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._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.utils.data
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_div_mean_mul_pow_round_sub_0(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)
    tmp2 = 0.25
    tmp3 = tmp1 * tmp2
    tmp4 = libdevice.nearbyint(tmp3)
    tmp5 = 4.0
    tmp6 = tmp4 * tmp5
    tmp7 = libdevice.nearbyint(tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tmp8 * tmp8
    tmp10 = tl.broadcast_to(tmp9, [RBLOCK])
    tmp12 = triton_helpers.promote_to_tensor(tl.sum(tmp10, 0))
    tmp13 = 256.0
    tmp14 = tmp12 / tmp13
    tmp15 = tmp14 * tmp5
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp15, None)


@triton.jit
def triton_per_fused_abs_index_mean_mul_relu_rsub_sub_1(in_out_ptr0,
    in_ptr0, in_ptr1, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 1024
    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)
    r2 = rindex // 256
    r3 = rindex % 256
    r0 = rindex % 64
    tmp0 = tl.load(in_ptr0 + r2, None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + r3, None, eviction_policy='evict_last')
    tmp1 = tl.full([RBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert((0 <= tmp4) & (tmp4 < 4),
        'index out of bounds: 0 <= tmp4 < 4')
    tmp7 = tl.load(in_ptr1 + (r0 + 64 * tmp4), None)
    tmp8 = tmp6 - tmp7
    tmp9 = tl_math.abs(tmp8)
    tmp10 = 8.0
    tmp11 = tmp10 - tmp9
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tmp14 = tl.broadcast_to(tmp13, [RBLOCK])
    tmp16 = triton_helpers.promote_to_tensor(tl.sum(tmp14, 0))
    tmp17 = 1024.0
    tmp18 = tmp16 / tmp17
    tmp19 = 4.0
    tmp20 = tmp18 * tmp19
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp20, 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,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_div_mean_mul_pow_round_sub_0[grid(1)](buf2, arg1_1,
            arg0_1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf1
        del buf1
        triton_per_fused_abs_index_mean_mul_relu_rsub_sub_1[grid(1)](buf3,
            arg3_1, arg2_1, 1, 1024, num_warps=8, num_stages=1)
        del arg2_1
        del arg3_1
    return buf2, buf3


class AELossNew(nn.Module):

    def __init__(self, pull_factor, push_factor, distance, margin_push):
        super(AELossNew, self).__init__()
        self.pull_factor = pull_factor
        self.push_factor = push_factor
        self.distance = distance
        self.margin_push = margin_push

    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], output[1]


Measured latency_ms: 0.028484