# Copyright 2024 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 Hiera model."""

import math
import unittest

from transformers import HieraConfig
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_backbone_common import BackboneTesterMixin
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 HieraBackbone, HieraForImageClassification, HieraForPreTraining, HieraModel

if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor


class HieraModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        image_size=[64, 64],
        mlp_ratio=1.0,
        num_channels=3,
        depths=[1, 1, 1, 1],
        patch_stride=[4, 4],
        patch_size=[7, 7],
        patch_padding=[3, 3],
        masked_unit_size=[8, 8],
        num_heads=[1, 1, 1, 1],
        embed_dim_multiplier=2.0,
        is_training=True,
        use_labels=True,
        embed_dim=8,
        hidden_act="gelu",
        decoder_hidden_size=2,
        decoder_depth=1,
        decoder_num_heads=1,
        initializer_range=0.02,
        scope=None,
        type_sequence_label_size=10,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.image_size = image_size
        self.mlp_ratio = mlp_ratio
        self.num_channels = num_channels
        self.depths = depths
        self.patch_stride = patch_stride
        self.patch_size = patch_size
        self.patch_padding = patch_padding
        self.masked_unit_size = masked_unit_size
        self.num_heads = num_heads
        self.embed_dim_multiplier = embed_dim_multiplier
        self.is_training = is_training
        self.use_labels = use_labels
        self.embed_dim = embed_dim
        self.hidden_act = hidden_act
        self.decoder_hidden_size = decoder_hidden_size
        self.decoder_depth = decoder_depth
        self.decoder_num_heads = decoder_num_heads
        self.initializer_range = initializer_range
        self.scope = scope
        self.type_sequence_label_size = type_sequence_label_size

    def prepare_config_and_inputs(self):
        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]])

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

        config = self.get_config()

        return config, pixel_values, labels

    def get_config(self):
        return HieraConfig(
            embed_dim=self.embed_dim,
            image_size=self.image_size,
            patch_stride=self.patch_stride,
            patch_size=self.patch_size,
            patch_padding=self.patch_padding,
            masked_unit_size=self.masked_unit_size,
            mlp_ratio=self.mlp_ratio,
            num_channels=self.num_channels,
            depths=self.depths,
            num_heads=self.num_heads,
            embed_dim_multiplier=self.embed_dim_multiplier,
            hidden_act=self.hidden_act,
            decoder_hidden_size=self.decoder_hidden_size,
            decoder_depth=self.decoder_depth,
            decoder_num_heads=self.decoder_num_heads,
            initializer_range=self.initializer_range,
        )

    def create_and_check_model(self, config, pixel_values, labels):
        model = HieraModel(config=config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values)

        tokens_spatial_shape = [i // s for i, s in zip(self.image_size, config.patch_stride)]
        expected_seq_len = math.prod(tokens_spatial_shape) // math.prod(config.query_stride) ** (config.num_query_pool)
        expected_dim = int(config.embed_dim * config.embed_dim_multiplier ** (len(config.depths) - 1))

        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, expected_seq_len, expected_dim))

    def create_and_check_backbone(self, config, pixel_values, labels):
        model = HieraBackbone(config=config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values)

        # verify hidden states
        self.parent.assertEqual(len(result.feature_maps), len(config.out_features))
        num_patches = config.image_size[0] // config.patch_stride[0] // config.masked_unit_size[0]
        self.parent.assertListEqual(
            list(result.feature_maps[0].shape), [self.batch_size, model.channels[0], num_patches, num_patches]
        )

        # verify channels
        self.parent.assertEqual(len(model.channels), len(config.out_features))

        # verify backbone works with out_features=None
        config.out_features = None
        model = HieraBackbone(config=config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values)

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps), 1)
        self.parent.assertListEqual(
            list(result.feature_maps[0].shape), [self.batch_size, model.channels[-1], num_patches, num_patches]
        )

        # verify channels
        self.parent.assertEqual(len(model.channels), 1)

    def create_and_check_for_pretraining(self, config, pixel_values, labels):
        model = HieraForPreTraining(config=config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values)
        pred_stride = config.patch_stride[-1] * (config.query_stride[-1] ** config.num_query_pool)
        num_patches = self.image_size[0] // pred_stride
        self.parent.assertEqual(
            result.logits.shape, (self.batch_size, num_patches**2, self.num_channels * pred_stride**2)
        )

        # test greyscale images
        config.num_channels = 1
        model = HieraForPreTraining(config)
        model.to(torch_device)
        model.eval()

        pixel_values = floats_tensor([self.batch_size, 1, self.image_size[0], self.image_size[0]])
        result = model(pixel_values)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, num_patches**2, pred_stride**2))

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

        # test greyscale images
        config.num_channels = 1
        model = HieraForImageClassification(config)
        model.to(torch_device)
        model.eval()

        pixel_values = floats_tensor([self.batch_size, 1, self.image_size[0], self.image_size[0]])
        result = model(pixel_values)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))

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


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

    all_model_classes = (
        (
            HieraModel,
            HieraBackbone,
            HieraForImageClassification,
            HieraForPreTraining,
        )
        if is_torch_available()
        else ()
    )
    pipeline_model_mapping = (
        {"image-feature-extraction": HieraModel, "image-classification": HieraForImageClassification}
        if is_torch_available()
        else {}
    )
    fx_compatible = True

    test_pruning = False
    test_resize_embeddings = False
    test_head_masking = False
    test_torch_exportable = True

    def setUp(self):
        self.model_tester = HieraModelTester(self)
        self.config_tester = ConfigTester(self, config_class=HieraConfig, 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()

    # Overriding as Hiera `get_input_embeddings` returns HieraPatchEmbeddings
    def test_model_get_set_embeddings(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))

    # Overriding as attention shape depends on patch_stride and mask_unit_size
    def test_attention_outputs(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.return_dict = True

        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._from_config(config, attn_implementation="eager")
            config = model.config
            model.to(torch_device)
            model.eval()
            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
            attentions = outputs.attentions
            expected_num_attentions = len(self.model_tester.depths)
            self.assertEqual(len(attentions), expected_num_attentions)

            # check that output_attentions also work using config
            del inputs_dict["output_attentions"]
            config.output_attentions = True
            seq_len = math.prod([i // s for i, s in zip(config.image_size, config.patch_stride)])
            mask_unit_area = math.prod(config.masked_unit_size)
            num_windows = seq_len // mask_unit_area
            if model_class.__name__ == "HieraForPreTraining":
                num_windows = int(num_windows * (1 - config.mask_ratio))
                seq_len = int(num_windows * mask_unit_area)
            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), expected_num_attentions)

            self.assertListEqual(
                list(attentions[0].shape[-4:]),
                [self.model_tester.num_heads[0], num_windows, mask_unit_area, seq_len // num_windows],
            )
            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))

            # also another +1 for reshaped_hidden_states
            added_hidden_states = 1 if model_class.__name__ == "HieraBackbone" else 2
            self.assertEqual(out_len + added_hidden_states, len(outputs))

            self_attentions = outputs.attentions

            self.assertEqual(len(self_attentions), expected_num_attentions)

            self.assertListEqual(
                list(self_attentions[0].shape[-4:]),
                [self.model_tester.num_heads[0], num_windows, mask_unit_area, seq_len // num_windows],
            )

    # Overriding as attention shape depends on patch_stride and mask_unit_size
    def test_hidden_states_output(self):
        def check_hidden_states_output(inputs_dict, config, model_class, image_size):
            model = model_class(config)
            model.to(torch_device)
            model.eval()

            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))

            hidden_states = outputs.hidden_states

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

            # Hiera has a different seq_length
            patch_size = config.patch_stride

            num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
            if model_class.__name__ == "HieraForPreTraining":
                mask_unit_area = math.prod(config.masked_unit_size)
                num_windows = num_patches // mask_unit_area
                num_windows = int(num_windows * (1 - config.mask_ratio))
                num_patches = int(num_windows * mask_unit_area)

            self.assertListEqual(
                list(hidden_states[0].shape[-2:]),
                [num_patches, self.model_tester.embed_dim],
            )

            if not model_class.__name__ == "HieraBackbone":
                reshaped_hidden_states = outputs.reshaped_hidden_states
                self.assertEqual(len(reshaped_hidden_states), expected_num_layers)

                batch_size = reshaped_hidden_states[0].shape[0]
                num_channels = reshaped_hidden_states[0].shape[-1]

                reshaped_hidden_states = reshaped_hidden_states[0].view(batch_size, -1, num_channels)
                self.assertListEqual(
                    list(reshaped_hidden_states.shape[-2:]),
                    [num_patches, self.model_tester.embed_dim],
                )

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        image_size = self.model_tester.image_size

        for model_class in self.all_model_classes:
            inputs_dict["output_hidden_states"] = True
            check_hidden_states_output(inputs_dict, config, model_class, image_size)

            # 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, image_size)

    # Overriding since HieraForPreTraining outputs bool_masked_pos which has to be converted to float in the msg
    def test_model_outputs_equivalence(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        def set_nan_tensor_to_zero(t):
            t[t != t] = 0
            return t

        def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
            with torch.no_grad():
                tuple_output = model(**tuple_inputs, return_dict=False, **additional_kwargs)
                dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs).to_tuple()

                def recursive_check(tuple_object, dict_object):
                    if isinstance(tuple_object, (list, tuple)):
                        for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
                            recursive_check(tuple_iterable_value, dict_iterable_value)
                    elif isinstance(tuple_object, dict):
                        for tuple_iterable_value, dict_iterable_value in zip(
                            tuple_object.values(), dict_object.values()
                        ):
                            recursive_check(tuple_iterable_value, dict_iterable_value)
                    elif tuple_object is None:
                        return
                    else:
                        self.assertTrue(
                            torch.allclose(
                                set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5
                            ),
                            msg=(
                                "Tuple and dict output are not equal. Difference:"
                                f" {torch.max(torch.abs(tuple_object.float() - dict_object.float()))}. Tuple has `nan`:"
                                f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has"
                                f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}."
                            ),
                        )

                recursive_check(tuple_output, dict_output)

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

            additional_kwargs = {}

            tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
            dict_inputs = self._prepare_for_class(inputs_dict, model_class)
            check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs)

            tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs)

            tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
            dict_inputs = self._prepare_for_class(inputs_dict, model_class)
            additional_kwargs["output_hidden_states"] = True
            check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs)

            tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs)

            if self.has_attentions:
                # Removing "output_hidden_states"
                del additional_kwargs["output_hidden_states"]

                tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
                dict_inputs = self._prepare_for_class(inputs_dict, model_class)
                additional_kwargs["output_attentions"] = True
                check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs)

                tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
                dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
                check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs)

                tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
                dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
                additional_kwargs["output_hidden_states"] = True
                check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs)

    @unittest.skip(reason="Hiera Transformer does not use feedforward chunking")
    def test_feed_forward_chunking(self):
        pass

    @unittest.skip(reason="Hiera 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)
            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_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)

    def test_backbone(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_backbone(*config_and_inputs)

    def test_for_pretraining(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*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 ["facebook/hiera-tiny-224-hf"]:
            model = HieraModel.from_pretrained(model_name)
            self.assertIsNotNone(model)


# 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 HieraModelIntegrationTest(unittest.TestCase):
    @cached_property
    def default_image_processor(self):
        return AutoImageProcessor.from_pretrained("facebook/hiera-tiny-224-in1k-hf") if is_vision_available() else None

    @slow
    def test_inference_image_classification_head(self):
        model = HieraForImageClassification.from_pretrained("facebook/hiera-tiny-224-in1k-hf").to(torch_device)

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

        expected_pixel_values = torch.tensor(
            [
                [[0.2967, 0.4679, 0.4508], [0.3309, 0.4337, 0.3309], [0.3309, 0.3823, 0.3309]],
                [[-1.5455, -1.4930, -1.5455], [-1.5280, -1.4755, -1.5980], [-1.5630, -1.5280, -1.4755]],
                [[-0.6367, -0.4973, -0.5321], [-0.7936, -0.6715, -0.6715], [-0.8284, -0.7413, -0.5670]],
            ]
        ).to(torch_device)

        torch.testing.assert_close(inputs.pixel_values[0, :3, :3, :3], expected_pixel_values, rtol=1e-4, atol=1e-4)

        # 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([[0.8028, 0.2409, -0.2254, -0.3712, -0.2848]]).to(torch_device)

        torch.testing.assert_close(outputs.logits[0, :5], expected_slice, rtol=1e-4, atol=1e-4)

    def test_inference_interpolate_pos_encoding(self):
        model = HieraModel.from_pretrained("facebook/hiera-tiny-224-hf").to(torch_device)

        image_processor = AutoImageProcessor.from_pretrained(
            "facebook/hiera-tiny-224-hf", size={"shortest_edge": 448}, crop_size={"height": 448, "width": 448}
        )
        image = prepare_img()
        inputs = image_processor(images=image, return_tensors="pt")
        pixel_values = inputs.pixel_values.to(torch_device)

        # forward pass
        with torch.no_grad():
            outputs = model(pixel_values, interpolate_pos_encoding=True)

        # verify the logits
        expected_shape = torch.Size((1, 196, 768))
        self.assertEqual(outputs.last_hidden_state.shape, expected_shape)

        expected_slice = torch.tensor(
            [[1.7853, 0.0690, 0.3177], [2.6853, -0.2334, 0.0889], [1.5445, -0.1515, -0.0300]]
        ).to(torch_device)

        torch.testing.assert_close(outputs.last_hidden_state[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)

    @slow
    def test_inference_for_pretraining(self):
        # make random mask reproducible
        torch.manual_seed(2)

        model = HieraForPreTraining.from_pretrained("facebook/hiera-tiny-224-mae-hf").to(torch_device)
        image_processor = self.default_image_processor

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

        config = model.config
        mask_spatial_shape = [
            i // s // ms for i, s, ms in zip(config.image_size, config.patch_stride, config.masked_unit_size)
        ]
        num_windows = math.prod(mask_spatial_shape)
        noise = torch.rand(1, num_windows).to(torch_device)

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

        # verify the logits
        expected_shape = torch.Size((1, 196, 768))
        self.assertEqual(outputs.logits.shape, expected_shape)

        expected_slice = torch.tensor(
            [
                [1.6407, 1.6506, 1.6541, 1.6617, 1.6703],
                [1.9730, 1.9842, 1.9848, 1.9896, 1.9947],
                [1.5949, 1.8262, 1.2602, 1.4801, 1.4448],
                [1.2341, 1.7907, 0.8618, 1.5202, 1.4523],
                [2.0140, 1.9846, 1.9434, 1.9019, 1.8648],
            ]
        )

        torch.testing.assert_close(outputs.logits[0, :5, :5], expected_slice.to(torch_device), rtol=1e-4, atol=1e-4)


@require_torch
class HieraBackboneTest(unittest.TestCase, BackboneTesterMixin):
    all_model_classes = (HieraBackbone,) if is_torch_available() else ()
    config_class = HieraConfig

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