# 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 SwiftFormer model. """


import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
    import torch
    from torch import nn

    from transformers import SwiftFormerForImageClassification, SwiftFormerModel
    from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor


class SwiftFormerModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        num_channels=3,
        is_training=True,
        use_labels=True,
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        image_size=224,
        num_labels=1000,
        layer_depths=[3, 3, 6, 4],
        embed_dims=[48, 56, 112, 220],
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.num_channels = num_channels
        self.is_training = is_training
        self.use_labels = use_labels
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.num_labels = num_labels
        self.image_size = image_size
        self.layer_depths = layer_depths
        self.embed_dims = embed_dims

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

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

        config = self.get_config()

        return config, pixel_values, labels

    def get_config(self):
        return SwiftFormerConfig(
            depths=self.layer_depths,
            embed_dims=self.embed_dims,
            mlp_ratio=4,
            downsamples=[True, True, True, True],
            hidden_act="gelu",
            num_labels=self.num_labels,
            down_patch_size=3,
            down_stride=2,
            down_pad=1,
            drop_rate=0.0,
            drop_path_rate=0.0,
            use_layer_scale=True,
            layer_scale_init_value=1e-5,
        )

    def create_and_check_model(self, config, pixel_values, labels):
        model = SwiftFormerModel(config=config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values)
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.embed_dims[-1], 7, 7))

    def create_and_check_for_image_classification(self, config, pixel_values, labels):
        config.num_labels = self.num_labels
        model = SwiftFormerForImageClassification(config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values, labels=labels)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))

        model = SwiftFormerForImageClassification(config)
        model.to(torch_device)
        model.eval()

        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
        result = model(pixel_values)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))

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


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

    all_model_classes = (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
    pipeline_model_mapping = (
        {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
        if is_torch_available()
        else {}
    )

    fx_compatible = False
    test_pruning = False
    test_resize_embeddings = False
    test_head_masking = False
    has_attentions = False

    def setUp(self):
        self.model_tester = SwiftFormerModelTester(self)
        self.config_tester = ConfigTester(
            self,
            config_class=SwiftFormerConfig,
            has_text_modality=False,
            hidden_size=37,
            num_attention_heads=12,
            num_hidden_layers=12,
        )

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

    @unittest.skip(reason="SwiftFormer 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)
            x = model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(x, nn.Linear))

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

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

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

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

    def test_for_image_classification(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*config_and_inputs)

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

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

    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_stages = 8
            self.assertEqual(len(hidden_states), expected_num_stages)  # TODO

            # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
            # with the width and height being successively divided by 2, after every 2 blocks
            for i in range(len(hidden_states)):
                self.assertEqual(
                    hidden_states[i].shape,
                    torch.Size(
                        [
                            self.model_tester.batch_size,
                            self.model_tester.embed_dims[i // 2],
                            (self.model_tester.image_size // 4) // 2 ** (i // 2),
                            (self.model_tester.image_size // 4) // 2 ** (i // 2),
                        ]
                    ),
                )

        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_initialization(self):
        def _config_zero_init(config):
            configs_no_init = copy.deepcopy(config)
            for key in configs_no_init.__dict__.keys():
                if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
                    setattr(configs_no_init, key, 1e-10)
                if isinstance(getattr(configs_no_init, key, None), PretrainedConfig):
                    no_init_subconfig = _config_zero_init(getattr(configs_no_init, key))
                    setattr(configs_no_init, key, no_init_subconfig)
            return configs_no_init

        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:
                    self.assertIn(
                        ((param.data.mean() * 1e9) / 1e9).round().item(),
                        [0.0, 1.0],
                        msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                    )

    @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 an image of cute cats
def prepare_img():
    image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
    return image


@require_torch
@require_vision
class SwiftFormerModelIntegrationTest(unittest.TestCase):
    @cached_property
    def default_image_processor(self):
        return ViTImageProcessor.from_pretrained("MBZUAI/swiftformer-xs") if is_vision_available() else None

    @slow
    def test_inference_image_classification_head(self):
        model = SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs").to(torch_device)

        image_processor = self.default_image_processor
        image = prepare_img()
        inputs = image_processor(images=image, return_tensors="pt").to(torch_device)

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

        # verify the logits
        expected_shape = torch.Size((1, 1000))
        self.assertEqual(outputs.logits.shape, expected_shape)

        expected_slice = torch.tensor([[-2.1703e00, 2.1107e00, -2.0811e00]]).to(torch_device)
        self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))

# 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 SwiftFormer model. """


import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
    import torch
    from torch import nn

    from transformers import SwiftFormerForImageClassification, SwiftFormerModel
    from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor


class SwiftFormerModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        num_channels=3,
        is_training=True,
        use_labels=True,
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        image_size=224,
        num_labels=3,
        layer_depths=[1, 1, 1, 1],
        embed_dims=[16, 16, 32, 32],
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.num_channels = num_channels
        self.is_training = is_training
        self.use_labels = use_labels
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.num_labels = num_labels
        self.image_size = image_size
        self.layer_depths = layer_depths
        self.embed_dims = embed_dims

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

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

        config = self.get_config()

        return config, pixel_values, labels

    def get_config(self):
        return SwiftFormerConfig(
            depths=self.layer_depths,
            embed_dims=self.embed_dims,
            mlp_ratio=4,
            downsamples=[True, True, True, True],
            hidden_act="gelu",
            num_labels=self.num_labels,
            down_patch_size=3,
            down_stride=2,
            down_pad=1,
            drop_rate=0.0,
            drop_path_rate=0.0,
            use_layer_scale=True,
            layer_scale_init_value=1e-5,
        )

    def create_and_check_model(self, config, pixel_values, labels):
        model = SwiftFormerModel(config=config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values)
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.embed_dims[-1], 7, 7))

    def create_and_check_for_image_classification(self, config, pixel_values, labels):
        config.num_labels = self.num_labels
        model = SwiftFormerForImageClassification(config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values, labels=labels)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))

        model = SwiftFormerForImageClassification(config)
        model.to(torch_device)
        model.eval()

        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
        result = model(pixel_values)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))

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


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

    all_model_classes = (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
    pipeline_model_mapping = (
        {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
        if is_torch_available()
        else {}
    )

    fx_compatible = False
    test_pruning = False
    test_resize_embeddings = False
    test_head_masking = False
    has_attentions = False

    def setUp(self):
        self.model_tester = SwiftFormerModelTester(self)
        self.config_tester = ConfigTester(
            self,
            config_class=SwiftFormerConfig,
            has_text_modality=False,
            hidden_size=37,
            num_attention_heads=12,
            num_hidden_layers=12,
        )

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

    @unittest.skip(reason="SwiftFormer 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)
            x = model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(x, nn.Linear))

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

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

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

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

    def test_for_image_classification(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*config_and_inputs)

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

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

    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_stages = 8
            self.assertEqual(len(hidden_states), expected_num_stages)  # TODO

            # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
            # with the width and height being successively divided by 2, after every 2 blocks
            for i in range(len(hidden_states)):
                self.assertEqual(
                    hidden_states[i].shape,
                    torch.Size(
                        [
                            self.model_tester.batch_size,
                            self.model_tester.embed_dims[i // 2],
                            (self.model_tester.image_size // 4) // 2 ** (i // 2),
                            (self.model_tester.image_size // 4) // 2 ** (i // 2),
                        ]
                    ),
                )

        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_initialization(self):
        def _config_zero_init(config):
            configs_no_init = copy.deepcopy(config)
            for key in configs_no_init.__dict__.keys():
                if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
                    setattr(configs_no_init, key, 1e-10)
                if isinstance(getattr(configs_no_init, key, None), PretrainedConfig):
                    no_init_subconfig = _config_zero_init(getattr(configs_no_init, key))
                    setattr(configs_no_init, key, no_init_subconfig)
            return configs_no_init

        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:
                    self.assertIn(
                        ((param.data.mean() * 1e9) / 1e9).round().item(),
                        [0.0, 1.0],
                        msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                    )


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


@require_torch
@require_vision
class SwiftFormerModelIntegrationTest(unittest.TestCase):
    @cached_property
    def default_image_processor(self):
        return ViTImageProcessor.from_pretrained("MBZUAI/swiftformer-xs") if is_vision_available() else None

    @slow
    def test_inference_image_classification_head(self):
        model = SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs").to(torch_device)

        image_processor = self.default_image_processor
        image = prepare_img()
        inputs = image_processor(images=image, return_tensors="pt").to(torch_device)

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

        # verify the logits
        expected_shape = torch.Size((1, 1000))
        self.assertEqual(outputs.logits.shape, expected_shape)

        expected_slice = torch.tensor([[-2.1703e00, 2.1107e00, -2.0811e00]]).to(torch_device)
        self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))
