import torch



def init_weights(m, mean=0.0, std=0.01):
    if m.__class__.__name__.find("Conv") != -1: m.weight.data.normal_(mean, std)

def get_padding(kernel_size, dilation=1):
    return int((kernel_size * dilation - dilation) / 2)

def convert_pad_shape(pad_shape):
    return [item for sublist in pad_shape[::-1] for item in sublist]

def slice_segments(x, ids_str, segment_size = 4, dim = 2):
    if dim == 2: ret = torch.zeros_like(x[:, :segment_size])
    elif dim == 3: ret = torch.zeros_like(x[:, :, :segment_size])

    for i in range(x.size(0)):
        idx_str = ids_str[i].item()
        idx_end = idx_str + segment_size

        if dim == 2: ret[i] = x[i, idx_str:idx_end]
        else: ret[i] = x[i, :, idx_str:idx_end]

    return ret

def rand_slice_segments(x, x_lengths=None, segment_size=4):
    b, _, t = x.size()
    if x_lengths is None: x_lengths = t

    ids_str = (torch.rand([b]).to(device=x.device) * (x_lengths - segment_size + 1)).to(dtype=torch.long)

    return slice_segments(x, ids_str, segment_size, dim=3), ids_str

@torch.jit.script
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
    n_channels_int = n_channels[0]
    
    in_act = input_a + input_b

    return torch.tanh(in_act[:, :n_channels_int, :]) * torch.sigmoid(in_act[:, n_channels_int:, :])

def sequence_mask(length, max_length = None):
    if max_length is None: max_length = length.max()

    return torch.arange(max_length, dtype=length.dtype, device=length.device).unsqueeze(0) < length.unsqueeze(1)

def clip_grad_value(parameters, clip_value, norm_type=2):
    if isinstance(parameters, torch.Tensor): parameters = [parameters]
    norm_type = float(norm_type)

    if clip_value is not None: clip_value = float(clip_value)
    total_norm = 0

    for p in list(filter(lambda p: p.grad is not None, parameters)):
        total_norm += (p.grad.data.norm(norm_type)).item() ** norm_type

        if clip_value is not None: p.grad.data.clamp_(min=-clip_value, max=clip_value)

    return total_norm ** (1.0 / norm_type)