from typing import Union, Optional, List, Any, Tuple
import os
import torch
import logging
from functools import partial
from tensorboardX import SummaryWriter

from ding.envs import get_vec_env_setting, create_env_manager
from ding.worker import BaseLearner, SampleSerialCollector, InteractionSerialEvaluator, BaseSerialCommander, \
    create_buffer, create_serial_collector
from ding.config import read_config, compile_config
from ding.policy import create_policy, PolicyFactory
from ding.reward_model import create_reward_model
from ding.utils import set_pkg_seed


def serial_pipeline_onpolicy(
        input_cfg: Union[str, Tuple[dict, dict]],
        seed: int = 0,
        env_setting: Optional[List[Any]] = None,
        model: Optional[torch.nn.Module] = None,
        max_iterations: Optional[int] = int(1e10),
) -> 'Policy':  # noqa
    """
    Overview:
        Serial pipeline entry on-policy version.
    Arguments:
        - input_cfg (:obj:`Union[str, Tuple[dict, dict]]`): Config in dict type. \
            ``str`` type means config file path. \
            ``Tuple[dict, dict]`` type means [user_config, create_cfg].
        - seed (:obj:`int`): Random seed.
        - env_setting (:obj:`Optional[List[Any]]`): A list with 3 elements: \
            ``BaseEnv`` subclass, collector env config, and evaluator env config.
        - model (:obj:`Optional[torch.nn.Module]`): Instance of torch.nn.Module.
        - max_iterations (:obj:`Optional[torch.nn.Module]`): Learner's max iteration. Pipeline will stop \
            when reaching this iteration.
    Returns:
        - policy (:obj:`Policy`): Converged policy.
    """
    if isinstance(input_cfg, str):
        cfg, create_cfg = read_config(input_cfg)
    else:
        cfg, create_cfg = input_cfg
    create_cfg.policy.type = create_cfg.policy.type + '_command'
    env_fn = None if env_setting is None else env_setting[0]
    cfg = compile_config(cfg, seed=seed, env=env_fn, auto=True, create_cfg=create_cfg, save_cfg=True)
    # Create main components: env, policy
    if env_setting is None:
        env_fn, collector_env_cfg, evaluator_env_cfg = get_vec_env_setting(cfg.env)
    else:
        env_fn, collector_env_cfg, evaluator_env_cfg = env_setting
    collector_env = create_env_manager(cfg.env.manager, [partial(env_fn, cfg=c) for c in collector_env_cfg])
    evaluator_env = create_env_manager(cfg.env.manager, [partial(env_fn, cfg=c) for c in evaluator_env_cfg])
    collector_env.seed(cfg.seed)
    evaluator_env.seed(cfg.seed, dynamic_seed=False)
    set_pkg_seed(cfg.seed, use_cuda=cfg.policy.cuda)
    policy = create_policy(cfg.policy, model=model, enable_field=['learn', 'collect', 'eval', 'command'])

    # Create worker components: learner, collector, evaluator, replay buffer, commander.
    tb_logger = SummaryWriter(os.path.join('./{}/log/'.format(cfg.exp_name), 'serial'))
    learner = BaseLearner(cfg.policy.learn.learner, policy.learn_mode, tb_logger, exp_name=cfg.exp_name)
    collector = create_serial_collector(
        cfg.policy.collect.collector,
        env=collector_env,
        policy=policy.collect_mode,
        tb_logger=tb_logger,
        exp_name=cfg.exp_name
    )
    evaluator = InteractionSerialEvaluator(
        cfg.policy.eval.evaluator, evaluator_env, policy.eval_mode, tb_logger, exp_name=cfg.exp_name
    )
    commander = BaseSerialCommander(
        cfg.policy.other.commander, learner, collector, evaluator, None, policy.command_mode
    )

    # ==========
    # Main loop
    # ==========
    # Learner's before_run hook.
    learner.call_hook('before_run')

    for _ in range(max_iterations):
        collect_kwargs = commander.step()
        # Evaluate policy performance
        if evaluator.should_eval(learner.train_iter):
            stop, reward = evaluator.eval(learner.save_checkpoint, learner.train_iter, collector.envstep)
            if stop:
                break
        # Collect data by default config n_sample/n_episode
        new_data = collector.collect(train_iter=learner.train_iter, policy_kwargs=collect_kwargs)

        # Learn policy from collected data
        learner.train(new_data, collector.envstep)

    # Learner's after_run hook.
    learner.call_hook('after_run')
    return policy
