import torch
from torch._inductor.select_algorithm import extern_kernels
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._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_eq_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 + x0, xmask)
    tmp1 = 1.0
    tmp2 = tmp0 == tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused__softmax_masked_fill_1(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x1), xmask).to(tl.int1)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + 0)
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK])
    tmp7 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask).to(tl.int1)
    tmp8 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask).to(tl.int1)
    tmp13 = tl.load(in_ptr1 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask).to(tl.int1)
    tmp18 = tl.load(in_ptr1 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tmp1 + tmp3
    tmp5 = float('-inf')
    tmp6 = tl.where(tmp0, tmp5, tmp4)
    tmp9 = tmp8 + tmp3
    tmp10 = tl.where(tmp7, tmp5, tmp9)
    tmp11 = triton_helpers.maximum(tmp6, tmp10)
    tmp14 = tmp13 + tmp3
    tmp15 = tl.where(tmp12, tmp5, tmp14)
    tmp16 = triton_helpers.maximum(tmp11, tmp15)
    tmp19 = tmp18 + tmp3
    tmp20 = tl.where(tmp17, tmp5, tmp19)
    tmp21 = triton_helpers.maximum(tmp16, tmp20)
    tmp22 = tmp6 - tmp21
    tmp23 = tl_math.exp(tmp22)
    tmp24 = tmp10 - tmp21
    tmp25 = tl_math.exp(tmp24)
    tmp26 = tmp23 + tmp25
    tmp27 = tmp15 - tmp21
    tmp28 = tl_math.exp(tmp27)
    tmp29 = tmp26 + tmp28
    tmp30 = tmp20 - tmp21
    tmp31 = tl_math.exp(tmp30)
    tmp32 = tmp29 + tmp31
    tl.store(out_ptr0 + x2, tmp21, xmask)
    tl.store(out_ptr1 + x2, tmp32, xmask)


@triton.jit
def triton_poi_fused__softmax_masked_fill_2(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x4 = xindex % 16
    x0 = xindex % 4
    x2 = xindex // 16
    tmp0 = tl.load(in_ptr0 + x3, xmask).to(tl.int1)
    tmp1 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + 0)
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK])
    tmp7 = tl.load(in_ptr3 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp10 = tl.load(in_ptr4 + (x0 + 4 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tmp1 + tmp3
    tmp5 = float('-inf')
    tmp6 = tl.where(tmp0, tmp5, tmp4)
    tmp8 = tmp6 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tmp11 = tmp9 / tmp10
    tl.store(out_ptr0 + x3, tmp11, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (1, 4), (4, 1))
    assert_size_stride(primals_3, (1,), (1,))
    assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 1), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_eq_0[grid(64)](primals_4, buf1, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_4
        buf2 = empty_strided_cuda((4, 1, 4), (4, 16, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 1, 4), (4, 16, 1), torch.float32)
        triton_poi_fused__softmax_masked_fill_1[grid(16)](buf1, buf0,
            primals_3, buf2, buf3, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_masked_fill_2[grid(64)](buf1, buf0,
            primals_3, buf2, buf3, buf4, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del buf0
        del buf2
        del buf3
        del primals_3
    return buf4, reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), buf1, buf4


class LinearAttentionLayerNew(nn.Module):

    def __init__(self, input_dim):
        super().__init__()
        self.linear = nn.Linear(input_dim, 1)

    def forward(self, input_0, input_1):
        primals_2 = self.linear.weight
        primals_3 = self.linear.bias
        primals_1 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]


Measured latency_ms: 0.0775