# 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 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch TVLT model. """

import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import (
    TvltConfig,
    is_datasets_available,
    is_speech_available,
    is_torch_available,
    is_vision_available,
)
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property

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


if is_torch_available():
    import torch
    import torch.nn as nn

    from transformers import TvltForAudioVisualClassification, TvltForPreTraining, TvltModel
    from transformers.models.tvlt.modeling_tvlt import TVLT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_datasets_available():
    from datasets import load_dataset

if is_vision_available():
    from transformers import TvltImageProcessor

if is_speech_available():
    from transformers import TvltFeatureExtractor


class TvltModelTester:
    def __init__(
        self,
        parent,
        batch_size=2,
        image_size=32,
        spectrogram_length=32,
        frequency_length=16,
        image_patch_size=[2, 2],
        audio_patch_size=[2, 2],
        num_image_channels=3,
        num_audio_channels=1,
        num_frames=2,
        hidden_size=128,
        num_hidden_layers=12,
        num_attention_heads=4,
        intermediate_size=128,
        hidden_act="gelu",
        hidden_dropout_prob=0.0,
        attention_probs_dropout_prob=0.0,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        qkv_bias=True,
        use_mean_pooling=True,
        decoder_num_attention_heads=4,
        decoder_hidden_size=64,
        decoder_num_hidden_layers=2,
        decoder_intermediate_size=128,
        image_mask_ratio=0.75,
        audio_mask_ratio=0.15,
        audio_mask_type="frame-level",
        task_matching=True,
        task_mae=True,
        num_labels=1,
        is_training=True,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.image_size = image_size
        self.spectrogram_length = spectrogram_length
        self.frequency_length = frequency_length
        self.image_patch_size = image_patch_size
        self.audio_patch_size = audio_patch_size
        self.num_image_channels = num_image_channels
        self.num_audio_channels = num_audio_channels
        self.num_frames = num_frames

        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.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.qkv_bias = qkv_bias
        self.use_mean_pooling = use_mean_pooling

        self.decoder_num_attention_heads = decoder_num_attention_heads
        self.decoder_hidden_size = decoder_hidden_size
        self.decoder_num_hidden_layers = decoder_num_hidden_layers
        self.decoder_intermediate_size = decoder_intermediate_size
        self.image_mask_ratio = image_mask_ratio
        self.audio_mask_ratio = audio_mask_ratio

        self.task_matching = task_matching
        self.task_mae = task_mae
        self.num_labels = num_labels

        self.expected_pixel_seq_len = (self.image_size // self.image_patch_size[0]) ** 2 * self.num_frames
        self.expected_audio_seq_len = (self.spectrogram_length // self.audio_patch_size[0]) * (
            self.frequency_length // self.audio_patch_size[1]
        )
        # we set the expected sequence length (which is used in several tests)
        # this is equal to the seq length of number of image/video patches + number of audio patches
        self.expected_seq_len = self.expected_pixel_seq_len + self.expected_audio_seq_len + 1

        self.image_mae_output_dim = image_patch_size[0] ** 2 * num_image_channels
        self.audio_mae_output_dim = audio_patch_size[0] * audio_patch_size[1] * num_audio_channels
        self.is_training = is_training

    def prepare_config_and_inputs(self):
        pixel_values = floats_tensor(
            [self.batch_size, self.num_frames, self.num_image_channels, self.image_size, self.image_size]
        )
        audio_values = floats_tensor(
            [self.batch_size, self.num_audio_channels, self.spectrogram_length, self.frequency_length]
        )

        pixel_mask = floats_tensor([self.batch_size, self.expected_pixel_seq_len])
        audio_mask = floats_tensor([self.batch_size, self.expected_audio_seq_len])

        config = self.get_config()

        return (config, pixel_values, audio_values, pixel_mask, audio_mask)

    def prepare_config_and_inputs_for_pretraining(self):
        pixel_values = floats_tensor(
            [self.batch_size, self.num_frames, self.num_image_channels, self.image_size, self.image_size]
        )
        audio_values = floats_tensor(
            [self.batch_size, self.num_audio_channels, self.spectrogram_length, self.frequency_length]
        )

        pixel_mask = floats_tensor([self.batch_size, self.expected_pixel_seq_len])
        audio_mask = floats_tensor([self.batch_size, self.expected_audio_seq_len])

        pixel_values_mixed = floats_tensor(
            [self.batch_size, self.num_frames, self.num_image_channels, self.image_size, self.image_size]
        )
        pixel_mask_mixed = floats_tensor([self.batch_size, self.expected_pixel_seq_len])
        labels = floats_tensor([self.batch_size])
        config = self.get_config()

        return (
            config,
            pixel_values,
            audio_values,
            pixel_mask,
            audio_mask,
            pixel_values_mixed,
            pixel_mask_mixed,
            labels,
        )

    def get_config(self):
        return TvltConfig(
            image_size=self.image_size,
            spectrogram_length=self.spectrogram_length,
            frequency_length=self.frequency_length,
            image_patch_size=self.image_patch_size,
            audio_patch_size=self.audio_patch_size,
            num_image_channels=self.num_image_channels,
            num_audio_channels=self.num_audio_channels,
            num_frames=self.num_frames,
            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,
            initializer_range=self.initializer_range,
            layer_norm_eps=self.layer_norm_eps,
            qkv_bias=self.qkv_bias,
            use_mean_pooling=self.use_mean_pooling,
            decoder_num_attention_heads=self.decoder_num_attention_heads,
            decoder_hidden_size=self.decoder_hidden_size,
            decoder_num_hidden_layers=self.decoder_num_hidden_layers,
            decoder_intermediate_size=self.decoder_intermediate_size,
            image_mask_ratio=self.image_mask_ratio,
            audio_mask_ratio=self.audio_mask_ratio,
            task_matching=self.task_matching,
            task_mae=self.task_mae,
            num_labels=self.num_labels,
        )

    def create_and_check_model(self, config, pixel_values, audio_values, pixel_mask, audio_mask):
        model = TvltModel(config=config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values, audio_values, pixel_mask=pixel_mask, audio_mask=audio_mask)
        result = model(pixel_values, audio_values)
        self.parent.assertEqual(
            result.last_hidden_state.shape, (self.batch_size, self.expected_seq_len, self.hidden_size)
        )

    def create_and_check_for_audiovisual_classification(
        self, config, pixel_values, audio_values, pixel_mask, audio_mask
    ):
        model = TvltForAudioVisualClassification(config=config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values, audio_values, pixel_mask=pixel_mask, audio_mask=audio_mask)
        result = model(pixel_values, audio_values)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))

    def create_and_check_for_pretraining(
        self,
        config,
        pixel_values,
        audio_values,
        pixel_mask,
        audio_mask,
        pixel_values_mixed,
        pixel_mask_mixed,
        labels,
    ):
        model = TvltForPreTraining(config=config)
        model.to(torch_device)
        model.train()
        result = model(
            pixel_values,
            audio_values,
            pixel_mask,
            audio_mask,
            pixel_values_mixed=pixel_values_mixed,
            pixel_mask_mixed=pixel_mask_mixed,
            labels=labels,
        )
        self.parent.assertEqual(
            result.pixel_logits.shape, (self.batch_size, self.expected_pixel_seq_len, self.image_mae_output_dim)
        )
        self.parent.assertEqual(
            result.audio_logits.shape, (self.batch_size, self.expected_audio_seq_len, self.audio_mae_output_dim)
        )
        self.parent.assertEqual(result.matching_logits.shape, (self.batch_size, self.num_labels))

    def create_and_check_for_pretraining_inference(
        self,
        config,
        pixel_values,
        audio_values,
        pixel_mask,
        audio_mask,
        pixel_values_mixed,
        pixel_mask_mixed,
        labels,
    ):
        model = TvltForPreTraining(config=config)
        model.to(torch_device)
        model.eval()
        result = model(
            pixel_values,
            audio_values,
            pixel_mask,
            audio_mask,
            pixel_values_mixed=pixel_values_mixed,
            pixel_mask_mixed=pixel_mask_mixed,
            labels=labels,
        )
        if result.pixel_logits is not None:
            self.parent.assertEqual(
                result.pixel_logits.shape, (self.batch_size, self.expected_pixel_seq_len, self.image_mae_output_dim)
            )
        if result.audio_logits is not None:
            self.parent.assertEqual(
                result.audio_logits.shape, (self.batch_size, self.expected_audio_seq_len, self.audio_mae_output_dim)
            )
        self.parent.assertEqual(result.matching_logits.shape, (self.batch_size, self.num_labels))

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

    def prepare_pixel_values(self):
        return floats_tensor(
            [self.batch_size, self.num_frames, self.num_image_channels, self.image_size, self.image_size]
        )

    def prepare_audio_values(self):
        return floats_tensor(
            [self.batch_size, self.num_audio_channels, self.spectrogram_length, self.frequency_length]
        )


@require_torch
class TvltModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (
        (TvltModel, TvltForPreTraining, TvltForAudioVisualClassification) if is_torch_available() else ()
    )
    pipeline_model_mapping = {"feature-extraction": TvltModel} if is_torch_available() else {}

    fx_compatible = False
    test_pruning = False
    test_headmasking = False
    test_torchscript = False
    test_resize_embeddings = False
    main_input_name = "pixel_values"

    # TvltForAudioVisualClassification and TvltForPreTraining require special treatment
    def _prepare_for_class(self, inputs_dict, model_class, return_labels=True):
        inputs_dict = copy.deepcopy(inputs_dict)

        if return_labels:
            if model_class.__name__ == "TvltForAudioVisualClassification":
                inputs_dict["labels"] = torch.zeros(
                    (self.model_tester.batch_size,), dtype=torch.long, device=torch_device
                )
            elif model_class.__name__ == "TvltForPreTraining":
                inputs_dict["labels"] = torch.zeros(
                    (self.model_tester.batch_size,), dtype=torch.float, device=torch_device
                )
                inputs_dict["pixel_values_mixed"] = torch.zeros(
                    (
                        self.model_tester.batch_size,
                        self.model_tester.num_frames,
                        self.model_tester.num_image_channels,
                        self.model_tester.image_size,
                        self.model_tester.image_size,
                    ),
                    dtype=torch.float,
                    device=torch_device,
                )
                inputs_dict["pixel_mask_mixed"] = torch.zeros(
                    (self.model_tester.batch_size, self.model_tester.expected_pixel_seq_len),
                    dtype=torch.float,
                    device=torch_device,
                )

        return inputs_dict

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

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

    @unittest.skip(reason="TVLT 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)
            input_embeddings = model.get_input_embeddings()
            self.assertIsInstance(input_embeddings, (tuple))
            for embedding in input_embeddings:
                self.assertIsInstance(embedding, (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", "audio_values"]
            self.assertListEqual(arg_names[:2], 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_audiovisual_classification(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_audiovisual_classification(*config_and_inputs)

    def test_for_pretraining(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs_for_pretraining()
        self.model_tester.create_and_check_for_pretraining(*config_and_inputs)
        self.model_tester.create_and_check_for_pretraining_inference(*config_and_inputs)

    @slow
    def test_model_from_pretrained(self):
        for model_name in TVLT_PRETRAINED_MODEL_ARCHIVE_LIST:
            model = TvltModel.from_pretrained(model_name)
            self.assertIsNotNone(model)

    def test_training(self):
        if not self.model_tester.is_training:
            return

        for model_class in self.all_model_classes[1:]:
            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
            config.return_dict = True

            model = model_class(config)
            model.to(torch_device)
            model.train()
            inputs = self._prepare_for_class(inputs_dict, model_class)
            for k, v in inputs.items():
                print(k, v.shape)
            loss = model(**inputs).loss
            loss.backward()

    def test_training_gradient_checkpointing(self):
        if not self.model_tester.is_training:
            return

        for model_class in self.all_model_classes[1:]:
            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
            config.use_cache = False
            config.return_dict = True

            model = model_class(config)
            model.to(torch_device)
            model.gradient_checkpointing_enable()
            model.train()
            inputs = self._prepare_for_class(inputs_dict, model_class)
            loss = model(**inputs).loss
            loss.backward()

    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[2:]:
                seq_len = self.model_tester.expected_seq_len

                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)

            seq_length = self.model_tester.expected_seq_len

            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[2:]:
            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(num_frames=8):
    file = hf_hub_download(
        repo_id="hf-internal-testing/spaghetti-video", filename="eating_spaghetti.npy", repo_type="dataset"
    )
    video = np.load(file)[:num_frames]
    return list(video)


def prepare_audio(num_samples=1):
    ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
    # automatic decoding with librispeech
    speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]
    return [x["array"] for x in speech_samples]


@require_torch
@require_vision
class TvltModelIntegrationTest(unittest.TestCase):
    @cached_property
    def default_processors(self):
        # logits were tested with a different mean and std, so we use the same here
        return (
            TvltImageProcessor() if is_vision_available() else None,
            TvltFeatureExtractor(),
        )

    def test_inference_for_base_model(self):
        model = TvltModel.from_pretrained("ZinengTang/tvlt-base").to(torch_device)

        image_processor, audio_feature_extractor = self.default_processors
        video = prepare_video()
        audio = prepare_audio()
        video_inputs = image_processor(video, return_tensors="pt").to(torch_device)
        audio_inputs = audio_feature_extractor(audio, return_tensors="pt").to(torch_device)
        inputs = {}
        inputs.update(video_inputs)
        inputs.update(audio_inputs)

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

        # verify the logits
        expected_last_hidden_state_slice = torch.tensor([[-0.0186, -0.0691], [0.0242, -0.0398]], device=torch_device)
        self.assertTrue(
            torch.allclose(outputs.last_hidden_state[:, :2, :2], expected_last_hidden_state_slice, atol=1e-4)
        )

    def test_inference_for_pretraining(self):
        model = TvltForPreTraining.from_pretrained("ZinengTang/tvlt-base").to(torch_device)

        image_processor, audio_feature_extractor = self.default_processors
        video = prepare_video()
        video_mixed = prepare_video()
        audio = prepare_audio()
        video_inputs = image_processor(video, return_tensors="pt", mask_pixel=True).to(torch_device)
        video_mixed_inputs = image_processor(video_mixed, is_mixed=True, return_tensors="pt").to(torch_device)
        audio_inputs = audio_feature_extractor(audio, return_tensors="pt", mask_audio=True).to(torch_device)
        labels = torch.tensor([[0.0]], device=torch_device)
        inputs = {}
        inputs.update(video_inputs)
        inputs.update(video_mixed_inputs)
        inputs.update(audio_inputs)
        inputs.update({"labels": labels})

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

        # verify the logits
        expected_pixel_logits_shape = torch.Size([1, 1568, 768])
        expected_audio_logits_shape = torch.Size([1, 96, 256])
        expected_matching_logits_shape = torch.Size([1, 1])

        if outputs.pixel_logits is not None:
            self.assertEqual(outputs.pixel_logits.shape, expected_pixel_logits_shape)
        if outputs.audio_logits is not None:
            self.assertEqual(outputs.audio_logits.shape, expected_audio_logits_shape)
        self.assertTrue(outputs.matching_logits.shape, expected_matching_logits_shape)

# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch TVLT model. """

import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import (
    TvltConfig,
    is_datasets_available,
    is_speech_available,
    is_torch_available,
    is_vision_available,
)
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property

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


if is_torch_available():
    import torch
    import torch.nn as nn

    from transformers import TvltForAudioVisualClassification, TvltForPreTraining, TvltModel
    from transformers.models.tvlt.modeling_tvlt import TVLT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_datasets_available():
    from datasets import load_dataset

if is_vision_available():
    from transformers import TvltImageProcessor

if is_speech_available():
    from transformers import TvltFeatureExtractor


class TvltModelTester:
    def __init__(
        self,
        parent,
        batch_size=2,
        image_size=32,
        spectrogram_length=32,
        frequency_length=16,
        image_patch_size=[2, 2],
        audio_patch_size=[2, 2],
        num_image_channels=3,
        num_audio_channels=1,
        num_frames=2,
        hidden_size=32,
        num_hidden_layers=3,
        num_attention_heads=4,
        intermediate_size=128,
        hidden_act="gelu",
        hidden_dropout_prob=0.0,
        attention_probs_dropout_prob=0.0,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        qkv_bias=True,
        use_mean_pooling=True,
        decoder_num_attention_heads=4,
        decoder_hidden_size=32,
        decoder_num_hidden_layers=2,
        decoder_intermediate_size=128,
        image_mask_ratio=0.75,
        audio_mask_ratio=0.15,
        audio_mask_type="frame-level",
        task_matching=True,
        task_mae=True,
        num_labels=1,
        is_training=True,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.image_size = image_size
        self.spectrogram_length = spectrogram_length
        self.frequency_length = frequency_length
        self.image_patch_size = image_patch_size
        self.audio_patch_size = audio_patch_size
        self.num_image_channels = num_image_channels
        self.num_audio_channels = num_audio_channels
        self.num_frames = num_frames

        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.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.qkv_bias = qkv_bias
        self.use_mean_pooling = use_mean_pooling

        self.decoder_num_attention_heads = decoder_num_attention_heads
        self.decoder_hidden_size = decoder_hidden_size
        self.decoder_num_hidden_layers = decoder_num_hidden_layers
        self.decoder_intermediate_size = decoder_intermediate_size
        self.image_mask_ratio = image_mask_ratio
        self.audio_mask_ratio = audio_mask_ratio

        self.task_matching = task_matching
        self.task_mae = task_mae
        self.num_labels = num_labels

        self.expected_pixel_seq_len = (self.image_size // self.image_patch_size[0]) ** 2 * self.num_frames
        self.expected_audio_seq_len = (self.spectrogram_length // self.audio_patch_size[0]) * (
            self.frequency_length // self.audio_patch_size[1]
        )
        # we set the expected sequence length (which is used in several tests)
        # this is equal to the seq length of number of image/video patches + number of audio patches
        self.expected_seq_len = self.expected_pixel_seq_len + self.expected_audio_seq_len + 1

        self.image_mae_output_dim = image_patch_size[0] ** 2 * num_image_channels
        self.audio_mae_output_dim = audio_patch_size[0] * audio_patch_size[1] * num_audio_channels
        self.is_training = is_training

    def prepare_config_and_inputs(self):
        pixel_values = floats_tensor(
            [self.batch_size, self.num_frames, self.num_image_channels, self.image_size, self.image_size]
        )
        audio_values = floats_tensor(
            [self.batch_size, self.num_audio_channels, self.spectrogram_length, self.frequency_length]
        )

        pixel_mask = floats_tensor([self.batch_size, self.expected_pixel_seq_len])
        audio_mask = floats_tensor([self.batch_size, self.expected_audio_seq_len])

        config = self.get_config()

        return (config, pixel_values, audio_values, pixel_mask, audio_mask)

    def prepare_config_and_inputs_for_pretraining(self):
        pixel_values = floats_tensor(
            [self.batch_size, self.num_frames, self.num_image_channels, self.image_size, self.image_size]
        )
        audio_values = floats_tensor(
            [self.batch_size, self.num_audio_channels, self.spectrogram_length, self.frequency_length]
        )

        pixel_mask = floats_tensor([self.batch_size, self.expected_pixel_seq_len])
        audio_mask = floats_tensor([self.batch_size, self.expected_audio_seq_len])

        pixel_values_mixed = floats_tensor(
            [self.batch_size, self.num_frames, self.num_image_channels, self.image_size, self.image_size]
        )
        pixel_mask_mixed = floats_tensor([self.batch_size, self.expected_pixel_seq_len])
        labels = floats_tensor([self.batch_size])
        config = self.get_config()

        return (
            config,
            pixel_values,
            audio_values,
            pixel_mask,
            audio_mask,
            pixel_values_mixed,
            pixel_mask_mixed,
            labels,
        )

    def get_config(self):
        return TvltConfig(
            image_size=self.image_size,
            spectrogram_length=self.spectrogram_length,
            frequency_length=self.frequency_length,
            image_patch_size=self.image_patch_size,
            audio_patch_size=self.audio_patch_size,
            num_image_channels=self.num_image_channels,
            num_audio_channels=self.num_audio_channels,
            num_frames=self.num_frames,
            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,
            initializer_range=self.initializer_range,
            layer_norm_eps=self.layer_norm_eps,
            qkv_bias=self.qkv_bias,
            use_mean_pooling=self.use_mean_pooling,
            decoder_num_attention_heads=self.decoder_num_attention_heads,
            decoder_hidden_size=self.decoder_hidden_size,
            decoder_num_hidden_layers=self.decoder_num_hidden_layers,
            decoder_intermediate_size=self.decoder_intermediate_size,
            image_mask_ratio=self.image_mask_ratio,
            audio_mask_ratio=self.audio_mask_ratio,
            task_matching=self.task_matching,
            task_mae=self.task_mae,
            num_labels=self.num_labels,
        )

    def create_and_check_model(self, config, pixel_values, audio_values, pixel_mask, audio_mask):
        model = TvltModel(config=config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values, audio_values, pixel_mask=pixel_mask, audio_mask=audio_mask)
        result = model(pixel_values, audio_values)
        self.parent.assertEqual(
            result.last_hidden_state.shape, (self.batch_size, self.expected_seq_len, self.hidden_size)
        )

    def create_and_check_for_audiovisual_classification(
        self, config, pixel_values, audio_values, pixel_mask, audio_mask
    ):
        model = TvltForAudioVisualClassification(config=config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values, audio_values, pixel_mask=pixel_mask, audio_mask=audio_mask)
        result = model(pixel_values, audio_values)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))

    def create_and_check_for_pretraining(
        self,
        config,
        pixel_values,
        audio_values,
        pixel_mask,
        audio_mask,
        pixel_values_mixed,
        pixel_mask_mixed,
        labels,
    ):
        model = TvltForPreTraining(config=config)
        model.to(torch_device)
        model.train()
        result = model(
            pixel_values,
            audio_values,
            pixel_mask,
            audio_mask,
            pixel_values_mixed=pixel_values_mixed,
            pixel_mask_mixed=pixel_mask_mixed,
            labels=labels,
        )
        self.parent.assertEqual(
            result.pixel_logits.shape, (self.batch_size, self.expected_pixel_seq_len, self.image_mae_output_dim)
        )
        self.parent.assertEqual(
            result.audio_logits.shape, (self.batch_size, self.expected_audio_seq_len, self.audio_mae_output_dim)
        )
        self.parent.assertEqual(result.matching_logits.shape, (self.batch_size, self.num_labels))

    def create_and_check_for_pretraining_inference(
        self,
        config,
        pixel_values,
        audio_values,
        pixel_mask,
        audio_mask,
        pixel_values_mixed,
        pixel_mask_mixed,
        labels,
    ):
        model = TvltForPreTraining(config=config)
        model.to(torch_device)
        model.eval()
        result = model(
            pixel_values,
            audio_values,
            pixel_mask,
            audio_mask,
            pixel_values_mixed=pixel_values_mixed,
            pixel_mask_mixed=pixel_mask_mixed,
            labels=labels,
        )
        if result.pixel_logits is not None:
            self.parent.assertEqual(
                result.pixel_logits.shape, (self.batch_size, self.expected_pixel_seq_len, self.image_mae_output_dim)
            )
        if result.audio_logits is not None:
            self.parent.assertEqual(
                result.audio_logits.shape, (self.batch_size, self.expected_audio_seq_len, self.audio_mae_output_dim)
            )
        self.parent.assertEqual(result.matching_logits.shape, (self.batch_size, self.num_labels))

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

    def prepare_pixel_values(self):
        return floats_tensor(
            [self.batch_size, self.num_frames, self.num_image_channels, self.image_size, self.image_size]
        )

    def prepare_audio_values(self):
        return floats_tensor(
            [self.batch_size, self.num_audio_channels, self.spectrogram_length, self.frequency_length]
        )


@require_torch
class TvltModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (
        (TvltModel, TvltForPreTraining, TvltForAudioVisualClassification) if is_torch_available() else ()
    )
    pipeline_model_mapping = {"feature-extraction": TvltModel} if is_torch_available() else {}

    fx_compatible = False
    test_pruning = False
    test_headmasking = False
    test_torchscript = False
    test_resize_embeddings = False
    main_input_name = "pixel_values"

    # TvltForAudioVisualClassification and TvltForPreTraining require special treatment
    def _prepare_for_class(self, inputs_dict, model_class, return_labels=True):
        inputs_dict = copy.deepcopy(inputs_dict)

        if return_labels:
            if model_class.__name__ == "TvltForAudioVisualClassification":
                inputs_dict["labels"] = torch.zeros(
                    (self.model_tester.batch_size,), dtype=torch.long, device=torch_device
                )
            elif model_class.__name__ == "TvltForPreTraining":
                inputs_dict["labels"] = torch.zeros(
                    (self.model_tester.batch_size,), dtype=torch.float, device=torch_device
                )
                inputs_dict["pixel_values_mixed"] = torch.zeros(
                    (
                        self.model_tester.batch_size,
                        self.model_tester.num_frames,
                        self.model_tester.num_image_channels,
                        self.model_tester.image_size,
                        self.model_tester.image_size,
                    ),
                    dtype=torch.float,
                    device=torch_device,
                )
                inputs_dict["pixel_mask_mixed"] = torch.zeros(
                    (self.model_tester.batch_size, self.model_tester.expected_pixel_seq_len),
                    dtype=torch.float,
                    device=torch_device,
                )

        return inputs_dict

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

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

    @unittest.skip(reason="TVLT 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)
            input_embeddings = model.get_input_embeddings()
            self.assertIsInstance(input_embeddings, (tuple))
            for embedding in input_embeddings:
                self.assertIsInstance(embedding, (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", "audio_values"]
            self.assertListEqual(arg_names[:2], 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_audiovisual_classification(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_audiovisual_classification(*config_and_inputs)

    def test_for_pretraining(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs_for_pretraining()
        self.model_tester.create_and_check_for_pretraining(*config_and_inputs)
        self.model_tester.create_and_check_for_pretraining_inference(*config_and_inputs)

    @slow
    def test_model_from_pretrained(self):
        for model_name in TVLT_PRETRAINED_MODEL_ARCHIVE_LIST:
            model = TvltModel.from_pretrained(model_name)
            self.assertIsNotNone(model)

    def test_training(self):
        if not self.model_tester.is_training:
            return

        for model_class in self.all_model_classes[1:]:
            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
            config.return_dict = True

            model = model_class(config)
            model.to(torch_device)
            model.train()
            inputs = self._prepare_for_class(inputs_dict, model_class)
            for k, v in inputs.items():
                print(k, v.shape)
            loss = model(**inputs).loss
            loss.backward()

    def test_training_gradient_checkpointing(self):
        if not self.model_tester.is_training:
            return

        for model_class in self.all_model_classes[1:]:
            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
            config.use_cache = False
            config.return_dict = True

            model = model_class(config)
            model.to(torch_device)
            model.gradient_checkpointing_enable()
            model.train()
            inputs = self._prepare_for_class(inputs_dict, model_class)
            loss = model(**inputs).loss
            loss.backward()

    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[2:]:
                seq_len = self.model_tester.expected_seq_len

                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)

            seq_length = self.model_tester.expected_seq_len

            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[2:]:
            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(num_frames=8):
    file = hf_hub_download(
        repo_id="hf-internal-testing/spaghetti-video", filename="eating_spaghetti.npy", repo_type="dataset"
    )
    video = np.load(file)[:num_frames]
    return list(video)


def prepare_audio(num_samples=1):
    ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
    # automatic decoding with librispeech
    speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]
    return [x["array"] for x in speech_samples]


@require_torch
@require_vision
class TvltModelIntegrationTest(unittest.TestCase):
    @cached_property
    def default_processors(self):
        # logits were tested with a different mean and std, so we use the same here
        return (
            TvltImageProcessor() if is_vision_available() else None,
            TvltFeatureExtractor(),
        )

    def test_inference_for_base_model(self):
        model = TvltModel.from_pretrained("ZinengTang/tvlt-base").to(torch_device)

        image_processor, audio_feature_extractor = self.default_processors
        video = prepare_video()
        audio = prepare_audio()
        video_inputs = image_processor(video, return_tensors="pt").to(torch_device)
        audio_inputs = audio_feature_extractor(audio, return_tensors="pt").to(torch_device)
        inputs = {}
        inputs.update(video_inputs)
        inputs.update(audio_inputs)

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

        # verify the logits
        expected_last_hidden_state_slice = torch.tensor([[-0.0186, -0.0691], [0.0242, -0.0398]], device=torch_device)
        self.assertTrue(
            torch.allclose(outputs.last_hidden_state[:, :2, :2], expected_last_hidden_state_slice, atol=1e-4)
        )

    def test_inference_for_pretraining(self):
        model = TvltForPreTraining.from_pretrained("ZinengTang/tvlt-base").to(torch_device)

        image_processor, audio_feature_extractor = self.default_processors
        video = prepare_video()
        video_mixed = prepare_video()
        audio = prepare_audio()
        video_inputs = image_processor(video, return_tensors="pt", mask_pixel=True).to(torch_device)
        video_mixed_inputs = image_processor(video_mixed, is_mixed=True, return_tensors="pt").to(torch_device)
        audio_inputs = audio_feature_extractor(audio, return_tensors="pt", mask_audio=True).to(torch_device)
        labels = torch.tensor([[0.0]], device=torch_device)
        inputs = {}
        inputs.update(video_inputs)
        inputs.update(video_mixed_inputs)
        inputs.update(audio_inputs)
        inputs.update({"labels": labels})

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

        # verify the logits
        expected_pixel_logits_shape = torch.Size([1, 1568, 768])
        expected_audio_logits_shape = torch.Size([1, 96, 256])
        expected_matching_logits_shape = torch.Size([1, 1])

        if outputs.pixel_logits is not None:
            self.assertEqual(outputs.pixel_logits.shape, expected_pixel_logits_shape)
        if outputs.audio_logits is not None:
            self.assertEqual(outputs.audio_logits.shape, expected_audio_logits_shape)
        self.assertTrue(outputs.matching_logits.shape, expected_matching_logits_shape)
