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
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_0(in_ptr0, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    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 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_clone_1(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 12
    xnumel = 4
    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 % 3
    y1 = yindex // 3
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 3 * x2 + 12 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1, 1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp4, xmask & ymask)


@triton.jit
def triton_poi_fused_add_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 48
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_3(in_ptr0, in_ptr1,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 48
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 3 % 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, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(16, 4)](primals_1, buf0, 16, 4,
            XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,),
            padding=(1,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 3), (12, 3, 1))
        del buf0
        buf2 = empty_strided_cuda((4, 3, 4), (12, 4, 1), torch.float32)
        triton_poi_fused_clone_1[grid(12, 4)](buf1, primals_3, buf2, 12, 4,
            XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((12, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (12, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf3)
        buf4 = reinterpret_tensor(buf3, (4, 3, 4), (12, 4, 1), 0)
        del buf3
        triton_poi_fused_add_2[grid(48)](buf4, primals_5, 48, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_5
        buf5 = empty_strided_cuda((4, 4, 3), (12, 3, 1), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_3[grid(48)](buf1,
            primals_3, buf5, 48, XBLOCK=64, num_warps=1, num_stages=1)
        del buf1
        del primals_3
    return buf4, primals_2, reinterpret_tensor(primals_1, (4, 4, 4), (16, 1,
        4), 0), reinterpret_tensor(buf2, (12, 4), (4, 1), 0), primals_4, buf5


class Conv1dLinearNew(torch.nn.Module):
    """Conv1D + Linear for Transformer block.

    A variant of MultiLayeredConv1d, which replaces second conv-layer to linear.

    """

    def __init__(self, in_chans, hidden_chans, kernel_size, dropout_rate):
        """Initialize Conv1dLinear module.

        Args:
            in_chans (int): Number of input channels.
            hidden_chans (int): Number of hidden channels.
            kernel_size (int): Kernel size of conv1d.
            dropout_rate (float): Dropout rate.

        """
        super(Conv1dLinearNew, self).__init__()
        self.w_1 = torch.nn.Conv1d(in_chans, hidden_chans, kernel_size,
            stride=1, padding=(kernel_size - 1) // 2)
        self.w_2 = torch.nn.Linear(hidden_chans, in_chans)
        self.dropout = torch.nn.Dropout(dropout_rate)

    def forward(self, input_0):
        primals_1 = self.w_1.weight
        primals_3 = self.w_1.bias
        primals_4 = self.w_2.weight
        primals_5 = self.w_2.bias
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.040071