"""
Dataset script for VLM Plant Sim - enables proper JSONL loading with explicit schema definition.
This prevents HF's auto-inference from failing on complex multi-valued fields.
"""

import json
from typing import Generator

import datasets
from datasets import Features, SplitGenerator, Value, DatasetInfo


# Dataset configuration
_CITATION = """\
@misc{yun2026usingvisionlanguagefoundation,
      title={Using Vision Language Foundation Models to Generate Plant Simulation Configurations via In-Context Learning},
      author={Heesup Yun and Isaac Kazuo Uyehara and Earl Ranario and Lars Lundqvist and Christine H. Diepenbrock and Brian N. Bailey and J. Mason Earles},
      year={2026},
      eprint={2603.08930},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
}
"""

_DESCRIPTION = """\
VLM Plant Sim is a multi-source plant and crop image dataset combining HELIOS synthetic renders,
real drone orthophoto patches, and annotated field data for evaluating Vision Language Models
in generating plant simulation configurations.
"""

_HOMEPAGE = "https://huggingface.co/datasets/heesup/vlm-plant-sim"
_LICENSE = "apache-2.0"


class VlmPlantSimDataset(datasets.GeneratorBasedBuilder):
    """HF Dataset builder for VLM Plant Sim."""

    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="default", version=VERSION, description="Default config"),
    ]

    DEFAULT_CONFIG_NAME = "default"

    def _info(self) -> DatasetInfo:
        """Define the dataset schema explicitly."""
        features = Features({
            "id": Value("string"),
            "split": Value("string"),
            "source": Value("string"),
            "dap": Value("int32"),
            "bed": Value("int32"),
            "tier": Value("int32"),
            "image_path": Value("string"),
            "annotation_path": Value("string"),
            "scene_json_path": Value("string"),
            "camera_json_path": Value("string"),
            "params_json_path": Value("string"),
            "classes_txt_path": Value("string"),
            "boxes_txt_path": Value("string"),
            "masks_json_path": Value("string"),
            "notes": Value("string"),
        })

        return DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Define the splits."""
        return [
            SplitGenerator(
                name="synthetic",
                gen_kwargs={"manifest_file": "manifests/synthetic.jsonl"},
            ),
            SplitGenerator(
                name="real",
                gen_kwargs={"manifest_file": "manifests/real.jsonl"},
            ),
            SplitGenerator(
                name="real_dap10_boxes",
                gen_kwargs={"manifest_file": "manifests/initial_plant.jsonl"},
            ),
        ]

    def _generate_examples(self, manifest_file: str) -> Generator:
        """Generate examples from JSONL manifest file."""
        idx = 0
        try:
            with open(manifest_file, "r", encoding="utf-8") as f:
                for line in f:
                    line = line.strip()
                    if not line:
                        continue
                    try:
                        record = json.loads(line)
                        # Ensure all expected fields are present
                        example = {
                            "id": record.get("id", ""),
                            "split": record.get("split", ""),
                            "source": record.get("source", ""),
                            "dap": int(record.get("dap", 0)),
                            "bed": int(record.get("bed", record.get("tier", record.get("location", 0)))),
                            "tier": int(record.get("tier", record.get("bed", record.get("location", 0)))),
                            "image_path": record.get("image_path", ""),
                            "annotation_path": record.get("annotation_path", ""),
                            "scene_json_path": record.get("scene_json_path", ""),
                            "camera_json_path": record.get("camera_json_path", ""),
                            "params_json_path": record.get("params_json_path", ""),
                            "classes_txt_path": record.get("classes_txt_path", ""),
                            "boxes_txt_path": record.get("boxes_txt_path", ""),
                            "masks_json_path": record.get("masks_json_path", ""),
                            "notes": record.get("notes", ""),
                        }
                        yield idx, example
                        idx += 1
                    except (json.JSONDecodeError, ValueError) as e:
                        # Skip invalid lines
                        continue
        except FileNotFoundError:
            # File not found - return empty result
            pass