"""HuggingFace `datasets` loader for Rad-R v1.0.

Usage:
    from datasets import load_dataset
    ds = load_dataset("radr-anon-2026/radr", trust_remote_code=True)
    sample = ds["train"][0]
    # sample.keys() == {"rd_map", "iq_real", "iq_imag", "fault_label",
    #                   "severity_label", "capture", "frame_idx"}
"""
from __future__ import annotations

import datasets
import h5py
import numpy as np


_CITATION = """\
@inproceedings{radr2026,
  title   = {Rad-R: A Real-World Raw-ADC Dataset and Benchmark for mmWave Automotive Radar Robustness},
  author  = {Anonymous},
  booktitle = {NeurIPS Evaluations and Datasets Track},
  year    = {2026}
}
"""

_DESCRIPTION = """\
The first publicly released radar dataset combining raw ADC captures from a
TI MMWCAS-RF-EVM 4-chip cascade (12 Tx x 16 Rx = 192 virtual channels at 77
GHz) with physically induced hardware fault annotations. v1.0 contains 9
capture runs (5 min each at 10 fps): healthy baseline, plus 4 fault types
(vibration, misalignment, blockage, Rx degradation) at 2 severity levels each.
Synchronized with BNO055 IMU, DHT22 board/ambient temperature, three GPS
streams, and dual Raspberry Pi cameras.
"""

_HOMEPAGE = "https://huggingface.co/datasets/radr-anon-2026/radr"
_LICENSE = "CC BY 4.0"

_FAULT_NAMES = ["healthy", "vibration", "misalignment", "blockage", "rx_degradation"]
_SEVERITY_NAMES = ["s0_healthy", "s1_mild", "s2_severe"]


class RadRConfig(datasets.BuilderConfig):
    def __init__(self, **kwargs):
        super().__init__(version=datasets.Version("1.0.0"), **kwargs)


class RadR(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.0")
    BUILDER_CONFIGS = [
        RadRConfig(name="default", description="Pre-processed RD maps + raw IQ subset (1800 frames)."),
    ]
    DEFAULT_CONFIG_NAME = "default"

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features({
                "rd_map":         datasets.Array2D(shape=(224, 224), dtype="float32"),
                "iq_real":        datasets.Array3D(shape=(64, 256, 16), dtype="float32"),
                "iq_imag":        datasets.Array3D(shape=(64, 256, 16), dtype="float32"),
                "fault_label":    datasets.ClassLabel(names=_FAULT_NAMES),
                "severity_label": datasets.ClassLabel(names=_SEVERITY_NAMES),
                "capture":        datasets.Value("string"),
                "frame_idx":      datasets.Value("int32"),
            }),
            supervised_keys=("rd_map", "fault_label"),
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        cache_url = "training_cache.h5"
        local = dl_manager.download(cache_url)
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"path": local}),
        ]

    def _generate_examples(self, path):
        with h5py.File(path, "r") as f:
            n = int(f.attrs.get("n_total", f["fault_label"].shape[0]))
            rd        = f["rd_map"]
            iq        = f["iq"]
            fault     = f["fault_label"]
            severity  = f["severity_label"]
            capture   = f["capture"]
            frame_idx = f["frame_idx"]

            for i in range(n):
                iq_complex = iq[i]
                yield i, {
                    "rd_map":         rd[i],
                    "iq_real":        iq_complex.real.astype(np.float32),
                    "iq_imag":        iq_complex.imag.astype(np.float32),
                    "fault_label":    int(fault[i]),
                    "severity_label": int(severity[i]),
                    "capture":        capture[i].decode() if isinstance(capture[i], bytes) else str(capture[i]),
                    "frame_idx":      int(frame_idx[i]),
                }