# 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.

import copy
import inspect

from transformers.testing_utils import require_torch, torch_device
from transformers.utils.backbone_utils import BackboneType


@require_torch
class BackboneTesterMixin:
    all_model_classes = ()
    has_attentions = True

    def test_config(self):
        config_class = self.config_class

        # test default config
        config = config_class()
        self.assertIsNotNone(config)
        expected_stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(config.depths) + 1)]
        self.assertEqual(config.stage_names, expected_stage_names)
        self.assertTrue(set(config.out_features).issubset(set(config.stage_names)))

        # Test out_features and out_indices are correctly set
        # out_features and out_indices both None
        config = config_class(out_features=None, out_indices=None)
        self.assertEqual(config.out_features, [config.stage_names[-1]])
        self.assertEqual(config.out_indices, [len(config.stage_names) - 1])

        # out_features and out_indices both set
        config = config_class(out_features=["stem", "stage1"], out_indices=[0, 1])
        self.assertEqual(config.out_features, ["stem", "stage1"])
        self.assertEqual(config.out_indices, [0, 1])

        # Only out_features set
        config = config_class(out_features=["stage1", "stage3"])
        self.assertEqual(config.out_features, ["stage1", "stage3"])
        self.assertEqual(config.out_indices, [1, 3])

        # Only out_indices set
        config = config_class(out_indices=[0, 2])
        self.assertEqual(config.out_features, [config.stage_names[0], config.stage_names[2]])
        self.assertEqual(config.out_indices, [0, 2])

        # Error raised when out_indices do not correspond to out_features
        with self.assertRaises(ValueError):
            config = config_class(out_features=["stage1", "stage2"], out_indices=[0, 2])

    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_channels(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            self.assertEqual(len(model.channels), len(config.out_features))
            num_features = model.num_features
            out_indices = [config.stage_names.index(feat) for feat in config.out_features]
            out_channels = [num_features[idx] for idx in out_indices]
            self.assertListEqual(model.channels, out_channels)

            new_config = copy.deepcopy(config)
            new_config.out_features = None
            model = model_class(new_config)
            self.assertEqual(len(model.channels), 1)
            self.assertListEqual(model.channels, [num_features[-1]])

            new_config = copy.deepcopy(config)
            new_config.out_indices = None
            model = model_class(new_config)
            self.assertEqual(len(model.channels), 1)
            self.assertListEqual(model.channels, [num_features[-1]])

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

        for model_class in self.all_model_classes:
            model = model_class(config)
            model.to(torch_device)
            model.eval()
            result = model(**inputs_dict)

            self.assertEqual(len(result.feature_maps), len(config.out_features))
            self.assertEqual(len(model.channels), len(config.out_features))
            self.assertEqual(len(result.feature_maps), len(config.out_indices))
            self.assertEqual(len(model.channels), len(config.out_indices))

            # Check output of last stage is taken if out_features=None, out_indices=None
            modified_config = copy.deepcopy(config)
            modified_config.out_features = None
            model = model_class(modified_config)
            model.to(torch_device)
            model.eval()
            result = model(**inputs_dict)

            self.assertEqual(len(result.feature_maps), 1)
            self.assertEqual(len(model.channels), 1)

            modified_config = copy.deepcopy(config)
            modified_config.out_indices = None
            model = model_class(modified_config)
            model.to(torch_device)
            model.eval()
            result = model(**inputs_dict)

            self.assertEqual(len(result.feature_maps), 1)
            self.assertEqual(len(model.channels), 1)

            # Check backbone can be initialized with fresh weights
            modified_config = copy.deepcopy(config)
            modified_config.use_pretrained_backbone = False
            model = model_class(modified_config)
            model.to(torch_device)
            model.eval()
            result = model(**inputs_dict)

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

        for backbone_class in self.all_model_classes:
            backbone = backbone_class(config)

            self.assertTrue(hasattr(backbone, "backbone_type"))
            self.assertTrue(hasattr(backbone, "stage_names"))
            self.assertTrue(hasattr(backbone, "num_features"))
            self.assertTrue(hasattr(backbone, "out_indices"))
            self.assertTrue(hasattr(backbone, "out_features"))
            self.assertTrue(hasattr(backbone, "out_feature_channels"))
            self.assertTrue(hasattr(backbone, "channels"))

            self.assertIsInstance(backbone.backbone_type, BackboneType)
            # Verify num_features has been initialized in the backbone init
            self.assertIsNotNone(backbone.num_features)
            self.assertTrue(len(backbone.channels) == len(backbone.out_indices))
            self.assertTrue(len(backbone.stage_names) == len(backbone.num_features))
            self.assertTrue(len(backbone.channels) <= len(backbone.num_features))
            self.assertTrue(len(backbone.out_feature_channels) == len(backbone.stage_names))

    def test_backbone_outputs(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        batch_size = inputs_dict["pixel_values"].shape[0]

        for backbone_class in self.all_model_classes:
            backbone = backbone_class(config)
            backbone.to(torch_device)
            backbone.eval()

            outputs = backbone(**inputs_dict)

            # Test default outputs and verify feature maps
            self.assertIsInstance(outputs.feature_maps, tuple)
            self.assertTrue(len(outputs.feature_maps) == len(backbone.channels))
            for feature_map, n_channels in zip(outputs.feature_maps, backbone.channels):
                self.assertTrue(feature_map.shape[:2], (batch_size, n_channels))
            self.assertIsNone(outputs.hidden_states)
            self.assertIsNone(outputs.attentions)

            # Test output_hidden_states=True
            outputs = backbone(**inputs_dict, output_hidden_states=True)
            self.assertIsNotNone(outputs.hidden_states)
            self.assertTrue(len(outputs.hidden_states), len(backbone.stage_names))
            for hidden_state, n_channels in zip(outputs.hidden_states, backbone.channels):
                self.assertTrue(hidden_state.shape[:2], (batch_size, n_channels))

            # Test output_attentions=True
            if self.has_attentions:
                outputs = backbone(**inputs_dict, output_attentions=True)
                self.assertIsNotNone(outputs.attentions)

# 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.

import copy
import inspect

from transformers.testing_utils import require_torch, torch_device
from transformers.utils.backbone_utils import BackboneType


@require_torch
class BackboneTesterMixin:
    all_model_classes = ()
    has_attentions = True

    def test_config(self):
        config_class = self.config_class

        # test default config
        config = config_class()
        self.assertIsNotNone(config)
        expected_stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(config.depths) + 1)]
        self.assertEqual(config.stage_names, expected_stage_names)
        self.assertTrue(set(config.out_features).issubset(set(config.stage_names)))

        # Test out_features and out_indices are correctly set
        # out_features and out_indices both None
        config = config_class(out_features=None, out_indices=None)
        self.assertEqual(config.out_features, [config.stage_names[-1]])
        self.assertEqual(config.out_indices, [len(config.stage_names) - 1])

        # out_features and out_indices both set
        config = config_class(out_features=["stem", "stage1"], out_indices=[0, 1])
        self.assertEqual(config.out_features, ["stem", "stage1"])
        self.assertEqual(config.out_indices, [0, 1])

        # Only out_features set
        config = config_class(out_features=["stage1", "stage3"])
        self.assertEqual(config.out_features, ["stage1", "stage3"])
        self.assertEqual(config.out_indices, [1, 3])

        # Only out_indices set
        config = config_class(out_indices=[0, 2])
        self.assertEqual(config.out_features, [config.stage_names[0], config.stage_names[2]])
        self.assertEqual(config.out_indices, [0, 2])

        # Error raised when out_indices do not correspond to out_features
        with self.assertRaises(ValueError):
            config = config_class(out_features=["stage1", "stage2"], out_indices=[0, 2])

    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_channels(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            self.assertEqual(len(model.channels), len(config.out_features))
            num_features = model.num_features
            out_indices = [config.stage_names.index(feat) for feat in config.out_features]
            out_channels = [num_features[idx] for idx in out_indices]
            self.assertListEqual(model.channels, out_channels)

            new_config = copy.deepcopy(config)
            new_config.out_features = None
            model = model_class(new_config)
            self.assertEqual(len(model.channels), 1)
            self.assertListEqual(model.channels, [num_features[-1]])

            new_config = copy.deepcopy(config)
            new_config.out_indices = None
            model = model_class(new_config)
            self.assertEqual(len(model.channels), 1)
            self.assertListEqual(model.channels, [num_features[-1]])

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

        for model_class in self.all_model_classes:
            model = model_class(config)
            model.to(torch_device)
            model.eval()
            result = model(**inputs_dict)

            self.assertEqual(len(result.feature_maps), len(config.out_features))
            self.assertEqual(len(model.channels), len(config.out_features))
            self.assertEqual(len(result.feature_maps), len(config.out_indices))
            self.assertEqual(len(model.channels), len(config.out_indices))

            # Check output of last stage is taken if out_features=None, out_indices=None
            modified_config = copy.deepcopy(config)
            modified_config.out_features = None
            model = model_class(modified_config)
            model.to(torch_device)
            model.eval()
            result = model(**inputs_dict)

            self.assertEqual(len(result.feature_maps), 1)
            self.assertEqual(len(model.channels), 1)

            modified_config = copy.deepcopy(config)
            modified_config.out_indices = None
            model = model_class(modified_config)
            model.to(torch_device)
            model.eval()
            result = model(**inputs_dict)

            self.assertEqual(len(result.feature_maps), 1)
            self.assertEqual(len(model.channels), 1)

            # Check backbone can be initialized with fresh weights
            modified_config = copy.deepcopy(config)
            modified_config.use_pretrained_backbone = False
            model = model_class(modified_config)
            model.to(torch_device)
            model.eval()
            result = model(**inputs_dict)

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

        for backbone_class in self.all_model_classes:
            backbone = backbone_class(config)

            self.assertTrue(hasattr(backbone, "backbone_type"))
            self.assertTrue(hasattr(backbone, "stage_names"))
            self.assertTrue(hasattr(backbone, "num_features"))
            self.assertTrue(hasattr(backbone, "out_indices"))
            self.assertTrue(hasattr(backbone, "out_features"))
            self.assertTrue(hasattr(backbone, "out_feature_channels"))
            self.assertTrue(hasattr(backbone, "channels"))

            self.assertIsInstance(backbone.backbone_type, BackboneType)
            # Verify num_features has been initialized in the backbone init
            self.assertIsNotNone(backbone.num_features)
            self.assertTrue(len(backbone.channels) == len(backbone.out_indices))
            self.assertTrue(len(backbone.stage_names) == len(backbone.num_features))
            self.assertTrue(len(backbone.channels) <= len(backbone.num_features))
            self.assertTrue(len(backbone.out_feature_channels) == len(backbone.stage_names))

    def test_backbone_outputs(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        batch_size = inputs_dict["pixel_values"].shape[0]

        for backbone_class in self.all_model_classes:
            backbone = backbone_class(config)
            backbone.to(torch_device)
            backbone.eval()

            outputs = backbone(**inputs_dict)

            # Test default outputs and verify feature maps
            self.assertIsInstance(outputs.feature_maps, tuple)
            self.assertTrue(len(outputs.feature_maps) == len(backbone.channels))
            for feature_map, n_channels in zip(outputs.feature_maps, backbone.channels):
                self.assertTrue(feature_map.shape[:2], (batch_size, n_channels))
            self.assertIsNone(outputs.hidden_states)
            self.assertIsNone(outputs.attentions)

            # Test output_hidden_states=True
            outputs = backbone(**inputs_dict, output_hidden_states=True)
            self.assertIsNotNone(outputs.hidden_states)
            self.assertTrue(len(outputs.hidden_states), len(backbone.stage_names))
            for hidden_state, n_channels in zip(outputs.hidden_states, backbone.channels):
                self.assertTrue(hidden_state.shape[:2], (batch_size, n_channels))

            # Test output_attentions=True
            if self.has_attentions:
                outputs = backbone(**inputs_dict, output_attentions=True)
                self.assertIsNotNone(outputs.attentions)
