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 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_relu_threshold_backward_0(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask)
    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 + x0, tmp4, xmask)
    tl.store(out_ptr0 + x0, tmp6, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(reinterpret_tensor(primals_3, (1,
            4, 4), (16, 4, 1), 0), primals_1, stride=(1,), padding=(0,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf0, (1, 4, 1), (4, 1, 1))
        buf1 = reinterpret_tensor(buf0, (4, 1), (1, 1), 0)
        del buf0
        buf2 = empty_strided_cuda((4, 1), (1, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(4)](buf1, primals_2,
            buf2, 4, XBLOCK=4, num_warps=1, num_stages=1)
        del primals_2
    return buf1, primals_1, reinterpret_tensor(primals_3, (1, 4, 4), (16, 4,
        1), 0), buf2


class Conv1dBlockNew(nn.Module):

    def __init__(self, input_dim, output_dim, kernel_size, stride, padding=
        0, norm='none', activation='relu', pad_type='zero'):
        super(Conv1dBlockNew, self).__init__()
        self.use_bias = True
        if pad_type == 'reflect':
            self.pad = nn.ReflectionPad1d(padding)
        elif pad_type == 'replicate':
            self.pad = nn.ReplicationPad1d(padding)
        elif pad_type == 'zero':
            self.pad = None
        elif pad_type == 'none':
            self.pad = None
        else:
            assert 0, 'Unsupported padding type: {}'.format(pad_type)
        norm_dim = output_dim
        if norm == 'bn':
            self.norm = nn.BatchNorm1d(norm_dim)
        elif norm == 'in':
            self.norm = nn.InstanceNorm1d(norm_dim)
        elif norm == 'ln':
            self.norm = nn.LayerNorm(norm_dim)
        elif norm == 'none':
            self.norm = None
        else:
            assert 0, 'Unsupported normalization: {}'.format(norm)
        if activation == 'relu':
            self.activation = nn.ReLU(inplace=True)
        elif activation == 'lrelu':
            self.activation = nn.LeakyReLU(0.2, inplace=True)
        elif activation == 'prelu':
            self.activation = nn.PReLU()
        elif activation == 'selu':
            self.activation = nn.SELU(inplace=True)
        elif activation == 'tanh':
            self.activation = nn.Tanh()
        elif activation == 'none':
            self.activation = None
        else:
            assert 0, 'Unsupported activation: {}'.format(activation)
        if pad_type == 'zero':
            self.conv = nn.Conv1d(input_dim, output_dim, kernel_size,
                stride, padding, bias=self.use_bias)
        else:
            self.conv = nn.Conv1d(input_dim, output_dim, kernel_size,
                stride, bias=self.use_bias)

    @staticmethod
    def calc_samepad_size(input_dim, kernel_size, stride, dilation=1):
        return ((input_dim - 1) * stride - input_dim + kernel_size + (
            kernel_size - 1) * (dilation - 1)) / 2

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


Measured latency_ms: 0.026399