# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_7/inductor_cache/ar/carsg3p745s5ppxeuixbyjxcr54jnmhedwc66ey5vzh5g4gw5jj7.py
# Topologically Sorted Source Nodes: [norm], Original ATen: [aten.linalg_vector_norm]
# Source node to ATen node mapping:
#   norm => abs_1, sum_1
# Graph fragment:
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%arg0_1,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%abs_1, [0, 2, 3], True), kwargs = {})
triton_per_fused_linalg_vector_norm_0 = async_compile.triton('triton_per_fused_linalg_vector_norm_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.persistent_reduction(
    size_hints=[4, 64],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_linalg_vector_norm_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_linalg_vector_norm_0(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    rnumel = 64
    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, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex % 16
    r2 = (rindex // 16)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (16*x0) + (64*r2)), xmask, other=0.0)
    tmp1 = tl_math.abs(tmp0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.where(xmask, tmp2, 0)
    tmp5 = tl.sum(tmp4, 1)[:, None]
    tl.store(out_ptr0 + (x0), tmp5, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/vp/cvpb5ed6ib2g6fmkhoxxi5krhkewfixourrixg3akkokpaeivlew.py
# Topologically Sorted Source Nodes: [z, mul, r], Original ATen: [aten.div, aten.mul, aten.linalg_vector_norm]
# Source node to ATen node mapping:
#   mul => mul
#   r => abs_2, sum_2
#   z => div
# Graph fragment:
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%arg0_1, %expand), kwargs = {})
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div, %arg1_1), kwargs = {})
#   %abs_2 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%mul,), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%abs_2, [1], True), kwargs = {})
triton_poi_fused_div_linalg_vector_norm_mul_1 = async_compile.triton('triton_poi_fused_div_linalg_vector_norm_mul_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_div_linalg_vector_norm_mul_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 12, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_div_linalg_vector_norm_mul_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = (xindex // 16)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (64*x1)), xmask)
    tmp1 = tl.load(in_ptr1 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp6 = tl.load(in_ptr2 + (x0 + (64*x1)), xmask)
    tmp9 = tl.load(in_ptr0 + (16 + x0 + (64*x1)), xmask)
    tmp10 = tl.load(in_ptr1 + (1))
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK])
    tmp14 = tl.load(in_ptr2 + (16 + x0 + (64*x1)), xmask)
    tmp18 = tl.load(in_ptr0 + (32 + x0 + (64*x1)), xmask)
    tmp19 = tl.load(in_ptr1 + (2))
    tmp20 = tl.broadcast_to(tmp19, [XBLOCK])
    tmp23 = tl.load(in_ptr2 + (32 + x0 + (64*x1)), xmask)
    tmp27 = tl.load(in_ptr0 + (48 + x0 + (64*x1)), xmask)
    tmp28 = tl.load(in_ptr1 + (3))
    tmp29 = tl.broadcast_to(tmp28, [XBLOCK])
    tmp32 = tl.load(in_ptr2 + (48 + x0 + (64*x1)), xmask)
    tmp3 = 1e-12
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = tmp0 / tmp4
    tmp7 = tmp5 * tmp6
    tmp8 = tl_math.abs(tmp7)
    tmp12 = triton_helpers.maximum(tmp11, tmp3)
    tmp13 = tmp9 / tmp12
    tmp15 = tmp13 * tmp14
    tmp16 = tl_math.abs(tmp15)
    tmp17 = tmp8 + tmp16
    tmp21 = triton_helpers.maximum(tmp20, tmp3)
    tmp22 = tmp18 / tmp21
    tmp24 = tmp22 * tmp23
    tmp25 = tl_math.abs(tmp24)
    tmp26 = tmp17 + tmp25
    tmp30 = triton_helpers.maximum(tmp29, tmp3)
    tmp31 = tmp27 / tmp30
    tmp33 = tmp31 * tmp32
    tmp34 = tl_math.abs(tmp33)
    tmp35 = tmp26 + tmp34
    tl.store(out_ptr0 + (x2), tmp35, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/hx/chx67zqpbivuhkxhp5ezn3tepf6nwv2gqxnaf2klisg3dvihzx7o.py
# Topologically Sorted Source Nodes: [einsum], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   einsum => clone, clone_1
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_2,), kwargs = {memory_format: torch.contiguous_format})
#   %clone_1 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_3,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_2 = async_compile.triton('triton_poi_fused_clone_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16, 16], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_clone_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2 + (16*y3)), xmask & ymask)
    tmp1 = tl.load(in_ptr1 + (y0), ymask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + (x2 + (16*y3)), xmask & ymask)
    tmp7 = tl.load(in_ptr3 + (x2 + (16*y1)), xmask & ymask, eviction_policy='evict_last')
    tmp2 = 1e-12
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp4 = tmp0 / tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = triton_helpers.maximum(tmp7, tmp2)
    tmp9 = tmp6 / tmp8
    tmp10 = tl_math.log(tmp9)
    tmp11 = tl_math.log(tmp0)
    tmp12 = tmp10 - tmp11
    tl.store(out_ptr0 + (y0 + (4*x2) + (64*y1)), tmp9, xmask & ymask)
    tl.store(out_ptr1 + (y0 + (4*x2) + (64*y1)), tmp12, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/j3/cj3rjanwhrokncxqzelpshcwemj6ytvab74b32utvu3rezmkjl52.py
# Topologically Sorted Source Nodes: [loss], Original ATen: [aten.mean]
# Source node to ATen node mapping:
#   loss => mean
# Graph fragment:
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%view_3,), kwargs = {})
triton_per_fused_mean_3 = async_compile.triton('triton_per_fused_mean_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.persistent_reduction(
    size_hints=[1, 64],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_mean_3', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_mean_3(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.sum(tmp1, 1)[:, None]
    tmp4 = 64.0
    tmp5 = tmp3 / tmp4
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp5, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

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((1, 4, 1, 1), (4, 1, 4, 4), torch.float32)
        # Topologically Sorted Source Nodes: [norm], Original ATen: [aten.linalg_vector_norm]
        stream0 = get_raw_stream(0)
        triton_per_fused_linalg_vector_norm_0.run(arg0_1, buf0, 4, 64, grid=grid(4), stream=stream0)
        buf1 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [z, mul, r], Original ATen: [aten.div, aten.mul, aten.linalg_vector_norm]
        triton_poi_fused_div_linalg_vector_norm_mul_1.run(arg0_1, buf0, arg1_1, buf1, 64, grid=grid(64), stream=stream0)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [einsum], Original ATen: [aten.clone]
        triton_poi_fused_clone_2.run(arg0_1, buf0, arg1_1, buf1, buf2, buf3, 16, 16, grid=grid(16, 16), stream=stream0)
        del arg0_1
        del arg1_1
        del buf0
        buf4 = reinterpret_tensor(buf1, (64, 1, 1), (1, 1, 1), 0); del buf1  # reuse
        # Topologically Sorted Source Nodes: [einsum], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf2, (64, 1, 4), (4, 0, 1), 0), reinterpret_tensor(buf3, (64, 4, 1), (4, 1, 0), 0), out=buf4)
        del buf2
        del buf3
        buf5 = empty_strided_cuda((), (), torch.float32)
        buf6 = buf5; del buf5  # reuse
        # Topologically Sorted Source Nodes: [loss], Original ATen: [aten.mean]
        triton_per_fused_mean_3.run(buf6, buf4, 1, 64, grid=grid(1), stream=stream0)
        del buf4
    return (buf6, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

from torch.nn import Module
import torch
from typing import cast
from torch.nn.modules import Module
import torch.nn.functional as F


class RQLoss(Module):
    """The RQ (backwards) loss between class probabilities and predictions.

    This loss is defined in `'Resolving label uncertainty with implicit generative
    models' <https://openreview.net/forum?id=AEa_UepnMDX>`_.

    .. versionadded:: 0.2
    """

    def forward(self, probs: 'torch.Tensor', target: 'torch.Tensor'
        ) ->torch.Tensor:
        """Computes the RQ (backwards) loss on prior.

        Args:
            probs: probabilities of predictions, expected shape B x C x H x W
            target: prior probabilities, expected shape B x C x H x W

        Returns:
            qr loss
        """
        q = probs
        z = q / q.norm(p=1, dim=(0, 2, 3), keepdim=True).clamp_min(1e-12
            ).expand_as(q)
        r = F.normalize(z * target, p=1, dim=1)
        loss = torch.einsum('bcxy,bcxy->bxy', r, torch.log(r) - torch.log(q)
            ).mean()
        return cast(torch.Tensor, loss)


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {}]

import torch
from torch._inductor.select_algorithm import extern_kernels
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.nn import Module
from torch.nn.modules import Module
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_per_fused_linalg_vector_norm_0(in_ptr0, out_ptr0, 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 % 16
    r2 = rindex // 16
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0 + 64 * r2), xmask, other=0.0)
    tmp1 = tl_math.abs(tmp0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.where(xmask, tmp2, 0)
    tmp5 = tl.sum(tmp4, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp5, xmask)


@triton.jit
def triton_poi_fused_div_linalg_vector_norm_mul_1(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp6 = tl.load(in_ptr2 + (x0 + 64 * x1), xmask)
    tmp9 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp10 = tl.load(in_ptr1 + 1)
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK])
    tmp14 = tl.load(in_ptr2 + (16 + x0 + 64 * x1), xmask)
    tmp18 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp19 = tl.load(in_ptr1 + 2)
    tmp20 = tl.broadcast_to(tmp19, [XBLOCK])
    tmp23 = tl.load(in_ptr2 + (32 + x0 + 64 * x1), xmask)
    tmp27 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp28 = tl.load(in_ptr1 + 3)
    tmp29 = tl.broadcast_to(tmp28, [XBLOCK])
    tmp32 = tl.load(in_ptr2 + (48 + x0 + 64 * x1), xmask)
    tmp3 = 1e-12
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = tmp0 / tmp4
    tmp7 = tmp5 * tmp6
    tmp8 = tl_math.abs(tmp7)
    tmp12 = triton_helpers.maximum(tmp11, tmp3)
    tmp13 = tmp9 / tmp12
    tmp15 = tmp13 * tmp14
    tmp16 = tl_math.abs(tmp15)
    tmp17 = tmp8 + tmp16
    tmp21 = triton_helpers.maximum(tmp20, tmp3)
    tmp22 = tmp18 / tmp21
    tmp24 = tmp22 * tmp23
    tmp25 = tl_math.abs(tmp24)
    tmp26 = tmp17 + tmp25
    tmp30 = triton_helpers.maximum(tmp29, tmp3)
    tmp31 = tmp27 / tmp30
    tmp33 = tmp31 * tmp32
    tmp34 = tl_math.abs(tmp33)
    tmp35 = tmp26 + tmp34
    tl.store(out_ptr0 + x2, tmp35, xmask)


@triton.jit
def triton_poi_fused_clone_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    out_ptr1, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 4
    y1 = yindex // 4
    tmp0 = tl.load(in_ptr0 + (x2 + 16 * y3), xmask & ymask)
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + (x2 + 16 * y3), xmask & ymask)
    tmp7 = tl.load(in_ptr3 + (x2 + 16 * y1), xmask & ymask, eviction_policy
        ='evict_last')
    tmp2 = 1e-12
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp4 = tmp0 / tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = triton_helpers.maximum(tmp7, tmp2)
    tmp9 = tmp6 / tmp8
    tmp10 = tl_math.log(tmp9)
    tmp11 = tl_math.log(tmp0)
    tmp12 = tmp10 - tmp11
    tl.store(out_ptr0 + (y0 + 4 * x2 + 64 * y1), tmp9, xmask & ymask)
    tl.store(out_ptr1 + (y0 + 4 * x2 + 64 * y1), tmp12, xmask & ymask)


@triton.jit
def triton_per_fused_mean_3(in_out_ptr0, in_ptr0, 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
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.sum(tmp1, 1)[:, None]
    tmp4 = 64.0
    tmp5 = tmp3 / tmp4
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp5, None)


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((1, 4, 1, 1), (4, 1, 4, 4), torch.float32)
        get_raw_stream(0)
        triton_per_fused_linalg_vector_norm_0[grid(4)](arg0_1, buf0, 4, 64,
            XBLOCK=1, num_warps=2, num_stages=1)
        buf1 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        triton_poi_fused_div_linalg_vector_norm_mul_1[grid(64)](arg0_1,
            buf0, arg1_1, buf1, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_2[grid(16, 16)](arg0_1, buf0, arg1_1, buf1,
            buf2, buf3, 16, 16, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1
            )
        del arg0_1
        del arg1_1
        del buf0
        buf4 = reinterpret_tensor(buf1, (64, 1, 1), (1, 1, 1), 0)
        del buf1
        extern_kernels.bmm(reinterpret_tensor(buf2, (64, 1, 4), (4, 0, 1), 
            0), reinterpret_tensor(buf3, (64, 4, 1), (4, 1, 0), 0), out=buf4)
        del buf2
        del buf3
        buf5 = empty_strided_cuda((), (), torch.float32)
        buf6 = buf5
        del buf5
        triton_per_fused_mean_3[grid(1)](buf6, buf4, 1, 64, XBLOCK=1,
            num_warps=2, num_stages=1)
        del buf4
    return buf6,


class RQLossNew(Module):
    """The RQ (backwards) loss between class probabilities and predictions.

    This loss is defined in `'Resolving label uncertainty with implicit generative
    models' <https://openreview.net/forum?id=AEa_UepnMDX>`_.

    .. versionadded:: 0.2
    """

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