# 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
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
    import torch

    from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin


class TimmBackboneModelTester:
    def __init__(
        self,
        parent,
        out_indices=None,
        out_features=None,
        stage_names=None,
        backbone="resnet50",
        batch_size=3,
        image_size=32,
        num_channels=3,
        is_training=True,
        use_pretrained_backbone=True,
    ):
        self.parent = parent
        self.out_indices = out_indices if out_indices is not None else [4]
        self.stage_names = stage_names
        self.out_features = out_features
        self.backbone = backbone
        self.batch_size = batch_size
        self.image_size = image_size
        self.num_channels = num_channels
        self.use_pretrained_backbone = use_pretrained_backbone
        self.is_training = is_training

    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 TimmBackboneConfig(
            image_size=self.image_size,
            num_channels=self.num_channels,
            out_features=self.out_features,
            out_indices=self.out_indices,
            stage_names=self.stage_names,
            use_pretrained_backbone=self.use_pretrained_backbone,
            backbone=self.backbone,
        )

    def create_and_check_model(self, config, pixel_values):
        model = TimmBackbone(config=config)
        model.to(torch_device)
        model.eval()
        with torch.no_grad():
            result = model(pixel_values)
        self.parent.assertEqual(
            result.feature_map[-1].shape,
            (self.batch_size, model.channels[-1], 14, 14),
        )

    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
@require_timm
class TimmBackboneModelTest(ModelTesterMixin, BackboneTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (TimmBackbone,) if is_torch_available() else ()
    pipeline_model_mapping = {"feature-extraction": TimmBackbone} if is_torch_available() else {}
    test_resize_embeddings = False
    test_head_masking = False
    test_pruning = False
    has_attentions = False

    def setUp(self):
        self.model_tester = TimmBackboneModelTester(self)
        self.config_tester = ConfigTester(self, config_class=PretrainedConfig, has_text_modality=False)

    def test_config(self):
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()

    def test_timm_transformer_backbone_equivalence(self):
        timm_checkpoint = "resnet18"
        transformers_checkpoint = "microsoft/resnet-18"

        timm_model = AutoBackbone.from_pretrained(timm_checkpoint, use_timm_backbone=True)
        transformers_model = AutoBackbone.from_pretrained(transformers_checkpoint)

        self.assertEqual(len(timm_model.out_features), len(transformers_model.out_features))
        self.assertEqual(len(timm_model.stage_names), len(transformers_model.stage_names))
        self.assertEqual(timm_model.channels, transformers_model.channels)
        # Out indices are set to the last layer by default. For timm models, we don't know
        # the number of layers in advance, so we set it to (-1,), whereas for transformers
        # models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
        self.assertEqual(timm_model.out_indices, (-1,))
        self.assertEqual(transformers_model.out_indices, [len(timm_model.stage_names) - 1])

        timm_model = AutoBackbone.from_pretrained(timm_checkpoint, use_timm_backbone=True, out_indices=[1, 2, 3])
        transformers_model = AutoBackbone.from_pretrained(transformers_checkpoint, out_indices=[1, 2, 3])

        self.assertEqual(timm_model.out_indices, transformers_model.out_indices)
        self.assertEqual(len(timm_model.out_features), len(transformers_model.out_features))
        self.assertEqual(timm_model.channels, transformers_model.channels)

    @unittest.skip("TimmBackbone doesn't support feed forward chunking")
    def test_feed_forward_chunking(self):
        pass

    @unittest.skip("TimmBackbone doesn't have num_hidden_layers attribute")
    def test_hidden_states_output(self):
        pass

    @unittest.skip("TimmBackbone initialization is managed on the timm side")
    def test_initialization(self):
        pass

    @unittest.skip("TimmBackbone models doesn't have inputs_embeds")
    def test_inputs_embeds(self):
        pass

    @unittest.skip("TimmBackbone models doesn't have inputs_embeds")
    def test_model_common_attributes(self):
        pass

    @unittest.skip("TimmBackbone model cannot be created without specifying a backbone checkpoint")
    def test_from_pretrained_no_checkpoint(self):
        pass

    @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone")
    def test_save_load(self):
        pass

    @unittest.skip("model weights aren't tied in TimmBackbone.")
    def test_tie_model_weights(self):
        pass

    @unittest.skip("model weights aren't tied in TimmBackbone.")
    def test_tied_model_weights_key_ignore(self):
        pass

    @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone")
    def test_load_save_without_tied_weights(self):
        pass

    @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone")
    def test_model_weights_reload_no_missing_tied_weights(self):
        pass

    @unittest.skip("TimmBackbone doesn't have hidden size info in its configuration.")
    def test_channels(self):
        pass

    @unittest.skip("TimmBackbone doesn't support output_attentions.")
    def test_torchscript_output_attentions(self):
        pass

    @unittest.skip("Safetensors is not supported by timm.")
    def test_can_use_safetensors(self):
        pass

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

    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_retain_grad_hidden_states_attentions(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.output_hidden_states = True
        config.output_attentions = self.has_attentions

        # no need to test all models as different heads yield the same functionality
        model_class = self.all_model_classes[0]
        model = model_class(config)
        model.to(torch_device)

        inputs = self._prepare_for_class(inputs_dict, model_class)
        outputs = model(**inputs)
        output = outputs[0][-1]

        # Encoder-/Decoder-only models
        hidden_states = outputs.hidden_states[0]
        hidden_states.retain_grad()

        if self.has_attentions:
            attentions = outputs.attentions[0]
            attentions.retain_grad()

        output.flatten()[0].backward(retain_graph=True)

        self.assertIsNotNone(hidden_states.grad)

        if self.has_attentions:
            self.assertIsNotNone(attentions.grad)

    # TimmBackbone config doesn't have out_features attribute
    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_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_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)

# 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
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
    import torch

    from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin


class TimmBackboneModelTester:
    def __init__(
        self,
        parent,
        out_indices=None,
        out_features=None,
        stage_names=None,
        backbone="resnet18",
        batch_size=3,
        image_size=32,
        num_channels=3,
        is_training=True,
        use_pretrained_backbone=True,
    ):
        self.parent = parent
        self.out_indices = out_indices if out_indices is not None else [4]
        self.stage_names = stage_names
        self.out_features = out_features
        self.backbone = backbone
        self.batch_size = batch_size
        self.image_size = image_size
        self.num_channels = num_channels
        self.use_pretrained_backbone = use_pretrained_backbone
        self.is_training = is_training

    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 TimmBackboneConfig(
            image_size=self.image_size,
            num_channels=self.num_channels,
            out_features=self.out_features,
            out_indices=self.out_indices,
            stage_names=self.stage_names,
            use_pretrained_backbone=self.use_pretrained_backbone,
            backbone=self.backbone,
        )

    def create_and_check_model(self, config, pixel_values):
        model = TimmBackbone(config=config)
        model.to(torch_device)
        model.eval()
        with torch.no_grad():
            result = model(pixel_values)
        self.parent.assertEqual(
            result.feature_map[-1].shape,
            (self.batch_size, model.channels[-1], 14, 14),
        )

    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
@require_timm
class TimmBackboneModelTest(ModelTesterMixin, BackboneTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (TimmBackbone,) if is_torch_available() else ()
    pipeline_model_mapping = {"feature-extraction": TimmBackbone} if is_torch_available() else {}
    test_resize_embeddings = False
    test_head_masking = False
    test_pruning = False
    has_attentions = False

    def setUp(self):
        self.model_tester = TimmBackboneModelTester(self)
        self.config_tester = ConfigTester(self, config_class=PretrainedConfig, has_text_modality=False)

    def test_config(self):
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()

    def test_timm_transformer_backbone_equivalence(self):
        timm_checkpoint = "resnet18"
        transformers_checkpoint = "microsoft/resnet-18"

        timm_model = AutoBackbone.from_pretrained(timm_checkpoint, use_timm_backbone=True)
        transformers_model = AutoBackbone.from_pretrained(transformers_checkpoint)

        self.assertEqual(len(timm_model.out_features), len(transformers_model.out_features))
        self.assertEqual(len(timm_model.stage_names), len(transformers_model.stage_names))
        self.assertEqual(timm_model.channels, transformers_model.channels)
        # Out indices are set to the last layer by default. For timm models, we don't know
        # the number of layers in advance, so we set it to (-1,), whereas for transformers
        # models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
        self.assertEqual(timm_model.out_indices, (-1,))
        self.assertEqual(transformers_model.out_indices, [len(timm_model.stage_names) - 1])

        timm_model = AutoBackbone.from_pretrained(timm_checkpoint, use_timm_backbone=True, out_indices=[1, 2, 3])
        transformers_model = AutoBackbone.from_pretrained(transformers_checkpoint, out_indices=[1, 2, 3])

        self.assertEqual(timm_model.out_indices, transformers_model.out_indices)
        self.assertEqual(len(timm_model.out_features), len(transformers_model.out_features))
        self.assertEqual(timm_model.channels, transformers_model.channels)

    @unittest.skip("TimmBackbone doesn't support feed forward chunking")
    def test_feed_forward_chunking(self):
        pass

    @unittest.skip("TimmBackbone doesn't have num_hidden_layers attribute")
    def test_hidden_states_output(self):
        pass

    @unittest.skip("TimmBackbone initialization is managed on the timm side")
    def test_initialization(self):
        pass

    @unittest.skip("TimmBackbone models doesn't have inputs_embeds")
    def test_inputs_embeds(self):
        pass

    @unittest.skip("TimmBackbone models doesn't have inputs_embeds")
    def test_model_common_attributes(self):
        pass

    @unittest.skip("TimmBackbone model cannot be created without specifying a backbone checkpoint")
    def test_from_pretrained_no_checkpoint(self):
        pass

    @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone")
    def test_save_load(self):
        pass

    @unittest.skip("model weights aren't tied in TimmBackbone.")
    def test_tie_model_weights(self):
        pass

    @unittest.skip("model weights aren't tied in TimmBackbone.")
    def test_tied_model_weights_key_ignore(self):
        pass

    @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone")
    def test_load_save_without_tied_weights(self):
        pass

    @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone")
    def test_model_weights_reload_no_missing_tied_weights(self):
        pass

    @unittest.skip("TimmBackbone doesn't have hidden size info in its configuration.")
    def test_channels(self):
        pass

    @unittest.skip("TimmBackbone doesn't support output_attentions.")
    def test_torchscript_output_attentions(self):
        pass

    @unittest.skip("Safetensors is not supported by timm.")
    def test_can_use_safetensors(self):
        pass

    @unittest.skip("Need to use a timm backbone and there is no tiny model available.")
    def test_model_is_small(self):
        pass

    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_retain_grad_hidden_states_attentions(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.output_hidden_states = True
        config.output_attentions = self.has_attentions

        # no need to test all models as different heads yield the same functionality
        model_class = self.all_model_classes[0]
        model = model_class(config)
        model.to(torch_device)

        inputs = self._prepare_for_class(inputs_dict, model_class)
        outputs = model(**inputs)
        output = outputs[0][-1]

        # Encoder-/Decoder-only models
        hidden_states = outputs.hidden_states[0]
        hidden_states.retain_grad()

        if self.has_attentions:
            attentions = outputs.attentions[0]
            attentions.retain_grad()

        output.flatten()[0].backward(retain_graph=True)

        self.assertIsNotNone(hidden_states.grad)

        if self.has_attentions:
            self.assertIsNotNone(attentions.grad)

    # TimmBackbone config doesn't have out_features attribute
    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_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_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)
