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.utils.data
from torch import nn
import torch
import torch.nn.parallel
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_clone_eq_ones_like_sign_where_0(in_ptr0, out_ptr0,
    out_ptr1, 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.full([1], 0, tl.int32)
    tmp2 = tmp1 < tmp0
    tmp3 = tmp2.to(tl.int8)
    tmp4 = tmp0 < tmp1
    tmp5 = tmp4.to(tl.int8)
    tmp6 = tmp3 - tmp5
    tmp7 = tmp6.to(tmp0.dtype)
    tmp8 = 0.0
    tmp9 = tmp7 == tmp8
    tmp10 = 1.0
    tmp11 = tl.where(tmp9, tmp10, tmp7)
    tl.store(out_ptr0 + x0, tmp0, xmask)
    tl.store(out_ptr1 + x0, tmp11, xmask)


@triton.jit
def triton_poi_fused_eq_ones_like_sign_where_1(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 144
    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.full([1], 0, tl.int32)
    tmp2 = tmp1 < tmp0
    tmp3 = tmp2.to(tl.int8)
    tmp4 = tmp0 < tmp1
    tmp5 = tmp4.to(tl.int8)
    tmp6 = tmp3 - tmp5
    tmp7 = tmp6.to(tmp0.dtype)
    tmp8 = 0.0
    tmp9 = tmp7 == tmp8
    tmp10 = 1.0
    tmp11 = tl.where(tmp9, tmp10, tmp7)
    tl.store(out_ptr0 + x0, tmp11, xmask)


@triton.jit
def triton_poi_fused__native_batch_norm_legit_no_training_hardtanh_2(in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, in_ptr4, 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
    x1 = xindex // 16 % 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')
    tmp12 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 1e-05
    tmp5 = tmp3 + tmp4
    tmp6 = libdevice.sqrt(tmp5)
    tmp7 = tl.full([1], 1, tl.int32)
    tmp8 = tmp7 / tmp6
    tmp9 = 1.0
    tmp10 = tmp8 * tmp9
    tmp11 = tmp2 * tmp10
    tmp13 = tmp11 * tmp12
    tmp15 = tmp13 + tmp14
    tmp16 = -1.0
    tmp17 = triton_helpers.maximum(tmp15, tmp16)
    tmp18 = triton_helpers.minimum(tmp17, tmp9)
    tl.store(out_ptr0 + x3, tmp18, 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_3, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_eq_ones_like_sign_where_0[grid(256)](primals_1,
            buf0, buf1, 256, XBLOCK=128, num_warps=4, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 3, 3), (36, 9, 3, 1), torch.float32)
        triton_poi_fused_eq_ones_like_sign_where_1[grid(144)](primals_3,
            buf2, 144, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_3
        buf3 = extern_kernels.convolution(buf1, buf2, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__native_batch_norm_legit_no_training_hardtanh_2[grid
            (256)](buf3, primals_4, primals_5, primals_6, primals_7, buf4, 
            256, XBLOCK=128, num_warps=4, num_stages=1)
        buf5 = torch.ops.aten.set_.source_Tensor(primals_1, buf1)
        assert_size_stride(buf5, (4, 4, 4, 4), (64, 16, 4, 1))
        del primals_1
        buf10 = torch.ops.aten.set_.source_Tensor(primals_2, buf2)
        assert_size_stride(buf10, (4, 4, 3, 3), (36, 9, 3, 1))
        del primals_2
    return (buf4, buf0, primals_4, primals_5, primals_6, primals_7, buf1,
        buf2, buf3)


def Binarize(tensor, quant_mode='det'):
    if quant_mode == 'det':
        tensor = tensor.sign()
        zero = torch.zeros_like(tensor)
        one = torch.ones_like(tensor)
        zero - one
        tensor = torch.where(tensor == 0, one, tensor)
        return tensor
    else:
        return tensor.add_(1).div_(2).add_(torch.rand(tensor.size()).add(-0.5)
            ).clamp_(0, 1).round().mul_(2).add_(-1)


def Binaryconv3x3(in_planes, out_planes, stride=1):
    """3x3 convolution with padding"""
    return BinarizeConv2d(in_planes, out_planes, kernel_size=3, stride=
        stride, padding=1, bias=False)


class BinarizeConv2d(nn.Conv2d):

    def __init__(self, *kargs, **kwargs):
        super(BinarizeConv2d, self).__init__(*kargs, **kwargs)

    def forward(self, input):
        input.data = Binarize(input.data)
        if not hasattr(self.weight, 'org'):
            self.weight.org = self.weight.data.clone()
        self.weight.data = Binarize(self.weight.org)
        out = nn.functional.conv2d(input, self.weight, None, self.stride,
            self.padding, self.dilation, self.groups)
        if self.bias is not None:
            self.bias.org = self.bias.data.clone()
            out += self.bias.view(1, -1, 1, 1).expand_as(out)
        return out


class BasicBlockNew(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None,
        do_bntan=True):
        super(BasicBlockNew, self).__init__()
        self.conv1 = Binaryconv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.tanh1 = nn.Hardtanh(inplace=True)
        self.conv2 = Binaryconv3x3(planes, planes)
        self.tanh2 = nn.Hardtanh(inplace=True)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.do_bntan = do_bntan
        self.stride = stride

    def forward(self, input_0):
        primals_2 = self.conv1.weight
        primals_4 = self.bn1.weight
        primals_5 = self.bn1.bias
        primals_3 = self.conv2.weight
        primals_6 = self.bn2.weight
        primals_7 = self.bn2.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: 0.015148