transformers/tests/models/flava/test_modeling_flava.py

# coding=utf-8
# Copyright 2022 Meta Platforms authors and 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 FLAVA model. """


import inspect
import os
import random
import tempfile
import unittest

import numpy as np
import requests

from transformers import (
    FlavaConfig,
    FlavaImageCodebookConfig,
    FlavaImageConfig,
    FlavaMultimodalConfig,
    FlavaTextConfig,
)
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import (
    ModelTesterMixin,
    _config_zero_init,
    floats_tensor,
    ids_tensor,
    random_attention_mask,
)
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import (
        FlavaForPreTraining,
        FlavaImageCodebook,
        FlavaImageModel,
        FlavaModel,
        FlavaMultimodalModel,
        FlavaTextModel,
    )
    from transformers.models.flava.modeling_flava import (
        FLAVA_CODEBOOK_PRETRAINED_MODEL_ARCHIVE_LIST,
        FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
    )
else:
    FlavaModel = None
    FlavaForPreTraining = None
    torch = {}


if is_vision_available():
    from PIL import Image

    from transformers import FlavaProcessor


class FlavaImageModelTester:
    def __init__(
        self,
        parent,
        batch_size=12,
        hidden_size=32,
        num_hidden_layers=2,
        num_attention_heads=4,
        intermediate_size=37,
        hidden_act="gelu",
        hidden_dropout_prob=0.0,
        attention_probs_dropout_prob=0.0,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        image_size=30,
        patch_size=2,
        num_channels=3,
        qkv_bias=True,
        mask_token=True,
        vocab_size=99,
    ):
        self.parent = parent
        self.batch_size = batch_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.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.qkv_bias = qkv_bias
        self.mask_token = mask_token
        self.vocab_size = vocab_size

    def prepare_config_and_inputs(self):
        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
        num_patches = self.image_size // self.patch_size
        bool_masked_pos = (
            torch.rand((self.batch_size, num_patches, num_patches), device=pixel_values.device) < 0.9
        ).long()
        config = self.get_config()
        return config, pixel_values, bool_masked_pos

    def get_config(self):
        return FlavaImageConfig(
            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,
            image_size=self.image_size,
            patch_size=self.patch_size,
            num_channels=self.num_channels,
            qkv_bias=self.qkv_bias,
            mask_token=self.mask_token,
            vocab_size=self.vocab_size,
        )

    def create_and_check_model(self, config, pixel_values, bool_masked_pos):
        model = FlavaImageModel(config=config)
        model.to(torch_device)
        model.eval()
        with torch.no_grad():
            result = model(pixel_values, bool_masked_pos)
        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
        image_size = (self.image_size, self.image_size)
        patch_size = (self.patch_size, self.patch_size)
        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size))
        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))

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


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

    all_model_classes = (FlavaImageModel,) 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 = FlavaImageModelTester(self)
        self.config_tester = ConfigTester(self, config_class=FlavaImageConfig, has_text_modality=False, hidden_size=37)

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

    def test_inputs_embeds(self):
        # FLAVA does not use inputs_embeds
        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_attention_outputs(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.return_dict = True

        # in FLAVA, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
        image_size = (self.model_tester.image_size, self.model_tester.image_size)
        patch_size = (self.model_tester.patch_size, self.model_tester.patch_size)
        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
        seq_len = num_patches + 1

        for model_class in self.all_model_classes:
            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)

            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))

            added_hidden_states = 1
            self.assertEqual(out_len + added_hidden_states, 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.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            expected_num_layers = getattr(
                self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
            )
            self.assertEqual(len(hidden_states), expected_num_layers)

            # FLAVA has a different seq_length
            image_size = (self.model_tester.image_size, self.model_tester.image_size)
            patch_size = (self.model_tester.patch_size, self.model_tester.patch_size)
            num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
            seq_length = num_patches + 1

            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)

    def test_training(self):
        pass

    def test_training_gradient_checkpointing(self):
        pass

    # skip this test as FlavaImageModel has no base class and is
    # not available in MODEL_MAPPING
    def test_save_load_fast_init_from_base(self):
        pass

    # skip this test as FlavaImageModel has no base class and is
    # not available in MODEL_MAPPING
    def test_save_load_fast_init_to_base(self):
        pass

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


class FlavaTextModelTester:
    def __init__(
        self,
        parent,
        batch_size=12,
        seq_length=7,
        is_training=True,
        use_input_mask=True,
        use_token_type_ids=True,
        vocab_size=102,
        type_vocab_size=2,
        max_position_embeddings=512,
        position_embedding_type="absolute",
        hidden_size=32,
        num_hidden_layers=2,
        num_attention_heads=4,
        intermediate_size=37,
        hidden_act="gelu",
        hidden_dropout_prob=0.0,
        attention_probs_dropout_prob=0.0,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        pad_token_id=0,
        qkv_bias=True,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.is_training = is_training
        self.use_input_mask = use_input_mask
        self.use_token_type_ids = use_token_type_ids
        self.seq_length = seq_length
        self.vocab_size = vocab_size
        self.type_vocab_size = type_vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.position_embedding_type = position_embedding_type
        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.pad_token_id = pad_token_id

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

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

        if input_mask is not None:
            batch_size, seq_length = input_mask.shape
            rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,))
            for batch_idx, start_index in enumerate(rnd_start_indices):
                input_mask[batch_idx, :start_index] = 1
                input_mask[batch_idx, start_index:] = 0

        token_type_ids = None

        if self.use_token_type_ids:
            token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)

        config = self.get_config()

        return config, input_ids, token_type_ids, input_mask

    def get_config(self):
        return FlavaTextConfig(
            vocab_size=self.vocab_size,
            type_vocab_size=self.type_vocab_size,
            max_position_embeddings=self.max_position_embeddings,
            position_embedding_type=self.position_embedding_type,
            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,
            pad_token_id=self.pad_token_id,
            qkv_bias=self.qkv_bias,
        )

    def create_and_check_model(self, config, input_ids, token_type_ids, input_mask):
        model = FlavaTextModel(config=config)
        model.to(torch_device)
        model.eval()
        with torch.no_grad():
            result = model(input_ids, token_type_ids=token_type_ids, attention_mask=input_mask)
            result = model(input_ids)
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))

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


@require_torch
class FlavaTextModelTest(ModelTesterMixin, unittest.TestCase):
    all_model_classes = (FlavaTextModel,) if is_torch_available() else ()
    test_pruning = False
    test_head_masking = False
    test_torchscript = False

    def setUp(self):
        self.model_tester = FlavaTextModelTester(self)
        self.config_tester = ConfigTester(self, config_class=FlavaTextConfig, hidden_size=37)

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

    def test_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)

    def test_training(self):
        pass

    def test_training_gradient_checkpointing(self):
        pass

    def test_inputs_embeds(self):
        # FLAVA does not use inputs_embeds
        pass

    # skip this test as FlavaTextModel has no base class and is
    # not available in MODEL_MAPPING
    def test_save_load_fast_init_from_base(self):
        pass

    # skip this test as FlavaTextModel has no base class and is
    # not available in MODEL_MAPPING
    def test_save_load_fast_init_to_base(self):
        pass

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


class FlavaMultimodalModelTester:
    def __init__(
        self,
        parent,
        batch_size=12,
        seq_length=44,
        use_input_mask=True,
        hidden_size=32,
        num_hidden_layers=2,
        num_attention_heads=4,
        intermediate_size=37,
        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,
        ce_ignore_index=-100,
        use_cls_token=True,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.seq_length = seq_length
        self.use_input_mask = use_input_mask
        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.ce_ignore_index = ce_ignore_index
        self.use_cls_token = use_cls_token

    def prepare_config_and_inputs(self):
        hidden_states = floats_tensor([self.batch_size, self.seq_length - 1, self.hidden_size])

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

        if input_mask is not None:
            batch_size, seq_length = input_mask.shape
            rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,))
            for batch_idx, start_index in enumerate(rnd_start_indices):
                input_mask[batch_idx, :start_index] = 1
                input_mask[batch_idx, start_index:] = 0

        config = self.get_config()

        return config, hidden_states, input_mask

    def get_config(self):
        return FlavaMultimodalConfig(
            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_cls_token=self.use_cls_token,
            ce_ignore_index=self.ce_ignore_index,
        )

    def create_and_check_model(self, config, hidden_states, input_mask):
        model = FlavaMultimodalModel(config=config)
        model.to(torch_device)
        model.eval()
        with torch.no_grad():
            result = model(hidden_states, attention_mask=input_mask)
            result = model(hidden_states)
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        config, hidden_states, input_mask = config_and_inputs
        inputs_dict = {"hidden_states": hidden_states, "attention_mask": input_mask}
        return config, inputs_dict


@require_torch
class FlavaMultimodalModelTest(ModelTesterMixin, unittest.TestCase):
    all_model_classes = (FlavaMultimodalModel,) if is_torch_available() else ()
    test_pruning = False
    test_head_masking = False
    test_resize_embeddings = False
    test_torchscript = False

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

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

    def test_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)

    def test_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 = ["hidden_states"]
            self.assertListEqual(arg_names[:1], expected_arg_names)

    def test_model_common_attributes(self):
        # No embedding in multimodal model
        pass

    def test_training(self):
        pass

    def test_training_gradient_checkpointing(self):
        pass

    def test_inputs_embeds(self):
        # FLAVA does not use inputs_embeds
        pass

    # skip this test as FlavaMultimodalModel has no base class and is
    # not available in MODEL_MAPPING
    def test_save_load_fast_init_from_base(self):
        pass

    # skip this test as FlavaMultimodalModel has no base class and is
    # not available in MODEL_MAPPING
    def test_save_load_fast_init_to_base(self):
        pass

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


class FlavaImageCodebookTester:
    def __init__(
        self,
        parent,
        batch_size=12,
        image_size=112,
        num_channels=3,
        hidden_size=32,
        num_groups=2,
        vocab_size=99,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.image_size = image_size
        self.num_channels = num_channels
        self.hidden_size = hidden_size
        self.num_groups = num_groups
        self.vocab_size = vocab_size

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

        return config, pixel_values

    def get_config(self):
        return FlavaImageCodebookConfig(
            hidden_size=self.hidden_size, num_groups=self.num_groups, vocab_size=self.vocab_size
        )

    def create_and_check_model(self, config, pixel_values):
        model = FlavaImageCodebook(config=config)
        model.to(torch_device)
        model.eval()
        with torch.no_grad():
            result = model(pixel_values)
        self.parent.assertEqual(
            result.shape, (self.batch_size, config.vocab_size, self.image_size // 8, self.image_size // 8)
        )

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


@require_torch
class FlavaImageCodebookTest(ModelTesterMixin, unittest.TestCase):
    all_model_classes = (FlavaImageCodebook,) if is_torch_available() else ()
    test_pruning = False
    test_head_masking = False
    test_resize_embeddings = False
    test_torchscript = False
    has_attentions = False

    def setUp(self):
        self.model_tester = FlavaImageCodebookTester(self)
        self.config_tester = ConfigTester(self, config_class=FlavaImageCodebookConfig, has_text_modality=False)

    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_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)

    @unittest.skip(reason="Flava does not output attentions")
    def test_attention_outputs(self):
        pass

    def test_model_common_attributes(self):
        # No embedding in multimodal model
        pass

    def test_training(self):
        pass

    def test_hidden_states_output(self):
        pass

    def test_retain_grad_hidden_states_attentions(self):
        # no attentions
        pass

    def test_training_gradient_checkpointing(self):
        pass

    def test_inputs_embeds(self):
        # FLAVA does not use inputs_embeds
        pass

    def test_model_outputs_equivalence(self):
        pass

    # skip this test as FlavaImageCodebook has no base class and is
    # not available in MODEL_MAPPING
    def test_save_load_fast_init_from_base(self):
        pass

    # skip this test as FlavaImageCodebook has no base class and is
    # not available in MODEL_MAPPING
    def test_save_load_fast_init_to_base(self):
        pass

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


class FlavaModelTester:
    model_class = FlavaModel

    def __init__(
        self,
        parent,
        text_kwargs=None,
        image_kwargs=None,
        multimodal_kwargs=None,
        image_codebook_kwargs=None,
        is_training=True,
        hidden_size=32,
        projection_dim=32,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
    ):
        if text_kwargs is None:
            text_kwargs = {}
        if image_kwargs is None:
            image_kwargs = {}
        if multimodal_kwargs is None:
            multimodal_kwargs = {}
        if image_codebook_kwargs is None:
            image_codebook_kwargs = {}

        self.parent = parent
        self.image_model_tester = FlavaImageModelTester(parent, **image_kwargs)
        self.text_model_tester = FlavaTextModelTester(parent, **text_kwargs)
        self.multimodal_model_tester = FlavaMultimodalModelTester(parent, **multimodal_kwargs)
        self.image_codebook_tester = FlavaImageCodebookTester(parent, **image_codebook_kwargs)
        self.is_training = is_training
        self.config_tester = ConfigTester(self, config_class=FlavaConfig, hidden_size=37)
        self.hidden_size = hidden_size
        self.projection_dim = projection_dim
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps

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

    def prepare_config_and_inputs_for_common(self):
        _, pixel_values, bool_masked_pos = self.image_model_tester.prepare_config_and_inputs()
        _, input_ids, token_type_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()

        config = self.get_config()

        return config, {
            "input_ids": input_ids,
            "token_type_ids": token_type_ids,
            "attention_mask": attention_mask,
            "pixel_values": pixel_values,
            "bool_masked_pos": bool_masked_pos,
        }

    def get_config(self):
        return FlavaConfig.from_configs(
            self.image_model_tester.get_config(),
            self.text_model_tester.get_config(),
            self.multimodal_model_tester.get_config(),
            self.image_codebook_tester.get_config(),
            hidden_size=self.hidden_size,
            projection_dim=self.projection_dim,
            initializer_range=self.initializer_range,
            layer_norm_eps=self.layer_norm_eps,
        )

    def create_and_check_model(self, config, inputs):
        self._test_model(config, inputs, test_image=True)
        self._test_model(config, inputs, test_text=True)
        self._test_model(config, inputs, test_image=True, test_text=True)

    def _test_model(self, config, inputs, test_image=False, test_text=False):
        model = self.model_class(config).to(torch_device).eval()
        with torch.no_grad():
            result = model(
                input_ids=inputs["input_ids"] if test_text else None,
                attention_mask=inputs["attention_mask"] if test_text else None,
                token_type_ids=inputs["token_type_ids"] if test_text else None,
                pixel_values=inputs["pixel_values"] if test_image else None,
                bool_masked_pos=inputs["bool_masked_pos"] if test_image else None,
            )
        image_size = (self.image_model_tester.image_size, self.image_model_tester.image_size)
        patch_size = (self.image_model_tester.patch_size, self.image_model_tester.patch_size)
        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

        if test_image:
            self.parent.assertEqual(
                result.image_embeddings.shape,
                (self.image_model_tester.batch_size, num_patches + 1, self.image_model_tester.hidden_size),
            )
        else:
            self.parent.assertIsNone(result.image_embeddings)

        if test_text:
            self.parent.assertEqual(
                result.text_embeddings.shape,
                (
                    self.text_model_tester.batch_size,
                    self.text_model_tester.seq_length,
                    self.text_model_tester.hidden_size,
                ),
            )
        else:
            self.parent.assertIsNone(result.text_embeddings)

        if test_image and test_text:
            self.parent.assertEqual(
                result.multimodal_embeddings.shape,
                (
                    self.multimodal_model_tester.batch_size,
                    self.text_model_tester.seq_length + num_patches + 2,
                    self.multimodal_model_tester.hidden_size,
                ),
            )
        else:
            self.parent.assertIsNone(result.multimodal_embeddings)


@require_torch
class FlavaModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (FlavaModel,) if is_torch_available() else ()
    pipeline_model_mapping = {"feature-extraction": FlavaModel} if is_torch_available() else {}
    class_for_tester = FlavaModelTester
    test_head_masking = False
    test_pruning = False
    test_resize_embeddings = False
    test_attention_outputs = False

    def setUp(self):
        self.model_tester = self.class_for_tester(self)

    def test_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.create_and_check_model(*config_and_inputs)

    # hidden_states are tested in individual model tests
    def test_hidden_states_output(self):
        pass

    # input_embeds are tested in individual model tests
    def test_inputs_embeds(self):
        pass

    # tested in individual model tests
    def test_retain_grad_hidden_states_attentions(self):
        pass

    # FlavaModel does not have input/output embeddings
    def test_model_common_attributes(self):
        pass

    # override as the `logit_scale` parameter initilization is different for FLAVA
    def test_initialization(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        configs_no_init = _config_zero_init(config)
        for model_class in self.all_model_classes:
            model = model_class(config=configs_no_init)
            for name, param in model.named_parameters():
                if param.requires_grad:
                    # check if `logit_scale` is initilized as per the original implementation
                    if name == "logit_scale" or name == "flava.logit_scale":
                        self.assertAlmostEqual(
                            param.data.item(),
                            np.log(1 / 0.07),
                            delta=1e-3,
                            msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                        )
                    else:
                        self.assertIn(
                            ((param.data.mean() * 1e9).round() / 1e9).item(),
                            [0.0, 1.0],
                            msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                        )

    def _create_and_check_torchscript(self, config, inputs_dict):
        if not self.test_torchscript:
            return

        configs_no_init = _config_zero_init(config)  # To be sure we have no Nan
        configs_no_init.torchscript = True
        configs_no_init.return_dict = False
        configs_no_init.return_loss = False
        for model_class in self.all_model_classes:
            model = model_class(config=configs_no_init)
            model.to(torch_device)
            model.eval()

            try:
                input_ids = inputs_dict["input_ids"]
                pixel_values = inputs_dict["pixel_values"]  # FLAVA needs pixel_values

                if "input_ids_masked" in inputs_dict:
                    # For pretraining
                    inputs = (input_ids, inputs_dict["input_ids_masked"], pixel_values)
                else:
                    inputs = (input_ids, pixel_values)

                traced_model = torch.jit.trace(model, inputs)
            except RuntimeError:
                self.fail("Couldn't trace module.")

            with tempfile.TemporaryDirectory() as tmp_dir_name:
                pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")

                try:
                    torch.jit.save(traced_model, pt_file_name)
                except Exception:
                    self.fail("Couldn't save module.")

                try:
                    loaded_model = torch.jit.load(pt_file_name)
                except Exception:
                    self.fail("Couldn't load module.")

            model.to(torch_device)
            model.eval()

            loaded_model.to(torch_device)
            loaded_model.eval()

            model_state_dict = model.state_dict()
            loaded_model_state_dict = loaded_model.state_dict()
            # Non persistent buffers won't be in original state dict
            loaded_model_state_dict.pop("text_model.embeddings.token_type_ids", None)

            non_persistent_buffers = {}
            for key in loaded_model_state_dict.keys():
                if key not in model_state_dict.keys():
                    non_persistent_buffers[key] = loaded_model_state_dict[key]

            loaded_model_state_dict = {
                key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers
            }

            self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))

            model_buffers = list(model.buffers())
            for non_persistent_buffer in non_persistent_buffers.values():
                found_buffer = False
                for i, model_buffer in enumerate(model_buffers):
                    if torch.equal(non_persistent_buffer, model_buffer):
                        found_buffer = True
                        break

                self.assertTrue(found_buffer)
                model_buffers.pop(i)

            models_equal = True
            for layer_name, p1 in model_state_dict.items():
                p2 = loaded_model_state_dict[layer_name]
                if p1.data.ne(p2.data).sum() > 0:
                    models_equal = False

            self.assertTrue(models_equal)

    def test_load_image_text_config(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        # Save FlavaConfig and check if we can load FlavaImageConfig from it
        with tempfile.TemporaryDirectory() as tmp_dir_name:
            config.save_pretrained(tmp_dir_name)
            image_config = FlavaImageConfig.from_pretrained(tmp_dir_name)
            self.assertDictEqual(config.image_config.to_dict(), image_config.to_dict())

        # Save FlavaConfig and check if we can load FlavaTextConfig from it
        with tempfile.TemporaryDirectory() as tmp_dir_name:
            config.save_pretrained(tmp_dir_name)
            text_config = FlavaTextConfig.from_pretrained(tmp_dir_name)
            self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict())

        # Save FlavaConfig and check if we can load FlavaMultimodalConfig from it
        with tempfile.TemporaryDirectory() as tmp_dir_name:
            config.save_pretrained(tmp_dir_name)
            multimodal_config = FlavaMultimodalConfig.from_pretrained(tmp_dir_name)
            self.assertDictEqual(config.multimodal_config.to_dict(), multimodal_config.to_dict())

    # overwrite from common since FlavaModel/TFFlavaModel return FLAVAOutput/TFFLAVAOutput
    @slow
    def test_model_from_pretrained(self):
        for model_name in FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = FlavaModel.from_pretrained(model_name)
            self.assertIsNotNone(model)


class FlavaForPreTrainingTester(FlavaModelTester):
    model_class = FlavaForPreTraining

    def prepare_config_and_inputs_for_common(self):
        _, pixel_values, bool_masked_pos = self.image_model_tester.prepare_config_and_inputs()
        _, input_ids, token_type_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
        config = self.get_config()

        input_ids_masked = input_ids.detach().clone()
        input_ids_masked[:, 1:3] = 100
        mlm_labels = input_ids.detach().clone()
        mlm_labels[:, :] = config.ce_ignore_index
        mlm_labels[:, 1:3] = input_ids[:, 1:3]
        mim_labels = torch.randint(
            0, self.image_model_tester.vocab_size, bool_masked_pos.size(), device=bool_masked_pos.device
        ).long()
        mim_labels[bool_masked_pos.ne(True)] = config.ce_ignore_index
        itm_labels = torch.ones(mlm_labels.size(0), device=bool_masked_pos.device).long()

        return config, {
            "input_ids": input_ids,
            "input_ids_masked": input_ids_masked,
            "token_type_ids": token_type_ids,
            "attention_mask": attention_mask,
            "pixel_values": pixel_values,
            "bool_masked_pos": bool_masked_pos,
            "mlm_labels": mlm_labels,
            "mim_labels": mim_labels,
            "itm_labels": itm_labels,
            "return_loss": True,
        }

    def _test_model(self, config, inputs, test_image=False, test_text=False):
        model = self.model_class(config).to(torch_device).eval()
        with torch.no_grad():
            result = model(
                input_ids=inputs["input_ids"] if test_text else None,
                input_ids_masked=inputs["input_ids_masked"] if test_text else None,
                attention_mask=inputs["attention_mask"] if test_text else None,
                token_type_ids=inputs["token_type_ids"] if test_text else None,
                pixel_values=inputs["pixel_values"] if test_image else None,
                bool_masked_pos=inputs["bool_masked_pos"] if test_image else None,
                mlm_labels=inputs["mlm_labels"],
                mim_labels=inputs["mim_labels"],
                itm_labels=inputs["itm_labels"],
                return_loss=inputs["return_loss"],
            )
        image_size = (self.image_model_tester.image_size, self.image_model_tester.image_size)
        patch_size = (self.image_model_tester.patch_size, self.image_model_tester.patch_size)
        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

        if test_image:
            self.parent.assertEqual(
                result.image_embeddings.shape,
                (self.image_model_tester.batch_size, num_patches + 1, self.image_model_tester.hidden_size),
            )
            if not test_text:
                self.parent.assertEqual(
                    result.loss_info.mim.dim(),
                    0,
                )
                self.parent.assertEqual(
                    result.mim_logits.shape,
                    (inputs["bool_masked_pos"].sum().item(), self.image_model_tester.vocab_size),
                )

        else:
            self.parent.assertIsNone(result.image_embeddings)

        if test_text:
            self.parent.assertEqual(
                result.text_embeddings.shape,
                (
                    self.text_model_tester.batch_size,
                    self.text_model_tester.seq_length,
                    self.text_model_tester.hidden_size,
                ),
            )
            if not test_image:
                self.parent.assertEqual(result.loss_info.mlm.dim(), 0)
                self.parent.assertEqual(
                    result.mlm_logits.shape,
                    (
                        (inputs["mlm_labels"] != self.multimodal_model_tester.ce_ignore_index).sum().item(),
                        self.text_model_tester.vocab_size,
                    ),
                )
        else:
            self.parent.assertIsNone(result.text_embeddings)

        if test_image and test_text:
            self.parent.assertEqual(
                result.multimodal_masked_embeddings.shape,
                (
                    self.multimodal_model_tester.batch_size,
                    self.text_model_tester.seq_length + num_patches + 2,
                    self.multimodal_model_tester.hidden_size,
                ),
            )
            self.parent.assertEqual(
                result.itm_logits.shape,
                (self.text_model_tester.batch_size, 2),
            )
            self.parent.assertEqual(
                result.mmm_text_logits.shape,
                (
                    (inputs["mlm_labels"] != self.multimodal_model_tester.ce_ignore_index).sum().item(),
                    self.text_model_tester.vocab_size,
                ),
            )
            self.parent.assertEqual(
                result.mmm_image_logits.shape,
                (inputs["bool_masked_pos"].sum().item(), self.image_model_tester.vocab_size),
            )
            self.parent.assertEqual(
                result.contrastive_logits_per_image.shape,
                (self.image_model_tester.batch_size, self.text_model_tester.batch_size),
            )
            self.parent.assertEqual(
                result.contrastive_logits_per_text.shape,
                (self.text_model_tester.batch_size, self.image_model_tester.batch_size),
            )

            for item in [
                result.loss_info.global_contrastive,
                result.loss_info.itm,
                result.loss_info.mmm_text,
                result.loss_info.mmm_image,
            ]:
                self.parent.assertEqual(item.dim(), 0)

            for item in [result.loss_info.mim, result.loss_info.mlm]:
                self.parent.assertIsNone(item)

        else:
            self.parent.assertIsNone(result.multimodal_masked_embeddings)
            for item in [
                result.loss_info.global_contrastive,
                result.loss_info.itm,
                result.loss_info.mmm_text,
                result.loss_info.mmm_image,
            ]:
                self.parent.assertIsNone(item)

        self.parent.assertIsNone(result.multimodal_embeddings)


@require_torch
class FlavaForPreTrainingTest(FlavaModelTest):
    all_model_classes = (FlavaForPreTraining,) if is_torch_available() else ()
    class_for_tester = FlavaForPreTrainingTester
    test_torchscript = False


# We will verify our results on an image of cute cats
def prepare_img():
    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    im = Image.open(requests.get(url, stream=True).raw)
    return im


@require_vision
@require_torch
class FlavaModelIntegrationTest(unittest.TestCase):
    @slow
    def test_inference(self):
        model_name = "facebook/flava-full"
        model = FlavaModel.from_pretrained(model_name).to(torch_device)
        processor = FlavaProcessor.from_pretrained(model_name)

        image = prepare_img()
        inputs = processor(
            text=["a photo of a cat", "a photo of a dog"],
            images=[image, image],
            padding="max_length",
            max_length=77,
            return_tensors="pt",
        ).to(torch_device)

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

        # verify the embeddings
        self.assertAlmostEqual(outputs.image_embeddings.sum().item(), -1352.53540, places=4)
        self.assertAlmostEqual(outputs.text_embeddings.sum().item(), -198.98225, places=4)
        self.assertAlmostEqual(outputs.multimodal_embeddings.sum().item(), -3988.51367, places=4)


@require_vision
@require_torch
class FlavaForPreTrainingIntegrationTest(unittest.TestCase):
    @slow
    def test_inference(self):
        model_name = "facebook/flava-full"
        model = FlavaForPreTraining.from_pretrained(model_name).to(torch_device)
        processor = FlavaProcessor.from_pretrained(model_name)
        torch.manual_seed(1)
        random.seed(1)

        image = prepare_img()
        inputs = processor(
            text=["a photo of a cat", "a photo of a dog"],
            images=[image, image],
            padding="max_length",
            max_length=77,
            return_tensors="pt",
            return_codebook_pixels=True,
            return_image_mask=True,
        )
        inputs["input_ids_masked"] = inputs["input_ids"].clone()
        inputs["input_ids_masked"][0, 4:6] = 103
        inputs["mlm_labels"] = inputs["input_ids"].clone()
        inputs["mlm_labels"][:, :] = -100
        inputs["mlm_labels"][0, 4:6] = inputs["input_ids"][0, 4:6]
        inputs = inputs.to(torch_device)
        # forward pass
        with torch.no_grad():
            outputs = model(**inputs)

        # verify the logits
        self.assertEqual(
            outputs.contrastive_logits_per_image.shape,
            torch.Size((inputs.pixel_values.shape[0], inputs.input_ids.shape[0])),
        )
        self.assertEqual(
            outputs.contrastive_logits_per_text.shape,
            torch.Size((inputs.input_ids.shape[0], inputs.pixel_values.shape[0])),
        )

        expected_logits = torch.tensor([[16.1291, 8.4033], [16.1291, 8.4033]], device=torch_device)
        self.assertTrue(torch.allclose(outputs.contrastive_logits_per_image, expected_logits, atol=1e-3))
        self.assertAlmostEqual(outputs.loss_info.mmm_text.item(), 1.75533199, places=4)
        self.assertAlmostEqual(outputs.loss_info.mmm_image.item(), 7.0290069, places=4)
        self.assertAlmostEqual(outputs.loss.item(), 11.0626, places=4)