# -*- coding: utf-8 -*-
"""Common util functions for all algorithms.

- Author: Curt Park
- Contact: curt.park@medipixel.io
"""

import random
from collections import deque

import numpy as np
import torch

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")


def identity(x):
    """Return input without any change."""
    return x


def soft_update(local, target, tau):
    """Soft-update: target = tau*local + (1-tau)*target."""
    for t_param, l_param in zip(target.parameters(), local.parameters()):
        t_param.data.copy_(tau * l_param.data + (1.0 - tau) * t_param.data)


def set_random_seed(seed, env):
    """Set random seed"""
    env.seed(seed)
    torch.manual_seed(seed)
    np.random.seed(seed)
    random.seed(seed)


def get_n_step_info_from_demo(demo, n_step, gamma):
    """Return 1 step and n step demos."""
    assert demo
    assert n_step > 1

    demos_1_step = list()
    demos_n_step = list()
    n_step_buffer = deque(maxlen=n_step)

    for transition in demo:
        n_step_buffer.append(transition)

        if len(n_step_buffer) == n_step:
            # add a single step transition
            demos_1_step.append(n_step_buffer[0])

            # add a multi step transition
            curr_state, action = n_step_buffer[0][:2]
            reward, next_state, done = get_n_step_info(n_step_buffer, gamma)
            transition = (curr_state, action, reward, next_state, done)
            demos_n_step.append(transition)

    return demos_1_step, demos_n_step


def get_n_step_info(n_step_buffer, gamma):
    """Return n step reward, next state, and done."""
    # info of the last transition
    reward, next_state, done = n_step_buffer[-1][-3:]

    for transition in reversed(list(n_step_buffer)[:-1]):
        r, n_s, d = transition[-3:]

        reward = r + gamma * reward * (1 - d)
        next_state, done = (n_s, d) if d else (next_state, done)

    return reward, next_state, done