import torch
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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_per_fused_add_mse_loss_mul_pow_relu_rsub_sub_sum_0(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.load(in_ptr1 + r0, None)
    tmp21 = tl.load(in_ptr2 + r0, None)
    tmp22 = tl.load(in_ptr3 + r0, None)
    tmp2 = 0.9
    tmp3 = tmp2 - tmp1
    tmp4 = tl.full([1], 0, tl.int32)
    tmp5 = triton_helpers.maximum(tmp4, tmp3)
    tmp6 = tmp5 * tmp5
    tmp7 = tmp0 * tmp6
    tmp8 = tl.broadcast_to(tmp7, [RBLOCK])
    tmp10 = triton_helpers.promote_to_tensor(tl.sum(tmp8, 0))
    tmp11 = 1.0
    tmp12 = tmp11 - tmp0
    tmp13 = 0.1
    tmp14 = tmp1 - tmp13
    tmp15 = triton_helpers.maximum(tmp4, tmp14)
    tmp16 = tmp15 * tmp15
    tmp17 = tmp12 * tmp16
    tmp18 = tl.broadcast_to(tmp17, [RBLOCK])
    tmp20 = triton_helpers.promote_to_tensor(tl.sum(tmp18, 0))
    tmp23 = tmp21 - tmp22
    tmp24 = tmp23 * tmp23
    tmp25 = tl.broadcast_to(tmp24, [RBLOCK])
    tmp27 = triton_helpers.promote_to_tensor(tl.sum(tmp25, 0))
    tmp28 = 0.5
    tmp29 = tmp20 * tmp28
    tmp30 = tmp10 + tmp29
    tmp31 = 0.0005
    tmp32 = tmp27 * tmp31
    tmp33 = tmp30 + tmp32
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp33, None)


def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg3_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_add_mse_loss_mul_pow_relu_rsub_sub_sum_0[grid(1)](buf3
            , arg1_1, arg0_1, arg2_1, arg3_1, 1, 256, num_warps=2, num_stages=1
            )
        del arg0_1
        del arg1_1
        del arg2_1
        del arg3_1
    return buf3,


class CapsuleLossNew(nn.Module):
    """Combine margin loss & reconstruction loss of capsule network."""

    def __init__(self, upper_bound=0.9, lower_bound=0.1, lmda=0.5):
        super(CapsuleLossNew, self).__init__()
        self.upper = upper_bound
        self.lower = lower_bound
        self.lmda = lmda
        self.reconstruction_loss_scalar = 0.0005
        self.mse = nn.MSELoss(reduction='sum')

    def forward(self, input_0, input_1, input_2, input_3):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        arg3_1 = input_3
        output = call([arg0_1, arg1_1, arg2_1, arg3_1])
        return output[0]


Measured latency_ms: 0.033684