# 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_4/inductor_cache/nl/cnlmc4rotaarrtvfztzaiegrcnaxsa6ub4pi24tghi44y22jtozp.py
# Topologically Sorted Source Nodes: [mul, sum_1, sum_2, pow_1, sum_4, sum_5, pow_2, sum_7, sum_8], Original ATen: [aten.mul, aten.sum, aten.pow]
# Source node to ATen node mapping:
#   mul => mul
#   pow_1 => pow_1
#   pow_2 => pow_2
#   sum_1 => sum_1
#   sum_2 => sum_2
#   sum_4 => sum_4
#   sum_5 => sum_5
#   sum_7 => sum_7
#   sum_8 => sum_8
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%squeeze, %arg1_1), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [1]), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%sum_1, [1]), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%squeeze, 2), kwargs = {})
#   %sum_4 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [1]), kwargs = {})
#   %sum_5 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%sum_4, [1]), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%arg1_1, 2), kwargs = {})
#   %sum_7 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_2, [1]), kwargs = {})
#   %sum_8 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%sum_7, [1]), kwargs = {})
triton_poi_fused_mul_pow_sum_0 = async_compile.triton('triton_poi_fused_mul_pow_sum_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.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_pow_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 32, '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_mul_pow_sum_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (64*x1)), xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + (64*x1)), xmask)
    tmp3 = tl.load(in_ptr0 + (16 + x0 + (64*x1)), xmask)
    tmp4 = tl.load(in_ptr1 + (16 + x0 + (64*x1)), xmask)
    tmp7 = tl.load(in_ptr0 + (32 + x0 + (64*x1)), xmask)
    tmp8 = tl.load(in_ptr1 + (32 + x0 + (64*x1)), xmask)
    tmp11 = tl.load(in_ptr0 + (48 + x0 + (64*x1)), xmask)
    tmp12 = tl.load(in_ptr1 + (48 + x0 + (64*x1)), xmask)
    tmp15 = tl.load(in_ptr0 + (4 + x0 + (64*x1)), xmask)
    tmp16 = tl.load(in_ptr1 + (4 + x0 + (64*x1)), xmask)
    tmp18 = tl.load(in_ptr0 + (20 + x0 + (64*x1)), xmask)
    tmp19 = tl.load(in_ptr1 + (20 + x0 + (64*x1)), xmask)
    tmp22 = tl.load(in_ptr0 + (36 + x0 + (64*x1)), xmask)
    tmp23 = tl.load(in_ptr1 + (36 + x0 + (64*x1)), xmask)
    tmp26 = tl.load(in_ptr0 + (52 + x0 + (64*x1)), xmask)
    tmp27 = tl.load(in_ptr1 + (52 + x0 + (64*x1)), xmask)
    tmp31 = tl.load(in_ptr0 + (8 + x0 + (64*x1)), xmask)
    tmp32 = tl.load(in_ptr1 + (8 + x0 + (64*x1)), xmask)
    tmp34 = tl.load(in_ptr0 + (24 + x0 + (64*x1)), xmask)
    tmp35 = tl.load(in_ptr1 + (24 + x0 + (64*x1)), xmask)
    tmp38 = tl.load(in_ptr0 + (40 + x0 + (64*x1)), xmask)
    tmp39 = tl.load(in_ptr1 + (40 + x0 + (64*x1)), xmask)
    tmp42 = tl.load(in_ptr0 + (56 + x0 + (64*x1)), xmask)
    tmp43 = tl.load(in_ptr1 + (56 + x0 + (64*x1)), xmask)
    tmp47 = tl.load(in_ptr0 + (12 + x0 + (64*x1)), xmask)
    tmp48 = tl.load(in_ptr1 + (12 + x0 + (64*x1)), xmask)
    tmp50 = tl.load(in_ptr0 + (28 + x0 + (64*x1)), xmask)
    tmp51 = tl.load(in_ptr1 + (28 + x0 + (64*x1)), xmask)
    tmp54 = tl.load(in_ptr0 + (44 + x0 + (64*x1)), xmask)
    tmp55 = tl.load(in_ptr1 + (44 + x0 + (64*x1)), xmask)
    tmp58 = tl.load(in_ptr0 + (60 + x0 + (64*x1)), xmask)
    tmp59 = tl.load(in_ptr1 + (60 + x0 + (64*x1)), xmask)
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tmp17 = tmp15 * tmp16
    tmp20 = tmp18 * tmp19
    tmp21 = tmp17 + tmp20
    tmp24 = tmp22 * tmp23
    tmp25 = tmp21 + tmp24
    tmp28 = tmp26 * tmp27
    tmp29 = tmp25 + tmp28
    tmp30 = tmp14 + tmp29
    tmp33 = tmp31 * tmp32
    tmp36 = tmp34 * tmp35
    tmp37 = tmp33 + tmp36
    tmp40 = tmp38 * tmp39
    tmp41 = tmp37 + tmp40
    tmp44 = tmp42 * tmp43
    tmp45 = tmp41 + tmp44
    tmp46 = tmp30 + tmp45
    tmp49 = tmp47 * tmp48
    tmp52 = tmp50 * tmp51
    tmp53 = tmp49 + tmp52
    tmp56 = tmp54 * tmp55
    tmp57 = tmp53 + tmp56
    tmp60 = tmp58 * tmp59
    tmp61 = tmp57 + tmp60
    tmp62 = tmp46 + tmp61
    tmp63 = tmp0 * tmp0
    tmp64 = tmp3 * tmp3
    tmp65 = tmp63 + tmp64
    tmp66 = tmp7 * tmp7
    tmp67 = tmp65 + tmp66
    tmp68 = tmp11 * tmp11
    tmp69 = tmp67 + tmp68
    tmp70 = tmp15 * tmp15
    tmp71 = tmp18 * tmp18
    tmp72 = tmp70 + tmp71
    tmp73 = tmp22 * tmp22
    tmp74 = tmp72 + tmp73
    tmp75 = tmp26 * tmp26
    tmp76 = tmp74 + tmp75
    tmp77 = tmp69 + tmp76
    tmp78 = tmp31 * tmp31
    tmp79 = tmp34 * tmp34
    tmp80 = tmp78 + tmp79
    tmp81 = tmp38 * tmp38
    tmp82 = tmp80 + tmp81
    tmp83 = tmp42 * tmp42
    tmp84 = tmp82 + tmp83
    tmp85 = tmp77 + tmp84
    tmp86 = tmp47 * tmp47
    tmp87 = tmp50 * tmp50
    tmp88 = tmp86 + tmp87
    tmp89 = tmp54 * tmp54
    tmp90 = tmp88 + tmp89
    tmp91 = tmp58 * tmp58
    tmp92 = tmp90 + tmp91
    tmp93 = tmp85 + tmp92
    tmp94 = tmp1 * tmp1
    tmp95 = tmp4 * tmp4
    tmp96 = tmp94 + tmp95
    tmp97 = tmp8 * tmp8
    tmp98 = tmp96 + tmp97
    tmp99 = tmp12 * tmp12
    tmp100 = tmp98 + tmp99
    tmp101 = tmp16 * tmp16
    tmp102 = tmp19 * tmp19
    tmp103 = tmp101 + tmp102
    tmp104 = tmp23 * tmp23
    tmp105 = tmp103 + tmp104
    tmp106 = tmp27 * tmp27
    tmp107 = tmp105 + tmp106
    tmp108 = tmp100 + tmp107
    tmp109 = tmp32 * tmp32
    tmp110 = tmp35 * tmp35
    tmp111 = tmp109 + tmp110
    tmp112 = tmp39 * tmp39
    tmp113 = tmp111 + tmp112
    tmp114 = tmp43 * tmp43
    tmp115 = tmp113 + tmp114
    tmp116 = tmp108 + tmp115
    tmp117 = tmp48 * tmp48
    tmp118 = tmp51 * tmp51
    tmp119 = tmp117 + tmp118
    tmp120 = tmp55 * tmp55
    tmp121 = tmp119 + tmp120
    tmp122 = tmp59 * tmp59
    tmp123 = tmp121 + tmp122
    tmp124 = tmp116 + tmp123
    tl.store(out_ptr0 + (x2), tmp62, xmask)
    tl.store(out_ptr1 + (x2), tmp93, xmask)
    tl.store(out_ptr2 + (x2), tmp124, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/bq/cbqgfdsdskelqjbw4r3hudlpfrzmjhr2qjb4hibcjvwgwcovvolq.py
# Topologically Sorted Source Nodes: [sum_3, mul_1, sum_6, sum_9, add, add_1, dice, sub, mean], Original ATen: [aten.sum, aten.mul, aten.add, aten.div, aten.rsub, aten.mean]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   dice => div
#   mean => mean
#   mul_1 => mul_1
#   sub => sub
#   sum_3 => sum_3
#   sum_6 => sum_6
#   sum_9 => sum_9
# Graph fragment:
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%sum_2, [1]), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_3, 2), kwargs = {})
#   %sum_6 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%sum_5, [1]), kwargs = {})
#   %sum_9 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%sum_8, [1]), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_6, %sum_9), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, 1e-05), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul_1, %add_1), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %div), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sub,), kwargs = {})
triton_per_fused_add_div_mean_mul_rsub_sum_1 = async_compile.triton('triton_per_fused_add_div_mean_mul_rsub_sum_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.persistent_reduction(
    size_hints=[1, 4],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: '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': {4: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=(4,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_mean_mul_rsub_sum_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 12, '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_add_div_mean_mul_rsub_sum_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 4
    RBLOCK: tl.constexpr = 4
    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 + (4*r0), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + (4*r0), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr1 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr2 + (4*r0), None, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr2 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr2 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr2 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 2.0
    tmp8 = tmp6 * tmp7
    tmp11 = tmp9 + tmp10
    tmp13 = tmp11 + tmp12
    tmp15 = tmp13 + tmp14
    tmp18 = tmp16 + tmp17
    tmp20 = tmp18 + tmp19
    tmp22 = tmp20 + tmp21
    tmp23 = tmp15 + tmp22
    tmp24 = 1e-05
    tmp25 = tmp23 + tmp24
    tmp26 = tmp8 / tmp25
    tmp27 = 1.0
    tmp28 = tmp27 - tmp26
    tmp29 = tl.broadcast_to(tmp28, [XBLOCK, RBLOCK])
    tmp31 = tl.sum(tmp29, 1)[:, None]
    tmp32 = 4.0
    tmp33 = tmp31 / tmp32
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp33, 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((4, 4), (4, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, sum_1, sum_2, pow_1, sum_4, sum_5, pow_2, sum_7, sum_8], Original ATen: [aten.mul, aten.sum, aten.pow]
        stream0 = get_raw_stream(0)
        triton_poi_fused_mul_pow_sum_0.run(arg0_1, arg1_1, buf0, buf1, buf2, 16, grid=grid(16), stream=stream0)
        del arg0_1
        del arg1_1
        buf4 = empty_strided_cuda((), (), torch.float32)
        buf5 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [sum_3, mul_1, sum_6, sum_9, add, add_1, dice, sub, mean], Original ATen: [aten.sum, aten.mul, aten.add, aten.div, aten.rsub, aten.mean]
        triton_per_fused_add_div_mean_mul_rsub_sum_1.run(buf5, buf0, buf1, buf2, 1, 4, grid=grid(1), stream=stream0)
        del buf0
        del buf1
        del buf2
    return (buf5, )


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)

import torch
import torch.nn as nn
import torch.utils.data
import torch


class DiceLoss(nn.Module):

    def __init__(self):
        super(DiceLoss, self).__init__()

    def forward(self, pred, target):
        pred = pred.squeeze(dim=1)
        dice = 2 * (pred * target).sum(dim=1).sum(dim=1).sum(dim=1) / (pred
            .pow(2).sum(dim=1).sum(dim=1).sum(dim=1) + target.pow(2).sum(
            dim=1).sum(dim=1).sum(dim=1) + 1e-05)
        return (1 - dice).mean()


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


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

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
import torch.nn as nn
import torch.utils.data
import torch
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_mul_pow_sum_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    out_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + 64 * x1), xmask)
    tmp3 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp4 = tl.load(in_ptr1 + (16 + x0 + 64 * x1), xmask)
    tmp7 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp8 = tl.load(in_ptr1 + (32 + x0 + 64 * x1), xmask)
    tmp11 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp12 = tl.load(in_ptr1 + (48 + x0 + 64 * x1), xmask)
    tmp15 = tl.load(in_ptr0 + (4 + x0 + 64 * x1), xmask)
    tmp16 = tl.load(in_ptr1 + (4 + x0 + 64 * x1), xmask)
    tmp18 = tl.load(in_ptr0 + (20 + x0 + 64 * x1), xmask)
    tmp19 = tl.load(in_ptr1 + (20 + x0 + 64 * x1), xmask)
    tmp22 = tl.load(in_ptr0 + (36 + x0 + 64 * x1), xmask)
    tmp23 = tl.load(in_ptr1 + (36 + x0 + 64 * x1), xmask)
    tmp26 = tl.load(in_ptr0 + (52 + x0 + 64 * x1), xmask)
    tmp27 = tl.load(in_ptr1 + (52 + x0 + 64 * x1), xmask)
    tmp31 = tl.load(in_ptr0 + (8 + x0 + 64 * x1), xmask)
    tmp32 = tl.load(in_ptr1 + (8 + x0 + 64 * x1), xmask)
    tmp34 = tl.load(in_ptr0 + (24 + x0 + 64 * x1), xmask)
    tmp35 = tl.load(in_ptr1 + (24 + x0 + 64 * x1), xmask)
    tmp38 = tl.load(in_ptr0 + (40 + x0 + 64 * x1), xmask)
    tmp39 = tl.load(in_ptr1 + (40 + x0 + 64 * x1), xmask)
    tmp42 = tl.load(in_ptr0 + (56 + x0 + 64 * x1), xmask)
    tmp43 = tl.load(in_ptr1 + (56 + x0 + 64 * x1), xmask)
    tmp47 = tl.load(in_ptr0 + (12 + x0 + 64 * x1), xmask)
    tmp48 = tl.load(in_ptr1 + (12 + x0 + 64 * x1), xmask)
    tmp50 = tl.load(in_ptr0 + (28 + x0 + 64 * x1), xmask)
    tmp51 = tl.load(in_ptr1 + (28 + x0 + 64 * x1), xmask)
    tmp54 = tl.load(in_ptr0 + (44 + x0 + 64 * x1), xmask)
    tmp55 = tl.load(in_ptr1 + (44 + x0 + 64 * x1), xmask)
    tmp58 = tl.load(in_ptr0 + (60 + x0 + 64 * x1), xmask)
    tmp59 = tl.load(in_ptr1 + (60 + x0 + 64 * x1), xmask)
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tmp17 = tmp15 * tmp16
    tmp20 = tmp18 * tmp19
    tmp21 = tmp17 + tmp20
    tmp24 = tmp22 * tmp23
    tmp25 = tmp21 + tmp24
    tmp28 = tmp26 * tmp27
    tmp29 = tmp25 + tmp28
    tmp30 = tmp14 + tmp29
    tmp33 = tmp31 * tmp32
    tmp36 = tmp34 * tmp35
    tmp37 = tmp33 + tmp36
    tmp40 = tmp38 * tmp39
    tmp41 = tmp37 + tmp40
    tmp44 = tmp42 * tmp43
    tmp45 = tmp41 + tmp44
    tmp46 = tmp30 + tmp45
    tmp49 = tmp47 * tmp48
    tmp52 = tmp50 * tmp51
    tmp53 = tmp49 + tmp52
    tmp56 = tmp54 * tmp55
    tmp57 = tmp53 + tmp56
    tmp60 = tmp58 * tmp59
    tmp61 = tmp57 + tmp60
    tmp62 = tmp46 + tmp61
    tmp63 = tmp0 * tmp0
    tmp64 = tmp3 * tmp3
    tmp65 = tmp63 + tmp64
    tmp66 = tmp7 * tmp7
    tmp67 = tmp65 + tmp66
    tmp68 = tmp11 * tmp11
    tmp69 = tmp67 + tmp68
    tmp70 = tmp15 * tmp15
    tmp71 = tmp18 * tmp18
    tmp72 = tmp70 + tmp71
    tmp73 = tmp22 * tmp22
    tmp74 = tmp72 + tmp73
    tmp75 = tmp26 * tmp26
    tmp76 = tmp74 + tmp75
    tmp77 = tmp69 + tmp76
    tmp78 = tmp31 * tmp31
    tmp79 = tmp34 * tmp34
    tmp80 = tmp78 + tmp79
    tmp81 = tmp38 * tmp38
    tmp82 = tmp80 + tmp81
    tmp83 = tmp42 * tmp42
    tmp84 = tmp82 + tmp83
    tmp85 = tmp77 + tmp84
    tmp86 = tmp47 * tmp47
    tmp87 = tmp50 * tmp50
    tmp88 = tmp86 + tmp87
    tmp89 = tmp54 * tmp54
    tmp90 = tmp88 + tmp89
    tmp91 = tmp58 * tmp58
    tmp92 = tmp90 + tmp91
    tmp93 = tmp85 + tmp92
    tmp94 = tmp1 * tmp1
    tmp95 = tmp4 * tmp4
    tmp96 = tmp94 + tmp95
    tmp97 = tmp8 * tmp8
    tmp98 = tmp96 + tmp97
    tmp99 = tmp12 * tmp12
    tmp100 = tmp98 + tmp99
    tmp101 = tmp16 * tmp16
    tmp102 = tmp19 * tmp19
    tmp103 = tmp101 + tmp102
    tmp104 = tmp23 * tmp23
    tmp105 = tmp103 + tmp104
    tmp106 = tmp27 * tmp27
    tmp107 = tmp105 + tmp106
    tmp108 = tmp100 + tmp107
    tmp109 = tmp32 * tmp32
    tmp110 = tmp35 * tmp35
    tmp111 = tmp109 + tmp110
    tmp112 = tmp39 * tmp39
    tmp113 = tmp111 + tmp112
    tmp114 = tmp43 * tmp43
    tmp115 = tmp113 + tmp114
    tmp116 = tmp108 + tmp115
    tmp117 = tmp48 * tmp48
    tmp118 = tmp51 * tmp51
    tmp119 = tmp117 + tmp118
    tmp120 = tmp55 * tmp55
    tmp121 = tmp119 + tmp120
    tmp122 = tmp59 * tmp59
    tmp123 = tmp121 + tmp122
    tmp124 = tmp116 + tmp123
    tl.store(out_ptr0 + x2, tmp62, xmask)
    tl.store(out_ptr1 + x2, tmp93, xmask)
    tl.store(out_ptr2 + x2, tmp124, xmask)


@triton.jit
def triton_per_fused_add_div_mean_mul_rsub_sum_1(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 4
    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 + 4 * r0, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + 4 * r0, None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr1 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr2 + 4 * r0, None, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr2 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr2 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr2 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 2.0
    tmp8 = tmp6 * tmp7
    tmp11 = tmp9 + tmp10
    tmp13 = tmp11 + tmp12
    tmp15 = tmp13 + tmp14
    tmp18 = tmp16 + tmp17
    tmp20 = tmp18 + tmp19
    tmp22 = tmp20 + tmp21
    tmp23 = tmp15 + tmp22
    tmp24 = 1e-05
    tmp25 = tmp23 + tmp24
    tmp26 = tmp8 / tmp25
    tmp27 = 1.0
    tmp28 = tmp27 - tmp26
    tmp29 = tl.broadcast_to(tmp28, [XBLOCK, RBLOCK])
    tmp31 = tl.sum(tmp29, 1)[:, None]
    tmp32 = 4.0
    tmp33 = tmp31 / tmp32
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp33, 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((4, 4), (4, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mul_pow_sum_0[grid(16)](arg0_1, arg1_1, buf0, buf1,
            buf2, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del arg0_1
        del arg1_1
        buf4 = empty_strided_cuda((), (), torch.float32)
        buf5 = buf4
        del buf4
        triton_per_fused_add_div_mean_mul_rsub_sum_1[grid(1)](buf5, buf0,
            buf1, buf2, 1, 4, XBLOCK=1, num_warps=2, num_stages=1)
        del buf0
        del buf1
        del buf2
    return buf5,


class DiceLossNew(nn.Module):

    def __init__(self):
        super(DiceLossNew, self).__init__()

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