# 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_6/inductor_cache/sw/cswigq6welcg2ea6b2om22mf7opqse7wgus3uvcqvwzvge7pz3qd.py
# Topologically Sorted Source Nodes: [mul_3, sub_3, mul_4, alpha_t, ce_loss, p, mul, sub, sub_1, mul_1, p_t, sub_2, pow_1, loss, loss_1, loss_2], Original ATen: [aten.mul, aten.rsub, aten.add, aten.binary_cross_entropy_with_logits, aten.sigmoid, aten.pow, aten.mean]
# Source node to ATen node mapping:
#   alpha_t => add_1
#   ce_loss => abs_1, exp, full_default, log1p, minimum, mul, neg, sub, sub_1, sub_2
#   loss => mul_3
#   loss_1 => mul_6
#   loss_2 => mean
#   mul => mul_1
#   mul_1 => mul_2
#   mul_3 => mul_4
#   mul_4 => mul_5
#   p => sigmoid
#   p_t => add
#   pow_1 => pow_1
#   sub => sub_3
#   sub_1 => sub_4
#   sub_2 => sub_5
#   sub_3 => sub_6
# Graph fragment:
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg1_1, 0.25), kwargs = {})
#   %sub_6 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg1_1), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_6, 0.75), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_4, %mul_5), kwargs = {})
#   %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=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul, %sub_1), kwargs = {})
#   %sigmoid : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%arg0_1,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %arg1_1), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sigmoid), kwargs = {})
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg1_1), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_3, %sub_4), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %mul_2), kwargs = {})
#   %sub_5 : [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_5, 2.0), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_2, %pow_1), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_1, %mul_3), 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_mean_mul_pow_rsub_sigmoid_0 = async_compile.triton('triton_per_fused_add_binary_cross_entropy_with_logits_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_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_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 = 0.25
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp3 - tmp0
    tmp5 = 0.75
    tmp6 = tmp4 * tmp5
    tmp7 = tmp2 + tmp6
    tmp9 = tmp4 * tmp8
    tmp10 = 0.0
    tmp11 = triton_helpers.minimum(tmp10, tmp8)
    tmp12 = tl_math.abs(tmp8)
    tmp13 = -tmp12
    tmp14 = tl_math.exp(tmp13)
    tmp15 = libdevice.log1p(tmp14)
    tmp16 = tmp11 - tmp15
    tmp17 = tmp9 - tmp16
    tmp18 = tl.sigmoid(tmp8)
    tmp19 = tmp18 * tmp0
    tmp20 = tmp3 - tmp18
    tmp21 = tmp20 * tmp4
    tmp22 = tmp19 + tmp21
    tmp23 = tmp3 - tmp22
    tmp24 = tmp23 * tmp23
    tmp25 = tmp17 * tmp24
    tmp26 = tmp7 * tmp25
    tmp27 = tl.broadcast_to(tmp26, [RBLOCK])
    tmp29 = triton_helpers.promote_to_tensor(tl.sum(tmp27, 0))
    tmp30 = 256.0
    tmp31 = tmp29 / tmp30
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp31, 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((), (), torch.float32)
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [mul_3, sub_3, mul_4, alpha_t, ce_loss, p, mul, sub, sub_1, mul_1, p_t, sub_2, pow_1, loss, loss_1, loss_2], Original ATen: [aten.mul, aten.rsub, aten.add, aten.binary_cross_entropy_with_logits, aten.sigmoid, aten.pow, aten.mean]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_binary_cross_entropy_with_logits_mean_mul_pow_rsub_sigmoid_0.run(buf1, arg1_1, arg0_1, 1, 256, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf1, )


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


def sigmoid_focal_loss(inputs: 'torch.Tensor', targets: 'torch.Tensor',
    alpha: 'float'=-1, gamma: 'float'=2, reduction: 'str'='none'
    ) ->torch.Tensor:
    """
    Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.
    Args:
        inputs: A float tensor of arbitrary shape.
                The predictions for each example.
        targets: A float tensor with the same shape as inputs. Stores the binary
                 classification label for each element in inputs
                (0 for the negative class and 1 for the positive class).
        alpha: (optional) Weighting factor in range (0,1) to balance
                positive vs negative examples. Default = -1 (no weighting).
        gamma: Exponent of the modulating factor (1 - p_t) to
               balance easy vs hard examples.
        reduction: 'none' | 'mean' | 'sum'
                 'none': No reduction will be applied to the output.
                 'mean': The output will be averaged.
                 'sum': The output will be summed.
    Returns:
        Loss tensor with the reduction option applied.
    """
    p = torch.sigmoid(inputs)
    ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction
        ='none')
    p_t = p * targets + (1 - p) * (1 - targets)
    loss = ce_loss * (1 - p_t) ** gamma
    if alpha >= 0:
        alpha_t = alpha * targets + (1 - alpha) * (1 - targets)
        loss = alpha_t * loss
    if reduction == 'mean':
        loss = loss.mean()
    elif reduction == 'sum':
        loss = loss.sum()
    return loss


class SigmoidFocalLoss(nn.Module):

    def __init__(self, alpha: 'float'=0.25, gamma: 'float'=2.0, reduction:
        'str'='mean'):
        super().__init__()
        self.alpha = alpha
        self.gamma = gamma
        self.reduction = reduction

    def forward(self, preds, targets):
        return sigmoid_focal_loss(preds, targets, self.alpha, self.gamma,
            self.reduction)


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
from torch.nn import 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_per_fused_add_binary_cross_entropy_with_logits_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 = 0.25
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp3 - tmp0
    tmp5 = 0.75
    tmp6 = tmp4 * tmp5
    tmp7 = tmp2 + tmp6
    tmp9 = tmp4 * tmp8
    tmp10 = 0.0
    tmp11 = triton_helpers.minimum(tmp10, tmp8)
    tmp12 = tl_math.abs(tmp8)
    tmp13 = -tmp12
    tmp14 = tl_math.exp(tmp13)
    tmp15 = libdevice.log1p(tmp14)
    tmp16 = tmp11 - tmp15
    tmp17 = tmp9 - tmp16
    tmp18 = tl.sigmoid(tmp8)
    tmp19 = tmp18 * tmp0
    tmp20 = tmp3 - tmp18
    tmp21 = tmp20 * tmp4
    tmp22 = tmp19 + tmp21
    tmp23 = tmp3 - tmp22
    tmp24 = tmp23 * tmp23
    tmp25 = tmp17 * tmp24
    tmp26 = tmp7 * tmp25
    tmp27 = tl.broadcast_to(tmp26, [RBLOCK])
    tmp29 = triton_helpers.promote_to_tensor(tl.sum(tmp27, 0))
    tmp30 = 256.0
    tmp31 = tmp29 / tmp30
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp31, 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((), (), torch.float32)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_add_binary_cross_entropy_with_logits_mean_mul_pow_rsub_sigmoid_0[
            grid(1)](buf1, arg1_1, arg0_1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf1,


def sigmoid_focal_loss(inputs: 'torch.Tensor', targets: 'torch.Tensor',
    alpha: 'float'=-1, gamma: 'float'=2, reduction: 'str'='none'
    ) ->torch.Tensor:
    """
    Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.
    Args:
        inputs: A float tensor of arbitrary shape.
                The predictions for each example.
        targets: A float tensor with the same shape as inputs. Stores the binary
                 classification label for each element in inputs
                (0 for the negative class and 1 for the positive class).
        alpha: (optional) Weighting factor in range (0,1) to balance
                positive vs negative examples. Default = -1 (no weighting).
        gamma: Exponent of the modulating factor (1 - p_t) to
               balance easy vs hard examples.
        reduction: 'none' | 'mean' | 'sum'
                 'none': No reduction will be applied to the output.
                 'mean': The output will be averaged.
                 'sum': The output will be summed.
    Returns:
        Loss tensor with the reduction option applied.
    """
    p = torch.sigmoid(inputs)
    ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction
        ='none')
    p_t = p * targets + (1 - p) * (1 - targets)
    loss = ce_loss * (1 - p_t) ** gamma
    if alpha >= 0:
        alpha_t = alpha * targets + (1 - alpha) * (1 - targets)
        loss = alpha_t * loss
    if reduction == 'mean':
        loss = loss.mean()
    elif reduction == 'sum':
        loss = loss.sum()
    return loss


class SigmoidFocalLossNew(nn.Module):

    def __init__(self, alpha: 'float'=0.25, gamma: 'float'=2.0, reduction:
        'str'='mean'):
        super().__init__()
        self.alpha = alpha
        self.gamma = gamma
        self.reduction = reduction

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