# coding=utf-8
# Copyright 2020 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.
"""TODO: Add a description here."""


import csv
import json
import os

import datasets


_DESCRIPTION = """\
This is an alloy composition dataset
"""

_LICENSE = "MIT"

# link to the dataset
_URL = "https://drive.google.com/uc?export=download&id="
_URLs = {
'train': _URL+'1wAERHsEBvWvCgfiWjtodM_5lV2OPlapC',
'test': _URL+'1TvC3R0gIjFNj2HWyvMZuuubv78vuZpSF',
}

class GlassAlloyComposition(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="train", version=VERSION, description="Training split of the complete dataset"),
        datasets.BuilderConfig(name="test", version=VERSION, description="Testing split of the complete dataset"),
    ]

    DEFAULT_CONFIG_NAME = "train" 
    
    def _info(self):
        """Basic information about the dataset is specified here"""

        features = datasets.Features(
            {
                "alloy_composition": datasets.Value("string")
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=None,
            license=_LICENSE
        )

    def _split_generators(self, dl_manager):
        """Generates the training and testing split of the dataset"""

        urls_to_download = _URLs
        downloaded_data = dl_manager.download_and_extract(urls_to_download)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": downloaded_data['train']
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": downloaded_data['test']
                },
            ),
        ]

    def _generate_examples(
        self, filepath
    ):
        # Specify the format in which the data is to be returned
        with open(filepath, encoding="utf-8") as f:
            for i, line in enumerate(f.readlines()):
                _id = i
                row = ' '.join(w for w in line.strip().split(","))
                yield _id, {"alloy_composition": row}