"""dataset_loader.py — Data loading for NSGF/NSGF++ experiments.

Handles:
  - 2D synthetic datasets (8gaussians, moons, scurve, checkerboard)
  - MNIST / CIFAR-10 for image experiments
  - Source distributions (standard Gaussian)

Reference: arXiv:2401.14069, Appendix E.1 and E.2
"""

import math
import numpy as np
import torch
from torch.utils.data import DataLoader, TensorDataset
from sklearn.datasets import make_moons, make_s_curve


# ============================================================
# 2D Synthetic Datasets (following Tong et al. 2023 / Grathwohl et al. 2018)
# ============================================================

def sample_8gaussians(n: int, scale: float = 4.0, std: float = 0.5) -> torch.Tensor:
    """8 Gaussian modes arranged in a circle."""
    centers = []
    for i in range(8):
        angle = 2 * math.pi * i / 8
        centers.append((scale * math.cos(angle), scale * math.sin(angle)))
    centers = np.array(centers)
    idx = np.random.randint(0, 8, n)
    data = centers[idx] + np.random.randn(n, 2) * std
    return torch.FloatTensor(data)


def sample_moons(n: int, noise: float = 0.05) -> torch.Tensor:
    """Two interleaving half-circles (scikit-learn moons)."""
    data, _ = make_moons(n_samples=n, noise=noise)
    data = data * 3.0 - np.array([1.0, 0.0])
    return torch.FloatTensor(data)


def sample_scurve(n: int, noise: float = 0.0) -> torch.Tensor:
    """S-curve projected to 2D."""
    data, _ = make_s_curve(n_samples=n, noise=noise)
    data = data[:, [0, 2]] * 3.0
    return torch.FloatTensor(data)


def sample_checkerboard(n: int) -> torch.Tensor:
    """4x4 checkerboard pattern."""
    x1 = np.random.rand(n) * 4 - 2
    x2_ = np.random.rand(n) - np.random.randint(0, 2, n) * 2
    x2 = x2_ + (np.floor(x1) % 2)
    data = np.column_stack([x1, x2]) * 2
    return torch.FloatTensor(data)


def sample_8gaussians_moons(n: int) -> torch.Tensor:
    """Mixture: half from 8gaussians, half from moons."""
    n1 = n // 2
    n2 = n - n1
    g = sample_8gaussians(n1)
    m = sample_moons(n2)
    data = torch.cat([g, m], dim=0)
    perm = torch.randperm(n)
    return data[perm]


DATASET_2D = {
    "8gaussians": sample_8gaussians,
    "moons": sample_moons,
    "scurve": sample_scurve,
    "checkerboard": sample_checkerboard,
    "8gaussians_moons": sample_8gaussians_moons,
}


def get_2d_dataset(name: str, n: int) -> torch.Tensor:
    if name not in DATASET_2D:
        raise ValueError(f"Unknown 2D dataset: {name}. Available: {list(DATASET_2D.keys())}")
    return DATASET_2D[name](n)


def sample_source_2d(n: int, dim: int = 2) -> torch.Tensor:
    return torch.randn(n, dim)


# ============================================================
# Image Datasets (MNIST, CIFAR-10)
# ============================================================

def get_image_dataloader(
    dataset_name: str,
    batch_size: int,
    train: bool = True,
    data_root: str = "./data",
    num_workers: int = 2,
    normalize_range: tuple = (-1.0, 1.0),
) -> DataLoader:
    import torchvision
    import torchvision.transforms as T

    lo, hi = normalize_range
    transforms_list = [T.ToTensor()]
    transforms_list.append(T.Normalize(
        mean=[0.5] * (1 if dataset_name == "mnist" else 3),
        std=[0.5] * (1 if dataset_name == "mnist" else 3),
    ))
    transform = T.Compose(transforms_list)

    if dataset_name == "mnist":
        ds = torchvision.datasets.MNIST(
            root=data_root, train=train, download=True, transform=transform
        )
    elif dataset_name == "cifar10":
        ds = torchvision.datasets.CIFAR10(
            root=data_root, train=train, download=True, transform=transform
        )
    else:
        raise ValueError(f"Unknown image dataset: {dataset_name}")

    return DataLoader(
        ds, batch_size=batch_size, shuffle=train,
        num_workers=num_workers, pin_memory=True, drop_last=True,
    )


def sample_source_image(n: int, channels: int, image_size: int) -> torch.Tensor:
    return torch.randn(n, channels, image_size, image_size)


# ============================================================
# DatasetLoader class (unified interface)
# ============================================================

class DatasetLoader:
    def __init__(self, config: dict):
        self.config = config
        self.dataset_name = config.get("dataset", "8gaussians")
        self.is_image = self.dataset_name in ("mnist", "cifar10")

    def sample_target(self, n: int, device: str = "cpu") -> torch.Tensor:
        if self.is_image:
            # Recreate DataLoader if batch size changed (different training phases
            # use different batch sizes, e.g. 256 for pool building, 128 for NSF)
            if not hasattr(self, "_image_loader") or self._image_batch_size != n:
                self._image_batch_size = n
                self._image_loader = get_image_dataloader(
                    self.dataset_name, batch_size=n, train=True
                )
                self._image_iter = iter(self._image_loader)
            try:
                images, _ = next(self._image_iter)
            except StopIteration:
                self._image_iter = iter(self._image_loader)
                images, _ = next(self._image_iter)
            return images.to(device)
        else:
            return get_2d_dataset(self.dataset_name, n).to(device)

    def sample_source(self, n: int, device: str = "cpu") -> torch.Tensor:
        if self.is_image:
            channels = self.config.get("in_channels", 1)
            image_size = self.config.get("image_size", 28)
            return sample_source_image(n, channels, image_size).to(device)
        else:
            dim = self.config.get("model", {}).get("input_dim", 2)
            return sample_source_2d(n, dim).to(device)

    def get_test_samples(self, n: int, device: str = "cpu") -> torch.Tensor:
        if self.is_image:
            loader = get_image_dataloader(
                self.dataset_name, batch_size=n, train=False
            )
            images, _ = next(iter(loader))
            return images.to(device)
        else:
            return get_2d_dataset(self.dataset_name, n).to(device)

    @property
    def data_dim(self) -> int:
        if self.is_image:
            c = self.config.get("in_channels", 1)
            s = self.config.get("image_size", 28)
            return c * s * s
        else:
            return self.config.get("model", {}).get("input_dim", 2)