# 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/um/cum65j23qchrjf5dndblqgbw6zomhgwfj2obfidtgy7b5j3zwklm.py
# Topologically Sorted Source Nodes: [pred], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   pred => amax, exp, sub
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%arg0_1, [1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
triton_poi_fused__softmax_0 = async_compile.triton('triton_poi_fused__softmax_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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__softmax_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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__softmax_0(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
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/wk/cwk2wao7opapqbjj7klnqrd6tgist3ts3nc5veryzhzstwpx7d4l.py
# Topologically Sorted Source Nodes: [pred], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   pred => div, sum_1
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
#   %div : [num_users=4] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_1 = async_compile.triton('triton_poi_fused__softmax_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=[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__softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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__softmax_1(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
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/uq/cuqmt6h2h2qrwlfougoxbc4cnmi6o42afntw3nwkdzak2kpz2xum.py
# Topologically Sorted Source Nodes: [mul, ne, valid_mask, valid_mask_1, mul_1, sum_1, mul_2, num, pow_1, pow_2, add_1, sum_2, den, truediv, loss, loss_1, total_loss, mul_3, valid_mask_2, mul_4, sum_3, mul_5, num_1, pow_3, pow_4, add_5, sum_4, den_1, truediv_1, loss_2, loss_3, total_loss_1, mul_6, valid_mask_3, mul_7, sum_5, mul_8, num_2, pow_5, pow_6, add_8, sum_6, den_2, truediv_2, loss_4, loss_5, total_loss_2, mul_9, valid_mask_4, mul_10, sum_7, mul_11, num_3, pow_7, pow_8, add_11, sum_8, den_3, truediv_3, loss_6, loss_7, total_loss_3, loss_8, loss_9, loss_10], Original ATen: [aten.mul, aten.ne, aten._to_copy, aten.view, aten.sum, aten.add, aten.pow, aten.div, aten.rsub, aten.mean]
# Source node to ATen node mapping:
#   add_1 => add_1
#   add_11 => add_13
#   add_5 => add_5
#   add_8 => add_9
#   den => add_2
#   den_1 => add_6
#   den_2 => add_10
#   den_3 => add_14
#   loss => sub_1
#   loss_1 => mean
#   loss_10 => mul_12
#   loss_2 => sub_2
#   loss_3 => mean_1
#   loss_4 => sub_3
#   loss_5 => mean_2
#   loss_6 => sub_4
#   loss_7 => mean_3
#   loss_8 => div_5
#   loss_9 => mean_4
#   mul => mul
#   mul_1 => mul_1
#   mul_10 => mul_10
#   mul_11 => mul_11
#   mul_2 => mul_2
#   mul_3 => mul_3
#   mul_4 => mul_4
#   mul_5 => mul_5
#   mul_6 => mul_6
#   mul_7 => mul_7
#   mul_8 => mul_8
#   mul_9 => mul_9
#   ne => ne
#   num => add
#   num_1 => add_4
#   num_2 => add_8
#   num_3 => add_12
#   pow_1 => pow_1
#   pow_2 => pow_2
#   pow_3 => pow_3
#   pow_4 => pow_4
#   pow_5 => pow_5
#   pow_6 => pow_6
#   pow_7 => pow_7
#   pow_8 => pow_8
#   sum_1 => sum_2
#   sum_2 => sum_3
#   sum_3 => sum_4
#   sum_4 => sum_5
#   sum_5 => sum_6
#   sum_6 => sum_7
#   sum_7 => sum_8
#   sum_8 => sum_9
#   total_loss => add_3
#   total_loss_1 => add_7
#   total_loss_2 => add_11
#   total_loss_3 => add_15
#   truediv => div_1
#   truediv_1 => div_2
#   truediv_2 => div_3
#   truediv_3 => div_4
#   valid_mask => convert_element_type_2
#   valid_mask_1 => view_2
#   valid_mask_2 => view_5
#   valid_mask_3 => view_8
#   valid_mask_4 => view_11
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view, %select_1), kwargs = {})
#   %ne : [num_users=1] = call_function[target=torch.ops.aten.ne.Scalar](args = (%arg1_1, 255), kwargs = {})
#   %convert_element_type_2 : [num_users=4] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%ne, torch.int64), kwargs = {})
#   %view_2 : [num_users=1] = call_function[target=torch.ops.aten.reshape.default](args = (%convert_element_type_2, [4, -1]), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %view_2), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_1, [1]), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_2, 2), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_2, 1), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%view, 2), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%select_1, 2), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%pow_1, %pow_2), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%add_1, [1]), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_3, 1), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add, %add_2), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %div_1), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sub_1,), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean, 0), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_3, %select_3), kwargs = {})
#   %view_5 : [num_users=1] = call_function[target=torch.ops.aten.reshape.default](args = (%convert_element_type_2, [4, -1]), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_3, %view_5), kwargs = {})
#   %sum_4 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_4, [1]), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_4, 2), kwargs = {})
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_5, 1), kwargs = {})
#   %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%view_3, 2), kwargs = {})
#   %pow_4 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%select_3, 2), kwargs = {})
#   %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%pow_3, %pow_4), kwargs = {})
#   %sum_5 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%add_5, [1]), kwargs = {})
#   %add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_5, 1), kwargs = {})
#   %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add_4, %add_6), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %div_2), kwargs = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sub_2,), kwargs = {})
#   %add_7 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_3, %mean_1), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_6, %select_5), kwargs = {})
#   %view_8 : [num_users=1] = call_function[target=torch.ops.aten.reshape.default](args = (%convert_element_type_2, [4, -1]), kwargs = {})
#   %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_6, %view_8), kwargs = {})
#   %sum_6 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_7, [1]), kwargs = {})
#   %mul_8 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_6, 2), kwargs = {})
#   %add_8 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_8, 1), kwargs = {})
#   %pow_5 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%view_6, 2), kwargs = {})
#   %pow_6 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%select_5, 2), kwargs = {})
#   %add_9 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%pow_5, %pow_6), kwargs = {})
#   %sum_7 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%add_9, [1]), kwargs = {})
#   %add_10 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_7, 1), kwargs = {})
#   %div_3 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add_8, %add_10), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %div_3), kwargs = {})
#   %mean_2 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sub_3,), kwargs = {})
#   %add_11 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_7, %mean_2), kwargs = {})
#   %mul_9 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_9, %select_7), kwargs = {})
#   %view_11 : [num_users=1] = call_function[target=torch.ops.aten.reshape.default](args = (%convert_element_type_2, [4, -1]), kwargs = {})
#   %mul_10 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_9, %view_11), kwargs = {})
#   %sum_8 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_10, [1]), kwargs = {})
#   %mul_11 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_8, 2), kwargs = {})
#   %add_12 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_11, 1), kwargs = {})
#   %pow_7 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%view_9, 2), kwargs = {})
#   %pow_8 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%select_7, 2), kwargs = {})
#   %add_13 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%pow_7, %pow_8), kwargs = {})
#   %sum_9 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%add_13, [1]), kwargs = {})
#   %add_14 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_9, 1), kwargs = {})
#   %div_4 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add_12, %add_14), kwargs = {})
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %div_4), kwargs = {})
#   %mean_3 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sub_4,), kwargs = {})
#   %add_15 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_11, %mean_3), kwargs = {})
#   %div_5 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add_15, 4), kwargs = {})
#   %mean_4 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%div_5,), kwargs = {})
#   %mul_12 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mean_4, 1.0), kwargs = {})
triton_per_fused__to_copy_add_div_mean_mul_ne_pow_rsub_sum_view_2 = async_compile.triton('triton_per_fused__to_copy_add_div_mean_mul_ne_pow_rsub_sum_view_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.persistent_reduction(
    size_hints=[1, 4],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=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__to_copy_add_div_mean_mul_ne_pow_rsub_sum_view_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 8, '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__to_copy_add_div_mean_mul_ne_pow_rsub_sum_view_2(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 4
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (4*r0), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (4*r0), None, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp29 = tl.load(in_ptr1 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp42 = tl.load(in_ptr1 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp71 = tl.load(in_ptr0 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp112 = tl.load(in_ptr0 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp153 = tl.load(in_ptr0 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp2 = tmp1.to(tl.int64)
    tmp3 = tl.full([1, 1], 0, tl.int64)
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = tl.full([1, 1], 3, tl.int64)
    tmp6 = triton_helpers.minimum(tmp4, tmp5)
    tmp7 = tmp6 == tmp3
    tmp8 = tmp7.to(tl.int64)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp0 * tmp9
    tmp11 = 255.0
    tmp12 = tmp1 != tmp11
    tmp13 = tmp12.to(tl.int64)
    tmp14 = tmp13.to(tl.float32)
    tmp15 = tmp10 * tmp14
    tmp17 = tmp16.to(tl.int64)
    tmp18 = triton_helpers.maximum(tmp17, tmp3)
    tmp19 = triton_helpers.minimum(tmp18, tmp5)
    tmp20 = tmp19 == tmp3
    tmp21 = tmp20.to(tl.int64)
    tmp22 = tmp21.to(tl.float32)
    tmp23 = tmp0 * tmp22
    tmp24 = tmp16 != tmp11
    tmp25 = tmp24.to(tl.int64)
    tmp26 = tmp25.to(tl.float32)
    tmp27 = tmp23 * tmp26
    tmp28 = tmp15 + tmp27
    tmp30 = tmp29.to(tl.int64)
    tmp31 = triton_helpers.maximum(tmp30, tmp3)
    tmp32 = triton_helpers.minimum(tmp31, tmp5)
    tmp33 = tmp32 == tmp3
    tmp34 = tmp33.to(tl.int64)
    tmp35 = tmp34.to(tl.float32)
    tmp36 = tmp0 * tmp35
    tmp37 = tmp29 != tmp11
    tmp38 = tmp37.to(tl.int64)
    tmp39 = tmp38.to(tl.float32)
    tmp40 = tmp36 * tmp39
    tmp41 = tmp28 + tmp40
    tmp43 = tmp42.to(tl.int64)
    tmp44 = triton_helpers.maximum(tmp43, tmp3)
    tmp45 = triton_helpers.minimum(tmp44, tmp5)
    tmp46 = tmp45 == tmp3
    tmp47 = tmp46.to(tl.int64)
    tmp48 = tmp47.to(tl.float32)
    tmp49 = tmp0 * tmp48
    tmp50 = tmp42 != tmp11
    tmp51 = tmp50.to(tl.int64)
    tmp52 = tmp51.to(tl.float32)
    tmp53 = tmp49 * tmp52
    tmp54 = tmp41 + tmp53
    tmp55 = tmp0 * tmp0
    tmp56 = tmp8 * tmp8
    tmp57 = tmp56.to(tl.float32)
    tmp58 = tmp55 + tmp57
    tmp59 = tmp21 * tmp21
    tmp60 = tmp59.to(tl.float32)
    tmp61 = tmp55 + tmp60
    tmp62 = tmp58 + tmp61
    tmp63 = tmp34 * tmp34
    tmp64 = tmp63.to(tl.float32)
    tmp65 = tmp55 + tmp64
    tmp66 = tmp62 + tmp65
    tmp67 = tmp47 * tmp47
    tmp68 = tmp67.to(tl.float32)
    tmp69 = tmp55 + tmp68
    tmp70 = tmp66 + tmp69
    tmp72 = tl.full([1, 1], 1, tl.int64)
    tmp73 = tmp6 == tmp72
    tmp74 = tmp73.to(tl.int64)
    tmp75 = tmp74.to(tl.float32)
    tmp76 = tmp71 * tmp75
    tmp77 = tmp76 * tmp14
    tmp78 = tmp19 == tmp72
    tmp79 = tmp78.to(tl.int64)
    tmp80 = tmp79.to(tl.float32)
    tmp81 = tmp71 * tmp80
    tmp82 = tmp81 * tmp26
    tmp83 = tmp77 + tmp82
    tmp84 = tmp32 == tmp72
    tmp85 = tmp84.to(tl.int64)
    tmp86 = tmp85.to(tl.float32)
    tmp87 = tmp71 * tmp86
    tmp88 = tmp87 * tmp39
    tmp89 = tmp83 + tmp88
    tmp90 = tmp45 == tmp72
    tmp91 = tmp90.to(tl.int64)
    tmp92 = tmp91.to(tl.float32)
    tmp93 = tmp71 * tmp92
    tmp94 = tmp93 * tmp52
    tmp95 = tmp89 + tmp94
    tmp96 = tmp71 * tmp71
    tmp97 = tmp74 * tmp74
    tmp98 = tmp97.to(tl.float32)
    tmp99 = tmp96 + tmp98
    tmp100 = tmp79 * tmp79
    tmp101 = tmp100.to(tl.float32)
    tmp102 = tmp96 + tmp101
    tmp103 = tmp99 + tmp102
    tmp104 = tmp85 * tmp85
    tmp105 = tmp104.to(tl.float32)
    tmp106 = tmp96 + tmp105
    tmp107 = tmp103 + tmp106
    tmp108 = tmp91 * tmp91
    tmp109 = tmp108.to(tl.float32)
    tmp110 = tmp96 + tmp109
    tmp111 = tmp107 + tmp110
    tmp113 = tl.full([1, 1], 2, tl.int64)
    tmp114 = tmp6 == tmp113
    tmp115 = tmp114.to(tl.int64)
    tmp116 = tmp115.to(tl.float32)
    tmp117 = tmp112 * tmp116
    tmp118 = tmp117 * tmp14
    tmp119 = tmp19 == tmp113
    tmp120 = tmp119.to(tl.int64)
    tmp121 = tmp120.to(tl.float32)
    tmp122 = tmp112 * tmp121
    tmp123 = tmp122 * tmp26
    tmp124 = tmp118 + tmp123
    tmp125 = tmp32 == tmp113
    tmp126 = tmp125.to(tl.int64)
    tmp127 = tmp126.to(tl.float32)
    tmp128 = tmp112 * tmp127
    tmp129 = tmp128 * tmp39
    tmp130 = tmp124 + tmp129
    tmp131 = tmp45 == tmp113
    tmp132 = tmp131.to(tl.int64)
    tmp133 = tmp132.to(tl.float32)
    tmp134 = tmp112 * tmp133
    tmp135 = tmp134 * tmp52
    tmp136 = tmp130 + tmp135
    tmp137 = tmp112 * tmp112
    tmp138 = tmp115 * tmp115
    tmp139 = tmp138.to(tl.float32)
    tmp140 = tmp137 + tmp139
    tmp141 = tmp120 * tmp120
    tmp142 = tmp141.to(tl.float32)
    tmp143 = tmp137 + tmp142
    tmp144 = tmp140 + tmp143
    tmp145 = tmp126 * tmp126
    tmp146 = tmp145.to(tl.float32)
    tmp147 = tmp137 + tmp146
    tmp148 = tmp144 + tmp147
    tmp149 = tmp132 * tmp132
    tmp150 = tmp149.to(tl.float32)
    tmp151 = tmp137 + tmp150
    tmp152 = tmp148 + tmp151
    tmp154 = tmp6 == tmp5
    tmp155 = tmp154.to(tl.int64)
    tmp156 = tmp155.to(tl.float32)
    tmp157 = tmp153 * tmp156
    tmp158 = tmp157 * tmp14
    tmp159 = tmp19 == tmp5
    tmp160 = tmp159.to(tl.int64)
    tmp161 = tmp160.to(tl.float32)
    tmp162 = tmp153 * tmp161
    tmp163 = tmp162 * tmp26
    tmp164 = tmp158 + tmp163
    tmp165 = tmp32 == tmp5
    tmp166 = tmp165.to(tl.int64)
    tmp167 = tmp166.to(tl.float32)
    tmp168 = tmp153 * tmp167
    tmp169 = tmp168 * tmp39
    tmp170 = tmp164 + tmp169
    tmp171 = tmp45 == tmp5
    tmp172 = tmp171.to(tl.int64)
    tmp173 = tmp172.to(tl.float32)
    tmp174 = tmp153 * tmp173
    tmp175 = tmp174 * tmp52
    tmp176 = tmp170 + tmp175
    tmp177 = tmp153 * tmp153
    tmp178 = tmp155 * tmp155
    tmp179 = tmp178.to(tl.float32)
    tmp180 = tmp177 + tmp179
    tmp181 = tmp160 * tmp160
    tmp182 = tmp181.to(tl.float32)
    tmp183 = tmp177 + tmp182
    tmp184 = tmp180 + tmp183
    tmp185 = tmp166 * tmp166
    tmp186 = tmp185.to(tl.float32)
    tmp187 = tmp177 + tmp186
    tmp188 = tmp184 + tmp187
    tmp189 = tmp172 * tmp172
    tmp190 = tmp189.to(tl.float32)
    tmp191 = tmp177 + tmp190
    tmp192 = tmp188 + tmp191
    tmp193 = 2.0
    tmp194 = tmp54 * tmp193
    tmp195 = 1.0
    tmp196 = tmp194 + tmp195
    tmp197 = tmp70 + tmp195
    tmp198 = tmp196 / tmp197
    tmp199 = tmp195 - tmp198
    tmp200 = tl.broadcast_to(tmp199, [XBLOCK, RBLOCK])
    tmp202 = tl.sum(tmp200, 1)[:, None]
    tmp203 = tmp95 * tmp193
    tmp204 = tmp203 + tmp195
    tmp205 = tmp111 + tmp195
    tmp206 = tmp204 / tmp205
    tmp207 = tmp195 - tmp206
    tmp208 = tl.broadcast_to(tmp207, [XBLOCK, RBLOCK])
    tmp210 = tl.sum(tmp208, 1)[:, None]
    tmp211 = tmp136 * tmp193
    tmp212 = tmp211 + tmp195
    tmp213 = tmp152 + tmp195
    tmp214 = tmp212 / tmp213
    tmp215 = tmp195 - tmp214
    tmp216 = tl.broadcast_to(tmp215, [XBLOCK, RBLOCK])
    tmp218 = tl.sum(tmp216, 1)[:, None]
    tmp219 = tmp176 * tmp193
    tmp220 = tmp219 + tmp195
    tmp221 = tmp192 + tmp195
    tmp222 = tmp220 / tmp221
    tmp223 = tmp195 - tmp222
    tmp224 = tl.broadcast_to(tmp223, [XBLOCK, RBLOCK])
    tmp226 = tl.sum(tmp224, 1)[:, None]
    tmp227 = 4.0
    tmp228 = tmp202 / tmp227
    tmp229 = 0.0
    tmp230 = tmp228 + tmp229
    tmp231 = tmp210 / tmp227
    tmp232 = tmp230 + tmp231
    tmp233 = tmp218 / tmp227
    tmp234 = tmp232 + tmp233
    tmp235 = tmp226 / tmp227
    tmp236 = tmp234 + tmp235
    tmp237 = 0.25
    tmp238 = tmp236 * tmp237
    tmp239 = tmp238 / tmp195
    tmp240 = tmp239 * tmp195
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp240, 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, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [pred], Original ATen: [aten._softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__softmax_0.run(arg0_1, buf0, 16, grid=grid(16), stream=stream0)
        del arg0_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [pred], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_1.run(buf0, buf1, 16, grid=grid(16), stream=stream0)
        del buf0
        buf10 = empty_strided_cuda((), (), torch.float32)
        buf14 = buf10; del buf10  # reuse
        # Topologically Sorted Source Nodes: [mul, ne, valid_mask, valid_mask_1, mul_1, sum_1, mul_2, num, pow_1, pow_2, add_1, sum_2, den, truediv, loss, loss_1, total_loss, mul_3, valid_mask_2, mul_4, sum_3, mul_5, num_1, pow_3, pow_4, add_5, sum_4, den_1, truediv_1, loss_2, loss_3, total_loss_1, mul_6, valid_mask_3, mul_7, sum_5, mul_8, num_2, pow_5, pow_6, add_8, sum_6, den_2, truediv_2, loss_4, loss_5, total_loss_2, mul_9, valid_mask_4, mul_10, sum_7, mul_11, num_3, pow_7, pow_8, add_11, sum_8, den_3, truediv_3, loss_6, loss_7, total_loss_3, loss_8, loss_9, loss_10], Original ATen: [aten.mul, aten.ne, aten._to_copy, aten.view, aten.sum, aten.add, aten.pow, aten.div, aten.rsub, aten.mean]
        triton_per_fused__to_copy_add_div_mean_mul_ne_pow_rsub_sum_view_2.run(buf14, buf1, arg1_1, 1, 4, grid=grid(1), stream=stream0)
        del arg1_1
        del buf1
    return (buf14, )


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, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4), (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 functools
import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
import torch._C
import torch.serialization


def reduce_loss(loss, reduction):
    """Reduce loss as specified.

    Args:
        loss (Tensor): Elementwise loss tensor.
        reduction (str): Options are "none", "mean" and "sum".

    Return:
        Tensor: Reduced loss tensor.
    """
    reduction_enum = F._Reduction.get_enum(reduction)
    if reduction_enum == 0:
        return loss
    elif reduction_enum == 1:
        return loss.mean()
    elif reduction_enum == 2:
        return loss.sum()


def weight_reduce_loss(loss, weight=None, reduction='mean', avg_factor=None):
    """Apply element-wise weight and reduce loss.

    Args:
        loss (Tensor): Element-wise loss.
        weight (Tensor): Element-wise weights.
        reduction (str): Same as built-in losses of PyTorch.
        avg_factor (float): Avarage factor when computing the mean of losses.

    Returns:
        Tensor: Processed loss values.
    """
    if weight is not None:
        assert weight.dim() == loss.dim()
        if weight.dim() > 1:
            assert weight.size(1) == 1 or weight.size(1) == loss.size(1)
        loss = loss * weight
    if avg_factor is None:
        loss = reduce_loss(loss, reduction)
    elif reduction == 'mean':
        loss = loss.sum() / avg_factor
    elif reduction != 'none':
        raise ValueError('avg_factor can not be used with reduction="sum"')
    return loss


def get_class_weight(class_weight):
    """Get class weight for loss function.

    Args:
        class_weight (list[float] | str | None): If class_weight is a str,
            take it as a file name and read from it.
    """
    if isinstance(class_weight, str):
        if class_weight.endswith('.npy'):
            class_weight = np.load(class_weight)
        else:
            class_weight = mmcv.load(class_weight)
    return class_weight


def weighted_loss(loss_func):
    """Create a weighted version of a given loss function.

    To use this decorator, the loss function must have the signature like
    `loss_func(pred, target, **kwargs)`. The function only needs to compute
    element-wise loss without any reduction. This decorator will add weight
    and reduction arguments to the function. The decorated function will have
    the signature like `loss_func(pred, target, weight=None, reduction='mean',
    avg_factor=None, **kwargs)`.

    :Example:

    >>> import torch
    >>> @weighted_loss
    >>> def l1_loss(pred, target):
    >>>     return (pred - target).abs()

    >>> pred = torch.Tensor([0, 2, 3])
    >>> target = torch.Tensor([1, 1, 1])
    >>> weight = torch.Tensor([1, 0, 1])

    >>> l1_loss(pred, target)
    tensor(1.3333)
    >>> l1_loss(pred, target, weight)
    tensor(1.)
    >>> l1_loss(pred, target, reduction='none')
    tensor([1., 1., 2.])
    >>> l1_loss(pred, target, weight, avg_factor=2)
    tensor(1.5000)
    """

    @functools.wraps(loss_func)
    def wrapper(pred, target, weight=None, reduction='mean', avg_factor=
        None, **kwargs):
        loss = loss_func(pred, target, **kwargs)
        loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
        return loss
    return wrapper


@weighted_loss
def binary_dice_loss(pred, target, valid_mask, smooth=1, exponent=2, **kwards):
    assert pred.shape[0] == target.shape[0]
    pred = pred.reshape(pred.shape[0], -1)
    target = target.reshape(target.shape[0], -1)
    valid_mask = valid_mask.reshape(valid_mask.shape[0], -1)
    num = torch.sum(torch.mul(pred, target) * valid_mask, dim=1) * 2 + smooth
    den = torch.sum(pred.pow(exponent) + target.pow(exponent), dim=1) + smooth
    return 1 - num / den


@weighted_loss
def dice_loss(pred, target, valid_mask, smooth=1, exponent=2, class_weight=
    None, ignore_index=255):
    assert pred.shape[0] == target.shape[0]
    total_loss = 0
    num_classes = pred.shape[1]
    for i in range(num_classes):
        if i != ignore_index:
            dice_loss = binary_dice_loss(pred[:, i], target[..., i],
                valid_mask=valid_mask, smooth=smooth, exponent=exponent)
            if class_weight is not None:
                dice_loss *= class_weight[i]
            total_loss += dice_loss
    return total_loss / num_classes


class DiceLoss(nn.Module):
    """DiceLoss.

    This loss is proposed in `V-Net: Fully Convolutional Neural Networks for
    Volumetric Medical Image Segmentation <https://arxiv.org/abs/1606.04797>`_.

    Args:
        loss_type (str, optional): Binary or multi-class loss.
            Default: 'multi_class'. Options are "binary" and "multi_class".
        smooth (float): A float number to smooth loss, and avoid NaN error.
            Default: 1
        exponent (float): An float number to calculate denominator
            value: \\sum{x^exponent} + \\sum{y^exponent}. Default: 2.
        reduction (str, optional): The method used to reduce the loss. Options
            are "none", "mean" and "sum". This parameter only works when
            per_image is True. Default: 'mean'.
        class_weight (list[float] | str, optional): Weight of each class. If in
            str format, read them from a file. Defaults to None.
        loss_weight (float, optional): Weight of the loss. Default to 1.0.
        ignore_index (int | None): The label index to be ignored. Default: 255.
    """

    def __init__(self, smooth=1, exponent=2, reduction='mean', class_weight
        =None, loss_weight=1.0, ignore_index=255, **kwards):
        super(DiceLoss, self).__init__()
        self.smooth = smooth
        self.exponent = exponent
        self.reduction = reduction
        self.class_weight = get_class_weight(class_weight)
        self.loss_weight = loss_weight
        self.ignore_index = ignore_index

    def forward(self, pred, target, avg_factor=None, reduction_override=
        None, **kwards):
        assert reduction_override in (None, 'none', 'mean', 'sum')
        reduction = (reduction_override if reduction_override else self.
            reduction)
        if self.class_weight is not None:
            class_weight = pred.new_tensor(self.class_weight)
        else:
            class_weight = None
        pred = F.softmax(pred, dim=1)
        num_classes = pred.shape[1]
        one_hot_target = F.one_hot(torch.clamp(target.long(), 0, 
            num_classes - 1), num_classes=num_classes)
        valid_mask = (target != self.ignore_index).long()
        loss = self.loss_weight * dice_loss(pred, one_hot_target,
            valid_mask=valid_mask, reduction=reduction, avg_factor=
            avg_factor, smooth=self.smooth, exponent=self.exponent,
            class_weight=class_weight, ignore_index=self.ignore_index)
        return loss


def get_inputs():
    return [torch.rand([4, 4]), torch.rand([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 math as tl_math
import functools
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
import torch._C
import torch.serialization
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused__softmax_0(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
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_1(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
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_per_fused__to_copy_add_div_mean_mul_ne_pow_rsub_sum_view_2(
    in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + 4 * r0, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * r0, None, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp29 = tl.load(in_ptr1 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp42 = tl.load(in_ptr1 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp71 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp112 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last'
        )
    tmp153 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last'
        )
    tmp2 = tmp1.to(tl.int64)
    tmp3 = tl.full([1, 1], 0, tl.int64)
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = tl.full([1, 1], 3, tl.int64)
    tmp6 = triton_helpers.minimum(tmp4, tmp5)
    tmp7 = tmp6 == tmp3
    tmp8 = tmp7.to(tl.int64)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp0 * tmp9
    tmp11 = 255.0
    tmp12 = tmp1 != tmp11
    tmp13 = tmp12.to(tl.int64)
    tmp14 = tmp13.to(tl.float32)
    tmp15 = tmp10 * tmp14
    tmp17 = tmp16.to(tl.int64)
    tmp18 = triton_helpers.maximum(tmp17, tmp3)
    tmp19 = triton_helpers.minimum(tmp18, tmp5)
    tmp20 = tmp19 == tmp3
    tmp21 = tmp20.to(tl.int64)
    tmp22 = tmp21.to(tl.float32)
    tmp23 = tmp0 * tmp22
    tmp24 = tmp16 != tmp11
    tmp25 = tmp24.to(tl.int64)
    tmp26 = tmp25.to(tl.float32)
    tmp27 = tmp23 * tmp26
    tmp28 = tmp15 + tmp27
    tmp30 = tmp29.to(tl.int64)
    tmp31 = triton_helpers.maximum(tmp30, tmp3)
    tmp32 = triton_helpers.minimum(tmp31, tmp5)
    tmp33 = tmp32 == tmp3
    tmp34 = tmp33.to(tl.int64)
    tmp35 = tmp34.to(tl.float32)
    tmp36 = tmp0 * tmp35
    tmp37 = tmp29 != tmp11
    tmp38 = tmp37.to(tl.int64)
    tmp39 = tmp38.to(tl.float32)
    tmp40 = tmp36 * tmp39
    tmp41 = tmp28 + tmp40
    tmp43 = tmp42.to(tl.int64)
    tmp44 = triton_helpers.maximum(tmp43, tmp3)
    tmp45 = triton_helpers.minimum(tmp44, tmp5)
    tmp46 = tmp45 == tmp3
    tmp47 = tmp46.to(tl.int64)
    tmp48 = tmp47.to(tl.float32)
    tmp49 = tmp0 * tmp48
    tmp50 = tmp42 != tmp11
    tmp51 = tmp50.to(tl.int64)
    tmp52 = tmp51.to(tl.float32)
    tmp53 = tmp49 * tmp52
    tmp54 = tmp41 + tmp53
    tmp55 = tmp0 * tmp0
    tmp56 = tmp8 * tmp8
    tmp57 = tmp56.to(tl.float32)
    tmp58 = tmp55 + tmp57
    tmp59 = tmp21 * tmp21
    tmp60 = tmp59.to(tl.float32)
    tmp61 = tmp55 + tmp60
    tmp62 = tmp58 + tmp61
    tmp63 = tmp34 * tmp34
    tmp64 = tmp63.to(tl.float32)
    tmp65 = tmp55 + tmp64
    tmp66 = tmp62 + tmp65
    tmp67 = tmp47 * tmp47
    tmp68 = tmp67.to(tl.float32)
    tmp69 = tmp55 + tmp68
    tmp70 = tmp66 + tmp69
    tmp72 = tl.full([1, 1], 1, tl.int64)
    tmp73 = tmp6 == tmp72
    tmp74 = tmp73.to(tl.int64)
    tmp75 = tmp74.to(tl.float32)
    tmp76 = tmp71 * tmp75
    tmp77 = tmp76 * tmp14
    tmp78 = tmp19 == tmp72
    tmp79 = tmp78.to(tl.int64)
    tmp80 = tmp79.to(tl.float32)
    tmp81 = tmp71 * tmp80
    tmp82 = tmp81 * tmp26
    tmp83 = tmp77 + tmp82
    tmp84 = tmp32 == tmp72
    tmp85 = tmp84.to(tl.int64)
    tmp86 = tmp85.to(tl.float32)
    tmp87 = tmp71 * tmp86
    tmp88 = tmp87 * tmp39
    tmp89 = tmp83 + tmp88
    tmp90 = tmp45 == tmp72
    tmp91 = tmp90.to(tl.int64)
    tmp92 = tmp91.to(tl.float32)
    tmp93 = tmp71 * tmp92
    tmp94 = tmp93 * tmp52
    tmp95 = tmp89 + tmp94
    tmp96 = tmp71 * tmp71
    tmp97 = tmp74 * tmp74
    tmp98 = tmp97.to(tl.float32)
    tmp99 = tmp96 + tmp98
    tmp100 = tmp79 * tmp79
    tmp101 = tmp100.to(tl.float32)
    tmp102 = tmp96 + tmp101
    tmp103 = tmp99 + tmp102
    tmp104 = tmp85 * tmp85
    tmp105 = tmp104.to(tl.float32)
    tmp106 = tmp96 + tmp105
    tmp107 = tmp103 + tmp106
    tmp108 = tmp91 * tmp91
    tmp109 = tmp108.to(tl.float32)
    tmp110 = tmp96 + tmp109
    tmp111 = tmp107 + tmp110
    tmp113 = tl.full([1, 1], 2, tl.int64)
    tmp114 = tmp6 == tmp113
    tmp115 = tmp114.to(tl.int64)
    tmp116 = tmp115.to(tl.float32)
    tmp117 = tmp112 * tmp116
    tmp118 = tmp117 * tmp14
    tmp119 = tmp19 == tmp113
    tmp120 = tmp119.to(tl.int64)
    tmp121 = tmp120.to(tl.float32)
    tmp122 = tmp112 * tmp121
    tmp123 = tmp122 * tmp26
    tmp124 = tmp118 + tmp123
    tmp125 = tmp32 == tmp113
    tmp126 = tmp125.to(tl.int64)
    tmp127 = tmp126.to(tl.float32)
    tmp128 = tmp112 * tmp127
    tmp129 = tmp128 * tmp39
    tmp130 = tmp124 + tmp129
    tmp131 = tmp45 == tmp113
    tmp132 = tmp131.to(tl.int64)
    tmp133 = tmp132.to(tl.float32)
    tmp134 = tmp112 * tmp133
    tmp135 = tmp134 * tmp52
    tmp136 = tmp130 + tmp135
    tmp137 = tmp112 * tmp112
    tmp138 = tmp115 * tmp115
    tmp139 = tmp138.to(tl.float32)
    tmp140 = tmp137 + tmp139
    tmp141 = tmp120 * tmp120
    tmp142 = tmp141.to(tl.float32)
    tmp143 = tmp137 + tmp142
    tmp144 = tmp140 + tmp143
    tmp145 = tmp126 * tmp126
    tmp146 = tmp145.to(tl.float32)
    tmp147 = tmp137 + tmp146
    tmp148 = tmp144 + tmp147
    tmp149 = tmp132 * tmp132
    tmp150 = tmp149.to(tl.float32)
    tmp151 = tmp137 + tmp150
    tmp152 = tmp148 + tmp151
    tmp154 = tmp6 == tmp5
    tmp155 = tmp154.to(tl.int64)
    tmp156 = tmp155.to(tl.float32)
    tmp157 = tmp153 * tmp156
    tmp158 = tmp157 * tmp14
    tmp159 = tmp19 == tmp5
    tmp160 = tmp159.to(tl.int64)
    tmp161 = tmp160.to(tl.float32)
    tmp162 = tmp153 * tmp161
    tmp163 = tmp162 * tmp26
    tmp164 = tmp158 + tmp163
    tmp165 = tmp32 == tmp5
    tmp166 = tmp165.to(tl.int64)
    tmp167 = tmp166.to(tl.float32)
    tmp168 = tmp153 * tmp167
    tmp169 = tmp168 * tmp39
    tmp170 = tmp164 + tmp169
    tmp171 = tmp45 == tmp5
    tmp172 = tmp171.to(tl.int64)
    tmp173 = tmp172.to(tl.float32)
    tmp174 = tmp153 * tmp173
    tmp175 = tmp174 * tmp52
    tmp176 = tmp170 + tmp175
    tmp177 = tmp153 * tmp153
    tmp178 = tmp155 * tmp155
    tmp179 = tmp178.to(tl.float32)
    tmp180 = tmp177 + tmp179
    tmp181 = tmp160 * tmp160
    tmp182 = tmp181.to(tl.float32)
    tmp183 = tmp177 + tmp182
    tmp184 = tmp180 + tmp183
    tmp185 = tmp166 * tmp166
    tmp186 = tmp185.to(tl.float32)
    tmp187 = tmp177 + tmp186
    tmp188 = tmp184 + tmp187
    tmp189 = tmp172 * tmp172
    tmp190 = tmp189.to(tl.float32)
    tmp191 = tmp177 + tmp190
    tmp192 = tmp188 + tmp191
    tmp193 = 2.0
    tmp194 = tmp54 * tmp193
    tmp195 = 1.0
    tmp196 = tmp194 + tmp195
    tmp197 = tmp70 + tmp195
    tmp198 = tmp196 / tmp197
    tmp199 = tmp195 - tmp198
    tmp200 = tl.broadcast_to(tmp199, [XBLOCK, RBLOCK])
    tmp202 = tl.sum(tmp200, 1)[:, None]
    tmp203 = tmp95 * tmp193
    tmp204 = tmp203 + tmp195
    tmp205 = tmp111 + tmp195
    tmp206 = tmp204 / tmp205
    tmp207 = tmp195 - tmp206
    tmp208 = tl.broadcast_to(tmp207, [XBLOCK, RBLOCK])
    tmp210 = tl.sum(tmp208, 1)[:, None]
    tmp211 = tmp136 * tmp193
    tmp212 = tmp211 + tmp195
    tmp213 = tmp152 + tmp195
    tmp214 = tmp212 / tmp213
    tmp215 = tmp195 - tmp214
    tmp216 = tl.broadcast_to(tmp215, [XBLOCK, RBLOCK])
    tmp218 = tl.sum(tmp216, 1)[:, None]
    tmp219 = tmp176 * tmp193
    tmp220 = tmp219 + tmp195
    tmp221 = tmp192 + tmp195
    tmp222 = tmp220 / tmp221
    tmp223 = tmp195 - tmp222
    tmp224 = tl.broadcast_to(tmp223, [XBLOCK, RBLOCK])
    tmp226 = tl.sum(tmp224, 1)[:, None]
    tmp227 = 4.0
    tmp228 = tmp202 / tmp227
    tmp229 = 0.0
    tmp230 = tmp228 + tmp229
    tmp231 = tmp210 / tmp227
    tmp232 = tmp230 + tmp231
    tmp233 = tmp218 / tmp227
    tmp234 = tmp232 + tmp233
    tmp235 = tmp226 / tmp227
    tmp236 = tmp234 + tmp235
    tmp237 = 0.25
    tmp238 = tmp236 * tmp237
    tmp239 = tmp238 / tmp195
    tmp240 = tmp239 * tmp195
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp240, None)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(16)](arg0_1, buf0, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del arg0_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(16)](buf0, buf1, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del buf0
        buf10 = empty_strided_cuda((), (), torch.float32)
        buf14 = buf10
        del buf10
        triton_per_fused__to_copy_add_div_mean_mul_ne_pow_rsub_sum_view_2[grid
            (1)](buf14, buf1, arg1_1, 1, 4, XBLOCK=1, num_warps=2, num_stages=1
            )
        del arg1_1
        del buf1
    return buf14,


def reduce_loss(loss, reduction):
    """Reduce loss as specified.

    Args:
        loss (Tensor): Elementwise loss tensor.
        reduction (str): Options are "none", "mean" and "sum".

    Return:
        Tensor: Reduced loss tensor.
    """
    reduction_enum = F._Reduction.get_enum(reduction)
    if reduction_enum == 0:
        return loss
    elif reduction_enum == 1:
        return loss.mean()
    elif reduction_enum == 2:
        return loss.sum()


def weight_reduce_loss(loss, weight=None, reduction='mean', avg_factor=None):
    """Apply element-wise weight and reduce loss.

    Args:
        loss (Tensor): Element-wise loss.
        weight (Tensor): Element-wise weights.
        reduction (str): Same as built-in losses of PyTorch.
        avg_factor (float): Avarage factor when computing the mean of losses.

    Returns:
        Tensor: Processed loss values.
    """
    if weight is not None:
        assert weight.dim() == loss.dim()
        if weight.dim() > 1:
            assert weight.size(1) == 1 or weight.size(1) == loss.size(1)
        loss = loss * weight
    if avg_factor is None:
        loss = reduce_loss(loss, reduction)
    elif reduction == 'mean':
        loss = loss.sum() / avg_factor
    elif reduction != 'none':
        raise ValueError('avg_factor can not be used with reduction="sum"')
    return loss


def get_class_weight(class_weight):
    """Get class weight for loss function.

    Args:
        class_weight (list[float] | str | None): If class_weight is a str,
            take it as a file name and read from it.
    """
    if isinstance(class_weight, str):
        if class_weight.endswith('.npy'):
            class_weight = np.load(class_weight)
        else:
            class_weight = mmcv.load(class_weight)
    return class_weight


def weighted_loss(loss_func):
    """Create a weighted version of a given loss function.

    To use this decorator, the loss function must have the signature like
    `loss_func(pred, target, **kwargs)`. The function only needs to compute
    element-wise loss without any reduction. This decorator will add weight
    and reduction arguments to the function. The decorated function will have
    the signature like `loss_func(pred, target, weight=None, reduction='mean',
    avg_factor=None, **kwargs)`.

    :Example:

    >>> import torch
    >>> @weighted_loss
    >>> def l1_loss(pred, target):
    >>>     return (pred - target).abs()

    >>> pred = torch.Tensor([0, 2, 3])
    >>> target = torch.Tensor([1, 1, 1])
    >>> weight = torch.Tensor([1, 0, 1])

    >>> l1_loss(pred, target)
    tensor(1.3333)
    >>> l1_loss(pred, target, weight)
    tensor(1.)
    >>> l1_loss(pred, target, reduction='none')
    tensor([1., 1., 2.])
    >>> l1_loss(pred, target, weight, avg_factor=2)
    tensor(1.5000)
    """

    @functools.wraps(loss_func)
    def wrapper(pred, target, weight=None, reduction='mean', avg_factor=
        None, **kwargs):
        loss = loss_func(pred, target, **kwargs)
        loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
        return loss
    return wrapper


@weighted_loss
def binary_dice_loss(pred, target, valid_mask, smooth=1, exponent=2, **kwards):
    assert pred.shape[0] == target.shape[0]
    pred = pred.reshape(pred.shape[0], -1)
    target = target.reshape(target.shape[0], -1)
    valid_mask = valid_mask.reshape(valid_mask.shape[0], -1)
    num = torch.sum(torch.mul(pred, target) * valid_mask, dim=1) * 2 + smooth
    den = torch.sum(pred.pow(exponent) + target.pow(exponent), dim=1) + smooth
    return 1 - num / den


@weighted_loss
def dice_loss(pred, target, valid_mask, smooth=1, exponent=2, class_weight=
    None, ignore_index=255):
    assert pred.shape[0] == target.shape[0]
    total_loss = 0
    num_classes = pred.shape[1]
    for i in range(num_classes):
        if i != ignore_index:
            dice_loss = binary_dice_loss(pred[:, i], target[..., i],
                valid_mask=valid_mask, smooth=smooth, exponent=exponent)
            if class_weight is not None:
                dice_loss *= class_weight[i]
            total_loss += dice_loss
    return total_loss / num_classes


class DiceLossNew(nn.Module):
    """DiceLoss.

    This loss is proposed in `V-Net: Fully Convolutional Neural Networks for
    Volumetric Medical Image Segmentation <https://arxiv.org/abs/1606.04797>`_.

    Args:
        loss_type (str, optional): Binary or multi-class loss.
            Default: 'multi_class'. Options are "binary" and "multi_class".
        smooth (float): A float number to smooth loss, and avoid NaN error.
            Default: 1
        exponent (float): An float number to calculate denominator
            value: \\sum{x^exponent} + \\sum{y^exponent}. Default: 2.
        reduction (str, optional): The method used to reduce the loss. Options
            are "none", "mean" and "sum". This parameter only works when
            per_image is True. Default: 'mean'.
        class_weight (list[float] | str, optional): Weight of each class. If in
            str format, read them from a file. Defaults to None.
        loss_weight (float, optional): Weight of the loss. Default to 1.0.
        ignore_index (int | None): The label index to be ignored. Default: 255.
    """

    def __init__(self, smooth=1, exponent=2, reduction='mean', class_weight
        =None, loss_weight=1.0, ignore_index=255, **kwards):
        super(DiceLossNew, self).__init__()
        self.smooth = smooth
        self.exponent = exponent
        self.reduction = reduction
        self.class_weight = get_class_weight(class_weight)
        self.loss_weight = loss_weight
        self.ignore_index = ignore_index

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