import glob
import logging
import os
import re
import subprocess

import numpy as np
import torch
from scipy.io.wavfile import read

from style_bert_vits2.logging import logger
from style_bert_vits2.models.utils import checkpoints  # type: ignore
from style_bert_vits2.models.utils import safetensors  # type: ignore


MATPLOTLIB_FLAG = False


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 is_resuming(dir_path):
    # JP-ExtraバージョンではDURがなくWDがあったり変わるため、Gのみで判断する
    g_list = glob.glob(os.path.join(dir_path, "G_*.pth"))
    # d_list = glob.glob(os.path.join(dir_path, "D_*.pth"))
    # dur_list = glob.glob(os.path.join(dir_path, "DUR_*.pth"))
    return len(g_list) > 0


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_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


def get_steps(model_path):
    matches = re.findall(r"\d+", model_path)
    return matches[-1] if matches else None


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.warning(
            "{} 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.warning(
                "git hash values are different. {}(saved) != {}(current)".format(
                    saved_hash[:8], cur_hash[:8]
                )
            )
    else:
        open(path, "w").write(cur_hash)