# 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 PyTorch VideoMAE model. """


import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
    import torch
    from torch import nn

    from transformers import (
        MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
        VideoMAEForPreTraining,
        VideoMAEForVideoClassification,
        VideoMAEModel,
    )
    from transformers.models.videomae.modeling_videomae import VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from transformers import VideoMAEImageProcessor


class VideoMAEModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        image_size=10,
        num_channels=3,
        patch_size=2,
        tubelet_size=2,
        num_frames=2,
        is_training=True,
        use_labels=True,
        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,
        type_sequence_label_size=10,
        initializer_range=0.02,
        mask_ratio=0.9,
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.image_size = image_size
        self.num_channels = num_channels
        self.patch_size = patch_size
        self.tubelet_size = tubelet_size
        self.num_frames = num_frames
        self.is_training = is_training
        self.use_labels = use_labels
        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.type_sequence_label_size = type_sequence_label_size
        self.initializer_range = initializer_range
        self.mask_ratio = mask_ratio
        self.scope = scope

        # in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
        self.num_patches_per_frame = (image_size // patch_size) ** 2
        self.seq_length = (num_frames // tubelet_size) * self.num_patches_per_frame

        # use this variable to define bool_masked_pos
        self.num_masks = int(mask_ratio * self.seq_length)

    def prepare_config_and_inputs(self):
        pixel_values = floats_tensor(
            [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]
        )

        labels = None
        if self.use_labels:
            labels = ids_tensor([self.batch_size], self.type_sequence_label_size)

        config = self.get_config()

        return config, pixel_values, labels

    def get_config(self):
        return VideoMAEConfig(
            image_size=self.image_size,
            patch_size=self.patch_size,
            num_channels=self.num_channels,
            num_frames=self.num_frames,
            tubelet_size=self.tubelet_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,
            is_decoder=False,
            initializer_range=self.initializer_range,
        )

    def create_and_check_model(self, config, pixel_values, labels):
        model = VideoMAEModel(config=config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values)
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))

    def create_and_check_for_pretraining(self, config, pixel_values, labels):
        model = VideoMAEForPreTraining(config)
        model.to(torch_device)
        model.eval()
        # important: each video needs to have the same number of masked patches
        # hence we define a single mask, which we then repeat for each example in the batch
        mask = torch.ones((self.num_masks,))
        mask = torch.cat([mask, torch.zeros(self.seq_length - mask.size(0))])
        bool_masked_pos = mask.expand(self.batch_size, -1).bool()

        result = model(pixel_values, bool_masked_pos)
        # model only returns predictions for masked patches
        num_masked_patches = mask.sum().item()
        decoder_num_labels = 3 * self.tubelet_size * self.patch_size**2
        self.parent.assertEqual(result.logits.shape, (self.batch_size, num_masked_patches, decoder_num_labels))

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        config, pixel_values, labels = config_and_inputs
        inputs_dict = {"pixel_values": pixel_values}
        return config, inputs_dict


@require_torch
class VideoMAEModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    """
    Here we also overwrite some of the tests of test_modeling_common.py, as VideoMAE does not use input_ids, inputs_embeds,
    attention_mask and seq_length.
    """

    all_model_classes = (
        (VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
    )
    pipeline_model_mapping = (
        {"feature-extraction": VideoMAEModel, "video-classification": VideoMAEForVideoClassification}
        if is_torch_available()
        else {}
    )

    test_pruning = False
    test_torchscript = False
    test_resize_embeddings = False
    test_head_masking = False

    def setUp(self):
        self.model_tester = VideoMAEModelTester(self)
        self.config_tester = ConfigTester(self, config_class=VideoMAEConfig, has_text_modality=False, hidden_size=37)

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

        if model_class == VideoMAEForPreTraining:
            # important: each video needs to have the same number of masked patches
            # hence we define a single mask, which we then repeat for each example in the batch
            mask = torch.ones((self.model_tester.num_masks,))
            mask = torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0))])
            bool_masked_pos = mask.expand(self.model_tester.batch_size, -1).bool()
            inputs_dict["bool_masked_pos"] = bool_masked_pos.to(torch_device)

        if return_labels:
            if model_class in [
                *get_values(MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING),
            ]:
                inputs_dict["labels"] = torch.zeros(
                    self.model_tester.batch_size, dtype=torch.long, device=torch_device
                )

        return inputs_dict

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

    @unittest.skip(reason="VideoMAE does not use inputs_embeds")
    def test_inputs_embeds(self):
        pass

    def test_model_common_attributes(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
            x = model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(x, nn.Linear))

    def test_forward_signature(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            signature = inspect.signature(model.forward)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            arg_names = [*signature.parameters.keys()]

            expected_arg_names = ["pixel_values"]
            self.assertListEqual(arg_names[:1], expected_arg_names)

    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_pretraining(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*config_and_inputs)

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

    def test_attention_outputs(self):
        if not self.has_attentions:
            pass

        else:
            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
            config.return_dict = True

            for model_class in self.all_model_classes:
                num_visible_patches = self.model_tester.seq_length - self.model_tester.num_masks
                seq_len = (
                    num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
                )

                inputs_dict["output_attentions"] = True
                inputs_dict["output_hidden_states"] = False
                config.return_dict = True
                model = model_class(config)
                model.to(torch_device)
                model.eval()
                with torch.no_grad():
                    outputs = model(**self._prepare_for_class(inputs_dict, model_class))
                attentions = outputs.attentions
                self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)

                # check that output_attentions also work using config
                del inputs_dict["output_attentions"]
                config.output_attentions = True
                model = model_class(config)
                model.to(torch_device)
                model.eval()
                with torch.no_grad():
                    outputs = model(**self._prepare_for_class(inputs_dict, model_class))
                attentions = outputs.attentions
                self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)

                self.assertListEqual(
                    list(attentions[0].shape[-3:]),
                    [self.model_tester.num_attention_heads, seq_len, seq_len],
                )
                out_len = len(outputs)

                # Check attention is always last and order is fine
                inputs_dict["output_attentions"] = True
                inputs_dict["output_hidden_states"] = True
                model = model_class(config)
                model.to(torch_device)
                model.eval()
                with torch.no_grad():
                    outputs = model(**self._prepare_for_class(inputs_dict, model_class))

                self.assertEqual(out_len + 1, len(outputs))

                self_attentions = outputs.attentions

                self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
                self.assertListEqual(
                    list(self_attentions[0].shape[-3:]),
                    [self.model_tester.num_attention_heads, seq_len, seq_len],
                )

    def test_hidden_states_output(self):
        def check_hidden_states_output(inputs_dict, config, model_class):
            model = model_class(config)
            model.to(torch_device)
            model.eval()

            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))

            hidden_states = outputs.hidden_states
            expected_num_layers = self.model_tester.num_hidden_layers + 1
            self.assertEqual(len(hidden_states), expected_num_layers)

            num_visible_patches = self.model_tester.seq_length - self.model_tester.num_masks
            seq_length = num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length

            self.assertListEqual(
                list(hidden_states[0].shape[-2:]),
                [seq_length, self.model_tester.hidden_size],
            )

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            inputs_dict["output_hidden_states"] = True
            check_hidden_states_output(inputs_dict, config, model_class)

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            config.output_hidden_states = True

            check_hidden_states_output(inputs_dict, config, model_class)

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


# We will verify our results on a video of eating spaghetti
# Frame indices used: [164 168 172 176 181 185 189 193 198 202 206 210 215 219 223 227]
def prepare_video():
    file = hf_hub_download(
        repo_id="hf-internal-testing/spaghetti-video", filename="eating_spaghetti.npy", repo_type="dataset"
    )
    video = np.load(file)
    return list(video)


@require_torch
@require_vision
class VideoMAEModelIntegrationTest(unittest.TestCase):
    @cached_property
    def default_image_processor(self):
        # logits were tested with a different mean and std, so we use the same here
        return (
            VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5])
            if is_vision_available()
            else None
        )

    @slow
    def test_inference_for_video_classification(self):
        model = VideoMAEForVideoClassification.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics").to(
            torch_device
        )

        image_processor = self.default_image_processor
        video = prepare_video()
        inputs = image_processor(video, return_tensors="pt").to(torch_device)

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

        # verify the logits
        expected_shape = torch.Size((1, 400))
        self.assertEqual(outputs.logits.shape, expected_shape)

        expected_slice = torch.tensor([0.3669, -0.0688, -0.2421]).to(torch_device)

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

    @slow
    def test_inference_for_pretraining(self):
        model = VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base-short").to(torch_device)

        image_processor = self.default_image_processor
        video = prepare_video()
        inputs = image_processor(video, return_tensors="pt").to(torch_device)

        # add boolean mask, indicating which patches to mask
        local_path = hf_hub_download(repo_id="hf-internal-testing/bool-masked-pos", filename="bool_masked_pos.pt")
        inputs["bool_masked_pos"] = torch.load(local_path)

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

        # verify the logits
        expected_shape = torch.Size([1, 1408, 1536])
        expected_slice = torch.tensor(
            [[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]], device=torch_device
        )
        self.assertEqual(outputs.logits.shape, expected_shape)
        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_slice, atol=1e-4))

        # verify the loss (`config.norm_pix_loss` = `True`)
        expected_loss = torch.tensor([0.5142], device=torch_device)
        self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=1e-4))

        # verify the loss (`config.norm_pix_loss` = `False`)
        model = VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base-short", norm_pix_loss=False).to(
            torch_device
        )

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

        expected_loss = torch.tensor(torch.tensor([0.6469]), device=torch_device)
        self.assertTrue(torch.allclose(outputs.loss, expected_loss, 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 PyTorch VideoMAE model. """


import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
    import torch
    from torch import nn

    from transformers import (
        MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
        VideoMAEForPreTraining,
        VideoMAEForVideoClassification,
        VideoMAEModel,
    )
    from transformers.models.videomae.modeling_videomae import VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from transformers import VideoMAEImageProcessor


class VideoMAEModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        image_size=10,
        num_channels=3,
        patch_size=2,
        tubelet_size=2,
        num_frames=2,
        is_training=True,
        use_labels=True,
        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,
        type_sequence_label_size=10,
        initializer_range=0.02,
        mask_ratio=0.9,
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.image_size = image_size
        self.num_channels = num_channels
        self.patch_size = patch_size
        self.tubelet_size = tubelet_size
        self.num_frames = num_frames
        self.is_training = is_training
        self.use_labels = use_labels
        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.type_sequence_label_size = type_sequence_label_size
        self.initializer_range = initializer_range
        self.mask_ratio = mask_ratio
        self.scope = scope

        # in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
        self.num_patches_per_frame = (image_size // patch_size) ** 2
        self.seq_length = (num_frames // tubelet_size) * self.num_patches_per_frame

        # use this variable to define bool_masked_pos
        self.num_masks = int(mask_ratio * self.seq_length)

    def prepare_config_and_inputs(self):
        pixel_values = floats_tensor(
            [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]
        )

        labels = None
        if self.use_labels:
            labels = ids_tensor([self.batch_size], self.type_sequence_label_size)

        config = self.get_config()

        return config, pixel_values, labels

    def get_config(self):
        return VideoMAEConfig(
            image_size=self.image_size,
            patch_size=self.patch_size,
            num_channels=self.num_channels,
            num_frames=self.num_frames,
            tubelet_size=self.tubelet_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,
            is_decoder=False,
            initializer_range=self.initializer_range,
            decoder_hidden_size=self.hidden_size,
            decoder_intermediate_size=self.intermediate_size,
            decoder_num_attention_heads=self.num_attention_heads,
            decoder_num_hidden_layers=self.num_hidden_layers,
        )

    def create_and_check_model(self, config, pixel_values, labels):
        model = VideoMAEModel(config=config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values)
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))

    def create_and_check_for_pretraining(self, config, pixel_values, labels):
        model = VideoMAEForPreTraining(config)
        model.to(torch_device)
        model.eval()
        # important: each video needs to have the same number of masked patches
        # hence we define a single mask, which we then repeat for each example in the batch
        mask = torch.ones((self.num_masks,))
        mask = torch.cat([mask, torch.zeros(self.seq_length - mask.size(0))])
        bool_masked_pos = mask.expand(self.batch_size, -1).bool()

        result = model(pixel_values, bool_masked_pos)
        # model only returns predictions for masked patches
        num_masked_patches = mask.sum().item()
        decoder_num_labels = 3 * self.tubelet_size * self.patch_size**2
        self.parent.assertEqual(result.logits.shape, (self.batch_size, num_masked_patches, decoder_num_labels))

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        config, pixel_values, labels = config_and_inputs
        inputs_dict = {"pixel_values": pixel_values}
        return config, inputs_dict


@require_torch
class VideoMAEModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    """
    Here we also overwrite some of the tests of test_modeling_common.py, as VideoMAE does not use input_ids, inputs_embeds,
    attention_mask and seq_length.
    """

    all_model_classes = (
        (VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
    )
    pipeline_model_mapping = (
        {"feature-extraction": VideoMAEModel, "video-classification": VideoMAEForVideoClassification}
        if is_torch_available()
        else {}
    )

    test_pruning = False
    test_torchscript = False
    test_resize_embeddings = False
    test_head_masking = False

    def setUp(self):
        self.model_tester = VideoMAEModelTester(self)
        self.config_tester = ConfigTester(self, config_class=VideoMAEConfig, has_text_modality=False, hidden_size=37)

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

        if model_class == VideoMAEForPreTraining:
            # important: each video needs to have the same number of masked patches
            # hence we define a single mask, which we then repeat for each example in the batch
            mask = torch.ones((self.model_tester.num_masks,))
            mask = torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0))])
            bool_masked_pos = mask.expand(self.model_tester.batch_size, -1).bool()
            inputs_dict["bool_masked_pos"] = bool_masked_pos.to(torch_device)

        if return_labels:
            if model_class in [
                *get_values(MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING),
            ]:
                inputs_dict["labels"] = torch.zeros(
                    self.model_tester.batch_size, dtype=torch.long, device=torch_device
                )

        return inputs_dict

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

    @unittest.skip(reason="VideoMAE does not use inputs_embeds")
    def test_inputs_embeds(self):
        pass

    def test_model_common_attributes(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
            x = model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(x, nn.Linear))

    def test_forward_signature(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            signature = inspect.signature(model.forward)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            arg_names = [*signature.parameters.keys()]

            expected_arg_names = ["pixel_values"]
            self.assertListEqual(arg_names[:1], expected_arg_names)

    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_pretraining(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*config_and_inputs)

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

    def test_attention_outputs(self):
        if not self.has_attentions:
            pass

        else:
            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
            config.return_dict = True

            for model_class in self.all_model_classes:
                num_visible_patches = self.model_tester.seq_length - self.model_tester.num_masks
                seq_len = (
                    num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
                )

                inputs_dict["output_attentions"] = True
                inputs_dict["output_hidden_states"] = False
                config.return_dict = True
                model = model_class(config)
                model.to(torch_device)
                model.eval()
                with torch.no_grad():
                    outputs = model(**self._prepare_for_class(inputs_dict, model_class))
                attentions = outputs.attentions
                self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)

                # check that output_attentions also work using config
                del inputs_dict["output_attentions"]
                config.output_attentions = True
                model = model_class(config)
                model.to(torch_device)
                model.eval()
                with torch.no_grad():
                    outputs = model(**self._prepare_for_class(inputs_dict, model_class))
                attentions = outputs.attentions
                self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)

                self.assertListEqual(
                    list(attentions[0].shape[-3:]),
                    [self.model_tester.num_attention_heads, seq_len, seq_len],
                )
                out_len = len(outputs)

                # Check attention is always last and order is fine
                inputs_dict["output_attentions"] = True
                inputs_dict["output_hidden_states"] = True
                model = model_class(config)
                model.to(torch_device)
                model.eval()
                with torch.no_grad():
                    outputs = model(**self._prepare_for_class(inputs_dict, model_class))

                self.assertEqual(out_len + 1, len(outputs))

                self_attentions = outputs.attentions

                self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
                self.assertListEqual(
                    list(self_attentions[0].shape[-3:]),
                    [self.model_tester.num_attention_heads, seq_len, seq_len],
                )

    def test_hidden_states_output(self):
        def check_hidden_states_output(inputs_dict, config, model_class):
            model = model_class(config)
            model.to(torch_device)
            model.eval()

            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))

            hidden_states = outputs.hidden_states
            expected_num_layers = self.model_tester.num_hidden_layers + 1
            self.assertEqual(len(hidden_states), expected_num_layers)

            num_visible_patches = self.model_tester.seq_length - self.model_tester.num_masks
            seq_length = num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length

            self.assertListEqual(
                list(hidden_states[0].shape[-2:]),
                [seq_length, self.model_tester.hidden_size],
            )

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            inputs_dict["output_hidden_states"] = True
            check_hidden_states_output(inputs_dict, config, model_class)

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            config.output_hidden_states = True

            check_hidden_states_output(inputs_dict, config, model_class)


# We will verify our results on a video of eating spaghetti
# Frame indices used: [164 168 172 176 181 185 189 193 198 202 206 210 215 219 223 227]
def prepare_video():
    file = hf_hub_download(
        repo_id="hf-internal-testing/spaghetti-video", filename="eating_spaghetti.npy", repo_type="dataset"
    )
    video = np.load(file)
    return list(video)


@require_torch
@require_vision
class VideoMAEModelIntegrationTest(unittest.TestCase):
    @cached_property
    def default_image_processor(self):
        # logits were tested with a different mean and std, so we use the same here
        return (
            VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5])
            if is_vision_available()
            else None
        )

    @slow
    def test_inference_for_video_classification(self):
        model = VideoMAEForVideoClassification.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics").to(
            torch_device
        )

        image_processor = self.default_image_processor
        video = prepare_video()
        inputs = image_processor(video, return_tensors="pt").to(torch_device)

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

        # verify the logits
        expected_shape = torch.Size((1, 400))
        self.assertEqual(outputs.logits.shape, expected_shape)

        expected_slice = torch.tensor([0.3669, -0.0688, -0.2421]).to(torch_device)

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

    @slow
    def test_inference_for_pretraining(self):
        model = VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base-short").to(torch_device)

        image_processor = self.default_image_processor
        video = prepare_video()
        inputs = image_processor(video, return_tensors="pt").to(torch_device)

        # add boolean mask, indicating which patches to mask
        local_path = hf_hub_download(repo_id="hf-internal-testing/bool-masked-pos", filename="bool_masked_pos.pt")
        inputs["bool_masked_pos"] = torch.load(local_path)

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

        # verify the logits
        expected_shape = torch.Size([1, 1408, 1536])
        expected_slice = torch.tensor(
            [[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]], device=torch_device
        )
        self.assertEqual(outputs.logits.shape, expected_shape)
        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_slice, atol=1e-4))

        # verify the loss (`config.norm_pix_loss` = `True`)
        expected_loss = torch.tensor([0.5142], device=torch_device)
        self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=1e-4))

        # verify the loss (`config.norm_pix_loss` = `False`)
        model = VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base-short", norm_pix_loss=False).to(
            torch_device
        )

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

        expected_loss = torch.tensor(torch.tensor([0.6469]), device=torch_device)
        self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=1e-4))
