# 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_6/inductor_cache/4d/c4dcyymmcgaffdelw4oi7cd7jv5yyjelxufef3522y5v7ybthw6v.py
# Topologically Sorted Source Nodes: [intersection, sum_1, sum_2, sum_3], Original ATen: [aten.mul, aten.sum]
# Source node to ATen node mapping:
#   intersection => mul
#   sum_1 => sum_1
#   sum_2 => sum_2
#   sum_3 => sum_3
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view, %view_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 = (%view, [1]), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%view_1, [1]), kwargs = {})
triton_per_fused_mul_sum_0 = async_compile.triton('triton_per_fused_mul_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.persistent_reduction(
    size_hints=[4, 16],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: '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, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_mul_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 3, '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_mul_sum_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    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 + (r1 + (64*x0)), xmask, other=0.0)
    tmp2 = tl.load(in_ptr1 + (r1 + (64*x0)), xmask, other=0.0)
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.where(xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp10 = tl.where(xmask, tmp8, 0)
    tmp11 = tl.sum(tmp10, 1)[:, None]
    tmp12 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp14 = tl.where(xmask, tmp12, 0)
    tmp15 = tl.sum(tmp14, 1)[:, None]
    tl.store(out_ptr0 + (x0), tmp7, xmask)
    tl.store(out_ptr1 + (x0), tmp11, xmask)
    tl.store(out_ptr2 + (x0), tmp15, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/6j/c6je6tk67db7lcbzlvykhcl4bbigvt5qvtrraruxnajjb4ouinpr.py
# Topologically Sorted Source Nodes: [intersection_2, sum_9, sum_10, sum_11], Original ATen: [aten.mul, aten.sum]
# Source node to ATen node mapping:
#   intersection_2 => mul_4
#   sum_10 => sum_10
#   sum_11 => sum_11
#   sum_9 => sum_9
# Graph fragment:
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_4, %view_5), kwargs = {})
#   %sum_9 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_4, [1]), kwargs = {})
#   %sum_10 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%view_4, [1]), kwargs = {})
#   %sum_11 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%view_5, [1]), kwargs = {})
triton_per_fused_mul_sum_1 = async_compile.triton('triton_per_fused_mul_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=[4, 16],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: '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, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_mul_sum_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 3, '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_mul_sum_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    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 + (32 + r1 + (64*x0)), xmask, other=0.0)
    tmp2 = tl.load(in_ptr1 + (32 + r1 + (64*x0)), xmask, other=0.0)
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.where(xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp10 = tl.where(xmask, tmp8, 0)
    tmp11 = tl.sum(tmp10, 1)[:, None]
    tmp12 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp14 = tl.where(xmask, tmp12, 0)
    tmp15 = tl.sum(tmp14, 1)[:, None]
    tl.store(out_ptr0 + (x0), tmp7, xmask)
    tl.store(out_ptr1 + (x0), tmp11, xmask)
    tl.store(out_ptr2 + (x0), tmp15, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/mo/cmolo25g2iaesx5queuzdwbachbqylq65gwhqmczzy7w2324bupw.py
# Topologically Sorted Source Nodes: [intersection_3, sum_13, sum_14, sum_15], Original ATen: [aten.mul, aten.sum]
# Source node to ATen node mapping:
#   intersection_3 => mul_6
#   sum_13 => sum_13
#   sum_14 => sum_14
#   sum_15 => sum_15
# Graph fragment:
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_6, %view_7), kwargs = {})
#   %sum_13 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_6, [1]), kwargs = {})
#   %sum_14 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%view_6, [1]), kwargs = {})
#   %sum_15 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%view_7, [1]), kwargs = {})
triton_per_fused_mul_sum_2 = async_compile.triton('triton_per_fused_mul_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=[4, 16],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: '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, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_mul_sum_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 3, '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_mul_sum_2(in_ptr0, in_ptr1, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    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 + (48 + r1 + (64*x0)), xmask, other=0.0)
    tmp2 = tl.load(in_ptr1 + (48 + r1 + (64*x0)), xmask, other=0.0)
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.where(xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp10 = tl.where(xmask, tmp8, 0)
    tmp11 = tl.sum(tmp10, 1)[:, None]
    tmp12 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp14 = tl.where(xmask, tmp12, 0)
    tmp15 = tl.sum(tmp14, 1)[:, None]
    tl.store(out_ptr0 + (x0), tmp7, xmask)
    tl.store(out_ptr1 + (x0), tmp11, xmask)
    tl.store(out_ptr2 + (x0), tmp15, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/gd/cgdw7wlhrxvoliweldjch6hsjibasixpmtt2jks3xjt555j7k3vw.py
# Topologically Sorted Source Nodes: [intersection_1, sum_5, sum_6, sum_7], Original ATen: [aten.mul, aten.sum]
# Source node to ATen node mapping:
#   intersection_1 => mul_2
#   sum_5 => sum_5
#   sum_6 => sum_6
#   sum_7 => sum_7
# Graph fragment:
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_2, %view_3), kwargs = {})
#   %sum_5 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_2, [1]), kwargs = {})
#   %sum_6 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%view_2, [1]), kwargs = {})
#   %sum_7 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%view_3, [1]), kwargs = {})
triton_per_fused_mul_sum_3 = async_compile.triton('triton_per_fused_mul_sum_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=[4, 16],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: '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, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_mul_sum_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 3, '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_mul_sum_3(in_ptr0, in_ptr1, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    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 + (16 + r1 + (64*x0)), xmask, other=0.0)
    tmp2 = tl.load(in_ptr1 + (16 + r1 + (64*x0)), xmask, other=0.0)
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.where(xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp10 = tl.where(xmask, tmp8, 0)
    tmp11 = tl.sum(tmp10, 1)[:, None]
    tmp12 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp14 = tl.where(xmask, tmp12, 0)
    tmp15 = tl.sum(tmp14, 1)[:, None]
    tl.store(out_ptr0 + (x0), tmp7, xmask)
    tl.store(out_ptr1 + (x0), tmp11, xmask)
    tl.store(out_ptr2 + (x0), tmp15, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/yd/cydt7aqo3safvxzunol6bcirceguofy272zlo7wb4gobi6glkk6t.py
# Topologically Sorted Source Nodes: [add, mul_1, add_1, add_2, loss, sum_4, truediv_1, loss_1, totalLoss, add_4, mul_3, add_5, add_6, loss_2, sum_8, truediv_3, loss_3, totalLoss_1, add_7, mul_5, add_8, add_9, loss_4, sum_12, truediv_5, loss_5, totalLoss_2, add_10, mul_7, add_11, add_12, loss_6, sum_16, truediv_7, loss_7, totalLoss_3], Original ATen: [aten.add, aten.mul, aten.div, aten.sum, aten.rsub]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   add_10 => add_12
#   add_11 => add_13
#   add_12 => add_14
#   add_2 => add_2
#   add_4 => add_4
#   add_5 => add_5
#   add_6 => add_6
#   add_7 => add_8
#   add_8 => add_9
#   add_9 => add_10
#   loss => div
#   loss_1 => sub
#   loss_2 => div_2
#   loss_3 => sub_1
#   loss_4 => div_4
#   loss_5 => sub_2
#   loss_6 => div_6
#   loss_7 => sub_3
#   mul_1 => mul_1
#   mul_3 => mul_3
#   mul_5 => mul_5
#   mul_7 => mul_7
#   sum_12 => sum_12
#   sum_16 => sum_16
#   sum_4 => sum_4
#   sum_8 => sum_8
#   totalLoss => add_3
#   totalLoss_1 => add_7
#   totalLoss_2 => add_11
#   totalLoss_3 => add_15
#   truediv_1 => div_1
#   truediv_3 => div_3
#   truediv_5 => div_5
#   truediv_7 => div_7
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, 1), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, 2), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_2, %sum_3), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_1, 1), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul_1, %add_2), kwargs = {})
#   %sum_4 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%div,), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_4, 4), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %div_1), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub, 0), kwargs = {})
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_5, 1), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_4, 2), kwargs = {})
#   %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_6, %sum_7), kwargs = {})
#   %add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_5, 1), kwargs = {})
#   %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul_3, %add_6), kwargs = {})
#   %sum_8 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%div_2,), kwargs = {})
#   %div_3 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_8, 4), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %div_3), kwargs = {})
#   %add_7 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_3, %sub_1), kwargs = {})
#   %add_8 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_9, 1), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_8, 2), kwargs = {})
#   %add_9 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_10, %sum_11), kwargs = {})
#   %add_10 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_9, 1), kwargs = {})
#   %div_4 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul_5, %add_10), kwargs = {})
#   %sum_12 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%div_4,), kwargs = {})
#   %div_5 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_12, 4), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %div_5), kwargs = {})
#   %add_11 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_7, %sub_2), kwargs = {})
#   %add_12 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_13, 1), kwargs = {})
#   %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_12, 2), kwargs = {})
#   %add_13 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_14, %sum_15), kwargs = {})
#   %add_14 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_13, 1), kwargs = {})
#   %div_6 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul_7, %add_14), kwargs = {})
#   %sum_16 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%div_6,), kwargs = {})
#   %div_7 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_16, 4), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %div_7), kwargs = {})
#   %add_15 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_11, %sub_3), kwargs = {})
triton_per_fused_add_div_mul_rsub_sum_4 = async_compile.triton('triton_per_fused_add_div_mul_rsub_sum_4', '''
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.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: '*fp32', 9: '*fp32', 10: '*fp32', 11: '*fp32', 12: '*fp32', 13: 'i32', 14: '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': {13: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), equal_to_1=(13,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_mul_rsub_sum_4', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 12, 'num_reduction': 4, '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_mul_rsub_sum_4(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, in_ptr8, in_ptr9, in_ptr10, in_ptr11, 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 + (r0), None)
    tmp5 = tl.load(in_ptr1 + (r0), None)
    tmp6 = tl.load(in_ptr2 + (r0), None)
    tmp13 = tl.load(in_ptr3 + (r0), None)
    tmp16 = tl.load(in_ptr4 + (r0), None)
    tmp17 = tl.load(in_ptr5 + (r0), None)
    tmp24 = tl.load(in_ptr6 + (r0), None)
    tmp27 = tl.load(in_ptr7 + (r0), None)
    tmp28 = tl.load(in_ptr8 + (r0), None)
    tmp35 = tl.load(in_ptr9 + (r0), None)
    tmp38 = tl.load(in_ptr10 + (r0), None)
    tmp39 = tl.load(in_ptr11 + (r0), None)
    tmp1 = 1.0
    tmp2 = tmp0 + tmp1
    tmp3 = 2.0
    tmp4 = tmp2 * tmp3
    tmp7 = tmp5 + tmp6
    tmp8 = tmp7 + tmp1
    tmp9 = tmp4 / tmp8
    tmp10 = tl.broadcast_to(tmp9, [XBLOCK, RBLOCK])
    tmp12 = tl.sum(tmp10, 1)[:, None]
    tmp14 = tmp13 + tmp1
    tmp15 = tmp14 * tmp3
    tmp18 = tmp16 + tmp17
    tmp19 = tmp18 + tmp1
    tmp20 = tmp15 / tmp19
    tmp21 = tl.broadcast_to(tmp20, [XBLOCK, RBLOCK])
    tmp23 = tl.sum(tmp21, 1)[:, None]
    tmp25 = tmp24 + tmp1
    tmp26 = tmp25 * tmp3
    tmp29 = tmp27 + tmp28
    tmp30 = tmp29 + tmp1
    tmp31 = tmp26 / tmp30
    tmp32 = tl.broadcast_to(tmp31, [XBLOCK, RBLOCK])
    tmp34 = tl.sum(tmp32, 1)[:, None]
    tmp36 = tmp35 + tmp1
    tmp37 = tmp36 * tmp3
    tmp40 = tmp38 + tmp39
    tmp41 = tmp40 + tmp1
    tmp42 = tmp37 / tmp41
    tmp43 = tl.broadcast_to(tmp42, [XBLOCK, RBLOCK])
    tmp45 = tl.sum(tmp43, 1)[:, None]
    tmp46 = 0.25
    tmp47 = tmp12 * tmp46
    tmp48 = tmp1 - tmp47
    tmp49 = 0.0
    tmp50 = tmp48 + tmp49
    tmp51 = tmp23 * tmp46
    tmp52 = tmp1 - tmp51
    tmp53 = tmp50 + tmp52
    tmp54 = tmp34 * tmp46
    tmp55 = tmp1 - tmp54
    tmp56 = tmp53 + tmp55
    tmp57 = tmp45 * tmp46
    tmp58 = tmp1 - tmp57
    tmp59 = tmp56 + tmp58
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp59, 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, ), (1, ), torch.float32)
        buf1 = empty_strided_cuda((4, ), (1, ), torch.float32)
        buf2 = empty_strided_cuda((4, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [intersection, sum_1, sum_2, sum_3], Original ATen: [aten.mul, aten.sum]
        stream0 = get_raw_stream(0)
        triton_per_fused_mul_sum_0.run(arg1_1, arg0_1, buf0, buf1, buf2, 4, 16, grid=grid(4), stream=stream0)
        buf8 = empty_strided_cuda((4, ), (1, ), torch.float32)
        buf9 = empty_strided_cuda((4, ), (1, ), torch.float32)
        buf10 = empty_strided_cuda((4, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [intersection_2, sum_9, sum_10, sum_11], Original ATen: [aten.mul, aten.sum]
        triton_per_fused_mul_sum_1.run(arg1_1, arg0_1, buf8, buf9, buf10, 4, 16, grid=grid(4), stream=stream0)
        buf12 = empty_strided_cuda((4, ), (1, ), torch.float32)
        buf13 = empty_strided_cuda((4, ), (1, ), torch.float32)
        buf14 = empty_strided_cuda((4, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [intersection_3, sum_13, sum_14, sum_15], Original ATen: [aten.mul, aten.sum]
        triton_per_fused_mul_sum_2.run(arg1_1, arg0_1, buf12, buf13, buf14, 4, 16, grid=grid(4), stream=stream0)
        buf4 = empty_strided_cuda((4, ), (1, ), torch.float32)
        buf5 = empty_strided_cuda((4, ), (1, ), torch.float32)
        buf6 = empty_strided_cuda((4, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [intersection_1, sum_5, sum_6, sum_7], Original ATen: [aten.mul, aten.sum]
        triton_per_fused_mul_sum_3.run(arg1_1, arg0_1, buf4, buf5, buf6, 4, 16, grid=grid(4), stream=stream0)
        del arg0_1
        del arg1_1
        buf11 = empty_strided_cuda((), (), torch.float32)
        buf16 = buf11; del buf11  # reuse
        # Topologically Sorted Source Nodes: [add, mul_1, add_1, add_2, loss, sum_4, truediv_1, loss_1, totalLoss, add_4, mul_3, add_5, add_6, loss_2, sum_8, truediv_3, loss_3, totalLoss_1, add_7, mul_5, add_8, add_9, loss_4, sum_12, truediv_5, loss_5, totalLoss_2, add_10, mul_7, add_11, add_12, loss_6, sum_16, truediv_7, loss_7, totalLoss_3], Original ATen: [aten.add, aten.mul, aten.div, aten.sum, aten.rsub]
        triton_per_fused_add_div_mul_rsub_sum_4.run(buf16, buf0, buf1, buf2, buf4, buf5, buf6, buf8, buf9, buf10, buf12, buf13, buf14, 1, 4, grid=grid(1), stream=stream0)
        del buf0
        del buf1
        del buf10
        del buf12
        del buf13
        del buf14
        del buf2
        del buf4
        del buf5
        del buf6
        del buf8
        del buf9
    return (buf16, )


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 DiceLoss(nn.Module):

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

    def forward(self, input, target, logits=True):
        if logits:
            input = nn.Sigmoid()(input)
        N = target.size(0)
        smooth = 1
        input_flat = input.view(N, -1)
        target_flat = target.view(N, -1)
        intersection = input_flat * target_flat
        loss = 2 * (intersection.sum(1) + smooth) / (input_flat.sum(1) +
            target_flat.sum(1) + smooth)
        loss = 1 - loss.sum() / N
        return loss


class MulticlassDiceLoss(nn.Module):
    """
    requires one hot encoded target. Applies DiceLoss on each class iteratively.
    requires input.shape[0:1] and target.shape[0:1] to be (N, C) where N is
      batch size and C is number of classes
    """

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

    def forward(self, input, target, weights=None, logits=True):
        C = target.shape[1]
        dice = DiceLoss()
        totalLoss = 0
        for i in range(C):
            diceLoss = dice(input[:, i], target[:, i], logits)
            if weights is not None:
                diceLoss *= weights[i]
            totalLoss += diceLoss
        return totalLoss


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
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_mul_sum_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    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 + (r1 + 64 * x0), xmask, other=0.0)
    tmp2 = tl.load(in_ptr1 + (r1 + 64 * x0), xmask, other=0.0)
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.where(xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp10 = tl.where(xmask, tmp8, 0)
    tmp11 = tl.sum(tmp10, 1)[:, None]
    tmp12 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp14 = tl.where(xmask, tmp12, 0)
    tmp15 = tl.sum(tmp14, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp7, xmask)
    tl.store(out_ptr1 + x0, tmp11, xmask)
    tl.store(out_ptr2 + x0, tmp15, xmask)


@triton.jit
def triton_per_fused_mul_sum_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    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 + (32 + r1 + 64 * x0), xmask, other=0.0)
    tmp2 = tl.load(in_ptr1 + (32 + r1 + 64 * x0), xmask, other=0.0)
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.where(xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp10 = tl.where(xmask, tmp8, 0)
    tmp11 = tl.sum(tmp10, 1)[:, None]
    tmp12 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp14 = tl.where(xmask, tmp12, 0)
    tmp15 = tl.sum(tmp14, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp7, xmask)
    tl.store(out_ptr1 + x0, tmp11, xmask)
    tl.store(out_ptr2 + x0, tmp15, xmask)


@triton.jit
def triton_per_fused_mul_sum_2(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    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 + (48 + r1 + 64 * x0), xmask, other=0.0)
    tmp2 = tl.load(in_ptr1 + (48 + r1 + 64 * x0), xmask, other=0.0)
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.where(xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp10 = tl.where(xmask, tmp8, 0)
    tmp11 = tl.sum(tmp10, 1)[:, None]
    tmp12 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp14 = tl.where(xmask, tmp12, 0)
    tmp15 = tl.sum(tmp14, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp7, xmask)
    tl.store(out_ptr1 + x0, tmp11, xmask)
    tl.store(out_ptr2 + x0, tmp15, xmask)


@triton.jit
def triton_per_fused_mul_sum_3(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    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 + (16 + r1 + 64 * x0), xmask, other=0.0)
    tmp2 = tl.load(in_ptr1 + (16 + r1 + 64 * x0), xmask, other=0.0)
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.where(xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp10 = tl.where(xmask, tmp8, 0)
    tmp11 = tl.sum(tmp10, 1)[:, None]
    tmp12 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp14 = tl.where(xmask, tmp12, 0)
    tmp15 = tl.sum(tmp14, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp7, xmask)
    tl.store(out_ptr1 + x0, tmp11, xmask)
    tl.store(out_ptr2 + x0, tmp15, xmask)


@triton.jit
def triton_per_fused_add_div_mul_rsub_sum_4(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, in_ptr8, in_ptr9,
    in_ptr10, in_ptr11, 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 + r0, None)
    tmp5 = tl.load(in_ptr1 + r0, None)
    tmp6 = tl.load(in_ptr2 + r0, None)
    tmp13 = tl.load(in_ptr3 + r0, None)
    tmp16 = tl.load(in_ptr4 + r0, None)
    tmp17 = tl.load(in_ptr5 + r0, None)
    tmp24 = tl.load(in_ptr6 + r0, None)
    tmp27 = tl.load(in_ptr7 + r0, None)
    tmp28 = tl.load(in_ptr8 + r0, None)
    tmp35 = tl.load(in_ptr9 + r0, None)
    tmp38 = tl.load(in_ptr10 + r0, None)
    tmp39 = tl.load(in_ptr11 + r0, None)
    tmp1 = 1.0
    tmp2 = tmp0 + tmp1
    tmp3 = 2.0
    tmp4 = tmp2 * tmp3
    tmp7 = tmp5 + tmp6
    tmp8 = tmp7 + tmp1
    tmp9 = tmp4 / tmp8
    tmp10 = tl.broadcast_to(tmp9, [XBLOCK, RBLOCK])
    tmp12 = tl.sum(tmp10, 1)[:, None]
    tmp14 = tmp13 + tmp1
    tmp15 = tmp14 * tmp3
    tmp18 = tmp16 + tmp17
    tmp19 = tmp18 + tmp1
    tmp20 = tmp15 / tmp19
    tmp21 = tl.broadcast_to(tmp20, [XBLOCK, RBLOCK])
    tmp23 = tl.sum(tmp21, 1)[:, None]
    tmp25 = tmp24 + tmp1
    tmp26 = tmp25 * tmp3
    tmp29 = tmp27 + tmp28
    tmp30 = tmp29 + tmp1
    tmp31 = tmp26 / tmp30
    tmp32 = tl.broadcast_to(tmp31, [XBLOCK, RBLOCK])
    tmp34 = tl.sum(tmp32, 1)[:, None]
    tmp36 = tmp35 + tmp1
    tmp37 = tmp36 * tmp3
    tmp40 = tmp38 + tmp39
    tmp41 = tmp40 + tmp1
    tmp42 = tmp37 / tmp41
    tmp43 = tl.broadcast_to(tmp42, [XBLOCK, RBLOCK])
    tmp45 = tl.sum(tmp43, 1)[:, None]
    tmp46 = 0.25
    tmp47 = tmp12 * tmp46
    tmp48 = tmp1 - tmp47
    tmp49 = 0.0
    tmp50 = tmp48 + tmp49
    tmp51 = tmp23 * tmp46
    tmp52 = tmp1 - tmp51
    tmp53 = tmp50 + tmp52
    tmp54 = tmp34 * tmp46
    tmp55 = tmp1 - tmp54
    tmp56 = tmp53 + tmp55
    tmp57 = tmp45 * tmp46
    tmp58 = tmp1 - tmp57
    tmp59 = tmp56 + tmp58
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp59, 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,), (1,), torch.float32)
        buf1 = empty_strided_cuda((4,), (1,), torch.float32)
        buf2 = empty_strided_cuda((4,), (1,), torch.float32)
        get_raw_stream(0)
        triton_per_fused_mul_sum_0[grid(4)](arg1_1, arg0_1, buf0, buf1,
            buf2, 4, 16, XBLOCK=1, num_warps=2, num_stages=1)
        buf8 = empty_strided_cuda((4,), (1,), torch.float32)
        buf9 = empty_strided_cuda((4,), (1,), torch.float32)
        buf10 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_per_fused_mul_sum_1[grid(4)](arg1_1, arg0_1, buf8, buf9,
            buf10, 4, 16, XBLOCK=1, num_warps=2, num_stages=1)
        buf12 = empty_strided_cuda((4,), (1,), torch.float32)
        buf13 = empty_strided_cuda((4,), (1,), torch.float32)
        buf14 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_per_fused_mul_sum_2[grid(4)](arg1_1, arg0_1, buf12, buf13,
            buf14, 4, 16, XBLOCK=1, num_warps=2, num_stages=1)
        buf4 = empty_strided_cuda((4,), (1,), torch.float32)
        buf5 = empty_strided_cuda((4,), (1,), torch.float32)
        buf6 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_per_fused_mul_sum_3[grid(4)](arg1_1, arg0_1, buf4, buf5,
            buf6, 4, 16, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        buf11 = empty_strided_cuda((), (), torch.float32)
        buf16 = buf11
        del buf11
        triton_per_fused_add_div_mul_rsub_sum_4[grid(1)](buf16, buf0, buf1,
            buf2, buf4, buf5, buf6, buf8, buf9, buf10, buf12, buf13, buf14,
            1, 4, XBLOCK=1, num_warps=2, num_stages=1)
        del buf0
        del buf1
        del buf10
        del buf12
        del buf13
        del buf14
        del buf2
        del buf4
        del buf5
        del buf6
        del buf8
        del buf9
    return buf16,


class DiceLoss(nn.Module):

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

    def forward(self, input, target, logits=True):
        if logits:
            input = nn.Sigmoid()(input)
        N = target.size(0)
        smooth = 1
        input_flat = input.view(N, -1)
        target_flat = target.view(N, -1)
        intersection = input_flat * target_flat
        loss = 2 * (intersection.sum(1) + smooth) / (input_flat.sum(1) +
            target_flat.sum(1) + smooth)
        loss = 1 - loss.sum() / N
        return loss


class MulticlassDiceLossNew(nn.Module):
    """
    requires one hot encoded target. Applies DiceLoss on each class iteratively.
    requires input.shape[0:1] and target.shape[0:1] to be (N, C) where N is
      batch size and C is number of classes
    """

    def __init__(self):
        super(MulticlassDiceLossNew, 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]
