# 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_1/inductor_cache/ez/cezdou3azfin5ilh3kwd4i2vvucikzhtsu4gqcobojprjh22un3o.py
# Topologically Sorted Source Nodes: [pow_1, sub, sub_1, add, log, mul, mul_1, temp1, sub_2, pow_2, add_1, log_1, mul_2, mul_3, temp2, temp, mean], Original ATen: [aten.pow, aten.rsub, aten.add, aten.log, aten.mul, aten.neg, aten.mean]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   log => log
#   log_1 => log_1
#   mean => mean
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   mul_3 => mul_3
#   pow_1 => pow_1
#   pow_2 => pow_2
#   sub => sub
#   sub_1 => sub_1
#   sub_2 => sub_2
#   temp => add_2
#   temp1 => neg
#   temp2 => neg_1
# Graph fragment:
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%arg0_1, 0), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg1_1), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg0_1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub_1, 1e-06), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %log), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_1, %mul), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%mul_1,), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg0_1), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_2, 0), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%arg0_1, 1e-06), kwargs = {})
#   %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add_1,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg1_1, %log_1), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_2, %mul_2), kwargs = {})
#   %neg_1 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%mul_3,), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%neg, %neg_1), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%add_2, [0, 1]), kwargs = {})
triton_per_fused_add_log_mean_mul_neg_pow_rsub_0 = async_compile.triton('triton_per_fused_add_log_mean_mul_neg_pow_rsub_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=[16, 16],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_log_mean_mul_neg_pow_rsub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 1, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_log_mean_mul_neg_pow_rsub_0(in_ptr0, in_ptr1, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (16*r1)), xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (x0 + (16*r1)), xmask, other=0.0)
    tmp2 = 1.0
    tmp3 = tmp2 - tmp1
    tmp4 = tmp2 - tmp0
    tmp5 = 1e-06
    tmp6 = tmp4 + tmp5
    tmp7 = tl_math.log(tmp6)
    tmp8 = tmp3 * tmp7
    tmp9 = tmp2 * tmp8
    tmp10 = -tmp9
    tmp11 = tmp0 + tmp5
    tmp12 = tl_math.log(tmp11)
    tmp13 = tmp1 * tmp12
    tmp14 = tmp2 * tmp13
    tmp15 = -tmp14
    tmp16 = tmp10 + tmp15
    tmp17 = tl.broadcast_to(tmp16, [XBLOCK, RBLOCK])
    tmp19 = tl.where(xmask, tmp17, 0)
    tmp20 = tl.sum(tmp19, 1)[:, None]
    tl.store(out_ptr0 + (x0), tmp20, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/jv/cjv34y65ewsws7xrm7m2itmkec345non5jnfp6izk4nmjaytqnqz.py
# Topologically Sorted Source Nodes: [mul_4, intersection_positive, add_4, abs_1, abs_2, add_3, cardinality_positive, add_5, dice_positive, sub_3, sub_4, mul_5, intersection_negative, add_6, abs_3, sub_5, abs_4, sub_6, cardinality_negative, add_7, dice_negative], Original ATen: [aten.mul, aten.sum, aten.add, aten.abs, aten.div, aten.rsub, aten.sub]
# Source node to ATen node mapping:
#   abs_1 => abs_1
#   abs_2 => abs_2
#   abs_3 => abs_3
#   abs_4 => abs_4
#   add_3 => add_3
#   add_4 => add_4
#   add_5 => add_5
#   add_6 => add_6
#   add_7 => add_7
#   cardinality_negative => sum_5
#   cardinality_positive => sum_3
#   dice_negative => div_1
#   dice_positive => div
#   intersection_negative => sum_4
#   intersection_positive => sum_2
#   mul_4 => mul_4
#   mul_5 => mul_5
#   sub_3 => sub_3
#   sub_4 => sub_4
#   sub_5 => sub_5
#   sub_6 => sub_6
# Graph fragment:
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, %arg1_1), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_4, [1]), kwargs = {})
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_2, 1e-06), kwargs = {})
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%arg0_1,), kwargs = {})
#   %abs_2 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%arg1_1,), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%abs_1, %abs_2), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%add_3, [1]), kwargs = {})
#   %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_3, 1e-06), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add_4, %add_5), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %arg0_1), kwargs = {})
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %arg1_1), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_3, %sub_4), kwargs = {})
#   %sum_4 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_5, [1]), kwargs = {})
#   %add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_4, 1e-06), kwargs = {})
#   %abs_3 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%arg0_1,), kwargs = {})
#   %sub_5 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (2, %abs_3), kwargs = {})
#   %abs_4 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%arg1_1,), kwargs = {})
#   %sub_6 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub_5, %abs_4), kwargs = {})
#   %sum_5 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%sub_6, [1]), kwargs = {})
#   %add_7 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_5, 1e-06), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add_6, %add_7), kwargs = {})
triton_poi_fused_abs_add_div_mul_rsub_sub_sum_1 = async_compile.triton('triton_poi_fused_abs_add_div_mul_rsub_sub_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.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=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_abs_add_div_mul_rsub_sub_sum_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_abs_add_div_mul_rsub_sub_sum_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, 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 + (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)
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tmp15 = 1e-06
    tmp16 = tmp14 + tmp15
    tmp17 = tl_math.abs(tmp0)
    tmp18 = tl_math.abs(tmp1)
    tmp19 = tmp17 + tmp18
    tmp20 = tl_math.abs(tmp3)
    tmp21 = tl_math.abs(tmp4)
    tmp22 = tmp20 + tmp21
    tmp23 = tmp19 + tmp22
    tmp24 = tl_math.abs(tmp7)
    tmp25 = tl_math.abs(tmp8)
    tmp26 = tmp24 + tmp25
    tmp27 = tmp23 + tmp26
    tmp28 = tl_math.abs(tmp11)
    tmp29 = tl_math.abs(tmp12)
    tmp30 = tmp28 + tmp29
    tmp31 = tmp27 + tmp30
    tmp32 = tmp31 + tmp15
    tmp33 = tmp16 / tmp32
    tmp34 = 1.0
    tmp35 = tmp34 - tmp0
    tmp36 = tmp34 - tmp1
    tmp37 = tmp35 * tmp36
    tmp38 = tmp34 - tmp3
    tmp39 = tmp34 - tmp4
    tmp40 = tmp38 * tmp39
    tmp41 = tmp37 + tmp40
    tmp42 = tmp34 - tmp7
    tmp43 = tmp34 - tmp8
    tmp44 = tmp42 * tmp43
    tmp45 = tmp41 + tmp44
    tmp46 = tmp34 - tmp11
    tmp47 = tmp34 - tmp12
    tmp48 = tmp46 * tmp47
    tmp49 = tmp45 + tmp48
    tmp50 = tmp49 + tmp15
    tmp51 = 2.0
    tmp52 = tmp51 - tmp17
    tmp53 = tmp52 - tmp18
    tmp54 = tmp51 - tmp20
    tmp55 = tmp54 - tmp21
    tmp56 = tmp53 + tmp55
    tmp57 = tmp51 - tmp24
    tmp58 = tmp57 - tmp25
    tmp59 = tmp56 + tmp58
    tmp60 = tmp51 - tmp28
    tmp61 = tmp60 - tmp29
    tmp62 = tmp59 + tmp61
    tmp63 = tmp62 + tmp15
    tmp64 = tmp50 / tmp63
    tl.store(out_ptr0 + (x2), tmp33, xmask)
    tl.store(out_ptr1 + (x2), tmp64, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/kz/ckzgxfstujogkk2b75muv7bj4vqqcpjzmorl3pj6ssqz2mkhtyqe.py
# Topologically Sorted Source Nodes: [pow_1, sub, sub_1, add, log, mul, mul_1, temp1, sub_2, pow_2, add_1, log_1, mul_2, mul_3, temp2, temp, mean, CELoss, sub_7, sub_8, temp3, DICELoss, mul_6, add_8], Original ATen: [aten.pow, aten.rsub, aten.add, aten.log, aten.mul, aten.neg, aten.mean, aten.sum, aten.sub]
# Source node to ATen node mapping:
#   CELoss => sum_1
#   DICELoss => sum_6
#   add => add
#   add_1 => add_1
#   add_8 => add_8
#   log => log
#   log_1 => log_1
#   mean => mean
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   mul_3 => mul_3
#   mul_6 => mul_6
#   pow_1 => pow_1
#   pow_2 => pow_2
#   sub => sub
#   sub_1 => sub_1
#   sub_2 => sub_2
#   sub_7 => sub_7
#   sub_8 => sub_8
#   temp => add_2
#   temp1 => neg
#   temp2 => neg_1
#   temp3 => mean_1
# Graph fragment:
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%arg0_1, 0), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg1_1), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg0_1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub_1, 1e-06), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %log), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_1, %mul), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%mul_1,), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg0_1), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_2, 0), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%arg0_1, 1e-06), kwargs = {})
#   %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add_1,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg1_1, %log_1), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_2, %mul_2), kwargs = {})
#   %neg_1 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%mul_3,), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%neg, %neg_1), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%add_2, [0, 1]), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mean,), kwargs = {})
#   %sub_7 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.5, %div), kwargs = {})
#   %sub_8 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub_7, %div_1), kwargs = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%sub_8, [0]), kwargs = {})
#   %sum_6 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mean_1,), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_6, 1.0), kwargs = {})
#   %add_8 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, %mul_6), kwargs = {})
triton_per_fused_add_log_mean_mul_neg_pow_rsub_sub_sum_2 = async_compile.triton('triton_per_fused_add_log_mean_mul_neg_pow_rsub_sub_sum_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.persistent_reduction(
    size_hints=[1, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {4: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 5), equal_to_1=(4,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_log_mean_mul_neg_pow_rsub_sub_sum_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 9, 'num_reduction': 2, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_log_mean_mul_neg_pow_rsub_sub_sum_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    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)
    tmp6 = tl.load(in_ptr1 + (r0), None)
    tmp9 = tl.load(in_ptr2 + (r0), None)
    tmp11 = tl.load(in_ptr1 + (16 + r0), None)
    tmp13 = tl.load(in_ptr2 + (16 + r0), None)
    tmp16 = tl.load(in_ptr1 + (32 + r0), None)
    tmp18 = tl.load(in_ptr2 + (32 + r0), None)
    tmp21 = tl.load(in_ptr1 + (48 + r0), None)
    tmp23 = tl.load(in_ptr2 + (48 + r0), None)
    tmp1 = 16.0
    tmp2 = tmp0 / tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.sum(tmp3, 1)[:, None]
    tmp7 = 1.5
    tmp8 = tmp7 - tmp6
    tmp10 = tmp8 - tmp9
    tmp12 = tmp7 - tmp11
    tmp14 = tmp12 - tmp13
    tmp15 = tmp10 + tmp14
    tmp17 = tmp7 - tmp16
    tmp19 = tmp17 - tmp18
    tmp20 = tmp15 + tmp19
    tmp22 = tmp7 - tmp21
    tmp24 = tmp22 - tmp23
    tmp25 = tmp20 + tmp24
    tmp26 = 4.0
    tmp27 = tmp25 / tmp26
    tmp28 = tl.broadcast_to(tmp27, [XBLOCK, RBLOCK])
    tmp30 = tl.sum(tmp28, 1)[:, None]
    tmp31 = 1.0
    tmp32 = tmp30 * tmp31
    tmp33 = tmp5 + 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)
        # Topologically Sorted Source Nodes: [pow_1, sub, sub_1, add, log, mul, mul_1, temp1, sub_2, pow_2, add_1, log_1, mul_2, mul_3, temp2, temp, mean], Original ATen: [aten.pow, aten.rsub, aten.add, aten.log, aten.mul, aten.neg, aten.mean]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_log_mean_mul_neg_pow_rsub_0.run(arg0_1, arg1_1, buf0, 16, 16, grid=grid(16), stream=stream0)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul_4, intersection_positive, add_4, abs_1, abs_2, add_3, cardinality_positive, add_5, dice_positive, sub_3, sub_4, mul_5, intersection_negative, add_6, abs_3, sub_5, abs_4, sub_6, cardinality_negative, add_7, dice_negative], Original ATen: [aten.mul, aten.sum, aten.add, aten.abs, aten.div, aten.rsub, aten.sub]
        triton_poi_fused_abs_add_div_mul_rsub_sub_sum_1.run(arg0_1, arg1_1, buf2, buf3, 64, grid=grid(64), stream=stream0)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf5 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [pow_1, sub, sub_1, add, log, mul, mul_1, temp1, sub_2, pow_2, add_1, log_1, mul_2, mul_3, temp2, temp, mean, CELoss, sub_7, sub_8, temp3, DICELoss, mul_6, add_8], Original ATen: [aten.pow, aten.rsub, aten.add, aten.log, aten.mul, aten.neg, aten.mean, aten.sum, aten.sub]
        triton_per_fused_add_log_mean_mul_neg_pow_rsub_sub_sum_2.run(buf5, buf0, buf2, buf3, 1, 16, grid=grid(1), stream=stream0)
        del buf0
        del buf2
        del buf3
    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


class EstimationLoss(nn.Module):

    def __init__(self):
        super(EstimationLoss, self).__init__()
        self.gamma = 0
        self.alpha = 0

    def forward(self, pred, target):
        temp1 = -torch.mul(pred ** self.gamma, torch.mul(1 - target, torch.
            log(1 - pred + 1e-06)))
        temp2 = -torch.mul((1 - pred) ** self.gamma, torch.mul(target,
            torch.log(pred + 1e-06)))
        temp = temp1 + temp2
        CELoss = torch.sum(torch.mean(temp, (0, 1)))
        intersection_positive = torch.sum(pred * target, 1)
        cardinality_positive = torch.sum(torch.abs(pred) + torch.abs(target), 1
            )
        dice_positive = (intersection_positive + 1e-06) / (cardinality_positive
             + 1e-06)
        intersection_negative = torch.sum((1.0 - pred) * (1.0 - target), 1)
        cardinality_negative = torch.sum(2 - torch.abs(pred) - torch.abs(
            target), 1)
        dice_negative = (intersection_negative + 1e-06) / (cardinality_negative
             + 1e-06)
        temp3 = torch.mean(1.5 - dice_positive - dice_negative, 0)
        DICELoss = torch.sum(temp3)
        return CELoss + 1.0 * DICELoss


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
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as 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_add_log_mean_mul_neg_pow_rsub_0(in_ptr0, in_ptr1,
    out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    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 + (x0 + 16 * r1), xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (x0 + 16 * r1), xmask, other=0.0)
    tmp2 = 1.0
    tmp3 = tmp2 - tmp1
    tmp4 = tmp2 - tmp0
    tmp5 = 1e-06
    tmp6 = tmp4 + tmp5
    tmp7 = tl_math.log(tmp6)
    tmp8 = tmp3 * tmp7
    tmp9 = tmp2 * tmp8
    tmp10 = -tmp9
    tmp11 = tmp0 + tmp5
    tmp12 = tl_math.log(tmp11)
    tmp13 = tmp1 * tmp12
    tmp14 = tmp2 * tmp13
    tmp15 = -tmp14
    tmp16 = tmp10 + tmp15
    tmp17 = tl.broadcast_to(tmp16, [XBLOCK, RBLOCK])
    tmp19 = tl.where(xmask, tmp17, 0)
    tmp20 = tl.sum(tmp19, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp20, xmask)


@triton.jit
def triton_poi_fused_abs_add_div_mul_rsub_sub_sum_1(in_ptr0, in_ptr1,
    out_ptr0, out_ptr1, 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 + (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)
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tmp15 = 1e-06
    tmp16 = tmp14 + tmp15
    tmp17 = tl_math.abs(tmp0)
    tmp18 = tl_math.abs(tmp1)
    tmp19 = tmp17 + tmp18
    tmp20 = tl_math.abs(tmp3)
    tmp21 = tl_math.abs(tmp4)
    tmp22 = tmp20 + tmp21
    tmp23 = tmp19 + tmp22
    tmp24 = tl_math.abs(tmp7)
    tmp25 = tl_math.abs(tmp8)
    tmp26 = tmp24 + tmp25
    tmp27 = tmp23 + tmp26
    tmp28 = tl_math.abs(tmp11)
    tmp29 = tl_math.abs(tmp12)
    tmp30 = tmp28 + tmp29
    tmp31 = tmp27 + tmp30
    tmp32 = tmp31 + tmp15
    tmp33 = tmp16 / tmp32
    tmp34 = 1.0
    tmp35 = tmp34 - tmp0
    tmp36 = tmp34 - tmp1
    tmp37 = tmp35 * tmp36
    tmp38 = tmp34 - tmp3
    tmp39 = tmp34 - tmp4
    tmp40 = tmp38 * tmp39
    tmp41 = tmp37 + tmp40
    tmp42 = tmp34 - tmp7
    tmp43 = tmp34 - tmp8
    tmp44 = tmp42 * tmp43
    tmp45 = tmp41 + tmp44
    tmp46 = tmp34 - tmp11
    tmp47 = tmp34 - tmp12
    tmp48 = tmp46 * tmp47
    tmp49 = tmp45 + tmp48
    tmp50 = tmp49 + tmp15
    tmp51 = 2.0
    tmp52 = tmp51 - tmp17
    tmp53 = tmp52 - tmp18
    tmp54 = tmp51 - tmp20
    tmp55 = tmp54 - tmp21
    tmp56 = tmp53 + tmp55
    tmp57 = tmp51 - tmp24
    tmp58 = tmp57 - tmp25
    tmp59 = tmp56 + tmp58
    tmp60 = tmp51 - tmp28
    tmp61 = tmp60 - tmp29
    tmp62 = tmp59 + tmp61
    tmp63 = tmp62 + tmp15
    tmp64 = tmp50 / tmp63
    tl.store(out_ptr0 + x2, tmp33, xmask)
    tl.store(out_ptr1 + x2, tmp64, xmask)


@triton.jit
def triton_per_fused_add_log_mean_mul_neg_pow_rsub_sub_sum_2(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 16
    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)
    tmp6 = tl.load(in_ptr1 + r0, None)
    tmp9 = tl.load(in_ptr2 + r0, None)
    tmp11 = tl.load(in_ptr1 + (16 + r0), None)
    tmp13 = tl.load(in_ptr2 + (16 + r0), None)
    tmp16 = tl.load(in_ptr1 + (32 + r0), None)
    tmp18 = tl.load(in_ptr2 + (32 + r0), None)
    tmp21 = tl.load(in_ptr1 + (48 + r0), None)
    tmp23 = tl.load(in_ptr2 + (48 + r0), None)
    tmp1 = 16.0
    tmp2 = tmp0 / tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.sum(tmp3, 1)[:, None]
    tmp7 = 1.5
    tmp8 = tmp7 - tmp6
    tmp10 = tmp8 - tmp9
    tmp12 = tmp7 - tmp11
    tmp14 = tmp12 - tmp13
    tmp15 = tmp10 + tmp14
    tmp17 = tmp7 - tmp16
    tmp19 = tmp17 - tmp18
    tmp20 = tmp15 + tmp19
    tmp22 = tmp7 - tmp21
    tmp24 = tmp22 - tmp23
    tmp25 = tmp20 + tmp24
    tmp26 = 4.0
    tmp27 = tmp25 / tmp26
    tmp28 = tl.broadcast_to(tmp27, [XBLOCK, RBLOCK])
    tmp30 = tl.sum(tmp28, 1)[:, None]
    tmp31 = 1.0
    tmp32 = tmp30 * tmp31
    tmp33 = tmp5 + 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)
        get_raw_stream(0)
        triton_per_fused_add_log_mean_mul_neg_pow_rsub_0[grid(16)](arg0_1,
            arg1_1, buf0, 16, 16, XBLOCK=1, num_warps=2, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_abs_add_div_mul_rsub_sub_sum_1[grid(64)](arg0_1,
            arg1_1, buf2, buf3, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf5 = buf1
        del buf1
        triton_per_fused_add_log_mean_mul_neg_pow_rsub_sub_sum_2[grid(1)](buf5,
            buf0, buf2, buf3, 1, 16, XBLOCK=1, num_warps=2, num_stages=1)
        del buf0
        del buf2
        del buf3
    return buf5,


class EstimationLossNew(nn.Module):

    def __init__(self):
        super(EstimationLossNew, self).__init__()
        self.gamma = 0
        self.alpha = 0

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