# Copyright 2021 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 import *

from deep_rl.agent.BaseAgent import BaseAgent





parser = argparse.ArgumentParser()

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

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

parser.add_argument('--num', type=int, default=1000)





def eval_step(state, weight_path):

    config = Config()

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

    config.eval_env = config.task_fn()

    config.history_length = 4

    model = VanillaNet(config.action_dim, NatureConvBody(in_channels=config.history_length))

    state_dict = torch.load(weight_path, map_location=torch.device('cpu'))

    model.load_state_dict(state_dict, strict=False)

    state = config.state_normalizer(state)

    network = model

    q = network(state)['q']

    action = to_np(q.argmax(-1))

    config.state_normalizer.unset_read_only()

    return action





def eval_episode(weight_path, output_file, num):

    game = 'BreakoutNoFrameskip-v4'

    task_fn = lambda: Task(game)

    env = task_fn()

    state = env.reset()

    a = 0

    while num:

        action = eval_step(state, weight_path)

        state, reward, done, info = env.step(action)

        torch.save(state, '{0}/state{1}.model'.format(output_file, a))

        num-=1

        a+=1





if __name__ == "__main__":

    args = parser.parse_args()

    input_file = args.pth_path

    output_file = args.state_path

    num = args.num

    eval_episode(input_file, output_file, num)