# 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/4q/c4qexuvtft75qv6hznb522cycv7wm3svqsx3scrho2mpfe2l4oyd.py
# Topologically Sorted Source Nodes: [balance_weights, setitem, setitem_1, setitem_2, setitem_3, setitem_4, setitem_5, sub, abs_1, mul, mae], Original ATen: [aten.zeros_like, aten.lift_fresh, aten.index_put, aten.sub, aten.abs, aten.mul, aten.mean]
# Source node to ATen node mapping:
#   abs_1 => abs_1
#   balance_weights => full_default
#   mae => mean
#   mul => mul
#   setitem => full_default_1, index_put
#   setitem_1 => full_default_2, index_put_1
#   setitem_2 => full_default_3, index_put_2
#   setitem_3 => full_default_4, index_put_3
#   setitem_4 => full_default_5, index_put_4
#   setitem_5 => full_default_6, index_put_5
#   sub => sub
# Graph fragment:
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([4, 4, 4, 4], 0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.5), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False})
#   %index_put : [num_users=1] = call_function[target=torch.ops.aten.index_put_.default](args = (%full_default, [%lt], %full_default_1), kwargs = {})
#   %full_default_2 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 1.2000000476837158), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False})
#   %index_put_1 : [num_users=1] = call_function[target=torch.ops.aten.index_put_.default](args = (%index_put, [%bitwise_and], %full_default_2), kwargs = {})
#   %full_default_3 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 1.399999976158142), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False})
#   %index_put_2 : [num_users=1] = call_function[target=torch.ops.aten.index_put_.default](args = (%index_put_1, [%bitwise_and_1], %full_default_3), kwargs = {})
#   %full_default_4 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 1.600000023841858), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False})
#   %index_put_3 : [num_users=1] = call_function[target=torch.ops.aten.index_put_.default](args = (%index_put_2, [%bitwise_and_2], %full_default_4), kwargs = {})
#   %full_default_5 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 1.7999999523162842), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False})
#   %index_put_4 : [num_users=1] = call_function[target=torch.ops.aten.index_put_.default](args = (%index_put_3, [%bitwise_and_3], %full_default_5), kwargs = {})
#   %full_default_6 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 2.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False})
#   %index_put_5 : [num_users=1] = call_function[target=torch.ops.aten.index_put_.default](args = (%index_put_4, [%ge_4], %full_default_6), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg1_1, %arg0_1), kwargs = {})
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%sub,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%index_put_5, %abs_1), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%mul,), kwargs = {})
triton_per_fused_abs_index_put_lift_fresh_mean_mul_sub_zeros_like_0 = async_compile.triton('triton_per_fused_abs_index_put_lift_fresh_mean_mul_sub_zeros_like_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_abs_index_put_lift_fresh_mean_mul_sub_zeros_like_0', 'mutated_arg_names': ['in_out_ptr1'], '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_abs_index_put_lift_fresh_mean_mul_sub_zeros_like_0(in_out_ptr1, 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)
    tmp33 = tl.load(in_ptr1 + (r0), None)
    tmp1 = 5.0
    tmp2 = tmp0 < tmp1
    tmp3 = 0.5
    tmp4 = 0.0
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = tmp0 >= tmp1
    tmp7 = 15.0
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = 1.2000000476837158
    tmp11 = tl.where(tmp9, tmp10, tmp5)
    tmp12 = tmp0 >= tmp7
    tmp13 = 30.0
    tmp14 = tmp0 < tmp13
    tmp15 = tmp12 & tmp14
    tmp16 = 1.399999976158142
    tmp17 = tl.where(tmp15, tmp16, tmp11)
    tmp18 = tmp0 >= tmp13
    tmp19 = 40.0
    tmp20 = tmp0 < tmp19
    tmp21 = tmp18 & tmp20
    tmp22 = 1.600000023841858
    tmp23 = tl.where(tmp21, tmp22, tmp17)
    tmp24 = tmp0 >= tmp19
    tmp25 = 45.0
    tmp26 = tmp0 < tmp25
    tmp27 = tmp24 & tmp26
    tmp28 = 1.7999999523162842
    tmp29 = tl.where(tmp27, tmp28, tmp23)
    tmp30 = tmp0 >= tmp25
    tmp31 = 2.0
    tmp32 = tl.where(tmp30, tmp31, tmp29)
    tmp34 = tmp33 - tmp0
    tmp35 = tl_math.abs(tmp34)
    tmp36 = tmp32 * tmp35
    tmp37 = tl.broadcast_to(tmp36, [RBLOCK])
    tmp39 = triton_helpers.promote_to_tensor(tl.sum(tmp37, 0))
    tmp40 = 256.0
    tmp41 = tmp39 / tmp40
    tl.debug_barrier()
    tl.store(in_out_ptr1 + (tl.full([1], 0, tl.int32)), tmp41, 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)
        buf6 = empty_strided_cuda((), (), torch.float32)
        buf7 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [balance_weights, setitem, setitem_1, setitem_2, setitem_3, setitem_4, setitem_5, sub, abs_1, mul, mae], Original ATen: [aten.zeros_like, aten.lift_fresh, aten.index_put, aten.sub, aten.abs, aten.mul, aten.mean]
        stream0 = get_raw_stream(0)
        triton_per_fused_abs_index_put_lift_fresh_mean_mul_sub_zeros_like_0.run(buf7, arg0_1, arg1_1, 1, 256, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf7, )


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 weighted_mae_windows(nn.Module):

    def __init__(self, weights=(0.5, 1.2, 1.4, 1.6, 1.8, 2.0), thresholds=(
        5.0, 15.0, 30.0, 40.0, 45.0)):
        super(weighted_mae_windows, self).__init__()
        assert len(thresholds) + 1 == len(weights)
        self.weights = weights
        self.threholds = thresholds

    def forward(self, predict, target):
        """
        :param input: nbatchs * nlengths * nheigths * nwidths
        :param target: nbatchs * nlengths * nheigths * nwidths
        :return:
        """
        balance_weights = torch.zeros_like(target)
        balance_weights[target < self.threholds[0]] = self.weights[0]
        for i, _ in enumerate(self.threholds[:-1]):
            balance_weights[(target >= self.threholds[i]) & (target < self.
                threholds[i + 1])] = self.weights[i + 1]
        balance_weights[target >= self.threholds[-1]] = self.weights[-1]
        mae = torch.mean(balance_weights * torch.abs(predict - target))
        return mae


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_abs_index_put_lift_fresh_mean_mul_sub_zeros_like_0(
    in_out_ptr1, 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)
    tmp33 = tl.load(in_ptr1 + r0, None)
    tmp1 = 5.0
    tmp2 = tmp0 < tmp1
    tmp3 = 0.5
    tmp4 = 0.0
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = tmp0 >= tmp1
    tmp7 = 15.0
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = 1.2000000476837158
    tmp11 = tl.where(tmp9, tmp10, tmp5)
    tmp12 = tmp0 >= tmp7
    tmp13 = 30.0
    tmp14 = tmp0 < tmp13
    tmp15 = tmp12 & tmp14
    tmp16 = 1.399999976158142
    tmp17 = tl.where(tmp15, tmp16, tmp11)
    tmp18 = tmp0 >= tmp13
    tmp19 = 40.0
    tmp20 = tmp0 < tmp19
    tmp21 = tmp18 & tmp20
    tmp22 = 1.600000023841858
    tmp23 = tl.where(tmp21, tmp22, tmp17)
    tmp24 = tmp0 >= tmp19
    tmp25 = 45.0
    tmp26 = tmp0 < tmp25
    tmp27 = tmp24 & tmp26
    tmp28 = 1.7999999523162842
    tmp29 = tl.where(tmp27, tmp28, tmp23)
    tmp30 = tmp0 >= tmp25
    tmp31 = 2.0
    tmp32 = tl.where(tmp30, tmp31, tmp29)
    tmp34 = tmp33 - tmp0
    tmp35 = tl_math.abs(tmp34)
    tmp36 = tmp32 * tmp35
    tmp37 = tl.broadcast_to(tmp36, [RBLOCK])
    tmp39 = triton_helpers.promote_to_tensor(tl.sum(tmp37, 0))
    tmp40 = 256.0
    tmp41 = tmp39 / tmp40
    tl.debug_barrier()
    tl.store(in_out_ptr1 + tl.full([1], 0, tl.int32), tmp41, 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)
        buf6 = empty_strided_cuda((), (), torch.float32)
        buf7 = buf6
        del buf6
        get_raw_stream(0)
        triton_per_fused_abs_index_put_lift_fresh_mean_mul_sub_zeros_like_0[
            grid(1)](buf7, arg0_1, arg1_1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf7,


class weighted_mae_windowsNew(nn.Module):

    def __init__(self, weights=(0.5, 1.2, 1.4, 1.6, 1.8, 2.0), thresholds=(
        5.0, 15.0, 30.0, 40.0, 45.0)):
        super(weighted_mae_windowsNew, self).__init__()
        assert len(thresholds) + 1 == len(weights)
        self.weights = weights
        self.threholds = thresholds

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