import forks.rlkit.rlkit.util.hyperparameter as hyp
from multiworld.envs.mujoco.cameras import sawyer_init_camera_zoomed_in
from forks.rlkit.rlkit.launchers.launcher_util import run_experiment
import forks.rlkit.rlkit.torch.vae.vae_schedules as vae_schedules
from forks.rlkit.rlkit.launchers.skewfit_experiments import skewfit_full_experiment
from forks.rlkit.rlkit.torch.vae.conv_vae import imsize48_default_architecture


if __name__ == "__main__":
    variant = dict(
        algorithm="Skew-Fit",
        double_algo=False,
        online_vae_exploration=False,
        imsize=48,
        init_camera=sawyer_init_camera_zoomed_in,
        env_id="SawyerPushNIPSEasy-v0",
        skewfit_variant=dict(
            save_video=True,
            custom_goal_sampler="replay_buffer",
            online_vae_trainer_kwargs=dict(beta=20, lr=1e-3),
            save_video_period=100,
            qf_kwargs=dict(hidden_sizes=[400, 300]),
            policy_kwargs=dict(hidden_sizes=[400, 300]),
            vf_kwargs=dict(hidden_sizes=[400, 300]),
            max_path_length=50,
            algo_kwargs=dict(
                batch_size=1024,
                num_epochs=1000,
                num_eval_steps_per_epoch=500,
                num_expl_steps_per_train_loop=500,
                num_trains_per_train_loop=1000,
                min_num_steps_before_training=10000,
                vae_training_schedule=vae_schedules.custom_schedule_2,
                oracle_data=False,
                vae_save_period=50,
                parallel_vae_train=False,
            ),
            twin_sac_trainer_kwargs=dict(
                discount=0.99,
                reward_scale=1,
                soft_target_tau=1e-3,
                target_update_period=1,  # 1
                use_automatic_entropy_tuning=True,
            ),
            replay_buffer_kwargs=dict(
                start_skew_epoch=10,
                max_size=int(100000),
                fraction_goals_rollout_goals=0.2,
                fraction_goals_env_goals=0.5,
                exploration_rewards_type="None",
                vae_priority_type="vae_prob",
                priority_function_kwargs=dict(
                    sampling_method="importance_sampling",
                    decoder_distribution="gaussian_identity_variance",
                    num_latents_to_sample=10,
                ),
                power=-1,
                relabeling_goal_sampling_mode="vae_prior",
            ),
            exploration_goal_sampling_mode="vae_prior",
            evaluation_goal_sampling_mode="reset_of_env",
            normalize=False,
            render=False,
            exploration_noise=0.0,
            exploration_type="ou",
            training_mode="train",
            testing_mode="test",
            reward_params=dict(type="latent_distance"),
            observation_key="latent_observation",
            desired_goal_key="latent_desired_goal",
            vae_wrapped_env_kwargs=dict(sample_from_true_prior=True),
        ),
        train_vae_variant=dict(
            representation_size=4,
            beta=20,
            num_epochs=0,
            dump_skew_debug_plots=False,
            decoder_activation="gaussian",
            generate_vae_dataset_kwargs=dict(
                N=40,
                test_p=0.9,
                use_cached=False,
                show=False,
                oracle_dataset=True,
                oracle_dataset_using_set_to_goal=True,
                n_random_steps=100,
                non_presampled_goal_img_is_garbage=True,
            ),
            vae_kwargs=dict(
                input_channels=3,
                architecture=imsize48_default_architecture,
                decoder_distribution="gaussian_identity_variance",
            ),
            # TODO: why the redundancy?
            algo_kwargs=dict(
                start_skew_epoch=5000,
                is_auto_encoder=False,
                batch_size=64,
                lr=1e-3,
                skew_config=dict(method="vae_prob", power=-1),
                skew_dataset=True,
                priority_function_kwargs=dict(
                    decoder_distribution="gaussian_identity_variance",
                    sampling_method="importance_sampling",
                    num_latents_to_sample=10,
                ),
                use_parallel_dataloading=False,
            ),
            save_period=25,
        ),
    )
    search_space = {}
    sweeper = hyp.DeterministicHyperparameterSweeper(
        search_space, default_parameters=variant
    )

    n_seeds = 1
    mode = "local"
    exp_prefix = "dev-{}".format(
        __file__.replace("/", "-").replace("_", "-").split(".")[0]
    )

    n_seeds = 3
    mode = "ec2"
    exp_prefix = "rlkit-skew-fit-pusher-reference-sample-from-true-prior-take2"

    for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
        for _ in range(n_seeds):
            run_experiment(
                skewfit_full_experiment,
                exp_prefix=exp_prefix,
                mode=mode,
                variant=variant,
                use_gpu=True,
                num_exps_per_instance=3,
                gcp_kwargs=dict(
                    terminate=True,
                    zone="us-east1-c",
                    gpu_kwargs=dict(gpu_model="nvidia-tesla-k80", num_gpu=1),
                ),
            )
