# 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
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# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# Lint as: python3
"""SQUAD: The Stanford Question Answering Dataset."""


import json

import datasets
from datasets.tasks import QuestionAnsweringExtractive


logger = datasets.logging.get_logger(__name__)


_CITATION = """\
@article{2016arXiv160605250R,
       author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
                 Konstantin and {Liang}, Percy},
        title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
      journal = {arXiv e-prints},
         year = 2016,
          eid = {arXiv:1606.05250},
        pages = {arXiv:1606.05250},
archivePrefix = {arXiv},
       eprint = {1606.05250},
}
"""

_DESCRIPTION = """\
Stanford Question Answering Dataset (SQuAD) is a reading comprehension \
dataset, consisting of questions posed by crowdworkers on a set of Wikipedia \
articles, where the answer to every question is a segment of text, or span, \
from the corresponding reading passage, or the question might be unanswerable.
"""

_URL = "https://huggingface.co/datasets/proan/fashion/resolve/main/images.tar.gz"




class Fashion(datasets.GeneratorBasedBuilder):
    """SQUAD: The Stanford Question Answering Dataset. Version 1.1."""



    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("int64"),
                    "color": datasets.Value("string"),
                    "description": datasets.Value("string"),
                    "shop_id": datasets.Value("string"),
                    "year": datasets.Value("float64"),
                    "image": 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/proan/fashion",
            citation=_CITATION,
            
        )

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

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

    def _generate_examples(self, images):
  
        idx = 0
        #iterate through images
        for filepath, image in images:
            yield idx, {
                "image": {"path": filepath, "bytes": image.read()},
                "id": datasets.Value("int64"),
                "color": datasets.Value("string"),
                "description": datasets.Value("string"),
                "shop_id": datasets.Value("string"),
                "year": datasets.Value("float64"),
                "image": datasets.Image(),  
            }
            idx +=1