# coding=utf-8
# Copyright 2021 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.
"""CPPE-5 dataset."""


import collections
import json
import os

import datasets


_CITATION = """\
@misc{dagli2021cppe5,
      title={CPPE-5: Medical Personal Protective Equipment Dataset},
      author={Rishit Dagli and Ali Mustufa Shaikh},
      year={2021},
      eprint={2112.09569},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
"""

_DESCRIPTION = """\
CPPE - 5 (Medical Personal Protective Equipment) is a new challenging dataset with the goal
to allow the study of subordinate categorization of medical personal protective equipments,
which is not possible with other popular data sets that focus on broad level categories.
"""

_HOMEPAGE = "https://sites.google.com/view/cppe5"

_LICENSE = "Unknown"

# _URL = "https://drive.google.com/uc?id=1MGnaAfbckUmigGUvihz7uiHGC6rBIbvr"
_URL = "https://huggingface.co/datasets/yunusskeete/cppe5/resolve/main/cppe5.tar.gz"

_CATEGORIES = ["automobile", "bike", "motorbike", "traffic_light", "traffic_sign"]


class CPPE5(datasets.GeneratorBasedBuilder):
    """CPPE - 5 dataset."""

    VERSION = datasets.Version("1.0.0")

    def _info(self):
        features = datasets.Features(
            {
                "image_id": datasets.Value("int64"),
                "image": datasets.Image(),
                "width": datasets.Value("int32"),
                "height": datasets.Value("int32"),
                "objects": datasets.Sequence(
                    {
                        "id": datasets.Value("int64"),
                        "area": datasets.Value("int64"),
                        "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
                        "category": datasets.ClassLabel(names=_CATEGORIES),
                    }
                ),
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        archive = dl_manager.download(_URL)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "annotation_file_path": "annotations/train.json",
                    "files": dl_manager.iter_archive(archive),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "annotation_file_path": "annotations/test.json",
                    "files": dl_manager.iter_archive(archive),
                },
            ),
        ]

    def _generate_examples(self, annotation_file_path, files):
        def process_annot(annot, category_id_to_category):
            return {
                "id": annot["id"],
                "area": annot["area"],
                "bbox": annot["bbox"],
                "category": category_id_to_category[annot["category_id"]],
            }

        image_id_to_image = {}
        idx = 0
        # This loop relies on the ordering of the files in the archive:
        # Annotation files come first, then the images.
        for path, f in files:
            file_name = os.path.basename(path)
            if path == annotation_file_path:
                annotations = json.load(f)
                category_id_to_category = {category["id"]: category["name"] for category in annotations["categories"]}
                image_id_to_annotations = collections.defaultdict(list)
                for annot in annotations["annotations"]:
                    image_id_to_annotations[annot["image_id"]].append(annot)
                image_id_to_image = {annot["file_name"]: annot for annot in annotations["images"]}
            elif file_name in image_id_to_image:
                image = image_id_to_image[file_name]
                objects = [
                    process_annot(annot, category_id_to_category) for annot in image_id_to_annotations[image["id"]]
                ]
                yield idx, {
                    "image_id": image["id"],
                    "image": {"path": path, "bytes": f.read()},
                    "width": image["width"],
                    "height": image["height"],
                    "objects": objects,
                }
                idx += 1

# # coding=utf-8
# # Permission is hereby granted, free of charge, to any person obtaining
# # a copy of this software and associated documentation files (the
# # "Software"), to deal in the Software without restriction, including
# # without limitation the rights to use, copy, modify, merge, publish,
# # distribute, sublicense, and/or sell copies of the Software, and to
# # permit persons to whom the Software is furnished to do so, subject to
# # the following conditions:

# # The above copyright notice and this permission notice shall be
# # included in all copies or substantial portions of the Software.

# # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
# # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
# # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
# # NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
# # LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
# # OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
# # WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
# """Carla-COCO-Object-Detection-Dataset"""

# import collections
# import json
# import os

# import datasets


# logger = datasets.logging.get_logger(__name__)

# _DESCRIPTION = """\
# This dataset contains 1028 images each 640x380 pixels.
# The dataset is split into 249 test and 779 training examples.
# Every image comes with MS COCO format annotations.
# The dataset was collected in Carla Simulator, driving around in autopilot mode in various environments
# (Town01, Town02, Town03, Town04, Town05) and saving every i-th frame.
# The labels where then automatically generated using the semantic segmentation information.
# """

# _HOMEPAGE = "https://github.com/yunusskeete/Carla-COCO-Object-Detection-Dataset"

# _LICENSE = "MIT"

# # _URL = "https://drive.google.com/uc?id=1QeveFt1jDNrafJeeCV1N_KoIKQEZyhuf"
# # # _URL = "https://drive.google.com/uc?id=1xUPwrMBBrGFIapLx_fyLjmH4HN16A4iZ"
# _URL = "https://huggingface.co/datasets/yunusskeete/Carla-COCO-Object-Detection-Dataset/resolve/main/Carla-COCO-Object-Detection-Dataset.tar.gz"

# _CATEGORIES = ["automobile", "bike", "motorbike", "traffic_light", "traffic_sign"]

# class CARLA_COCO(datasets.GeneratorBasedBuilder):
#     """Carla-COCO-Object-Detection-Dataset"""

#     VERSION = datasets.Version("1.1.0")

#     def _info(self):
#         """This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset"""

#         features = datasets.Features(
#             {
#                 "id": datasets.Value("int64"),
#                 "image_id": datasets.Value("string"),
#                 "image": datasets.Image(),
#                 "width": datasets.Value("int32"),
#                 "height": datasets.Value("int32"),
#                 "file_name": datasets.Value("string"),
#                 "url": datasets.Value("string"),
#                 "objects": datasets.Sequence(
#                     {
#                         "id": datasets.Sequence(datasets.Value("int64")),
#                         "area": datasets.Sequence(datasets.Value("int64")),
#                         "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
#                         "category": datasets.Sequence(datasets.ClassLabel(names=_CATEGORIES)),
#                     }
#                 ),
#             }
#         )
#         return datasets.DatasetInfo(
#             description=_DESCRIPTION,
#             features=features,
#             homepage=_HOMEPAGE,
#             license=_LICENSE,
#         )

#     def _split_generators(self, dl_manager):
#         """This method is tasked with downloading/extracting the data and defining the splits depending on the configuration"""

#         archive = dl_manager.download_and_extract(_URL)

#         return [
#             datasets.SplitGenerator(
#                 name=datasets.Split.TRAIN,
#                 # These kwargs will be passed to _generate_examples
#                 gen_kwargs={
#                     "annotation_file_path": "annotations/train.json",
#                     "files": dl_manager.iter_archive(archive),
#                 }
#             ),
#             datasets.SplitGenerator(
#                 name=datasets.Split.TEST,
#                 # These kwargs will be passed to _generate_examples
#                 gen_kwargs={
#                     "annotation_file_path": "annotations/test.json",
#                     "files": dl_manager.iter_archive(archive),
#                 }
#             ),
#         ]

#     # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
#     def _generate_examples(self, annotation_file_path, files):
#         """
#         This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
#         The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
#         """

#         logger.info("generating examples from = %s", annotation_file_path)

#         def process_annot(annot, category_id_to_category):
#             return {
#                 "id": annot["id"],
#                 "area": annot["area"],
#                 "bbox": annot["bbox"],
#                 "category": category_id_to_category[annot["category_id"]],
#             }

#         image_id_to_image = {}
#         idx = 0
#         # This loop relies on the ordering of the files in the archive:
#         # Annotation files come first, then the images.
#         for path, f in files:
#             file_name = os.path.basename(path)
#             if path == annotation_file_path:
#                 annotations = json.load(f)
#                 category_id_to_category = {category["id"]: category["name"] for category in annotations["categories"]}
#                 image_id_to_annotations = collections.defaultdict(list)
#                 for annot in annotations["annotations"]:
#                     image_id_to_annotations[annot["image_id"]].append(annot)
#                 image_id_to_image = {annot["file_name"]: annot for annot in annotations["images"]}
#             elif file_name in image_id_to_image:
#                 image = image_id_to_image[file_name]
#                 objects = [
#                     process_annot(annot, category_id_to_category) for annot in image_id_to_annotations[image["id"]]
#                 ]
#                 yield idx, {
#                     "image_id": image["id"],
#                     "image": {"path": path, "bytes": f.read()},
#                     "width": image["width"],
#                     "height": image["height"],
#                     "objects": objects,
#                 }
#                 idx += 1