import hipdnn
import torch


def build_bn_finalize_graph(
    hipdnn_handle,
    torch_tensor_sum,
    torch_tensor_sq_sum,
    torch_tensor_scale,
    torch_tensor_bias,
    torch_tensor_prev_running_mean,
    torch_tensor_prev_running_variance,
    torch_tensor_momentum,
    torch_tensor_epsilon,
    torch_tensor_accum_count,
    hipdnn_data_type,
):
    graph = hipdnn.pygraph(
        handle=hipdnn_handle,
        io_data_type=hipdnn_data_type,
        intermediate_data_type=hipdnn.data_type.FLOAT,
        compute_data_type=hipdnn.data_type.FLOAT,
        name="bn_finalize",
    )

    hipdnn_tensor_sum = graph.tensor_like(torch_tensor_sum)
    hipdnn_tensor_sq_sum = graph.tensor_like(torch_tensor_sq_sum)
    hipdnn_tensor_scale = graph.tensor_like(torch_tensor_scale)
    hipdnn_tensor_bias = graph.tensor_like(torch_tensor_bias)
    hipdnn_tensor_prev_running_mean = graph.tensor_like(torch_tensor_prev_running_mean)
    hipdnn_tensor_prev_running_variance = graph.tensor_like(torch_tensor_prev_running_variance)
    hipdnn_tensor_momentum = graph.tensor_like(torch_tensor_momentum)
    hipdnn_tensor_momentum.set_value(0.001)
    hipdnn_tensor_epsilon = graph.tensor_like(torch_tensor_epsilon)
    hipdnn_tensor_epsilon.set_value(1e-5)
    hipdnn_tensor_accum_count = graph.tensor_like(torch_tensor_accum_count)
    hipdnn_tensor_accum_count.set_value(torch_tensor_accum_count.item())

    (
        hipdnn_tensor_eq_scale,
        hipdnn_tensor_eq_bias,
        hipdnn_tensor_mean,
        hipdnn_tensor_inv_variance,
        hipdnn_tensor_next_running_mean,
        hipdnn_tensor_next_running_variance,
    ) = graph.bn_finalize(
        sum=hipdnn_tensor_sum,
        sq_sum=hipdnn_tensor_sq_sum,
        scale=hipdnn_tensor_scale,
        bias=hipdnn_tensor_bias,
        epsilon=hipdnn_tensor_epsilon,
        accum_count=hipdnn_tensor_accum_count,
        prev_running_mean=hipdnn_tensor_prev_running_mean,
        prev_running_variance=hipdnn_tensor_prev_running_variance,
        momentum=hipdnn_tensor_momentum,
        name="bn_finalize_node",
    )
    hipdnn_tensor_eq_scale.set_output(True)
    hipdnn_tensor_eq_bias.set_output(True)
    hipdnn_tensor_mean.set_output(True)
    hipdnn_tensor_inv_variance.set_output(True)
    hipdnn_tensor_next_running_mean.set_output(True)
    hipdnn_tensor_next_running_variance.set_output(True)

    graph.build(hipdnn_handle)

    return (
        graph,
        hipdnn_tensor_sum,
        hipdnn_tensor_sq_sum,
        hipdnn_tensor_scale,
        hipdnn_tensor_bias,
        hipdnn_tensor_prev_running_mean,
        hipdnn_tensor_prev_running_variance,
        hipdnn_tensor_momentum,
        hipdnn_tensor_epsilon,
        hipdnn_tensor_accum_count,
        hipdnn_tensor_eq_scale,
        hipdnn_tensor_eq_bias,
        hipdnn_tensor_mean,
        hipdnn_tensor_inv_variance,
        hipdnn_tensor_next_running_mean,
        hipdnn_tensor_next_running_variance,
    )


if __name__ == "__main__":
    n = 1
    c = 32
    h = 1
    w = 1

    hipdnn_data_type = hipdnn.data_type.FLOAT
    torch_data_type = torch.float32

    torch_tensor_sum = torch.rand(n, c, h, w, dtype=torch_data_type, device="cuda")
    torch_tensor_sq_sum = torch.rand(n, c, h, w, dtype=torch_data_type, device="cuda")
    torch_tensor_scale = torch.rand(n, c, h, w, dtype=torch_data_type, device="cuda")
    torch_tensor_bias = torch.rand(n, c, h, w, dtype=torch_data_type, device="cuda")
    torch_tensor_prev_running_mean = torch.rand(n, c, h, w, dtype=torch_data_type, device="cuda")
    torch_tensor_prev_running_variance = torch.rand(
        n, c, h, w, dtype=torch_data_type, device="cuda"
    )
    torch_tensor_momentum = torch.full(
        (1, 1, 1, 1), 0.001, dtype=torch.float32, requires_grad=False, device="cuda"
    )
    torch_tensor_epsilon = torch.full(
        (1, 1, 1, 1), 1e-5, dtype=torch.float32, requires_grad=False, device="cuda"
    )
    torch_tensor_accum_count = torch.full(
        (1, 1, 1, 1), n * h * w, dtype=torch.int32, requires_grad=False, device="cuda"
    )

    hipdnn_handle = hipdnn.create_handle()

    (
        graph,
        hipdnn_tensor_sum,
        hipdnn_tensor_sq_sum,
        hipdnn_tensor_scale,
        hipdnn_tensor_bias,
        hipdnn_tensor_prev_running_mean,
        hipdnn_tensor_prev_running_variance,
        hipdnn_tensor_momentum,
        hipdnn_tensor_epsilon,
        hipdnn_tensor_accum_count,
        hipdnn_tensor_eq_scale,
        hipdnn_tensor_eq_bias,
        hipdnn_tensor_mean,
        hipdnn_tensor_inv_variance,
        hipdnn_tensor_next_running_mean,
        hipdnn_tensor_next_running_variance,
    ) = build_bn_finalize_graph(
        hipdnn_handle,
        torch_tensor_sum,
        torch_tensor_sq_sum,
        torch_tensor_scale,
        torch_tensor_bias,
        torch_tensor_prev_running_mean,
        torch_tensor_prev_running_variance,
        torch_tensor_momentum,
        torch_tensor_epsilon,
        torch_tensor_accum_count,
        hipdnn_data_type,
    )

    torch_tensor_eq_scale = torch.empty(
        hipdnn_tensor_eq_scale.get_dim(), dtype=torch_data_type, device="cuda"
    )
    torch_tensor_eq_bias = torch.empty(
        hipdnn_tensor_eq_bias.get_dim(), dtype=torch_data_type, device="cuda"
    )
    torch_tensor_mean = torch.empty(
        hipdnn_tensor_mean.get_dim(), dtype=torch_data_type, device="cuda"
    )
    torch_tensor_inv_variance = torch.empty(
        hipdnn_tensor_inv_variance.get_dim(), dtype=torch_data_type, device="cuda"
    )
    torch_tensor_next_running_mean = torch.empty(
        hipdnn_tensor_next_running_mean.get_dim(), dtype=torch_data_type, device="cuda"
    )
    torch_tensor_next_running_variance = torch.empty(
        hipdnn_tensor_next_running_variance.get_dim(), dtype=torch_data_type, device="cuda"
    )

    variant_pack = {
        hipdnn_tensor_sum: torch_tensor_sum.data_ptr(),
        hipdnn_tensor_sq_sum: torch_tensor_sq_sum.data_ptr(),
        hipdnn_tensor_scale: torch_tensor_scale.data_ptr(),
        hipdnn_tensor_bias: torch_tensor_bias.data_ptr(),
        hipdnn_tensor_prev_running_mean: torch_tensor_prev_running_mean.data_ptr(),
        hipdnn_tensor_prev_running_variance: torch_tensor_prev_running_variance.data_ptr(),
        hipdnn_tensor_momentum: torch_tensor_momentum.data_ptr(),
        hipdnn_tensor_epsilon: torch_tensor_epsilon.data_ptr(),
        hipdnn_tensor_accum_count: torch_tensor_accum_count.data_ptr(),
        hipdnn_tensor_eq_scale: torch_tensor_eq_scale.data_ptr(),
        hipdnn_tensor_eq_bias: torch_tensor_eq_bias.data_ptr(),
        hipdnn_tensor_mean: torch_tensor_mean.data_ptr(),
        hipdnn_tensor_inv_variance: torch_tensor_inv_variance.data_ptr(),
        hipdnn_tensor_next_running_mean: torch_tensor_next_running_mean.data_ptr(),
        hipdnn_tensor_next_running_variance: torch_tensor_next_running_variance.data_ptr(),
    }
    workspace = torch.empty(graph.get_workspace_size(), dtype=torch.uint8, device="cuda")

    graph.exec(variant_pack=variant_pack, workspace=workspace.data_ptr())
    print("Batch normalization finalize graph execution complete.")