# 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 TensorFlow ViTMAE model."""

from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import TFViTMAEForPreTraining, TFViTMAEModel
    from transformers.modeling_tf_utils import keras


if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor


class TFViTMAEModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        image_size=30,
        patch_size=2,
        num_channels=3,
        is_training=True,
        use_labels=True,
        hidden_size=32,
        num_hidden_layers=2,
        num_attention_heads=4,
        intermediate_size=37,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        type_sequence_label_size=10,
        initializer_range=0.02,
        num_labels=3,
        mask_ratio=0.6,
        scope=None,
        attn_implementation="eager",
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.is_training = is_training
        self.use_labels = use_labels
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.hidden_act = hidden_act
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.type_sequence_label_size = type_sequence_label_size
        self.initializer_range = initializer_range
        self.mask_ratio = mask_ratio
        self.scope = scope
        self.attn_implementation = attn_implementation

        # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
        # (we add 1 for the [CLS] token)
        num_patches = (image_size // patch_size) ** 2
        self.seq_length = int(math.ceil((1 - mask_ratio) * (num_patches + 1)))

    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.type_sequence_label_size)

        config = self.get_config()

        return config, pixel_values, labels

    def get_config(self):
        return ViTMAEConfig(
            image_size=self.image_size,
            patch_size=self.patch_size,
            num_channels=self.num_channels,
            hidden_size=self.hidden_size,
            num_hidden_layers=self.num_hidden_layers,
            num_attention_heads=self.num_attention_heads,
            intermediate_size=self.intermediate_size,
            decoder_hidden_size=self.hidden_size,
            decoder_num_hidden_layers=self.num_hidden_layers,
            decoder_num_attention_heads=self.num_attention_heads,
            decoder_intermediate_size=self.intermediate_size,
            hidden_act=self.hidden_act,
            hidden_dropout_prob=self.hidden_dropout_prob,
            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
            is_decoder=False,
            initializer_range=self.initializer_range,
            mask_ratio=self.mask_ratio,
            attn_implementation=self.attn_implementation,
        )

    def create_and_check_model(self, config, pixel_values, labels):
        model = TFViTMAEModel(config=config)
        result = model(pixel_values, training=False)
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))

    def create_and_check_for_pretraining(self, config, pixel_values, labels):
        model = TFViTMAEForPreTraining(config)
        result = model(pixel_values, training=False)
        # expected sequence length = num_patches
        num_patches = (self.image_size // self.patch_size) ** 2
        expected_num_channels = self.patch_size**2 * self.num_channels
        self.parent.assertEqual(result.logits.shape, (self.batch_size, num_patches, expected_num_channels))

        # test greyscale images
        config.num_channels = 1
        model = TFViTMAEForPreTraining(config)

        pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
        result = model(pixel_values, training=False)
        expected_num_channels = self.patch_size**2
        self.parent.assertEqual(result.logits.shape, (self.batch_size, num_patches, expected_num_channels))

    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_tf
class TFViTMAEModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    """
    Here we also overwrite some of the tests of test_modeling_common.py, as ViTMAE does not use input_ids, inputs_embeds,
    attention_mask and seq_length.
    """

    all_model_classes = (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
    pipeline_model_mapping = {"feature-extraction": TFViTMAEModel} if is_tf_available() else {}

    test_pruning = False
    test_onnx = False
    test_resize_embeddings = False
    test_head_masking = False

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

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

    @unittest.skip(reason="ViTMAE 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(), (keras.layers.Layer))
            x = model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(x, keras.layers.Layer))

    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.call)
            # 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_pretraining(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*config_and_inputs)

    # overwrite from common since TFViTMAEForPretraining has random masking, we need to fix the noise
    # to generate masks during test
    def test_keyword_and_dict_args(self):
        # make the mask reproducible
        np.random.seed(2)

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        num_patches = int((config.image_size // config.patch_size) ** 2)
        noise = np.random.uniform(size=(self.model_tester.batch_size, num_patches))

        for model_class in self.all_model_classes:
            model = model_class(config)
            inputs = self._prepare_for_class(inputs_dict, model_class)

            outputs_dict = model(inputs, noise=noise)

            inputs_keywords = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
            outputs_keywords = model(**inputs_keywords, noise=noise)
            output_dict = outputs_dict[0].numpy()
            output_keywords = outputs_keywords[0].numpy()

            self.assertLess(np.sum(np.abs(output_dict - output_keywords)), 1e-6)

    # overwrite from common since TFViTMAEForPretraining has random masking, we need to fix the noise
    # to generate masks during test
    def test_numpy_arrays_inputs(self):
        # make the mask reproducible
        np.random.seed(2)

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        num_patches = int((config.image_size // config.patch_size) ** 2)
        noise = np.random.uniform(size=(self.model_tester.batch_size, num_patches))

        def prepare_numpy_arrays(inputs_dict):
            inputs_np_dict = {}
            for k, v in inputs_dict.items():
                if tf.is_tensor(v):
                    inputs_np_dict[k] = v.numpy()
                else:
                    inputs_np_dict[k] = np.array(k)

            return inputs_np_dict

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

            inputs = self._prepare_for_class(inputs_dict, model_class)
            inputs_np = prepare_numpy_arrays(inputs)

            output_for_dict_input = model(inputs_np, noise=noise)
            output_for_kw_input = model(**inputs_np, noise=noise)
            self.assert_outputs_same(output_for_dict_input, output_for_kw_input)

    # overwrite from common since TFViTMAEForPretraining has random masking, we need to fix the noise
    # to generate masks during test
    def check_pt_tf_models(self, tf_model, pt_model, tf_inputs_dict):
        # make masks reproducible
        np.random.seed(2)

        num_patches = int((tf_model.config.image_size // tf_model.config.patch_size) ** 2)
        noise = np.random.uniform(size=(self.model_tester.batch_size, num_patches))
        tf_noise = tf.constant(noise)

        # Add `noise` argument.
        # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
        tf_inputs_dict["noise"] = tf_noise

        super().check_pt_tf_models(tf_model, pt_model, tf_inputs_dict)

    # overwrite from common since TFViTMAEForPretraining has random masking, we need to fix the noise
    # to generate masks during test
    def test_keras_save_load(self):
        # make mask reproducible
        np.random.seed(2)

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        tf_main_layer_classes = {
            module_member
            for model_class in self.all_model_classes
            for module in (import_module(model_class.__module__),)
            for module_member_name in dir(module)
            if module_member_name.endswith("MainLayer")
            # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
            and module_member_name[: -len("MainLayer")] == model_class.__name__[: -len("Model")]
            for module_member in (getattr(module, module_member_name),)
            if isinstance(module_member, type)
            and keras.layers.Layer in module_member.__bases__
            and getattr(module_member, "_keras_serializable", False)
        }

        num_patches = int((config.image_size // config.patch_size) ** 2)
        noise = np.random.uniform(size=(self.model_tester.batch_size, num_patches))
        noise = tf.convert_to_tensor(noise)
        inputs_dict.update({"noise": noise})

        for main_layer_class in tf_main_layer_classes:
            main_layer = main_layer_class(config)

            symbolic_inputs = {
                name: keras.Input(tensor.shape[1:], dtype=tensor.dtype) for name, tensor in inputs_dict.items()
            }

            model = keras.Model(symbolic_inputs, outputs=main_layer(symbolic_inputs))
            outputs = model(inputs_dict)

            with tempfile.TemporaryDirectory() as tmpdirname:
                filepath = os.path.join(tmpdirname, "keras_model.h5")
                model.save(filepath)
                model = keras.models.load_model(filepath, custom_objects={main_layer_class.__name__: main_layer_class})
                assert isinstance(model, keras.Model)
                after_outputs = model(inputs_dict)
                self.assert_outputs_same(after_outputs, outputs)

    # overwrite from common since TFViTMAEForPretraining has random masking, we need to fix the noise
    # to generate masks during test
    @slow
    def test_save_load(self):
        # make mask reproducible
        np.random.seed(2)

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        num_patches = int((config.image_size // config.patch_size) ** 2)
        noise = np.random.uniform(size=(self.model_tester.batch_size, num_patches))

        for model_class in self.all_model_classes:
            model = model_class(config)
            model_input = self._prepare_for_class(inputs_dict, model_class)
            outputs = model(model_input, noise=noise)

            if model_class.__name__ == "TFViTMAEModel":
                out_2 = outputs.last_hidden_state.numpy()
                out_2[np.isnan(out_2)] = 0
            else:
                out_2 = outputs.logits.numpy()
                out_2[np.isnan(out_2)] = 0

            with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(tmpdirname, saved_model=False)
                model = model_class.from_pretrained(tmpdirname)
                after_outputs = model(model_input, noise=noise)

                if model_class.__name__ == "TFViTMAEModel":
                    out_1 = after_outputs["last_hidden_state"].numpy()
                    out_1[np.isnan(out_1)] = 0
                else:
                    out_1 = after_outputs["logits"].numpy()
                    out_1[np.isnan(out_1)] = 0

                max_diff = np.amax(np.abs(out_1 - out_2))
                self.assertLessEqual(max_diff, 1e-5)

    # overwrite from common since TFViTMAEForPretraining has random masking, we need to fix the noise
    # to generate masks during test
    def test_save_load_config(self):
        # make mask reproducible
        np.random.seed(2)

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        num_patches = int((config.image_size // config.patch_size) ** 2)
        noise = np.random.uniform(size=(self.model_tester.batch_size, num_patches))

        for model_class in self.all_model_classes:
            model = model_class(config)
            model_inputs = self._prepare_for_class(inputs_dict, model_class)

            outputs = model(model_inputs, noise=noise)
            model_config = model.get_config()
            # make sure that returned config is jsonifiable, which is required by keras
            json.dumps(model_config)
            new_model = model_class.from_config(model.get_config())
            # make sure it also accepts a normal config
            _ = model_class.from_config(model.config)
            _ = new_model(model_inputs)  # Build model
            new_model.set_weights(model.get_weights())
            after_outputs = new_model(model_inputs, noise=noise)

            self.assert_outputs_same(after_outputs, outputs)

    @unittest.skip(
        reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""
    )
    def test_determinism(self):
        pass

    @unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load""")
    def test_model_outputs_equivalence(self):
        pass

    @slow
    def test_model_from_pretrained(self):
        model = TFViTMAEModel.from_pretrained("google/vit-base-patch16-224")
        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_tf
@require_vision
class TFViTMAEModelIntegrationTest(unittest.TestCase):
    @cached_property
    def default_image_processor(self):
        return ViTImageProcessor.from_pretrained("facebook/vit-mae-base")

    @slow
    def test_inference_for_pretraining(self):
        # make random mask reproducible across the PT and TF model
        np.random.seed(2)

        model = TFViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base")

        image_processor = self.default_image_processor
        image = prepare_img()
        inputs = image_processor(images=image, return_tensors="tf")

        # prepare a noise vector that will be also used for testing the TF model
        # (this way we can ensure that the PT and TF models operate on the same inputs)
        vit_mae_config = ViTMAEConfig()
        num_patches = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2)
        noise = np.random.uniform(size=(1, num_patches))

        # forward pass
        outputs = model(**inputs, noise=noise)

        # verify the logits
        expected_shape = tf.convert_to_tensor([1, 196, 768])
        self.assertEqual(outputs.logits.shape, expected_shape)

        expected_slice = tf.convert_to_tensor(
            [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]
        )

        tf.debugging.assert_near(outputs.logits[0, :3, :3], expected_slice, atol=1e-4)

    @slow
    def test_inference_interpolate_pos_encoding(self):
        # ViTMAE models have an `interpolate_pos_encoding` argument in their forward method,
        # allowing to interpolate the pre-trained position embeddings in order to use
        # the model on higher resolutions. The DINO model by Facebook AI leverages this
        # to visualize self-attention on higher resolution images.

        # make random mask reproducible across the PT and TF model
        np.random.seed(2)

        model = TFViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base")

        image_processor = self.default_image_processor
        image = prepare_img()
        inputs = image_processor(images=image, do_resize=False, return_tensors="tf")

        # prepare a noise vector that will be also used for testing the TF model
        # (this way we can ensure that the PT and TF models operate on the same inputs)
        vit_mae_config = ViTMAEConfig()
        num_patches = (image.height // vit_mae_config.patch_size) * (image.width // vit_mae_config.patch_size)
        noise = np.random.uniform(size=(1, num_patches))

        # forward pass
        outputs = model(**inputs, noise=noise, interpolate_pos_encoding=True)

        # verify the logits
        expected_shape = tf.convert_to_tensor([1, 1200, 768])
        self.assertEqual(outputs.logits.shape, expected_shape)
