"""PPO-style GPRO agent with group-relative advantages for corridor control."""
from __future__ import annotations

from typing import Dict, List, Tuple

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim


class MultiDiscreteActorCritic(nn.Module):
    """Shared actor-critic backbone with one categorical head per control segment."""

    def __init__(
        self,
        state_dim: int,
        action_dims: List[int],
        hidden_layers: List[int] = [256, 256],
    ):
        super().__init__()
        self.action_dims = action_dims

        layers = []
        prev_dim = state_dim
        for hidden_dim in hidden_layers:
            layers.append(nn.Linear(prev_dim, hidden_dim))
            layers.append(nn.LayerNorm(hidden_dim))
            layers.append(nn.ReLU())
            prev_dim = hidden_dim
        self.feature_extractor = nn.Sequential(*layers)

        self.actor_heads = nn.ModuleList(
            [nn.Linear(prev_dim, action_dim) for action_dim in action_dims]
        )
        self.critic = nn.Sequential(
            nn.Linear(prev_dim, 128),
            nn.ReLU(),
            nn.Linear(128, 1),
        )
        self._init_weights()

    def _init_weights(self):
        for module in self.modules():
            if isinstance(module, nn.Linear):
                nn.init.orthogonal_(module.weight, gain=np.sqrt(2))
                nn.init.constant_(module.bias, 0)
        for head in self.actor_heads:
            nn.init.orthogonal_(head.weight, gain=0.01)
        nn.init.orthogonal_(self.critic[-1].weight, gain=1.0)

    def forward(self, state: torch.Tensor) -> Tuple[List[torch.Tensor], torch.Tensor]:
        features = self.feature_extractor(state)
        logits_list = [head(features) for head in self.actor_heads]
        value = self.critic(features)
        return logits_list, value

    def get_action_probs(self, state: torch.Tensor) -> Tuple[List[torch.Tensor], torch.Tensor]:
        logits_list, value = self.forward(state)
        probs_list = [F.softmax(logits, dim=-1) for logits in logits_list]
        return probs_list, value

    def get_value(self, state: torch.Tensor) -> torch.Tensor:
        features = self.feature_extractor(state)
        return self.critic(features)


class GPROAgent:
    """PPO actor-critic with group-relative trajectory ranking."""

    def __init__(
        self,
        state_dim: int,
        action_dims: List[int],
        hidden_layers: List[int] = [256, 256],
        learning_rate: float = 3e-4,
        gamma: float = 0.99,
        gae_lambda: float = 0.95,
        clip_epsilon: float = 0.2,
        value_coef: float = 0.5,
        entropy_coef: float = 0.01,
        max_grad_norm: float = 0.5,
        ppo_epochs: int = 4,
        minibatch_size: int = 64,
        group_size: int = 4,
        group_advantage_coef: float = 0.35,
        advantage_epsilon: float = 1e-8,
        device: str = "cuda",
        lr_schedule: str = "cosine",
        total_episodes: int = 300,
    ):
        self.device = torch.device(device if torch.cuda.is_available() else "cpu")
        self.gamma = gamma
        self.gae_lambda = gae_lambda
        self.clip_epsilon = clip_epsilon
        self.value_coef = value_coef
        self.entropy_coef = entropy_coef
        self.max_grad_norm = max_grad_norm
        self.ppo_epochs = ppo_epochs
        self.minibatch_size = minibatch_size
        self.group_size = max(int(group_size), 2)
        self.group_advantage_coef = float(np.clip(group_advantage_coef, 0.0, 1.0))
        self.advantage_epsilon = advantage_epsilon
        self.action_dims = action_dims
        self.num_heads = len(action_dims)

        self.policy = MultiDiscreteActorCritic(state_dim, action_dims, hidden_layers).to(
            self.device
        )
        self.optimizer = optim.Adam(self.policy.parameters(), lr=learning_rate, eps=1e-5)

        self.lr_schedule = lr_schedule
        if lr_schedule == "cosine":
            self.scheduler = optim.lr_scheduler.CosineAnnealingLR(
                self.optimizer, T_max=total_episodes, eta_min=learning_rate * 0.1
            )
        else:
            self.scheduler = None

        self.reset_group_buffers()
        self._reset_episode_buffer()

    def _reset_episode_buffer(self):
        self.current_states: List[np.ndarray] = []
        self.current_actions: List[np.ndarray] = []
        self.current_rewards: List[float] = []
        self.current_values: List[float] = []
        self.current_log_probs: List[float] = []
        self.current_dones: List[float] = []

    def reset_group_buffers(self):
        self.trajectories: List[Dict[str, np.ndarray | float]] = []
        self._reset_episode_buffer()

    def select_action(
        self, state: np.ndarray, deterministic: bool = False
    ) -> Tuple[np.ndarray, float, float]:
        state_tensor = torch.FloatTensor(state).unsqueeze(0).to(self.device)
        with torch.no_grad():
            probs_list, value = self.policy.get_action_probs(state_tensor)

        actions = []
        total_log_prob = 0.0
        for probs in probs_list:
            if deterministic:
                action = torch.argmax(probs, dim=-1).item()
            else:
                dist = torch.distributions.Categorical(probs)
                action = dist.sample().item()
            log_prob = torch.log(probs[0, action] + 1e-10).item()
            actions.append(action)
            total_log_prob += log_prob

        return np.array(actions, dtype=np.int64), total_log_prob, value.item()

    def store_transition(self, state, action, reward, value, log_prob, done):
        self.current_states.append(np.asarray(state, dtype=np.float32))
        self.current_actions.append(np.asarray(action, dtype=np.int64))
        self.current_rewards.append(float(reward))
        self.current_values.append(float(value))
        self.current_log_probs.append(float(log_prob))
        self.current_dones.append(float(done))

    def finish_episode(self, score: float):
        if not self.current_states:
            return
        self.trajectories.append(
            {
                "states": np.asarray(self.current_states, dtype=np.float32),
                "actions": np.asarray(self.current_actions, dtype=np.int64),
                "rewards": np.asarray(self.current_rewards, dtype=np.float32),
                "values": np.asarray(self.current_values, dtype=np.float32),
                "log_probs": np.asarray(self.current_log_probs, dtype=np.float32),
                "dones": np.asarray(self.current_dones, dtype=np.float32),
                "score": float(score),
            }
        )
        self._reset_episode_buffer()

    def _compute_gae(
        self,
        rewards: np.ndarray,
        values: np.ndarray,
        dones: np.ndarray,
        next_value: float = 0.0,
    ) -> Tuple[np.ndarray, np.ndarray]:
        advantages = np.zeros_like(rewards, dtype=np.float32)
        gae = 0.0

        for t in reversed(range(len(rewards))):
            if t == len(rewards) - 1:
                next_val = next_value
            else:
                next_val = values[t + 1]
            delta = rewards[t] + self.gamma * next_val * (1.0 - dones[t]) - values[t]
            gae = delta + self.gamma * self.gae_lambda * (1.0 - dones[t]) * gae
            advantages[t] = gae

        returns = advantages + values
        return advantages, returns

    def _normalize(self, values: np.ndarray) -> np.ndarray:
        if values.size == 0:
            return values
        mean = float(values.mean())
        std = float(values.std())
        if std < self.advantage_epsilon:
            return np.zeros_like(values, dtype=np.float32)
        return ((values - mean) / (std + self.advantage_epsilon)).astype(np.float32)

    def _build_training_targets(self) -> Tuple[np.ndarray, np.ndarray]:
        if not self.trajectories:
            return np.array([], dtype=np.float32), np.array([], dtype=np.float32)

        gae_advantages = []
        returns = []
        for trajectory in self.trajectories:
            trajectory_adv, trajectory_ret = self._compute_gae(
                rewards=trajectory["rewards"],
                values=trajectory["values"],
                dones=trajectory["dones"],
                next_value=0.0,
            )
            gae_advantages.append(trajectory_adv)
            returns.append(trajectory_ret)

        normalized_gae = self._normalize(np.concatenate(gae_advantages, axis=0))
        group_scores = np.asarray(
            [trajectory["score"] for trajectory in self.trajectories], dtype=np.float32
        )
        normalized_group_scores = self._normalize(group_scores)
        repeated_group_advantages = np.concatenate(
            [
                np.full(len(trajectory["states"]), normalized_group_scores[idx], dtype=np.float32)
                for idx, trajectory in enumerate(self.trajectories)
            ],
            axis=0,
        )

        combined_advantages = (
            (1.0 - self.group_advantage_coef) * normalized_gae
            + self.group_advantage_coef * repeated_group_advantages
        )
        combined_advantages = self._normalize(combined_advantages)
        return combined_advantages, np.concatenate(returns, axis=0).astype(np.float32)

    def update(self) -> Dict[str, float]:
        if not self.trajectories:
            return {}

        advantages, returns = self._build_training_targets()
        states = torch.FloatTensor(
            np.concatenate([trajectory["states"] for trajectory in self.trajectories], axis=0)
        ).to(self.device)
        actions = torch.LongTensor(
            np.concatenate([trajectory["actions"] for trajectory in self.trajectories], axis=0)
        ).to(self.device)
        old_log_probs = torch.FloatTensor(
            np.concatenate([trajectory["log_probs"] for trajectory in self.trajectories], axis=0)
        ).to(self.device)
        advantages_t = torch.FloatTensor(advantages).to(self.device)
        returns_t = torch.FloatTensor(returns).to(self.device)

        total_loss = 0.0
        total_policy_loss = 0.0
        total_value_loss = 0.0
        total_entropy_value = 0.0
        update_count = 0
        dataset_size = states.shape[0]

        for _ in range(self.ppo_epochs):
            indices = np.random.permutation(dataset_size)
            for start_idx in range(0, dataset_size, self.minibatch_size):
                end_idx = min(start_idx + self.minibatch_size, dataset_size)
                batch_idx = indices[start_idx:end_idx]

                batch_states = states[batch_idx]
                batch_actions = actions[batch_idx]
                batch_old_log_probs = old_log_probs[batch_idx]
                batch_advantages = advantages_t[batch_idx]
                batch_returns = returns_t[batch_idx]

                logits_list, values = self.policy(batch_states)
                total_new_log_probs = torch.zeros(len(batch_idx), device=self.device)
                entropy_terms = torch.zeros(len(batch_idx), device=self.device)

                for head_idx in range(self.num_heads):
                    probs = F.softmax(logits_list[head_idx], dim=-1)
                    dist = torch.distributions.Categorical(probs)
                    total_new_log_probs += dist.log_prob(batch_actions[:, head_idx])
                    entropy_terms += dist.entropy()

                entropy = entropy_terms.mean()
                ratio = torch.exp(total_new_log_probs - batch_old_log_probs)
                surr1 = ratio * batch_advantages
                surr2 = (
                    torch.clamp(ratio, 1 - self.clip_epsilon, 1 + self.clip_epsilon)
                    * batch_advantages
                )
                policy_loss = -torch.min(surr1, surr2).mean()
                value_loss = F.mse_loss(values.squeeze(-1), batch_returns)
                loss = (
                    policy_loss
                    + self.value_coef * value_loss
                    - self.entropy_coef * entropy
                )

                self.optimizer.zero_grad()
                loss.backward()
                nn.utils.clip_grad_norm_(self.policy.parameters(), self.max_grad_norm)
                self.optimizer.step()

                total_loss += float(loss.item())
                total_policy_loss += float(policy_loss.item())
                total_value_loss += float(value_loss.item())
                total_entropy_value += float(entropy.item())
                update_count += 1

        if self.scheduler is not None:
            self.scheduler.step()

        scores = np.asarray(
            [trajectory["score"] for trajectory in self.trajectories], dtype=np.float32
        )
        stats = {
            "loss": total_loss / max(update_count, 1),
            "policy_loss": total_policy_loss / max(update_count, 1),
            "value_loss": total_value_loss / max(update_count, 1),
            "entropy": total_entropy_value / max(update_count, 1),
            "lr": self.optimizer.param_groups[0]["lr"],
            "group_score_mean": float(scores.mean()) if scores.size else 0.0,
            "group_score_std": float(scores.std()) if scores.size else 0.0,
            "group_size": float(len(self.trajectories)),
            "group_advantage_coef": float(self.group_advantage_coef),
        }
        self.reset_group_buffers()
        return stats

    def save(self, path: str):
        torch.save(
            {
                "policy_state_dict": self.policy.state_dict(),
                "optimizer_state_dict": self.optimizer.state_dict(),
            },
            path,
        )

    def load(self, path: str):
        checkpoint = torch.load(path, map_location=self.device, weights_only=False)
        self.policy.load_state_dict(checkpoint["policy_state_dict"])
        self.optimizer.load_state_dict(checkpoint["optimizer_state_dict"])