import torch

from benchmark_model_configs import MODEL_REGISTRY
from benchmark_model_configs import build_model_config_sweep
from benchmark_model_configs import build_token_length_sweep
from benchmark_model_configs import get_benchmark_model_config
from utils import SingleBenchmarkRunInput
from utils import build_memory_bench_fn
from utils import build_speed_bench_fn
from utils import parse_benchmark_script_args
from utils import run_benchmarks

from liger_kernel.transformers.jsd import LigerJSD
from liger_kernel.utils import infer_device

device = infer_device()


class TorchJSD(torch.nn.Module):
    def __init__(
        self,
        beta: float = 0.5,
        ignore_index: int = -100,
        dtype: torch.dtype = torch.float,
    ):
        super(TorchJSD, self).__init__()
        self.kl = torch.nn.KLDivLoss(reduction="none", log_target=True)
        self.beta = beta
        self.ignore_index = ignore_index
        self.dtype = dtype

    def forward(
        self,
        log_q: torch.Tensor,  # input
        log_p: torch.Tensor,  # target
        label=None,
    ):
        log_p, log_q = log_p.to(torch.float), log_q.to(torch.float)
        log_p, log_q = log_p.view(-1, log_p.size(-1)), log_q.view(-1, log_q.size(-1))
        m = torch.lerp(torch.exp(log_q), torch.exp(log_p), self.beta)
        loss = self.beta * self.kl(torch.log(m), log_p).sum(dim=-1) + (1 - self.beta) * self.kl(
            torch.log(m), log_q
        ).sum(dim=-1)

        if label is not None:
            loss = torch.where(label != self.ignore_index, loss, 0.0)
            n_non_ignore = (label != self.ignore_index).sum().item()
            if n_non_ignore == 0:
                loss = 0.0
            else:
                loss = (loss / n_non_ignore).sum()
        else:
            loss = (loss / log_q.shape[0]).sum()
        return loss.to(self.dtype)


def setup_jsd(input: SingleBenchmarkRunInput):
    """Create input tensors and JSD loss from benchmark config."""
    cfg = input.extra_benchmark_config
    if isinstance(input.x, str):
        model_cfg = MODEL_REGISTRY[input.x]
        BT = cfg["seq_len"] * cfg["bsz"]
        V = model_cfg.vocab_size
    else:
        BT = input.x
        V = cfg["vocab_size"]

    _input = torch.randn(BT, V, requires_grad=True, device=device).log_softmax(dim=-1)
    target = torch.randn(BT, V, device=device).log_softmax(dim=-1)

    if input.kernel_provider == "liger":
        loss_fn = LigerJSD()
    elif input.kernel_provider == "torch":
        loss_fn = TorchJSD()
    else:
        raise ValueError(f"Invalid provider: {input.kernel_provider} for JSD")
    return _input, lambda _: loss_fn(_input, target)


if __name__ == "__main__":
    args = parse_benchmark_script_args()

    if args.sweep_mode == "model_config":
        common_configs = build_model_config_sweep(
            kernel_name="jsd",
            setup_fn=setup_jsd,
            model_keys=["vocab_size"],
            probe_provider="torch",
            probe_dim="BT",
            bt=args.bt,
            overwrite=args.overwrite,
        )
    else:
        model = get_benchmark_model_config(args.model)
        probe_seq_len = 1024

        common_configs = build_token_length_sweep(
            kernel_name="jsd",
            probe_x=probe_seq_len,
            model=model,
            setup_fn=setup_jsd,
            model_keys=["vocab_size"],
            scale_dim="BT",
            x_label="total tokens",
            probe_provider="torch",
            overwrite=args.overwrite,
        )

    common_configs["kernel_providers"] = ["torch", "liger"]

    run_benchmarks(
        bench_test_fn=build_speed_bench_fn(setup_jsd),
        kernel_operation_modes=["full", "forward", "backward"],
        metric_name="speed",
        metric_unit="ms",
        **common_configs,
    )
    run_benchmarks(
        bench_test_fn=build_memory_bench_fn(setup_jsd),
        kernel_operation_modes=["full"],
        metric_name="memory",
        metric_unit="MB",
        **common_configs,
    )
