import os
import csv
from PIL import Image
import datasets

# Define configurations for each flavor.
BUILDER_CONFIGS = [
    datasets.BuilderConfig(
        name="sound_baseline",
        description="Physical dataset: baseline variant",
        data_dir="./physicsgen/urban_sound_25k_baseline"
    ),
    datasets.BuilderConfig(
        name="sound_reflection",
        description="Physical dataset: reflection variant",
        data_dir="./physicsgen/urban_sound_25k_reflection"
    ),
    datasets.BuilderConfig(
        name="sound_diffraction",
        description="Physical dataset: reflection variant",
        data_dir="./physicsgen/urban_sound_25k_diffraction"
    ),
    datasets.BuilderConfig(
        name="sound_combined",
        description="Physical dataset: reflection variant",
        data_dir="./physicsgen/urban_sound_25k_combined"
    ),
    datasets.BuilderConfig(
        name="lens_p1",
        description="Distortion dataset variant",
        data_dir="./physicsgen/lens_distortion_p1"
    ),
    datasets.BuilderConfig(
        name="lens_p2",
        description="Distortion dataset variant",
        data_dir="./physicsgen/lens_distortion_p2"
    ),
    datasets.BuilderConfig(
        name="ball_roll",
        description="Double image dataset variant",
        data_dir="./physicsgen/ball_roll"
    ),
    datasets.BuilderConfig(
        name="ball_bounce",
        description="Double image dataset variant",
        data_dir="./physicsgen/ball_bounce"
    ),
]

class MyPhysicalDataset(datasets.GeneratorBasedBuilder):
    BUILDER_CONFIGS = BUILDER_CONFIGS
    VERSION = datasets.Version("1.0.2")

    def _info(self):
        if self.config.name in ["sound_baseline", "sound_reflection", "sound_diffraction", "sound_combined"]:
            features = datasets.Features({
                "lat": datasets.Value("float"),
                "long": datasets.Value("float"),
                "db": datasets.Value("string"),
                "soundmap": datasets.Image(),
                "osm": datasets.Image(),
                "temperature": datasets.Value("int32"),
                "humidity": datasets.Value("int32"),
                "yaw": datasets.Value("float"),
                "sample_id": datasets.Value("int32"),
                "soundmap_512": datasets.Image(),
            })
        elif self.config.name in ["lens_p1", "lens_p2"]:
            features = datasets.Features({
                "label_path": datasets.Value("string"),
                "fx": datasets.Value("float"),
                "k1": datasets.Value("float"),
                "k2": datasets.Value("float"),
                "k3": datasets.Value("float"),
                "p1": datasets.Value("float"),
                "p2": datasets.Value("float"),
                "cx": datasets.Value("float"),
                "distortion_path": datasets.Value("string"),
            })
        elif self.config.name in ["ball_roll", "ball_bounce"]:
            features = datasets.Features({
                "ImgName": datasets.Value("string"),
                "StartHeight": datasets.Value("int32"),
                "GroundIncli": datasets.Value("float"),
                "InputTime": datasets.Value("int32"),
                "TargetTime": datasets.Value("int32"),
                "input_image": datasets.Image(),
                "target_image": datasets.Image(),
            })
        else:
            raise ValueError(f"Unknown config name: {self.config.name}")
        return datasets.DatasetInfo(
            description="Multiple variant physical tasks dataset.",
            features=features,
        )

    def _split_generators(self, dl_manager):
        data_dir = self.config.data_dir
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"split_dir": os.path.join(data_dir, "train")},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={"split_dir": os.path.join(data_dir, "test")},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={"split_dir": os.path.join(data_dir, "eval")},
            ),
        ]

    def _generate_examples(self, split_dir):
        if self.config.name in ["sound_baseline", "sound_reflection", "sound_diffraction", "sound_combined"]:
            csv_path = os.path.join(split_dir, "meta_data.csv")
            with open(csv_path, encoding="utf-8") as f:
                reader = csv.DictReader(f)
                for idx, row in enumerate(reader):
                    row["soundmap"] = os.path.join(split_dir, row["soundmap"])
                    row["osm"] = os.path.join(split_dir, row["osm"])
                    row["soundmap_512"] = os.path.join(split_dir, row["soundmap_512"])
                    row["lat"] = float(row["lat"])
                    row["long"] = float(row["long"])
                    row["temperature"] = int(row["temperature"])
                    row["humidity"] = int(row["humidity"])
                    row["sample_id"] = int(row["sample_id"])
                    row["yaw"] = float(row["yaw"]) if row["yaw"] else 0.0
                    yield idx, row

        elif self.config.name in ["lens_p1", "lens_p2"]:
            csv_path = os.path.join(split_dir, "meta_data.csv")
            with open(csv_path, encoding="utf-8") as f:
                reader = csv.DictReader(f)
                for idx, row in enumerate(reader):
                    row["label_path"] = str(row["label_path"])
                    row["distortion_path"] = str(row["distortion_path"])
                    row["fx"] = float(row["fx"])
                    row["k1"] = float(row["k1"])
                    row["k2"] = float(row["k2"])
                    row["k3"] = float(row["k3"])
                    row["p1"] = float(row["p1"])
                    row["p2"] = float(row["p2"])
                    row["cx"] = float(row["cx"])
                    yield idx, row

        elif self.config.name in ["ball_roll", "ball_bounce"]:
            csv_path = os.path.join(split_dir, "meta_data.csv")
            with open(csv_path, encoding="utf-8") as f:
                reader = csv.DictReader(f)
                for idx, row in enumerate(reader):
                    # Construct image path from ImgName, e.g., "DoubleImg_0.jpg"
                    image_filename = "DoubleImg_" + row["ImgName"] + ".jpg"
                    input_image_path = os.path.join(split_dir, "x", image_filename)
                    target_image_path = os.path.join(split_dir, "y", image_filename)
                    row["input_image"] = input_image_path
                    row["target_image"] = target_image_path
                    row["ImgName"] = row["ImgName"]
                    row["StartHeight"] = int(row["StartHeight"])
                    row["GroundIncli"] = float(row["GroundIncli"])
                    row["InputTime"] = int(row["InputTime"])
                    row["TargetTime"] = int(row["TargetTime"])
                    yield idx, row