# 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/ot/cot3iokaq5qj7fk7xcq34uf742bupzp5cerqfhhwmxoilvk3c2ml.py
# Topologically Sorted Source Nodes: [mul_2, sub_2, mul_3, at, sub, pred_sig, sub_1, mul, mul_1, pt, sub_3, pow_1, wt, clamp, bce, mul_5, mean], Original ATen: [aten.mul, aten.rsub, aten.add, aten.sigmoid, aten.pow, aten.clamp, aten.binary_cross_entropy_with_logits, aten.mean]
# Source node to ATen node mapping:
#   at => add_1
#   bce => abs_1, exp, full_default, log1p, minimum, mul_5, neg, sub_4, sub_5, sub_6
#   clamp => clamp_max, clamp_min
#   mean => mean
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   mul_3 => mul_3
#   mul_5 => mul_6
#   pow_1 => pow_1
#   pred_sig => sigmoid
#   pt => add
#   sub => sub
#   sub_1 => sub_1
#   sub_2 => sub_2
#   sub_3 => sub_3
#   wt => mul_4
# Graph fragment:
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg1_1, 0.75), kwargs = {})
#   %sub_2 : [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_2, 0.25), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_2, %mul_3), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg1_1), kwargs = {})
#   %sigmoid : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%arg0_1,), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sigmoid), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %sub_1), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg1_1, %sigmoid), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %add), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_3, 2.0), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_1, %pow_1), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%arg1_1, 0.0), kwargs = {})
#   %clamp_max : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 1.0), kwargs = {})
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %clamp_max), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_4, %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_5 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%minimum, %log1p), kwargs = {})
#   %sub_6 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_5, %sub_5), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_4, %sub_6), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%mul_6,), kwargs = {})
triton_per_fused_add_binary_cross_entropy_with_logits_clamp_mean_mul_pow_rsub_sigmoid_0 = async_compile.triton('triton_per_fused_add_binary_cross_entropy_with_logits_clamp_mean_mul_pow_rsub_sigmoid_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=[1, 256],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_binary_cross_entropy_with_logits_clamp_mean_mul_pow_rsub_sigmoid_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 2, 'num_reduction': 1, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_add_binary_cross_entropy_with_logits_clamp_mean_mul_pow_rsub_sigmoid_0(in_out_ptr0, in_ptr0, in_ptr1, 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)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp8 = tl.load(in_ptr1 + (r0), None)
    tmp1 = 0.75
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp3 - tmp0
    tmp5 = 0.25
    tmp6 = tmp4 * tmp5
    tmp7 = tmp2 + tmp6
    tmp9 = tl.sigmoid(tmp8)
    tmp10 = tmp3 - tmp9
    tmp11 = tmp4 * tmp10
    tmp12 = tmp0 * tmp9
    tmp13 = tmp11 + tmp12
    tmp14 = tmp3 - tmp13
    tmp15 = tmp14 * tmp14
    tmp16 = tmp7 * tmp15
    tmp17 = 0.0
    tmp18 = triton_helpers.maximum(tmp0, tmp17)
    tmp19 = triton_helpers.minimum(tmp18, tmp3)
    tmp20 = tmp3 - tmp19
    tmp21 = tmp20 * tmp8
    tmp22 = triton_helpers.minimum(tmp17, tmp8)
    tmp23 = tl_math.abs(tmp8)
    tmp24 = -tmp23
    tmp25 = tl_math.exp(tmp24)
    tmp26 = libdevice.log1p(tmp25)
    tmp27 = tmp22 - tmp26
    tmp28 = tmp21 - tmp27
    tmp29 = tmp16 * tmp28
    tmp30 = tl.broadcast_to(tmp29, [RBLOCK])
    tmp32 = triton_helpers.promote_to_tensor(tl.sum(tmp30, 0))
    tmp33 = 256.0
    tmp34 = tmp32 / tmp33
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp34, 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)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [mul_2, sub_2, mul_3, at, sub, pred_sig, sub_1, mul, mul_1, pt, sub_3, pow_1, wt, clamp, bce, mul_5, mean], Original ATen: [aten.mul, aten.rsub, aten.add, aten.sigmoid, aten.pow, aten.clamp, aten.binary_cross_entropy_with_logits, aten.mean]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_binary_cross_entropy_with_logits_clamp_mean_mul_pow_rsub_sigmoid_0.run(buf2, arg1_1, arg0_1, 1, 256, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
    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 torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel


class WeightedBCEFocalLoss(nn.Module):
    """Weighted binary focal loss with logits.
    """

    def __init__(self, gamma=2.0, alpha=0.25, eps=0.0):
        super().__init__()
        self.eps = eps
        self.gamma = gamma
        self.alpha = alpha

    def forward(self, pred, target, weight_mask=None):
        pred_sig = pred.sigmoid()
        pt = (1 - target) * (1 - pred_sig) + target * pred_sig
        at = (1 - self.alpha) * target + self.alpha * (1 - target)
        wt = at * (1 - pt) ** self.gamma
        if weight_mask is not None:
            wt *= weight_mask
        bce = F.binary_cross_entropy_with_logits(pred, target.clamp(self.
            eps, 1 - self.eps), reduction='none')
        return (wt * bce).mean()


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.utils.data
import torch.nn as nn
import torch.nn.parallel
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_add_binary_cross_entropy_with_logits_clamp_mean_mul_pow_rsub_sigmoid_0(
    in_out_ptr0, in_ptr0, in_ptr1, 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)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp8 = tl.load(in_ptr1 + r0, None)
    tmp1 = 0.75
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp3 - tmp0
    tmp5 = 0.25
    tmp6 = tmp4 * tmp5
    tmp7 = tmp2 + tmp6
    tmp9 = tl.sigmoid(tmp8)
    tmp10 = tmp3 - tmp9
    tmp11 = tmp4 * tmp10
    tmp12 = tmp0 * tmp9
    tmp13 = tmp11 + tmp12
    tmp14 = tmp3 - tmp13
    tmp15 = tmp14 * tmp14
    tmp16 = tmp7 * tmp15
    tmp17 = 0.0
    tmp18 = triton_helpers.maximum(tmp0, tmp17)
    tmp19 = triton_helpers.minimum(tmp18, tmp3)
    tmp20 = tmp3 - tmp19
    tmp21 = tmp20 * tmp8
    tmp22 = triton_helpers.minimum(tmp17, tmp8)
    tmp23 = tl_math.abs(tmp8)
    tmp24 = -tmp23
    tmp25 = tl_math.exp(tmp24)
    tmp26 = libdevice.log1p(tmp25)
    tmp27 = tmp22 - tmp26
    tmp28 = tmp21 - tmp27
    tmp29 = tmp16 * tmp28
    tmp30 = tl.broadcast_to(tmp29, [RBLOCK])
    tmp32 = triton_helpers.promote_to_tensor(tl.sum(tmp30, 0))
    tmp33 = 256.0
    tmp34 = tmp32 / tmp33
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp34, 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)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_per_fused_add_binary_cross_entropy_with_logits_clamp_mean_mul_pow_rsub_sigmoid_0[
            grid(1)](buf2, arg1_1, arg0_1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf2,


class WeightedBCEFocalLossNew(nn.Module):
    """Weighted binary focal loss with logits.
    """

    def __init__(self, gamma=2.0, alpha=0.25, eps=0.0):
        super().__init__()
        self.eps = eps
        self.gamma = gamma
        self.alpha = alpha

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