import os
import glob
import json
import torch
import logging
import argparse
import requests
import subprocess
import numpy as np
from tqdm import tqdm
from scipy.io.wavfile import read


MATPLOTLIB_FLAG = False

logger = logging.getLogger(__name__)


def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=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
        and not skip_optimizer
        and checkpoint_dict["optimizer"] is not None
    ):
        optimizer.load_state_dict(checkpoint_dict["optimizer"])

    elif optimizer is None and not skip_optimizer:
        # else:      Disable this line if Infer and resume checkpoint,then enable the line upper
        new_opt_dict = optimizer.state_dict()
        new_opt_dict_params = new_opt_dict["param_groups"][0]["params"]
        new_opt_dict["param_groups"] = checkpoint_dict["optimizer"]["param_groups"]
        new_opt_dict["param_groups"][0]["params"] = new_opt_dict_params
        optimizer.load_state_dict(new_opt_dict)

    saved_state_dict = checkpoint_dict["model"]
    if hasattr(model, "module"):
        state_dict = model.module.state_dict()

    else:
        state_dict = model.state_dict()

    new_state_dict = {}
    for k, v in state_dict.items():
        try:
            # assert "emb_g" not in k
            # print("load", k)
            new_state_dict[k] = saved_state_dict[k]
            assert saved_state_dict[k].shape == v.shape, (
                saved_state_dict[k].shape,
                v.shape,
            )

        except:
            logger.error("%s is not in the checkpoint" % k)
            new_state_dict[k] = v

    if hasattr(model, "module"):
        model.module.load_state_dict(new_state_dict, strict=False)

    else:
        model.load_state_dict(new_state_dict, strict=False)

    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("./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 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))
    ]
    name_key = lambda _f: int(re.compile("._(\d+)\.pth").match(_f).group(1))
    time_key = lambda _f: os.path.getmtime(os.path.join(path_to_models, _f))
    sort_key = time_key if sort_by_time else name_key
    x_sorted = lambda _x: 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])
    ]
    del_info = lambda fn: logger.info(f".. Free up space by deleting ckpt {fn}")
    del_routine = lambda x: [os.remove(x), del_info(x)]
    rs = [del_routine(fn) for fn in to_del]
    print(rs)


def get_hparams_from_dir(model_dir):
    config_save_path = os.path.join(model_dir, "config.json")
    with open(config_save_path, "r", encoding="utf-8") as f:
        data = f.read()

    config = json.loads(data)
    hparams = HParams(**config)
    hparams.model_dir = model_dir
    return hparams


def download_file(file_url: str):
    filename = file_url.split("&FilePath=")[-1]
    if os.path.exists(filename):
        return filename

    response = requests.get(file_url, stream=True)
    # 检查请求是否成功
    if response.status_code == 200:
        # 获取文件总大小
        file_size = int(response.headers.get("Content-Length", 0))
        # 打开文件以写入二进制数据
        with open(filename, "wb") as file:
            # 创建进度条
            progress_bar = tqdm(
                total=file_size,
                unit="B",
                unit_scale=True,
                desc=f"Downloading {filename}...",
            )
            # 以块的形式下载文件
            for chunk in response.iter_content(chunk_size=8192):
                if chunk:  # 过滤掉保持连接的新块
                    file.write(chunk)
                    progress_bar.update(len(chunk))  # 更新进度条

            progress_bar.close()  # 关闭进度条

        print(f"模型文件 '{file_url}' 下载成功。")

    else:
        print(f"下载失败，状态码：{response.status_code}")

    return filename


def get_hparams_from_url(config_url):
    response = requests.get(config_url)
    config = response.json()
    return HParams(**config)


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__()