# Source: https://github.com/huggingface/datasets/blob/main/templates/new_dataset_script.py
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# TODO: Address all TODOs and remove all explanatory comments


import csv
import json
import os

import datasets


# Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {Boat dataset},
author={Tzu-Chi Chen, Inc.
},
year={2024}
}
"""

# Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This dataset is designed to solve object detection task.
"""

# Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://huggingface.co/datasets/zhuchi76/Boat_dataset"

# Add the licence for the dataset here if you can find it
_LICENSE = ""

# Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLS = {
    "images": f"{_HOMEPAGE}/data/images.tar.gz",
    "anno": {
        "train": f"{_HOMEPAGE}/data/instances_train2023.jsonl",
        "val": f"{_HOMEPAGE}/data/instances_val2023.jsonl",
        "test": f"{_HOMEPAGE}/data/instances_val2023r.jsonl"
    },
}

class BoatDataset(datasets.GeneratorBasedBuilder):

    VERSION = datasets.Version("1.1.0")

    def _info(self):
        features=datasets.Features({
            'image_id': datasets.Value('int32'),
            'image_path': datasets.Value('string'),
            'width': datasets.Value('int32'),
            'height': datasets.Value('int32'),
            'objects': datasets.Features({
                'id': datasets.Sequence(datasets.Value('int32')),
                'area': datasets.Sequence(datasets.Value('float32')),
                'bbox': datasets.Sequence(datasets.Sequence(datasets.Value('float32'), length=4)),  # [x, y, width, height]
                'category': datasets.Sequence(datasets.Value('int32'))
            }),
        })
        
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        downloaded_files = dl_manager.download_and_extract(_URLS)

        image_dir = dl_manager.extract(downloaded_files['images'])
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    'image_dir': image_dir,
                    'annotations_file': downloaded_files['anno']['train'],
                    'split': 'train'
                }
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    'image_dir': image_dir,
                    'annotations_file': downloaded_files['anno']['val'],
                    'split': 'val'
                }
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    'image_dir': image_dir,
                    'annotations_file': downloaded_files['anno']['test'],
                    'split': 'val_real'
                }
            )
        ]

    def _generate_examples(self, image_dir, annotations_file, split):
        with open(annotations_file, encoding="utf-8") as f:
            for key, row in enumerate(f):
                try:
                    data = json.loads(row.strip())
                    yield key, {
                        "image_id": data["image_id"],
                        "image_path": data["image_path"],
                        "width": data["width"],
                        "height": data["height"],
                        "objects": data["objects"],
                    }
                except json.JSONDecodeError:
                    print(f"Skipping invalid JSON at line {key + 1}: {row}")
                    continue