# 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_4/inductor_cache/ua/cua2bnjsaylwpuipejxq7alpkun7mpul6bg5aqbuwcaqjmveshag.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.clamp]
# Source node to ATen node mapping:
#   x => clamp_max, clamp_min
# Graph fragment:
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%select_7, 0.0001), kwargs = {})
#   %clamp_max : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 0.9999), kwargs = {})
triton_poi_fused_clamp_0 = async_compile.triton('triton_poi_fused_clamp_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clamp_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, '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},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_clamp_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp9 = tl.load(in_ptr0 + (x0), xmask)
    tmp0 = tl.full([1], 0, tl.int64)
    tmp1 = tmp0 == tmp0
    tmp2 = tl.full([1], 0, tl.int32)
    tmp3 = tmp2 == tmp2
    tmp4 = tl.load(in_ptr0 + (x0), tmp1 & xmask, other=0.0)
    tmp5 = tl.sigmoid(tmp4)
    tmp6 = tl.where(tmp3, tmp5, tmp4)
    tmp7 = tl.full(tmp6.shape, 0.0, tmp6.dtype)
    tmp8 = tl.where(tmp1, tmp6, tmp7)
    tmp10 = tl.where(tmp1, tmp8, tmp9)
    tmp11 = 0.0001
    tmp12 = triton_helpers.maximum(tmp10, tmp11)
    tmp13 = 0.9999
    tmp14 = triton_helpers.minimum(tmp12, tmp13)
    tl.store(out_ptr0 + (x0), tmp14, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/mh/cmh7t7hbssmx7h2wgsf5ccigk54th3hlmocn6pgix55rx6dftjkk.py
# Topologically Sorted Source Nodes: [sigmoid_, sigmoid__1], Original ATen: [aten.sigmoid]
# Source node to ATen node mapping:
#   sigmoid_ => sigmoid
#   sigmoid__1 => sigmoid_1
# Graph fragment:
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%select_6,), kwargs = {})
#   %select_scatter_default : [num_users=1] = call_function[target=torch.ops.aten.select_scatter.default](args = (%slice_tensor, %sigmoid, 0, 0), kwargs = {})
#   %slice_scatter_default : [num_users=5] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%arg0_1, %select_scatter_default, 0, 0, 9223372036854775807, 5), kwargs = {})
#   %sigmoid_1 : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%select_9,), kwargs = {})
#   %select_scatter_default_1 : [num_users=1] = call_function[target=torch.ops.aten.select_scatter.default](args = (%slice_tensor_1, %sigmoid_1, 0, 0), kwargs = {})
#   %slice_scatter_default_1 : [num_users=5] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%slice_scatter_default, %select_scatter_default_1, 0, 1, 9223372036854775807, 5), kwargs = {})
triton_poi_fused_sigmoid_1 = async_compile.triton('triton_poi_fused_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_sigmoid_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, '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},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_sigmoid_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 64)
    x0 = xindex % 64
    x2 = xindex
    tmp35 = tl.load(in_ptr0 + (x2), xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 1, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = ((-1) + x1) % 5
    tmp4 = tl.full([1], 0, tl.int64)
    tmp5 = tmp3 == tmp4
    tmp6 = tmp2 & tmp5
    tmp7 = triton_helpers.div_floor_integer((-1) + x1,  5)
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = tmp7 == tmp8
    tmp10 = tmp1 == tmp4
    tmp11 = tmp10 & tmp6
    tmp12 = tmp8 == tmp8
    tmp13 = tl.load(in_ptr0 + (x0), tmp11 & xmask, eviction_policy='evict_last', other=0.0)
    tmp14 = tl.sigmoid(tmp13)
    tmp15 = tl.where(tmp12, tmp14, tmp13)
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp11, tmp15, tmp16)
    tmp18 = tl.load(in_ptr0 + (64 + x0), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
    tmp19 = tl.where(tmp10, tmp17, tmp18)
    tmp20 = tl.sigmoid(tmp19)
    tmp21 = tl.where(tmp9, tmp14, tmp13)
    tmp22 = tl.full(tmp21.shape, 0.0, tmp21.dtype)
    tmp23 = tl.where(tmp11, tmp21, tmp22)
    tmp24 = tl.load(in_ptr0 + (64 + x0 + (320*(triton_helpers.div_floor_integer((-1) + x1,  5)))), tmp6 & xmask, other=0.0)
    tmp25 = tl.where(tmp10, tmp23, tmp24)
    tmp26 = tl.where(tmp9, tmp20, tmp25)
    tmp27 = tl.full(tmp26.shape, 0.0, tmp26.dtype)
    tmp28 = tl.where(tmp6, tmp26, tmp27)
    tmp29 = tmp0 == tmp4
    tmp30 = tl.load(in_ptr0 + (x0), tmp29 & xmask, eviction_policy='evict_last', other=0.0)
    tmp31 = tl.sigmoid(tmp30)
    tmp32 = tl.where(tmp12, tmp31, tmp30)
    tmp33 = tl.full(tmp32.shape, 0.0, tmp32.dtype)
    tmp34 = tl.where(tmp29, tmp32, tmp33)
    tmp36 = tl.where(tmp29, tmp34, tmp35)
    tmp37 = tl.where(tmp6, tmp28, tmp36)
    tl.store(out_ptr0 + (x2), tmp37, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/f6/cf6ofsya263vgczihfxnr5nnn33lab2qgcajxxcoptlikatpuxuh.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.clamp]
# Source node to ATen node mapping:
#   x_1 => clamp_max_1, clamp_min_1
# Graph fragment:
#   %clamp_min_1 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%select_10, 0.0001), kwargs = {})
#   %clamp_max_1 : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min_1, 0.9999), kwargs = {})
triton_poi_fused_clamp_2 = async_compile.triton('triton_poi_fused_clamp_2', '''
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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clamp_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, '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},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_clamp_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (64 + x0), xmask)
    tmp1 = 0.0001
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = 0.9999
    tmp4 = triton_helpers.minimum(tmp2, tmp3)
    tl.store(out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ix/cixfy53zive25wtuojmtv2a5vyfsvmtse7mblsgqi3xzwz5mmfku.py
# Topologically Sorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
# Graph fragment:
#   %copy_ : [num_users=0] = call_function[target=torch.ops.aten.copy_.default](args = (%arg0_1, %slice_scatter_default_1), kwargs = {})
triton_poi_fused_3 = async_compile.triton('triton_poi_fused_3', '''
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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_3', 'mutated_arg_names': ['out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, '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},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_3(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tl.store(out_ptr0 + (x0), tmp0, xmask)
''', 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, (6, 4, 4, 4), (64, 16, 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], Original ATen: [aten.clamp]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clamp_0.run(arg0_1, buf0, 64, grid=grid(64), stream=stream0)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sigmoid_, sigmoid__1], Original ATen: [aten.sigmoid]
        triton_poi_fused_sigmoid_1.run(arg0_1, buf1, 256, grid=grid(256), stream=stream0)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.clamp]
        triton_poi_fused_clamp_2.run(buf1, buf2, 64, grid=grid(64), stream=stream0)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        triton_poi_fused_3.run(buf1, arg0_1, 256, grid=grid(256), stream=stream0)
        del arg0_1
    return (buf0, reinterpret_tensor(arg1_1, (4, 4, 4), (16, 4, 1), 0), buf2, reinterpret_tensor(buf1, (1, 4, 4, 4), (320, 16, 4, 1), 128), reinterpret_tensor(buf1, (1, 4, 4, 4), (320, 16, 4, 1), 192), reinterpret_tensor(buf1, (0, 4, 4, 4), (320, 16, 4, 1), 256), reinterpret_tensor(arg1_1, (4, 4, 4), (16, 4, 1), 64), reinterpret_tensor(arg1_1, (4, 4, 4), (16, 4, 1), 128), reinterpret_tensor(arg1_1, (4, 4, 4), (16, 4, 1), 192), reinterpret_tensor(arg1_1, (4, 4, 4), (16, 4, 1), 256), reinterpret_tensor(arg1_1, (4, 4, 4), (16, 4, 1), 320), )


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((6, 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
import torch.cuda


def _ae_loss(tag0, tag1, mask):
    num = mask.sum(dim=1, keepdim=True).float()
    tag0 = tag0.squeeze()
    tag1 = tag1.squeeze()
    tag_mean = (tag0 + tag1) / 2
    tag0 = torch.pow(tag0 - tag_mean, 2) / (num + 0.0001)
    tag0 = tag0[mask].sum()
    tag1 = torch.pow(tag1 - tag_mean, 2) / (num + 0.0001)
    tag1 = tag1[mask].sum()
    pull = tag0 + tag1
    mask = mask.unsqueeze(1) + mask.unsqueeze(2)
    mask = mask.eq(2)
    num = num.unsqueeze(2)
    num2 = (num - 1) * num
    dist = tag_mean.unsqueeze(1) - tag_mean.unsqueeze(2)
    dist = 1 - torch.abs(dist)
    dist = nn.functional.relu(dist, inplace=True)
    dist = dist - 1 / (num + 0.0001)
    dist = dist / (num2 + 0.0001)
    dist = dist[mask]
    push = dist.sum()
    return pull, push


def _neg_loss(preds, gt, lamda, lamdb):
    pos_inds = gt.eq(1)
    neg_inds = gt.lt(1)
    neg_weights = torch.pow(1 - gt[neg_inds], lamda)
    loss = 0
    for pred in preds:
        pos_pred = pred[pos_inds]
        neg_pred = pred[neg_inds]
        pos_loss = torch.log(pos_pred) * torch.pow(1 - pos_pred, lamdb)
        neg_loss = torch.log(1 - neg_pred) * torch.pow(neg_pred, lamdb
            ) * neg_weights
        num_pos = pos_inds.float().sum()
        pos_loss = pos_loss.sum()
        neg_loss = neg_loss.sum()
        if pos_pred.nelement() == 0:
            loss = loss - neg_loss
        else:
            loss = loss - (pos_loss + neg_loss) / num_pos
    return loss


def _regr_loss(regr, gt_regr, mask):
    num = mask.float().sum()
    mask = mask.unsqueeze(2).expand_as(gt_regr)
    regr = regr[mask]
    gt_regr = gt_regr[mask]
    regr_loss = nn.functional.smooth_l1_loss(regr, gt_regr, size_average=False)
    regr_loss = regr_loss / (num + 0.0001)
    return regr_loss


def _sigmoid(x):
    x = torch.clamp(x.sigmoid_(), min=0.0001, max=1 - 0.0001)
    return x


class AELossPurePie(nn.Module):

    def __init__(self, lamda, lamdb, regr_weight=1, focal_loss=_neg_loss):
        super(AELossPurePie, self).__init__()
        self.regr_weight = regr_weight
        self.focal_loss = focal_loss
        self.ae_loss = _ae_loss
        self.regr_loss = _regr_loss
        self.lamda = lamda
        self.lamdb = lamdb

    def forward(self, outs, targets):
        stride = 5
        center_heats = outs[0::stride]
        key_heats = outs[1::stride]
        center_regrs = outs[2::stride]
        key_regrs_tl = outs[3::stride]
        key_regrs_br = outs[4::stride]
        gt_center_heat = targets[0]
        gt_key_heat = targets[1]
        gt_mask = targets[2]
        gt_center_regr = targets[3]
        gt_key_regr_tl = targets[4]
        gt_key_regr_br = targets[5]
        focal_loss = 0
        center_heats = [_sigmoid(t) for t in center_heats]
        key_heats = [_sigmoid(b) for b in key_heats]
        focal_loss += self.focal_loss(center_heats, gt_center_heat, self.
            lamda, self.lamdb)
        focal_loss += self.focal_loss(key_heats, gt_key_heat, self.lamda,
            self.lamdb)
        regr_loss = 0
        for center_regr, key_regr_tl, key_regr_br in zip(center_regrs,
            key_regrs_tl, key_regrs_br):
            regr_loss += self.regr_loss(center_regr, gt_center_regr, gt_mask)
            regr_loss += self.regr_loss(key_regr_tl, gt_key_regr_tl, gt_mask
                ) / 2
            regr_loss += self.regr_loss(key_regr_br, gt_key_regr_br, gt_mask
                ) / 2
        regr_loss = self.regr_weight * regr_loss
        loss = (focal_loss + regr_loss) / len(center_heats)
        return loss.unsqueeze(0)


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), torch.rand([6, 4, 4, 4])]


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

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
import torch.nn as nn
import torch.cuda
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_clamp_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp9 = tl.load(in_ptr0 + x0, xmask)
    tmp0 = tl.full([1], 0, tl.int64)
    tmp1 = tmp0 == tmp0
    tmp2 = tl.full([1], 0, tl.int32)
    tmp3 = tmp2 == tmp2
    tmp4 = tl.load(in_ptr0 + x0, tmp1 & xmask, other=0.0)
    tmp5 = tl.sigmoid(tmp4)
    tmp6 = tl.where(tmp3, tmp5, tmp4)
    tmp7 = tl.full(tmp6.shape, 0.0, tmp6.dtype)
    tmp8 = tl.where(tmp1, tmp6, tmp7)
    tmp10 = tl.where(tmp1, tmp8, tmp9)
    tmp11 = 0.0001
    tmp12 = triton_helpers.maximum(tmp10, tmp11)
    tmp13 = 0.9999
    tmp14 = triton_helpers.minimum(tmp12, tmp13)
    tl.store(out_ptr0 + x0, tmp14, xmask)


@triton.jit
def triton_poi_fused_sigmoid_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 64
    x0 = xindex % 64
    x2 = xindex
    tmp35 = tl.load(in_ptr0 + x2, xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 1, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = (-1 + x1) % 5
    tmp4 = tl.full([1], 0, tl.int64)
    tmp5 = tmp3 == tmp4
    tmp6 = tmp2 & tmp5
    tmp7 = triton_helpers.div_floor_integer(-1 + x1, 5)
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = tmp7 == tmp8
    tmp10 = tmp1 == tmp4
    tmp11 = tmp10 & tmp6
    tmp12 = tmp8 == tmp8
    tmp13 = tl.load(in_ptr0 + x0, tmp11 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp14 = tl.sigmoid(tmp13)
    tmp15 = tl.where(tmp12, tmp14, tmp13)
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp11, tmp15, tmp16)
    tmp18 = tl.load(in_ptr0 + (64 + x0), tmp6 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp19 = tl.where(tmp10, tmp17, tmp18)
    tmp20 = tl.sigmoid(tmp19)
    tmp21 = tl.where(tmp9, tmp14, tmp13)
    tmp22 = tl.full(tmp21.shape, 0.0, tmp21.dtype)
    tmp23 = tl.where(tmp11, tmp21, tmp22)
    tmp24 = tl.load(in_ptr0 + (64 + x0 + 320 * triton_helpers.
        div_floor_integer(-1 + x1, 5)), tmp6 & xmask, other=0.0)
    tmp25 = tl.where(tmp10, tmp23, tmp24)
    tmp26 = tl.where(tmp9, tmp20, tmp25)
    tmp27 = tl.full(tmp26.shape, 0.0, tmp26.dtype)
    tmp28 = tl.where(tmp6, tmp26, tmp27)
    tmp29 = tmp0 == tmp4
    tmp30 = tl.load(in_ptr0 + x0, tmp29 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp31 = tl.sigmoid(tmp30)
    tmp32 = tl.where(tmp12, tmp31, tmp30)
    tmp33 = tl.full(tmp32.shape, 0.0, tmp32.dtype)
    tmp34 = tl.where(tmp29, tmp32, tmp33)
    tmp36 = tl.where(tmp29, tmp34, tmp35)
    tmp37 = tl.where(tmp6, tmp28, tmp36)
    tl.store(out_ptr0 + x2, tmp37, xmask)


@triton.jit
def triton_poi_fused_clamp_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (64 + x0), xmask)
    tmp1 = 0.0001
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = 0.9999
    tmp4 = triton_helpers.minimum(tmp2, tmp3)
    tl.store(out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_poi_fused_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tl.store(out_ptr0 + x0, tmp0, xmask)


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, (6, 4, 4, 4), (64, 16, 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_clamp_0[grid(64)](arg0_1, buf0, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_sigmoid_1[grid(256)](arg0_1, buf1, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_clamp_2[grid(64)](buf1, buf2, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        triton_poi_fused_3[grid(256)](buf1, arg0_1, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del arg0_1
    return buf0, reinterpret_tensor(arg1_1, (4, 4, 4), (16, 4, 1), 0
        ), buf2, reinterpret_tensor(buf1, (1, 4, 4, 4), (320, 16, 4, 1), 128
        ), reinterpret_tensor(buf1, (1, 4, 4, 4), (320, 16, 4, 1), 192
        ), reinterpret_tensor(buf1, (0, 4, 4, 4), (320, 16, 4, 1), 256
        ), reinterpret_tensor(arg1_1, (4, 4, 4), (16, 4, 1), 64
        ), reinterpret_tensor(arg1_1, (4, 4, 4), (16, 4, 1), 128
        ), reinterpret_tensor(arg1_1, (4, 4, 4), (16, 4, 1), 192
        ), reinterpret_tensor(arg1_1, (4, 4, 4), (16, 4, 1), 256
        ), reinterpret_tensor(arg1_1, (4, 4, 4), (16, 4, 1), 320)


def _ae_loss(tag0, tag1, mask):
    num = mask.sum(dim=1, keepdim=True).float()
    tag0 = tag0.squeeze()
    tag1 = tag1.squeeze()
    tag_mean = (tag0 + tag1) / 2
    tag0 = torch.pow(tag0 - tag_mean, 2) / (num + 0.0001)
    tag0 = tag0[mask].sum()
    tag1 = torch.pow(tag1 - tag_mean, 2) / (num + 0.0001)
    tag1 = tag1[mask].sum()
    pull = tag0 + tag1
    mask = mask.unsqueeze(1) + mask.unsqueeze(2)
    mask = mask.eq(2)
    num = num.unsqueeze(2)
    num2 = (num - 1) * num
    dist = tag_mean.unsqueeze(1) - tag_mean.unsqueeze(2)
    dist = 1 - torch.abs(dist)
    dist = nn.functional.relu(dist, inplace=True)
    dist = dist - 1 / (num + 0.0001)
    dist = dist / (num2 + 0.0001)
    dist = dist[mask]
    push = dist.sum()
    return pull, push


def _neg_loss(preds, gt, lamda, lamdb):
    pos_inds = gt.eq(1)
    neg_inds = gt.lt(1)
    neg_weights = torch.pow(1 - gt[neg_inds], lamda)
    loss = 0
    for pred in preds:
        pos_pred = pred[pos_inds]
        neg_pred = pred[neg_inds]
        pos_loss = torch.log(pos_pred) * torch.pow(1 - pos_pred, lamdb)
        neg_loss = torch.log(1 - neg_pred) * torch.pow(neg_pred, lamdb
            ) * neg_weights
        num_pos = pos_inds.float().sum()
        pos_loss = pos_loss.sum()
        neg_loss = neg_loss.sum()
        if pos_pred.nelement() == 0:
            loss = loss - neg_loss
        else:
            loss = loss - (pos_loss + neg_loss) / num_pos
    return loss


def _regr_loss(regr, gt_regr, mask):
    num = mask.float().sum()
    mask = mask.unsqueeze(2).expand_as(gt_regr)
    regr = regr[mask]
    gt_regr = gt_regr[mask]
    regr_loss = nn.functional.smooth_l1_loss(regr, gt_regr, size_average=False)
    regr_loss = regr_loss / (num + 0.0001)
    return regr_loss


def _sigmoid(x):
    x = torch.clamp(x.sigmoid_(), min=0.0001, max=1 - 0.0001)
    return x


class AELossPurePieNew(nn.Module):

    def __init__(self, lamda, lamdb, regr_weight=1, focal_loss=_neg_loss):
        super(AELossPurePieNew, self).__init__()
        self.regr_weight = regr_weight
        self.focal_loss = focal_loss
        self.ae_loss = _ae_loss
        self.regr_loss = _regr_loss
        self.lamda = lamda
        self.lamdb = lamdb

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