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
from scipy.stats import truncnorm
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_per_fused__native_batch_norm_legit_0(in_ptr0, out_ptr0, out_ptr1,
    out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex % 4
    r2 = rindex // 4
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 4 * x0 + 16 * r2), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tl.where(xmask, tmp1, 0)
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp6 = tl.where(xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.full([XBLOCK, 1], 16, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp1 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp15 = tl.where(xmask, tmp13, 0)
    tmp16 = tl.sum(tmp15, 1)[:, None]
    tmp17 = 16.0
    tmp18 = tmp16 / tmp17
    tmp19 = 0.001
    tmp20 = tmp18 + tmp19
    tmp21 = libdevice.rsqrt(tmp20)
    tl.store(out_ptr2 + x0, tmp21, xmask)
    tl.store(out_ptr0 + x0, tmp10, xmask)
    tl.store(out_ptr1 + x0, tmp16, xmask)


@triton.jit
def triton_poi_fused__native_batch_norm_legit_relu_threshold_backward_1(in_ptr0
    , in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, out_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_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 16.0
    tmp5 = tmp3 / tmp4
    tmp6 = 0.001
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tmp16 = 0.0
    tmp17 = tmp15 <= tmp16
    tl.store(out_ptr0 + x3, tmp15, xmask)
    tl.store(out_ptr1 + x3, tmp17, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (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,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((1, 4, 1), (4, 1, 4), torch.float32)
        buf2 = empty_strided_cuda((1, 4, 1), (4, 1, 4), torch.float32)
        buf4 = empty_strided_cuda((1, 4, 1), (4, 1, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused__native_batch_norm_legit_0[grid(4)](buf0, buf1,
            buf2, buf4, 4, 16, XBLOCK=1, num_warps=2, num_stages=1)
        buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        triton_poi_fused__native_batch_norm_legit_relu_threshold_backward_1[
            grid(64)](buf0, buf1, buf2, primals_4, primals_5, buf5, buf6, 
            64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf2
        del primals_5
    return buf5, primals_4, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0
        ), buf0, reinterpret_tensor(buf4, (4,), (1,), 0
        ), buf6, reinterpret_tensor(buf1, (1, 4, 1), (4, 1, 1), 0)


def truncated_normal_(tensor, mean=0.0, std=1.0):
    values = truncnorm.rvs(-2, 2, size=tensor.shape)
    values = mean + std * values
    tensor.copy_(torch.from_numpy(values))
    return tensor


def fc_init_(module):
    if hasattr(module, 'weight') and module.weight is not None:
        truncated_normal_(module.weight.data, mean=0.0, std=0.01)
    if hasattr(module, 'bias') and module.bias is not None:
        torch.nn.init.constant_(module.bias.data, 0.0)
    return module


class LinearBlockNew(torch.nn.Module):

    def __init__(self, input_size, output_size):
        super(LinearBlockNew, self).__init__()
        self.relu = torch.nn.ReLU()
        self.normalize = torch.nn.BatchNorm1d(output_size, affine=True,
            momentum=0.999, eps=0.001, track_running_stats=False)
        self.linear = torch.nn.Linear(input_size, output_size)
        fc_init_(self.linear)

    def forward(self, input_0):
        primals_2 = self.normalize.weight
        primals_4 = self.normalize.bias
        primals_1 = self.linear.weight
        primals_5 = self.linear.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.01504