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.nn as nn
from torch.nn import Parameter
import torch.utils.data
import torch.optim
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_clamp_div_linalg_vector_norm_mul_scatter_sub_0(in_out_ptr0
    , in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr2 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr2 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp18 = tl.load(in_ptr2 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp21 = tl.load(in_ptr2 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp29 = tl.load(in_ptr3 + x1, 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)
    tmp14 = tmp13 * tmp13
    tmp16 = tmp15 * tmp15
    tmp17 = tmp14 + tmp16
    tmp19 = tmp18 * tmp18
    tmp20 = tmp17 + tmp19
    tmp22 = tmp21 * tmp21
    tmp23 = tmp20 + tmp22
    tmp24 = libdevice.sqrt(tmp23)
    tmp25 = tmp12 * tmp24
    tmp26 = 1e-08
    tmp27 = triton_helpers.maximum(tmp25, tmp26)
    tmp28 = tmp0 / tmp27
    tmp30 = x0
    tmp31 = tmp29 == tmp30
    tmp32 = 1.0
    tmp33 = 0.0
    tmp34 = tl.where(tmp31, tmp32, tmp33)
    tmp35 = 0.4
    tmp36 = tmp34 * tmp35
    tmp37 = tmp28 - tmp36
    tmp38 = 30.0
    tmp39 = tmp37 * tmp38
    tl.store(in_out_ptr0 + x2, tmp39, 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)
        extern_kernels.mm(primals_2, reinterpret_tensor(primals_1, (4, 4),
            (1, 4), 0), out=buf0)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_poi_fused_clamp_div_linalg_vector_norm_mul_scatter_sub_0[grid
            (16)](buf2, buf0, primals_2, primals_1, primals_3, 16, XBLOCK=
            16, num_warps=1, num_stages=1)
        del primals_3
    return buf2, primals_1, primals_2, buf0


def cosine_sim(x1, x2, dim=1, eps=1e-08):
    ip = torch.mm(x1, x2.t())
    w1 = torch.norm(x1, 2, dim)
    w2 = torch.norm(x2, 2, dim)
    return ip / torch.ger(w1, w2).clamp(min=eps)


class MarginCosineProductNew(nn.Module):
    """Implement of large margin cosine distance: :
    Args:
        in_features: size of each input sample
        out_features: size of each output sample
        s: norm of input feature
        m: margin
    """

    def __init__(self, in_features, out_features, s=30.0, m=0.4):
        super(MarginCosineProductNew, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.s = s
        self.m = m
        self.weight = Parameter(torch.Tensor(out_features, in_features))
        nn.init.xavier_uniform_(self.weight)

    def __repr__(self):
        return self.__class__.__name__ + '(' + 'in_features=' + str(self.
            in_features) + ', out_features=' + str(self.out_features
            ) + ', s=' + str(self.s) + ', m=' + str(self.m) + ')'

    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: 0.02329