# 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_7/inductor_cache/ar/car4w6k5xtvxdgstpntr74b3lsmdap665zjhk74ct4st3net2y3x.py
# Topologically Sorted Source Nodes: [sub, pow_1, mse, wmse, mean], Original ATen: [aten.sub, aten.pow, aten.squeeze, aten.mul, aten.mean]
# Source node to ATen node mapping:
#   mean => mean
#   mse => squeeze
#   pow_1 => pow_1
#   sub => sub
#   wmse => mul
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%slice_2, %slice_3), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %squeeze : [num_users=2] = call_function[target=torch.ops.aten.squeeze.default](args = (%pow_1,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%squeeze, %primals_3), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%mul,), kwargs = {})
triton_per_fused_mean_mul_pow_squeeze_sub_0 = async_compile.triton('triton_per_fused_mean_mul_pow_squeeze_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, 64],
    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), equal_to_1=(5,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_mean_mul_pow_squeeze_sub_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, '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_mean_mul_pow_squeeze_sub_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 36
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = rindex < rnumel
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), rmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (r0), rmask, other=0.0)
    tmp4 = tl.load(in_ptr2 + (r0), rmask, other=0.0)
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp5 = tmp3 * tmp4
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp8 = tl.where(rmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = 36.0
    tmp11 = tmp9 / tmp10
    tl.store(out_ptr0 + (tl.broadcast_to(r0, [XBLOCK, RBLOCK])), tmp3, rmask)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp11, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 9), (9, 1))
    assert_size_stride(primals_2, (4, 9), (9, 1))
    assert_size_stride(primals_3, (4, 9), (9, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 9), (9, 1), torch.float32)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [sub, pow_1, mse, wmse, mean], Original ATen: [aten.sub, aten.pow, aten.squeeze, aten.mul, aten.mean]
        stream0 = get_raw_stream(0)
        triton_per_fused_mean_mul_pow_squeeze_sub_0.run(buf2, primals_1, primals_2, primals_3, buf0, 1, 36, grid=grid(1), stream=stream0)
        del primals_1
        del primals_2
        del primals_3
    return (buf2, buf0, )


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, 9), (9, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 9), (9, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 9), (9, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    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.utils.data


class LearnableTimeDepWeightedCost(torch.nn.Module):

    def __init__(self, time_horizon, dim=9, weights=None):
        super(LearnableTimeDepWeightedCost, self).__init__()
        if weights is None:
            self.weights = torch.nn.Parameter(0.01 * torch.ones([
                time_horizon, dim]))
        else:
            self.weights = weights
        self.clip = torch.nn.ReLU()
        self.dim = dim
        self.meta_grads = [[] for _, _ in enumerate(self.parameters())]

    def forward(self, y_in, y_target):
        assert y_in.dim() == 2
        mse = ((y_in[:, -self.dim:] - y_target[-self.dim:]) ** 2).squeeze()
        wmse = mse * self.weights
        return wmse.mean()


def get_inputs():
    return [torch.rand([4, 9]), torch.rand([4, 9])]


def get_init_inputs():
    return [[], {'time_horizon': 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
import torch.utils.data
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_mean_mul_pow_squeeze_sub_0(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
    rnumel = 36
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    rmask = rindex < rnumel
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, rmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + r0, rmask, other=0.0)
    tmp4 = tl.load(in_ptr2 + r0, rmask, other=0.0)
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp5 = tmp3 * tmp4
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp8 = tl.where(rmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = 36.0
    tmp11 = tmp9 / tmp10
    tl.store(out_ptr0 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp3, rmask)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp11, None)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 9), (9, 1))
    assert_size_stride(primals_2, (4, 9), (9, 1))
    assert_size_stride(primals_3, (4, 9), (9, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 9), (9, 1), torch.float32)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_per_fused_mean_mul_pow_squeeze_sub_0[grid(1)](buf2,
            primals_1, primals_2, primals_3, buf0, 1, 36, XBLOCK=1,
            num_warps=2, num_stages=1)
        del primals_1
        del primals_2
        del primals_3
    return buf2, buf0


class LearnableTimeDepWeightedCostNew(torch.nn.Module):

    def __init__(self, time_horizon, dim=9, weights=None):
        super(LearnableTimeDepWeightedCostNew, self).__init__()
        if weights is None:
            self.weights = torch.nn.Parameter(0.01 * torch.ones([
                time_horizon, dim]))
        else:
            self.weights = weights
        self.clip = torch.nn.ReLU()
        self.dim = dim
        self.meta_grads = [[] for _, _ in enumerate(self.parameters())]

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