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 torch_nn
from torch.nn import Parameter
import torch.utils
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_mul_renorm_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-05
    tmp14 = tmp12 > tmp13
    tmp15 = 1e-07
    tmp16 = tmp12 + tmp15
    tmp17 = tl.full([1], 1, tl.int32)
    tmp18 = tmp17 / tmp16
    tmp19 = tmp18 * tmp13
    tmp20 = 1.0
    tmp21 = tl.where(tmp14, tmp19, tmp20)
    tmp22 = tmp0 * tmp21
    tmp23 = 100000.0
    tmp24 = tmp22 * tmp23
    tl.store(out_ptr0 + x2, tmp24, xmask)


@triton.jit
def triton_poi_fused_clamp_div_ge_le_logical_and_1(in_out_ptr0, in_ptr0,
    out_ptr0, out_ptr1, 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_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + 4 * 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)
    tmp13 = tmp0 / tmp12
    tmp14 = -1.0
    tmp15 = triton_helpers.maximum(tmp13, tmp14)
    tmp16 = 1.0
    tmp17 = triton_helpers.minimum(tmp15, tmp16)
    tmp18 = tmp13 >= tmp14
    tmp19 = tmp13 <= tmp16
    tmp20 = tmp18 & tmp19
    tl.store(out_ptr0 + x2, tmp17, xmask)
    tl.store(out_ptr1 + x2, tmp20, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (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_mul_renorm_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)
        buf2 = buf1
        del buf1
        buf3 = buf0
        del buf0
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        triton_poi_fused_clamp_div_ge_le_logical_and_1[grid(16)](buf2,
            primals_2, buf3, buf4, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del buf2
    return buf3, primals_1, primals_2, buf4


class P2SActivationLayerNew(torch_nn.Module):
    """ Output layer that produces cos	heta between activation vector x
    and class vector w_j
    in_dim:     dimension of input feature vectors
    output_dim: dimension of output feature vectors 
                (i.e., number of classes)
    
    
    Usage example:
      batchsize = 64
      input_dim = 10
      class_num = 5
      l_layer = P2SActivationLayer(input_dim, class_num)
      l_loss = P2SGradLoss()
      data = torch.rand(batchsize, input_dim, requires_grad=True)
      target = (torch.rand(batchsize) * class_num).clamp(0, class_num-1)
      target = target.to(torch.long)
      scores = l_layer(data)
      loss = l_loss(scores, target)
      loss.backward()
    """

    def __init__(self, in_dim, out_dim):
        super(P2SActivationLayerNew, self).__init__()
        self.in_dim = in_dim
        self.out_dim = out_dim
        self.weight = Parameter(torch.Tensor(in_dim, out_dim))
        self.weight.data.uniform_(-1, 1).renorm_(2, 1, 1e-05).mul_(100000.0)
        return

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


Measured latency_ms: 0.033919