# 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 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 TensorFlow LayoutLMv3 model. """

from __future__ import annotations

import copy
import inspect
import unittest

import numpy as np

from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import (
        TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
        TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
        TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
        TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
        LayoutLMv3Config,
        TFLayoutLMv3ForQuestionAnswering,
        TFLayoutLMv3ForSequenceClassification,
        TFLayoutLMv3ForTokenClassification,
        TFLayoutLMv3Model,
    )

if is_vision_available():
    from PIL import Image

    from transformers import LayoutLMv3ImageProcessor


class TFLayoutLMv3ModelTester:
    def __init__(
        self,
        parent,
        batch_size=2,
        num_channels=3,
        image_size=4,
        patch_size=2,
        text_seq_length=7,
        is_training=True,
        use_input_mask=True,
        use_token_type_ids=True,
        use_labels=True,
        vocab_size=99,
        hidden_size=36,
        num_hidden_layers=2,
        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,
        coordinate_size=6,
        shape_size=6,
        num_labels=3,
        num_choices=4,
        scope=None,
        range_bbox=1000,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.num_channels = num_channels
        self.image_size = image_size
        self.patch_size = patch_size
        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.coordinate_size = coordinate_size
        self.shape_size = shape_size
        self.num_labels = num_labels
        self.num_choices = num_choices
        self.scope = scope
        self.range_bbox = range_bbox

        # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
        self.text_seq_length = text_seq_length
        self.image_seq_length = (image_size // patch_size) ** 2 + 1
        self.seq_length = self.text_seq_length + self.image_seq_length

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

        bbox = ids_tensor([self.batch_size, self.text_seq_length, 4], self.range_bbox)
        bbox = bbox.numpy()
        # 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]:
                    tmp_coordinate = bbox[i, j, 3]
                    bbox[i, j, 3] = bbox[i, j, 1]
                    bbox[i, j, 1] = tmp_coordinate
                if bbox[i, j, 2] < bbox[i, j, 0]:
                    tmp_coordinate = bbox[i, j, 2]
                    bbox[i, j, 2] = bbox[i, j, 0]
                    bbox[i, j, 0] = tmp_coordinate
        bbox = tf.constant(bbox)

        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

        input_mask = None
        if self.use_input_mask:
            input_mask = random_attention_mask([self.batch_size, self.text_seq_length])

        token_type_ids = None
        if self.use_token_type_ids:
            token_type_ids = ids_tensor([self.batch_size, self.text_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.text_seq_length], self.num_labels)

        config = LayoutLMv3Config(
            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,
            coordinate_size=self.coordinate_size,
            shape_size=self.shape_size,
            input_size=self.image_size,
            patch_size=self.patch_size,
        )

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

    def create_and_check_model(self, config, input_ids, bbox, pixel_values, token_type_ids, input_mask):
        model = TFLayoutLMv3Model(config=config)

        # text + image
        result = model(input_ids, pixel_values=pixel_values, training=False)
        result = model(
            input_ids,
            bbox=bbox,
            pixel_values=pixel_values,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
            training=False,
        )
        result = model(input_ids, bbox=bbox, pixel_values=pixel_values, training=False)

        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))

        # text only
        result = model(input_ids, training=False)
        self.parent.assertEqual(
            result.last_hidden_state.shape, (self.batch_size, self.text_seq_length, self.hidden_size)
        )

        # image only
        result = model({"pixel_values": pixel_values}, training=False)
        self.parent.assertEqual(
            result.last_hidden_state.shape, (self.batch_size, self.image_seq_length, self.hidden_size)
        )

    def create_and_check_for_sequence_classification(
        self, config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels
    ):
        config.num_labels = self.num_labels
        model = TFLayoutLMv3ForSequenceClassification(config=config)
        result = model(
            input_ids,
            bbox=bbox,
            pixel_values=pixel_values,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
            labels=sequence_labels,
            training=False,
        )
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))

    def create_and_check_for_token_classification(
        self, config, input_ids, bbox, pixel_values, token_type_ids, input_mask, token_labels
    ):
        config.num_labels = self.num_labels
        model = TFLayoutLMv3ForTokenClassification(config=config)
        result = model(
            input_ids,
            bbox=bbox,
            pixel_values=pixel_values,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
            labels=token_labels,
            training=False,
        )
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.text_seq_length, self.num_labels))

    def create_and_check_for_question_answering(
        self, config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels
    ):
        config.num_labels = 2
        model = TFLayoutLMv3ForQuestionAnswering(config=config)
        result = model(
            input_ids,
            bbox=bbox,
            pixel_values=pixel_values,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
            start_positions=sequence_labels,
            end_positions=sequence_labels,
            training=False,
        )
        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, pixel_values, token_type_ids, input_mask, _, _) = config_and_inputs
        inputs_dict = {
            "input_ids": input_ids,
            "bbox": bbox,
            "pixel_values": pixel_values,
            "token_type_ids": token_type_ids,
            "attention_mask": input_mask,
        }
        return config, inputs_dict


@require_tf
class TFLayoutLMv3ModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (
        (
            TFLayoutLMv3Model,
            TFLayoutLMv3ForQuestionAnswering,
            TFLayoutLMv3ForSequenceClassification,
            TFLayoutLMv3ForTokenClassification,
        )
        if is_tf_available()
        else ()
    )
    pipeline_model_mapping = (
        {"document-question-answering": TFLayoutLMv3ForQuestionAnswering, "feature-extraction": TFLayoutLMv3Model}
        if is_tf_available()
        else {}
    )

    test_pruning = False
    test_resize_embeddings = False
    test_onnx = False

    # TODO: Fix the failed tests
    def is_pipeline_test_to_skip(
        self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
    ):
        return True

    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False) -> dict:
        inputs_dict = copy.deepcopy(inputs_dict)

        if model_class in get_values(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
            inputs_dict = {
                k: tf.tile(tf.expand_dims(v, 1), (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1))
                if isinstance(v, tf.Tensor) and v.ndim > 0
                else v
                for k, v in inputs_dict.items()
            }

        if return_labels:
            if model_class in get_values(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
                inputs_dict["labels"] = tf.ones(self.model_tester.batch_size, dtype=tf.int32)
            elif model_class in get_values(TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING):
                inputs_dict["start_positions"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
                inputs_dict["end_positions"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
            elif model_class in get_values(TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING):
                inputs_dict["labels"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
            elif model_class in get_values(TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING):
                inputs_dict["labels"] = tf.zeros(
                    (self.model_tester.batch_size, self.model_tester.text_seq_length), dtype=tf.int32
                )

        return inputs_dict

    def setUp(self):
        self.model_tester = TFLayoutLMv3ModelTester(self)
        self.config_tester = ConfigTester(self, config_class=LayoutLMv3Config, hidden_size=37)

    def test_config(self):
        self.config_tester.run_common_tests()

    def test_loss_computation(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
            model = model_class(config)
            if getattr(model, "hf_compute_loss", None):
                # The number of elements in the loss should be the same as the number of elements in the label
                prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
                added_label = prepared_for_class[
                    sorted(prepared_for_class.keys() - inputs_dict.keys(), reverse=True)[0]
                ]
                expected_loss_size = added_label.shape.as_list()[:1]

                # Test that model correctly compute the loss with kwargs
                prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
                input_ids = prepared_for_class.pop("input_ids")

                loss = model(input_ids, **prepared_for_class)[0]
                self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])

                # Test that model correctly compute the loss when we mask some positions
                prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
                input_ids = prepared_for_class.pop("input_ids")
                if "labels" in prepared_for_class:
                    labels = prepared_for_class["labels"].numpy()
                    if len(labels.shape) > 1 and labels.shape[1] != 1:
                        labels[0] = -100
                        prepared_for_class["labels"] = tf.convert_to_tensor(labels)
                        loss = model(input_ids, **prepared_for_class)[0]
                        self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])
                        self.assertTrue(not np.any(np.isnan(loss.numpy())))

                # Test that model correctly compute the loss with a dict
                prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
                loss = model(prepared_for_class)[0]
                self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])

                # Test that model correctly compute the loss with a tuple
                prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)

                # Get keys that were added with the _prepare_for_class function
                label_keys = prepared_for_class.keys() - inputs_dict.keys()
                signature = inspect.signature(model.call).parameters
                signature_names = list(signature.keys())

                # Create a dictionary holding the location of the tensors in the tuple
                tuple_index_mapping = {0: "input_ids"}
                for label_key in label_keys:
                    label_key_index = signature_names.index(label_key)
                    tuple_index_mapping[label_key_index] = label_key
                sorted_tuple_index_mapping = sorted(tuple_index_mapping.items())
                # Initialize a list with their default values, update the values and convert to a tuple
                list_input = []

                for name in signature_names:
                    if name != "kwargs":
                        list_input.append(signature[name].default)

                for index, value in sorted_tuple_index_mapping:
                    list_input[index] = prepared_for_class[value]

                tuple_input = tuple(list_input)

                # Send to model
                loss = model(tuple_input[:-1])[0]

                self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])

    def test_model(self):
        (
            config,
            input_ids,
            bbox,
            pixel_values,
            token_type_ids,
            input_mask,
            _,
            _,
        ) = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(config, input_ids, bbox, pixel_values, token_type_ids, input_mask)

    def test_model_various_embeddings(self):
        (
            config,
            input_ids,
            bbox,
            pixel_values,
            token_type_ids,
            input_mask,
            _,
            _,
        ) = self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
            config.position_embedding_type = type
            self.model_tester.create_and_check_model(config, input_ids, bbox, pixel_values, token_type_ids, input_mask)

    def test_for_sequence_classification(self):
        (
            config,
            input_ids,
            bbox,
            pixel_values,
            token_type_ids,
            input_mask,
            sequence_labels,
            _,
        ) = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(
            config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels
        )

    def test_for_token_classification(self):
        (
            config,
            input_ids,
            bbox,
            pixel_values,
            token_type_ids,
            input_mask,
            _,
            token_labels,
        ) = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(
            config, input_ids, bbox, pixel_values, token_type_ids, input_mask, token_labels
        )

    def test_for_question_answering(self):
        (
            config,
            input_ids,
            bbox,
            pixel_values,
            token_type_ids,
            input_mask,
            sequence_labels,
            _,
        ) = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(
            config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels
        )

    @slow
    def test_model_from_pretrained(self):
        for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = TFLayoutLMv3Model.from_pretrained(model_name)
            self.assertIsNotNone(model)


# We will verify our results on an image of cute cats
def prepare_img():
    image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
    return image


@require_tf
class TFLayoutLMv3ModelIntegrationTest(unittest.TestCase):
    @cached_property
    def default_image_processor(self):
        return LayoutLMv3ImageProcessor(apply_ocr=False) if is_vision_available() else None

    @slow
    def test_inference_no_head(self):
        model = TFLayoutLMv3Model.from_pretrained("microsoft/layoutlmv3-base")

        image_processor = self.default_image_processor
        image = prepare_img()
        pixel_values = image_processor(images=image, return_tensors="tf").pixel_values

        input_ids = tf.constant([[1, 2]])
        bbox = tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]), axis=0)

        # forward pass
        outputs = model(input_ids=input_ids, bbox=bbox, pixel_values=pixel_values, training=False)

        # verify the logits
        expected_shape = (1, 199, 768)
        self.assertEqual(outputs.last_hidden_state.shape, expected_shape)

        expected_slice = tf.constant(
            [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]
        )

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

# coding=utf-8
# Copyright 2022 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 TensorFlow LayoutLMv3 model. """

from __future__ import annotations

import copy
import inspect
import unittest

import numpy as np

from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import (
        TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
        TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
        TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
        TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
        LayoutLMv3Config,
        TFLayoutLMv3ForQuestionAnswering,
        TFLayoutLMv3ForSequenceClassification,
        TFLayoutLMv3ForTokenClassification,
        TFLayoutLMv3Model,
    )

if is_vision_available():
    from PIL import Image

    from transformers import LayoutLMv3ImageProcessor


class TFLayoutLMv3ModelTester:
    def __init__(
        self,
        parent,
        batch_size=2,
        num_channels=3,
        image_size=4,
        patch_size=2,
        text_seq_length=7,
        is_training=True,
        use_input_mask=True,
        use_token_type_ids=True,
        use_labels=True,
        vocab_size=99,
        hidden_size=36,
        num_hidden_layers=2,
        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,
        coordinate_size=6,
        shape_size=6,
        num_labels=3,
        num_choices=4,
        scope=None,
        range_bbox=1000,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.num_channels = num_channels
        self.image_size = image_size
        self.patch_size = patch_size
        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.coordinate_size = coordinate_size
        self.shape_size = shape_size
        self.num_labels = num_labels
        self.num_choices = num_choices
        self.scope = scope
        self.range_bbox = range_bbox

        # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
        self.text_seq_length = text_seq_length
        self.image_seq_length = (image_size // patch_size) ** 2 + 1
        self.seq_length = self.text_seq_length + self.image_seq_length

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

        bbox = ids_tensor([self.batch_size, self.text_seq_length, 4], self.range_bbox)
        bbox = bbox.numpy()
        # 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]:
                    tmp_coordinate = bbox[i, j, 3]
                    bbox[i, j, 3] = bbox[i, j, 1]
                    bbox[i, j, 1] = tmp_coordinate
                if bbox[i, j, 2] < bbox[i, j, 0]:
                    tmp_coordinate = bbox[i, j, 2]
                    bbox[i, j, 2] = bbox[i, j, 0]
                    bbox[i, j, 0] = tmp_coordinate
        bbox = tf.constant(bbox)

        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

        input_mask = None
        if self.use_input_mask:
            input_mask = random_attention_mask([self.batch_size, self.text_seq_length])

        token_type_ids = None
        if self.use_token_type_ids:
            token_type_ids = ids_tensor([self.batch_size, self.text_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.text_seq_length], self.num_labels)

        config = LayoutLMv3Config(
            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,
            coordinate_size=self.coordinate_size,
            shape_size=self.shape_size,
            input_size=self.image_size,
            patch_size=self.patch_size,
        )

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

    def create_and_check_model(self, config, input_ids, bbox, pixel_values, token_type_ids, input_mask):
        model = TFLayoutLMv3Model(config=config)

        # text + image
        result = model(input_ids, pixel_values=pixel_values, training=False)
        result = model(
            input_ids,
            bbox=bbox,
            pixel_values=pixel_values,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
            training=False,
        )
        result = model(input_ids, bbox=bbox, pixel_values=pixel_values, training=False)

        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))

        # text only
        result = model(input_ids, training=False)
        self.parent.assertEqual(
            result.last_hidden_state.shape, (self.batch_size, self.text_seq_length, self.hidden_size)
        )

        # image only
        result = model({"pixel_values": pixel_values}, training=False)
        self.parent.assertEqual(
            result.last_hidden_state.shape, (self.batch_size, self.image_seq_length, self.hidden_size)
        )

    def create_and_check_for_sequence_classification(
        self, config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels
    ):
        config.num_labels = self.num_labels
        model = TFLayoutLMv3ForSequenceClassification(config=config)
        result = model(
            input_ids,
            bbox=bbox,
            pixel_values=pixel_values,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
            labels=sequence_labels,
            training=False,
        )
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))

    def create_and_check_for_token_classification(
        self, config, input_ids, bbox, pixel_values, token_type_ids, input_mask, token_labels
    ):
        config.num_labels = self.num_labels
        model = TFLayoutLMv3ForTokenClassification(config=config)
        result = model(
            input_ids,
            bbox=bbox,
            pixel_values=pixel_values,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
            labels=token_labels,
            training=False,
        )
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.text_seq_length, self.num_labels))

    def create_and_check_for_question_answering(
        self, config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels
    ):
        config.num_labels = 2
        model = TFLayoutLMv3ForQuestionAnswering(config=config)
        result = model(
            input_ids,
            bbox=bbox,
            pixel_values=pixel_values,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
            start_positions=sequence_labels,
            end_positions=sequence_labels,
            training=False,
        )
        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, pixel_values, token_type_ids, input_mask, _, _) = config_and_inputs
        inputs_dict = {
            "input_ids": input_ids,
            "bbox": bbox,
            "pixel_values": pixel_values,
            "token_type_ids": token_type_ids,
            "attention_mask": input_mask,
        }
        return config, inputs_dict


@require_tf
class TFLayoutLMv3ModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (
        (
            TFLayoutLMv3Model,
            TFLayoutLMv3ForQuestionAnswering,
            TFLayoutLMv3ForSequenceClassification,
            TFLayoutLMv3ForTokenClassification,
        )
        if is_tf_available()
        else ()
    )
    pipeline_model_mapping = (
        {"document-question-answering": TFLayoutLMv3ForQuestionAnswering, "feature-extraction": TFLayoutLMv3Model}
        if is_tf_available()
        else {}
    )

    test_pruning = False
    test_resize_embeddings = False
    test_onnx = False

    # TODO: Fix the failed tests
    def is_pipeline_test_to_skip(
        self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
    ):
        return True

    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False) -> dict:
        inputs_dict = copy.deepcopy(inputs_dict)

        if model_class in get_values(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
            inputs_dict = {
                k: tf.tile(tf.expand_dims(v, 1), (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1))
                if isinstance(v, tf.Tensor) and v.ndim > 0
                else v
                for k, v in inputs_dict.items()
            }

        if return_labels:
            if model_class in get_values(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
                inputs_dict["labels"] = tf.ones(self.model_tester.batch_size, dtype=tf.int32)
            elif model_class in get_values(TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING):
                inputs_dict["start_positions"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
                inputs_dict["end_positions"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
            elif model_class in get_values(TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING):
                inputs_dict["labels"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
            elif model_class in get_values(TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING):
                inputs_dict["labels"] = tf.zeros(
                    (self.model_tester.batch_size, self.model_tester.text_seq_length), dtype=tf.int32
                )

        return inputs_dict

    def setUp(self):
        self.model_tester = TFLayoutLMv3ModelTester(self)
        self.config_tester = ConfigTester(self, config_class=LayoutLMv3Config, hidden_size=37)

    def test_config(self):
        self.config_tester.run_common_tests()

    def test_loss_computation(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
            model = model_class(config)
            if getattr(model, "hf_compute_loss", None):
                # The number of elements in the loss should be the same as the number of elements in the label
                prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
                added_label = prepared_for_class[
                    sorted(prepared_for_class.keys() - inputs_dict.keys(), reverse=True)[0]
                ]
                expected_loss_size = added_label.shape.as_list()[:1]

                # Test that model correctly compute the loss with kwargs
                prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
                input_ids = prepared_for_class.pop("input_ids")

                loss = model(input_ids, **prepared_for_class)[0]
                self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])

                # Test that model correctly compute the loss when we mask some positions
                prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
                input_ids = prepared_for_class.pop("input_ids")
                if "labels" in prepared_for_class:
                    labels = prepared_for_class["labels"].numpy()
                    if len(labels.shape) > 1 and labels.shape[1] != 1:
                        labels[0] = -100
                        prepared_for_class["labels"] = tf.convert_to_tensor(labels)
                        loss = model(input_ids, **prepared_for_class)[0]
                        self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])
                        self.assertTrue(not np.any(np.isnan(loss.numpy())))

                # Test that model correctly compute the loss with a dict
                prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
                loss = model(prepared_for_class)[0]
                self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])

                # Test that model correctly compute the loss with a tuple
                prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)

                # Get keys that were added with the _prepare_for_class function
                label_keys = prepared_for_class.keys() - inputs_dict.keys()
                signature = inspect.signature(model.call).parameters
                signature_names = list(signature.keys())

                # Create a dictionary holding the location of the tensors in the tuple
                tuple_index_mapping = {0: "input_ids"}
                for label_key in label_keys:
                    label_key_index = signature_names.index(label_key)
                    tuple_index_mapping[label_key_index] = label_key
                sorted_tuple_index_mapping = sorted(tuple_index_mapping.items())
                # Initialize a list with their default values, update the values and convert to a tuple
                list_input = []

                for name in signature_names:
                    if name != "kwargs":
                        list_input.append(signature[name].default)

                for index, value in sorted_tuple_index_mapping:
                    list_input[index] = prepared_for_class[value]

                tuple_input = tuple(list_input)

                # Send to model
                loss = model(tuple_input[:-1])[0]

                self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])

    def test_model(self):
        (
            config,
            input_ids,
            bbox,
            pixel_values,
            token_type_ids,
            input_mask,
            _,
            _,
        ) = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(config, input_ids, bbox, pixel_values, token_type_ids, input_mask)

    def test_model_various_embeddings(self):
        (
            config,
            input_ids,
            bbox,
            pixel_values,
            token_type_ids,
            input_mask,
            _,
            _,
        ) = self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
            config.position_embedding_type = type
            self.model_tester.create_and_check_model(config, input_ids, bbox, pixel_values, token_type_ids, input_mask)

    def test_for_sequence_classification(self):
        (
            config,
            input_ids,
            bbox,
            pixel_values,
            token_type_ids,
            input_mask,
            sequence_labels,
            _,
        ) = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(
            config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels
        )

    def test_for_token_classification(self):
        (
            config,
            input_ids,
            bbox,
            pixel_values,
            token_type_ids,
            input_mask,
            _,
            token_labels,
        ) = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(
            config, input_ids, bbox, pixel_values, token_type_ids, input_mask, token_labels
        )

    def test_for_question_answering(self):
        (
            config,
            input_ids,
            bbox,
            pixel_values,
            token_type_ids,
            input_mask,
            sequence_labels,
            _,
        ) = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(
            config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels
        )

    @slow
    def test_model_from_pretrained(self):
        for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = TFLayoutLMv3Model.from_pretrained(model_name)
            self.assertIsNotNone(model)


# We will verify our results on an image of cute cats
def prepare_img():
    image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
    return image


@require_tf
class TFLayoutLMv3ModelIntegrationTest(unittest.TestCase):
    @cached_property
    def default_image_processor(self):
        return LayoutLMv3ImageProcessor(apply_ocr=False) if is_vision_available() else None

    @slow
    def test_inference_no_head(self):
        model = TFLayoutLMv3Model.from_pretrained("microsoft/layoutlmv3-base")

        image_processor = self.default_image_processor
        image = prepare_img()
        pixel_values = image_processor(images=image, return_tensors="tf").pixel_values

        input_ids = tf.constant([[1, 2]])
        bbox = tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]), axis=0)

        # forward pass
        outputs = model(input_ids=input_ids, bbox=bbox, pixel_values=pixel_values, training=False)

        # verify the logits
        expected_shape = (1, 199, 768)
        self.assertEqual(outputs.last_hidden_state.shape, expected_shape)

        expected_slice = tf.constant(
            [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]
        )

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