from typing import Callable
import torch
import pytest

from aiter.ops.tilelang import mhc_post_fwd as mhc_post


def generate_mhc_post_test_data(
    num_tokens: int,
    h: int,
    mhc_mult: int,
    device: str = 'cuda',
) -> dict[str, torch.Tensor]:
    x = torch.randn((num_tokens, h), dtype=torch.bfloat16, device=device)
    residual = torch.randn((num_tokens, mhc_mult, h), dtype=torch.bfloat16, device=device)
    post_layer_mix = torch.randn((num_tokens, mhc_mult), dtype=torch.float32, device=device)
    comb_res_mix = torch.randn((num_tokens, mhc_mult, mhc_mult), dtype=torch.float32, device=device)

    o_grad = torch.randn((num_tokens, mhc_mult, h), dtype=torch.bfloat16, device=device)

    return {
        'x': x,
        'residual': residual,
        'post_layer_mix': post_layer_mix,
        'comb_res_mix': comb_res_mix,
        'o_grad': o_grad,
    }


def _tester(
    impl: Callable[[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor], torch.Tensor],
    test_data: dict[str, torch.Tensor],
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    x_ = test_data['x'].clone().requires_grad_()
    residual_ = test_data['residual'].clone().requires_grad_()
    post_layer_mix_ = test_data['post_layer_mix'].clone().requires_grad_()
    comb_res_mix_ = test_data['comb_res_mix'].clone().requires_grad_()
    out_ = impl(x_, residual_, post_layer_mix_, comb_res_mix_)
    torch.autograd.backward([out_], [test_data['o_grad']])
    return out_, x_.grad, residual_.grad, post_layer_mix_.grad, comb_res_mix_.grad


def _estimate_fwd_io_bytes(num_tokens: int, h: int, mhc_mult: int) -> int:
    n = num_tokens
    read_bytes = (
        n * h * 2
        + n * mhc_mult * h * 2
        + n * mhc_mult * 4
        + n * mhc_mult * mhc_mult * 4
    )
    write_bytes = n * mhc_mult * h * 2
    return read_bytes + write_bytes


def _estimate_bwd_io_bytes(num_tokens: int, h: int, mhc_mult: int) -> int:
    n = num_tokens
    read_bytes = (
        n * mhc_mult * h * 2
        + n * h * 2
        + n * mhc_mult * h * 2
        + n * mhc_mult * 4
        + n * mhc_mult * mhc_mult * 4
    )
    write_bytes = n * h * 2 + n * mhc_mult * h * 2 + n * mhc_mult * 4 + n * mhc_mult * mhc_mult * 4
    return read_bytes + write_bytes


@pytest.mark.skipif(not torch.cuda.is_available(), reason='CUDA is required')
@pytest.mark.benchmark
@pytest.mark.parametrize(
    'num_tokens,h,mhc_mult',
    [
        # (1, 4096, 1280, 4),
        # (1, 4096, 2560, 4),
        # (1, 4096, 7168, 4),
        # (2, 4096, 2560, 4),
        
        # h = 7168
        (1, 7168, 4),
        (32, 7168, 4),
        (64, 7168, 4),
        (128, 7168, 4),
        (256, 7168, 4),
        (512, 7168, 4),
        (1024, 7168, 4),
        (2048, 7168, 4),
        (8192, 7168, 4),
        (65536, 7168, 4),
        
        # h = 4096
        (1, 4096, 4),
        (32, 4096, 4),
        (64, 4096, 4),
        (128, 4096, 4),
        (256, 4096, 4),
        (512, 4096, 4),
        (1024, 4096, 4),
        (2048, 4096, 4),
        (8192, 4096, 4),
        (65536, 4096, 4),
        
    ],
)
def test_mhc_post_fwd_benchmark(
    num_tokens: int,
    h: int,
    mhc_mult: int,
    benchmark_timer,
    benchmark_record,
) -> None:
    test_data = generate_mhc_post_test_data(num_tokens=num_tokens, h=h, mhc_mult=mhc_mult)
    x = test_data['x']
    residual = test_data['residual']
    post_layer_mix = test_data['post_layer_mix']
    comb_res_mix = test_data['comb_res_mix']

    def fn_fwd() -> torch.Tensor:
        return mhc_post(x, residual, post_layer_mix, comb_res_mix)

    fn_fwd()
    t_tl_us = benchmark_timer(fn_fwd)
    io_bytes = _estimate_fwd_io_bytes(num_tokens, h, mhc_mult)
    bw_tl_gbs = io_bytes / t_tl_us / 1e3

    benchmark_record(
        kernel='mhc_post',
        operation='fwd',
        params={'num_tokens': num_tokens, 'h': h, 'mhc_mult': mhc_mult},
        time_us=t_tl_us,
        bandwidth_gbs=bw_tl_gbs,
        extras={'num_tokens': num_tokens, 'io_bytes': io_bytes},
    )