# coding=utf-8
# Copyright 2021 The TensorFlow 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.

"""Rock, Paper, Scissors dataset."""
import re
from pathlib import Path

import datasets
from datasets.tasks import ImageClassification


_CITATION = """\
@ONLINE {rps,
author = "Laurence Moroney",
title = "Rock, Paper, Scissors Dataset",
month = "feb",
year = "2019",
url = "http://laurencemoroney.com/rock-paper-scissors-dataset"
}
"""

_URLS = {
    'train': "https://storage.googleapis.com/download.tensorflow.org/data/rps.zip",
    'test': "https://storage.googleapis.com/download.tensorflow.org/data/rps-test-set.zip"
}
_NAMES = ["rock", "paper", "scissors"]

class RockPaperScissors(datasets.GeneratorBasedBuilder):
  """Rock, Paper, Scissors dataset."""

  def _info(self):
    return datasets.DatasetInfo(
        description="Images of hands playing rock, paper, scissor game.",
        features=datasets.Features({
            "file":
                datasets.Value("string"),
            "labels":
                datasets.features.ClassLabel(names=sorted(tuple(_NAMES))),
        }),
        supervised_keys=("file", "labels"),
        homepage="http://laurencemoroney.com/rock-paper-scissors-dataset",
        citation=_CITATION,
        task_templates=[ImageClassification(image_file_path_column="file", label_column="labels", labels=sorted(tuple(_NAMES)))]
    )

  def _split_generators(self, dl_manager):
    data_files = dl_manager.download_and_extract(_URLS)
    return [
        datasets.SplitGenerator(
            name=datasets.Split.TRAIN,
            gen_kwargs={
                "archive": data_files['train'],
            }),
        datasets.SplitGenerator(
            name=datasets.Split.TEST,
            gen_kwargs={
                "archive": data_files['test'],
            }),
    ]

  def _generate_examples(self, archive):
    labels = self.info.features['labels']
    for i, path in enumerate(Path(archive).glob('**/*')):
        if path.suffix == '.png':
            yield i, dict(file=path.as_posix(), labels=labels.encode_example(path.parent.name.lower()))