# Copyright (c) OpenMMLab. All rights reserved.
import copy
import os
from tempfile import NamedTemporaryFile, TemporaryDirectory
from typing import Any

import mmcv
import numpy as np
import pytest
import torch

import mmdeploy.backend.onnxruntime as ort_apis
from mmdeploy.apis import build_task_processor
from mmdeploy.codebase import import_codebase
from mmdeploy.utils import Codebase, load_config
from mmdeploy.utils.test import DummyModel, SwitchBackendWrapper

import_codebase(Codebase.MMCLS)

model_cfg_path = 'tests/test_codebase/test_mmcls/data/model.py'
model_cfg = load_config(model_cfg_path)[0]
deploy_cfg = mmcv.Config(
    dict(
        backend_config=dict(type='onnxruntime'),
        codebase_config=dict(type='mmcls', task='Classification'),
        onnx_config=dict(
            type='onnx',
            export_params=True,
            keep_initializers_as_inputs=False,
            opset_version=11,
            input_shape=None,
            input_names=['input'],
            output_names=['output'])))

onnx_file = NamedTemporaryFile(suffix='.onnx').name
task_processor = build_task_processor(model_cfg, deploy_cfg, 'cpu')
img_shape = (64, 64)
num_classes = 1000
img = np.random.rand(*img_shape, 3)


@pytest.mark.parametrize('from_mmrazor', [True, False, '123', 0])
def test_init_pytorch_model(from_mmrazor: Any):
    from mmcls.models.classifiers.base import BaseClassifier
    if from_mmrazor is False:
        _task_processor = task_processor
    else:
        _model_cfg_path = 'tests/test_codebase/test_mmcls/data/' \
            'mmrazor_model.py'
        _model_cfg = load_config(_model_cfg_path)[0]
        _model_cfg.algorithm.architecture.model.type = 'mmcls.ImageClassifier'
        _model_cfg.algorithm.architecture.model.backbone = dict(
            type='SearchableShuffleNetV2', widen_factor=1.0)
        _deploy_cfg = copy.deepcopy(deploy_cfg)
        _deploy_cfg.codebase_config['from_mmrazor'] = from_mmrazor
        _task_processor = build_task_processor(_model_cfg, _deploy_cfg, 'cpu')

    if not isinstance(from_mmrazor, bool):
        with pytest.raises(
                TypeError,
                match='`from_mmrazor` attribute must be '
                'boolean type! '
                f'but got: {from_mmrazor}'):
            _ = _task_processor.from_mmrazor
        return
    assert from_mmrazor == _task_processor.from_mmrazor

    model = _task_processor.init_pytorch_model(None)
    assert isinstance(model, BaseClassifier)


@pytest.fixture
def backend_model():
    from mmdeploy.backend.onnxruntime import ORTWrapper
    ort_apis.__dict__.update({'ORTWrapper': ORTWrapper})
    wrapper = SwitchBackendWrapper(ORTWrapper)
    wrapper.set(outputs={
        'output': torch.rand(1, num_classes),
    })

    yield task_processor.init_backend_model([''])

    wrapper.recover()


def test_init_backend_model(backend_model):
    assert isinstance(backend_model, torch.nn.Module)


def test_create_input():
    inputs = task_processor.create_input(img, input_shape=img_shape)
    assert isinstance(inputs, tuple) and len(inputs) == 2


def test_run_inference(backend_model):
    input_dict, _ = task_processor.create_input(img, input_shape=img_shape)
    results = task_processor.run_inference(backend_model, input_dict)
    assert results is not None


def test_visualize(backend_model):
    input_dict, _ = task_processor.create_input(img, input_shape=img_shape)
    results = task_processor.run_inference(backend_model, input_dict)
    with TemporaryDirectory() as dir:
        filename = dir + 'tmp.jpg'
        task_processor.visualize(backend_model, img, results[0], filename, '')
        assert os.path.exists(filename)


def test_get_tensor_from_input():
    input_data = {'img': torch.ones(3, 4, 5)}
    inputs = task_processor.get_tensor_from_input(input_data)
    assert torch.equal(inputs, torch.ones(3, 4, 5))


def test_get_partition_cfg():
    try:
        _ = task_processor.get_partition_cfg(partition_type='')
    except NotImplementedError:
        pass


def test_build_dataset_and_dataloader():
    from torch.utils.data import DataLoader, Dataset
    dataset = task_processor.build_dataset(
        dataset_cfg=model_cfg, dataset_type='test')
    assert isinstance(dataset, Dataset), 'Failed to build dataset'
    dataloader = task_processor.build_dataloader(dataset, 1, 1)
    assert isinstance(dataloader, DataLoader), 'Failed to build dataloader'


def test_single_gpu_test_and_evaluate():
    from mmcv.parallel import MMDataParallel
    dataset = task_processor.build_dataset(
        dataset_cfg=model_cfg, dataset_type='test')
    dataloader = task_processor.build_dataloader(dataset, 1, 1)

    # Prepare dummy model
    model = DummyModel(outputs=[torch.rand([1, 1000])])
    model = MMDataParallel(model, device_ids=[0])
    assert model is not None
    # Run test
    outputs = task_processor.single_gpu_test(model, dataloader)
    assert outputs is not None
    task_processor.evaluate_outputs(model_cfg, outputs, dataset)