subscene.py

# 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: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""


import csv
import json
import os

import datasets
import gzip

# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLS = "https://huggingface.co/datasets/khalidalt/subscene/resolve/main/{Lang}/{Lang}_subscene_{split}{index}.json.gz"

_N_FILES_PER_SPLIT = {
    'arabic': {'train':33 },
    'english': {'train': 82},
}

_LangID = ['arabic', 'english']
# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case

class SubsceneConfig(datasets.BuilderConfig):
    """ Builder config for Subscene Dataset. """

    def __init__(self, subset, **kwargs):
        super(SubsceneConfig, self).__init__(**kwargs)

        if subset !="all":

            self.subset = [subset]
        else:
            self.subset = _LangID

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

    VERSION = datasets.Version("1.1.0")
    BUILDER_CONFIGS_CLASS = SubsceneConfig
    BUILDER_CONFIGS = [
        SubsceneConfig(name=subset,
        subset=subset,
        version=datasets.Version("1.1.0", ""),
        description='')
        for subset in _LangID
    ]


    def _info(self):
        # information about the datasets and feature type of the datasets items.

        features = datasets.Features(
            {
                "subtitle_name": datasets.Value("string"),
                "file_name": datasets.Value("string"),
                "transcript": datasets.Value("string"),
            }
        )



        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        #split = 'train'
        #print("Split")
        data_urls = {}
        for split in ['train']: #'validation']:
            #if self.config.subset = "all":

            data_urls[split] = [
                _URLS.format(
                    Lang = subset,
                    split='validation' if split=='_val' else '',
                    index = i,
                )
                for subset in self.config.subset
                for i in range(_N_FILES_PER_SPLIT[subset][split])
            ]

        train_downloaded_files = dl_manager.download(data_urls["train"])
        #validation_downloaded_files = dl_manager.download(data_urls["validation"])
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": train_downloaded_files}),
            #datasets.SplitGenerator(
            #    name=datasets.Split.VALIDATION, gen_kwargs={"filepaths": validation_downloaded_files}
            #),
        ]


    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepaths):

        id_ = 0
        for filepath in filepaths:
            with gzip.open(open(filepath,"rb"), "rt", encoding = "utf-8") as f:
                for row in f:
                    if row:

                        data = json.loads(row)
                        yield id_, data
                        id_ +=1