"""LibriTTS dataset with phone alignments, prosody and mel spectrograms."""

import os
from pathlib import Path
import hashlib
import pickle

import datasets
import pandas as pd
import numpy as np
from tqdm.contrib.concurrent import process_map
from tqdm.auto import tqdm
from multiprocessing import cpu_count
from PIL import Image

logger = datasets.logging.get_logger(__name__)

_VERSION = "0.0.1"

_CITATION = """\
@inproceedings{48008,
title	= {LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech},
author	= {Heiga Zen and Rob Clark and Ron J. Weiss and Viet Dang and Ye Jia and Yonghui Wu and Yu Zhang and Zhifeng Chen},
year	= {2019},
URL	= {https://arxiv.org/abs/1904.02882},
booktitle	= {Interspeech}
}
"""

_DESCRIPTION = """\
Dataset containing Mel Spectrograms, Prosody and Phone Alignments for the LibriTTS dataset.
"""

_URLS = {
    "dev.clean": "https://huggingface.co/datasets/cdminix/libritts-phones-and-mel/resolve/main/data/dev_clean.tar.gz",
    "dev.other": "https://huggingface.co/datasets/cdminix/libritts-phones-and-mel/resolve/main/data/dev_other.tar.gz",
    "test.clean": "https://huggingface.co/datasets/cdminix/libritts-phones-and-mel/resolve/main/data/test_clean.tar.gz",
    "test.other": "https://huggingface.co/datasets/cdminix/libritts-phones-and-mel/resolve/main/data/test_other.tar.gz",
    "train.clean.100": "https://huggingface.co/datasets/cdminix/libritts-phones-and-mel/resolve/main/data/train_clean_100.tar.gz",
    "train.clean.360": "https://huggingface.co/datasets/cdminix/libritts-phones-and-mel/resolve/main/data/train_clean_360.tar.gz",
    "train.other.500": "https://huggingface.co/datasets/cdminix/libritts-phones-and-mel/resolve/main/data/train_other_500.tar.gz",
}


class LibriTTSConfig(datasets.BuilderConfig):

    def __init__(self, **kwargs):
        """

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


class LibriTTS(datasets.GeneratorBasedBuilder):

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

    def _info(self):
        features = {
            "id": datasets.Value("string"),
            "speaker_id": datasets.Value("string"),
            "chapter_id": datasets.Value("string"),
            "phones": datasets.Value("string"),
            "mel": datasets.Value("string"),
            "prosody": datasets.Value("string"),
            "speaker_utterance": datasets.Value("string"),
            "mean_speaker_utterance": datasets.Value("string"),
            "mean_speaker": datasets.Value("string"),
            "text": datasets.Value("string"),
        }

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(features),
            supervised_keys=None,
            homepage="https://huggingface.co/datasets/cdminix/libritts-phones-and-mel",
            citation=_CITATION,
            task_templates=None,
        )

    def _split_generators(self, dl_manager):
        splits = [
            datasets.SplitGenerator(
                name=key,
                gen_kwargs={"data_path": dl_manager.download_and_extract(value)},
            )
            for key, value in _URLS.items()
        ]

        return splits

    def _df_from_path(self, path):
        mel_path = Path(path)
        prosody_path = Path(str(mel_path).replace("_mel.png", "_prosody.png"))
        phones_path = Path(str(mel_path).replace("_mel.png", "_phones.npy"))
        overall_speaker_path = Path(str(mel_path).replace("_mel.png", "_speaker.npy"))
        temporal_speaker_path = Path(str(mel_path).replace("_mel.png", "_speaker.png"))
        mean_speaker_path = mel_path.parent.parent / "mean_speaker.npy"
        text = Path(str(mel_path).replace("_mel.png", "_text.txt")).read_text().lower()

        speaker_id = mel_path.parent.parent.name
        chapter_id = mel_path.parent.name
        _id = str(mel_path).replace("_mel.png", "")

        return {
            "id": _id,
            "speaker_id": speaker_id,
            "chapter_id": chapter_id,
            "phones": phones_path,
            "mel": mel_path,
            "prosody": prosody_path,
            "speaker_utterance": temporal_speaker_path,
            "mean_speaker_utterance": overall_speaker_path,
            "mean_speaker": mean_speaker_path,
            "text": text,
        }

    def _df_from_paths_mp(self, paths):
        return pd.DataFrame(
            process_map(
                self._df_from_path,
                paths,
                desc="Reading files",
                max_workers=cpu_count(),
                chunksize=100,
            )
        )

    def _create_mean_speaker(self, df):
        # Create mean speaker for each speaker
        for _, speaker_df in df.groupby("speaker_id"):
            if not Path(speaker_df["mean_speaker"].iloc[0]).exists():
                mean_speaker = np.mean(
                    np.array(
                        [np.load(path) for path in speaker_df["mean_speaker_utterance"]]
                    ),
                    axis=0,
                )
                np.save(speaker_df["mean_speaker"].iloc[0], mean_speaker)

    def _generate_examples(self, data_path):
        """Generate examples."""
        logger.info("⏳ Generating examples from = %s", data_path)

        paths = sorted(list(Path(data_path).rglob("*_mel.png")))

        df = self._df_from_paths_mp(paths)

        self._create_mean_speaker(df)

        for i, row in tqdm(df.iterrows(), desc="Generating examples"):
            yield row["id"], row.to_dict()