# Copyright (c) Alibaba, Inc. and its affiliates.

import math
import os
import shutil
from multiprocessing import Pool
from typing import Any, Dict, Union

# import kaldiio
import librosa
import numpy as np
import torch
import torchaudio
import torchaudio.compliance.kaldi as kaldi


def ndarray_resample(audio_in: np.ndarray,
                     fs_in: int = 16000,
                     fs_out: int = 16000) -> np.ndarray:
    audio_out = audio_in
    if fs_in != fs_out:
        audio_out = librosa.resample(audio_in, orig_sr=fs_in, target_sr=fs_out)
    return audio_out


def torch_resample(audio_in: torch.Tensor,
                   fs_in: int = 16000,
                   fs_out: int = 16000) -> torch.Tensor:
    audio_out = audio_in
    if fs_in != fs_out:
        audio_out = torchaudio.transforms.Resample(orig_freq=fs_in,
                                                   new_freq=fs_out)(audio_in)
    return audio_out


def extract_CMVN_featrures(mvn_file):
    """
    extract CMVN from cmvn.ark
    """

    if not os.path.exists(mvn_file):
        return None
    try:
        cmvn = kaldiio.load_mat(mvn_file)
        means = []
        variance = []

        for i in range(cmvn.shape[1] - 1):
            means.append(float(cmvn[0][i]))

        count = float(cmvn[0][-1])

        for i in range(cmvn.shape[1] - 1):
            variance.append(float(cmvn[1][i]))

        for i in range(len(means)):
            means[i] /= count
            variance[i] = variance[i] / count - means[i] * means[i]
            if variance[i] < 1.0e-20:
                variance[i] = 1.0e-20
            variance[i] = 1.0 / math.sqrt(variance[i])

        cmvn = np.array([means, variance])
        return cmvn
    except Exception:
        cmvn = extract_CMVN_features_txt(mvn_file)
        return cmvn


def extract_CMVN_features_txt(mvn_file):  # noqa
    with open(mvn_file, 'r', encoding='utf-8') as f:
        lines = f.readlines()

    add_shift_list = []
    rescale_list = []
    for i in range(len(lines)):
        line_item = lines[i].split()
        if line_item[0] == '<AddShift>':
            line_item = lines[i + 1].split()
            if line_item[0] == '<LearnRateCoef>':
                add_shift_line = line_item[3:(len(line_item) - 1)]
                add_shift_list = list(add_shift_line)
                continue
        elif line_item[0] == '<Rescale>':
            line_item = lines[i + 1].split()
            if line_item[0] == '<LearnRateCoef>':
                rescale_line = line_item[3:(len(line_item) - 1)]
                rescale_list = list(rescale_line)
                continue
    add_shift_list_f = [float(s) for s in add_shift_list]
    rescale_list_f = [float(s) for s in rescale_list]
    cmvn = np.array([add_shift_list_f, rescale_list_f])
    return cmvn


def build_LFR_features(inputs, m=7, n=6):  # noqa
    """
    Actually, this implements stacking frames and skipping frames.
    if m = 1 and n = 1, just return the origin features.
    if m = 1 and n > 1, it works like skipping.
    if m > 1 and n = 1, it works like stacking but only support right frames.
    if m > 1 and n > 1, it works like LFR.

    Args:
        inputs_batch: inputs is T x D np.ndarray
        m: number of frames to stack
        n: number of frames to skip
    """
    # LFR_inputs_batch = []
    # for inputs in inputs_batch:
    LFR_inputs = []
    T = inputs.shape[0]
    T_lfr = int(np.ceil(T / n))
    left_padding = np.tile(inputs[0], ((m - 1) // 2, 1))
    inputs = np.vstack((left_padding, inputs))
    T = T + (m - 1) // 2
    for i in range(T_lfr):
        if m <= T - i * n:
            LFR_inputs.append(np.hstack(inputs[i * n:i * n + m]))
        else:  # process last LFR frame
            num_padding = m - (T - i * n)
            frame = np.hstack(inputs[i * n:])
            for _ in range(num_padding):
                frame = np.hstack((frame, inputs[-1]))
            LFR_inputs.append(frame)
    return np.vstack(LFR_inputs)


def compute_fbank(wav_file,
                  num_mel_bins=80,
                  frame_length=25,
                  frame_shift=10,
                  dither=0.0,
                  is_pcm=False,
                  fs: Union[int, Dict[Any, int]] = 16000):
    audio_sr: int = 16000
    model_sr: int = 16000
    if isinstance(fs, int):
        model_sr = fs
        audio_sr = fs
    else:
        model_sr = fs['model_fs']
        audio_sr = fs['audio_fs']

    if is_pcm is True:
        # byte(PCM16) to float32, and resample
        value = wav_file
        middle_data = np.frombuffer(value, dtype=np.int16)
        middle_data = np.asarray(middle_data)
        if middle_data.dtype.kind not in 'iu':
            raise TypeError("'middle_data' must be an array of integers")
        dtype = np.dtype('float32')
        if dtype.kind != 'f':
            raise TypeError("'dtype' must be a floating point type")

        i = np.iinfo(middle_data.dtype)
        abs_max = 2 ** (i.bits - 1)
        offset = i.min + abs_max
        waveform = np.frombuffer(
            (middle_data.astype(dtype) - offset) / abs_max, dtype=np.float32)
        waveform = ndarray_resample(waveform, audio_sr, model_sr)
        waveform = torch.from_numpy(waveform.reshape(1, -1))
    else:
        # load pcm from wav, and resample
        waveform, audio_sr = torchaudio.load(wav_file)
        waveform = waveform * (1 << 15)
        waveform = torch_resample(waveform, audio_sr, model_sr)

    mat = kaldi.fbank(waveform,
                      num_mel_bins=num_mel_bins,
                      frame_length=frame_length,
                      frame_shift=frame_shift,
                      dither=dither,
                      energy_floor=0.0,
                      window_type='hamming',
                      sample_frequency=model_sr)

    input_feats = mat

    return input_feats


def wav2num_frame(wav_path, frontend_conf):
    waveform, sampling_rate = torchaudio.load(wav_path)
    speech_length = (waveform.shape[1] / sampling_rate) * 1000.
    n_frames = (waveform.shape[1] * 1000.0) / (sampling_rate * frontend_conf["frame_shift"] * frontend_conf["lfr_n"])
    feature_dim = frontend_conf["n_mels"] * frontend_conf["lfr_m"]
    return n_frames, feature_dim, speech_length


def calc_shape_core(root_path, frontend_conf, speech_length_min, speech_length_max, idx):
    wav_scp_file = os.path.join(root_path, "wav.scp.{}".format(idx))
    shape_file = os.path.join(root_path, "speech_shape.{}".format(idx))
    with open(wav_scp_file) as f:
        lines = f.readlines()
    with open(shape_file, "w") as f:
        for line in lines:
            sample_name, wav_path = line.strip().split()
            n_frames, feature_dim, speech_length = wav2num_frame(wav_path, frontend_conf)
            write_flag = True
            if speech_length_min > 0 and speech_length < speech_length_min:
                write_flag = False
            if speech_length_max > 0 and speech_length > speech_length_max:
                write_flag = False
            if write_flag:
                f.write("{} {},{}\n".format(sample_name, str(int(np.ceil(n_frames))), str(int(feature_dim))))
                f.flush()


def calc_shape(data_dir, dataset, frontend_conf, speech_length_min=-1, speech_length_max=-1, nj=32):
    shape_path = os.path.join(data_dir, dataset, "shape_files")
    if os.path.exists(shape_path):
        assert os.path.exists(os.path.join(data_dir, dataset, "speech_shape"))
        print('Shape file for small dataset already exists.')
        return
    os.makedirs(shape_path, exist_ok=True)

    # split
    wav_scp_file = os.path.join(data_dir, dataset, "wav.scp")
    with open(wav_scp_file) as f:
        lines = f.readlines()
        num_lines = len(lines)
        num_job_lines = num_lines // nj
    start = 0
    for i in range(nj):
        end = start + num_job_lines
        file = os.path.join(shape_path, "wav.scp.{}".format(str(i + 1)))
        with open(file, "w") as f:
            if i == nj - 1:
                f.writelines(lines[start:])
            else:
                f.writelines(lines[start:end])
        start = end

    p = Pool(nj)
    for i in range(nj):
        p.apply_async(calc_shape_core,
                      args=(shape_path, frontend_conf, speech_length_min, speech_length_max, str(i + 1)))
    print('Generating shape files, please wait a few minutes...')
    p.close()
    p.join()

    # combine
    file = os.path.join(data_dir, dataset, "speech_shape")
    with open(file, "w") as f:
        for i in range(nj):
            job_file = os.path.join(shape_path, "speech_shape.{}".format(str(i + 1)))
            with open(job_file) as job_f:
                lines = job_f.readlines()
                f.writelines(lines)
    print('Generating shape files done.')


def generate_data_list(data_dir, dataset, nj=100):
    split_dir = os.path.join(data_dir, dataset, "split")
    if os.path.exists(split_dir):
        assert os.path.exists(os.path.join(data_dir, dataset, "data.list"))
        print('Data list for large dataset already exists.')
        return
    os.makedirs(split_dir, exist_ok=True)

    with open(os.path.join(data_dir, dataset, "wav.scp")) as f_wav:
        wav_lines = f_wav.readlines()
    with open(os.path.join(data_dir, dataset, "text")) as f_text:
        text_lines = f_text.readlines()
    total_num_lines = len(wav_lines)
    num_lines = total_num_lines // nj
    start_num = 0
    for i in range(nj):
        end_num = start_num + num_lines
        split_dir_nj = os.path.join(split_dir, str(i + 1))
        os.mkdir(split_dir_nj)
        wav_file = os.path.join(split_dir_nj, 'wav.scp')
        text_file = os.path.join(split_dir_nj, "text")
        with open(wav_file, "w") as fw, open(text_file, "w") as ft:
            if i == nj - 1:
                fw.writelines(wav_lines[start_num:])
                ft.writelines(text_lines[start_num:])
            else:
                fw.writelines(wav_lines[start_num:end_num])
                ft.writelines(text_lines[start_num:end_num])
        start_num = end_num

    data_list_file = os.path.join(data_dir, dataset, "data.list")
    with open(data_list_file, "w") as f_data:
        for i in range(nj):
            wav_path = os.path.join(split_dir, str(i + 1), "wav.scp")
            text_path = os.path.join(split_dir, str(i + 1), "text")
            f_data.write(wav_path + " " + text_path + "\n")

def filter_wav_text(data_dir, dataset):
    wav_file = os.path.join(data_dir,dataset,"wav.scp")
    text_file = os.path.join(data_dir, dataset, "text")
    with open(wav_file) as f_wav, open(text_file) as f_text:
        wav_lines = f_wav.readlines()
        text_lines = f_text.readlines()
    os.rename(wav_file, "{}.bak".format(wav_file))
    os.rename(text_file, "{}.bak".format(text_file))
    wav_dict = {}
    for line in wav_lines:
        parts = line.strip().split()
        if len(parts) != 2:
            continue
        sample_name, wav_path = parts
        wav_dict[sample_name] = wav_path
    text_dict = {}
    for line in text_lines:
        parts = line.strip().split()
        if len(parts) < 2:
            continue
        sample_name = parts[0]
        text_dict[sample_name] = " ".join(parts[1:]).lower()
    filter_count = 0
    with open(wav_file, "w") as f_wav, open(text_file, "w") as f_text:
        for sample_name, wav_path in wav_dict.items():
            if sample_name in text_dict.keys():
                f_wav.write(sample_name + " " + wav_path  + "\n")
                f_text.write(sample_name + " " + text_dict[sample_name] + "\n")
            else:
                filter_count += 1
    print("{}/{} samples in {} are filtered because of the mismatch between wav.scp and text".format(len(wav_lines), filter_count, dataset))