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.triton_helpers import libdevice
from torch.autograd import Function
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_mean_sub_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = 4.0
    tmp9 = tmp7 / tmp8
    tmp10 = tmp0 - tmp9
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_mean_sub_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = 4.0
    tmp9 = tmp7 / tmp8
    tmp10 = tmp0 - tmp9
    tl.store(out_ptr0 + x3, tmp10, xmask)


@triton.jit
def triton_poi_fused_div_sign_std_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = 4.0
    tmp9 = tmp7 / tmp8
    tmp10 = tmp1 - tmp9
    tmp11 = tmp10 * tmp10
    tmp12 = tmp2 - tmp9
    tmp13 = tmp12 * tmp12
    tmp14 = tmp11 + tmp13
    tmp15 = tmp4 - tmp9
    tmp16 = tmp15 * tmp15
    tmp17 = tmp14 + tmp16
    tmp18 = tmp6 - tmp9
    tmp19 = tmp18 * tmp18
    tmp20 = tmp17 + tmp19
    tmp21 = 3.0
    tmp22 = tmp20 / tmp21
    tmp23 = libdevice.sqrt(tmp22)
    tmp24 = tmp0 / tmp23
    tmp25 = tl.full([1], 0, tl.int32)
    tmp26 = tmp25 < tmp24
    tmp27 = tmp26.to(tl.int8)
    tmp28 = tmp24 < tmp25
    tmp29 = tmp28.to(tl.int8)
    tmp30 = tmp27 - tmp29
    tmp31 = tmp30.to(tmp24.dtype)
    tl.store(out_ptr0 + x3, tmp31, xmask)


@triton.jit
def triton_poi_fused_div_sign_std_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = 4.0
    tmp9 = tmp7 / tmp8
    tmp10 = tmp1 - tmp9
    tmp11 = tmp10 * tmp10
    tmp12 = tmp2 - tmp9
    tmp13 = tmp12 * tmp12
    tmp14 = tmp11 + tmp13
    tmp15 = tmp4 - tmp9
    tmp16 = tmp15 * tmp15
    tmp17 = tmp14 + tmp16
    tmp18 = tmp6 - tmp9
    tmp19 = tmp18 * tmp18
    tmp20 = tmp17 + tmp19
    tmp21 = 3.0
    tmp22 = tmp20 / tmp21
    tmp23 = libdevice.sqrt(tmp22)
    tmp24 = tmp0 / tmp23
    tmp25 = tl.full([1], 0, tl.int32)
    tmp26 = tmp25 < tmp24
    tmp27 = tmp26.to(tl.int8)
    tmp28 = tmp24 < tmp25
    tmp29 = tmp28.to(tl.int8)
    tmp30 = tmp27 - tmp29
    tmp31 = tmp30.to(tmp24.dtype)
    tl.store(out_ptr0 + x2, tmp31, xmask)


@triton.jit
def triton_poi_fused_mul_4(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 * tmp2
    tl.store(out_ptr0 + x0, tmp3, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (), ())
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mean_sub_0[grid(16)](primals_2, buf0, 16, XBLOCK=
            16, num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_mean_sub_1[grid(256)](primals_1, buf1, 256, XBLOCK
            =128, num_warps=4, num_stages=1)
        del primals_1
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_div_sign_std_2[grid(256)](buf1, buf2, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_div_sign_std_3[grid(16)](buf0, buf3, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del buf0
        buf4 = reinterpret_tensor(buf1, (64, 4), (4, 1), 0)
        del buf1
        extern_kernels.addmm(primals_3, reinterpret_tensor(buf2, (64, 4), (
            4, 1), 0), reinterpret_tensor(buf3, (4, 4), (1, 4), 0), alpha=1,
            beta=1, out=buf4)
        del buf3
        del primals_3
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_mul_4[grid(256)](buf4, primals_4, buf5, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
    return buf5, primals_2, primals_4, reinterpret_tensor(buf2, (64, 4), (4,
        1), 0), buf4


class XNOR_BinaryQuantize(Function):

    @staticmethod
    def forward(ctx, input):
        ctx.save_for_backward(input)
        out = torch.sign(input)
        return out

    @staticmethod
    def backward(ctx, grad_output):
        grad_input = grad_output.clone().clamp(min=-1, max=1)
        return grad_input


class XNOR_BinaryQuantize_a(Function):

    @staticmethod
    def forward(ctx, input):
        ctx.save_for_backward(input)
        input = torch.sign(input)
        return input

    @staticmethod
    def backward(ctx, grad_output):
        input = ctx.saved_tensors
        grad_input = grad_output.clone()
        grad_input[input[0].ge(1)] = 0
        grad_input[input[1].le(-1)] = 0
        return grad_input


class binary_last_fcNew(nn.Linear):

    def __init__(self, in_features, out_features, bias=True):
        super(binary_last_fcNew, self).__init__(in_features, out_features, bias
            )
        w = self.weight
        sw = w.abs().mean().float().detach()
        self.alpha = nn.Parameter(sw, requires_grad=True)

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


Measured latency_ms: 0.076155