# 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 Swin model. """

import copy
import inspect
import unittest

from transformers import SwinConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
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 torch import nn

    from transformers import SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
    from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST, to_2tuple

if is_vision_available():
    from PIL import Image

    from transformers import AutoFeatureExtractor


def _config_zero_init(config):
    configs_no_init = copy.deepcopy(config)
    for key in configs_no_init.__dict__.keys():
        if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
            setattr(configs_no_init, key, 1e-10)
    return configs_no_init


class SwinModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        image_size=32,
        patch_size=2,
        num_channels=3,
        embed_dim=16,
        depths=[1],
        num_heads=[2],
        window_size=2,
        mlp_ratio=2.0,
        qkv_bias=True,
        hidden_dropout_prob=0.0,
        attention_probs_dropout_prob=0.0,
        drop_path_rate=0.1,
        hidden_act="gelu",
        use_absolute_embeddings=False,
        patch_norm=True,
        initializer_range=0.02,
        layer_norm_eps=1e-5,
        is_training=True,
        scope=None,
        use_labels=True,
        type_sequence_label_size=10,
        encoder_stride=2,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.embed_dim = embed_dim
        self.depths = depths
        self.num_heads = num_heads
        self.window_size = window_size
        self.mlp_ratio = mlp_ratio
        self.qkv_bias = qkv_bias
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.drop_path_rate = drop_path_rate
        self.hidden_act = hidden_act
        self.use_absolute_embeddings = use_absolute_embeddings
        self.patch_norm = patch_norm
        self.layer_norm_eps = layer_norm_eps
        self.initializer_range = initializer_range
        self.is_training = is_training
        self.scope = scope
        self.use_labels = use_labels
        self.type_sequence_label_size = type_sequence_label_size
        self.encoder_stride = encoder_stride

    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 SwinConfig(
            image_size=self.image_size,
            patch_size=self.patch_size,
            num_channels=self.num_channels,
            embed_dim=self.embed_dim,
            depths=self.depths,
            num_heads=self.num_heads,
            window_size=self.window_size,
            mlp_ratio=self.mlp_ratio,
            qkv_bias=self.qkv_bias,
            hidden_dropout_prob=self.hidden_dropout_prob,
            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
            drop_path_rate=self.drop_path_rate,
            hidden_act=self.hidden_act,
            use_absolute_embeddings=self.use_absolute_embeddings,
            path_norm=self.patch_norm,
            layer_norm_eps=self.layer_norm_eps,
            initializer_range=self.initializer_range,
            encoder_stride=self.encoder_stride,
        )

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

        # since the model we're testing only consists of a single layer, expected_seq_len = number of patches
        expected_seq_len = (config.image_size // config.patch_size) ** 2
        expected_dim = int(config.embed_dim * 2 ** (len(config.depths) - 1))

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

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

    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 SwinModelTest(ModelTesterMixin, unittest.TestCase):

    all_model_classes = (
        (
            SwinModel,
            SwinForImageClassification,
            SwinForMaskedImageModeling,
        )
        if is_torch_available()
        else ()
    )

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

    def setUp(self):
        self.model_tester = SwinModelTester(self)
        self.config_tester = ConfigTester(self, config_class=SwinConfig, embed_dim=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

    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_inputs_embeds(self):
        # Swin does not use inputs_embeds
        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_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_attention_outputs(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.return_dict = True

        image_size = to_2tuple(self.model_tester.image_size)
        patch_size = to_2tuple(self.model_tester.patch_size)
        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
        seq_len = num_patches
        encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
        chunk_length = getattr(self.model_tester, "chunk_length", None)
        if chunk_length is not None and hasattr(self.model_tester, "num_hashes"):
            encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes

        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(config)
            model.to(torch_device)
            model.eval()
            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
            attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
            self.assertEqual(len(attentions), len(self.model_tester.depths))

            # check that output_attentions also work using config
            del inputs_dict["output_attentions"]
            config.output_attentions = True
            window_size_squared = config.window_size**2
            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.encoder_attentions if config.is_encoder_decoder else outputs.attentions
            self.assertEqual(len(attentions), len(self.model_tester.depths))

            if chunk_length is not None:
                self.assertListEqual(
                    list(attentions[0].shape[-4:]),
                    [self.model_tester.num_heads[0], window_size_squared, chunk_length, window_size_squared],
                )
            else:
                self.assertListEqual(
                    list(attentions[0].shape[-3:]),
                    [self.model_tester.num_heads[0], window_size_squared, window_size_squared],
                )
            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))

            if hasattr(self.model_tester, "num_hidden_states_types"):
                added_hidden_states = self.model_tester.num_hidden_states_types
            elif self.is_encoder_decoder:
                added_hidden_states = 2
            else:
                added_hidden_states = 1
            self.assertEqual(out_len + added_hidden_states, len(outputs))

            self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

            self.assertEqual(len(self_attentions), len(self.model_tester.depths))
            if chunk_length is not None:
                self.assertListEqual(
                    list(self_attentions[0].shape[-4:]),
                    [self.model_tester.num_heads[0], window_size_squared, chunk_length, window_size_squared],
                )
            else:
                self.assertListEqual(
                    list(self_attentions[0].shape[-3:]),
                    [self.model_tester.num_heads[0], window_size_squared, window_size_squared],
                )

    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.encoder_hidden_states if config.is_encoder_decoder else 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)

            # Swin has a different seq_length
            image_size = to_2tuple(self.model_tester.image_size)
            patch_size = to_2tuple(self.model_tester.patch_size)
            num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

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

        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 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 SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = SwinModel.from_pretrained(model_name)
            self.assertIsNotNone(model)

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

        configs_no_init = _config_zero_init(config)
        for model_class in self.all_model_classes:
            model = model_class(config=configs_no_init)
            for name, param in model.named_parameters():
                if "embeddings" not in name and param.requires_grad:
                    self.assertIn(
                        ((param.data.mean() * 1e9).round() / 1e9).item(),
                        [0.0, 1.0],
                        msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                    )


@require_vision
@require_torch
class SwinModelIntegrationTest(unittest.TestCase):
    @cached_property
    def default_feature_extractor(self):
        return (
            AutoFeatureExtractor.from_pretrained("microsoft/swin-tiny-patch4-window7-224")
            if is_vision_available()
            else None
        )

    @slow
    def test_inference_image_classification_head(self):
        model = SwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224").to(torch_device)
        feature_extractor = self.default_feature_extractor

        image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
        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([-0.0948, -0.6454, -0.0921]).to(torch_device)

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