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 torch.nn import Module
import math
import torch._utils
from torch.nn import Parameter
from itertools import product as product
import torch.utils.data.distributed
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_div_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
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = libdevice.sqrt(tmp11)
    tmp13 = 1e-12
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp15 = tmp0 / tmp14
    tl.store(out_ptr0 + x2, tmp15, xmask)


@triton.jit
def triton_poi_fused_arange_1(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 = x0
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused_gt_sub_2(in_ptr0, in_ptr1, 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_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.full([XBLOCK], 4, tl.int32)
    tmp3 = tmp1 + tmp2
    tmp4 = tmp1 < 0
    tmp5 = tl.where(tmp4, tmp3, tmp1)
    tl.device_assert((0 <= tmp5) & (tmp5 < 4) | ~xmask,
        'index out of bounds: 0 <= tmp5 < 4')
    tmp7 = tl.load(in_ptr0 + (tmp5 + 4 * x1), xmask, eviction_policy=
        'evict_last')
    tmp8 = 0.35
    tmp9 = tmp7 - tmp8
    tmp10 = tmp0 > tmp9
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_gt_sub_where_3(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    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_ptr0 + x0, xmask)
    tmp1 = tl.full([XBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert((0 <= tmp4) & (tmp4 < 4) | ~xmask,
        'index out of bounds: 0 <= tmp4 < 4')
    tmp6 = tl.load(in_ptr1 + (tmp4 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp7 = 0.35
    tmp8 = tmp6 > tmp7
    tmp9 = tmp6 - tmp7
    tmp10 = tl.where(tmp8, tmp9, tmp6)
    tl.store(out_ptr0 + x0, tmp8, xmask)
    tl.store(out_ptr1 + x0, tmp10, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (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((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_div_0[grid(16)](primals_1, buf0, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_2, buf0, out=buf1)
        del buf0
        buf2 = empty_strided_cuda((4,), (1,), torch.int64)
        triton_poi_fused_arange_1[grid(4)](buf2, 4, XBLOCK=4, num_warps=1,
            num_stages=1)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        triton_poi_fused_gt_sub_2[grid(16)](buf1, primals_3, buf3, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf4 = empty_strided_cuda((4, 1), (1, 1), torch.bool)
        buf5 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        triton_poi_fused_gt_sub_where_3[grid(4)](primals_3, buf1, buf4,
            buf5, 4, XBLOCK=4, num_warps=1, num_stages=1)
    return (buf1, buf3, buf5, primals_1, primals_3, buf2, buf4,
        reinterpret_tensor(primals_2, (4, 4), (1, 4), 0))


class MV_SoftmaxNew(Module):
    """Implementation for "Mis-classified Vector Guided Softmax Loss for Face Recognition"
    """

    def __init__(self, feat_dim, num_class, is_am, margin=0.35, mv_weight=
        1.12, scale=32):
        super(MV_SoftmaxNew, self).__init__()
        self.weight = Parameter(torch.Tensor(feat_dim, num_class))
        self.weight.data.uniform_(-1, 1).renorm_(2, 1, 1e-05).mul_(100000.0)
        self.margin = margin
        self.mv_weight = mv_weight
        self.scale = scale
        self.is_am = is_am
        self.cos_m = math.cos(margin)
        self.sin_m = math.sin(margin)
        self.threshold = math.cos(math.pi - margin)
        self.mm = self.sin_m * margin

    def forward(self, input_0, input_1):
        primals_1 = self.weight
        primals_2 = input_0
        primals_3 = input_1
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.063496