import io
import json
from pathlib import Path
from typing import Any, Dict, List, Union

import pandas as pd
from datasets import load_dataset


class ADTDatasetLoader:
    """Loader for ADT Dataset from Hugging Face."""

    def __init__(self, repo_id: str = "ariakang/ADT-test", sequence_name: str = None):
        """
        Initialize the dataset loader.

        Args:
            repo_id: Hugging Face repository ID
            sequence_name: Specific sequence to load. If None, loads first available sequence
        """
        self.dataset = load_dataset(repo_id)

        if sequence_name is None:
            sequence_name = list(self.dataset.keys())[0]

        self.sequence_name = sequence_name
        self.sequence = self.dataset[sequence_name]

        # Create a mapping of filenames to indices for faster access
        self.file_index = {
            filename: idx for idx, filename in enumerate(self.sequence["filename"])
        }

    def _deserialize_csv(self, csv_string: str) -> pd.DataFrame:
        """Convert CSV string to DataFrame."""
        return pd.read_csv(io.StringIO(csv_string))

    def _deserialize_json(self, json_string: str) -> Union[Dict, List]:
        """Convert JSON string to Python object."""
        return json.loads(json_string)

    def get_available_files(self) -> List[Dict[str, str]]:
        """Get list of all available files and their types."""
        return [
            {"filename": filename, "type": dtype}
            for filename, dtype in zip(
                self.sequence["filename"], self.sequence["data_type"]
            )
        ]

    def load_file_by_name(self, filename: str) -> Any:
        """
        Load specific file by name.

        Args:
            filename: Name of the file to load (e.g., "2d_bounding_box.csv")

        Returns:
            DataFrame for CSV files, dict/list for JSON files
        """
        if filename not in self.file_index:
            raise ValueError(f"File {filename} not found in dataset")

        idx = self.file_index[filename]
        data_type = self.sequence["data_type"][idx]
        data = self.sequence["data"][idx]

        if data_type == "csv":
            return self._deserialize_csv(data)
        elif data_type in ["json", "jsonl"]:
            return self._deserialize_json(data)
        else:
            return data

    def load_2d_bounding_boxes(self) -> pd.DataFrame:
        """Load 2D bounding box data."""
        return self.load_file_by_name("2d_bounding_box.csv")

    def load_3d_bounding_boxes(self) -> pd.DataFrame:
        """Load 3D bounding box data."""
        return self.load_file_by_name("3d_bounding_box.csv")

    def load_aria_trajectory(self) -> pd.DataFrame:
        """Load Aria device trajectory data."""
        return self.load_file_by_name("aria_trajectory.csv")

    def load_eyegaze(self) -> pd.DataFrame:
        """Load eye gaze data."""
        return self.load_file_by_name("eyegaze.csv")

    def load_scene_objects(self) -> pd.DataFrame:
        """Load scene objects data."""
        return self.load_file_by_name("scene_objects.csv")

    def load_instances(self) -> Dict:
        """Load instances data."""
        return self.load_file_by_name("instances.json")

    def load_metadata(self) -> Dict:
        """Load metadata."""
        return self.load_file_by_name("metadata.json")

    def load_mps_eye_gaze(self) -> Dict[str, Union[pd.DataFrame, Dict]]:
        """Load MPS eye gaze data."""
        return {
            "general": self.load_file_by_name("mps/eye_gaze/general_eye_gaze.csv"),
            "summary": self.load_file_by_name("mps/eye_gaze/summary.json"),
        }

    def load_mps_slam(self) -> Dict[str, Union[pd.DataFrame, List]]:
        """Load MPS SLAM data."""
        return {
            "closed_loop": self.load_file_by_name(
                "mps/slam/closed_loop_trajectory.csv"
            ),
            "open_loop": self.load_file_by_name("mps/slam/open_loop_trajectory.csv"),
            "calibration": self.load_file_by_name("mps/slam/online_calibration.jsonl"),
        }

    def get_vrs_files_info(self) -> List[Dict]:
        """Get information about VRS files."""
        vrs_info = self.load_file_by_name("vrs_files_info.json")
        return self._deserialize_json(vrs_info)


def print_dataset_summary(data: Any, name: str):
    """Print summary of loaded data."""
    if isinstance(data, pd.DataFrame):
        print(f"\n{name}:")
        print(f"Shape: {data.shape}")
        print("Columns:", list(data.columns))
        print("Sample data:")
        print(data.head(2))
    elif isinstance(data, dict):
        print(f"\n{name}:")
        print("Keys:", list(data.keys()))
    elif isinstance(data, list):
        print(f"\n{name}:")
        print(f"Number of items: {len(data)}")
        if data:
            print("First item sample:", data[0])


def main():
    """Example usage of the dataset loader."""
    loader = ADTDatasetLoader()

    print(f"Loading sequence: {loader.sequence_name}")

    # Print available files
    print("\nAvailable files:")
    for file_info in loader.get_available_files():
        print(f"- {file_info['filename']} ({file_info['type']})")

    # Load and print summaries of all data types
    print("\n=== Loading all data types ===")

    # Bounding boxes
    print_dataset_summary(loader.load_2d_bounding_boxes(), "2D Bounding Boxes")
    print_dataset_summary(loader.load_3d_bounding_boxes(), "3D Bounding Boxes")

    # Trajectory and eye gaze
    print_dataset_summary(loader.load_aria_trajectory(), "Aria Trajectory")
    print_dataset_summary(loader.load_eyegaze(), "Eye Gaze")

    # Scene objects
    print_dataset_summary(loader.load_scene_objects(), "Scene Objects")

    # JSON data
    print_dataset_summary(loader.load_instances(), "Instances")
    print_dataset_summary(loader.load_metadata(), "Metadata")

    # MPS data
    print_dataset_summary(loader.load_mps_eye_gaze(), "MPS Eye Gaze")
    print_dataset_summary(loader.load_mps_slam(), "MPS SLAM")

    # VRS files info
    print_dataset_summary(loader.get_vrs_files_info(), "VRS Files")


if __name__ == "__main__":
    main()