import os
import numpy as np
import pickle
import time
from agents.orderenforcingwrapper import OrderEnforcingAgent
from citylearn.citylearn import CityLearnEnv

"""
This file is used to generate offline data for a decision transformer.
Data is saved as pickle file.
Data structure:
list(
    dict(
        "observations": nparray(nparray(np.float32)),
        "next_observations": nparray(nparray(np.float32)),
        "actions": nparray(nparray(np.float32)),
        "rewards": nparray(np.oat32),
        "terminals": nparray(np.bool_)
        )
    )
"""


class Constants:
    file_to_save = "non.pkl"
    sequence_length = 720
    episodes = 1
    state_dim = 28  # size of state space
    action_dim = 1  # size of action space
    schema_path = './data/citylearn_challenge_2022_phase_1/schema.json'


def action_space_to_dict(aspace):
    """ Only for box space """
    return {"high": aspace.high,
            "low": aspace.low,
            "shape": aspace.shape,
            "dtype": str(aspace.dtype)
            }


def env_reset(env):
    observations = env.reset()
    action_space = env.action_space
    observation_space = env.observation_space
    building_info = env.get_building_information()
    building_info = list(building_info.values())
    action_space_dicts = [action_space_to_dict(asp) for asp in action_space]
    observation_space_dicts = [action_space_to_dict(osp) for osp in observation_space]
    obs_dict = {"action_space": action_space_dicts,
                "observation_space": observation_space_dicts,
                "building_info": building_info,
                "observation": observations}
    return obs_dict


def generate_data():
    print("========================= Start Data Collection ========================")

    env = CityLearnEnv(schema=Constants.schema_path)
    agent = OrderEnforcingAgent()

    dataset = []
    observation_data = []
    next_observation_data = []
    action_data = []
    reward_data = []
    done_data = []

    obs_dict = env_reset(env)
    observations = obs_dict["observation"]

    agent_time_elapsed = 0

    step_start = time.perf_counter()
    actions = agent.register_reset(obs_dict)
    agent_time_elapsed += time.perf_counter() - step_start

    episodes_completed = 0
    sequences_completed = 0
    current_step_total = 0
    current_step_in_sequence = 0
    interrupted = False
    episode_metrics = []

    try:
        while True:
            current_step_in_sequence += 1
            current_step_total += 1
            next_observations, reward, done, info = env.step(actions)
            # ACTION [-1,1] attempts to decrease or increase the electricity stored in the battery by an amount
            # equivalent to action times its maximum capacity
            # Save environment interactions:
            observation_data.append(observations)
            next_observation_data.append(next_observations)
            action_data.append(actions)
            reward_data.append(reward)
            done_data.append(False)  # always False

            observations = next_observations  # observations of next time step

            if current_step_in_sequence >= Constants.sequence_length:  # Sequence completed
                current_step_in_sequence = 0
                sequences_completed += 1

                for bi in range(len(env.buildings)):
                    obs_building_i = np.zeros((Constants.sequence_length, Constants.state_dim), dtype=np.float32)
                    n_obs_building_i = np.zeros((Constants.sequence_length, Constants.state_dim), dtype=np.float32)
                    acts_building_i = np.zeros((Constants.sequence_length, Constants.action_dim), dtype=np.float32)
                    rwds_building_i = np.zeros(Constants.sequence_length, dtype=np.float32)
                    dones_building_i = np.zeros(Constants.sequence_length, dtype=np.bool_)
                    for ti in range(Constants.sequence_length):
                        obs_building_i[ti] = np.array(observation_data[ti][bi])
                        n_obs_building_i[ti] = np.array(next_observation_data[ti][bi])
                        acts_building_i[ti] = np.array(action_data[ti][bi])
                        rwds_building_i[ti] = reward_data[ti][bi]
                        dones_building_i[ti] = done_data[ti]

                    dict_building_i = {
                        "observations": obs_building_i,
                        "next_observations": n_obs_building_i,
                        "actions": acts_building_i,
                        "rewards": rwds_building_i,
                        "terminals": dones_building_i
                    }
                    dataset.append(dict_building_i)

                observation_data = []
                next_observation_data = []
                action_data = []
                reward_data = []
                done_data = []
                print("Sequence completed:", sequences_completed)

            if done:
                episodes_completed += 1

                metrics_t = env.evaluate()
                metrics = {"price_cost": metrics_t[0], "emmision_cost": metrics_t[1], "grid_cost": metrics_t[2]}
                if np.any(np.isnan(metrics_t)):
                    raise ValueError("Episode metrics are nan, please contant organizers")
                episode_metrics.append(metrics)
                print(f"Episode complete: {episodes_completed} | Latest episode metrics: {metrics}", )

                obs_dict = env_reset(env)
                observations = obs_dict["observation"]

                step_start = time.perf_counter()
                actions = agent.register_reset(obs_dict)
                agent_time_elapsed += time.perf_counter() - step_start
            else:
                step_start = time.perf_counter()
                actions = agent.compute_action(next_observations)
                agent_time_elapsed += time.perf_counter() - step_start

            if current_step_total % 1000 == 0:
                print(f"Num Steps: {current_step_total}, Num episodes: {episodes_completed}")

            if episodes_completed >= Constants.episodes:
                break
    except KeyboardInterrupt:
        print("========================= Stopping Generation ==========================")
        interrupted = True

    if not interrupted:
        print("========================= Generation Completed =========================")

    if len(episode_metrics) > 0:
        print("Agent Performance:")
        print("Average Price Cost:", np.mean([e['price_cost'] for e in episode_metrics]))
        print("Average Emission Cost:", np.mean([e['emmision_cost'] for e in episode_metrics]))
        print("Average Grid Cost:", np.mean([e['grid_cost'] for e in episode_metrics]))
    print(f"Total time taken by agent: {agent_time_elapsed}s")

    print("========================= Writing Data File ============================")

    length = 0
    for data in dataset:
        if len(data["observations"]) > length:
            length = len(data["observations"])

    print("Amount Of Sequences: ", len(dataset))
    print("Longest Sequence: ", length)
    total_values = (2 * Constants.state_dim + Constants.action_dim + 2) * length * len(dataset)
    print("Total values to store: ", total_values)

    # create or overwrite pickle file
    with open(Constants.file_to_save, "wb") as f:
        pickle.dump(dataset, f)

    print("========================= Writing Completed ============================")
    file_size = os.stat(Constants.file_to_save).st_size
    if file_size > 1e+6:
        string_byte = "(" + str(round(file_size / 1e+6)) + " MB)"
    else:
        string_byte = "(" + str(round(file_size / 1e+3)) + " kB)"
    print("==> Data saved in", Constants.file_to_save, string_byte)


if __name__ == '__main__':
    generate_data()