# Copyright 2022 Huawei Technologies Co., Ltd

#

# 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.



from deep_rl.agent.DQN_agent import *

from deep_rl.network.network_bodies import *





parser = argparse.ArgumentParser()

parser.add_argument('--pth-path', type=str)

parser.add_argument('--onnx-path', type=str)





class DQNAgent(DQNAgent):

    def __init__(self):

        config = Config()

        config.task_fn = lambda: Task('BreakoutNoFrameskip-v4')

        config.eval_env = config.task_fn()

        config.history_length = 4

        config.network_fn = lambda: VanillaNet(config.action_dim, NatureConvBody(in_channels=config.history_length))

        self.network = config.network_fn()



    def pth2onnx(self, input_file, output_file):

        state_dict = torch.load(input_file, map_location=lambda storage, loc: storage)

        self.network.load_state_dict(state_dict)

        self.network.eval()

        input_names = ["input"]

        output_names = ["output"]

        dynamic_axes = {'input': {0: '-1'}, 'output': {0: '-1'}}

        dummy_input = torch.randn(16, 4, 84, 84)

        torch.onnx.export(self.network, dummy_input, output_file, input_names=input_names, dynamic_axes=dynamic_axes,

                          output_names=output_names, opset_version=11, verbose=True)





def pth2onnx(input_file, output_file):

    agent = DQNAgent()

    agent.pth2onnx(input_file, output_file)





if __name__ == "__main__":

    args = parser.parse_args()

    input_file = args.pth_path

    output_file = args.onnx_path

    pth2onnx(input_file, output_file)