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
import torch.nn as nn
import torch.nn.functional as F
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_mul_pow_relu_rsub_sub_sum_0(in_ptr0, in_ptr1,
    out_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex % 16
    r1 = rindex // 16
    tmp0 = tl.load(in_ptr0 + (r0 + 64 * r1), None)
    tmp1 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
    tmp18 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp19 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
    tmp32 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp33 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
    tmp46 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
    tmp47 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
    tmp2 = 0.9
    tmp3 = tmp2 - tmp1
    tmp4 = tl.full([1, 1], 0, tl.int32)
    tmp5 = triton_helpers.maximum(tmp4, tmp3)
    tmp6 = tmp5 * tmp5
    tmp7 = tmp0 * tmp6
    tmp8 = 1.0
    tmp9 = tmp8 - tmp0
    tmp10 = 0.5
    tmp11 = tmp9 * tmp10
    tmp12 = 0.1
    tmp13 = tmp1 - tmp12
    tmp14 = triton_helpers.maximum(tmp4, tmp13)
    tmp15 = tmp14 * tmp14
    tmp16 = tmp11 * tmp15
    tmp17 = tmp7 + tmp16
    tmp20 = tmp2 - tmp19
    tmp21 = triton_helpers.maximum(tmp4, tmp20)
    tmp22 = tmp21 * tmp21
    tmp23 = tmp18 * tmp22
    tmp24 = tmp8 - tmp18
    tmp25 = tmp24 * tmp10
    tmp26 = tmp19 - tmp12
    tmp27 = triton_helpers.maximum(tmp4, tmp26)
    tmp28 = tmp27 * tmp27
    tmp29 = tmp25 * tmp28
    tmp30 = tmp23 + tmp29
    tmp31 = tmp17 + tmp30
    tmp34 = tmp2 - tmp33
    tmp35 = triton_helpers.maximum(tmp4, tmp34)
    tmp36 = tmp35 * tmp35
    tmp37 = tmp32 * tmp36
    tmp38 = tmp8 - tmp32
    tmp39 = tmp38 * tmp10
    tmp40 = tmp33 - tmp12
    tmp41 = triton_helpers.maximum(tmp4, tmp40)
    tmp42 = tmp41 * tmp41
    tmp43 = tmp39 * tmp42
    tmp44 = tmp37 + tmp43
    tmp45 = tmp31 + tmp44
    tmp48 = tmp2 - tmp47
    tmp49 = triton_helpers.maximum(tmp4, tmp48)
    tmp50 = tmp49 * tmp49
    tmp51 = tmp46 * tmp50
    tmp52 = tmp8 - tmp46
    tmp53 = tmp52 * tmp10
    tmp54 = tmp47 - tmp12
    tmp55 = triton_helpers.maximum(tmp4, tmp54)
    tmp56 = tmp55 * tmp55
    tmp57 = tmp53 * tmp56
    tmp58 = tmp51 + tmp57
    tmp59 = tmp45 + tmp58
    tmp60 = tl.broadcast_to(tmp59, [XBLOCK, RBLOCK])
    tmp62 = tl.sum(tmp60, 1)[:, None]
    tl.store(out_ptr1 + tl.full([XBLOCK, 1], 0, tl.int32), tmp62, None)


@triton.jit
def triton_per_fused_add_mse_loss_mul_1(in_out_ptr0, in_ptr0, in_ptr1,
    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)
    tmp7 = tl.load(in_out_ptr0 + 0)
    tmp8 = tl.broadcast_to(tmp7, [1])
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
    tmp9 = 0.0005
    tmp10 = tmp6 * tmp9
    tmp11 = tmp8 + tmp10
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp11, 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)
        buf1 = empty_strided_cuda((), (), torch.float32)
        get_raw_stream(0)
        triton_per_fused_add_mul_pow_relu_rsub_sub_sum_0[grid(1)](arg1_1,
            arg0_1, buf1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        buf3 = buf1
        del buf1
        triton_per_fused_add_mse_loss_mul_1[grid(1)](buf3, arg3_1, arg2_1, 
            1, 256, num_warps=2, num_stages=1)
        del arg2_1
        del arg3_1
    return buf3,


class MarginLoss(nn.Module):

    def __init__(self, size_average=False, loss_lambda=0.5):
        """
		Margin loss for digit existence
		Eq. (4): L_k = T_k * max(0, m+ - ||v_k||)^2 + lambda * (1 - T_k) * max(0, ||v_k|| - m-)^2
		
		Args:
			size_average: should the losses be averaged (True) or summed (False) over observations for each minibatch.
			loss_lambda: parameter for down-weighting the loss for missing digits
		"""
        super(MarginLoss, self).__init__()
        self.size_average = size_average
        self.m_plus = 0.9
        self.m_minus = 0.1
        self.loss_lambda = loss_lambda

    def forward(self, inputs, labels):
        L_k = labels * F.relu(self.m_plus - inputs) ** 2 + self.loss_lambda * (
            1 - labels) * F.relu(inputs - self.m_minus) ** 2
        L_k = L_k.sum(dim=1)
        if self.size_average:
            return L_k.mean()
        else:
            return L_k.sum()


class CapsuleLossNew(nn.Module):

    def __init__(self, loss_lambda=0.5, recon_loss_scale=0.0005,
        size_average=False):
        """
		Combined margin loss and reconstruction loss. Margin loss see above.
		Sum squared error (SSE) was used as a reconstruction loss.
		
		Args:
			recon_loss_scale: 	param for scaling down the the reconstruction loss
			size_average:		if True, reconstruction loss becomes MSE instead of SSE
		"""
        super(CapsuleLossNew, self).__init__()
        self.size_average = size_average
        self.margin_loss = MarginLoss(size_average=size_average,
            loss_lambda=loss_lambda)
        self.reconstruction_loss = nn.MSELoss(size_average=size_average)
        self.recon_loss_scale = recon_loss_scale

    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: 0.033483