# Copyright 2020 The HuggingFace Datasets Authors.
#
# 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.
"""Oxford_IIT pet loading script."""


import xml.etree.ElementTree as ET
from pathlib import Path

import datasets

_CITATION = """\
@InProceedings{parkhi12a,
  author       = "Parkhi, O. M. and Vedaldi, A. and Zisserman, A. and Jawahar, C.~V.",
  title        = "Cats and Dogs",
  booktitle    = "IEEE Conference on Computer Vision and Pattern Recognition",
  year         = "2012",
}

"""

_DESCRIPTION = """\
37 category pet dataset with roughly 200 images for each class. The images have a large variations in scale, pose and lighting. All images have an associated ground truth annotation of breed, head ROI, and pixel level trimap segmentation.
"""

_HOMEPAGE = "https://www.robots.ox.ac.uk/~vgg/data/pets/"

_LICENSE = "CC BY-SA 4.0"

# TODO: 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": "https://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz",
    "annotations": "https://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz",
}


_LABEL_CLASSES = [
    "Abyssinian",
    "american_bulldog",
    "american_pit_bull_terrier",
    "basset_hound",
    "beagle",
    "Bengal",
    "Birman",
    "Bombay",
    "boxer",
    "British_Shorthair",
    "chihuahua",
    "Egyptian_Mau",
    "english_cocker_spaniel",
    "english_setter",
    "german_shorthaired",
    "great_pyrenees",
    "havanese",
    "japanese_chin",
    "keeshond",
    "leonberger",
    "Maine_Coon",
    "miniature_pinscher",
    "newfoundland",
    "Persian",
    "pomeranian",
    "pug",
    "Ragdoll",
    "Russian_Blue",
    "saint_bernard",
    "samoyed",
    "scottish_terrier",
    "shiba_inu",
    "Siamese",
    "Sphynx",
    "staffordshire_bull_terrier",
    "wheaten_terrier",
    "yorkshire_terrier",
]
_SPECIES_CLASSES = ["Cat", "Dog"]

# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class NewDataset(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    VERSION = datasets.Version("1.0.0")

    def _info(self):
        features = datasets.Features(
            {
                "image": datasets.Image(),
                "label": datasets.features.ClassLabel(names=_LABEL_CLASSES),
                "species": datasets.features.ClassLabel(names=_SPECIES_CLASSES),
                "segmentation_mask": datasets.Image(),
                # "bbox": datasets.features.Array2D(shape=(1, 4), dtype="int64"),
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        urls = _URLS
        data_dir = dl_manager.download_and_extract(urls)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "images_dir": Path(data_dir["images"]) / "images",
                    "annotations_dir": Path(data_dir["annotations"]) / "annotations",
                    "images_list": Path(data_dir["annotations"])
                    / "annotations"
                    / "trainval.txt",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "images_dir": Path(data_dir["images"]) / "images",
                    "annotations_dir": Path(data_dir["annotations"]) / "annotations",
                    "images_list": Path(data_dir["annotations"])
                    / "annotations"
                    / "test.txt",
                },
            ),
        ]

    # Not used because missing xml file for Abyssinian_104
    # def _get_data_from_xml_file(self, xlm_annotation_file):
    #     # From https://huggingface.co/datasets/fuliucansheng/pascal_voc/blob/main/pascal_voc.py
    #     anno_tree = ET.parse(xlm_annotation_file)
    #     objects = []
    #     for obj in anno_tree.findall("./object"):
    #         info = {
    #             "class": obj.findall("./name")[0].text,
    #             "bbox": [[
    #                 int(float(obj.findall("./bndbox/xmin")[0].text)),
    #                 int(float(obj.findall("./bndbox/ymin")[0].text)),
    #                 int(float(obj.findall("./bndbox/xmax")[0].text)),
    #                 int(float(obj.findall("./bndbox/ymax")[0].text)),]
    #             ],
    #         }

    #         if obj.findall("./pose"):
    #             info["pose"] = obj.findall("./pose")[0].text
    #         if obj.findall("./truncated"):
    #             info["truncated"] = int(obj.findall("./truncated")[0].text)
    #         if obj.findall("./difficult"):
    #             info["difficult"] = int(obj.findall("./difficult")[0].text)
    #         else:
    #             info["difficult"] = 0
    #         if obj.findall("./occluded"):
    #             info["occluded"] = int(obj.findall("./occluded")[0].text)

    #         if obj.findall("./actions"):
    #             info["action"] = [
    #                 action.tag
    #                 for action in obj.findall("./actions/")
    #                 if int(action.text) == 1
    #             ][0]

    #         objects.append(info)
    #     print(len(objects))
    #     return objects

    def _generate_examples(self, images_dir, annotations_dir, images_list):
        bounding_box_dir = annotations_dir / "xmls"
        trimaps_dir = annotations_dir / "trimaps"

        with open(images_list, encoding="utf-8") as f:
            for row in f:
                image_name, label, species, _ = row.strip().split(" ")

                trimap_name = f"{image_name}.png"
                # bbox_name = f"{image_name}.xml"
                image_name = f"{image_name}.jpg"

                label = _LABEL_CLASSES[int(label) - 1]
                species = _SPECIES_CLASSES[int(species) - 1]
                # objects = self._get_data_from_xml_file(str(bounding_box_dir / bbox_name))

                record = {
                    "image": str(images_dir / image_name),
                    "label": label,
                    "species": species,
                    "segmentation_mask": str(trimaps_dir / trimap_name),
                    # "bbox": [[ymin, ymax, xmin, xmax]]
                }
                yield image_name, record