from curses.ascii import isalpha
import os
import csv
import re
from typing import Sequence
import json
import ast
import datasets



_DESCRIPTION = """\
 Example dataset toxic
"""
_DATA_URL = "https://drive.google.com/uc?id=1Ldnn3YYt_ErYq4ZGSon1MvcP3uJO0_PX"
_DATA_ENG = "https://drive.google.com/uc?id=1p-iyKTRhUXaDmqsx69Zvb4ivjaCmVVr8"

_TEXT = {
    "sen_vi": [" thất vọng", " bình thường", " hài lòng"],
    "sen_en": [" negative", " neutral", " positive"],
    "top_vi": [" giảng viên", " môn học", " phòng học", " tổng thể"],
    "top_en": [" lecturer", " curriculum", " facility", " general"],
    "top_en_": ["lecturer", "curriculum", "facility", "general"],
    "sen_en_": ["negative", "neutral", "positive"],
    "sen_vi_": ["thất vọng", "bình thường", "hài lòng"],
    "top_vi_": ["giảng viên", "môn học", "phòng học", "tổng thể"],
}

class Config(datasets.BuilderConfig):
    """BuilderConfig for GLUE."""

    def __init__(self, data_url, **kwargs):
        """BuilderConfig
        Args:
          data_url: `string`, url to the dataset (word or raw level)
          **kwargs: keyword arguments forwarded to super.
        """
        super(Config, self).__init__(
            version=datasets.Version(
                "1.0.0",
            ),
            **kwargs,
        )
        self.data_url = data_url


class Guess(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("0.1.0")
    BUILDER_CONFIGS = [
        Config(
            name="top_vi",
            data_url=_DATA_URL,
            description="data",
        ),
        Config(
            name="top_en",
            data_url=_DATA_ENG,
            description="data",
        ),
        Config(
            name="sen_vi",
            data_url=_DATA_URL,
            description="data",
        ),
        Config(
            name="sen_en",
            data_url=_DATA_ENG,
            description="data",
        ),
        Config(
            name="sen_en_",
            data_url=_DATA_ENG,
            description="data",
        ),
        Config(
            name="top_en_",
            data_url=_DATA_ENG,
            description="data",
        ),
        Config(
            name="top_vi_",
            data_url=_DATA_URL,
            description="data",
        ),
        Config(
            name="sen_vi_",
            data_url=_DATA_URL,
            description="data",
        ),
    ]

    def _info(self):
        # TODO(wikitext): Specifies the datasets.DatasetInfo object
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # datasets.features.FeatureConnectors
            features=datasets.Features(
                {
                    "text": datasets.Value("string"),
                    "classes": datasets.Sequence(datasets.Value("string")),
                    "target": datasets.Value("int8")
                }
            ),
            # If there's a common (input, target) tuple from the features,
            # specify them here. They'll be used if as_supervised=True in
            # builder.as_dataset.
            supervised_keys=None,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        # TODO(wikitext): Downloads the data and defines the splits
        # dl_manager is a datasets.download.DownloadManager that can be used to
        # download and extract URLs
        data_file = dl_manager.download(self.config.data_url)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={"data_file": data_file, "type": self.config.name},
            ),
        ]
        

    def _generate_examples(self, data_file, type):

        """Yields examples."""
        # TODO(wikitext): Yields (key, example) tuples from the dataset
        with open(data_file, 'r') as f:
            lines = list(f)
        
        if type[:3] == 'sen':
            _CLASS = {
                "negative": 0,
                "neutral": 1,
                "positive": 2,
            }
        else:
            _CLASS = {
                "lecturer": 0,
                "curriculum": 1,
                "facility": 2,
                "others": 3
            }
        
        TEXT_ = _TEXT[type]

        for idx, line in enumerate(lines):
            json_object = ast.literal_eval(line)
            if type[:3] == 'top':
                label = json_object['topic']
            else:
                label = json_object['sentiment']

            if label not in _CLASS:
                continue

            _text = json_object['text']
            _classes = []

            _PROMPT = {
                "sen_vi": f'{_text} Cảm thấy ',
                "sen_en": f'{_text} The sentiment of this sentence is ',
                "top_vi": f'Nói về ',
                "top_en": f'Comment about ',
                "sen_en_": f'{_text} The sentiment of this sentence is ',
                "top_en_": f'Comment about ',
                "sen_vi_": f'{_text} Cảm thấy ',
                "top_vi_": f'Nói về ',
            }
    
            for _cl in TEXT_:
                if type[:3] == 'sen':
                    _classes.append(_cl)
                else:
                    _classes.append(f'{_cl}. {_text}')
                

            yield idx, {
                    "text" : _PROMPT[type],
                    "classes" : _classes,
                    "target" : _CLASS[label]
                }