# 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_8/inductor_cache/d5/cd52mlrkv7fg7ptxw5vaz664ocriv2bgu4unk4oe24v6nbgmv7uv.py
# Topologically Sorted Source Nodes: [mul, intersection, ground_o, pred_o], Original ATen: [aten.mul, aten.sum]
# Source node to ATen node mapping:
#   ground_o => sum_2
#   intersection => sum_1
#   mul => mul
#   pred_o => sum_3
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg1_1, %arg0_1), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [2, 3]), kwargs = {})
#   %sum_2 : [num_users=2] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%arg1_1, [2, 3]), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%arg0_1, [2, 3]), 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=[16, 16],
    reduction_hint=ReductionHint.INNER,
    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, 5, 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 = 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 + (r1 + (16*x0)), xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (r1 + (16*x0)), xmask, other=0.0)
    tmp2 = tmp0 * tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(xmask, tmp3, 0)
    tmp6 = tl.sum(tmp5, 1)[:, None]
    tmp7 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp9 = tl.where(xmask, tmp7, 0)
    tmp10 = tl.sum(tmp9, 1)[:, None]
    tmp11 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp13 = tl.where(xmask, tmp11, 0)
    tmp14 = tl.sum(tmp13, 1)[:, None]
    tl.store(out_ptr0 + (x0), tmp6, xmask)
    tl.store(out_ptr1 + (x0), tmp10, xmask)
    tl.store(out_ptr2 + (x0), tmp14, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/6l/c6l2gkrph5u6qhzfpwcqqfu277drwwd7ra3dyhys4nf64xwbbpre.py
# Topologically Sorted Source Nodes: [setitem], Original ATen: [aten.lift_fresh, aten.index_put]
# Source node to ATen node mapping:
#   setitem => full_default, index_put
# Graph fragment:
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False})
#   %index_put : [num_users=1] = call_function[target=torch.ops.aten.index_put.default](args = (%select, [%isinf], %full_default), kwargs = {})
triton_poi_fused_index_put_lift_fresh_1 = async_compile.triton('triton_poi_fused_index_put_lift_fresh_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=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_index_put_lift_fresh_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_index_put_lift_fresh_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = tmp0 * tmp0
    tmp2 = tl.full([1], 1, tl.int32)
    tmp3 = tmp2 / tmp1
    tmp4 = libdevice.isinf(tmp3).to(tl.int1)
    tmp5 = 0.0
    tmp6 = tl.where(tmp4, tmp5, tmp3)
    tl.store(out_ptr0 + (x0), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/cy/ccy7idfxu272yvofjbbp54inr6a5zn7jkvgbq7732tfnhhq2lmr7.py
# Topologically Sorted Source Nodes: [mul_1, w], Original ATen: [aten.mul, aten.reciprocal]
# Source node to ATen node mapping:
#   mul_1 => mul_1
#   w => reciprocal
# Graph fragment:
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_2, %sum_2), kwargs = {})
#   %reciprocal : [num_users=2] = call_function[target=torch.ops.aten.reciprocal.default](args = (%mul_1,), kwargs = {})
#   %select_scatter_default : [num_users=2] = call_function[target=torch.ops.aten.select_scatter.default](args = (%reciprocal, %index_put, 0, 0), kwargs = {})
triton_poi_fused_mul_reciprocal_2 = async_compile.triton('triton_poi_fused_mul_reciprocal_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_reciprocal_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_reciprocal_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4)
    x0 = xindex % 4
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (x2), xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp5 = tmp4 * tmp4
    tmp6 = tl.full([1], 1, tl.int32)
    tmp7 = tmp6 / tmp5
    tmp8 = tl.where(tmp2, tmp3, tmp7)
    tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/jk/cjkuxs5m7ejvswg7hrk2llstygqftgn2i6wdk7h25jxqaj665z6b.py
# Topologically Sorted Source Nodes: [max_1, setitem_1], Original ATen: [aten.max, aten.index_put]
# Source node to ATen node mapping:
#   max_1 => max_1
#   setitem_1 => index_put_1
# Graph fragment:
#   %max_1 : [num_users=1] = call_function[target=torch.ops.aten.max.default](args = (%select_4,), kwargs = {})
#   %index_put_1 : [num_users=1] = call_function[target=torch.ops.aten.index_put_.default](args = (%select_4, [%isinf], %max_1), kwargs = {})
triton_per_fused_index_put_max_3 = async_compile.triton('triton_per_fused_index_put_max_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.persistent_reduction(
    size_hints=[1, 4],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_index_put_max_3', 'mutated_arg_names': ['out_ptr2'], 'no_x_dim': False, 'num_load': 2, '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_index_put_max_3(in_ptr0, in_ptr1, out_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
    tmp2 = tl.load(in_ptr0 + (r0), None)
    tmp3 = tl.load(in_ptr1 + (r0), None)
    tmp0 = tl.full([1, 1], 0, tl.int32)
    tmp1 = tmp0 == tmp0
    tmp4 = tmp3 * tmp3
    tmp5 = tl.full([1, 1], 1, tl.int32)
    tmp6 = tmp5 / tmp4
    tmp7 = tl.where(tmp1, tmp2, tmp6)
    tmp8 = tl.broadcast_to(tmp7, [XBLOCK, RBLOCK])
    tmp10 = triton_helpers.max2(tmp8, 1)[:, None]
    tmp11 = libdevice.isinf(tmp6).to(tl.int1)
    tmp12 = tl.where(tmp11, tmp10, tmp7)
    tl.store(out_ptr2 + (tl.broadcast_to(r0, [XBLOCK, RBLOCK])), tmp12, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/4q/c4ql4xyg2xlaa3uezk7gjgywmt3fw3tynazq4ckpuwcsgzw6zhei.py
# Topologically Sorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
# Graph fragment:
#   %select_scatter_default_1 : [num_users=2] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_scatter_default, %index_put_1, 0, 0), kwargs = {})
triton_poi_fused_4 = async_compile.triton('triton_poi_fused_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.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_4(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4)
    x0 = xindex % 4
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (x2), xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tl.store(out_ptr0 + (x2), tmp5, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/o5/co5ocweef7wvpqwak7rmrihdxwrqz6mechmdzkcte5vjlsv5tdhx.py
# Topologically Sorted Source Nodes: [setitem_2], Original ATen: [aten.lift_fresh, aten.index_put]
# Source node to ATen node mapping:
#   setitem_2 => full_default_1, index_put_2
# Graph fragment:
#   %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False})
#   %index_put_2 : [num_users=1] = call_function[target=torch.ops.aten.index_put_.default](args = (%select_6, [%isinf_1], %full_default_1), kwargs = {})
triton_poi_fused_index_put_lift_fresh_5 = async_compile.triton('triton_poi_fused_index_put_lift_fresh_5', '''
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=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_index_put_lift_fresh_5', 'mutated_arg_names': ['out_ptr1'], 'no_x_dim': False, 'num_load': 2, '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_index_put_lift_fresh_5(in_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp3 = tl.load(in_ptr0 + (x0), xmask)
    tmp4 = tl.load(in_ptr0 + (4 + x0), xmask)
    tmp0 = tl.full([1], 1, tl.int32)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = libdevice.isinf(tmp5).to(tl.int1)
    tmp7 = 0.0
    tmp8 = tl.where(tmp6, tmp7, tmp5)
    tl.store(out_ptr1 + (4 + x0), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/yv/cyvyap3cqstn6kbri4i6qtdbvfru2ywjrkts4h2byghwjatyop4k.py
# Topologically Sorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
# Graph fragment:
#   %select_scatter_default_2 : [num_users=2] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_scatter_default_1, %index_put_2, 0, 1), kwargs = {})
triton_poi_fused_6 = async_compile.triton('triton_poi_fused_6', '''
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: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_6(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4)
    x0 = xindex % 4
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (x2), xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 1, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tl.store(out_ptr0 + (x2), tmp5, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/tg/ctgfsxxk2urovszhep4mbnqcv7roo2a5o633p6v6krrzhegbz6tr.py
# Topologically Sorted Source Nodes: [max_2, setitem_3], Original ATen: [aten.max, aten.index_put]
# Source node to ATen node mapping:
#   max_2 => max_2
#   setitem_3 => index_put_3
# Graph fragment:
#   %max_2 : [num_users=1] = call_function[target=torch.ops.aten.max.default](args = (%select_7,), kwargs = {})
#   %index_put_3 : [num_users=1] = call_function[target=torch.ops.aten.index_put_.default](args = (%select_7, [%isinf_1], %max_2), kwargs = {})
triton_per_fused_index_put_max_7 = async_compile.triton('triton_per_fused_index_put_max_7', '''
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: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_index_put_max_7', 'mutated_arg_names': ['out_ptr2'], 'no_x_dim': False, 'num_load': 3, '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_index_put_max_7(in_ptr0, in_ptr1, out_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
    tmp2 = tl.load(in_ptr0 + (4 + r0), None)
    tmp9 = tl.load(in_ptr1 + (r0), None)
    tmp10 = tl.load(in_ptr1 + (4 + r0), None)
    tmp0 = tl.full([1, 1], 1, tl.int32)
    tmp1 = tmp0 == tmp0
    tmp3 = tl.where(tmp1, tmp2, tmp2)
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = triton_helpers.max2(tmp4, 1)[:, None]
    tmp7 = tl.full([1, 1], 0, tl.int32)
    tmp8 = tmp0 == tmp7
    tmp11 = tl.where(tmp8, tmp9, tmp10)
    tmp12 = libdevice.isinf(tmp11).to(tl.int1)
    tmp13 = tl.where(tmp12, tmp6, tmp3)
    tl.store(out_ptr2 + (tl.broadcast_to(4 + r0, [XBLOCK, RBLOCK])), tmp13, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/fy/cfy4vgr7piqugeft2crnhfbw5ielxsgean22zjdvpklq4txeer5o.py
# Topologically Sorted Source Nodes: [setitem_4], Original ATen: [aten.lift_fresh, aten.index_put]
# Source node to ATen node mapping:
#   setitem_4 => full_default_2, index_put_4
# Graph fragment:
#   %full_default_2 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False})
#   %index_put_4 : [num_users=1] = call_function[target=torch.ops.aten.index_put_.default](args = (%select_9, [%isinf_2], %full_default_2), kwargs = {})
triton_poi_fused_index_put_lift_fresh_8 = async_compile.triton('triton_poi_fused_index_put_lift_fresh_8', '''
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=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_index_put_lift_fresh_8', 'mutated_arg_names': ['out_ptr1'], 'no_x_dim': False, 'num_load': 2, '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_index_put_lift_fresh_8(in_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp3 = tl.load(in_ptr0 + (4 + x0), xmask)
    tmp4 = tl.load(in_ptr0 + (8 + x0), xmask)
    tmp0 = tl.full([1], 2, tl.int32)
    tmp1 = tl.full([1], 1, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = libdevice.isinf(tmp5).to(tl.int1)
    tmp7 = 0.0
    tmp8 = tl.where(tmp6, tmp7, tmp5)
    tl.store(out_ptr1 + (8 + x0), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/zj/czjcmriyujs5brmyh7stjrkfk37e4twzbljor3wofssbrxsuw63t.py
# Topologically Sorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
# Graph fragment:
#   %select_scatter_default_4 : [num_users=2] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_scatter_default_3, %index_put_4, 0, 2), kwargs = {})
triton_poi_fused_9 = async_compile.triton('triton_poi_fused_9', '''
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: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_9', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_9(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4)
    x0 = xindex % 4
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (x2), xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 2, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tl.store(out_ptr0 + (x2), tmp5, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/hx/chxbr5aueebc6hjsn4ej4rftudtvp5pbxyu5cbojq5zy2lksc2pa.py
# Topologically Sorted Source Nodes: [max_3, setitem_5], Original ATen: [aten.max, aten.index_put]
# Source node to ATen node mapping:
#   max_3 => max_3
#   setitem_5 => index_put_5
# Graph fragment:
#   %max_3 : [num_users=1] = call_function[target=torch.ops.aten.max.default](args = (%select_10,), kwargs = {})
#   %index_put_5 : [num_users=1] = call_function[target=torch.ops.aten.index_put_.default](args = (%select_10, [%isinf_2], %max_3), kwargs = {})
triton_per_fused_index_put_max_10 = async_compile.triton('triton_per_fused_index_put_max_10', '''
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: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_index_put_max_10', 'mutated_arg_names': ['out_ptr2'], 'no_x_dim': False, 'num_load': 3, '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_index_put_max_10(in_ptr0, in_ptr1, out_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
    tmp2 = tl.load(in_ptr0 + (8 + r0), None)
    tmp9 = tl.load(in_ptr1 + (4 + r0), None)
    tmp10 = tl.load(in_ptr1 + (8 + r0), None)
    tmp0 = tl.full([1, 1], 2, tl.int32)
    tmp1 = tmp0 == tmp0
    tmp3 = tl.where(tmp1, tmp2, tmp2)
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = triton_helpers.max2(tmp4, 1)[:, None]
    tmp7 = tl.full([1, 1], 1, tl.int32)
    tmp8 = tmp0 == tmp7
    tmp11 = tl.where(tmp8, tmp9, tmp10)
    tmp12 = libdevice.isinf(tmp11).to(tl.int1)
    tmp13 = tl.where(tmp12, tmp6, tmp3)
    tl.store(out_ptr2 + (tl.broadcast_to(8 + r0, [XBLOCK, RBLOCK])), tmp13, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/wq/cwqjjijigoiri7ebhb452bt4tizigzg2o6ji6lsekghkviga7xp3.py
# Topologically Sorted Source Nodes: [setitem_6], Original ATen: [aten.lift_fresh, aten.index_put]
# Source node to ATen node mapping:
#   setitem_6 => full_default_3, index_put_6
# Graph fragment:
#   %full_default_3 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False})
#   %index_put_6 : [num_users=1] = call_function[target=torch.ops.aten.index_put_.default](args = (%select_12, [%isinf_3], %full_default_3), kwargs = {})
triton_poi_fused_index_put_lift_fresh_11 = async_compile.triton('triton_poi_fused_index_put_lift_fresh_11', '''
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=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_index_put_lift_fresh_11', 'mutated_arg_names': ['out_ptr1'], 'no_x_dim': False, 'num_load': 2, '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_index_put_lift_fresh_11(in_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp3 = tl.load(in_ptr0 + (8 + x0), xmask)
    tmp4 = tl.load(in_ptr0 + (12 + x0), xmask)
    tmp0 = tl.full([1], 3, tl.int32)
    tmp1 = tl.full([1], 2, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = libdevice.isinf(tmp5).to(tl.int1)
    tmp7 = 0.0
    tmp8 = tl.where(tmp6, tmp7, tmp5)
    tl.store(out_ptr1 + (12 + x0), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/ux/cuxhakfkc47pmz4gov3quzmpxdbvtixikhnf3nicsxfr6rvsdnli.py
# Topologically Sorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
# Graph fragment:
#   %select_scatter_default_6 : [num_users=2] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_scatter_default_5, %index_put_6, 0, 3), kwargs = {})
triton_poi_fused_12 = async_compile.triton('triton_poi_fused_12', '''
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: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_12', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_12(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4)
    x0 = xindex % 4
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (x2), xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 3, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tl.store(out_ptr0 + (x2), tmp5, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/lf/clf4pxfzowgmhow2bm4nhu2drefrnqqu6uqv5egtoum633aeciwm.py
# Topologically Sorted Source Nodes: [max_4, setitem_7], Original ATen: [aten.max, aten.index_put]
# Source node to ATen node mapping:
#   max_4 => max_4
#   setitem_7 => index_put_7
# Graph fragment:
#   %max_4 : [num_users=1] = call_function[target=torch.ops.aten.max.default](args = (%select_13,), kwargs = {})
#   %index_put_7 : [num_users=1] = call_function[target=torch.ops.aten.index_put_.default](args = (%select_13, [%isinf_3], %max_4), kwargs = {})
triton_per_fused_index_put_max_13 = async_compile.triton('triton_per_fused_index_put_max_13', '''
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: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_index_put_max_13', 'mutated_arg_names': ['out_ptr2'], 'no_x_dim': False, 'num_load': 3, '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_index_put_max_13(in_ptr0, in_ptr1, out_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
    tmp2 = tl.load(in_ptr0 + (12 + r0), None)
    tmp9 = tl.load(in_ptr1 + (8 + r0), None)
    tmp10 = tl.load(in_ptr1 + (12 + r0), None)
    tmp0 = tl.full([1, 1], 3, tl.int32)
    tmp1 = tmp0 == tmp0
    tmp3 = tl.where(tmp1, tmp2, tmp2)
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = triton_helpers.max2(tmp4, 1)[:, None]
    tmp7 = tl.full([1, 1], 2, tl.int32)
    tmp8 = tmp0 == tmp7
    tmp11 = tl.where(tmp8, tmp9, tmp10)
    tmp12 = libdevice.isinf(tmp11).to(tl.int1)
    tmp13 = tl.where(tmp12, tmp6, tmp3)
    tl.store(out_ptr2 + (tl.broadcast_to(12 + r0, [XBLOCK, RBLOCK])), tmp13, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/s2/cs2wyfvtdb7vmxx7u5otf7upxsggzrwonnusuhl3h5txytfu73v3.py
# Topologically Sorted Source Nodes: [mul_2, sum_4, mul_3, add_1, denominator, mul_4, sum_5, add_2, truediv, f, f_1], Original ATen: [aten.mul, aten.sum, aten.add, aten.div, aten.rsub, aten.mean]
# Source node to ATen node mapping:
#   add_1 => add_1
#   add_2 => add_2
#   denominator => add
#   f => sub
#   f_1 => mean
#   mul_2 => mul_2
#   mul_3 => mul_3
#   mul_4 => mul_4
#   sum_4 => sum_4
#   sum_5 => sum_5
#   truediv => div
# Graph fragment:
#   %select_scatter_default_7 : [num_users=2] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_scatter_default_6, %index_put_7, 0, 3), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_1, %select_scatter_default_7), kwargs = {})
#   %sum_4 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_2, [1]), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_4, 2.0), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_3, 1e-05), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_2, %sum_3), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, %select_scatter_default_7), kwargs = {})
#   %sum_5 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_4, [1]), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_5, 1e-05), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add_1, %add_2), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %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_14 = async_compile.triton('triton_per_fused_add_div_mean_mul_rsub_sum_14', '''
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: '*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': {5: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=(5,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_mean_mul_rsub_sum_14', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 20, '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_14(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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')
    tmp4 = tl.load(in_ptr1 + (12))
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK, RBLOCK])
    tmp6 = tl.load(in_ptr1 + (4*r0), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (13))
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
    tmp12 = tl.load(in_ptr1 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr0 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr1 + (14))
    tmp18 = tl.broadcast_to(tmp17, [XBLOCK, RBLOCK])
    tmp19 = tl.load(in_ptr1 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr0 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp24 = tl.load(in_ptr1 + (15))
    tmp25 = tl.broadcast_to(tmp24, [XBLOCK, RBLOCK])
    tmp26 = tl.load(in_ptr1 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp30 = tl.load(in_ptr2 + (4*r0), None, eviction_policy='evict_last')
    tmp31 = tl.load(in_ptr3 + (4*r0), None, eviction_policy='evict_last')
    tmp34 = tl.load(in_ptr2 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp35 = tl.load(in_ptr3 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr2 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp40 = tl.load(in_ptr3 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp44 = tl.load(in_ptr2 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp45 = tl.load(in_ptr3 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp1 = r0
    tmp2 = tl.full([1, 1], 3, tl.int32)
    tmp3 = tmp1 == tmp2
    tmp7 = tl.where(tmp3, tmp5, tmp6)
    tmp8 = tmp0 * tmp7
    tmp13 = tl.where(tmp3, tmp11, tmp12)
    tmp14 = tmp9 * tmp13
    tmp15 = tmp8 + tmp14
    tmp20 = tl.where(tmp3, tmp18, tmp19)
    tmp21 = tmp16 * tmp20
    tmp22 = tmp15 + tmp21
    tmp27 = tl.where(tmp3, tmp25, tmp26)
    tmp28 = tmp23 * tmp27
    tmp29 = tmp22 + tmp28
    tmp32 = tmp30 + tmp31
    tmp33 = tmp32 * tmp7
    tmp36 = tmp34 + tmp35
    tmp37 = tmp36 * tmp13
    tmp38 = tmp33 + tmp37
    tmp41 = tmp39 + tmp40
    tmp42 = tmp41 * tmp20
    tmp43 = tmp38 + tmp42
    tmp46 = tmp44 + tmp45
    tmp47 = tmp46 * tmp27
    tmp48 = tmp43 + tmp47
    tmp49 = 2.0
    tmp50 = tmp29 * tmp49
    tmp51 = 1e-05
    tmp52 = tmp50 + tmp51
    tmp53 = tmp48 + tmp51
    tmp54 = tmp52 / tmp53
    tmp55 = 1.0
    tmp56 = tmp55 - tmp54
    tmp57 = tl.broadcast_to(tmp56, [XBLOCK, RBLOCK])
    tmp59 = tl.sum(tmp57, 1)[:, None]
    tmp60 = 4.0
    tmp61 = tmp59 / tmp60
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp61, 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)
        buf29 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, intersection, ground_o, pred_o], Original ATen: [aten.mul, aten.sum]
        stream0 = get_raw_stream(0)
        triton_per_fused_mul_sum_0.run(arg1_1, arg0_1, buf0, buf1, buf29, 16, 16, grid=grid(16), stream=stream0)
        del arg0_1
        del arg1_1
        buf2 = empty_strided_cuda((4, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [setitem], Original ATen: [aten.lift_fresh, aten.index_put]
        triton_poi_fused_index_put_lift_fresh_1.run(buf1, buf2, 4, grid=grid(4), stream=stream0)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul_1, w], Original ATen: [aten.mul, aten.reciprocal]
        triton_poi_fused_mul_reciprocal_2.run(buf2, buf1, buf4, 16, grid=grid(16), stream=stream0)
        # Topologically Sorted Source Nodes: [max_1, setitem_1], Original ATen: [aten.max, aten.index_put]
        triton_per_fused_index_put_max_3.run(buf2, buf1, buf4, 1, 4, grid=grid(1), stream=stream0)
        del buf2
        buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        triton_poi_fused_4.run(buf4, buf7, 16, grid=grid(16), stream=stream0)
        # Topologically Sorted Source Nodes: [setitem_2], Original ATen: [aten.lift_fresh, aten.index_put]
        triton_poi_fused_index_put_lift_fresh_5.run(buf4, buf7, 4, grid=grid(4), stream=stream0)
        buf11 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        triton_poi_fused_6.run(buf7, buf11, 16, grid=grid(16), stream=stream0)
        # Topologically Sorted Source Nodes: [max_2, setitem_3], Original ATen: [aten.max, aten.index_put]
        triton_per_fused_index_put_max_7.run(buf7, buf4, buf11, 1, 4, grid=grid(1), stream=stream0)
        buf14 = buf7; del buf7  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        triton_poi_fused_6.run(buf11, buf14, 16, grid=grid(16), stream=stream0)
        # Topologically Sorted Source Nodes: [setitem_4], Original ATen: [aten.lift_fresh, aten.index_put]
        triton_poi_fused_index_put_lift_fresh_8.run(buf11, buf14, 4, grid=grid(4), stream=stream0)
        buf18 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        triton_poi_fused_9.run(buf14, buf18, 16, grid=grid(16), stream=stream0)
        # Topologically Sorted Source Nodes: [max_3, setitem_5], Original ATen: [aten.max, aten.index_put]
        triton_per_fused_index_put_max_10.run(buf14, buf11, buf18, 1, 4, grid=grid(1), stream=stream0)
        buf21 = buf14; del buf14  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        triton_poi_fused_9.run(buf18, buf21, 16, grid=grid(16), stream=stream0)
        # Topologically Sorted Source Nodes: [setitem_6], Original ATen: [aten.lift_fresh, aten.index_put]
        triton_poi_fused_index_put_lift_fresh_11.run(buf18, buf21, 4, grid=grid(4), stream=stream0)
        buf25 = buf11; del buf11  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        triton_poi_fused_12.run(buf21, buf25, 16, grid=grid(16), stream=stream0)
        # Topologically Sorted Source Nodes: [max_4, setitem_7], Original ATen: [aten.max, aten.index_put]
        triton_per_fused_index_put_max_13.run(buf21, buf18, buf25, 1, 4, grid=grid(1), stream=stream0)
        del buf18
        del buf21
        buf31 = empty_strided_cuda((), (), torch.float32)
        buf32 = buf31; del buf31  # reuse
        # Topologically Sorted Source Nodes: [mul_2, sum_4, mul_3, add_1, denominator, mul_4, sum_5, add_2, truediv, f, f_1], Original ATen: [aten.mul, aten.sum, aten.add, aten.div, aten.rsub, aten.mean]
        triton_per_fused_add_div_mean_mul_rsub_sum_14.run(buf32, buf0, buf25, buf1, buf29, 1, 4, grid=grid(1), stream=stream0)
        del buf0
        del buf1
        del buf25
        del buf29
    return (buf32, )


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 warnings
import numpy as np
from typing import Callable
from torch.nn.modules.loss import _Loss


def one_hot(labels, num_classes):
    """
    Converts label image `labels` to a one-hot vector with `num_classes` number of channels as last dimension.
    """
    labels = labels % num_classes
    y = np.eye(num_classes)
    onehot = y[labels.flatten()]
    return onehot.reshape(tuple(labels.shape) + (num_classes,)).astype(labels
        .dtype)


class GeneralizedDiceLoss(_Loss):
    """
    Compute the generalised Dice loss defined in:

        Sudre, C. et. al. (2017) Generalised Dice overlap as a deep learning
        loss function for highly unbalanced segmentations. DLMIA 2017.

    Adapted from:
        https://github.com/NifTK/NiftyNet/blob/v0.6.0/niftynet/layer/loss_segmentation.py#L279
    """

    def __init__(self, include_background: 'bool'=True, to_onehot_y: 'bool'
        =False, sigmoid: 'bool'=False, softmax: 'bool'=False, w_type: 'str'
        ='square', reduction: 'str'='mean'):
        """
        Args:
            include_background: If False channel index 0 (background category) is excluded from the calculation.
            to_onehot_y: whether to convert `y` into the one-hot format. Defaults to False.
            sigmoid: If True, apply a sigmoid function to the prediction.
            softmax: If True, apply a softmax function to the prediction.
            w_type ('square'|'simple'|'uniform'): type of function to transform ground truth volume to a weight factor.
                Default: `'square'`
            reduction (`none|mean|sum`): Specifies the reduction to apply to the output:
                ``'none'``: no reduction will be applied,
                ``'mean'``: the sum of the output will be divided by the batch size in the output,
                ``'sum'``: the output will be summed over the batch dim.
                Default: ``'mean'``.
        """
        super().__init__(reduction=reduction)
        if reduction not in ['none', 'mean', 'sum']:
            raise ValueError(
                f'reduction={reduction} is invalid. Valid options are: none, mean or sum.'
                )
        self.include_background = include_background
        self.to_onehot_y = to_onehot_y
        if sigmoid and softmax:
            raise ValueError(
                'sigmoid=True and softmax=True are not compatible.')
        self.sigmoid = sigmoid
        self.softmax = softmax
        self.w_func: 'Callable' = torch.ones_like
        if w_type == 'simple':
            self.w_func = torch.reciprocal
        elif w_type == 'square':
            self.w_func = lambda x: torch.reciprocal(x * x)

    def forward(self, input: 'torch.Tensor', target: 'torch.Tensor', smooth:
        'float'=1e-05):
        """
        Args:
            input (tensor): the shape should be BNH[WD].
            target (tensor): the shape should be BNH[WD].
            smooth: a small constant to avoid nan.
        """
        if self.sigmoid:
            input = torch.sigmoid(input)
        n_pred_ch = input.shape[1]
        if n_pred_ch == 1:
            if self.softmax:
                warnings.warn(
                    'single channel prediction, `softmax=True` ignored.')
            if self.to_onehot_y:
                warnings.warn(
                    'single channel prediction, `to_onehot_y=True` ignored.')
            if not self.include_background:
                warnings.warn(
                    'single channel prediction, `include_background=False` ignored.'
                    )
        else:
            if self.softmax:
                input = torch.softmax(input, 1)
            if self.to_onehot_y:
                target = one_hot(target, n_pred_ch)
            if not self.include_background:
                target = target[:, 1:]
                input = input[:, 1:]
        assert target.shape == input.shape, f'ground truth has differing shape ({target.shape}) from input ({input.shape})'
        reduce_axis = list(range(2, len(input.shape)))
        intersection = torch.sum(target * input, reduce_axis)
        ground_o = torch.sum(target, reduce_axis)
        pred_o = torch.sum(input, reduce_axis)
        denominator = ground_o + pred_o
        w = self.w_func(ground_o.float())
        for b in w:
            infs = torch.isinf(b)
            b[infs] = 0.0
            b[infs] = torch.max(b)
        f = 1.0 - (2.0 * (intersection * w).sum(1) + smooth) / ((
            denominator * w).sum(1) + smooth)
        if self.reduction == 'mean':
            f = torch.mean(f)
        elif self.reduction == 'sum':
            f = torch.sum(f)
        elif self.reduction == 'none':
            pass
        else:
            raise ValueError(f'reduction={self.reduction} is invalid.')
        return f


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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import numpy as np
from typing import Callable
from torch.nn.modules.loss import _Loss
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 = 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 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (r1 + 16 * x0), xmask, other=0.0)
    tmp2 = tmp0 * tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(xmask, tmp3, 0)
    tmp6 = tl.sum(tmp5, 1)[:, None]
    tmp7 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp9 = tl.where(xmask, tmp7, 0)
    tmp10 = tl.sum(tmp9, 1)[:, None]
    tmp11 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp13 = tl.where(xmask, tmp11, 0)
    tmp14 = tl.sum(tmp13, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp6, xmask)
    tl.store(out_ptr1 + x0, tmp10, xmask)
    tl.store(out_ptr2 + x0, tmp14, xmask)


@triton.jit
def triton_poi_fused_index_put_lift_fresh_1(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tmp0 * tmp0
    tmp2 = tl.full([1], 1, tl.int32)
    tmp3 = tmp2 / tmp1
    tmp4 = libdevice.isinf(tmp3).to(tl.int1)
    tmp5 = 0.0
    tmp6 = tl.where(tmp4, tmp5, tmp3)
    tl.store(out_ptr0 + x0, tmp6, xmask)


@triton.jit
def triton_poi_fused_mul_reciprocal_2(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + x2, xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp5 = tmp4 * tmp4
    tmp6 = tl.full([1], 1, tl.int32)
    tmp7 = tmp6 / tmp5
    tmp8 = tl.where(tmp2, tmp3, tmp7)
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_per_fused_index_put_max_3(in_ptr0, in_ptr1, out_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
    tmp2 = tl.load(in_ptr0 + r0, None)
    tmp3 = tl.load(in_ptr1 + r0, None)
    tmp0 = tl.full([1, 1], 0, tl.int32)
    tmp1 = tmp0 == tmp0
    tmp4 = tmp3 * tmp3
    tmp5 = tl.full([1, 1], 1, tl.int32)
    tmp6 = tmp5 / tmp4
    tmp7 = tl.where(tmp1, tmp2, tmp6)
    tmp8 = tl.broadcast_to(tmp7, [XBLOCK, RBLOCK])
    tmp10 = triton_helpers.max2(tmp8, 1)[:, None]
    tmp11 = libdevice.isinf(tmp6).to(tl.int1)
    tmp12 = tl.where(tmp11, tmp10, tmp7)
    tl.store(out_ptr2 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp12, None)


@triton.jit
def triton_poi_fused_4(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + x2, xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tl.store(out_ptr0 + x2, tmp5, xmask)


@triton.jit
def triton_poi_fused_index_put_lift_fresh_5(in_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp3 = tl.load(in_ptr0 + x0, xmask)
    tmp4 = tl.load(in_ptr0 + (4 + x0), xmask)
    tmp0 = tl.full([1], 1, tl.int32)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = libdevice.isinf(tmp5).to(tl.int1)
    tmp7 = 0.0
    tmp8 = tl.where(tmp6, tmp7, tmp5)
    tl.store(out_ptr1 + (4 + x0), tmp8, xmask)


@triton.jit
def triton_poi_fused_6(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + x2, xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 1, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tl.store(out_ptr0 + x2, tmp5, xmask)


@triton.jit
def triton_per_fused_index_put_max_7(in_ptr0, in_ptr1, out_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
    tmp2 = tl.load(in_ptr0 + (4 + r0), None)
    tmp9 = tl.load(in_ptr1 + r0, None)
    tmp10 = tl.load(in_ptr1 + (4 + r0), None)
    tmp0 = tl.full([1, 1], 1, tl.int32)
    tmp1 = tmp0 == tmp0
    tmp3 = tl.where(tmp1, tmp2, tmp2)
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = triton_helpers.max2(tmp4, 1)[:, None]
    tmp7 = tl.full([1, 1], 0, tl.int32)
    tmp8 = tmp0 == tmp7
    tmp11 = tl.where(tmp8, tmp9, tmp10)
    tmp12 = libdevice.isinf(tmp11).to(tl.int1)
    tmp13 = tl.where(tmp12, tmp6, tmp3)
    tl.store(out_ptr2 + tl.broadcast_to(4 + r0, [XBLOCK, RBLOCK]), tmp13, None)


@triton.jit
def triton_poi_fused_index_put_lift_fresh_8(in_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp3 = tl.load(in_ptr0 + (4 + x0), xmask)
    tmp4 = tl.load(in_ptr0 + (8 + x0), xmask)
    tmp0 = tl.full([1], 2, tl.int32)
    tmp1 = tl.full([1], 1, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = libdevice.isinf(tmp5).to(tl.int1)
    tmp7 = 0.0
    tmp8 = tl.where(tmp6, tmp7, tmp5)
    tl.store(out_ptr1 + (8 + x0), tmp8, xmask)


@triton.jit
def triton_poi_fused_9(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + x2, xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 2, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tl.store(out_ptr0 + x2, tmp5, xmask)


@triton.jit
def triton_per_fused_index_put_max_10(in_ptr0, in_ptr1, out_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
    tmp2 = tl.load(in_ptr0 + (8 + r0), None)
    tmp9 = tl.load(in_ptr1 + (4 + r0), None)
    tmp10 = tl.load(in_ptr1 + (8 + r0), None)
    tmp0 = tl.full([1, 1], 2, tl.int32)
    tmp1 = tmp0 == tmp0
    tmp3 = tl.where(tmp1, tmp2, tmp2)
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = triton_helpers.max2(tmp4, 1)[:, None]
    tmp7 = tl.full([1, 1], 1, tl.int32)
    tmp8 = tmp0 == tmp7
    tmp11 = tl.where(tmp8, tmp9, tmp10)
    tmp12 = libdevice.isinf(tmp11).to(tl.int1)
    tmp13 = tl.where(tmp12, tmp6, tmp3)
    tl.store(out_ptr2 + tl.broadcast_to(8 + r0, [XBLOCK, RBLOCK]), tmp13, None)


@triton.jit
def triton_poi_fused_index_put_lift_fresh_11(in_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp3 = tl.load(in_ptr0 + (8 + x0), xmask)
    tmp4 = tl.load(in_ptr0 + (12 + x0), xmask)
    tmp0 = tl.full([1], 3, tl.int32)
    tmp1 = tl.full([1], 2, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = libdevice.isinf(tmp5).to(tl.int1)
    tmp7 = 0.0
    tmp8 = tl.where(tmp6, tmp7, tmp5)
    tl.store(out_ptr1 + (12 + x0), tmp8, xmask)


@triton.jit
def triton_poi_fused_12(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + x2, xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 3, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tl.store(out_ptr0 + x2, tmp5, xmask)


@triton.jit
def triton_per_fused_index_put_max_13(in_ptr0, in_ptr1, out_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
    tmp2 = tl.load(in_ptr0 + (12 + r0), None)
    tmp9 = tl.load(in_ptr1 + (8 + r0), None)
    tmp10 = tl.load(in_ptr1 + (12 + r0), None)
    tmp0 = tl.full([1, 1], 3, tl.int32)
    tmp1 = tmp0 == tmp0
    tmp3 = tl.where(tmp1, tmp2, tmp2)
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = triton_helpers.max2(tmp4, 1)[:, None]
    tmp7 = tl.full([1, 1], 2, tl.int32)
    tmp8 = tmp0 == tmp7
    tmp11 = tl.where(tmp8, tmp9, tmp10)
    tmp12 = libdevice.isinf(tmp11).to(tl.int1)
    tmp13 = tl.where(tmp12, tmp6, tmp3)
    tl.store(out_ptr2 + tl.broadcast_to(12 + r0, [XBLOCK, RBLOCK]), tmp13, None
        )


@triton.jit
def triton_per_fused_add_div_mean_mul_rsub_sum_14(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, 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')
    tmp4 = tl.load(in_ptr1 + 12)
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK, RBLOCK])
    tmp6 = tl.load(in_ptr1 + 4 * r0, None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + 13)
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
    tmp12 = tl.load(in_ptr1 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr1 + 14)
    tmp18 = tl.broadcast_to(tmp17, [XBLOCK, RBLOCK])
    tmp19 = tl.load(in_ptr1 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp24 = tl.load(in_ptr1 + 15)
    tmp25 = tl.broadcast_to(tmp24, [XBLOCK, RBLOCK])
    tmp26 = tl.load(in_ptr1 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp30 = tl.load(in_ptr2 + 4 * r0, None, eviction_policy='evict_last')
    tmp31 = tl.load(in_ptr3 + 4 * r0, None, eviction_policy='evict_last')
    tmp34 = tl.load(in_ptr2 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp35 = tl.load(in_ptr3 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr2 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp40 = tl.load(in_ptr3 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp44 = tl.load(in_ptr2 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp45 = tl.load(in_ptr3 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp1 = r0
    tmp2 = tl.full([1, 1], 3, tl.int32)
    tmp3 = tmp1 == tmp2
    tmp7 = tl.where(tmp3, tmp5, tmp6)
    tmp8 = tmp0 * tmp7
    tmp13 = tl.where(tmp3, tmp11, tmp12)
    tmp14 = tmp9 * tmp13
    tmp15 = tmp8 + tmp14
    tmp20 = tl.where(tmp3, tmp18, tmp19)
    tmp21 = tmp16 * tmp20
    tmp22 = tmp15 + tmp21
    tmp27 = tl.where(tmp3, tmp25, tmp26)
    tmp28 = tmp23 * tmp27
    tmp29 = tmp22 + tmp28
    tmp32 = tmp30 + tmp31
    tmp33 = tmp32 * tmp7
    tmp36 = tmp34 + tmp35
    tmp37 = tmp36 * tmp13
    tmp38 = tmp33 + tmp37
    tmp41 = tmp39 + tmp40
    tmp42 = tmp41 * tmp20
    tmp43 = tmp38 + tmp42
    tmp46 = tmp44 + tmp45
    tmp47 = tmp46 * tmp27
    tmp48 = tmp43 + tmp47
    tmp49 = 2.0
    tmp50 = tmp29 * tmp49
    tmp51 = 1e-05
    tmp52 = tmp50 + tmp51
    tmp53 = tmp48 + tmp51
    tmp54 = tmp52 / tmp53
    tmp55 = 1.0
    tmp56 = tmp55 - tmp54
    tmp57 = tl.broadcast_to(tmp56, [XBLOCK, RBLOCK])
    tmp59 = tl.sum(tmp57, 1)[:, None]
    tmp60 = 4.0
    tmp61 = tmp59 / tmp60
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp61, 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)
        buf29 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_mul_sum_0[grid(16)](arg1_1, arg0_1, buf0, buf1,
            buf29, 16, 16, XBLOCK=8, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        buf2 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_poi_fused_index_put_lift_fresh_1[grid(4)](buf1, buf2, 4,
            XBLOCK=4, num_warps=1, num_stages=1)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_mul_reciprocal_2[grid(16)](buf2, buf1, buf4, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        triton_per_fused_index_put_max_3[grid(1)](buf2, buf1, buf4, 1, 4,
            XBLOCK=1, num_warps=2, num_stages=1)
        del buf2
        buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_4[grid(16)](buf4, buf7, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        triton_poi_fused_index_put_lift_fresh_5[grid(4)](buf4, buf7, 4,
            XBLOCK=4, num_warps=1, num_stages=1)
        buf11 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_6[grid(16)](buf7, buf11, 16, XBLOCK=16, num_warps=
            1, num_stages=1)
        triton_per_fused_index_put_max_7[grid(1)](buf7, buf4, buf11, 1, 4,
            XBLOCK=1, num_warps=2, num_stages=1)
        buf14 = buf7
        del buf7
        triton_poi_fused_6[grid(16)](buf11, buf14, 16, XBLOCK=16, num_warps
            =1, num_stages=1)
        triton_poi_fused_index_put_lift_fresh_8[grid(4)](buf11, buf14, 4,
            XBLOCK=4, num_warps=1, num_stages=1)
        buf18 = buf4
        del buf4
        triton_poi_fused_9[grid(16)](buf14, buf18, 16, XBLOCK=16, num_warps
            =1, num_stages=1)
        triton_per_fused_index_put_max_10[grid(1)](buf14, buf11, buf18, 1, 
            4, XBLOCK=1, num_warps=2, num_stages=1)
        buf21 = buf14
        del buf14
        triton_poi_fused_9[grid(16)](buf18, buf21, 16, XBLOCK=16, num_warps
            =1, num_stages=1)
        triton_poi_fused_index_put_lift_fresh_11[grid(4)](buf18, buf21, 4,
            XBLOCK=4, num_warps=1, num_stages=1)
        buf25 = buf11
        del buf11
        triton_poi_fused_12[grid(16)](buf21, buf25, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        triton_per_fused_index_put_max_13[grid(1)](buf21, buf18, buf25, 1, 
            4, XBLOCK=1, num_warps=2, num_stages=1)
        del buf18
        del buf21
        buf31 = empty_strided_cuda((), (), torch.float32)
        buf32 = buf31
        del buf31
        triton_per_fused_add_div_mean_mul_rsub_sum_14[grid(1)](buf32, buf0,
            buf25, buf1, buf29, 1, 4, XBLOCK=1, num_warps=2, num_stages=1)
        del buf0
        del buf1
        del buf25
        del buf29
    return buf32,


def one_hot(labels, num_classes):
    """
    Converts label image `labels` to a one-hot vector with `num_classes` number of channels as last dimension.
    """
    labels = labels % num_classes
    y = np.eye(num_classes)
    onehot = y[labels.flatten()]
    return onehot.reshape(tuple(labels.shape) + (num_classes,)).astype(labels
        .dtype)


class GeneralizedDiceLossNew(_Loss):
    """
    Compute the generalised Dice loss defined in:

        Sudre, C. et. al. (2017) Generalised Dice overlap as a deep learning
        loss function for highly unbalanced segmentations. DLMIA 2017.

    Adapted from:
        https://github.com/NifTK/NiftyNet/blob/v0.6.0/niftynet/layer/loss_segmentation.py#L279
    """

    def __init__(self, include_background: 'bool'=True, to_onehot_y: 'bool'
        =False, sigmoid: 'bool'=False, softmax: 'bool'=False, w_type: 'str'
        ='square', reduction: 'str'='mean'):
        """
        Args:
            include_background: If False channel index 0 (background category) is excluded from the calculation.
            to_onehot_y: whether to convert `y` into the one-hot format. Defaults to False.
            sigmoid: If True, apply a sigmoid function to the prediction.
            softmax: If True, apply a softmax function to the prediction.
            w_type ('square'|'simple'|'uniform'): type of function to transform ground truth volume to a weight factor.
                Default: `'square'`
            reduction (`none|mean|sum`): Specifies the reduction to apply to the output:
                ``'none'``: no reduction will be applied,
                ``'mean'``: the sum of the output will be divided by the batch size in the output,
                ``'sum'``: the output will be summed over the batch dim.
                Default: ``'mean'``.
        """
        super().__init__(reduction=reduction)
        if reduction not in ['none', 'mean', 'sum']:
            raise ValueError(
                f'reduction={reduction} is invalid. Valid options are: none, mean or sum.'
                )
        self.include_background = include_background
        self.to_onehot_y = to_onehot_y
        if sigmoid and softmax:
            raise ValueError(
                'sigmoid=True and softmax=True are not compatible.')
        self.sigmoid = sigmoid
        self.softmax = softmax
        self.w_func: 'Callable' = torch.ones_like
        if w_type == 'simple':
            self.w_func = torch.reciprocal
        elif w_type == 'square':
            self.w_func = lambda x: torch.reciprocal(x * x)

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