# coding=utf-8
# Copyright 2022 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 LeViT model. """


import inspect
import unittest
import warnings
from math import ceil, floor

from packaging import version

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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


if is_torch_available():
    import torch

    from transformers import (
        MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
        MODEL_MAPPING,
        LevitForImageClassification,
        LevitForImageClassificationWithTeacher,
        LevitModel,
    )
    from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import LevitFeatureExtractor


class LevitConfigTester(ConfigTester):
    def create_and_test_config_common_properties(self):
        config = self.config_class(**self.inputs_dict)
        self.parent.assertTrue(hasattr(config, "hidden_sizes"))
        self.parent.assertTrue(hasattr(config, "num_attention_heads"))


class LevitModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        image_size=64,
        num_channels=3,
        kernel_size=3,
        stride=2,
        padding=1,
        patch_size=16,
        hidden_sizes=[128, 256, 384],
        num_attention_heads=[4, 6, 8],
        depths=[2, 3, 4],
        key_dim=[16, 16, 16],
        drop_path_rate=0,
        mlp_ratio=[2, 2, 2],
        attention_ratio=[2, 2, 2],
        initializer_range=0.02,
        is_training=True,
        use_labels=True,
        num_labels=2,  # Check
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.image_size = image_size
        self.num_channels = num_channels
        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = padding
        self.hidden_sizes = hidden_sizes
        self.num_attention_heads = num_attention_heads
        self.depths = depths
        self.key_dim = key_dim
        self.drop_path_rate = drop_path_rate
        self.patch_size = patch_size
        self.attention_ratio = attention_ratio
        self.mlp_ratio = mlp_ratio
        self.initializer_range = initializer_range
        self.down_ops = [
            ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
            ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
        ]
        self.is_training = is_training
        self.use_labels = use_labels
        self.num_labels = num_labels
        self.initializer_range = initializer_range

    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 LevitConfig(
            image_size=self.image_size,
            num_channels=self.num_channels,
            kernel_size=self.kernel_size,
            stride=self.stride,
            padding=self.padding,
            patch_size=self.patch_size,
            hidden_sizes=self.hidden_sizes,
            num_attention_heads=self.num_attention_heads,
            depths=self.depths,
            key_dim=self.key_dim,
            drop_path_rate=self.drop_path_rate,
            mlp_ratio=self.mlp_ratio,
            attention_ratio=self.attention_ratio,
            initializer_range=self.initializer_range,
            down_ops=self.down_ops,
        )

    def create_and_check_model(self, config, pixel_values, labels):
        model = LevitModel(config=config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values)
        image_size = (self.image_size, self.image_size)
        height, width = image_size[0], image_size[1]
        for _ in range(4):
            height = floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1)
            width = floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1)
        self.parent.assertEqual(
            result.last_hidden_state.shape,
            (self.batch_size, ceil(height / 4) * ceil(width / 4), self.hidden_sizes[-1]),
        )

    def create_and_check_for_image_classification(self, config, pixel_values, labels):
        config.num_labels = self.num_labels
        model = LevitForImageClassification(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))

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

    all_model_classes = (
        (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
        if is_torch_available()
        else ()
    )

    test_pruning = False
    test_torchscript = False
    test_resize_embeddings = False
    test_head_masking = False
    has_attentions = False

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

    def test_config(self):
        self.create_and_test_config_common_properties()
        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 create_and_test_config_common_properties(self):
        return

    @unittest.skip(reason="Levit does not use inputs_embeds")
    def test_inputs_embeds(self):
        pass

    @unittest.skip(reason="Levit does not support input and output embeddings")
    def test_model_common_attributes(self):
        pass

    @unittest.skip(reason="Levit does not output attentions")
    def test_attention_outputs(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_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_layers = len(self.model_tester.depths) + 1
            self.assertEqual(len(hidden_states), expected_num_layers)

            image_size = (self.model_tester.image_size, self.model_tester.image_size)
            height, width = image_size[0], image_size[1]
            for _ in range(4):
                height = floor(
                    (
                        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                        / self.model_tester.stride
                    )
                    + 1
                )
                width = floor(
                    (
                        (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                        / self.model_tester.stride
                    )
                    + 1
                )
            # verify the first hidden states (first block)
            self.assertListEqual(
                list(hidden_states[0].shape[-2:]),
                [
                    height * width,
                    self.model_tester.hidden_sizes[0],
                ],
            )

        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 _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
        inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)

        if return_labels:
            if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                del inputs_dict["labels"]

        return inputs_dict

    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)

    # special case for LevitForImageClassificationWithTeacher model
    def test_training(self):
        if not self.model_tester.is_training:
            return

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.return_dict = True

        for model_class in self.all_model_classes:
            # LevitForImageClassificationWithTeacher supports inference-only
            if (
                model_class in get_values(MODEL_MAPPING)
                or model_class.__name__ == "LevitForImageClassificationWithTeacher"
            ):
                continue
            model = model_class(config)
            model.to(torch_device)
            model.train()
            inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            loss = model(**inputs).loss
            loss.backward()

    def test_training_gradient_checkpointing(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        if not self.model_tester.is_training:
            return

        config.use_cache = False
        config.return_dict = True

        for model_class in self.all_model_classes:
            if model_class in get_values(MODEL_MAPPING) or not model_class.supports_gradient_checkpointing:
                continue
            # LevitForImageClassificationWithTeacher supports inference-only
            if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                continue
            model = model_class(config)
            model.gradient_checkpointing_enable()
            model.to(torch_device)
            model.train()
            inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            loss = model(**inputs).loss
            loss.backward()

    def test_problem_types(self):

        parsed_torch_version_base = version.parse(version.parse(torch.__version__).base_version)
        if parsed_torch_version_base.base_version.startswith("1.9"):
            self.skipTest(reason="This test fails with PyTorch 1.9.x: some CUDA issue")

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        problem_types = [
            {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
            {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
            {"title": "regression", "num_labels": 1, "dtype": torch.float},
        ]

        for model_class in self.all_model_classes:
            if (
                model_class
                not in [
                    *get_values(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING),
                ]
                or model_class.__name__ == "LevitForImageClassificationWithTeacher"
            ):
                continue

            for problem_type in problem_types:
                with self.subTest(msg=f"Testing {model_class} with {problem_type['title']}"):

                    config.problem_type = problem_type["title"]
                    config.num_labels = problem_type["num_labels"]

                    model = model_class(config)
                    model.to(torch_device)
                    model.train()

                    inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)

                    if problem_type["num_labels"] > 1:
                        inputs["labels"] = inputs["labels"].unsqueeze(1).repeat(1, problem_type["num_labels"])

                    inputs["labels"] = inputs["labels"].to(problem_type["dtype"])

                    # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                    # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                    # they have the same size." which is a symptom something in wrong for the regression problem.
                    # See https://github.com/huggingface/transformers/issues/11780
                    with warnings.catch_warnings(record=True) as warning_list:
                        loss = model(**inputs).loss
                    for w in warning_list:
                        if "Using a target size that is different to the input size" in str(w.message):
                            raise ValueError(
                                f"Something is going wrong in the regression problem: intercepted {w.message}"
                            )

                    loss.backward()

    @slow
    def test_model_from_pretrained(self):
        for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = LevitModel.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 LevitModelIntegrationTest(unittest.TestCase):
    @cached_property
    def default_feature_extractor(self):
        return LevitFeatureExtractor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0])

    @slow
    def test_inference_image_classification_head(self):
        model = LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(
            torch_device
        )

        feature_extractor = self.default_feature_extractor
        image = prepare_img()
        inputs = feature_extractor(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([1.0448, -0.3745, -1.8317]).to(torch_device)

        self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))
