"""
This is the huggingface data loader for TOPVIEWRS Benchmark.
"""

import json
import os
import shutil

import datasets


_CITATION = """
@misc{li2024topviewrs,
        title={TopViewRS: Vision-Language Models as Top-View Spatial Reasoners},
        author={Chengzu Li and Caiqi Zhang and Han Zhou and Nigel Collier and Anna Korhonen and Ivan Vulić},
        year={2024},
        eprint={2406.02537},
        archivePrefix={arXiv},
        primaryClass={cs.CL}
}
"""

_DESCRIPTION = """
TopViewRS dataset, comprising 11,384 multiple-choice questions with either photo-realistic 
or semantic top-view maps of real-world scenarios through a pipeline of automatic collection followed by human alignment.
"""

_HOMEPAGE = "https://topviewrs.github.io/"

_LICENSE = "MIT"

TASK_SPLIT = ['top_view_recognition', 'top_view_localization', 'static_spatial_reasoning', 'dynamic_spatial_reasoning']

_URLS = {
    "realistic_json": f"released_realistic_datasets.json",
    "semantic_json": f"released_semantic_datasets.json",
    "images": f"data.zip"
}


class TOPVIEWRSConfig(datasets.BuilderConfig):
    """BuilderConfig for TOPVIEWRS."""

    def __init__(self, task_split, map_type, image_save_dir, **kwargs):
        """BuilderConfig for TOPVIEWRS.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(TOPVIEWRSConfig, self).__init__(**kwargs)
        self.task_split = task_split
        self.map_type = map_type
        self.image_save_dir = image_save_dir


class TOPVIEWRS(datasets.GeneratorBasedBuilder):
    """TOPVIEWRS Dataset"""

    BUILDER_CONFIG_CLASS = TOPVIEWRSConfig
    BUILDER_CONFIGS = [
        TOPVIEWRSConfig(
            name="topviewrs",
            version=datasets.Version("0.0.0"),
            description=_DESCRIPTION,
            task_split=None,
            map_type=None,
            image_save_dir="."
        )
    ]

    DEFAULT_CONFIG_NAME = "topviewrs"

    def _info(self):
        features = datasets.Features(
            {
                "index": datasets.Value("int32"),
                "scene_id": datasets.Value("string"),
                "question": datasets.Value("string"),
                "choices": datasets.Sequence(datasets.Value("string")),
                "labels": datasets.Sequence(datasets.Value("string")),
                "choice_type": datasets.Value("string"),
                "map_path": datasets.Value("string"),
                "question_ability": datasets.Value("string"),
            }
        )
        if self.config.task_split == "dynamic_spatial_reasoning":
            features = datasets.Features(
                {
                    "index": datasets.Value("int32"),
                    "scene_id": datasets.Value("string"),
                    "question": datasets.Value("string"),
                    "choices": datasets.Sequence(datasets.Value("string")),
                    "labels": datasets.Sequence(datasets.Value("string")),
                    "choice_type": datasets.Value("string"),
                    "map_path": datasets.Value("string"),
                    "question_ability": datasets.Value("string"),
                    "reference_path": datasets.Sequence(datasets.Sequence(datasets.Value("int32")))
                }
            )

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        zip_file = dl_manager.download({"images": _URLS['images']})
        os.rename(zip_file['images'], os.path.join(os.path.dirname(zip_file['images']), _URLS['images']))
        try:
            shutil.unpack_archive(os.path.join(os.path.dirname(zip_file['images']), _URLS['images']), self.config.image_save_dir)
        except:
            raise FileNotFoundError(f"Unpacking the image data.zip failed. Make sure that you have the zip file at {zip_file}. ")
        
        downloaded_files = dl_manager.download_and_extract({k: v for k, v in _URLS.items() if k != "images"})
        
        image_base_file_dir = self.config.image_save_dir
        json_file_path = downloaded_files[f"{self.config.map_type}_json"]

        return [
            datasets.SplitGenerator(
                name=datasets.Split('val'),
                gen_kwargs={
                    "json_file_path": json_file_path,
                    "image_base_dir": image_base_file_dir
                },
            )
        ]

    def _generate_examples(self, json_file_path: str, image_base_dir: str):
        task = self.config.task_split
        map_type = self.config.map_type

        map_key = "rgb" if map_type.lower() == "realistic" else map_type
        with open(json_file_path) as f:
            data_list = json.load(f)[task]

        for idx, data_item in enumerate(data_list):
            return_item = {
                "index": idx,
                "scene_id": data_item['scene_id'],
                "question": data_item['question'],
                "choices": data_item['choices'],
                "labels": data_item['labels'],
                "choice_type": str(data_item["question_meta_data"]["choices"]),
                "map_path": os.path.join(image_base_dir, data_item[f"{map_key}_map"]),
                "question_ability": data_item['ability'],
            }
            if "reference_path" in data_item.keys():
                return_item["reference_path"] = data_item["reference_path"]

            yield idx, return_item
            idx += 1