# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and 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.

# Lint as: python3


import json

import datasets
from datasets.tasks import QuestionAnsweringExtractive


logger = datasets.logging.get_logger(__name__)

_URL = "https://huggingface.co/datasets/jaradat/pidray-semantics/resolve/main/pixel_values.tar.gz"
_URL2 = "https://huggingface.co/datasets/jaradat/pidray-semantics/resolve/main/label.tar.gz"



class PIDrayTargz(datasets.GeneratorBasedBuilder):

    def _info(self):
        return datasets.DatasetInfo(
            features=datasets.Features(
                {
                    #"text": datasets.Value("string"),
                    "pixel_values": datasets.Image(),
                    "label": datasets.Image(),
                }
            ),
            # No default supervised_keys (as we have to pass both question
            # and context as input).
            supervised_keys=None,
            homepage="https://huggingface.co/datasets/jaradat/pidray-semantics",

        )

    def _split_generators(self, dl_manager):
        path = dl_manager.download(_URL)
        image_iters = dl_manager.iter_archive(path)

        
        path2 = dl_manager.download(_URL2)
        label_iters = dl_manager.iter_archive(path2)

        return [
            datasets.SplitGenerator(
            name=datasets.Split.TRAIN, 
            gen_kwargs={
                "images": image_iters,
                "label": label_iters
                }
            ),
            
        ]

    def _generate_examples(self, images, label):
        """This function returns the examples in the raw (text) form."""
        idx = 0
        # iterate through images
        for filepath, image in images
            
            text = filepath.split
            yield idx, {
                "pixel_values": {"filepath": filepath, "image": image.read()},
                "label": {"filepath": label[idx]['filepath'], "label": label[idx]['image'].read()},
            }
            idx += 1