"""LibriTTS dataset with forced alignments."""

import os
from pathlib import Path
import hashlib
import pickle

import datasets
import pandas as pd
import numpy as np
from alignments.datasets.librispeech import LibrittsRDataset
from tqdm.contrib.concurrent import process_map
from tqdm.auto import tqdm
from multiprocessing import cpu_count
from phones.convert import Converter
import torchaudio
import torchaudio.transforms as AT
from functools import lru_cache

logger = datasets.logging.get_logger(__name__)

_PHONESET = "arpabet"

_VERBOSE = os.environ.get("LIBRITTS_VERBOSE", True)
_MAX_WORKERS = os.environ.get("LIBRITTS_MAX_WORKERS", cpu_count())
_PATH = os.environ.get("LIBRITTS_PATH", os.environ.get("HF_DATASETS_CACHE", None))
if _PATH is not None and not os.path.exists(_PATH):
    os.makedirs(_PATH)

_VERSION = "1.1.0"

_CITATION = """\
@article{koizumi2023libritts,
  title={LibriTTS-R: A Restored Multi-Speaker Text-to-Speech Corpus},
  author={Koizumi, Yuma and Zen, Heiga and Karita, Shigeki and Ding, Yifan and Yatabe, Kohei and Morioka, Nobuyuki and Bacchiani, Michiel and Zhang, Yu and Han, Wei and Bapna, Ankur},
  journal={arXiv preprint arXiv:2305.18802},
  year={2023}
}

@article{zen2019libritts,
  title={LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech},
  author={Zen, Heiga and Dang, Viet and Clark, Rob and Zhang, Yu and Weiss, Ron J and Jia, Ye and Chen, Zhifeng and Wu, Yonghui},
  journal={Interspeech},
  year={2019}
}
@article{https://doi.org/10.48550/arxiv.2211.16049,
  author = {Minixhofer, Christoph and Klejch, Ondřej and Bell, Peter},
  title = {Evaluating and reducing the distance between synthetic and real speech distributions},
  year = {2022}
}
"""

_DESCRIPTION = """\
Dataset used for loading TTS spectrograms and waveform audio with alignments and a number of configurable "measures", which are extracted from the raw audio.
"""

_URL = "http://www.openslr.org/resources/141/"
_URLS = {
    "dev-clean": _URL + "dev_clean.tar.gz",
    "dev-other": _URL + "dev_other.tar.gz",
    "test-clean": _URL + "test_clean.tar.gz",
    "test-other": _URL + "test_other.tar.gz",
    "train-clean-100": _URL + "train_clean_100.tar.gz",
    "train-clean-360": _URL + "train_clean_360.tar.gz",
    "train-other-500": _URL + "train_other_500.tar.gz",
}

@lru_cache(maxsize=1000)
def get_speaker_prompts(speaker, hash_ds):
    ds = hash_ds.df
    speaker_prompts = ds[ds["speaker"] == speaker]
    speaker_prompts = tuple(speaker_prompts["audio"])
    return speaker_prompts

class LibriTTSAlignConfig(datasets.BuilderConfig):
    """BuilderConfig for LibriTTSAlign."""

    def __init__(self, sampling_rate=22050, hop_length=256, win_length=1024, **kwargs):
        """BuilderConfig for LibriTTSAlign.

        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(LibriTTSAlignConfig, self).__init__(**kwargs)

        self.sampling_rate = sampling_rate
        self.hop_length = hop_length
        self.win_length = win_length
        
        if _PATH is None:
            raise ValueError("Please set the environment variable LIBRITTS_PATH to point to the LibriTTS dataset directory.")
        elif _PATH == os.environ.get("HF_DATASETS_CACHE", None):
            logger.warning("Please set the environment variable LIBRITTS_PATH to point to the LibriTTS dataset directory. Using HF_DATASETS_CACHE as a fallback.")

class LibriTTSAlign(datasets.GeneratorBasedBuilder):
    """LibriTTSAlign dataset."""

    BUILDER_CONFIGS = [
        LibriTTSAlignConfig(
            name="libritts",
            version=datasets.Version(_VERSION, ""),
        ),
    ]

    def _info(self):
        features = {
            "id": datasets.Value("string"),
            "speaker": datasets.Value("string"),
            "text": datasets.Value("string"),
            "start": datasets.Value("float32"),
            "end": datasets.Value("float32"),
            # phone features
            "phones": datasets.Sequence(datasets.Value("string")),
            "phone_durations": datasets.Sequence(datasets.Value("int32")),
            # audio feature
            "audio": datasets.Value("string"),
            "audio_speaker_prompt": datasets.Sequence(datasets.Value("string")),
        }

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(features),
            supervised_keys=None,
            homepage="https://github.com/MiniXC/MeasureCollator",
            citation=_CITATION,
            task_templates=None,
        )

    def _split_generators(self, dl_manager):
        ds_dict = {}
        for name, url in _URLS.items():
            ds_dict[name] = self._create_alignments_ds(name, url)
        splits = [
            datasets.SplitGenerator(
                name=key.replace("-", "."),
                gen_kwargs={"ds": self._create_data(value)}
            ) 
            for key, value in ds_dict.items()
        ]
        # dataframe with all data
        data_train = self._create_data([ds_dict["train-clean-100"], ds_dict["train-clean-360"], ds_dict["train-other-500"]])
        data_dev = self._create_data([ds_dict["dev-clean"], ds_dict["dev-other"]])
        data_test = self._create_data([ds_dict["test-clean"], ds_dict["test-other"]])
        splits += [
            datasets.SplitGenerator(
                name="train.all",
                gen_kwargs={
                    "ds": data_train,
                }
            ),
            datasets.SplitGenerator(
                name="dev.all",
                gen_kwargs={
                    "ds": data_dev,
                }
            ),
            datasets.SplitGenerator(
                name="test.all",
                gen_kwargs={
                    "ds": data_test,
                }
            ),
        ]
        data_all = pd.concat([data_train, data_dev, data_test])
        # create a new split which takes one sample from each speaker in data_all and puts it into the dev split
        # we then remove these samples from data_all
        speakers = data_all["speaker"].unique()
        # seed for reproducibility
        np.random.seed(42)
        self.data_all = data_all
        del data_all
        data_dev_all = [
            x for x in 
            process_map(
                self._create_dev_split,
                speakers,
                chunksize=1000,
                max_workers=_MAX_WORKERS,
                desc="creating dev split",
                tqdm_class=tqdm,
            )
            if x is not None
        ]
        data_dev_all = pd.concat(data_dev_all)
        data_all = self.data_all
        data_all = data_all[data_all["speaker"].isin(data_dev_all["speaker"].unique())]
        data_all = data_all[~data_all["basename"].isin(data_dev_all["basename"].unique())]
        del self.data_all
        self.speaker2idxs = {}
        self.speaker2idxs["all"] = {speaker: idx for idx, speaker in enumerate(sorted(list(data_dev_all["speaker"].unique())))}
        self.speaker2idxs["train"] = {speaker: idx for idx, speaker in enumerate(sorted(list(data_train["speaker"].unique())))}
        self.speaker2idxs["dev"] = {speaker: idx for idx, speaker in enumerate(sorted(list(data_dev["speaker"].unique())))}
        self.speaker2idxs["test"] = {speaker: idx for idx, speaker in enumerate(sorted(list(data_test["speaker"].unique())))}
        splits += [
            datasets.SplitGenerator(
                name="train",
                gen_kwargs={
                    "ds": data_all,
                }
            ),
            datasets.SplitGenerator(
                name="dev",
                gen_kwargs={
                    "ds": data_dev_all,
                }
            ),
        ]
        self.alignments_ds = None
        self.data = None
        return splits
    
    def _create_dev_split(self, speaker):
        data_speaker = self.data_all[self.data_all["speaker"] == speaker]
        if len(data_speaker) < 10:
            print(f"Speaker {speaker} has only {len(data_speaker)} samples, skipping")
            return None
        else:
            data_speaker = data_speaker.sample(2)
            return data_speaker

    def _create_alignments_ds(self, name, url):
        self.empty_textgrids = 0
        ds_hash = hashlib.md5(os.path.join(_PATH, f"{name}-alignments").encode()).hexdigest()
        pkl_path = os.path.join(_PATH, f"{ds_hash}.pkl")
        if os.path.exists(pkl_path):
            ds = pickle.load(open(pkl_path, "rb"))
        else:
            tgt_dir = os.path.join(_PATH, f"{name}-alignments")
            src_dir = os.path.join(_PATH, f"{name}-data")
            if os.path.exists(tgt_dir):
                src_dir = None
                url = None
            if os.path.exists(src_dir):
                url = None
            ds = LibrittsRDataset(
                target_directory=tgt_dir,
                source_directory=src_dir,
                source_url=url,
                verbose=_VERBOSE,
                tmp_directory=os.path.join(_PATH, f"{name}-tmp"),
                chunk_size=1000,
            )
            pickle.dump(ds, open(pkl_path, "wb"))
        return ds, ds_hash

    def _create_data(self, data):
        entries = []
        self.phone_cache = {}
        self.phone_converter = Converter()
        if not isinstance(data, list):
            data = [data]
        hashes = [ds_hash for ds, ds_hash in data]
        ds = [ds for ds, ds_hash in data]
        self.ds = ds
        del data
        for i, ds in enumerate(ds):
            if os.path.exists(os.path.join(_PATH, f"{hashes[i]}-entries.pkl")):
                add_entries = pickle.load(open(os.path.join(_PATH, f"{hashes[i]}-entries.pkl"), "rb"))
            else:
                add_entries = [
                    entry
                    for entry in process_map(
                        self._create_entry,
                        zip([i] * len(ds), np.arange(len(ds))),
                        chunksize=10_000,
                        max_workers=_MAX_WORKERS,
                        desc=f"processing dataset {hashes[i]}",
                        tqdm_class=tqdm,
                    )
                    if entry is not None
                ]
                pickle.dump(add_entries, open(os.path.join(_PATH, f"{hashes[i]}-entries.pkl"), "wb"))
            entries += add_entries
        if self.empty_textgrids > 0:
            logger.warning(f"Found {self.empty_textgrids} empty textgrids")
        del self.ds, self.phone_cache, self.phone_converter
        df = pd.DataFrame(
            entries,
            columns=[
                "phones",
                "duration",
                "start",
                "end",
                "audio",
                "speaker",
                "text",
                "basename",
            ],
        )
        return df

    def _create_entry(self, dsi_idx):
        dsi, idx = dsi_idx
        item = self.ds[dsi][idx]
        start, end = item["phones"][0][0], item["phones"][-1][1]

        phones = []
        durations = []

        for i, p in enumerate(item["phones"]):
            s, e, phone = p
            phone.replace("ˌ", "")
            r_phone = phone.replace("0", "").replace("1", "")
            if len(r_phone) > 0:
                phone = r_phone
            if "[" not in phone:
                o_phone = phone
                if o_phone not in self.phone_cache:
                    phone = self.phone_converter(
                        phone, _PHONESET, lang=None
                    )[0]
                    self.phone_cache[o_phone] = phone
                phone = self.phone_cache[o_phone]
            phones.append(phone)
            durations.append(
                int(
                    np.round(e * self.config.sampling_rate / self.config.hop_length)
                    - np.round(s * self.config.sampling_rate / self.config.hop_length)
                )
            )

        if start >= end:
            self.empty_textgrids += 1
            return None
        
        return (
            phones,
            durations,
            start,
            end,
            item["wav"],
            str(item["speaker"]).split("/")[-1],
            item["transcript"],
            Path(item["wav"]).name,
        )

    def _generate_examples(self, ds):
        j = 0
        hash_col = "audio"
        hash_ds = HashableDataFrame(ds, hash_col)
        for i, row in ds.iterrows():
            # 10kB is the minimum size of a wav file for our purposes
            if Path(row["audio"]).stat().st_size >= 10_000:
                if len(row["phones"]) < 384:
                    speaker_prompts = get_speaker_prompts(row["speaker"], hash_ds)
                    result = {
                        "id": row["basename"],
                        "speaker": row["speaker"],
                        "text": row["text"],
                        "start": row["start"],
                        "end": row["end"],
                        "phones": row["phones"],
                        "phone_durations": row["duration"],
                        "audio": str(row["audio"]),
                        "audio_speaker_prompt": speaker_prompts,
                    }
                    yield j, result
                    j += 1

class HashableDataFrame():
    def __init__(self, df, hash_col):
        self.df = df
        self.hash_col = hash_col
        self.hash = hashlib.md5(self.df[self.hash_col].values).hexdigest()
        # to integer
        self.hash = int(self.hash, 16)
    
    def __hash__(self):
        return self.hash

    def __eq__(self, other):
        return self.hash == other.hash