# 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/wu/cwulnfzawsraojxjhrosfetzxwwjffv77d4rvq534gvtlqi6vyp4.py
# Topologically Sorted Source Nodes: [mul_2, sub_2, mul_3, at, neg_2, input_1, log, mul, sub, sub_1, log_1, mul_1, add, cross_entropy, logpt, balanced_cross_entropy, pt, sub_3, pow_1, focal_loss, sum_1], Original ATen: [aten.mul, aten.rsub, aten.add, aten.neg, aten.clamp, aten.log, aten.exp, aten.pow, aten.sum]
# Source node to ATen node mapping:
#   add => add
#   at => add_1
#   balanced_cross_entropy => mul_4
#   cross_entropy => neg
#   focal_loss => mul_5
#   input_1 => clamp_max, clamp_min
#   log => log
#   log_1 => log_1
#   logpt => neg_1
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   mul_3 => mul_3
#   neg_2 => neg_2
#   pow_1 => pow_1
#   pt => exp
#   sub => sub
#   sub_1 => sub_1
#   sub_2 => sub_2
#   sub_3 => sub_3
#   sum_1 => sum_1
# Graph fragment:
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg1_1, 0.25), 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, 0.75), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_2, %mul_3), kwargs = {})
#   %neg_2 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%add_1,), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%arg0_1, 1e-07), kwargs = {})
#   %clamp_max : [num_users=2] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 0.9999999), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%clamp_max,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg1_1, %log), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg1_1), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %clamp_max), kwargs = {})
#   %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sub_1,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %log_1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%add,), kwargs = {})
#   %neg_1 : [num_users=2] = call_function[target=torch.ops.aten.neg.default](args = (%neg,), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%neg_2, %neg_1), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%neg_1,), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %exp), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_3, 2), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_4, %pow_1), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mul_5,), kwargs = {})
triton_per_fused_add_clamp_exp_log_mul_neg_pow_rsub_sum_0 = async_compile.triton('triton_per_fused_add_clamp_exp_log_mul_neg_pow_rsub_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=[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_clamp_exp_log_mul_neg_pow_rsub_sum_0', 'mutated_arg_names': [], '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_clamp_exp_log_mul_neg_pow_rsub_sum_0(in_ptr0, in_ptr1, out_ptr0, 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)
    tmp9 = 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
    tmp8 = -tmp7
    tmp10 = 1e-07
    tmp11 = triton_helpers.maximum(tmp9, tmp10)
    tmp12 = 0.9999999
    tmp13 = triton_helpers.minimum(tmp11, tmp12)
    tmp14 = tl_math.log(tmp13)
    tmp15 = tmp0 * tmp14
    tmp16 = tmp3 - tmp13
    tmp17 = tl_math.log(tmp16)
    tmp18 = tmp4 * tmp17
    tmp19 = tmp15 + tmp18
    tmp20 = -tmp19
    tmp21 = -tmp20
    tmp22 = tmp8 * tmp21
    tmp23 = tl_math.exp(tmp21)
    tmp24 = tmp3 - tmp23
    tmp25 = tmp24 * tmp24
    tmp26 = tmp22 * tmp25
    tmp27 = tl.broadcast_to(tmp26, [RBLOCK])
    tmp29 = triton_helpers.promote_to_tensor(tl.sum(tmp27, 0))
    tl.store(out_ptr0 + (tl.full([1], 0, tl.int32)), tmp29, 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)
        # Topologically Sorted Source Nodes: [mul_2, sub_2, mul_3, at, neg_2, input_1, log, mul, sub, sub_1, log_1, mul_1, add, cross_entropy, logpt, balanced_cross_entropy, pt, sub_3, pow_1, focal_loss, sum_1], Original ATen: [aten.mul, aten.rsub, aten.add, aten.neg, aten.clamp, aten.log, aten.exp, aten.pow, aten.sum]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_clamp_exp_log_mul_neg_pow_rsub_sum_0.run(arg1_1, arg0_1, buf0, 1, 256, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf0, )


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


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

    .. seealso::
        Lin, Tsung-Yi, et al. "Focal loss for dense object detection."
        Proceedings of the IEEE international conference on computer vision. 2017.

    Args:
        gamma (float): Value from 0 to 5, Control between easy background and hard ROI
            training examples. If set to 0, equivalent to cross-entropy.
        alpha (float): Value from 0 to 1, usually corresponding to the inverse of class frequency to address class
            imbalance.
        eps (float): Epsilon to avoid division by zero.

    Attributes:
        gamma (float): Value from 0 to 5, Control between easy background and hard ROI
            training examples. If set to 0, equivalent to cross-entropy.
        alpha (float): Value from 0 to 1, usually corresponding to the inverse of class frequency to address class
            imbalance.
        eps (float): Epsilon to avoid division by zero.
    """

    def __init__(self, gamma=2, alpha=0.25, eps=1e-07):
        super(FocalLoss, self).__init__()
        self.gamma = gamma
        self.alpha = alpha
        self.eps = eps

    def forward(self, input, target):
        input = input.clamp(self.eps, 1.0 - self.eps)
        cross_entropy = -(target * torch.log(input) + (1 - target) * torch.
            log(1 - input))
        logpt = -cross_entropy
        pt = torch.exp(logpt)
        at = self.alpha * target + (1 - self.alpha) * (1 - target)
        balanced_cross_entropy = -at * logpt
        focal_loss = balanced_cross_entropy * (1 - pt) ** self.gamma
        return focal_loss.sum()


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 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_add_clamp_exp_log_mul_neg_pow_rsub_sum_0(in_ptr0,
    in_ptr1, out_ptr0, 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)
    tmp9 = 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
    tmp8 = -tmp7
    tmp10 = 1e-07
    tmp11 = triton_helpers.maximum(tmp9, tmp10)
    tmp12 = 0.9999999
    tmp13 = triton_helpers.minimum(tmp11, tmp12)
    tmp14 = tl_math.log(tmp13)
    tmp15 = tmp0 * tmp14
    tmp16 = tmp3 - tmp13
    tmp17 = tl_math.log(tmp16)
    tmp18 = tmp4 * tmp17
    tmp19 = tmp15 + tmp18
    tmp20 = -tmp19
    tmp21 = -tmp20
    tmp22 = tmp8 * tmp21
    tmp23 = tl_math.exp(tmp21)
    tmp24 = tmp3 - tmp23
    tmp25 = tmp24 * tmp24
    tmp26 = tmp22 * tmp25
    tmp27 = tl.broadcast_to(tmp26, [RBLOCK])
    tmp29 = triton_helpers.promote_to_tensor(tl.sum(tmp27, 0))
    tl.store(out_ptr0 + tl.full([1], 0, tl.int32), tmp29, 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)
        get_raw_stream(0)
        triton_per_fused_add_clamp_exp_log_mul_neg_pow_rsub_sum_0[grid(1)](
            arg1_1, arg0_1, buf0, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf0,


class FocalLossNew(nn.Module):
    """FocalLoss.

    .. seealso::
        Lin, Tsung-Yi, et al. "Focal loss for dense object detection."
        Proceedings of the IEEE international conference on computer vision. 2017.

    Args:
        gamma (float): Value from 0 to 5, Control between easy background and hard ROI
            training examples. If set to 0, equivalent to cross-entropy.
        alpha (float): Value from 0 to 1, usually corresponding to the inverse of class frequency to address class
            imbalance.
        eps (float): Epsilon to avoid division by zero.

    Attributes:
        gamma (float): Value from 0 to 5, Control between easy background and hard ROI
            training examples. If set to 0, equivalent to cross-entropy.
        alpha (float): Value from 0 to 1, usually corresponding to the inverse of class frequency to address class
            imbalance.
        eps (float): Epsilon to avoid division by zero.
    """

    def __init__(self, gamma=2, alpha=0.25, eps=1e-07):
        super(FocalLossNew, self).__init__()
        self.gamma = gamma
        self.alpha = alpha
        self.eps = eps

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