# # Unity ML Agents
# ## ML-Agent Learning (Imitation)
# Contains an implementation of Behavioral Cloning Algorithm

import logging
import os

import numpy as np
import tensorflow as tf

from unityagents import AllBrainInfo
from unitytrainers.bc.models import BehavioralCloningModel
from unitytrainers.buffer import Buffer
from unitytrainers.trainer import UnityTrainerException, Trainer

logger = logging.getLogger("unityagents")


class BehavioralCloningTrainer(Trainer):
    """The ImitationTrainer is an implementation of the imitation learning."""

    def __init__(self, sess, env, brain_name, trainer_parameters, training, seed):
        """
        Responsible for collecting experiences and training PPO model.
        :param sess: Tensorflow session.
        :param env: The UnityEnvironment.
        :param  trainer_parameters: The parameters for the trainer (dictionary).
        :param training: Whether the trainer is set for training.
        """
        self.param_keys = ['brain_to_imitate', 'batch_size', 'time_horizon', 'graph_scope',
                           'summary_freq', 'max_steps', 'batches_per_epoch', 'use_recurrent', 'hidden_units',
                           'num_layers', 'sequence_length', 'memory_size']

        for k in self.param_keys:
            if k not in trainer_parameters:
                raise UnityTrainerException("The hyperparameter {0} could not be found for the Imitation trainer of "
                                            "brain {1}.".format(k, brain_name))

        super(BehavioralCloningTrainer, self).__init__(sess, env, brain_name, trainer_parameters, training)

        self.variable_scope = trainer_parameters['graph_scope']
        self.brain_to_imitate = trainer_parameters['brain_to_imitate']
        self.batches_per_epoch = trainer_parameters['batches_per_epoch']
        self.use_recurrent = trainer_parameters['use_recurrent']
        self.step = 0
        self.sequence_length = 1
        self.m_size = None
        if self.use_recurrent:
            self.m_size = trainer_parameters["memory_size"]
            self.sequence_length = trainer_parameters["sequence_length"]
        self.n_sequences = max(int(trainer_parameters['batch_size'] / self.sequence_length), 1)
        self.cumulative_rewards = {}
        self.episode_steps = {}
        self.stats = {'losses': [], 'episode_length': [], 'cumulative_reward': []}

        self.training_buffer = Buffer()
        self.is_continuous = (env.brains[brain_name].vector_action_space_type == "continuous")
        self.use_observations = (env.brains[brain_name].number_visual_observations > 0)
        if self.use_observations:
            logger.info('Cannot use observations with imitation learning')
        self.use_states = (env.brains[brain_name].vector_observation_space_size > 0)
        self.summary_path = trainer_parameters['summary_path']
        if not os.path.exists(self.summary_path):
            os.makedirs(self.summary_path)

        self.summary_writer = tf.summary.FileWriter(self.summary_path)
        with tf.variable_scope(self.variable_scope):
            tf.set_random_seed(seed)
            self.model = BehavioralCloningModel(
                h_size=int(trainer_parameters['hidden_units']),
                lr=float(trainer_parameters['learning_rate']),
                n_layers=int(trainer_parameters['num_layers']),
                m_size=self.m_size,
                normalize=False,
                use_recurrent=trainer_parameters['use_recurrent'],
                brain=self.brain)

    def __str__(self):

        return '''Hyperparameters for the Imitation Trainer of brain {0}: \n{1}'''.format(
            self.brain_name, '\n'.join(['\t{0}:\t{1}'.format(x, self.trainer_parameters[x]) for x in self.param_keys]))

    @property
    def parameters(self):
        """
        Returns the trainer parameters of the trainer.
        """
        return self.trainer_parameters

    @property
    def graph_scope(self):
        """
        Returns the graph scope of the trainer.
        """
        return self.variable_scope

    @property
    def get_max_steps(self):
        """
        Returns the maximum number of steps. Is used to know when the trainer should be stopped.
        :return: The maximum number of steps of the trainer
        """
        return float(self.trainer_parameters['max_steps'])

    @property
    def get_step(self):
        """
        Returns the number of steps the trainer has performed
        :return: the step count of the trainer
        """
        return self.step

    @property
    def get_last_reward(self):
        """
        Returns the last reward the trainer has had
        :return: the new last reward
        """
        if len(self.stats['cumulative_reward']) > 0:
            return np.mean(self.stats['cumulative_reward'])
        else:
            return 0

    def increment_step(self):
        """
        Increment the step count of the trainer
        """
        self.step += 1

    def update_last_reward(self):
        """
        Updates the last reward
        """
        return

    def take_action(self, all_brain_info: AllBrainInfo):
        """
        Decides actions given state/observation information, and takes them in environment.
        :param all_brain_info: AllBrainInfo from environment.
        :return: a tuple containing action, memories, values and an object
        to be passed to add experiences
        """
        if len(all_brain_info[self.brain_name].agents) == 0:
            return [], [], [], None

        agent_brain = all_brain_info[self.brain_name]
        feed_dict = {self.model.dropout_rate: 1.0, self.model.sequence_length: 1}
        run_list = [self.model.sample_action]
        if self.use_observations:
            for i, _ in enumerate(agent_brain.visual_observations):
                feed_dict[self.model.observation_in[i]] = agent_brain.visual_observations[i]
        if self.use_states:
            feed_dict[self.model.state_in] = agent_brain.vector_observations
        if self.use_recurrent:
            if agent_brain.memories.shape[1] == 0:
                agent_brain.memories = np.zeros((len(agent_brain.agents), self.m_size))
            feed_dict[self.model.memory_in] = agent_brain.memories
            run_list += [self.model.memory_out]
        if self.use_recurrent:
            agent_action, memories = self.sess.run(run_list, feed_dict)
            return agent_action, memories, None, None
        else:
            agent_action = self.sess.run(run_list, feed_dict)
        return agent_action, None, None, None

    def add_experiences(self, curr_info: AllBrainInfo, next_info: AllBrainInfo, take_action_outputs):
        """
        Adds experiences to each agent's experience history.
        :param curr_info: Current AllBrainInfo (Dictionary of all current brains and corresponding BrainInfo).
        :param next_info: Next AllBrainInfo (Dictionary of all current brains and corresponding BrainInfo).
        :param take_action_outputs: The outputs of the take action method.
        """
        info_expert = curr_info[self.brain_to_imitate]
        next_info_expert = next_info[self.brain_to_imitate]
        for agent_id in info_expert.agents:
            idx = info_expert.agents.index(agent_id)
            self.training_buffer[agent_id].last_brain_info = info_expert
            self.training_buffer[agent_id].last_take_action_outputs = take_action_outputs

        info_expert = None
        take_action_outputs = None
        for agent_id in next_info_expert.agents:
            stored_info_expert = self.training_buffer[agent_id].last_brain_info
            if stored_info_expert is None:
                continue
            else:
                idx = stored_info_expert.agents.index(agent_id)
                next_idx = next_info_expert.agents.index(agent_id)
                if not stored_info_expert.local_done[idx]:
                    if self.use_observations:
                        for i, _ in enumerate(stored_info_expert.visual_observations):
                            self.training_buffer[agent_id]['observations%d' % i].append(stored_info_expert.visual_observations[i][idx])
                    if self.use_states:
                        self.training_buffer[agent_id]['states'].append(stored_info_expert.vector_observations[idx])
                    if self.use_recurrent:
                        if stored_info_expert.memories.shape[1] == 0:
                            stored_info_expert.memories = np.zeros((len(stored_info_expert.agents), self.m_size))
                        self.training_buffer[agent_id]['memory'].append(stored_info_expert.memories[idx])
                    self.training_buffer[agent_id]['actions'].append(next_info_expert.previous_actions[next_idx])

        info_student = curr_info[self.brain_name]
        next_info_student = next_info[self.brain_name]
        for agent_id in info_student.agents:
            idx = info_student.agents.index(agent_id)
            self.training_buffer[agent_id].last_brain_info = info_student
        
        info_student = None
        for agent_id in next_info_student.agents:
            stored_info_student = self.training_buffer[agent_id].last_brain_info
            if stored_info_student is None:
                continue
            else:
                idx = stored_info_student.agents.index(agent_id)
                next_idx = next_info_student.agents.index(agent_id)
                if not stored_info_student.local_done[idx]:
                    if agent_id not in self.cumulative_rewards:
                        self.cumulative_rewards[agent_id] = 0
                    self.cumulative_rewards[agent_id] += next_info_student.rewards[next_idx]
                    if agent_id not in self.episode_steps:
                        self.episode_steps[agent_id] = 0
                    self.episode_steps[agent_id] += 1

    def process_experiences(self, info: AllBrainInfo):
        """
        Checks agent histories for processing condition, and processes them as necessary.
        Processing involves calculating value and advantage targets for model updating step.
        :param info: Current AllBrainInfo
        """
        info_expert = info[self.brain_to_imitate]
        for l in range(len(info_expert.agents)):
            if ((info_expert.local_done[l] or
                 len(self.training_buffer[info_expert.agents[l]]['actions']) > self.trainer_parameters[
                 'time_horizon'])
                    and len(self.training_buffer[info_expert.agents[l]]['actions']) > 0):
                agent_id = info_expert.agents[l]
                self.training_buffer.append_update_buffer(agent_id, batch_size=None, training_length=self.sequence_length)
                self.training_buffer[agent_id].reset_agent()

        info_student = info[self.brain_name]
        for l in range(len(info_student.agents)):
            if info_student.local_done[l]:
                agent_id = info_student.agents[l]
                self.stats['cumulative_reward'].append(self.cumulative_rewards[agent_id])
                self.stats['episode_length'].append(self.episode_steps[agent_id])
                self.cumulative_rewards[agent_id] = 0
                self.episode_steps[agent_id] = 0

    def end_episode(self):
        """
        A signal that the Episode has ended. The buffer must be reset. 
        Get only called when the academy resets.
        """
        self.training_buffer.reset_all()
        for agent_id in self.cumulative_rewards:
            self.cumulative_rewards[agent_id] = 0
        for agent_id in self.episode_steps:
            self.episode_steps[agent_id] = 0

    def is_ready_update(self):
        """
        Returns whether or not the trainer has enough elements to run update model
        :return: A boolean corresponding to whether or not update_model() can be run
        """
        return len(self.training_buffer.update_buffer['actions']) > self.n_sequences

    def update_model(self):
        """
        Uses training_buffer to update model.
        """

        self.training_buffer.update_buffer.shuffle()
        batch_losses = []
        for j in range(
                min(len(self.training_buffer.update_buffer['actions']) // self.n_sequences, self.batches_per_epoch)):
            _buffer = self.training_buffer.update_buffer
            start = j * self.n_sequences
            end = (j + 1) * self.n_sequences
            batch_states = np.array(_buffer['states'][start:end])
            batch_actions = np.array(_buffer['actions'][start:end])
            feed_dict = {self.model.true_action: batch_actions.reshape([-1, self.brain.vector_action_space_size]),
                         self.model.dropout_rate: 0.5,
                         self.model.batch_size: self.n_sequences,
                         self.model.sequence_length: self.sequence_length}
            if not self.is_continuous:
                feed_dict[self.model.state_in] = batch_states.reshape([-1, 1])
            else:
                feed_dict[self.model.state_in] = batch_states.reshape([-1, self.brain.vector_observation_space_size *
                                                                       self.brain.num_stacked_vector_observations])
            if self.use_observations:
                for i, _ in enumerate(self.model.observation_in):
                    _obs = np.array(_buffer['observations%d' % i][start:end])
                    (_batch, _seq, _w, _h, _c) = _obs.shape
                    feed_dict[self.model.observation_in[i]] = _obs.reshape([-1, _w, _h, _c])
            if self.use_recurrent:
                feed_dict[self.model.memory_in] = np.zeros([self.n_sequences, self.m_size])

            loss, _ = self.sess.run([self.model.loss, self.model.update], feed_dict=feed_dict)
            batch_losses.append(loss)
        if len(batch_losses) > 0:
            self.stats['losses'].append(np.mean(batch_losses))
        else:
            self.stats['losses'].append(0)

    def write_summary(self, lesson_number):
        """
        Saves training statistics to Tensorboard.
        :param lesson_number: The lesson the trainer is at.
        """
        if (self.get_step % self.trainer_parameters['summary_freq'] == 0 and self.get_step != 0 and
                self.is_training and self.get_step <= self.get_max_steps):
            steps = self.get_step
            if len(self.stats['cumulative_reward']) > 0:
                mean_reward = np.mean(self.stats['cumulative_reward'])
                logger.info("{0} : Step: {1}. Mean Reward: {2}. Std of Reward: {3}."
                            .format(self.brain_name, steps, mean_reward, np.std(self.stats['cumulative_reward'])))
            summary = tf.Summary()
            for key in self.stats:
                if len(self.stats[key]) > 0:
                    stat_mean = float(np.mean(self.stats[key]))
                    summary.value.add(tag='Info/{}'.format(key), simple_value=stat_mean)
                    self.stats[key] = []
            summary.value.add(tag='Info/Lesson', simple_value=lesson_number)
            self.summary_writer.add_summary(summary, steps)
            self.summary_writer.flush()
