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from ray.rllib.agents.ppo.ppo_torch_policy import PPOTorchPolicy, KLCoeffMixin, ppo_surrogate_loss
from ray.rllib.agents.ppo.ppo import PPOTrainer, DEFAULT_CONFIG as PPO_CONFIG
from ray.rllib.models.action_dist import ActionDistribution
import gym
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.policy.policy import Policy
from ray.rllib.policy.sample_batch import SampleBatch
from ray.rllib.policy.torch_policy import EntropyCoeffSchedule, \
    LearningRateSchedule
from ray.rllib.utils.typing import TensorType, TrainerConfigDict
from ray.rllib.utils.torch_ops import convert_to_torch_tensor
from marllib.marl.algos.utils.centralized_critic import CentralizedValueMixin, centralized_critic_postprocessing
from marllib.marl.algos.core import setup_torch_mixins

from marllib.marl.algos.utils.centralized_critic import reorder_obs
import inspect 
#############
### MAPPO ###
#############

def central_critic_ppo_loss(policy: Policy, model: ModelV2,
                            dist_class: ActionDistribution,
                            train_batch: SampleBatch) -> TensorType:
    """Constructs the loss for Centralized PPO Objective.
    Args:
        policy (Policy): The Policy to calculate the loss for.
        model (ModelV2): The Model to calculate the loss for.
        dist_class (Type[ActionDistribution]: The action distr. class.
        train_batch (SampleBatch): The training data.

    Returns:
        Union[TensorType, List[TensorType]]: A single loss tensor or a list
            of loss tensors.
    """
    CentralizedValueMixin.__init__(policy)
    func = ppo_surrogate_loss

    vf_saved = model.value_function
    opp_action_in_cc = policy.config["model"]["custom_model_config"]["opp_action_in_cc"]
    custom_config = policy.config["model"]["custom_model_config"]

    central_vf_parameters = inspect.signature(policy.model.central_value_function).parameters.keys()
    if 'agent_mask' in central_vf_parameters:
        n_agents = policy.model.n_agents
        tmp_obs = reorder_obs(train_batch["obs"].cpu().numpy(), custom_config["space_obs"])
        agent_mask = convert_to_torch_tensor(tmp_obs[:, -2*n_agents:-n_agents], policy.device)
        model.value_function = lambda: policy.model.central_value_function(train_batch["state"], 
                
            train_batch["opponent_actions"] if opp_action_in_cc else None,
            agent_mask=agent_mask)
    else:
        model.value_function = lambda: policy.model.central_value_function(train_batch["state"],
                                                                       train_batch[
                                                                           "opponent_actions"] if opp_action_in_cc else None)

    policy._central_value_out = model.value_function()
    loss = func(policy, model, dist_class, train_batch)

    model.value_function = vf_saved

    return loss


MAPPOTorchPolicy = PPOTorchPolicy.with_updates(
    name="MAPPOTorchPolicy",
    get_default_config=lambda: PPO_CONFIG,
    postprocess_fn=centralized_critic_postprocessing,
    loss_fn=central_critic_ppo_loss,
    before_init=setup_torch_mixins,
    mixins=[
        LearningRateSchedule, EntropyCoeffSchedule, KLCoeffMixin,
        CentralizedValueMixin
    ])


def get_policy_class_mappo(config_):
    if config_["framework"] == "torch":
        return MAPPOTorchPolicy


MAPPOTrainer = PPOTrainer.with_updates(
    name="MAPPOTrainer",
    default_policy=None,
    get_policy_class=get_policy_class_mappo,
)
