from typing import Dict, Optional, Tuple, List
from mlagents.torch_utils import torch
import numpy as np

from mlagents.trainers.buffer import AgentBuffer
from mlagents.trainers.trajectory import SplitObservations
from mlagents.trainers.torch.components.bc.module import BCModule
from mlagents.trainers.torch.components.reward_providers import create_reward_provider

from mlagents.trainers.policy.torch_policy import TorchPolicy
from mlagents.trainers.optimizer import Optimizer
from mlagents.trainers.settings import TrainerSettings
from mlagents.trainers.torch.utils import ModelUtils


class TorchOptimizer(Optimizer):  # pylint: disable=W0223
    def __init__(self, policy: TorchPolicy, trainer_settings: TrainerSettings):
        super().__init__()
        self.policy = policy
        self.trainer_settings = trainer_settings
        self.update_dict: Dict[str, torch.Tensor] = {}
        self.value_heads: Dict[str, torch.Tensor] = {}
        self.memory_in: torch.Tensor = None
        self.memory_out: torch.Tensor = None
        self.m_size: int = 0
        self.global_step = torch.tensor(0)
        self.bc_module: Optional[BCModule] = None
        self.create_reward_signals(trainer_settings.reward_signals)
        if trainer_settings.behavioral_cloning is not None:
            self.bc_module = BCModule(
                self.policy,
                trainer_settings.behavioral_cloning,
                policy_learning_rate=trainer_settings.hyperparameters.learning_rate,
                default_batch_size=trainer_settings.hyperparameters.batch_size,
                default_num_epoch=3,
            )

    def update(self, batch: AgentBuffer, num_sequences: int) -> Dict[str, float]:
        pass

    def create_reward_signals(self, reward_signal_configs):
        """
        Create reward signals
        :param reward_signal_configs: Reward signal config.
        """
        for reward_signal, settings in reward_signal_configs.items():
            # Name reward signals by string in case we have duplicates later
            self.reward_signals[reward_signal.value] = create_reward_provider(
                reward_signal, self.policy.behavior_spec, settings
            )

    def get_trajectory_value_estimates(
        self, batch: AgentBuffer, next_obs: List[np.ndarray], done: bool
    ) -> Tuple[Dict[str, np.ndarray], Dict[str, float]]:
        vector_obs = [ModelUtils.list_to_tensor(batch["vector_obs"])]
        if self.policy.use_vis_obs:
            visual_obs = []
            for idx, _ in enumerate(
                self.policy.actor_critic.network_body.visual_processors
            ):
                visual_ob = ModelUtils.list_to_tensor(batch["visual_obs%d" % idx])
                visual_obs.append(visual_ob)
        else:
            visual_obs = []

        memory = torch.zeros([1, 1, self.policy.m_size])

        vec_vis_obs = SplitObservations.from_observations(next_obs)
        next_vec_obs = [
            ModelUtils.list_to_tensor(vec_vis_obs.vector_observations).unsqueeze(0)
        ]
        next_vis_obs = [
            ModelUtils.list_to_tensor(_vis_ob).unsqueeze(0)
            for _vis_ob in vec_vis_obs.visual_observations
        ]

        value_estimates, next_memory = self.policy.actor_critic.critic_pass(
            vector_obs, visual_obs, memory, sequence_length=batch.num_experiences
        )

        next_value_estimate, _ = self.policy.actor_critic.critic_pass(
            next_vec_obs, next_vis_obs, next_memory, sequence_length=1
        )

        for name, estimate in value_estimates.items():
            value_estimates[name] = ModelUtils.to_numpy(estimate)
            next_value_estimate[name] = ModelUtils.to_numpy(next_value_estimate[name])

        if done:
            for k in next_value_estimate:
                if not self.reward_signals[k].ignore_done:
                    next_value_estimate[k] = 0.0

        return value_estimates, next_value_estimate