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
import torch.nn as nn
import torch.nn.parallel
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_clone_transpose_0(in_ptr0, out_ptr0, out_ptr1, ynumel,
    xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x1 = xindex
    y0 = yindex
    y2 = yindex % 4
    y3 = yindex // 4
    tmp0 = tl.load(in_ptr0 + (x1 + 16 * y0), xmask & ymask)
    tl.store(out_ptr0 + (x1 + 16 * y0), tmp0, xmask & ymask)
    tl.store(out_ptr1 + (y2 + 4 * x1 + 64 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused_clone_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 64 * y1), xmask & ymask)
    tmp1 = tl.load(in_ptr0 + (4 * x2 + 64 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x2 + 64 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x2 + 64 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x2 + 64 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + (x2 + 16 * y3), tmp8, xmask & ymask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 1, 1), (4, 1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(reinterpret_tensor(primals_2, (4,
            4, 4, 1), (16, 4, 1, 1), 0), primals_3, stride=(1, 1), padding=
            (0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0
            ), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 1), (16, 4, 1, 1))
        buf1 = empty_strided_cuda((4, 16, 4), (64, 1, 16), torch.float32)
        buf6 = empty_strided_cuda((4, 4, 16), (64, 1, 4), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_transpose_0[grid(16, 16)](primals_1, buf1,
            buf6, 16, 16, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
        del primals_1
        buf2 = empty_strided_cuda((4, 16, 4), (64, 4, 1), torch.float32)
        extern_kernels.bmm(buf1, reinterpret_tensor(buf0, (4, 4, 4), (16, 4,
            1), 0), out=buf2)
        buf3 = reinterpret_tensor(buf1, (64, 4), (4, 1), 0)
        del buf1
        triton_poi_fused__softmax_1[grid(256)](buf2, buf3, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf4 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
        triton_poi_fused_clone_2[grid(16, 16)](buf3, buf4, 16, 16, XBLOCK=
            16, YBLOCK=16, num_warps=4, num_stages=1)
        buf5 = reinterpret_tensor(buf3, (4, 4, 16), (64, 16, 1), 0)
        del buf3
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 
            0), buf4, out=buf5)
    return reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), primals_3, reinterpret_tensor(primals_2, (4, 4, 4, 1), (16, 4, 1,
        1), 0), buf2, reinterpret_tensor(buf0, (4, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf4, (4, 16, 4), (64, 1, 16), 0), buf6


def conv1x1(in_planes, out_planes, bias=False):
    """1x1 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1,
        padding=0, bias=bias)


class GlobalAttentionGeneralNew(nn.Module):

    def __init__(self, idf, cdf):
        super(GlobalAttentionGeneralNew, self).__init__()
        self.conv_context = conv1x1(cdf, idf)
        self.sm = nn.Softmax(dim=1)
        self.mask = None

    def applyMask(self, mask):
        self.mask = mask

    def forward(self, input_0, input_1):
        primals_3 = self.conv_context.weight
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3])
        return output[0], output[1]


Measured latency_ms: 0.073594