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 import 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_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 8
    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_add_mul_rsub_sigmoid_1(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 2 % 4
    x2 = xindex // 8
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x1 + 8 * x2), xmask, eviction_policy='evict_last'
        )
    tmp2 = tl.load(in_ptr0 + (4 + x1 + 8 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr1 + x3, xmask)
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tmp4 = 1.0
    tmp5 = tmp4 - tmp1
    tmp7 = tmp5 * tmp6
    tmp8 = tmp3 + tmp7
    tl.store(out_ptr0 + x3, tmp8, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (8, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (8,), (1,))
    assert_size_stride(primals_3, (4, 4, 2), (8, 2, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,),
            padding=(1,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf0, (4, 8, 1), (8, 1, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(32)](buf1, primals_2, 32,
            XBLOCK=32, num_warps=1, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((4, 4, 2), (8, 2, 1), torch.float32)
        triton_poi_fused_add_mul_rsub_sigmoid_1[grid(32)](buf1, primals_3,
            buf2, 32, XBLOCK=32, num_warps=1, num_stages=1)
    return buf2, primals_1, primals_3, buf1


class NonCausalConv1d(nn.Module):
    """Non causal Conv1d with appropriate padding to ensure sequence length stays the same.

    Note Convolutions always have stride of 1 following layout in paper.
    
    """

    def __init__(self, in_channels, out_channels, kernel_size, dilation):
        super().__init__()
        padding = (kernel_size - 1) * dilation // 2
        self.conv = nn.Conv1d(in_channels, out_channels, kernel_size, 1,
            padding, dilation)

    def forward(self, x):
        """
        Inputs:
            x(batch_size x input_dim x seq_len)

        """
        return self.conv(x)


class CausalConv1d(nn.Module):
    """Causal conv1d with appropriate padding to ensure sequence length stays the same.

    Note Convolutions always have stride of 1 following layout in paper.
    
    """

    def __init__(self, in_channels, out_channels, kernel_size, dilation):
        super().__init__()
        self.padding = (kernel_size - 1) * dilation
        self.conv = nn.Conv1d(in_channels, out_channels, kernel_size, 1,
            self.padding, dilation)

    def forward(self, x):
        """
        Inputs:
            x(batch_size x input_dim x seq_len)
        
        """
        x = self.conv(x)
        if self.padding > 0:
            return x[:, :, :-self.padding].contiguous()
        else:
            return x


class HighwayNew(nn.Module):
    """Highway network with conv1d
    
    """

    def __init__(self, hidden, kernel_size, dilation, causal=False):
        self.d = hidden
        super().__init__()
        if causal:
            self.conv = CausalConv1d(hidden, 2 * hidden, kernel_size, dilation)
        else:
            self.conv = NonCausalConv1d(hidden, 2 * hidden, kernel_size,
                dilation)

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


Measured latency_ms: 0.036144