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
import torch.nn as nn
import torch.optim
import torch.nn.parallel
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_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 4
    x2 = xindex // 16
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x1 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (4 + x1 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (8 + x1 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (12 + x1 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(in_out_ptr0 + x3, tmp8, xmask)


@triton.jit
def triton_poi_fused_add_div_mul_neg_sub_1(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex // 4
    x0 = xindex % 4
    x2 = xindex // 16
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x4, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x4), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (2 + 4 * x4), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (3 + 4 * x4), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr1 + (4 * x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp13 = tl.load(in_ptr1 + (1 + 4 * x0 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + (2 + 4 * x0 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr1 + (3 + 4 * x0 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tmp23 = tl.load(in_out_ptr0 + x3, xmask)
    tmp29 = tl.load(in_ptr2 + 0)
    tmp30 = tl.broadcast_to(tmp29, [XBLOCK])
    tmp1 = tmp0 * tmp0
    tmp3 = tmp2 * tmp2
    tmp4 = tmp1 + tmp3
    tmp6 = tmp5 * tmp5
    tmp7 = tmp4 + tmp6
    tmp9 = tmp8 * tmp8
    tmp10 = tmp7 + tmp9
    tmp12 = tmp11 * tmp11
    tmp14 = tmp13 * tmp13
    tmp15 = tmp12 + tmp14
    tmp17 = tmp16 * tmp16
    tmp18 = tmp15 + tmp17
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = tmp10 + tmp21
    tmp24 = 2.0
    tmp25 = tmp23 * tmp24
    tmp26 = tmp22 - tmp25
    tmp27 = 0.25
    tmp28 = tmp26 * tmp27
    tmp31 = -tmp30
    tmp32 = tmp31 * tmp28
    tl.store(in_out_ptr0 + x3, tmp28, xmask)
    tl.store(out_ptr0 + x3, tmp32, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(primals_2, (4, 4, 4), (16, 1,
            4), 0), primals_1, out=buf0)
        del primals_1
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_div_0[grid(64)](buf1, primals_2, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(primals_3, reinterpret_tensor(buf1, (4, 4, 4), (
            16, 1, 4), 0), out=buf2)
        buf3 = buf2
        del buf2
        buf4 = buf3
        del buf3
        buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_div_mul_neg_sub_1[grid(64)](buf4, primals_3,
            buf1, primals_4, buf5, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf1
        del primals_3
        del primals_4
    return buf5, buf4


def L2SquareDist(A, B, average=True):
    assert A.dim() == 3
    assert B.dim() == 3
    assert A.size(0) == B.size(0) and A.size(2) == B.size(2)
    nB = A.size(0)
    Na = A.size(1)
    Nb = B.size(1)
    nC = A.size(2)
    AB = torch.bmm(A, B.transpose(1, 2))
    AA = (A * A).sum(dim=2, keepdim=True).view(nB, Na, 1)
    BB = (B * B).sum(dim=2, keepdim=True).view(nB, 1, Nb)
    dist = AA.expand_as(AB) + BB.expand_as(AB) - 2 * AB
    if average:
        dist = dist / nC
    return dist


class PrototypicalNetworkNew(nn.Module):

    def __init__(self, opt):
        super(PrototypicalNetworkNew, self).__init__()
        scale_cls = opt['scale_cls'] if 'scale_cls' in opt else 1.0
        self.scale_cls = nn.Parameter(torch.FloatTensor(1).fill_(scale_cls),
            requires_grad=True)

    def forward(self, input_0, input_1, input_2):
        primals_4 = self.scale_cls
        primals_1 = input_0
        primals_2 = input_1
        primals_3 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]


Measured latency_ms: 0.020099