# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_4/inductor_cache/mr/cmr3tv5ws2snvq6sc6sxr656wx47kzohgg6mk4czehbbxdzlihyt.py
# Topologically Sorted Source Nodes: [softmax, y_pred_log], Original ATen: [aten._softmax, aten._log_softmax]
# Source node to ATen node mapping:
#   softmax => amax_1, exp_1, sub_2
#   y_pred_log => amax, sub
# Graph fragment:
#   %amax_1 : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%arg1_1, [1], True), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg1_1, %amax_1), kwargs = {})
#   %exp_1 : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_2,), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%arg1_1, [1], True), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg1_1, %amax), kwargs = {})
triton_poi_fused__log_softmax__softmax_0 = async_compile.triton('triton_poi_fused__log_softmax__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=[256], 
    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__log_softmax__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__log_softmax__softmax_0(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = (xindex // 64)
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + (64*x2)), 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 + (x3), tmp9, xmask)
    tl.store(out_ptr1 + (x3), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/rf/crfebo4jtldh4ottmga4ufkdllxhfuapjkpacsafnyj7tiyytfp2.py
# Topologically Sorted Source Nodes: [weight, softmax, sub, fweight, weight_1, y_pred_log, mul_3], Original ATen: [aten.pow, aten._softmax, aten.rsub, aten.mul, aten._log_softmax]
# Source node to ATen node mapping:
#   fweight => pow_2
#   mul_3 => mul_3
#   softmax => div_1, sum_5
#   sub => sub_3
#   weight => pow_1
#   weight_1 => mul_2
#   y_pred_log => exp, log_1, sub_1, sum_4
# Graph fragment:
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%arg0_1, 1.0), kwargs = {})
#   %sum_5 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp_1, [1], True), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp_1, %sum_5), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %div_1), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_3, 2.0), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_1, %pow_2), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_4 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
#   %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_4,), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub, %log_1), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_2, %sub_1), kwargs = {})
triton_poi_fused__log_softmax__softmax_mul_pow_rsub_1 = async_compile.triton('triton_poi_fused__log_softmax__softmax_mul_pow_rsub_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__log_softmax__softmax_mul_pow_rsub_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 11, '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__log_softmax__softmax_mul_pow_rsub_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = (xindex // 64)
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x3), xmask)
    tmp2 = tl.load(in_ptr1 + (x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (16 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (32 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (48 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr2 + (x3), xmask)
    tmp15 = tl.load(in_ptr2 + (x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr2 + (16 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr2 + (32 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr2 + (48 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = tmp1 / tmp8
    tmp10 = 1.0
    tmp11 = tmp10 - tmp9
    tmp12 = tmp11 * tmp11
    tmp13 = tmp0 * tmp12
    tmp16 = tl_math.exp(tmp15)
    tmp18 = tl_math.exp(tmp17)
    tmp19 = tmp16 + tmp18
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp19 + tmp21
    tmp24 = tl_math.exp(tmp23)
    tmp25 = tmp22 + tmp24
    tmp26 = tl_math.log(tmp25)
    tmp27 = tmp14 - tmp26
    tmp28 = tmp13 * tmp27
    tl.store(out_ptr0 + (x3), tmp28, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/7d/c7dhb4kb7dagmuuq26vnnnfwal3vbltjgcxrxsbpfudttfjy5zfl.py
# Topologically Sorted Source Nodes: [mul_4, logpy, mean, loss, eq, dice_target, mul, intersection, mul_1, sum_2, sum_3, add, union, truediv, log, loss_dice, mul_5, loss_1], Original ATen: [aten.mul, aten.sum, aten.mean, aten.neg, aten.eq, aten._to_copy, aten.add, aten.div, aten.log]
# Source node to ATen node mapping:
#   add => add
#   dice_target => convert_element_type
#   eq => eq
#   intersection => sum_1
#   log => log
#   logpy => sum_6
#   loss => neg_1
#   loss_1 => add_2
#   loss_dice => neg
#   mean => mean
#   mul => mul
#   mul_1 => mul_1
#   mul_4 => mul_4
#   mul_5 => mul_5
#   sum_2 => sum_2
#   sum_3 => sum_3
#   truediv => div
#   union => add_1
# Graph fragment:
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_3, %arg2_1), kwargs = {})
#   %sum_6 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_4, [1]), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sum_6,), kwargs = {})
#   %neg_1 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%mean,), kwargs = {})
#   %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%select, 1), kwargs = {})
#   %convert_element_type : [num_users=2] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%eq, torch.float32), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_1, %convert_element_type), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mul,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_1, 2), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%select_1,), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%convert_element_type,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_2, %sum_3), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, 1e-15), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul_1, %add_1), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%div,), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%log,), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%neg, 1.0), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%neg_1, %mul_5), kwargs = {})
triton_per_fused__to_copy_add_div_eq_log_mean_mul_neg_sum_2 = async_compile.triton('triton_per_fused__to_copy_add_div_eq_log_mean_mul_neg_sum_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.persistent_reduction(
    size_hints=[1, 64],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {4: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 5), equal_to_1=(4,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__to_copy_add_div_eq_log_mean_mul_neg_sum_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 9, '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_eq_log_mean_mul_neg_sum_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    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 % 16
    r1 = (rindex // 16)
    tmp0 = tl.load(in_ptr0 + (r0 + (64*r1)), None)
    tmp1 = tl.load(in_ptr1 + (r0 + (64*r1)), None)
    tmp3 = tl.load(in_ptr0 + (16 + r0 + (64*r1)), None)
    tmp4 = tl.load(in_ptr1 + (16 + r0 + (64*r1)), None)
    tmp7 = tl.load(in_ptr0 + (32 + r0 + (64*r1)), None)
    tmp8 = tl.load(in_ptr1 + (32 + r0 + (64*r1)), None)
    tmp11 = tl.load(in_ptr0 + (48 + r0 + (64*r1)), None)
    tmp12 = tl.load(in_ptr1 + (48 + r0 + (64*r1)), None)
    tmp18 = tl.load(in_ptr2 + (16 + r0 + (64*r1)), None)
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.sum(tmp15, 1)[:, None]
    tmp19 = tl.sigmoid(tmp18)
    tmp20 = 1.0
    tmp21 = tmp4 == tmp20
    tmp22 = tmp21.to(tl.float32)
    tmp23 = tmp19 * tmp22
    tmp24 = tl.broadcast_to(tmp23, [XBLOCK, RBLOCK])
    tmp26 = tl.sum(tmp24, 1)[:, None]
    tmp27 = tl.broadcast_to(tmp19, [XBLOCK, RBLOCK])
    tmp29 = tl.sum(tmp27, 1)[:, None]
    tmp30 = tl.broadcast_to(tmp22, [XBLOCK, RBLOCK])
    tmp32 = tl.sum(tmp30, 1)[:, None]
    tmp33 = 64.0
    tmp34 = tmp17 / tmp33
    tmp35 = -tmp34
    tmp36 = 2.0
    tmp37 = tmp26 * tmp36
    tmp38 = tmp29 + tmp32
    tmp39 = 1e-15
    tmp40 = tmp38 + tmp39
    tmp41 = tmp37 / tmp40
    tmp42 = tl_math.log(tmp41)
    tmp43 = -tmp42
    tmp44 = tmp43 * tmp20
    tmp45 = tmp35 + tmp44
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp45, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_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))
    assert_size_stride(arg2_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, 4), (64, 16, 4, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [softmax, y_pred_log], Original ATen: [aten._softmax, aten._log_softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__log_softmax__softmax_0.run(arg1_1, buf0, buf1, 256, grid=grid(256), stream=stream0)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [weight, softmax, sub, fweight, weight_1, y_pred_log, mul_3], Original ATen: [aten.pow, aten._softmax, aten.rsub, aten.mul, aten._log_softmax]
        triton_poi_fused__log_softmax__softmax_mul_pow_rsub_1.run(arg0_1, buf0, buf1, buf2, 256, grid=grid(256), stream=stream0)
        del arg0_1
        del buf0
        del buf1
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf7 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [mul_4, logpy, mean, loss, eq, dice_target, mul, intersection, mul_1, sum_2, sum_3, add, union, truediv, log, loss_dice, mul_5, loss_1], Original ATen: [aten.mul, aten.sum, aten.mean, aten.neg, aten.eq, aten._to_copy, aten.add, aten.div, aten.log]
        triton_per_fused__to_copy_add_div_eq_log_mean_mul_neg_sum_2.run(buf7, buf2, arg2_1, arg1_1, 1, 64, grid=grid(1), stream=stream0)
        del arg1_1
        del arg2_1
        del buf2
    return (buf7, )


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)
    arg2_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_1])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
import torch.nn.functional
import torch.nn as nn
import torch.nn.functional as F


def centercrop(image, w, h):
    _nt, _ct, ht, wt = image.size()
    padw, padh = (wt - w) // 2, (ht - h) // 2
    if padw > 0 and padh > 0:
        image = image[:, :, padh:-padh, padw:-padw]
    return image


class WeightedMCEFocalloss(nn.Module):

    def __init__(self, gamma=2.0):
        super(WeightedMCEFocalloss, self).__init__()
        self.gamma = gamma

    def forward(self, y_pred, y_true, weight):
        _n, _ch, h, w = y_pred.size()
        y_true = centercrop(y_true, w, h)
        weight = centercrop(weight, w, h)
        y_pred_log = F.log_softmax(y_pred, dim=1)
        fweight = (1 - F.softmax(y_pred, dim=1)) ** self.gamma
        weight = weight * fweight
        logpy = torch.sum(weight * y_pred_log * y_true, dim=1)
        loss = -torch.mean(logpy)
        return loss


class BLogDiceLoss(nn.Module):

    def __init__(self, classe=1):
        super(BLogDiceLoss, self).__init__()
        self.sigmoid = nn.Sigmoid()
        self.classe = classe

    def forward(self, y_pred, y_true, weight=None):
        _n, _ch, h, w = y_pred.size()
        y_true = centercrop(y_true, w, h)
        y_pred = self.sigmoid(y_pred)
        eps = 1e-15
        dice_target = (y_true[:, self.classe, ...] == 1).float()
        dice_output = y_pred[:, self.classe, ...]
        intersection = (dice_output * dice_target).sum()
        union = dice_output.sum() + dice_target.sum() + eps
        return -torch.log(2 * intersection / union)


class WeightedMCEDiceLoss(nn.Module):

    def __init__(self, alpha=1.0, gamma=1.0):
        super(WeightedMCEDiceLoss, self).__init__()
        self.loss_mce = WeightedMCEFocalloss()
        self.loss_dice = BLogDiceLoss()
        self.alpha = alpha
        self.gamma = gamma

    def forward(self, y_pred, y_true, weight):
        alpha = self.alpha
        weight = torch.pow(weight, self.gamma)
        loss_dice = self.loss_dice(y_pred, y_true)
        loss_mce = self.loss_mce(y_pred, y_true, weight)
        loss = loss_mce + alpha * loss_dice
        return loss


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), 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 math as tl_math
import torch.nn.functional
import torch.nn as nn
import torch.nn.functional as F
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__log_softmax__softmax_0(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), 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 + x3, tmp9, xmask)
    tl.store(out_ptr1 + x3, tmp8, xmask)


@triton.jit
def triton_poi_fused__log_softmax__softmax_mul_pow_rsub_1(in_ptr0, in_ptr1,
    in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x3, xmask)
    tmp2 = tl.load(in_ptr1 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr1 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp5 = tl.load(in_ptr1 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp7 = tl.load(in_ptr1 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp14 = tl.load(in_ptr2 + x3, xmask)
    tmp15 = tl.load(in_ptr2 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp17 = tl.load(in_ptr2 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp20 = tl.load(in_ptr2 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp23 = tl.load(in_ptr2 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = tmp1 / tmp8
    tmp10 = 1.0
    tmp11 = tmp10 - tmp9
    tmp12 = tmp11 * tmp11
    tmp13 = tmp0 * tmp12
    tmp16 = tl_math.exp(tmp15)
    tmp18 = tl_math.exp(tmp17)
    tmp19 = tmp16 + tmp18
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp19 + tmp21
    tmp24 = tl_math.exp(tmp23)
    tmp25 = tmp22 + tmp24
    tmp26 = tl_math.log(tmp25)
    tmp27 = tmp14 - tmp26
    tmp28 = tmp13 * tmp27
    tl.store(out_ptr0 + x3, tmp28, xmask)


@triton.jit
def triton_per_fused__to_copy_add_div_eq_log_mean_mul_neg_sum_2(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    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 % 16
    r1 = rindex // 16
    tmp0 = tl.load(in_ptr0 + (r0 + 64 * r1), None)
    tmp1 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
    tmp3 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp4 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
    tmp7 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp8 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
    tmp11 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
    tmp12 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
    tmp18 = tl.load(in_ptr2 + (16 + r0 + 64 * r1), None)
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.sum(tmp15, 1)[:, None]
    tmp19 = tl.sigmoid(tmp18)
    tmp20 = 1.0
    tmp21 = tmp4 == tmp20
    tmp22 = tmp21.to(tl.float32)
    tmp23 = tmp19 * tmp22
    tmp24 = tl.broadcast_to(tmp23, [XBLOCK, RBLOCK])
    tmp26 = tl.sum(tmp24, 1)[:, None]
    tmp27 = tl.broadcast_to(tmp19, [XBLOCK, RBLOCK])
    tmp29 = tl.sum(tmp27, 1)[:, None]
    tmp30 = tl.broadcast_to(tmp22, [XBLOCK, RBLOCK])
    tmp32 = tl.sum(tmp30, 1)[:, None]
    tmp33 = 64.0
    tmp34 = tmp17 / tmp33
    tmp35 = -tmp34
    tmp36 = 2.0
    tmp37 = tmp26 * tmp36
    tmp38 = tmp29 + tmp32
    tmp39 = 1e-15
    tmp40 = tmp38 + tmp39
    tmp41 = tmp37 / tmp40
    tmp42 = tl_math.log(tmp41)
    tmp43 = -tmp42
    tmp44 = tmp43 * tmp20
    tmp45 = tmp35 + tmp44
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp45, None)


def call(args):
    arg0_1, arg1_1, arg2_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))
    assert_size_stride(arg2_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, 4), (64, 16, 4, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__log_softmax__softmax_0[grid(256)](arg1_1, buf0,
            buf1, 256, XBLOCK=128, num_warps=4, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__log_softmax__softmax_mul_pow_rsub_1[grid(256)](arg0_1
            , buf0, buf1, buf2, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
        del buf0
        del buf1
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf7 = buf3
        del buf3
        triton_per_fused__to_copy_add_div_eq_log_mean_mul_neg_sum_2[grid(1)](
            buf7, buf2, arg2_1, arg1_1, 1, 64, XBLOCK=1, num_warps=2,
            num_stages=1)
        del arg1_1
        del arg2_1
        del buf2
    return buf7,


def centercrop(image, w, h):
    _nt, _ct, ht, wt = image.size()
    padw, padh = (wt - w) // 2, (ht - h) // 2
    if padw > 0 and padh > 0:
        image = image[:, :, padh:-padh, padw:-padw]
    return image


class WeightedMCEFocalloss(nn.Module):

    def __init__(self, gamma=2.0):
        super(WeightedMCEFocalloss, self).__init__()
        self.gamma = gamma

    def forward(self, y_pred, y_true, weight):
        _n, _ch, h, w = y_pred.size()
        y_true = centercrop(y_true, w, h)
        weight = centercrop(weight, w, h)
        y_pred_log = F.log_softmax(y_pred, dim=1)
        fweight = (1 - F.softmax(y_pred, dim=1)) ** self.gamma
        weight = weight * fweight
        logpy = torch.sum(weight * y_pred_log * y_true, dim=1)
        loss = -torch.mean(logpy)
        return loss


class BLogDiceLoss(nn.Module):

    def __init__(self, classe=1):
        super(BLogDiceLoss, self).__init__()
        self.sigmoid = nn.Sigmoid()
        self.classe = classe

    def forward(self, y_pred, y_true, weight=None):
        _n, _ch, h, w = y_pred.size()
        y_true = centercrop(y_true, w, h)
        y_pred = self.sigmoid(y_pred)
        eps = 1e-15
        dice_target = (y_true[:, self.classe, ...] == 1).float()
        dice_output = y_pred[:, self.classe, ...]
        intersection = (dice_output * dice_target).sum()
        union = dice_output.sum() + dice_target.sum() + eps
        return -torch.log(2 * intersection / union)


class WeightedMCEDiceLossNew(nn.Module):

    def __init__(self, alpha=1.0, gamma=1.0):
        super(WeightedMCEDiceLossNew, self).__init__()
        self.loss_mce = WeightedMCEFocalloss()
        self.loss_dice = BLogDiceLoss()
        self.alpha = alpha
        self.gamma = gamma

    def forward(self, input_0, input_1, input_2):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        output = call([arg0_1, arg1_1, arg2_1])
        return output[0]
