# 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/zn/cznprkeuyccqrgaq2oqedmt6b75mnapuac3rarw7btkcaemyq7x5.py
# Topologically Sorted Source Nodes: [sum_1], Original ATen: [aten.sum]
# Source node to ATen node mapping:
#   sum_1 => sum_1
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%arg1_1, [2, 3], True), kwargs = {})
triton_per_fused_sum_0 = async_compile.triton('triton_per_fused_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: 'i32', 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_per_fused_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_sum_0(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (16*x0)), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tl.store(out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/zy/czygy27npr6ld27ec2ahrguwi3mku62rvqmjpse6uvkfa27p7vgp.py
# Topologically Sorted Source Nodes: [bce_loss, mul, weights, betas, mul_1, sub_1, mul_2, mul_3, bce_loss_1, sum_2, bce_loss_2], Original ATen: [aten.binary_cross_entropy_with_logits, aten.mul, aten.div, aten.rsub, aten.add, aten.sum]
# Source node to ATen node mapping:
#   bce_loss => abs_1, exp, full_default, log1p, minimum, mul, neg, sub, sub_1, sub_2
#   bce_loss_1 => add
#   bce_loss_2 => div_1
#   betas => sub_3
#   mul => mul_1
#   mul_1 => mul_2
#   mul_2 => mul_3
#   mul_3 => mul_4
#   sub_1 => sub_4
#   sum_2 => sum_2
#   weights => div
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg1_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %arg0_1), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %minimum : [num_users=1] = call_function[target=torch.ops.aten.minimum.default](args = (%full_default, %arg0_1), kwargs = {})
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%arg0_1,), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%abs_1,), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%neg,), kwargs = {})
#   %log1p : [num_users=1] = call_function[target=torch.ops.aten.log1p.default](args = (%exp,), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%minimum, %log1p), kwargs = {})
#   %sub_2 : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul, %sub_1), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg1_1, %sub_2), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_1, 16), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %div), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_1, %sub_3), kwargs = {})
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg1_1), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_4, %sub_2), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_3, %div), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_2, %mul_4), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%add,), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_2, 64), kwargs = {})
triton_per_fused_add_binary_cross_entropy_with_logits_div_mul_rsub_sum_1 = async_compile.triton('triton_per_fused_add_binary_cross_entropy_with_logits_div_mul_rsub_sum_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.persistent_reduction(
    size_hints=[1, 256],
    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_add_binary_cross_entropy_with_logits_div_mul_rsub_sum_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, '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_add_binary_cross_entropy_with_logits_div_mul_rsub_sum_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, rnumel):
    xnumel = 1
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r2 = rindex
    r1 = (rindex // 16)
    tmp0 = tl.load(in_ptr0 + (r2), None)
    tmp3 = tl.load(in_ptr1 + (r2), None)
    tmp14 = tl.load(in_ptr2 + (r1), None, eviction_policy='evict_last')
    tmp1 = 1.0
    tmp2 = tmp1 - tmp0
    tmp4 = tmp2 * tmp3
    tmp5 = 0.0
    tmp6 = triton_helpers.minimum(tmp5, tmp3)
    tmp7 = tl_math.abs(tmp3)
    tmp8 = -tmp7
    tmp9 = tl_math.exp(tmp8)
    tmp10 = libdevice.log1p(tmp9)
    tmp11 = tmp6 - tmp10
    tmp12 = tmp4 - tmp11
    tmp13 = tmp0 * tmp12
    tmp15 = 0.0625
    tmp16 = tmp14 * tmp15
    tmp17 = tmp1 - tmp16
    tmp18 = tmp13 * tmp17
    tmp19 = tmp2 * tmp12
    tmp20 = tmp19 * tmp16
    tmp21 = tmp18 + tmp20
    tmp22 = tl.broadcast_to(tmp21, [RBLOCK])
    tmp24 = triton_helpers.promote_to_tensor(tl.sum(tmp22, 0))
    tmp25 = 0.015625
    tmp26 = tmp24 * tmp25
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp26, 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, 1, 1), (4, 1, 16, 16), torch.float32)
        # Topologically Sorted Source Nodes: [sum_1], Original ATen: [aten.sum]
        stream0 = get_raw_stream(0)
        triton_per_fused_sum_0.run(arg1_1, buf0, 16, 16, grid=grid(16), stream=stream0)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [bce_loss, mul, weights, betas, mul_1, sub_1, mul_2, mul_3, bce_loss_1, sum_2, bce_loss_2], Original ATen: [aten.binary_cross_entropy_with_logits, aten.mul, aten.div, aten.rsub, aten.add, aten.sum]
        triton_per_fused_add_binary_cross_entropy_with_logits_div_mul_rsub_sum_1.run(buf2, arg1_1, arg0_1, buf0, 1, 256, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
        del buf0
    return (buf2, )


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 random
import torch
import torch.nn as nn
from random import random
import random


class MultiLabelSoftBinaryCrossEntropy(nn.Module):

    def __init__(self, smooth_factor: 'float'=0, weighted: 'bool'=True, mcb:
        'bool'=False, hp_lambda: 'int'=10, epsilon: 'float'=0.1, logits=
        True, first_class_bg=False):
        super(MultiLabelSoftBinaryCrossEntropy, self).__init__()
        self.smooth_factor = smooth_factor
        self.logits = logits
        if logits:
            self.criterion = nn.BCEWithLogitsLoss(reduction='none' if
                weighted else 'mean')
        else:
            self.criterion = nn.BCELoss(reduction='none' if weighted else
                'mean')
        self.weighted = weighted
        self.hp_lambda = hp_lambda
        self.MCB = mcb
        self.epsilon = epsilon
        self.first_class_bg = first_class_bg

    def forward(self, y_pred: 'torch.Tensor', y_true: 'torch.Tensor'
        ) ->torch.Tensor:
        if y_pred.size() != y_true.size():
            """
            Case in which y_pred.shape == b x c+1 x h x w and y_true.shape == b x c x h x w
            """
            y_pred = y_pred[:, 1:]
        b, _c, h, w = y_true.shape
        y_true = y_true.float()
        if self.smooth_factor:
            smooth = random.uniform(0, self.smooth_factor)
            soft_targets = (1 - y_true) * smooth + y_true * (1 - smooth)
        else:
            soft_targets = y_true
        bce_loss = self.criterion(y_pred, soft_targets)
        if self.weighted and not self.MCB:
            N = h * w
            weights = y_true.sum(dim=(2, 3), keepdim=True) / N
            betas = 1 - weights
            bce_loss = y_true * bce_loss * betas + (1 - y_true
                ) * bce_loss * weights
            bce_loss = bce_loss.sum() / (b * N)
        if self.weighted and self.MCB:
            Ypos = y_true.sum(dim=(0, 2, 3), keepdim=False)
            mcb_loss = 0
            for i, k in enumerate(Ypos):
                if self.first_class_bg and i == 0:
                    tmp = (y_true[:, i] * bce_loss[:, i]).flatten(1, 2)
                    mcb_loss += torch.topk(tmp, k=self.hp_lambda * 25, dim=
                        1, sorted=False).values.mean()
                else:
                    tmp = ((1 - y_true[:, i]) * bce_loss[:, i]).flatten(1, 2)
                    topk = max(min(k * self.hp_lambda // b, (1 - y_true[:,
                        i]).sum() // b), self.hp_lambda)
                    ik = torch.topk(tmp, k=int(topk), dim=1, sorted=False
                        ).values
                    beta_k = ik.shape[1] / (k / b + ik.shape[1] + self.epsilon)
                    mcb_loss += (ik * (1 - beta_k)).mean()
                    tmp = y_true[:, i] * bce_loss[:, i]
                    mcb_loss += (tmp * beta_k).sum() / (y_true[:, i].sum() +
                        self.epsilon)
            bce_loss = mcb_loss
        return bce_loss


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, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_per_fused_sum_0(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK: tl.
    constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_per_fused_add_binary_cross_entropy_with_logits_div_mul_rsub_sum_1(
    in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r2 = rindex
    r1 = rindex // 16
    tmp0 = tl.load(in_ptr0 + r2, None)
    tmp3 = tl.load(in_ptr1 + r2, None)
    tmp14 = tl.load(in_ptr2 + r1, None, eviction_policy='evict_last')
    tmp1 = 1.0
    tmp2 = tmp1 - tmp0
    tmp4 = tmp2 * tmp3
    tmp5 = 0.0
    tmp6 = triton_helpers.minimum(tmp5, tmp3)
    tmp7 = tl_math.abs(tmp3)
    tmp8 = -tmp7
    tmp9 = tl_math.exp(tmp8)
    tmp10 = libdevice.log1p(tmp9)
    tmp11 = tmp6 - tmp10
    tmp12 = tmp4 - tmp11
    tmp13 = tmp0 * tmp12
    tmp15 = 0.0625
    tmp16 = tmp14 * tmp15
    tmp17 = tmp1 - tmp16
    tmp18 = tmp13 * tmp17
    tmp19 = tmp2 * tmp12
    tmp20 = tmp19 * tmp16
    tmp21 = tmp18 + tmp20
    tmp22 = tl.broadcast_to(tmp21, [RBLOCK])
    tmp24 = triton_helpers.promote_to_tensor(tl.sum(tmp22, 0))
    tmp25 = 0.015625
    tmp26 = tmp24 * tmp25
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp26, 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, 1, 1), (4, 1, 16, 16), torch.float32)
        get_raw_stream(0)
        triton_per_fused_sum_0[grid(16)](arg1_1, buf0, 16, 16, XBLOCK=8,
            num_warps=2, num_stages=1)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        triton_per_fused_add_binary_cross_entropy_with_logits_div_mul_rsub_sum_1[
            grid(1)](buf2, arg1_1, arg0_1, buf0, 1, 256, num_warps=2,
            num_stages=1)
        del arg0_1
        del arg1_1
        del buf0
    return buf2,


class MultiLabelSoftBinaryCrossEntropyNew(nn.Module):

    def __init__(self, smooth_factor: 'float'=0, weighted: 'bool'=True, mcb:
        'bool'=False, hp_lambda: 'int'=10, epsilon: 'float'=0.1, logits=
        True, first_class_bg=False):
        super(MultiLabelSoftBinaryCrossEntropyNew, self).__init__()
        self.smooth_factor = smooth_factor
        self.logits = logits
        if logits:
            self.criterion = nn.BCEWithLogitsLoss(reduction='none' if
                weighted else 'mean')
        else:
            self.criterion = nn.BCELoss(reduction='none' if weighted else
                'mean')
        self.weighted = weighted
        self.hp_lambda = hp_lambda
        self.MCB = mcb
        self.epsilon = epsilon
        self.first_class_bg = first_class_bg

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