from typing import List
import os
import csv
import ast
import gzip

import datasets
from datasets.utils.logging import get_logger

logger = get_logger(__name__)

_URL = "https://asappresearch.github.io/slue-toolkit/"

_DL_URLS = {
    "slue-hvb": "data/slue-hvb_blind.zip",
}

_LICENSE = """
=======================================================
The license of this script
MIT License
Copyright (c) 2023 ASAPP Inc.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
=======================================================
SLUE-HVB dataset contains a subset of the Gridspace-Stanford Harper Valley speech dataset and the copyright of this subset remains the same with the original license, CC-BY-4.0. See also original license notice (https://github.com/cricketclub/gridspace-stanford-harper-valley/blob/master/LICENSE)

Additionally, we provide dialog act classification annotation and it is covered with the same license as CC-BY-4.0.
=======================================================

"""

_CITATION = """\
@inproceedings{shon2023slue_phase2,
  title={SLUE Phase-2: A Benchmark Suite of Diverse Spoken Language Understanding Tasks},
  author={Shon, Suwon and Arora, Siddhant and Lin, Chyi-Jiunn and Pasad, Ankita and Wu, Felix and Sharma, Roshan and Wu, Wei-Lun and Lee, Hung-Yi and Livescu, Karen and Watanabe, Shinji},
  booktitle={ACL},
  year={2023},
}
"""

_DESCRIPTION = """\
Spoken Language Understanding Evaluation (SLUE) benchmark Phase 2.
"""

class SLUE2Config(datasets.BuilderConfig):
    """BuilderConfig for SLUE."""

    def __init__(self, **kwargs):
        """
        Args:
          data_dir: `string`, the path to the folder containing the files in the
            downloaded .tar
          citation: `string`, citation for the data set
          url: `string`, url for information about the data set
          **kwargs: keyword arguments forwarded to super.
        """
        super(SLUE2Config, self).__init__(
            version=datasets.Version("2.4.0", ""), **kwargs
        )


class SLUE2(datasets.GeneratorBasedBuilder):
    """Librispeech dataset."""

    DEFAULT_WRITER_BATCH_SIZE = 256
    DEFAULT_CONFIG_NAME = "hvb"
    BUILDER_CONFIGS = [
        SLUE2Config(
            name="hvb",
            description="SLUE-HVB set.",
        ),
    ]

    def _info(self):
        if self.config.name == "hvb":
            features = {
                "issue_id": datasets.Value("string"),
                "audio": datasets.Audio(sampling_rate=16_000),
                "speaker_id": datasets.Value("string"),
                "text": datasets.Value("string"),
                "utt_index": datasets.Value("int32"),
                "channel": datasets.Value("int32"),
                "role": datasets.Value("string"),
                "start_ms": datasets.Value("int32"),
                "duration_ms": datasets.Value("int32"),
                "intent": datasets.Value("string"),
                "dialog_acts": datasets.Sequence(
                    datasets.Value("string"),
                ),
            }
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(features),
            supervised_keys=("file", "text"),
            homepage=_URL,
            citation=_CITATION,
            license=_LICENSE,
        )

    def _split_generators(
        self, dl_manager: datasets.DownloadManager
    ) -> List[datasets.SplitGenerator]:

        config_name = f"slue-{self.config.name}"

        dl_dir = dl_manager.download_and_extract(_DL_URLS[config_name])
        data_dir = os.path.join(dl_dir, config_name)
        print(data_dir)

        splits = [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": os.path.join(
                        data_dir or "", f"{config_name}_fine-tune.tsv"
                    ),
                    "data_dir": data_dir,
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "filepath": os.path.join(data_dir or "", f"{config_name}_dev.tsv"),
                    "data_dir": data_dir,
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": os.path.join(
                        data_dir or "", f"{config_name}_test_blind.tsv"
                    ),
                    "data_dir": data_dir,
                },
            ),
        ]
        return splits

    def _generate_examples(self, filepath, data_dir):
        logger.info(f"generating examples from = {filepath}")

        with open(filepath) as f:
            reader = csv.DictReader(f, delimiter="\t")

            for idx, row in enumerate(reader):
                if self.config.name == "hvb":
                    split = "test" if "test" in filepath else "dev" if "dev" in filepath else "fine-tune"
                    audio_file = os.path.join(
                        data_dir, split,
                        f'{row["issue_id"]}_{row["start_ms"]}_{int(row["start_ms"]) + int(row["duration_ms"])}.wav'
                    )
                    example = {
                        "issue_id": row["issue_id"],
                        "audio": audio_file,
                        "speaker_id": row["speaker_id"],
                        "text": row["text"],
                        "utt_index": int(row["utt_index"]),
                        "channel": int(row["channel"]),
                        "role": row["role"],
                        "start_ms": int(row["start_ms"]),
                        "duration_ms": int(row["duration_ms"]),
                        "intent": row["intent"],
                        "dialog_acts": eval(row.get("dialog_acts", "[]")),
                    }
                yield idx, example