import numpy as np
import matplotlib.pyplot as plt
from scipy.io import wavfile
import torch
from torch.nn import functional as F


def repeat_expand_2d(content, target_len):
    # align content with mel

    src_len = content.shape[-1]
    target = torch.zeros([content.shape[0], target_len], dtype=torch.float).to(content.device)
    temp = torch.arange(src_len+1) * target_len / src_len
    current_pos = 0
    for i in range(target_len):
        if i < temp[current_pos+1]:
            target[:, i] = content[:, current_pos]
        else:
            current_pos += 1
            target[:, i] = content[:, current_pos]

    return target


def save_plot(tensor, savepath):
    plt.style.use('default')
    fig, ax = plt.subplots(figsize=(12, 3))
    im = ax.imshow(tensor, aspect="auto", origin="lower", interpolation='none')
    plt.colorbar(im, ax=ax)
    plt.tight_layout()
    fig.canvas.draw()
    plt.savefig(savepath)
    plt.close()


def save_audio(file_path, sampling_rate, audio):
    audio = np.clip(audio.detach().cpu().squeeze().numpy(), -0.999, 0.999)
    wavfile.write(file_path, sampling_rate, (audio * 32767).astype("int16"))


def minmax_norm_diff(tensor: torch.Tensor, vmax: float = 2.5, vmin: float = -12) -> torch.Tensor:
    tensor = torch.clip(tensor, vmin, vmax)
    tensor = 2 * (tensor - vmin) / (vmax - vmin) - 1
    return tensor


def reverse_minmax_norm_diff(tensor: torch.Tensor, vmax: float = 2.5, vmin: float = -12) -> torch.Tensor:
    tensor = torch.clip(tensor, -1.0, 1.0)
    tensor = (tensor + 1) / 2
    tensor = tensor * (vmax - vmin) + vmin
    return tensor