# coding=utf-8
# Copyright 2022 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.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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from typing import List, Tuple, Dict
from pathlib import Path
import json

import datasets

_CITATION = """\
@inproceedings{larson-etal-2020-inconsistencies,
    title = "Inconsistencies in Crowdsourced Slot-Filling Annotations: A Typology and Identification Methods",
    author = "Larson, Stefan  and
      Cheung, Adrian  and
      Mahendran, Anish  and
      Leach, Kevin  and
      Kummerfeld, Jonathan K.",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://aclanthology.org/2020.coling-main.442",
    doi = "10.18653/v1/2020.coling-main.442",
    pages = "5035--5046",
    abstract = "Slot-filling models in task-driven dialog systems rely on carefully annotated training data. However, annotations by crowd workers are often inconsistent or contain errors. Simple solutions like manually checking annotations or having multiple workers label each sample are expensive and waste effort on samples that are correct. If we can identify inconsistencies, we can focus effort where it is needed. Toward this end, we define six inconsistency types in slot-filling annotations. Using three new noisy crowd-annotated datasets, we show that a wide range of inconsistencies occur and can impact system performance if not addressed. We then introduce automatic methods of identifying inconsistencies. Experiments on our new datasets show that these methods effectively reveal inconsistencies in data, though there is further scope for improvement.",
}

"""

_DATASETNAME = "inconsistencies_forex"

_DESCRIPTION = """\
This dataset is designed for Annotation Error Detection.
"""

_HOMEPAGE = ""

_LICENSE = "CC-BY 4.0"

_URLS = {
    _DATASETNAME: "https://drive.google.com/uc?export=download&id=1h6OB8rUmvjQNHl8L6erNjteXvRfI2sZF",
}

_SOURCE_VERSION = "1.0.0"

_SCHEMA = datasets.Features({
    "id": datasets.Value("string"),
    "official_split": datasets.Value("string"),
    "tokens":  datasets.Sequence(datasets.Value("string")),
    "tags_gold": datasets.Sequence(datasets.Value("string")),
    "tags": datasets.Sequence(datasets.Value("string")),
    "classification": datasets.Value("string"),
    "sentence": datasets.Value("string"),
})



class InconsistenciesForex(datasets.GeneratorBasedBuilder):

    _VERSION = datasets.Version(_SOURCE_VERSION)

    

    def _info(self) -> datasets.DatasetInfo:
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=_SCHEMA,
            supervised_keys=None,
            homepage=_HOMEPAGE,
            citation=_CITATION,
            license=_LICENSE,
        )
        

    def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
        """Returns SplitGenerators."""
        urls = _URLS[_DATASETNAME]
        data_dir = dl_manager.download_and_extract(urls)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # Whatever you put in gen_kwargs will be passed to _generate_examples
                gen_kwargs={
                    "data_dir": data_dir,
                },
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`

    def _generate_examples(self, data_dir) -> Tuple[int, Dict]:
        """Yields examples as (key, example) tuples."""
        data_dir = Path(data_dir)
        gold_file = data_dir / "data" / "forex_gold.json"
        crowd_file = data_dir / "data" / "forex_crowd.json"

        with open(gold_file, "r") as f:
            gold = json.load(f)["sentences"]

        with open(crowd_file, "r") as f:
            crowd = json.load(f)["sentences"]
        
        sentence_to_gold = {i["sentence"].lower(): i for i in gold}
        sentence_to_crowd = {i["sentence"].lower(): i for i in crowd}

        assert sentence_to_gold.keys() == sentence_to_crowd.keys()


        for idx_example, gold in enumerate(gold):
            crowd = sentence_to_crowd[gold["sentence"].lower()]

            yield idx_example, {
                "id": str(idx_example),
                "official_split": gold["official_split"],
                "tokens": [i["word"] for i in gold["svpLabels"]],
                "tags_gold": [i["label"] for i in gold["svpLabels"]],
                "tags": [i["label"] for i in crowd["svpLabels"]],
                "classification": gold["classification"],
                "sentence": gold["sentence"].lower(),
            }

if __name__ == "__main__":
    data = datasets.load_dataset(__file__)
    breakpoint()