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


@triton.jit
def triton_poi_fused_max_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 16 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (4 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp3 = tl.load(in_ptr0 + (8 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp5 = tl.load(in_ptr0 + (12 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp7 = tl.load(in_ptr0 + (1 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp8 = tl.load(in_ptr0 + (5 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp10 = tl.load(in_ptr0 + (9 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp12 = tl.load(in_ptr0 + (13 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp15 = tl.load(in_ptr0 + (2 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp16 = tl.load(in_ptr0 + (6 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp18 = tl.load(in_ptr0 + (10 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp20 = tl.load(in_ptr0 + (14 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp23 = tl.load(in_ptr0 + (3 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp24 = tl.load(in_ptr0 + (7 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp26 = tl.load(in_ptr0 + (11 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp28 = tl.load(in_ptr0 + (15 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp6 = triton_helpers.maximum(tmp4, tmp5)
    tmp9 = triton_helpers.maximum(tmp7, tmp8)
    tmp11 = triton_helpers.maximum(tmp9, tmp10)
    tmp13 = triton_helpers.maximum(tmp11, tmp12)
    tmp14 = triton_helpers.maximum(tmp6, tmp13)
    tmp17 = triton_helpers.maximum(tmp15, tmp16)
    tmp19 = triton_helpers.maximum(tmp17, tmp18)
    tmp21 = triton_helpers.maximum(tmp19, tmp20)
    tmp22 = triton_helpers.maximum(tmp14, tmp21)
    tmp25 = triton_helpers.maximum(tmp23, tmp24)
    tmp27 = triton_helpers.maximum(tmp25, tmp26)
    tmp29 = triton_helpers.maximum(tmp27, tmp28)
    tmp30 = triton_helpers.maximum(tmp22, tmp29)
    tl.store(out_ptr0 + x0, tmp30, xmask)


@triton.jit
def triton_per_fused_add_div_mean_mul_pow_rsub_sub_sum_1(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)
    r2 = rindex
    r1 = rindex // 16
    tmp0 = tl.load(in_ptr0 + r2, None)
    tmp1 = tl.load(in_ptr1 + r1, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + r2, None)
    tmp14 = tl.load(in_ptr3 + r2, None)
    tmp2 = tmp0 / tmp1
    tmp4 = tmp2 - tmp3
    tmp5 = tmp4 * tmp4
    tmp6 = 1.0
    tmp7 = tmp6 - tmp3
    tmp8 = 0.1
    tmp9 = tmp7 + tmp8
    tmp10 = tmp5 / tmp9
    tmp11 = tl.broadcast_to(tmp10, [RBLOCK])
    tmp13 = triton_helpers.promote_to_tensor(tl.sum(tmp11, 0))
    tmp15 = tmp6 - tmp14
    tmp16 = tmp15 * tmp15
    tmp17 = 0.0625
    tmp18 = tmp16 * tmp17
    tmp19 = tl.broadcast_to(tmp18, [RBLOCK])
    tmp21 = triton_helpers.promote_to_tensor(tl.sum(tmp19, 0))
    tmp22 = 256.0
    tmp23 = tmp13 / tmp22
    tmp24 = tmp23 + tmp21
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp24, None)


def call(args):
    arg0_1, arg1_1, arg2_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))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_max_0[grid(16)](arg1_1, buf0, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf1
        del buf1
        triton_per_fused_add_div_mean_mul_pow_rsub_sub_sum_1[grid(1)](buf3,
            arg1_1, buf0, arg2_1, arg0_1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
        del buf0
    return buf3,


class ModMSELossNew(torch.nn.Module):

    def __init__(self, shape_r_gt, shape_c_gt):
        super(ModMSELossNew, self).__init__()
        self.shape_r_gt = shape_r_gt
        self.shape_c_gt = shape_c_gt

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


Measured latency_ms: 0.029002