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
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_convolution_relu_0(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)
    x2 = xindex
    x0 = xindex % 1024
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, 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 + x2, tmp4, None)


@triton.jit
def triton_poi_fused_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)
    x2 = xindex
    x0 = xindex % 2048
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, 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 + x2, tmp4, None)


@triton.jit
def triton_poi_fused_relu_2(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)
    x2 = xindex
    x0 = xindex % 4096
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, 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 + x2, tmp4, None)


@triton.jit
def triton_poi_fused_sigmoid_3(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)
    x2 = xindex
    x0 = xindex % 1024
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tl.store(in_out_ptr0 + x2, tmp3, None)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 1024, 1), (1024, 1, 1))
    assert_size_stride(primals_2, (1024, 1024, 1), (1024, 1, 1))
    assert_size_stride(primals_3, (1024,), (1,))
    assert_size_stride(primals_4, (2048, 1024, 1), (1024, 1, 1))
    assert_size_stride(primals_5, (2048,), (1,))
    assert_size_stride(primals_6, (4096, 2048), (2048, 1))
    assert_size_stride(primals_7, (4096,), (1,))
    assert_size_stride(primals_8, (1024, 4096), (4096, 1))
    assert_size_stride(primals_9, (1024,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf0, (4, 1024, 1), (1024, 1, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(4096)](buf1, primals_3, 
            4096, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_3
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf2, (4, 2048, 1), (2048, 1, 1))
        buf3 = reinterpret_tensor(buf2, (4, 2048), (2048, 1), 0)
        del buf2
        triton_poi_fused_relu_1[grid(8192)](buf3, primals_5, 8192, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((4, 4096), (4096, 1), torch.float32)
        extern_kernels.mm(buf3, reinterpret_tensor(primals_6, (2048, 4096),
            (1, 2048), 0), out=buf4)
        buf5 = buf4
        del buf4
        triton_poi_fused_relu_2[grid(16384)](buf5, primals_7, 16384, XBLOCK
            =256, num_warps=4, num_stages=1)
        del primals_7
        buf6 = empty_strided_cuda((4, 1024), (1024, 1), torch.float32)
        extern_kernels.mm(buf5, reinterpret_tensor(primals_8, (4096, 1024),
            (1, 4096), 0), out=buf6)
        buf7 = buf6
        del buf6
        triton_poi_fused_sigmoid_3[grid(4096)](buf7, primals_9, 4096,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_9
    return (buf7, primals_1, primals_2, primals_4, buf1, buf3, buf5, buf7,
        primals_8, primals_6)


class AnomalyNew(nn.Module):

    def __init__(self, window=1024):
        self.window = window
        super(AnomalyNew, self).__init__()
        self.layer1 = nn.Conv1d(window, window, kernel_size=1, stride=1,
            padding=0)
        self.layer2 = nn.Conv1d(window, 2 * window, kernel_size=1, stride=1,
            padding=0)
        self.fc1 = nn.Linear(2 * window, 4 * window)
        self.fc2 = nn.Linear(4 * window, window)
        self.relu = nn.ReLU(inplace=True)

    def forward(self, input_0):
        primals_2 = self.layer1.weight
        primals_3 = self.layer1.bias
        primals_4 = self.layer2.weight
        primals_5 = self.layer2.bias
        primals_6 = self.fc1.weight
        primals_7 = self.fc1.bias
        primals_8 = self.fc2.weight
        primals_9 = self.fc2.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]


Measured latency_ms: 0.118665