import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import Tensor
from torch import nn
from torch.jit import Final
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_add_mul_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (192 + x0), xmask)
    tmp3 = tl.load(in_ptr0 + (128 + x0), xmask)
    tmp5 = tl.load(in_ptr0 + (64 + x0), xmask)
    tmp7 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = -3.0
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp1
    tmp6 = tmp5 * tmp1
    tmp8 = tmp7 * tmp1
    tmp9 = 4.0
    tmp10 = tmp7 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = tmp11 * tmp9
    tmp13 = tmp6 + tmp12
    tmp14 = tmp13 * tmp9
    tmp15 = tmp4 + tmp14
    tmp16 = tmp15 * tmp9
    tmp17 = tmp2 + tmp16
    tl.store(out_ptr0 + x0, tmp17, xmask)


@triton.jit
def triton_poi_fused_add_mul_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 64
    x0 = xindex % 64
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (128 + x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + (64 + x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp24 = tl.load(in_ptr2 + x2, xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 3, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp4 = tl.full([1], 2, tl.int32)
    tmp5 = tmp0 == tmp4
    tmp7 = -3.0
    tmp8 = tmp6 * tmp7
    tmp10 = tmp9 * tmp7
    tmp12 = tmp11 * tmp7
    tmp13 = 4.0
    tmp14 = tmp11 * tmp13
    tmp15 = tmp12 + tmp14
    tmp16 = tmp15 * tmp13
    tmp17 = tmp10 + tmp16
    tmp18 = tmp17 * tmp13
    tmp19 = tmp8 + tmp18
    tmp20 = tl.full([1], 1, tl.int32)
    tmp21 = tmp0 == tmp20
    tmp22 = tl.full([1], 0, tl.int32)
    tmp23 = tmp0 == tmp22
    tmp25 = tl.where(tmp23, tmp15, tmp24)
    tmp26 = tl.where(tmp21, tmp17, tmp25)
    tmp27 = tl.where(tmp5, tmp19, tmp26)
    tmp28 = tl.where(tmp2, tmp3, tmp27)
    tl.store(out_ptr0 + x2, tmp28, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mul_0[grid(64)](arg0_1, buf1, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_mul_1[grid(256)](buf1, arg0_1, buf0, buf2, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
        del buf0
    return buf2, buf1


class ExponentialUpdate(nn.Module):
    alpha: 'Final[int]'

    def __init__(self, alpha: 'float'):
        super().__init__()
        self.alpha = float(alpha)

    def forward(self, x: 'Tensor', state: 'Tensor') ->Tensor:
        return x * (1 - self.alpha) + state * self.alpha


class ExponentialDecayNew(nn.Module):

    def __init__(self, alpha: 'float'):
        super().__init__()
        self.update_rule = ExponentialUpdate(alpha)

    def forward(self, input_0):
        arg0_1 = input_0
        output = call([arg0_1])
        return output[0], output[1]


Measured latency_ms: 0.028616