# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# 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.

import yaml
import os.path as osp
import six
import copy
from collections import OrderedDict
import paddle.fluid as fluid
import utils.logging as logging
import models


def load_model(model_dir):
    if not osp.exists(osp.join(model_dir, "model.yml")):
        raise Exception("There's no model.yml in {}".format(model_dir))
    with open(osp.join(model_dir, "model.yml")) as f:
        info = yaml.load(f.read(), Loader=yaml.Loader)
    status = info['status']

    if not hasattr(models, info['Model']):
        raise Exception("There's no attribute {} in models".format(
            info['Model']))
    model = getattr(models, info['Model'])(**info['_init_params'])
    if status == "Normal":
        startup_prog = fluid.Program()
        model.test_prog = fluid.Program()
        with fluid.program_guard(model.test_prog, startup_prog):
            with fluid.unique_name.guard():
                model.test_inputs, model.test_outputs = model.build_net(
                    mode='test')
        model.test_prog = model.test_prog.clone(for_test=True)
        model.exe.run(startup_prog)
        import pickle
        with open(osp.join(model_dir, 'model.pdparams'), 'rb') as f:
            load_dict = pickle.load(f)
        fluid.io.set_program_state(model.test_prog, load_dict)

    elif status in ['Infer', 'Quant']:
        [prog, input_names, outputs] = fluid.io.load_inference_model(
            model_dir, model.exe, params_filename='__params__')
        model.test_prog = prog
        test_outputs_info = info['_ModelInputsOutputs']['test_outputs']
        model.test_inputs = OrderedDict()
        model.test_outputs = OrderedDict()
        for name in input_names:
            model.test_inputs[name] = model.test_prog.global_block().var(name)
        for i, out in enumerate(outputs):
            var_desc = test_outputs_info[i]
            model.test_outputs[var_desc[0]] = out
    if 'test_transforms' in info:
        model.test_transforms = build_transforms(info['test_transforms'])
        model.eval_transforms = copy.deepcopy(model.test_transforms)

    if '_Attributes' in info:
        for k, v in info['_Attributes'].items():
            if k in model.__dict__:
                model.__dict__[k] = v

    logging.info("Model[{}] loaded.".format(info['Model']))
    return model


def build_transforms(transforms_info):
    import transforms as T
    transforms = list()
    for op_info in transforms_info:
        op_name = list(op_info.keys())[0]
        op_attr = op_info[op_name]
        if not hasattr(T, op_name):
            raise Exception(
                "There's no operator named '{}' in transforms".format(op_name))
        transforms.append(getattr(T, op_name)(**op_attr))
    eval_transforms = T.Compose(transforms)
    return eval_transforms