# 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.

import datasets
import pandas as pd


_CITATION = """"""
_DESCRIPTION = """"""
_HOMEPAGE = ""
_LICENSE = ""
_URLS = {
    "qa": {
        "train": "data/qa/train.csv",
        "validation": "data/qa/validation.csv",
        "test": "data/qa/test.csv",
        "all": "data/qa/qa.csv",
    },
    "passages": {
        "train": "data/passages/train.tsv",
        "validation": "data/passages/validation.tsv",
        "test": "data/passages/test.tsv",
        "all": "data/passages/passages.tsv"
    },
}

_CONFIGS = {}

_CONFIGS["qa"] = {
    "description": "Answer bar exam questions",
    "features": {
        "idx": datasets.Value("string"),
        "dataset": datasets.Value("string"),
        "example_id": datasets.Value("string"),
        "prompt_id": datasets.Value("string"),
        "source": datasets.Value("string"),
        "subject": datasets.Value("string"),
        "question_number": datasets.Value("string"),
        "prompt": datasets.Value("string"),
        "question": datasets.Value("string"),
        "choice_a": datasets.Value("string"),
        "choice_b": datasets.Value("string"),
        "choice_c": datasets.Value("string"),
        "choice_d": datasets.Value("string"),
        "answer": datasets.Value("string"),
        "gold_passage": datasets.Value("string"),
        "gold_idx": datasets.Value("string"),
    },
    "license": None,
}

_CONFIGS["passages"] = {
    "description": "Passage corpus of bar exam question explanations, Wex definitions and primary sources, and caselaw",
    "features": {
        "idx": datasets.Value("string"),
        "source": datasets.Value("string"),
        "faiss_id": datasets.Value("string"),
        "case_id": datasets.Value("string"),
        "absolute_paragraph_id": datasets.Value("string"),
        "opinion_id": datasets.Value("string"),
        "relative_paragraph_id": datasets.Value("string"),
        "text": datasets.Value("string"),
    },
    "license": None,
}


class BarExamQA(datasets.GeneratorBasedBuilder):
    """Legal retrieval/QA dataset for the multistate bar exam"""
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name=task, version=datasets.Version("1.0.0"), description=task,
        )
        for task in _CONFIGS
    ]


    def _info(self):
        features = _CONFIGS[self.config.name]["features"]
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(features),
            homepage=_HOMEPAGE,
            citation=_CITATION,
            license=_CONFIGS[self.config.name]["license"],
        )


    def _split_generators(self, dl_manager):
        downloaded_file_dir = dl_manager.download_and_extract(_URLS[self.config.name])
        splits = [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "fpath": downloaded_file_dir["train"],
                    "name": self.config.name,
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "fpath": downloaded_file_dir["validation"],
                    "name": self.config.name,
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "fpath": downloaded_file_dir["test"],
                    "name": self.config.name,
                },
            ),
        ]
        return splits


    def _generate_examples(self, fpath, name):
        """Yields examples as (key, example) tuples."""
        if name in ["qa"]:
            data = pd.read_csv(fpath)
            data = data.to_dict(orient="records")
            for id_line, example in enumerate(data):
                yield id_line, example

        if name in ["passages"]:
            data = pd.read_csv(fpath, sep='\t')
            data = data.to_dict(orient="records")
            for id_line, example in enumerate(data):
                yield id_line, example