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 math as tl_math
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_abs_max_sub_sum_0(in_ptr0, out_ptr0, out_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.broadcast_to(tmp0, [RBLOCK])
    tmp3 = triton_helpers.promote_to_tensor(triton_helpers.max2(tmp1, 0))
    tmp4 = tmp0 - tmp3
    tmp5 = tl_math.abs(tmp4)
    tmp6 = tl.broadcast_to(tmp5, [RBLOCK])
    tmp8 = triton_helpers.promote_to_tensor(tl.sum(tmp6, 0))
    tl.store(out_ptr0 + tl.full([1], 0, tl.int32), tmp3, None)
    tl.store(out_ptr1 + tl.full([1], 0, tl.int32), tmp8, None)


@triton.jit
def triton_per_fused_mul_sum_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 64 * x0), xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (r1 + 64 * x0), xmask, other=0.0)
    tmp2 = tl.load(in_ptr2 + 0)
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp6 = tl.load(in_ptr3 + 0)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp4 = tmp1 - tmp3
    tmp5 = tl_math.abs(tmp4)
    tmp8 = tmp5 / tmp7
    tmp9 = tmp0 * tmp8
    tmp10 = tl.broadcast_to(tmp9, [XBLOCK, RBLOCK])
    tmp12 = tl.where(xmask, tmp10, 0)
    tmp13 = tl.sum(tmp12, 1)[:, None]
    tmp14 = 1.0
    tmp15 = tmp13 * tmp14
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp15, xmask)


def call(args):
    arg0_1, arg1_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))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        buf1 = empty_strided_cuda((), (), torch.float32)
        get_raw_stream(0)
        triton_per_fused_abs_max_sub_sum_0[grid(1)](arg1_1, buf0, buf1, 1, 
            256, num_warps=2, num_stages=1)
        buf2 = empty_strided_cuda((4,), (1,), torch.float32)
        buf3 = buf2
        del buf2
        triton_per_fused_mul_sum_1[grid(4)](buf3, arg0_1, arg1_1, buf0,
            buf1, 4, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        del buf0
        del buf1
    return buf3,


class RegionPenaltyLossNew(nn.Module):

    def __init__(self, scale=1.0):
        """
        Multiplicative penalty.

        Penalizes "forbidden" regions instead of exact distribution matches.
        Optionally used in tandem with MTCrossEntropyRegionAwareLoss.
        `scale` param allows caller to scale the loss in order to match
        magnitude of other loss terms
        """
        super().__init__()
        self.scale = scale

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


Measured latency_ms: 0.030743