# Copyright 2022 Lance Developers
#
# 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,
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# limitations under the License.

"""COCO: Microsoft COCO Dataset.

https://cocodataset.org/#home
"""

import os
from typing import List

import datasets
import lance
import pyarrow as pa
import pyarrow.compute as pc

_CLASS_MAP = {
    1: "person",
    2: "bicycle",
    3: "car",
    4: "motorcycle",
    5: "airplane",
    6: "bus",
    7: "train",
    8: "truck",
    9: "boat",
    10: "traffic light",
    11: "fire hydrant",
    13: "stop sign",
    14: "parking meter",
    15: "bench",
    16: "bird",
    17: "cat",
    18: "dog",
    19: "horse",
    20: "sheep",
    21: "cow",
    22: "elephant",
    23: "bear",
    24: "zebra",
    25: "giraffe",
    27: "backpack",
    28: "umbrella",
    31: "handbag",
    32: "tie",
    33: "suitcase",
    34: "frisbee",
    35: "skis",
    36: "snowboard",
    37: "sports ball",
    38: "kite",
    39: "baseball bat",
    40: "baseball glove",
    41: "skateboard",
    42: "surfboard",
    43: "tennis racket",
    44: "bottle",
    46: "wine glass",
    47: "cup",
    48: "fork",
    49: "knife",
    50: "spoon",
    51: "bowl",
    52: "banana",
    53: "apple",
    54: "sandwich",
    55: "orange",
    56: "broccoli",
    57: "carrot",
    58: "hot dog",
    59: "pizza",
    60: "donut",
    61: "cake",
    62: "chair",
    63: "couch",
    64: "potted plant",
    65: "bed",
    67: "dining table",
    70: "toilet",
    72: "tv",
    73: "laptop",
    74: "mouse",
    75: "remote",
    76: "keyboard",
    77: "cell phone",
    78: "microwave",
    79: "oven",
    80: "toaster",
    81: "sink",
    82: "refrigerator",
    84: "book",
    85: "clock",
    86: "vase",
    87: "scissors",
    88: "teddy bear",
    89: "hair drier",
    90: "toothbrush",
}
_DATASET_URI = (
    "https://eto-public.s3.us-west-2.amazonaws.com/datasets/coco/coco.lance.tar.gz"
)


class Coco(datasets.ArrowBasedBuilder):
    """COCO: Microsoft common object in context dataset"""

    def _info(self):
        class_names = []
        for i in range(0, max(_CLASS_MAP.keys()) + 1):
            class_names.append(_CLASS_MAP.get(i, f"N/A-{i}"))
        return datasets.DatasetInfo(
            description="COCO: Microsoft object detection dataset",
            features=datasets.Features(
                {
                    "image": datasets.Image(),
                    "split": datasets.Value("string"),
                    "annotations": datasets.Sequence(
                        {
                            "bbox": datasets.Sequence(
                                datasets.Value("float32"), length=4
                            ),
                            "category_id": datasets.ClassLabel(names=class_names),
                        }
                    ),
                }
            ),
            supervised_keys=None,
            homepage="https://github.com/eto-ai/lance/tree/main/python/benchmarks/coco",
        )

    def _split_generators(
        self, dl_manager: datasets.DownloadManager
    ) -> List[datasets.SplitGenerator]:
        extracted_dir = dl_manager.download_and_extract(_DATASET_URI)
        base_uri = os.path.join(extracted_dir, "coco.lance")
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"split": "train", "base_uri": base_uri},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={"split": "val", "base_uri": base_uri},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={"split": "test", "base_uri": base_uri},
            ),
        ]

    def _generate_tables(self, split, base_uri):
        idx = 0
        dataset = lance.dataset(base_uri)
        scanner = dataset.scanner(
            filter=pc.field("split") == split,
        )
        for batch in scanner.to_batches():  # type: pa.RecordBatch
            cols = []
            names = []

            annotations = batch.column("annotations")
            if len(annotations) == 0:
                continue
            cols.append(annotations)
            names.append("annotations")

            # Decode split because Huggingface does not support dictionary yet.
            split_arr = batch.column("split").dictionary_decode()
            cols.append(split_arr)
            names.append("split")

            bytes_arr = batch.column("image").storage
            arr = pa.StructArray.from_arrays([bytes_arr], ["bytes"])
            cols.append(arr)
            names.append("image")

            yield idx, pa.Table.from_arrays(cols, names)
            idx += 1