import os
import re
import xml.etree.ElementTree as ET
from glob import glob
from pathlib import Path
from typing import List

from tqdm import tqdm


########################
# DATASETS
########################


def tweb(root_path, meta_file):
    """Normalize TWEB dataset.
    https://www.kaggle.com/bryanpark/the-world-english-bible-speech-dataset
    """
    txt_file = os.path.join(root_path, meta_file)
    items = []
    speaker_name = "tweb"
    with open(txt_file, "r") as ttf:
        for line in ttf:
            cols = line.split("\t")
            wav_file = os.path.join(root_path, cols[0] + ".wav")
            text = cols[1]
            items.append([text, wav_file, speaker_name])
    return items


def mozilla(root_path, meta_file):
    """Normalizes Mozilla meta data files to TTS format"""
    txt_file = os.path.join(root_path, meta_file)
    items = []
    speaker_name = "mozilla"
    with open(txt_file, "r") as ttf:
        for line in ttf:
            cols = line.split("|")
            wav_file = cols[1].strip()
            text = cols[0].strip()
            wav_file = os.path.join(root_path, "wavs", wav_file)
            items.append([text, wav_file, speaker_name])
    return items


def mozilla_de(root_path, meta_file):
    """Normalizes Mozilla meta data files to TTS format"""
    txt_file = os.path.join(root_path, meta_file)
    items = []
    speaker_name = "mozilla"
    with open(txt_file, "r", encoding="ISO 8859-1") as ttf:
        for line in ttf:
            cols = line.strip().split("|")
            wav_file = cols[0].strip()
            text = cols[1].strip()
            folder_name = f"BATCH_{wav_file.split('_')[0]}_FINAL"
            wav_file = os.path.join(root_path, folder_name, wav_file)
            items.append([text, wav_file, speaker_name])
    return items


def mailabs(root_path, meta_files=None):
    """Normalizes M-AI-Labs meta data files to TTS format"""
    speaker_regex = re.compile("by_book/(male|female)/(?P<speaker_name>[^/]+)/")
    if meta_files is None:
        csv_files = glob(root_path + "/**/metadata.csv", recursive=True)
    else:
        csv_files = meta_files
    # meta_files = [f.strip() for f in meta_files.split(",")]
    items = []
    for csv_file in csv_files:
        txt_file = os.path.join(root_path, csv_file)
        folder = os.path.dirname(txt_file)
        # determine speaker based on folder structure...
        speaker_name_match = speaker_regex.search(txt_file)
        if speaker_name_match is None:
            continue
        speaker_name = speaker_name_match.group("speaker_name")
        print(" | > {}".format(csv_file))
        with open(txt_file, "r") as ttf:
            for line in ttf:
                cols = line.split("|")
                if meta_files is None:
                    wav_file = os.path.join(folder, "wavs", cols[0] + ".wav")
                else:
                    wav_file = os.path.join(root_path, folder.replace("metadata.csv", ""), "wavs", cols[0] + ".wav")
                if os.path.isfile(wav_file):
                    text = cols[1].strip()
                    items.append([text, wav_file, speaker_name])
                else:
                    raise RuntimeError("> File %s does not exist!" % (wav_file))
    return items


def ljspeech(root_path, meta_file):
    """Normalizes the LJSpeech meta data file to TTS format
    https://keithito.com/LJ-Speech-Dataset/"""
    txt_file = os.path.join(root_path, meta_file)
    items = []
    speaker_name = "ljspeech"
    with open(txt_file, "r", encoding="utf-8") as ttf:
        for line in ttf:
            cols = line.split("|")
            wav_file = os.path.join(root_path, "wavs", cols[0] + ".wav")
            text = cols[1]
            items.append([text, wav_file, speaker_name])
    return items


def sam_accenture(root_path, meta_file):
    """Normalizes the sam-accenture meta data file to TTS format
    https://github.com/Sam-Accenture-Non-Binary-Voice/non-binary-voice-files"""
    xml_file = os.path.join(root_path, "voice_over_recordings", meta_file)
    xml_root = ET.parse(xml_file).getroot()
    items = []
    speaker_name = "sam_accenture"
    for item in xml_root.findall("./fileid"):
        text = item.text
        wav_file = os.path.join(root_path, "vo_voice_quality_transformation", item.get("id") + ".wav")
        if not os.path.exists(wav_file):
            print(f" [!] {wav_file} in metafile does not exist. Skipping...")
            continue
        items.append([text, wav_file, speaker_name])
    return items


def ruslan(root_path, meta_file):
    """Normalizes the RUSLAN meta data file to TTS format
    https://ruslan-corpus.github.io/"""
    txt_file = os.path.join(root_path, meta_file)
    items = []
    speaker_name = "ljspeech"
    with open(txt_file, "r", encoding="utf-8") as ttf:
        for line in ttf:
            cols = line.split("|")
            wav_file = os.path.join(root_path, "RUSLAN", cols[0] + ".wav")
            text = cols[1]
            items.append([text, wav_file, speaker_name])
    return items


def css10(root_path, meta_file):
    """Normalizes the CSS10 dataset file to TTS format"""
    txt_file = os.path.join(root_path, meta_file)
    items = []
    speaker_name = "ljspeech"
    with open(txt_file, "r") as ttf:
        for line in ttf:
            cols = line.split("|")
            wav_file = os.path.join(root_path, cols[0])
            text = cols[1]
            items.append([text, wav_file, speaker_name])
    return items


def nancy(root_path, meta_file):
    """Normalizes the Nancy meta data file to TTS format"""
    txt_file = os.path.join(root_path, meta_file)
    items = []
    speaker_name = "nancy"
    with open(txt_file, "r") as ttf:
        for line in ttf:
            utt_id = line.split()[1]
            text = line[line.find('"') + 1 : line.rfind('"') - 1]
            wav_file = os.path.join(root_path, "wavn", utt_id + ".wav")
            items.append([text, wav_file, speaker_name])
    return items


def common_voice(root_path, meta_file):
    """Normalize the common voice meta data file to TTS format."""
    txt_file = os.path.join(root_path, meta_file)
    items = []
    with open(txt_file, "r") as ttf:
        for line in ttf:
            if line.startswith("client_id"):
                continue
            cols = line.split("\t")
            text = cols[2]
            speaker_name = cols[0]
            wav_file = os.path.join(root_path, "clips", cols[1].replace(".mp3", ".wav"))
            items.append([text, wav_file, "MCV_" + speaker_name])
    return items


def libri_tts(root_path, meta_files=None):
    """https://ai.google/tools/datasets/libri-tts/"""
    items = []
    if meta_files is None:
        meta_files = glob(f"{root_path}/**/*trans.tsv", recursive=True)
    for meta_file in meta_files:
        _meta_file = os.path.basename(meta_file).split(".")[0]
        speaker_name = _meta_file.split("_")[0]
        chapter_id = _meta_file.split("_")[1]
        _root_path = os.path.join(root_path, f"{speaker_name}/{chapter_id}")
        with open(meta_file, "r") as ttf:
            for line in ttf:
                cols = line.split("\t")
                wav_file = os.path.join(_root_path, cols[0] + ".wav")
                text = cols[1]
                items.append([text, wav_file, "LTTS_" + speaker_name])
    for item in items:
        assert os.path.exists(item[1]), f" [!] wav files don't exist - {item[1]}"
    return items


def custom_turkish(root_path, meta_file):
    txt_file = os.path.join(root_path, meta_file)
    items = []
    speaker_name = "turkish-female"
    skipped_files = []
    with open(txt_file, "r", encoding="utf-8") as ttf:
        for line in ttf:
            cols = line.split("|")
            wav_file = os.path.join(root_path, "wavs", cols[0].strip() + ".wav")
            if not os.path.exists(wav_file):
                skipped_files.append(wav_file)
                continue
            text = cols[1].strip()
            items.append([text, wav_file, speaker_name])
    print(f" [!] {len(skipped_files)} files skipped. They don't exist...")
    return items


# ToDo: add the dataset link when the dataset is released publicly
def brspeech(root_path, meta_file):
    """BRSpeech 3.0 beta"""
    txt_file = os.path.join(root_path, meta_file)
    items = []
    with open(txt_file, "r") as ttf:
        for line in ttf:
            if line.startswith("wav_filename"):
                continue
            cols = line.split("|")
            wav_file = os.path.join(root_path, cols[0])
            text = cols[2]
            speaker_name = cols[3]
            items.append([text, wav_file, speaker_name])
    return items


def vctk(root_path, meta_files=None, wavs_path="wav48"):
    """homepages.inf.ed.ac.uk/jyamagis/release/VCTK-Corpus.tar.gz"""
    test_speakers = meta_files
    items = []
    meta_files = glob(f"{os.path.join(root_path,'txt')}/**/*.txt", recursive=True)
    for meta_file in meta_files:
        _, speaker_id, txt_file = os.path.relpath(meta_file, root_path).split(os.sep)
        file_id = txt_file.split(".")[0]
        if isinstance(test_speakers, list):  # if is list ignore this speakers ids
            if speaker_id in test_speakers:
                continue
        with open(meta_file) as file_text:
            text = file_text.readlines()[0]
        wav_file = os.path.join(root_path, wavs_path, speaker_id, file_id + ".wav")
        items.append([text, wav_file, "VCTK_" + speaker_id])

    return items


def vctk_slim(root_path, meta_files=None, wavs_path="wav48"):
    """homepages.inf.ed.ac.uk/jyamagis/release/VCTK-Corpus.tar.gz"""
    items = []
    txt_files = glob(f"{os.path.join(root_path,'txt')}/**/*.txt", recursive=True)
    for text_file in txt_files:
        _, speaker_id, txt_file = os.path.relpath(text_file, root_path).split(os.sep)
        file_id = txt_file.split(".")[0]
        if isinstance(meta_files, list):  # if is list ignore this speakers ids
            if speaker_id in meta_files:
                continue
        wav_file = os.path.join(root_path, wavs_path, speaker_id, file_id + ".wav")
        items.append([None, wav_file, "VCTK_" + speaker_id])

    return items


# ======================================== VOX CELEB ===========================================
def voxceleb2(root_path, meta_file=None):
    """
    :param meta_file   Used only for consistency with load_meta_data api
    """
    return _voxcel_x(root_path, meta_file, voxcel_idx="2")


def voxceleb1(root_path, meta_file=None):
    """
    :param meta_file   Used only for consistency with load_meta_data api
    """
    return _voxcel_x(root_path, meta_file, voxcel_idx="1")


def _voxcel_x(root_path, meta_file, voxcel_idx):
    assert voxcel_idx in ["1", "2"]
    expected_count = 148_000 if voxcel_idx == "1" else 1_000_000
    voxceleb_path = Path(root_path)
    cache_to = voxceleb_path / f"metafile_voxceleb{voxcel_idx}.csv"
    cache_to.parent.mkdir(exist_ok=True)

    # if not exists meta file, crawl recursively for 'wav' files
    if meta_file is not None:
        with open(str(meta_file), "r") as f:
            return [x.strip().split("|") for x in f.readlines()]

    elif not cache_to.exists():
        cnt = 0
        meta_data = []
        wav_files = voxceleb_path.rglob("**/*.wav")
        for path in tqdm(
            wav_files,
            desc=f"Building VoxCeleb {voxcel_idx} Meta file ... this needs to be done only once.",
            total=expected_count,
        ):
            speaker_id = str(Path(path).parent.parent.stem)
            assert speaker_id.startswith("id")
            text = None  # VoxCel does not provide transciptions, and they are not needed for training the SE
            meta_data.append(f"{text}|{path}|voxcel{voxcel_idx}_{speaker_id}\n")
            cnt += 1
        with open(str(cache_to), "w") as f:
            f.write("".join(meta_data))
        if cnt < expected_count:
            raise ValueError(f"Found too few instances for Voxceleb. Should be around {expected_count}, is: {cnt}")

    with open(str(cache_to), "r") as f:
        return [x.strip().split("|") for x in f.readlines()]


def baker(root_path: str, meta_file: str) -> List[List[str]]:
    """Normalizes the Baker meta data file to TTS format

    Args:
        root_path (str): path to the baker dataset
        meta_file (str): name of the meta dataset containing names of wav to select and the transcript of the sentence
    Returns:
        List[List[str]]: List of (text, wav_path, speaker_name) associated with each sentences
    """
    txt_file = os.path.join(root_path, meta_file)
    items = []
    speaker_name = "baker"
    with open(txt_file, "r") as ttf:
        for line in ttf:
            wav_name, text = line.rstrip("\n").split("|")
            wav_path = os.path.join(root_path, "clips_22", wav_name)
            items.append([text, wav_path, speaker_name])
    return items


def kokoro(root_path, meta_file):
    """Japanese single-speaker dataset from https://github.com/kaiidams/Kokoro-Speech-Dataset"""
    txt_file = os.path.join(root_path, meta_file)
    items = []
    speaker_name = "kokoro"
    with open(txt_file, "r") as ttf:
        for line in ttf:
            cols = line.split("|")
            wav_file = os.path.join(root_path, "wavs", cols[0] + ".wav")
            text = cols[2].replace(" ", "")
            items.append([text, wav_file, speaker_name])
    return items