import torch
import torch.nn.functional as F

from .utils import nearest_power_of_two
from flashfftconv import FlashFFTConv


def convolve(u: torch.Tensor, v: torch.Tensor, n: int, use_approx: bool = True) -> tuple[torch.Tensor, torch.Tensor]:
    bsz, seq_len, d_in = u.shape

    sgn = torch.full((1, seq_len, 1), 1, device=u.device, dtype=torch.float32)
    sgn[:, 1::2] *= -1

    # Cast u and v to float32 for FFT
    u = u.to(torch.float32)
    v = v.to(torch.float32)

    if use_approx:
        _, d_out = v.shape
        v = v.view(1, -1, d_out, 1)
    else:
        _, K = v.shape
        sgn = sgn.unsqueeze(-1)
        v = v.view(1, -1, K, 1, 1)
        u = u.view(bsz, -1, 1, d_in).expand(bsz, -1, K, d_in)

    v = torch.fft.rfft(v, n=n, dim=1)
    U = torch.stack([u, u * sgn], dim=-1)
    U = torch.fft.rfft(U, n=n, dim=1)
    U_conv = torch.fft.irfft(v * U, n=n, dim=1)[:, :seq_len]
    U_plus, U_minus = torch.unbind(U_conv, dim=-1)
    U_minus = U_minus * sgn

    # Convert back to original dtype
    U_plus = U_plus.to(u.dtype)
    U_minus = U_minus.to(u.dtype)

    return U_plus, U_minus

def flash_convolve(
    u: torch.Tensor, v: torch.Tensor, flash_fft: FlashFFTConv, use_approx: bool = True,
) -> tuple[torch.Tensor, torch.Tensor]:
    dtype = u.dtype  # Store the original dtype
    u = u.to(torch.float32)
    v = v.to(torch.float32)

    bsz, seq_len, d_in = u.shape
    _, K = v.shape

    padded_len = nearest_power_of_two(seq_len, round_up=True)
    pad_len = padded_len - seq_len

    sgn = torch.full((1, 1, padded_len), 1, device=u.device, dtype=torch.float32)
    sgn[:, :, 1::2] = -1

    if use_approx:
        u_padded = F.pad(u.transpose(1, 2), (0, pad_len)).contiguous()
        v_padded = F.pad(v.transpose(0, 1), (0, pad_len)).contiguous()
        u_conv = torch.stack([u_padded, u_padded * sgn], dim=0).reshape(2 * bsz, d_in, padded_len)
    else:
        u_k_padded = F.pad(u.transpose(1, 2), (0, pad_len)).repeat_interleave(K, dim=1).contiguous()
        v_padded = F.pad(v.transpose(0, 1), (0, pad_len)).repeat(d_in, 1).contiguous()
        u_conv = torch.stack([u_k_padded, u_k_padded * sgn], dim=0).reshape(2 * bsz, K * d_in, padded_len)

    U_conv = flash_fft(u_conv, v_padded)

    # Trim the output back to the original sequence length
    U_conv = U_conv[..., :seq_len]

    u_plus, u_minus = torch.chunk(U_conv, 2, dim=0)

    if use_approx:
        u_minus = u_minus * sgn[:, :, :seq_len]
        U_plus, U_minus = u_plus.transpose(1, 2), u_minus.transpose(1, 2)
    else:
        sgn = sgn[:, :, :seq_len].unsqueeze(-1).transpose(1, 2)
        U_plus = u_plus.view(bsz, d_in, K, seq_len).permute(0, 3, 2, 1).contiguous()
        U_minus = u_minus.view(bsz, d_in, K, seq_len).permute(0, 3, 2, 1).contiguous() * sgn

    # Convert back to original dtype
    U_plus = U_plus.to(dtype)
    U_minus = U_minus.to(dtype)

    return U_plus, U_minus