# AOT ID: ['0_forward']
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_1/inductor_cache/y7/cy75pv62flzgueapzlnlplljec2lmbrjxeuhmgi2gb5pjyl6sna4.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   x_1 => convolution
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%permute, %primals_2, %primals_3, [1], [0], [1], False, [0], 1), kwargs = {})
triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_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.pointwise(
    size_hints=[16, 4], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (16*y1)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/fl/cflvio5unxft4ifotu3bteybiybckkv4oadyqdpkcksq4x7nkdye.py
# Topologically Sorted Source Nodes: [sub, mul, logits_2, scores], Original ATen: [aten.rsub, aten.mul, aten.add, aten.sigmoid]
# Source node to ATen node mapping:
#   logits_2 => add
#   mul => mul
#   scores => sigmoid
#   sub => sub
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %primals_4), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, -1e+30), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%squeeze, %mul), kwargs = {})
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%add,), kwargs = {})
triton_poi_fused_add_mul_rsub_sigmoid_1 = async_compile.triton('triton_poi_fused_add_mul_rsub_sigmoid_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.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_rsub_sigmoid_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_mul_rsub_sigmoid_1(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp1 = tl.load(in_ptr0 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr1 + (x0), xmask)
    tmp3 = tmp0 + tmp2
    tmp5 = 1.0
    tmp6 = tmp5 - tmp4
    tmp7 = -1e+30
    tmp8 = tmp6 * tmp7
    tmp9 = tmp3 + tmp8
    tmp10 = tl.sigmoid(tmp9)
    tl.store(in_out_ptr0 + (x0), tmp10, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (1, 4, 1), (4, 1, 1))
    assert_size_stride(primals_3, (1, ), (1, ))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.convolution]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_0.run(primals_1, buf0, 16, 4, grid=grid(16, 4), stream=stream0)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf1, (4, 1, 4), (4, 4, 1))
        del buf0
        buf2 = reinterpret_tensor(buf1, (4, 4), (4, 1), 0); del buf1  # reuse
        # Topologically Sorted Source Nodes: [sub, mul, logits_2, scores], Original ATen: [aten.rsub, aten.mul, aten.add, aten.sigmoid]
        triton_poi_fused_add_mul_rsub_sigmoid_1.run(buf2, primals_3, primals_4, 16, grid=grid(16), stream=stream0)
        del primals_3
        del primals_4
    return (buf2, primals_2, reinterpret_tensor(primals_1, (4, 4, 4), (16, 1, 4), 0), buf2, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((1, 4, 1), (4, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    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.parallel
import torch.nn as nn
import torch.utils.data
import torch.backends.cudnn


def mask_logits(inputs, mask, mask_value=-1e+30):
    mask = mask.type(torch.float32)
    return inputs + (1.0 - mask) * mask_value


class Conv1D(nn.Module):

    def __init__(self, in_dim, out_dim, kernel_size=1, stride=1, padding=0,
        bias=True):
        super(Conv1D, self).__init__()
        self.conv1d = nn.Conv1d(in_channels=in_dim, out_channels=out_dim,
            kernel_size=kernel_size, padding=padding, stride=stride, bias=bias)

    def forward(self, x):
        x = x.transpose(1, 2)
        x = self.conv1d(x)
        return x.transpose(1, 2)


class HighLightLayer(nn.Module):

    def __init__(self, dim):
        super(HighLightLayer, self).__init__()
        self.conv1d = Conv1D(in_dim=dim, out_dim=1, kernel_size=1, stride=1,
            padding=0, bias=True)

    def forward(self, x, mask):
        logits = self.conv1d(x)
        logits = logits.squeeze(2)
        logits = mask_logits(logits, mask)
        scores = nn.Sigmoid()(logits)
        return scores

    @staticmethod
    def compute_loss(scores, labels, mask, epsilon=1e-12):
        labels = labels.type(torch.float32)
        weights = torch.where(labels == 0.0, labels + 1.0, 2.0 * labels)
        loss_per_location = nn.BCELoss(reduction='none')(scores, labels)
        loss_per_location = loss_per_location * weights
        mask = mask.type(torch.float32)
        loss = torch.sum(loss_per_location * mask) / (torch.sum(mask) + epsilon
            )
        return loss


def get_inputs():
    return [torch.rand([4, 4, 4]), torch.rand([4, 4])]


def get_init_inputs():
    return [[], {'dim': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
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
import torch.nn.parallel
import torch.nn as nn
import torch.utils.data
import torch.backends.cudnn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_convolution_0(in_ptr0, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_add_mul_rsub_sigmoid_1(in_out_ptr0, in_ptr0, in_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr1 + x0, xmask)
    tmp3 = tmp0 + tmp2
    tmp5 = 1.0
    tmp6 = tmp5 - tmp4
    tmp7 = -1e+30
    tmp8 = tmp6 * tmp7
    tmp9 = tmp3 + tmp8
    tmp10 = tl.sigmoid(tmp9)
    tl.store(in_out_ptr0 + x0, tmp10, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (1, 4, 1), (4, 1, 1))
    assert_size_stride(primals_3, (1,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(16, 4)](primals_1, buf0, 16, 4,
            XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf1, (4, 1, 4), (4, 4, 1))
        del buf0
        buf2 = reinterpret_tensor(buf1, (4, 4), (4, 1), 0)
        del buf1
        triton_poi_fused_add_mul_rsub_sigmoid_1[grid(16)](buf2, primals_3,
            primals_4, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_3
        del primals_4
    return buf2, primals_2, reinterpret_tensor(primals_1, (4, 4, 4), (16, 1,
        4), 0), buf2


def mask_logits(inputs, mask, mask_value=-1e+30):
    mask = mask.type(torch.float32)
    return inputs + (1.0 - mask) * mask_value


class Conv1D(nn.Module):

    def __init__(self, in_dim, out_dim, kernel_size=1, stride=1, padding=0,
        bias=True):
        super(Conv1D, self).__init__()
        self.conv1d = nn.Conv1d(in_channels=in_dim, out_channels=out_dim,
            kernel_size=kernel_size, padding=padding, stride=stride, bias=bias)

    def forward(self, x):
        x = x.transpose(1, 2)
        x = self.conv1d(x)
        return x.transpose(1, 2)


class HighLightLayerNew(nn.Module):

    def __init__(self, dim):
        super(HighLightLayerNew, self).__init__()
        self.conv1d = Conv1D(in_dim=dim, out_dim=1, kernel_size=1, stride=1,
            padding=0, bias=True)

    @staticmethod
    def compute_loss(scores, labels, mask, epsilon=1e-12):
        labels = labels.type(torch.float32)
        weights = torch.where(labels == 0.0, labels + 1.0, 2.0 * labels)
        loss_per_location = nn.BCELoss(reduction='none')(scores, labels)
        loss_per_location = loss_per_location * weights
        mask = mask.type(torch.float32)
        loss = torch.sum(loss_per_location * mask) / (torch.sum(mask) + epsilon
            )
        return loss

    def forward(self, input_0, input_1):
        primals_2 = self.conv1d.conv1d.weight
        primals_3 = self.conv1d.conv1d.bias
        primals_1 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
