# 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,
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# Lint as: python3
"""Arabic Poetry Metric dataset."""


import os
import datasets
import pandas as pd

_DESCRIPTION = """\
Masader is the largest public catalogue for Arabic NLP datasets, which consists of more than 200 datasets annotated with 25 attributes. 
"""

_CITATION = """\
@misc{alyafeai2021masader,
      title={Masader: Metadata Sourcing for Arabic Text and Speech Data Resources}, 
      author={Zaid Alyafeai and Maraim Masoud and Mustafa Ghaleb and Maged S. Al-shaibani},
      year={2021},
      eprint={2110.06744},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
      }
"""


class AdawatConfig(datasets.BuilderConfig):
    """BuilderConfig for Masader."""

    def __init__(self, **kwargs):
        """BuilderConfig for Adawat.

        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(AdawatConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)


class Adawat(datasets.GeneratorBasedBuilder):
    """Adawatdataset."""

    BUILDER_CONFIGS = [
        AdawatConfig(
            name="plain_text",
            description="Plain text",
        )
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    'Id': datasets.Value("string"),
                    'Name': datasets.Value("string"),
                    'Link': datasets.Value("string"),
                    'Colab link': datasets.Value("string"),
                    'GitHub Repo': datasets.Value("string"),
                    'Pricing': datasets.Value("string"),
                    'Accessibility': datasets.Value("string"),
                    'License': datasets.Value("string"),
                    'Version': datasets.Value("string"),
                    'Description': datasets.Value("string"),
                    'Paper Title': datasets.Value("string"),
                    'Paper URL': datasets.Value("string"),
                    'Release Year': datasets.Value("int32"),
                    'Tasks': datasets.Value("string"),
                    'Supported language(s)': datasets.Value("string"),
                    'Tool Type': datasets.Value("string"),
                    'Interface': datasets.Value("string"),
                    'Programming Language': datasets.Value("string"),
                    'Added by': datasets.Value("string"),
                    'Evaluated datasets': datasets.Value("string"),
                }
            ),
            supervised_keys=None,
            homepage="https://github.com/arbml/Masader",
            citation=_CITATION,)

    def _split_generators(self, dl_manager):
        sheet_id = "1uLqCbygNS9Pvsp1UkkR4Qe9SQrJVwpZDXzcpBfufmE8"
        sheet_name = "main"
        url = f"https://docs.google.com/spreadsheets/d/{sheet_id}/gviz/tq?tqx=out:csv&sheet={sheet_name}"
        
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN, gen_kwargs={"url":url }
            ),
        ]

    def _generate_examples(self, url):
        """Generate examples."""
        # For labeled examples, extract the label from the path.
        
                
        df = pd.read_csv(url, usecols=range(22))
        entry_list = []
        i = 0
        idx = 0
         
        for i in range(len(df)):    
            masader_entry = {col.strip():df.values[i][j] for j,col in enumerate(df.columns) if j not in [18,20]}            
            yield i, masader_entry