import os
import glob
import sys
import argparse
import logging
import json
import subprocess
import numpy as np
from scipy.io.wavfile import read
import torch
import torchvision
from torch.nn import functional as F
from commons import sequence_mask
import hifigan

# 导入 pyworld 用于 F0 计算
import numpy as np
from transformers import (
    Wav2Vec2FeatureExtractor,
    HubertModel,
)

MATPLOTLIB_FLAG = False
logging.basicConfig(stream=sys.stdout, level=logging.WARNING)
logger = logging

model_dir = "HuBERT/chinese-hubert-large-fariseq-ckpt"

def get_cmodel(rank):
    # 加载 HuBERT 模型和特征提取器
    feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_dir)
    cmodel = HubertModel.from_pretrained(model_dir)
    cmodel = cmodel.to(rank)
    cmodel = cmodel.float()
    cmodel.eval()
    return cmodel, feature_extractor

def get_content(cmodel, feature_extractor, y, device):
    # 预处理音频
    input_values = feature_extractor(y, return_tensors="pt", sampling_rate=16000).input_values  # [1, T]
    input_values = input_values.to(device).float()

    # 提取特征
    with torch.no_grad():
        outputs = cmodel(input_values)
        c = outputs.last_hidden_state  # [batch, seq_len, hidden_size]
        c = c.transpose(1, 2)
    return c


def get_vocoder(rank):
    with open("hifigan/config.json", "r") as f:
        config = json.load(f)
    config = hifigan.AttrDict(config)
    vocoder = hifigan.Generator(config)
    ckpt = torch.load("hifigan/generator_v1.pth")
    vocoder.load_state_dict(ckpt["generator"])
    vocoder.eval()
    vocoder.remove_weight_norm()
    vocoder.cuda(rank)
    return vocoder


def transform(mel, height):  # 68-92
    # r = np.random.random()
    # rate = r * 0.3 + 0.85 # 0.85-1.15
    # height = int(mel.size(-2) * rate)
    tgt = torchvision.transforms.functional.resize(mel, (height, mel.size(-1)))
    if height >= mel.size(-2):
        return tgt[:, :mel.size(-2), :]
    else:
        silence = tgt[:, -1:, :].repeat(1, mel.size(-2) - height, 1)
        silence += torch.randn_like(silence) / 10
        return torch.cat((tgt, silence), 1)


def stretch(mel, width):  # 0.5-2
    return torchvision.transforms.functional.resize(mel, (mel.size(-2), width))


def load_checkpoint(checkpoint_path, model, optimizer=None, strict=False):
    assert os.path.isfile(checkpoint_path)
    checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
    iteration = checkpoint_dict['iteration']
    learning_rate = checkpoint_dict['learning_rate']
    if optimizer is not None:
        optimizer.load_state_dict(checkpoint_dict['optimizer'])
    saved_state_dict = checkpoint_dict['model']
    if hasattr(model, 'module'):
        state_dict = model.module.state_dict()
    else:
        state_dict = model.state_dict()
    if strict:
        assert state_dict.keys() == saved_state_dict.keys(), "Mismatched model config and checkpoint."
    new_state_dict = {}
    for k, v in state_dict.items():
        try:
            new_state_dict[k] = saved_state_dict[k]
        except:
            logger.info("%s is not in the checkpoint" % k)
            new_state_dict[k] = v
    if hasattr(model, 'module'):
        model.module.load_state_dict(new_state_dict)
    else:
        model.load_state_dict(new_state_dict)
    logger.info("Loaded checkpoint '{}' (iteration {})".format(
        checkpoint_path, iteration))
    return model, optimizer, learning_rate, iteration


def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
    logger.info("Saving model and optimizer state at iteration {} to {}".format(
        iteration, checkpoint_path))
    if hasattr(model, 'module'):
        state_dict = model.module.state_dict()
    else:
        state_dict = model.state_dict()
    torch.save({'model': state_dict,
                'iteration': iteration,
                'optimizer': optimizer.state_dict(),
                'learning_rate': learning_rate}, checkpoint_path)


def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
    for k, v in scalars.items():
        writer.add_scalar(k, v, global_step)
    for k, v in histograms.items():
        writer.add_histogram(k, v, global_step)
    for k, v in images.items():
        writer.add_image(k, v, global_step, dataformats='HWC')
    for k, v in audios.items():
        writer.add_audio(k, v, global_step, audio_sampling_rate)


def latest_checkpoint_path(dir_path, regex="G_*.pth"):
    f_list = glob.glob(os.path.join(dir_path, regex))
    f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
    x = f_list[-1]
    print(x)
    return x


def plot_spectrogram_to_numpy(spectrogram):
    global MATPLOTLIB_FLAG
    if not MATPLOTLIB_FLAG:
        import matplotlib
        matplotlib.use("Agg")
        MATPLOTLIB_FLAG = True
        mpl_logger = logging.getLogger('matplotlib')
        mpl_logger.setLevel(logging.WARNING)
    import matplotlib.pylab as plt
    import numpy as np

    fig, ax = plt.subplots(figsize=(10, 2))
    im = ax.imshow(spectrogram, aspect="auto", origin="lower",
                   interpolation='none')
    plt.colorbar(im, ax=ax)
    plt.xlabel("Frames")
    plt.ylabel("Channels")
    plt.tight_layout()

    fig.canvas.draw()
    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
    plt.close()
    return data


def plot_alignment_to_numpy(alignment, info=None):
    global MATPLOTLIB_FLAG
    if not MATPLOTLIB_FLAG:
        import matplotlib
        matplotlib.use("Agg")
        MATPLOTLIB_FLAG = True
        mpl_logger = logging.getLogger('matplotlib')
        mpl_logger.setLevel(logging.WARNING)
    import matplotlib.pylab as plt
    import numpy as np

    fig, ax = plt.subplots(figsize=(6, 4))
    im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
                   interpolation='none')
    fig.colorbar(im, ax=ax)
    xlabel = 'Decoder timestep'
    if info is not None:
        xlabel += '\n\n' + info
    plt.xlabel(xlabel)
    plt.ylabel('Encoder timestep')
    plt.tight_layout()

    fig.canvas.draw()
    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
    plt.close()
    return data


def load_wav_to_torch(full_path):
    sampling_rate, data = read(full_path)
    return torch.FloatTensor(data.astype(np.float32)), sampling_rate


def load_filepaths_and_text(filename, split="|"):
    with open(filename, encoding='utf-8') as f:
        filepaths_and_text = [line.strip().split(split) for line in f]
    return filepaths_and_text


def get_hparams(init=True):
    parser = argparse.ArgumentParser()
    parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
                        help='JSON file for configuration')
    parser.add_argument('-m', '--model', type=str, required=True,
                        help='Model name')

    args = parser.parse_args()
    model_dir = os.path.join("/data/logs/", args.model)

    if not os.path.exists(model_dir):
        os.makedirs(model_dir)

    config_path = args.config
    config_save_path = os.path.join(model_dir, "config.json")
    if init:
        with open(config_path, "r") as f:
            data = f.read()
        with open(config_save_path, "w") as f:
            f.write(data)
    else:
        with open(config_save_path, "r") as f:
            data = f.read()
    config = json.loads(data)

    hparams = HParams(**config)
    hparams.model_dir = model_dir
    return hparams


def get_hparams_from_dir(model_dir):
    config_save_path = os.path.join(model_dir, "config.json")
    with open(config_save_path, "r") as f:
        data = f.read()
    config = json.loads(data)

    hparams = HParams(**config)
    hparams.model_dir = model_dir
    return hparams


def get_hparams_from_file(config_path):
    with open(config_path, "r") as f:
        data = f.read()
    config = json.loads(data)

    hparams = HParams(**config)
    return hparams


def check_git_hash(model_dir):
    source_dir = os.path.dirname(os.path.realpath(__file__))
    if not os.path.exists(os.path.join(source_dir, ".git")):
        logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
            source_dir
        ))
        return

    cur_hash = subprocess.getoutput("git rev-parse HEAD")

    path = os.path.join(model_dir, "githash")
    if os.path.exists(path):
        saved_hash = open(path).read()
        if saved_hash != cur_hash:
            logger.warn("git hash values are different. {}(saved) != {}(current)".format(
                saved_hash[:8], cur_hash[:8]))
    else:
        open(path, "w").write(cur_hash)


def get_logger(model_dir, filename="train.log"):
    global logger
    logger = logging.getLogger(os.path.basename(model_dir))
    logger.setLevel(logging.DEBUG)

    formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
    if not os.path.exists(model_dir):
        os.makedirs(model_dir)
    h = logging.FileHandler(os.path.join(model_dir, filename))
    h.setLevel(logging.DEBUG)
    h.setFormatter(formatter)
    logger.addHandler(h)
    return logger


class HParams():
    def __init__(self, **kwargs):
        for k, v in kwargs.items():
            if type(v) == dict:
                v = HParams(**v)
            self[k] = v

    def keys(self):
        return self.__dict__.keys()

    def items(self):
        return self.__dict__.items()

    def values(self):
        return self.__dict__.values()

    def __len__(self):
        return len(self.__dict__)

    def __getitem__(self, key):
        return getattr(self, key)

    def __setitem__(self, key, value):
        return setattr(self, key, value)

    def __contains__(self, key):
        return key in self.__dict__

    def __repr__(self):
        return self.__dict__.__repr__()


def get_f0_torch(wav, sr=16000, hop_length=160):
    """
  使用 torchcrepe 在 GPU 上提取 F0
  输入 wav: (batch_size, time), 在 GPU 上
  返回 f0: (batch_size, f0_length), 在 GPU 上
  """
    f0 = torchcrepe.predict(wav, sample_rate=sr, hop_length=hop_length, batch_size=64, device=wav.device, model='full')
    return f0


def clean_checkpoints(path_to_models="logs/44k/", n_ckpts_to_keep=2, sort_by_time=True):
    """Freeing up space by deleting saved ckpts

  Arguments:
  path_to_models    --  Path to the model directory
  n_ckpts_to_keep   --  Number of ckpts to keep, excluding G_0.pth and D_0.pth
  sort_by_time      --  True -> chronologically delete ckpts
                        False -> lexicographically delete ckpts
  """
    import re

    ckpts_files = [
        f
        for f in os.listdir(path_to_models)
        if os.path.isfile(os.path.join(path_to_models, f))
    ]

    def name_key(_f):
        return int(re.compile("._(\\d+)\\.pth").match(_f).group(1))

    def time_key(_f):
        return os.path.getmtime(os.path.join(path_to_models, _f))

    sort_key = time_key if sort_by_time else name_key

    def x_sorted(_x):
        return sorted(
            [f for f in ckpts_files if f.startswith(_x) and not f.endswith("_0.pth")],
            key=sort_key,
        )

    to_del = [
        os.path.join(path_to_models, fn)
        for fn in (x_sorted("G")[:-n_ckpts_to_keep] + x_sorted("D")[:-n_ckpts_to_keep])
    ]

    def del_info(fn):
        return logger.info(f".. Free up space by deleting ckpt {fn}")

    def del_routine(x):
        return [os.remove(x), del_info(x)]

    [del_routine(fn) for fn in to_del]