# 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/am/camwpeehokwg5kta3nxp4pdqaj4l6nldns4yhguxa3yy3jfhld4k.py
# Topologically Sorted Source Nodes: [mul, sub, mul_1, pred_merged, loss, loss_1, mul_2], Original ATen: [aten.mul, aten.rsub, aten.add, aten.sub, aten.abs, aten.mean]
# Source node to ATen node mapping:
#   loss => abs_1, sub_1
#   loss_1 => mean
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   pred_merged => add
#   sub => sub
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, %arg1_1), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %arg0_1), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %arg2_1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %arg3_1), kwargs = {})
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%sub_1,), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%abs_1,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mean, 1.0), kwargs = {})
triton_per_fused_abs_add_mean_mul_rsub_sub_0 = async_compile.triton('triton_per_fused_abs_add_mean_mul_rsub_sub_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: '*fp32', 4: '*fp32', 5: 'i32', 6: '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': {5: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 6), equal_to_1=(5,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_abs_add_mean_mul_rsub_sub_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 4, '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_abs_add_mean_mul_rsub_sub_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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)
    tmp1 = tl.load(in_ptr1 + (r0), None)
    tmp5 = tl.load(in_ptr2 + (r0), None)
    tmp8 = tl.load(in_ptr3 + (r0), None)
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp3 - tmp0
    tmp6 = tmp4 * tmp5
    tmp7 = tmp2 + tmp6
    tmp9 = tmp7 - tmp8
    tmp10 = tl_math.abs(tmp9)
    tmp11 = tl.broadcast_to(tmp10, [RBLOCK])
    tmp13 = triton_helpers.promote_to_tensor(tl.sum(tmp11, 0))
    tmp14 = 256.0
    tmp15 = tmp13 / tmp14
    tmp16 = tmp15 * tmp3
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp16, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_1, arg3_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))
    assert_size_stride(arg3_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, sub, mul_1, pred_merged, loss, loss_1, mul_2], Original ATen: [aten.mul, aten.rsub, aten.add, aten.sub, aten.abs, aten.mean]
        stream0 = get_raw_stream(0)
        triton_per_fused_abs_add_mean_mul_rsub_sub_0.run(buf1, arg0_1, arg1_1, arg2_1, arg3_1, 1, 256, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
        del arg2_1
        del arg3_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)
    arg2_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg3_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, arg3_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 functools
import torch
from torch.nn import functional as F
import torch.nn as nn


def reduce_loss(loss, reduction):
    """Reduce loss as specified.

    Args:
        loss (Tensor): Elementwise loss tensor.
        reduction (str): Options are "none", "mean" and "sum".

    Returns:
        Tensor: Reduced loss tensor.
    """
    reduction_enum = F._Reduction.get_enum(reduction)
    if reduction_enum == 0:
        return loss
    if reduction_enum == 1:
        return loss.mean()
    return loss.sum()


def mask_reduce_loss(loss, weight=None, reduction='mean', sample_wise=False):
    """Apply element-wise weight and reduce loss.

    Args:
        loss (Tensor): Element-wise loss.
        weight (Tensor): Element-wise weights. Default: None.
        reduction (str): Same as built-in losses of PyTorch. Options are
            "none", "mean" and "sum". Default: 'mean'.
        sample_wise (bool): Whether calculate the loss sample-wise. This
            argument only takes effect when `reduction` is 'mean' and `weight`
            (argument of `forward()`) is not None. It will first reduces loss
            with 'mean' per-sample, and then it means over all the samples.
            Default: False.

    Returns:
        Tensor: Processed loss values.
    """
    if weight is not None:
        assert weight.dim() == loss.dim()
        assert weight.size(1) == 1 or weight.size(1) == loss.size(1)
        loss = loss * weight
    if weight is None or reduction == 'sum':
        loss = reduce_loss(loss, reduction)
    elif reduction == 'mean':
        if weight.size(1) == 1:
            weight = weight.expand_as(loss)
        eps = 1e-12
        if sample_wise:
            weight = weight.sum(dim=[1, 2, 3], keepdim=True)
            loss = (loss / (weight + eps)).sum() / weight.size(0)
        else:
            loss = loss.sum() / (weight.sum() + eps)
    return loss


def masked_loss(loss_func):
    """Create a masked version of a given loss function.

    To use this decorator, the loss function must have the signature like
    `loss_func(pred, target, **kwargs)`. The function only needs to compute
    element-wise loss without any reduction. This decorator will add weight
    and reduction arguments to the function. The decorated function will have
    the signature like `loss_func(pred, target, weight=None, reduction='mean',
    avg_factor=None, **kwargs)`.

    :Example:

    >>> import torch
    >>> @masked_loss
    >>> def l1_loss(pred, target):
    >>>     return (pred - target).abs()

    >>> pred = torch.Tensor([0, 2, 3])
    >>> target = torch.Tensor([1, 1, 1])
    >>> weight = torch.Tensor([1, 0, 1])

    >>> l1_loss(pred, target)
    tensor(1.3333)
    >>> l1_loss(pred, target, weight)
    tensor(1.5000)
    >>> l1_loss(pred, target, reduction='none')
    tensor([1., 1., 2.])
    >>> l1_loss(pred, target, weight, reduction='sum')
    tensor(3.)
    """

    @functools.wraps(loss_func)
    def wrapper(pred, target, weight=None, reduction='mean', sample_wise=
        False, **kwargs):
        loss = loss_func(pred, target, **kwargs)
        loss = mask_reduce_loss(loss, weight, reduction, sample_wise)
        return loss
    return wrapper


@masked_loss
def l1_loss(pred, target):
    """L1 loss.

    Args:
        pred (Tensor): Prediction Tensor with shape (n, c, h, w).
        target ([type]): Target Tensor with shape (n, c, h, w).

    Returns:
        Tensor: Calculated L1 loss.
    """
    return F.l1_loss(pred, target, reduction='none')


class L1CompositionLoss(nn.Module):
    """L1 composition loss.

    Args:
        loss_weight (float): Loss weight for L1 loss. Default: 1.0.
        reduction (str): Specifies the reduction to apply to the output.
            Supported choices are 'none' | 'mean' | 'sum'. Default: 'mean'.
        sample_wise (bool): Whether calculate the loss sample-wise. This
            argument only takes effect when `reduction` is 'mean' and `weight`
            (argument of `forward()`) is not None. It will first reduces loss
            with 'mean' per-sample, and then it means over all the samples.
            Default: False.
    """

    def __init__(self, loss_weight=1.0, reduction='mean', sample_wise=False):
        super().__init__()
        if reduction not in ['none', 'mean', 'sum']:
            raise ValueError(
                f'Unsupported reduction mode: {reduction}. Supported ones are: {_reduction_modes}'
                )
        self.loss_weight = loss_weight
        self.reduction = reduction
        self.sample_wise = sample_wise

    def forward(self, pred_alpha, fg, bg, ori_merged, weight=None, **kwargs):
        """
        Args:
            pred_alpha (Tensor): of shape (N, 1, H, W). Predicted alpha matte.
            fg (Tensor): of shape (N, 3, H, W). Tensor of foreground object.
            bg (Tensor): of shape (N, 3, H, W). Tensor of background object.
            ori_merged (Tensor): of shape (N, 3, H, W). Tensor of origin merged
                image before normalized by ImageNet mean and std.
            weight (Tensor, optional): of shape (N, 1, H, W). It is an
                indicating matrix: weight[trimap == 128] = 1. Default: None.
        """
        pred_merged = pred_alpha * fg + (1.0 - pred_alpha) * bg
        if weight is not None:
            weight = weight.expand(-1, 3, -1, -1)
        return self.loss_weight * l1_loss(pred_merged, ori_merged, weight,
            reduction=self.reduction, sample_wise=self.sample_wise)


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), 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 functools
from torch.nn import functional as F
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_abs_add_mean_mul_rsub_sub_0(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, 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)
    tmp1 = tl.load(in_ptr1 + r0, None)
    tmp5 = tl.load(in_ptr2 + r0, None)
    tmp8 = tl.load(in_ptr3 + r0, None)
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp3 - tmp0
    tmp6 = tmp4 * tmp5
    tmp7 = tmp2 + tmp6
    tmp9 = tmp7 - tmp8
    tmp10 = tl_math.abs(tmp9)
    tmp11 = tl.broadcast_to(tmp10, [RBLOCK])
    tmp13 = triton_helpers.promote_to_tensor(tl.sum(tmp11, 0))
    tmp14 = 256.0
    tmp15 = tmp13 / tmp14
    tmp16 = tmp15 * tmp3
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp16, None)


def call(args):
    arg0_1, arg1_1, arg2_1, arg3_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))
    assert_size_stride(arg3_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_abs_add_mean_mul_rsub_sub_0[grid(1)](buf1, arg0_1,
            arg1_1, arg2_1, arg3_1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
        del arg3_1
    return buf1,


def reduce_loss(loss, reduction):
    """Reduce loss as specified.

    Args:
        loss (Tensor): Elementwise loss tensor.
        reduction (str): Options are "none", "mean" and "sum".

    Returns:
        Tensor: Reduced loss tensor.
    """
    reduction_enum = F._Reduction.get_enum(reduction)
    if reduction_enum == 0:
        return loss
    if reduction_enum == 1:
        return loss.mean()
    return loss.sum()


def mask_reduce_loss(loss, weight=None, reduction='mean', sample_wise=False):
    """Apply element-wise weight and reduce loss.

    Args:
        loss (Tensor): Element-wise loss.
        weight (Tensor): Element-wise weights. Default: None.
        reduction (str): Same as built-in losses of PyTorch. Options are
            "none", "mean" and "sum". Default: 'mean'.
        sample_wise (bool): Whether calculate the loss sample-wise. This
            argument only takes effect when `reduction` is 'mean' and `weight`
            (argument of `forward()`) is not None. It will first reduces loss
            with 'mean' per-sample, and then it means over all the samples.
            Default: False.

    Returns:
        Tensor: Processed loss values.
    """
    if weight is not None:
        assert weight.dim() == loss.dim()
        assert weight.size(1) == 1 or weight.size(1) == loss.size(1)
        loss = loss * weight
    if weight is None or reduction == 'sum':
        loss = reduce_loss(loss, reduction)
    elif reduction == 'mean':
        if weight.size(1) == 1:
            weight = weight.expand_as(loss)
        eps = 1e-12
        if sample_wise:
            weight = weight.sum(dim=[1, 2, 3], keepdim=True)
            loss = (loss / (weight + eps)).sum() / weight.size(0)
        else:
            loss = loss.sum() / (weight.sum() + eps)
    return loss


def masked_loss(loss_func):
    """Create a masked version of a given loss function.

    To use this decorator, the loss function must have the signature like
    `loss_func(pred, target, **kwargs)`. The function only needs to compute
    element-wise loss without any reduction. This decorator will add weight
    and reduction arguments to the function. The decorated function will have
    the signature like `loss_func(pred, target, weight=None, reduction='mean',
    avg_factor=None, **kwargs)`.

    :Example:

    >>> import torch
    >>> @masked_loss
    >>> def l1_loss(pred, target):
    >>>     return (pred - target).abs()

    >>> pred = torch.Tensor([0, 2, 3])
    >>> target = torch.Tensor([1, 1, 1])
    >>> weight = torch.Tensor([1, 0, 1])

    >>> l1_loss(pred, target)
    tensor(1.3333)
    >>> l1_loss(pred, target, weight)
    tensor(1.5000)
    >>> l1_loss(pred, target, reduction='none')
    tensor([1., 1., 2.])
    >>> l1_loss(pred, target, weight, reduction='sum')
    tensor(3.)
    """

    @functools.wraps(loss_func)
    def wrapper(pred, target, weight=None, reduction='mean', sample_wise=
        False, **kwargs):
        loss = loss_func(pred, target, **kwargs)
        loss = mask_reduce_loss(loss, weight, reduction, sample_wise)
        return loss
    return wrapper


@masked_loss
def l1_loss(pred, target):
    """L1 loss.

    Args:
        pred (Tensor): Prediction Tensor with shape (n, c, h, w).
        target ([type]): Target Tensor with shape (n, c, h, w).

    Returns:
        Tensor: Calculated L1 loss.
    """
    return F.l1_loss(pred, target, reduction='none')


class L1CompositionLossNew(nn.Module):
    """L1 composition loss.

    Args:
        loss_weight (float): Loss weight for L1 loss. Default: 1.0.
        reduction (str): Specifies the reduction to apply to the output.
            Supported choices are 'none' | 'mean' | 'sum'. Default: 'mean'.
        sample_wise (bool): Whether calculate the loss sample-wise. This
            argument only takes effect when `reduction` is 'mean' and `weight`
            (argument of `forward()`) is not None. It will first reduces loss
            with 'mean' per-sample, and then it means over all the samples.
            Default: False.
    """

    def __init__(self, loss_weight=1.0, reduction='mean', sample_wise=False):
        super().__init__()
        if reduction not in ['none', 'mean', 'sum']:
            raise ValueError(
                f'Unsupported reduction mode: {reduction}. Supported ones are: {_reduction_modes}'
                )
        self.loss_weight = loss_weight
        self.reduction = reduction
        self.sample_wise = sample_wise

    def forward(self, input_0, input_1, input_2, input_3):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        arg3_1 = input_3
        output = call([arg0_1, arg1_1, arg2_1, arg3_1])
        return output[0]
