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 typing
import torch.multiprocessing
from torch import nn
from torch.nn import functional as F
import torch.optim
import torch.utils.data
import torch.distributed
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_constant_pad_nd_mul_0(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 112
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 7
    x1 = xindex // 7
    x2 = xindex
    tmp0 = -1 + x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + (-1 + x0 + 4 * x1), tmp5 & xmask, other=0.0)
    tmp7 = tl.load(in_ptr1 + (-1 + x0 + 4 * x1), tmp5 & xmask, other=0.0)
    tmp8 = tmp6 * tmp7
    tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
    tmp10 = tl.where(tmp5, tmp8, tmp9)
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_constant_pad_nd_convolution_mul_relu_1(in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 112
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 7
    x3 = xindex // 7
    x1 = xindex // 7 % 4
    x4 = xindex
    tmp0 = -1 + x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + (-1 + x0 + 4 * x3), tmp5 & xmask, other=0.0)
    tmp7 = tl.load(in_ptr1 + x1, tmp5 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full([1], 0, tl.int32)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tmp11 = tl.load(in_ptr2 + (-1 + x0 + 4 * x3), tmp5 & xmask, other=0.0)
    tmp12 = tmp10 * tmp11
    tmp13 = tl.full(tmp12.shape, 0.0, tmp12.dtype)
    tmp14 = tl.where(tmp5, tmp12, tmp13)
    tl.store(out_ptr0 + x4, tmp14, xmask)


@triton.jit
def triton_poi_fused_convolution_mul_2(in_out_ptr0, in_ptr0, in_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x3, xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 * tmp3
    tl.store(in_out_ptr0 + x3, tmp4, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_3(in_ptr0, in_ptr1,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 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, primals_6 = 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, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_6, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 7), (28, 7, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_mul_0[grid(112)](primals_1,
            primals_2, buf0, 112, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(buf0, primals_3, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4), (16, 4, 1))
        buf2 = empty_strided_cuda((4, 4, 7), (28, 7, 1), torch.float32)
        triton_poi_fused_constant_pad_nd_convolution_mul_relu_1[grid(112)](buf1
            , primals_4, primals_2, buf2, 112, XBLOCK=128, num_warps=4,
            num_stages=1)
        buf3 = extern_kernels.convolution(buf2, primals_5, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 4), (16, 4, 1))
        buf4 = buf3
        del buf3
        triton_poi_fused_convolution_mul_2[grid(64)](buf4, primals_6,
            primals_2, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_6
        buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_3[grid(64)](buf1,
            primals_4, buf5, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf1
        del primals_4
    return buf4, primals_2, primals_3, primals_5, buf0, buf2, buf5


class FFNNew(nn.Module):

    def __init__(self, in_channels: 'int', out_channels: 'int',
        filter_channels: 'int', kernel_size: 'int', p_dropout: 'float'=0.0,
        activation: 'typing.Optional[str]'=None, causal: 'bool'=False):
        super().__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.filter_channels = filter_channels
        self.kernel_size = kernel_size
        self.p_dropout = p_dropout
        self.activation = activation
        self.causal = causal
        if causal:
            self.padding = self._causal_padding
        else:
            self.padding = self._same_padding
        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
        self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
        self.drop = nn.Dropout(p_dropout)

    def _causal_padding(self, x):
        if self.kernel_size == 1:
            return x
        pad_l = self.kernel_size - 1
        pad_r = 0
        x = F.pad(x, (pad_l, pad_r, 0, 0, 0, 0))
        return x

    def _same_padding(self, x):
        if self.kernel_size == 1:
            return x
        pad_l = (self.kernel_size - 1) // 2
        pad_r = self.kernel_size // 2
        x = F.pad(x, (pad_l, pad_r, 0, 0, 0, 0))
        return x

    def forward(self, input_0, input_1):
        primals_1 = self.conv_1.weight
        primals_4 = self.conv_1.bias
        primals_2 = self.conv_2.weight
        primals_6 = self.conv_2.bias
        primals_3 = input_0
        primals_5 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]


Measured latency_ms: 0.032543