# -*- coding: utf-8 -*-
"""Untitled9.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1qRAN4BBFZkzQKFec3qaXZ_obxuWQa6c6
"""

# 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.
"""Beans leaf dataset with images of diseased and health leaves."""

import os

import datasets
from datasets.tasks import ImageClassification


_HOMEPAGE = "https://huggingface.co/datasets/poolrf2001/mask"

_CITATION = """\
@ONLINE {masksdata,
    author="Pool_rf",
    title="Mask face dataset",
    month="January",
    year="2023",
    url="https://huggingface.co/datasets/poolrf2001/mask"
}
"""

_DESCRIPTION = """\
MaskFace es un conjunto de datos de imágenes de personas con y sin mascarillas Consta de 3 clases: 1 clase de si la persona está puesta la mascarilla, 
otra clase si la persona no esta puesta la mascarilla y una clase donde la persona está puesta la mascarilla incorrectamente.
"""

_URLS = {
    "train": "https://huggingface.co/datasets/poolrf2001/mask/blob/main/train.zip",
    "validation": "https://huggingface.co/datasets/poolrf2001/mask/blob/main/validation.zip",
    "test": "https://huggingface.co/datasets/poolrf2001/mask/blob/main/test.zip",
}

_NAMES = ["mask_weared_incorrect", "width_mask", "without_mask"]


class mask(datasets.GeneratorBasedBuilder):
    """MaskFace images dataset."""

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    
                    "image": datasets.Image(),
                    "labels": datasets.features.ClassLabel(names=_NAMES),
                }
            ),
            supervised_keys=("image", "labels"),
            homepage=_HOMEPAGE,
            citation=_CITATION,
            task_templates=[ImageClassification(image_column="image", label_column="labels")],
        )

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

    def _generate_examples(self, files):
        for i, path in enumerate(files):
            file_name = os.path.basename(path)
            if file_name.endswith(".png"):
                yield i, {
                    
                    "image": path,
                    "labels": os.path.basename(os.path.dirname(path)).lower(),
                }