import os
import json
import numpy as np
import torch
import torch.nn.functional as F
from torch.optim import Adam
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
import matplotlib
import matplotlib.pyplot as plt
import warnings

from mode_disent_no_ssm.utils.skill_policy_wrapper import DiaynSkillPolicyWrapper
from mode_disent.memory.memory import MyLazyMemory
from mode_disent.env_wrappers.rlkit_wrapper import NormalizedBoxEnvForPytorch
from mode_disent.utils.mmd import compute_mmd_tutorial

from code_slac.utils import calc_kl_divergence, update_params

from mode_disent_no_ssm.network.mode_model import ModeLatentNetwork
from mode_disent_no_ssm.utils.empty_network import Empty
from mode_disent_no_ssm.utils.parse_args import json_save
from mode_disent_no_ssm.test.action_sampler import ActionSamplerNoSSM


matplotlib.use('Agg')


class DisentTrainerNoSSM:

    def __init__(self,
                 env: NormalizedBoxEnvForPytorch,
                 log_dir,
                 min_steps_sampling,
                 batch_size,
                 num_sequences,
                 train_steps,
                 lr,
                 mode_dim,
                 num_mode_repitions,
                 rnn_dim,
                 num_rnn_layers,
                 rnn_dropout,
                 hidden_units_mode_encoder,
                 std_decoder,
                 mode_latent_model: ModeLatentNetwork,
                 hidden_units_action_decoder,
                 memory_size,
                 skill_policy: DiaynSkillPolicyWrapper,
                 log_interval,
                 info_loss_params,
                 run_id,
                 run_hp,
                 params_for_testing,
                 device,
                 leaky_slope=0.2,
                 seed=0,
                 ):

        self.env = env
        self.observation_shape = self.env.observation_space.shape
        self.action_shape = self.env.action_space.shape

        self.feature_dim = int(self.observation_shape[0])
        self.mode_dim = mode_dim
        self.num_sequences = num_sequences
        self.min_steps_sampling = min_steps_sampling
        self.batch_size = batch_size
        self.train_steps = train_steps
        self.run_id = run_id
        self.learn_steps = 0
        self.episodes = 0

        self.seed = None
        self._set_seed(seed)

        self.device = None
        self._set_device(device)

        if self.observation_shape[0] == self.feature_dim:
            self.obs_encoder = Empty().to(self.device)
        else:
            self.obs_encoder = torch.nn.Linear(self.observation_shape[0],
                                               self.feature_dim)

        if mode_latent_model is None:
            self.mode_latent_model = ModeLatentNetwork(
                mode_dim=self.mode_dim,
                representation_dim=self.feature_dim,
                rnn_dim=rnn_dim,
                num_rnn_layers=num_rnn_layers,
                rnn_dropout=rnn_dropout,
                hidden_units_mode_encoder=hidden_units_mode_encoder,
                hidden_units_action_decoder=hidden_units_action_decoder,
                num_mode_repeat=num_mode_repitions,
                feature_dim=self.feature_dim,
                action_dim=self.action_shape[0],
                std_decoder=std_decoder,
                device=self.device,
                leaky_slope=leaky_slope,
            ).to(self.device)
            self.mode_model_loaded = False

        else:
            self.mode_latent_model = mode_latent_model.to(self.device)
            self.mode_model_loaded = True

        self.optim = Adam(self.mode_latent_model.parameters(), lr=lr)

        self.memory = MyLazyMemory(
            state_rep=True,
            capacity=memory_size,
            num_sequences=self.num_sequences,
            observation_shape=self.observation_shape,
            action_shape=self.action_shape,
            device=self.device
        )

        self.log_dir = log_dir
        self.model_dir = os.path.join(self.log_dir, 'model', str(self.run_id))
        self.summary_dir = os.path.join(self.log_dir, 'summary', str(self.run_id))
        if not os.path.exists(self.model_dir):
            os.makedirs(self.model_dir)
        if not os.path.exists(self.summary_dir):
            os.makedirs(self.summary_dir)

        testparams_save_path = os.path.join(self.model_dir, 'parameters_for_testing.json')
        json_save(params_for_testing, testparams_save_path)
        run_hp_save_path = os.path.join(self.model_dir, 'run_hyperparameters.json')
        json_save(run_hp, run_hp_save_path)

        self.writer = SummaryWriter(log_dir=self.summary_dir)
        self.log_interval = log_interval

        self.skill_policy = skill_policy
        self.info_loss_params = info_loss_params
        self.num_skills = self.skill_policy.num_skills
        self.steps = np.zeros(shape=self.num_skills, dtype=np.int)

    def run_training(self):
        self._sample_sequences(
            memory_to_fill=self.memory,
            min_steps=self.min_steps_sampling,
            step_cnt=self.steps
        )

        self._train()

        self._save_models()

    def _train(self):
        for _ in tqdm(range(self.train_steps)):
            self._learn_step()

            if self._is_interval(self.log_interval * 25, self.learn_steps):
                self._save_models()

    def _learn_step(self):
        sequences = self.memory.sample_sequence(self.batch_size)

        loss = self._calc_loss(sequences)
        update_params(self.optim, self.mode_latent_model, loss)

        self.learn_steps += 1

    def _calc_loss(self, sequence):
        actions_seq = sequence['actions_seq']
        features_seq = self.obs_encoder(sequence['states_seq'])
        skill_seq = sequence['skill_seq']
        skill_seq_np_squeezed =\
            self._tensor_to_numpy(skill_seq.float().mean(dim=1)) \
            .astype(np.uint8).squeeze()

        # Posterior and prior
        mode_post = self.mode_latent_model.sample_mode_posterior(features_seq=features_seq)
        mode_pri = self.mode_latent_model.sample_mode_prior(self.batch_size)

        # KLD
        kld = calc_kl_divergence([mode_post['dists']],
                                 [mode_pri['dists']])

        # MMD
        mmd = compute_mmd_tutorial(mode_pri['samples'],
                                   mode_post['samples'])

        # Reconstruction
        actions_seq_recon = self.mode_latent_model.action_decoder(
            state_rep_seq=features_seq[:, :-1, :],
            mode_sample=mode_post['samples']
        )

        # Reconstruction loss
        ll = actions_seq_recon['dists'].log_prob(actions_seq).mean(dim=0).sum()
        mse = F.mse_loss(actions_seq_recon['samples'], actions_seq)

        # Classic beta-VAE loss
        beta = 1.
        classic_loss = beta * kld - ll

        # Info-VAE loss
        alpha = self.info_loss_params.alpha
        lamda = self.info_loss_params.lamda
        kld_info = (1 - alpha) * kld
        mmd_info = (alpha + lamda - 1) * mmd
        info_loss = mse + kld_info + mmd_info
        if self.info_loss_params.kld_diff_desired is not None:
            kld_desired_scalar = self.info_loss_params.kld_diff_desired
            kld_desired = torch.tensor(kld_desired_scalar).to(self.device)
            kld_diff_control = 0.07 * F.mse_loss(kld_desired, kld)
            info_loss += kld_diff_control

        # Logging
        base_str_stats = 'Mode Model stats/'
        with torch.no_grad():
            base_str_info = 'Mode Model info-vae/'
            base_str_mode_map = 'Mode Model/'
            if self._is_interval(self.log_interval, self.learn_steps):
                self._summary_log_mode(base_str_stats + 'log-liklyhood', ll)
                self._summary_log_mode(base_str_stats + 'mse', mse)
                self._summary_log_mode(base_str_stats + 'kld', kld)
                self._summary_log_mode(base_str_stats + 'mmd', mmd)

                self._summary_log_mode(base_str_info + 'kld info-weighted', kld_info)
                self._summary_log_mode(base_str_info + 'mmd info weighted', mmd_info)
                self._summary_log_mode(
                    base_str_info + 'loss on latent', mmd_info + kld_info)

                mode_map_fig = self._plot_mode_map(
                    skill_seq=skill_seq,
                    mode_post_samples=mode_post['samples']
                )
                self._save_fig(
                    locations=['writer'],
                    fig=mode_map_fig,
                    base_str=base_str_mode_map + 'Mode Map'
                )

            base_str_recon = 'Mode Model Action Reconstruction'
            base_str_states_features = 'Mode Model States Features'
            if self._is_interval(self.log_interval, self.learn_steps):
                rand_batch_idx = np.random.randint(0, self.batch_size)

                fig_actions = self._plot_recon_comparison(
                    action_seq=actions_seq[rand_batch_idx],
                    action_seq_recon=actions_seq_recon['samples'][rand_batch_idx],
                    skill=skill_seq_np_squeezed[rand_batch_idx]
                )
                self._save_fig(fig_actions, ['writer'],
                               base_str=base_str_recon)

                fig_states = self._plot_state_features(
                    states_seq=sequence['states_seq'][rand_batch_idx, :-1, :],
                    features_seq=features_seq[rand_batch_idx, :-1, :],
                    skill=skill_seq_np_squeezed[rand_batch_idx]
                )
                self._save_fig(fig_states, ['writer'],
                               base_str=base_str_states_features)

            if self._is_interval(self.log_interval * 20, self.learn_steps) and self.env.observation_space.shape[0] < 4:
                self._test_mode_influence(mode_post_samples=mode_post['samples'])

        return info_loss

    def _sample_sequences(self,
                          memory_to_fill: MyLazyMemory,
                          min_steps,
                          step_cnt: np.ndarray):
        skill = 0
        while np.sum(step_cnt) < min_steps:
            self._sample_equal_skill_dist(memory=memory_to_fill,
                                          skill=skill,
                                          step_cnt=step_cnt)

            skill = min(skill + 1, (skill + 1) % self.num_skills)
            self.episodes += 1

        print(self.steps)
        memory_to_fill.skill_histogram(writer=self.writer)

    def _sample_equal_skill_dist(self,
                                 memory: MyLazyMemory,
                                 skill,
                                 step_cnt: np.ndarray):
        episode_steps = 0
        self.skill_policy.set_skill(skill)

        obs = self.env.reset()
        memory.set_initial_state(obs)

        next_state = obs
        done = False
        while self.steps[skill] <= np.max(self.steps):
            if done:
                next_state = self.env.reset()

            action = self.skill_policy.get_action(
                obs_denormalized=self.env.denormalize(next_state)
                if self.env.state_normalization else next_state
            )
            next_state, reward, done, _ = self.env.step(action)

            episode_steps += 1
            step_cnt[skill] += 1

            seq_pushed = memory.append(action=action,
                                       skill=np.array([skill], dtype=np.uint8),
                                       state=next_state,
                                       done=np.array([done], dtype=np.bool))
            if seq_pushed:
                break

        print(f'episode: {self.episodes:<4}  '
              f'episode_steps: {episode_steps:<4}  '
              f'skill: {skill: <4}  ')

    def _plot_mode_map(self,
                       skill_seq,
                       mode_post_samples,
                       ):
        """
        Args:
            skill_seq           : (N, S, 1) - tensor
            mode_post_samples   : (N, 2) - tensor
        """
        plot_dim = 2
        if not self.mode_dim == plot_dim:
            warnings.warn(f'Warning: Mode-Dimension is not equal to {plot_dim:<2}.'
                          f'No mode map is plotted')
            return None

        colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k',
                  'darkorange', 'gray', 'lightgreen']

        if self.num_skills > len(colors):
            raise ValueError(f'Not more than then {len(colors):<3} '
                             f'skill supported for mode'
                             f'plotting right now (more color needed)')

        assert mode_post_samples.shape == torch.Size((self.batch_size, plot_dim))

        skill_seq = self._tensor_to_numpy(skill_seq.float().mean(dim=1))\
            .astype(np.uint8).squeeze()
        mode_post_samples = self._tensor_to_numpy(mode_post_samples)

        plt.interactive(False)
        _, axes = plt.subplots()
        lim = [-3., 3.]
        axes.set_ylim(lim)
        axes.set_xlim(lim)

        for skill in range(skill_seq.max() + 1):
            bool_idx = skill_seq == skill
            plt.scatter(mode_post_samples[bool_idx, 0],
                        mode_post_samples[bool_idx, 1],
                        label=skill,
                        c=colors[skill])

        axes.legend()
        axes.grid(True)
        fig = plt.gcf()

        return fig

    def _plot_recon_comparison(self,
                               action_seq,
                               action_seq_recon,
                               skill: int,
                               ):
        """
        Args:
            action_seq         : (S, action_dim)
            action_seq_recon   : (S, action_dim)
        Return:
            figure
        """
        action_dim = self.action_shape[0]

        assert action_seq.size(1) == action_seq_recon.size(1) == action_dim
        assert action_seq.size(0) == action_seq_recon.size(0) \
               == self.num_sequences

        action_seq = self._tensor_to_numpy(action_seq)
        action_seq_recon = self._tensor_to_numpy(action_seq_recon)

        plt.interactive(False)
        _, axes1 = plt.subplots()
        lim = [-1.3, 1.3]
        axes1.set_ylim(lim)
        plt.title(f'Skill: {skill:<3}')

        for dim in range(action_dim):
            plt.plot(action_seq[:, dim], label=f'real action dim {dim:<2}')
            plt.plot(action_seq_recon[:, dim], label=f'recon action dim {dim:<2}')
        plt.legend()
        fig = plt.gcf()

        return fig

    def _plot_state_features(self,
                             states_seq,
                             features_seq,
                             skill: int,
                             ):
        """
        Args:
            states_seq       : (S, obs_dim)
            features_seq     : (S, feature_dim)
        Return:
            figure
        """
        obs_dim = self.observation_shape[0]
        feature_dim = self.feature_dim

        assert states_seq.size(0) == features_seq.size(0) == self.num_sequences
        assert features_seq.size(1) == feature_dim
        assert states_seq.size(1) == obs_dim

        states_seq = self._tensor_to_numpy(states_seq)
        features_seq = self._tensor_to_numpy(features_seq)

        plt.interactive(False)
        _, axes = plt.subplots()
        lim = [-1, 1]
        axes.set_ylim(lim)
        plt.title(f'Skill: {skill:<3}')

        for dim in range(obs_dim):
            plt.plot(states_seq[:, dim], label=f'state dim {dim:<2}')
        for dim in range(feature_dim):
            plt.plot(features_seq[:, dim], label=f'features dim {dim:<2}')

        plt.legend()
        fig = plt.gcf()

        return fig

    def _test_mode_influence(self, mode_post_samples, seq_len=250):
        with torch.no_grad():
            mode_action_sampler = ActionSamplerNoSSM(
                mode_model=self.mode_latent_model,
                device=self.device
            )

            if not(self.mode_dim == 2):
                modes = mode_post_samples[:10]
            else:
                modes = self._create_grid()

            for mode in modes:
                mode_action_sampler.reset(mode=mode.unsqueeze(0))
                obs = self.env.reset()
                action_save = []
                obs_save = []

                for _ in range(seq_len):
                    obs_tensor = torch.from_numpy(obs.astype(np.float))\
                        .unsqueeze(0).to(self.device).float()

                    action_tensor = mode_action_sampler(
                        state_rep=self.obs_encoder(obs_tensor)
                    )

                    action = action_tensor.squeeze().detach().cpu().numpy()
                    obs, _, done, _ = self.env.step(action)

                    action_save.append(action)
                    assert obs.shape == self.env.observation_space.shape
                    obs_save.append(obs)

                actions = np.stack(action_save, axis=0)
                obs = np.stack(obs_save, axis=0)

                plt.interactive(False)
                ax = plt.gca()
                ax.set_ylim([-3.5, 3.5])
                plt.plot(actions, label='actions')
                for dim in range(obs.shape[1]):
                    plt.plot(obs[:, dim], label='state_dim' + str(dim))
                plt.legend()
                fig = plt.gcf()
                self.writer.add_figure('mode_grid_plot_test/mode' + str(mode),
                                       figure=fig,
                                       global_step=self.learn_steps)

    def _create_grid(self):
        min = -1.7
        max = -min
        num_steps = 4

        grid_vec = torch.linspace(min, max, num_steps)
        grid_vec_list = [grid_vec] * self.mode_dim
        grid = torch.meshgrid(*grid_vec_list)
        modes = torch.stack(list(grid)).view(self.mode_dim, -1) \
                            .transpose(0, -1).to(self.device)
        return modes

    def _set_seed(self, seed):
        self.seed = seed
        torch.manual_seed(self.seed)
        np.random.seed(self.seed)
        self.env.seed(self.seed)

    def _set_device(self, device_str):
        self.device = torch.device(
            device_str if torch.cuda.is_available() else "cpu"
        )
        print("device set to " + str(self.device))

    def _save_models(self):
        # Models
        path_name_mode = os.path.join(self.model_dir, 'mode_model.pkl')
        path_name_obs_encoder = os.path.join(self.model_dir, 'obs_encoder.pkl')
        torch.save(self.mode_latent_model, path_name_mode)
        torch.save(self.obs_encoder, path_name_obs_encoder)

        # Mode mapping fig
        sequence = self.memory.sample_sequence(self.batch_size)
        features_seq = self.obs_encoder(sequence['states_seq'])
        fig = self._plot_mode_map(
            sequence['skill_seq'],
            self.mode_latent_model.sample_mode_posterior(features_seq)['samples']
        )
        self._save_fig(fig,
                       locations=['file'],
                       base_str='mode_mapping.fig')

    def _summary_log_mode(self, data_name, data):
        if type(data) == torch.Tensor:
            data = data.detach().cpu().item()
        self.writer.add_scalar(data_name, data, self.learn_steps)

    @staticmethod
    def _is_interval(log_interval, steps):
        return True if steps % log_interval == 0 else False

    @staticmethod
    def _tensor_to_numpy(tensor: torch.tensor):
        return tensor.detach().cpu().numpy()

    def _numpy_to_tensor(self, nd_array: np.ndarray):
        return torch.from_numpy(nd_array).to(self.device)

    def _save_fig(self, fig, locations: list, base_str):
        for loc in locations:
            if loc == 'writer':
                self.writer.add_figure(base_str + 'mode mapping',
                                       fig,
                                       global_step=self.learn_steps)

            elif loc == 'file':
                path_name_fig = os.path.join(self.model_dir, 'mode_mapping.fig')
                torch.save(obj=fig, f=path_name_fig)

            else:
                raise NotImplementedError(f'Location {loc} is not implemented')

        plt.clf()

matplotlib.pyplot.interactive matplotlib.pyplot.gcf matplotlib.pyplot.subplots matplotlib.pyplot.gca matplotlib.use matplotlib.pyplot.title matplotlib.pyplot.clf matplotlib.pyplot.plot matplotlib.pyplot.legend matplotlib.pyplot.scatter