import numpy as np
from lifelong_rl.torch.pytorch_util import from_numpy, get_numpy
import torch


def multitask_rollout(
        env,
        agent,
        max_path_length=np.inf,
        render=False,
        render_kwargs=None,
        observation_key=None,
        desired_goal_key=None,
        get_action_kwargs=None,
        return_dict_obs=False,
):
    if render_kwargs is None:
        render_kwargs = {}
    if get_action_kwargs is None:
        get_action_kwargs = {}
    dict_obs = []
    dict_next_obs = []
    observations = []
    actions = []
    rewards = []
    terminals = []
    agent_infos = []
    env_infos = []
    next_observations = []
    path_length = 0
    agent.reset()
    o = env.reset()
    if render:
        env.render(**render_kwargs)
    goal = o[desired_goal_key]
    while path_length < max_path_length:
        dict_obs.append(o)
        if observation_key:
            o = o[observation_key]
        new_obs = np.hstack((o, goal))
        a, agent_info = agent.get_action(new_obs, **get_action_kwargs)
        next_o, r, d, env_info = env.step(a)
        if render:
            env.render(**render_kwargs)
        observations.append(o)
        rewards.append(r)
        terminals.append(d)
        actions.append(a)
        next_observations.append(next_o)
        dict_next_obs.append(next_o)
        agent_infos.append(agent_info)
        env_infos.append(env_info)
        path_length += 1
        if d:
            break
        o = next_o
    actions = np.array(actions)
    if len(actions.shape) == 1:
        actions = np.expand_dims(actions, 1)
    observations = np.array(observations)
    next_observations = np.array(next_observations)
    if return_dict_obs:
        observations = dict_obs
        next_observations = dict_next_obs
    return dict(
        observations=observations,
        actions=actions,
        rewards=np.array(rewards).reshape(-1, 1),
        next_observations=next_observations,
        terminals=np.array(terminals).reshape(-1, 1),
        agent_infos=agent_infos,
        env_infos=env_infos,
        goals=np.repeat(goal[None], path_length, 0),
        full_observations=dict_obs,
    )


# CHANGE: add clipping on action
# TODO: make this function take a_min, a_max as arguments
def rollout(
        env,
        agent,
        max_path_length=np.inf,
        render=False,
        render_kwargs=None,
):
    """
    The following value for the following keys will be a 2D array, with the
    first dimension corresponding to the time dimension.
     - observations
     - actions
     - rewards
     - next_observations
     - terminals

    The next two elements will be lists of dictionaries, with the index into
    the list being the index into the time
     - agent_infos
     - env_infos
    """
    if render_kwargs is None:
        render_kwargs = {}
    observations = []
    actions = []
    rewards = []
    terminals = []
    agent_infos = []
    env_infos = []

    o = env.reset()
    agent.reset()
    next_o = None
    path_length = 0
    if render:
        env.render(**render_kwargs)
    while path_length < max_path_length:
        a, agent_info = agent.get_action(o)
        next_o, r, d, env_info = env.step(np.clip(a, -1, 1))
        observations.append(o)
        rewards.append(r)
        terminals.append(d)
        actions.append(a)
        agent_infos.append(agent_info)
        env_infos.append(env_info)

        path_length += 1
        if d:
            break
        o = next_o
        if render:
            env.render(**render_kwargs)

    actions = np.array(actions)
    if len(actions.shape) == 1:
        actions = np.expand_dims(actions, 1)
    observations = np.array(observations)
    if len(observations.shape) == 1:
        observations = np.expand_dims(observations, 1)
        next_o = np.array([next_o])
    next_observations = np.vstack(
        (observations[1:, :], np.expand_dims(next_o, 0)))


    return dict(
        observations=observations,
        actions=actions,
        rewards=np.array(rewards).reshape(-1, 1),
        next_observations=next_observations,
        terminals=np.array(terminals).reshape(-1, 1),
        agent_infos=agent_infos,
        env_infos=env_infos,
    )


def rollout_with_latent(
        env,
        agent,
        max_path_length=np.inf,
        render=False,
        render_kwargs=None,
        sample_latent_every=None,
):
    """
    The following value for the following keys will be a 2D array, with the
    first dimension corresponding to the time dimension.
     - observations
     - actions
     - rewards
     - next_observations
     - terminals

    The next two elements will be lists of dictionaries, with the index into
    the list being the index into the time
     - agent_infos
     - env_infos
    """
    if render_kwargs is None:
        render_kwargs = {}
    observations = []
    actions = []
    rewards = []
    terminals = []
    agent_infos = []
    env_infos = []
    latents = []
    o = env.reset()
    agent.reset()
    next_o = None
    path_length = 0
    if render:
        env.render(**render_kwargs)
    while path_length < max_path_length:
        if sample_latent_every is not None and path_length % sample_latent_every == 0:
            agent.sample_latent()
        a, agent_info = agent.get_action(o)
        next_o, r, d, env_info = env.step(a)
        observations.append(o)
        rewards.append(r)
        terminals.append(d)
        actions.append(a)
        agent_infos.append(agent_info)
        env_infos.append(env_info)
        latents.append(agent.get_current_latent())
        path_length += 1
        if d:
            break
        o = next_o
        if render:
            env.render(**render_kwargs)

    actions = np.array(actions)
    if len(actions.shape) == 1:
        actions = np.expand_dims(actions, 1)
    observations = np.array(observations)
    if len(observations.shape) == 1:
        observations = np.expand_dims(observations, 1)
        next_o = np.array([next_o])
    next_observations = np.vstack(
        (observations[1:, :], np.expand_dims(next_o, 0)))
    return dict(
        observations=observations,
        actions=actions,
        rewards=np.array(rewards).reshape(-1, 1),
        next_observations=next_observations,
        terminals=np.array(terminals).reshape(-1, 1),
        agent_infos=agent_infos,
        env_infos=env_infos,
        latents=np.array(latents),
    )


def rollout_with_attack(
        env,
        agent,
        attacker,
        max_path_length=np.inf,
        render=False,
        render_kwargs=None,
):
    """
    The following value for the following keys will be a 2D array, with the
    first dimension corresponding to the time dimension.
     - observations
     - actions
     - rewards
     - next_observations
     - terminals

    The next two elements will be lists of dictionaries, with the index into
    the list being the index into the time
     - agent_infos
     - env_infos
    """
    if render_kwargs is None:
        render_kwargs = {}
    observations = []
    actions = []
    rewards = []
    terminals = []
    agent_infos = []
    env_infos = []

    o = env.reset()
    agent.reset()
    next_o = None
    path_length = 0
    if render:
        env.render(**render_kwargs)
    while path_length < max_path_length:
        o_attack = attacker.attack_obs(o)
        a, agent_info = agent.get_action(o_attack)
        next_o, r, d, env_info = env.step(np.clip(a, -1, 1))
        observations.append(o)
        rewards.append(r)
        terminals.append(d)
        actions.append(a)
        agent_infos.append(agent_info)
        env_info['attack_obs'] = o_attack
        env_infos.append(env_info)

        path_length += 1
        if d:
            break
        o = next_o
        if render:
            env.render(**render_kwargs)

    actions = np.array(actions)
    if len(actions.shape) == 1:
        actions = np.expand_dims(actions, 1)
    observations = np.array(observations)
    if len(observations.shape) == 1:
        observations = np.expand_dims(observations, 1)
        next_o = np.array([next_o])
    next_observations = np.vstack(
        (observations[1:, :], np.expand_dims(next_o, 0)))


    return dict(
        observations=observations,
        actions=actions,
        rewards=np.array(rewards).reshape(-1, 1),
        next_observations=next_observations,
        terminals=np.array(terminals).reshape(-1, 1),
        agent_infos=agent_infos,
        env_infos=env_infos,
    )