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
import torch.nn as nn
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_mul_repeat_0(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x0 = xindex % 4096
    x2 = xindex // 16384
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + (x0 + 4096 * x2), None, eviction_policy=
        'evict_last')
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x3, tmp2, None)


@triton.jit
def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 4096 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_2(in_out_ptr0,
    in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 4096 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x3, tmp4, None)
    tl.store(out_ptr0 + x3, tmp6, None)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(primals_2, (4, 4, 64, 64), (16384, 4096, 64, 1))
    assert_size_stride(primals_3, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_6, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 64, 64), (16384, 4096, 64, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mul_repeat_0[grid(65536)](primals_2, primals_1,
            buf0, 65536, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_1
        del primals_2
        buf1 = extern_kernels.convolution(buf0, primals_3, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 64, 64), (16384, 4096, 64, 1))
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_relu_1[grid(65536)](buf2, primals_4, 
            65536, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_4
        buf3 = extern_kernels.convolution(buf2, primals_5, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 64, 64), (16384, 4096, 64, 1))
        buf4 = buf3
        del buf3
        buf5 = empty_strided_cuda((4, 4, 64, 64), (16384, 4096, 64, 1),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_2[grid(65536)](
            buf4, primals_6, buf5, 65536, XBLOCK=512, num_warps=4, num_stages=1
            )
        del primals_6
    return buf4, primals_3, primals_5, buf0, buf2, buf5


class QueryModuleNew(nn.Module):
    """ A neural module that takes as input a feature map and an attention and produces a feature
    map as output.

    Extended Summary
    ----------------
    A :class:`QueryModule` takes a feature map and an attention mask as input. It attends to the
    feature map via an elementwise multiplication with the attention mask, then processes this
    attended feature map via a series of convolutions to extract relevant information.

    For example, a :class:`QueryModule` tasked with determining the color of objects would output a
    feature map encoding what color the attended object is. A module intended to count would output
    a feature map encoding the number of attended objects in the scene.

    Attributes
    ----------
    dim : int
        The number of channels of each convolutional filter.
    """

    def __init__(self, dim):
        super().__init__()
        self.conv1 = nn.Conv2d(dim, dim, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(dim, dim, kernel_size=3, padding=1)
        torch.nn.init.kaiming_normal_(self.conv1.weight)
        torch.nn.init.kaiming_normal_(self.conv2.weight)
        self.dim = dim

    def forward(self, input_0, input_1):
        primals_3 = self.conv1.weight
        primals_4 = self.conv1.bias
        primals_5 = self.conv2.weight
        primals_6 = self.conv2.bias
        primals_2 = input_0
        primals_1 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]


Measured latency_ms: 0.015332