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


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


def scale_shift(x, scale, shift):
    return (x+shift) * scale


def scale_shift_re(x, scale, shift):
    return (x/scale) - shift


def align_seq(source, target_length, mapping_method='hard'):
    source_len = source.shape[1]
    if mapping_method == 'hard':
        mapping_idx = np.round(np.arange(target_length) * source_len / target_length)
        output = source[:, mapping_idx]
    else:
        # TBD
        raise NotImplementedError

    return output