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

import logging
import copy

from mlagents.trainers.bc.trainer import BCTrainer
from mlagents.trainers.demo_loader import demo_to_buffer
from mlagents.trainers.trainer import UnityTrainerException

logger = logging.getLogger("mlagents.trainers")


class OfflineBCTrainer(BCTrainer):
    """The OfflineBCTrainer is an implementation of Offline Behavioral Cloning."""

    def __init__(self, brain, trainer_parameters, training, load, seed, run_id):
        """
        Responsible for collecting experiences and training PPO model.
        :param  trainer_parameters: The parameters for the trainer (dictionary).
        :param training: Whether the trainer is set for training.
        :param load: Whether the model should be loaded.
        :param seed: The seed the model will be initialized with
        :param run_id: The The identifier of the current run
        """
        super(OfflineBCTrainer, self).__init__(
            brain, trainer_parameters, training, load, seed, run_id)

        self.param_keys = ['batch_size', 'summary_freq', 'max_steps',
                           'batches_per_epoch', 'use_recurrent',
                           'hidden_units', 'learning_rate', 'num_layers',
                           'sequence_length', 'memory_size', 'model_path',
                           'demo_path']

        self.check_param_keys()
        self.batches_per_epoch = trainer_parameters['batches_per_epoch']
        self.n_sequences = max(int(trainer_parameters['batch_size'] / self.policy.sequence_length),
                               1)

        brain_params, self.demonstration_buffer = demo_to_buffer(
            trainer_parameters['demo_path'],
            self.policy.sequence_length)

        policy_brain = copy.deepcopy(brain.__dict__)
        expert_brain = copy.deepcopy(brain_params.__dict__)
        policy_brain.pop('brain_name')
        expert_brain.pop('brain_name')
        if expert_brain != policy_brain:
            raise UnityTrainerException("The provided demonstration is not compatible with the "
                                        "brain being used for performance evaluation.")

    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]))