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 as 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 = 64
    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
    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)


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, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0)
        del buf0
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(64)](buf1,
            primals_3, buf2, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_3
    return buf1, reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), buf2


def reset_parameters_util_x(model):
    for module in model.modules():
        if isinstance(module, nn.Linear):
            nn.init.xavier_normal_(module.weight.data, 1)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.GRUCell):
            for mm in module.parameters():
                if mm.data.ndimension() == 2:
                    nn.init.xavier_normal_(mm.data, 1)
                elif mm.data.ndimension() == 1:
                    mm.data.zero_()


class TextProcessorNew(nn.Module):
    """Processes sentence representations to the correct hidden dimension"""

    def __init__(self, desc_dim, hid_dim):
        super(TextProcessorNew, self).__init__()
        self.desc_dim = desc_dim
        self.hid_dim = hid_dim
        self.transform = nn.Linear(desc_dim, hid_dim)
        self.reset_parameters()

    def reset_parameters(self):
        reset_parameters_util_x(self)

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


Measured latency_ms: 0.023675