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.utils.data
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_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 784
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 7 % 7
    x0 = xindex % 7
    x2 = xindex // 49
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 4, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = x0
    tmp4 = tmp3 < tmp1
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + (x0 + 4 * x1 + 16 * x2), tmp5 & xmask, other=0.0)
    tl.store(out_ptr0 + x3, tmp6, xmask)


@triton.jit
def triton_poi_fused_convolution_max_relu_1(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp6 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp7 = tl.load(in_ptr1 + 1)
    tmp8 = tl.broadcast_to(tmp7, [XBLOCK])
    tmp26 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp27 = tl.load(in_ptr1 + 2)
    tmp28 = tl.broadcast_to(tmp27, [XBLOCK])
    tmp45 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp46 = tl.load(in_ptr1 + 3)
    tmp47 = tl.broadcast_to(tmp46, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tl.full([1], 0, tl.int32)
    tmp5 = triton_helpers.maximum(tmp4, tmp3)
    tmp9 = tmp6 + tmp8
    tmp10 = triton_helpers.maximum(tmp4, tmp9)
    tmp11 = tmp5 > tmp10
    tmp12 = tmp5 == tmp10
    tmp13 = tmp5 != tmp5
    tmp14 = tmp10 != tmp10
    tmp15 = tmp13 > tmp14
    tmp16 = tmp11 | tmp15
    tmp17 = tmp13 & tmp14
    tmp18 = tmp12 | tmp17
    tmp19 = tl.full([1], 0, tl.int64)
    tmp20 = tl.full([1], 1, tl.int64)
    tmp21 = tmp19 < tmp20
    tmp22 = tmp18 & tmp21
    tmp23 = tmp16 | tmp22
    tmp24 = tl.where(tmp23, tmp5, tmp10)
    tmp25 = tl.where(tmp23, tmp19, tmp20)
    tmp29 = tmp26 + tmp28
    tmp30 = triton_helpers.maximum(tmp4, tmp29)
    tmp31 = tmp24 > tmp30
    tmp32 = tmp24 == tmp30
    tmp33 = tmp24 != tmp24
    tmp34 = tmp30 != tmp30
    tmp35 = tmp33 > tmp34
    tmp36 = tmp31 | tmp35
    tmp37 = tmp33 & tmp34
    tmp38 = tmp32 | tmp37
    tmp39 = tl.full([1], 2, tl.int64)
    tmp40 = tmp25 < tmp39
    tmp41 = tmp38 & tmp40
    tmp42 = tmp36 | tmp41
    tmp43 = tl.where(tmp42, tmp24, tmp30)
    tmp44 = tl.where(tmp42, tmp25, tmp39)
    tmp48 = tmp45 + tmp47
    tmp49 = triton_helpers.maximum(tmp4, tmp48)
    tmp50 = tmp43 > tmp49
    tmp51 = tmp43 == tmp49
    tmp52 = tmp43 != tmp43
    tmp53 = tmp49 != tmp49
    tmp54 = tmp52 > tmp53
    tmp55 = tmp50 | tmp54
    tmp56 = tmp52 & tmp53
    tmp57 = tmp51 | tmp56
    tmp58 = tl.full([1], 3, tl.int64)
    tmp59 = tmp44 < tmp58
    tmp60 = tmp57 & tmp59
    tmp61 = tmp55 | tmp60
    tl.where(tmp61, tmp43, tmp49)
    tmp63 = tl.where(tmp61, tmp44, tmp58)
    tmp64 = triton_helpers.maximum(tmp5, tmp10)
    tmp65 = triton_helpers.maximum(tmp64, tmp30)
    tmp66 = triton_helpers.maximum(tmp65, tmp49)
    tl.store(out_ptr0 + x2, tmp63, xmask)
    tl.store(out_ptr1 + x2, tmp66, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_2(in_ptr0, in_ptr1,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, 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(out_ptr0 + x3, tmp6, xmask)


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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 7, 7), (196, 49, 7, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0[grid(784)](primals_1, buf0, 784,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.int64)
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_convolution_max_relu_1[grid(64)](buf1, primals_3,
            buf2, buf3, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf4 = torch.ops.aten.topk.default(buf3, 4, 2)
        del buf3
        buf5 = buf4[0]
        buf6 = buf4[1]
        del buf4
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_2[grid(256)](buf1,
            primals_3, buf7, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf1
        del primals_3
    return buf5, primals_2, buf0, buf6, reinterpret_tensor(buf2, (4, 1, 4, 
        4), (16, 16, 4, 1), 0), buf7


class PACRRConvMax2dModuleNew(torch.nn.Module):

    def __init__(self, shape, n_filters, k, channels):
        super().__init__()
        self.shape = shape
        if shape != 1:
            self.pad = torch.nn.ConstantPad2d((0, shape - 1, 0, shape - 1), 0)
        else:
            self.pad = None
        self.conv = torch.nn.Conv2d(channels, n_filters, shape)
        self.activation = torch.nn.ReLU()
        self.k = k
        self.shape = shape
        self.channels = channels

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


Measured latency_ms: 0.023522