval()
        result = model(input_ids, 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.vocab_size))

    def create_and_check_for_sequence_classification(
        self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        config.num_labels = self.num_labels
        model = LayoutLMForSequenceClassification(config)
        model.to(torch_device)
        model.eval()
        result = model(
            input_ids, bbox, 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, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        config.num_labels = self.num_labels
        model = LayoutLMForTokenClassification(config=config)
        model.to(torch_device)
        model.eval()
        result = model(input_ids, 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_question_answering(
        self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        model = LayoutLMForQuestionAnswering(config=config)
        model.to(torch_device)
        model.eval()
        result = model(
            input_ids,
            bbox=bbox,
            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,
            bbox,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_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 LayoutLMModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (
        (
            LayoutLMModel,
            LayoutLMForMaskedLM,
            LayoutLMForSequenceClassification,
            LayoutLMForTokenClassification,
            LayoutLMForQuestionAnswering,
        )
        if is_torch_available()
        else None
    )
    pipeline_model_mapping = (
        {
            "document-question-answering": LayoutLMForQuestionAnswering,
            "feature-extraction": LayoutLMModel,
            "fill-mask": LayoutLMForMaskedLM,
            "text-classification": LayoutLMForSequenceClassification,
            "token-classification": LayoutLMForTokenClassification,
            "zero-shot": LayoutLMForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    fx_compatible = True

    def setUp(self):
        self.model_tester = LayoutLMModelTester(self)
        self.config_tester = ConfigTester(self, config_class=LayoutLMConfig, 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_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_masked_lm(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*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_layoutlm_batch_inputs():
    # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
    # fmt: off
    input_ids = torch.tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]],device=torch_device)  # noqa: E231
    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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],],device=torch_device)  # noqa: E231
    bbox = torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]],device=torch_device)  # noqa: E231
    token_type_ids = torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]],device=torch_device)  # noqa: E231
    # these are sequence labels (i.e. at the token level)
    labels = torch.tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]],device=torch_device)  # noqa: E231
    # fmt: on

    return input_ids, attention_mask, bbox, token_type_ids, labels


@require_torch
class LayoutLMModelIntegrationTest(unittest.TestCase):
    @slow
    def test_forward_pass_no_head(self):
        model = LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased").to(torch_device)

        input_ids, attention_mask, bbox, token_type_ids, labels = prepare_layoutlm_batch_inputs()

        # forward pass
        outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids)

        # test the sequence output on [0, :3, :3]
        expected_slice = torch.tensor(
            [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]],
            device=torch_device,
        )

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

        # test the pooled output on [1, :3]
        expected_slice = torch.tensor([-0.6580, -0.0214, 0.8552], device=torch_device)

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

    @slow
    def test_forward_pass_sequence_classification(self):
        # initialize model with randomly initialized sequence classification head
        model = LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased", num_labels=2).to(
            torch_device
        )

        input_ids, attention_mask, bbox, token_type_ids, _ = prepare_layoutlm_batch_inputs()

        # forward pass
        outputs = model(
            input_ids=input_ids,
            bbox=bbox,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            labels=torch.tensor([1, 1], device=torch_device),
        )

        # test whether we get a loss as a scalar
        loss = outputs.loss
        expected_shape = torch.Size([])
        self.assertEqual(loss.shape, expected_shape)

        # test the shape of the logits
        logits = outputs.logits
        expected_shape = torch.Size((2, 2))
        self.assertEqual(logits.shape, expected_shape)

    @slow
    def test_forward_pass_token_classification(self):
        # initialize model with randomly initialized token classification head
        model = LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased", num_labels=13).to(
            torch_device
        )

        input_ids, attention_mask, bbox, token_type_ids, labels = prepare_layoutlm_batch_inputs()

        # forward pass
        outputs = model(
            input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids, labels=labels
        )

        # test the loss calculation to be around 2.65
        # expected_loss = torch.tensor(2.65, device=torch_device)

        # The loss is currently somewhat random and can vary between 0.1-0.3 atol.
        # self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

        # test the shape of the logits
        logits = outputs.logits
        expected_shape = torch.Size((2, 25, 13))
        self.assertEqual(logits.shape, expected_shape)

    @slow
    def test_forward_pass_question_answering(self):
        # initialize model with randomly initialized token classification head
        model = LayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased").to(torch_device)

        input_ids, attention_mask, bbox, token_type_ids, labels = prepare_layoutlm_batch_inputs()

        # forward pass
        outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids)

        # test the shape of the logits
        expected_shape = torch.Size((2, 25))
        self.assertEqual(outputs.start_logits.shape, expected_shape)
        self.assertEqual(outputs.end_logits.shape, expected_shape)
