from __future__ import annotations

from pathlib import Path

import numpy as np
import torch
from cmaes import SepCMA
from rich.console import Console

from .config import ESConfig
from .io import write_json
from .schemas import RewardRecord
from .training_common import make_loader, make_model, move_batch, seed_everything

console = Console()


@torch.no_grad()
def _cached_features(model, loader) -> tuple[np.ndarray, np.ndarray]:
    model.eval()
    features = []
    rewards = []
    for batch in loader:
        inputs, batch_rewards = move_batch(batch, model.device_ref)
        features.append(model.encode_features(**inputs).cpu().numpy())
        rewards.append(batch_rewards.cpu().numpy())
    return np.concatenate(features), np.concatenate(rewards)


def _unpack(candidate: np.ndarray, worker_count: int, hidden_size: int):
    weight_size = worker_count * hidden_size
    weight = candidate[:weight_size].reshape(worker_count, hidden_size)
    bias = candidate[weight_size:]
    return weight, bias


def _candidate_utility(
    candidate: np.ndarray,
    features: np.ndarray,
    rewards: np.ndarray,
    worker_count: int,
    objective: str,
) -> float:
    weight, bias = _unpack(candidate, worker_count, features.shape[1])
    logits = features @ weight.T + bias
    if objective == "expected_utility":
        shifted = logits - logits.max(axis=1, keepdims=True)
        probabilities = np.exp(shifted)
        probabilities /= probabilities.sum(axis=1, keepdims=True)
        return float((probabilities * rewards).sum(axis=1).mean())
    actions = logits.argmax(axis=1)
    return float(rewards[np.arange(len(rewards)), actions].mean())


def train_es(
    records: list[RewardRecord],
    config: ESConfig,
    output_dir: str | Path,
    checkpoint: str | None = None,
) -> dict:
    seed_everything(config.training.seed)
    worker_ids = records[0].worker_ids
    model = make_model(config.model, worker_ids, checkpoint)
    train_loader = make_loader(records, model, "train", batch_size=32, shuffle=False)
    train_features, train_rewards = _cached_features(model, train_loader)
    try:
        validation_loader = make_loader(
            records, model, "validation", batch_size=32, shuffle=False
        )
        validation_features, validation_rewards = _cached_features(model, validation_loader)
    except ValueError:
        validation_features = validation_rewards = None

    initial = np.concatenate(
        [
            model.classifier.weight.detach().cpu().numpy().ravel(),
            model.classifier.bias.detach().cpu().numpy().ravel(),
        ]
    ).astype(np.float64)
    kwargs = {
        "mean": initial,
        "sigma": config.training.sigma,
        "seed": config.training.seed,
    }
    if config.training.population_size is not None:
        kwargs["population_size"] = config.training.population_size
    optimizer = SepCMA(**kwargs)
    best_candidate = initial.copy()
    best_train_utility = _candidate_utility(
        initial,
        train_features,
        train_rewards,
        len(worker_ids),
        config.training.objective,
    )
    history = []
    for generation in range(config.training.generations):
        solutions = []
        generation_best = -float("inf")
        for _ in range(optimizer.population_size):
            candidate = optimizer.ask()
            utility = _candidate_utility(
                candidate,
                train_features,
                train_rewards,
                len(worker_ids),
                config.training.objective,
            )
            solutions.append((candidate, -utility))
            generation_best = max(generation_best, utility)
            if utility > best_train_utility:
                best_train_utility = utility
                best_candidate = candidate.copy()
        optimizer.tell(solutions)
        row = {
            "generation": generation + 1,
            "generation_best_train_utility": generation_best,
            "best_train_utility": best_train_utility,
        }
        if validation_features is not None and validation_rewards is not None:
            row["validation_utility"] = _candidate_utility(
                best_candidate,
                validation_features,
                validation_rewards,
                len(worker_ids),
                config.training.objective,
            )
        history.append(row)
        console.print(row)

    weight, bias = _unpack(best_candidate, len(worker_ids), train_features.shape[1])
    with torch.no_grad():
        model.classifier.weight.copy_(torch.from_numpy(weight).to(model.classifier.weight))
        model.classifier.bias.copy_(torch.from_numpy(bias).to(model.classifier.bias))
    output = Path(output_dir)
    model.save_checkpoint(
        output,
        metadata={"stage": "sep-cma-es", "history": history},
    )
    report = {
        "stage": "sep-cma-es",
        "checkpoint": str(output),
        "worker_ids": worker_ids,
        "best_train_utility": best_train_utility,
        "history": history,
    }
    write_json(output / "training_report.json", report)
    return report