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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from collections import OrderedDict
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 = 4
    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
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x1), xmask & ymask)
    tl.store(out_ptr0 + (x1 + 4 * y0), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 24
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 6
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 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(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_2(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 8
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 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(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_tanh_tanh_backward_3(in_out_ptr0, in_ptr0, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 40
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 10
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.tanh(tmp2)
    tmp4 = tmp3 * tmp3
    tmp5 = 1.0
    tmp6 = tmp5 - tmp4
    tl.store(in_out_ptr0 + x2, tmp3, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 3), (12, 3, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 3), (12, 3, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4, 3), (12, 3, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(4, 4)](primals_1, buf0, 4, 4,
            XBLOCK=4, YBLOCK=4, num_warps=1, num_stages=1)
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,),
            padding=(2,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf1, (1, 4, 6), (24, 6, 1))
        del buf0
        buf2 = reinterpret_tensor(buf1, (4, 6), (6, 1), 0)
        del buf1
        buf9 = empty_strided_cuda((4, 6), (6, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(24)](buf2,
            primals_3, buf9, 24, XBLOCK=32, num_warps=1, num_stages=1)
        del primals_3
        buf3 = extern_kernels.convolution(reinterpret_tensor(buf2, (1, 4, 6
            ), (0, 6, 1), 0), primals_4, stride=(1,), padding=(2,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf3, (1, 4, 8), (32, 8, 1))
        buf4 = reinterpret_tensor(buf3, (4, 8), (8, 1), 0)
        del buf3
        buf8 = empty_strided_cuda((4, 8), (8, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_2[grid(32)](buf4,
            primals_5, buf8, 32, XBLOCK=32, num_warps=1, num_stages=1)
        del primals_5
        buf5 = extern_kernels.convolution(reinterpret_tensor(buf4, (1, 4, 8
            ), (0, 8, 1), 0), primals_6, stride=(1,), padding=(2,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf5, (1, 4, 10), (40, 10, 1))
        buf6 = reinterpret_tensor(buf5, (4, 10), (10, 1), 0)
        del buf5
        buf7 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
        triton_poi_fused_tanh_tanh_backward_3[grid(40)](buf6, primals_7,
            buf7, 40, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_7
    return reinterpret_tensor(buf6, (10, 4), (1, 10), 0
        ), primals_2, primals_4, primals_6, reinterpret_tensor(primals_1, (
        1, 4, 4), (4, 1, 4), 0), reinterpret_tensor(buf2, (1, 4, 6), (24, 6,
        1), 0), reinterpret_tensor(buf4, (1, 4, 8), (32, 8, 1), 0
        ), buf7, buf8, buf9


class Conv1DNew(nn.Module):

    def __init__(self, embedding_dim, hidden_dim):
        super(Conv1DNew, self).__init__()
        self.convs = nn.Sequential(OrderedDict([('conv1', nn.Conv1d(
            embedding_dim, hidden_dim, kernel_size=3, stride=1, padding=2)),
            ('relu1', nn.ReLU()), ('conv2', nn.Conv1d(hidden_dim,
            hidden_dim, 3, 1, 2)), ('relu2', nn.ReLU()), ('conv3', nn.
            Conv1d(hidden_dim, hidden_dim, 3, 1, 2)), ('tanh', nn.Tanh())]))

    def forward(self, input_0):
        primals_2 = self.convs.conv1.weight
        primals_3 = self.convs.conv1.bias
        primals_4 = self.convs.conv2.weight
        primals_5 = self.convs.conv2.bias
        primals_6 = self.convs.conv3.weight
        primals_7 = self.convs.conv3.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.091134