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

import mmcv
import numpy as np
import pytest
import torch
from torch.utils.data import DataLoader

from mmdeploy.apis import build_task_processor
from mmdeploy.utils import load_config
from mmdeploy.utils.test import DummyModel, SwitchBackendWrapper

model_cfg_path = 'tests/test_codebase/test_mmseg/data/model.py'


@pytest.fixture(scope='module')
def model_cfg():
    return load_config(model_cfg_path)[0]


@pytest.fixture(scope='module')
def deploy_cfg():
    return mmcv.Config(
        dict(
            backend_config=dict(type='onnxruntime'),
            codebase_config=dict(type='mmseg', task='Segmentation'),
            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'])))


@pytest.fixture(scope='module')
def task_processor(model_cfg, deploy_cfg):
    return build_task_processor(model_cfg, deploy_cfg, 'cpu')


img_shape = (32, 32)


@pytest.fixture(scope='module')
def img():
    return np.random.rand(*img_shape, 3)


@pytest.mark.parametrize('from_mmrazor', [True, False, '123', 0])
def test_init_pytorch_model(from_mmrazor: Any, task_processor, deploy_cfg):
    from mmseg.models.segmentors.base import BaseSegmentor
    if from_mmrazor is False:
        _task_processor = task_processor
    else:
        _model_cfg_path = 'tests/test_codebase/test_mmseg/data/' \
            'mmrazor_model.py'
        _model_cfg = load_config(_model_cfg_path)[0]
        _model_cfg.algorithm.architecture.model.type = 'mmseg.EncoderDecoder'
        _model_cfg.algorithm.distiller.teacher.type = 'mmseg.EncoderDecoder'
        _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
    if from_mmrazor:
        pytest.importorskip('mmrazor', reason='mmrazor is not installed.')
    model = _task_processor.init_pytorch_model(None)
    assert isinstance(model, BaseSegmentor)


@pytest.fixture(scope='module')
def backend_model(task_processor):
    from mmdeploy.backend.onnxruntime import ORTWrapper
    with SwitchBackendWrapper(ORTWrapper) as wrapper:
        wrapper.set(outputs={
            'output': torch.rand(1, 1, *img_shape),
        })

        yield task_processor.init_backend_model([''])


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


@pytest.fixture(scope='module')
def model_inputs(task_processor, img):
    return task_processor.create_input(img, input_shape=img_shape)


def test_create_input(model_inputs):
    assert isinstance(model_inputs, tuple) and len(model_inputs) == 2


def test_run_inference(backend_model, task_processor, img):
    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, task_processor, model_inputs, img, tmp_path):
    input_dict, _ = model_inputs
    results = task_processor.run_inference(backend_model, input_dict)
    filename = str(tmp_path / 'tmp.jpg')
    task_processor.visualize(backend_model, img, results[0], filename, '')
    assert os.path.exists(filename)


def test_get_tensort_from_input(task_processor):
    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(task_processor):
    with pytest.raises(NotImplementedError):
        _ = task_processor.get_partition_cfg(partition_type='')


def test_build_dataset_and_dataloader(task_processor, model_cfg):
    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(task_processor, model_cfg):
    from mmcv.parallel import MMDataParallel

    # Prepare dataloader
    dataloader = DataLoader([])

    # Prepare dummy model
    model = DummyModel(outputs=[torch.rand([1, 1, *img_shape])])
    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, [])