# 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_7/inductor_cache/gm/cgmetfdpcv6uwom37f23lo325l3q7v2hfw2me2przc7uxp6arc6s.py
# Topologically Sorted Source Nodes: [smoothing, residue], Original ATen: [aten.stack, aten.sub]
# Source node to ATen node mapping:
#   residue => sub
#   smoothing => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%div, %div_1, %div_2, %div_3], 1), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %view), kwargs = {})
triton_poi_fused_stack_sub_0 = async_compile.triton('triton_poi_fused_stack_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.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_stack_sub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 13, '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_stack_sub_0(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4) % 16
    x0 = xindex % 4
    x2 = (xindex // 64)
    x3 = xindex
    tmp54 = tl.load(in_ptr0 + (x3), xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (4*x1) + (64*x2)), tmp4 & xmask, other=0.0)
    tmp6 = tl.load(in_ptr0 + (16 + x0 + (4*x1) + (64*x2)), tmp4 & xmask, other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = 0.5
    tmp9 = tmp7 * tmp8
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tmp13 = tl.full([1], 8, tl.int64)
    tmp14 = tmp0 < tmp13
    tmp15 = tmp12 & tmp14
    tmp16 = tl.load(in_ptr0 + (x0 + (4*((-4) + x1)) + (64*x2)), tmp15 & xmask, other=0.0)
    tmp17 = tl.load(in_ptr0 + (16 + x0 + (4*((-4) + x1)) + (64*x2)), tmp15 & xmask, other=0.0)
    tmp18 = tmp16 + tmp17
    tmp19 = tl.load(in_ptr0 + (32 + x0 + (4*((-4) + x1)) + (64*x2)), tmp15 & xmask, other=0.0)
    tmp20 = tmp18 + tmp19
    tmp21 = 0.3333333333333333
    tmp22 = tmp20 * tmp21
    tmp23 = tl.full(tmp22.shape, 0.0, tmp22.dtype)
    tmp24 = tl.where(tmp15, tmp22, tmp23)
    tmp25 = tmp0 >= tmp13
    tmp26 = tl.full([1], 12, tl.int64)
    tmp27 = tmp0 < tmp26
    tmp28 = tmp25 & tmp27
    tmp29 = tl.load(in_ptr0 + (x0 + (4*((-8) + x1)) + (64*x2)), tmp28 & xmask, other=0.0)
    tmp30 = tl.load(in_ptr0 + (16 + x0 + (4*((-8) + x1)) + (64*x2)), tmp28 & xmask, other=0.0)
    tmp31 = tmp29 + tmp30
    tmp32 = tl.load(in_ptr0 + (32 + x0 + (4*((-8) + x1)) + (64*x2)), tmp28 & xmask, other=0.0)
    tmp33 = tmp31 + tmp32
    tmp34 = tl.load(in_ptr0 + (48 + x0 + (4*((-8) + x1)) + (64*x2)), tmp28 & xmask, other=0.0)
    tmp35 = tmp33 + tmp34
    tmp36 = 0.25
    tmp37 = tmp35 * tmp36
    tmp38 = tl.full(tmp37.shape, 0.0, tmp37.dtype)
    tmp39 = tl.where(tmp28, tmp37, tmp38)
    tmp40 = tmp0 >= tmp26
    tmp41 = tl.full([1], 16, tl.int64)
    tmp42 = tmp0 < tmp41
    tmp43 = tl.load(in_ptr0 + (16 + x0 + (4*((-12) + x1)) + (64*x2)), tmp40 & xmask, other=0.0)
    tmp44 = tl.load(in_ptr0 + (32 + x0 + (4*((-12) + x1)) + (64*x2)), tmp40 & xmask, other=0.0)
    tmp45 = tmp43 + tmp44
    tmp46 = tl.load(in_ptr0 + (48 + x0 + (4*((-12) + x1)) + (64*x2)), tmp40 & xmask, other=0.0)
    tmp47 = tmp45 + tmp46
    tmp48 = tmp47 * tmp21
    tmp49 = tl.full(tmp48.shape, 0.0, tmp48.dtype)
    tmp50 = tl.where(tmp40, tmp48, tmp49)
    tmp51 = tl.where(tmp28, tmp39, tmp50)
    tmp52 = tl.where(tmp15, tmp24, tmp51)
    tmp53 = tl.where(tmp4, tmp11, tmp52)
    tmp55 = tmp54 - tmp53
    tl.store(out_ptr0 + (x3), tmp53, xmask)
    tl.store(out_ptr1 + (x3), tmp55, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 16, 4), (64, 4, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [smoothing, residue], Original ATen: [aten.stack, aten.sub]
        stream0 = get_raw_stream(0)
        triton_poi_fused_stack_sub_0.run(arg0_1, buf0, buf1, 256, grid=grid(256), stream=stream0)
        del arg0_1
    return (reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0), 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)
    fn = lambda: call([arg0_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
from torch import Tensor
import torch as pt
from torch import nn


class MovingAverage(nn.Module):

    def __init__(self, cond_size: 'int', pred_size: 'int') ->None:
        super().__init__()
        self.left_window = cond_size // 2
        self.right_window = cond_size - self.left_window

    def forward(self, data: 'Tensor'):
        smoothing = []
        for step in range(data.size(1)):
            left = max(step - self.left_window, 0)
            right = min(step + self.right_window, data.size(1))
            ma = data[:, left:right].sum(dim=1).div(right - left)
            smoothing.append(ma)
        smoothing = pt.stack(smoothing, dim=1)
        residue = data - smoothing
        return smoothing, residue


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


def get_init_inputs():
    return [[], {'cond_size': 4, 'pred_size': 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 import nn
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_stack_sub_0(in_ptr0, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 16
    x0 = xindex % 4
    x2 = xindex // 64
    x3 = xindex
    tmp54 = tl.load(in_ptr0 + x3, xmask)
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 4 * x1 + 64 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tl.load(in_ptr0 + (16 + x0 + 4 * x1 + 64 * x2), tmp4 & xmask,
        other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = 0.5
    tmp9 = tmp7 * tmp8
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tmp13 = tl.full([1], 8, tl.int64)
    tmp14 = tmp0 < tmp13
    tmp15 = tmp12 & tmp14
    tmp16 = tl.load(in_ptr0 + (x0 + 4 * (-4 + x1) + 64 * x2), tmp15 & xmask,
        other=0.0)
    tmp17 = tl.load(in_ptr0 + (16 + x0 + 4 * (-4 + x1) + 64 * x2), tmp15 &
        xmask, other=0.0)
    tmp18 = tmp16 + tmp17
    tmp19 = tl.load(in_ptr0 + (32 + x0 + 4 * (-4 + x1) + 64 * x2), tmp15 &
        xmask, other=0.0)
    tmp20 = tmp18 + tmp19
    tmp21 = 0.3333333333333333
    tmp22 = tmp20 * tmp21
    tmp23 = tl.full(tmp22.shape, 0.0, tmp22.dtype)
    tmp24 = tl.where(tmp15, tmp22, tmp23)
    tmp25 = tmp0 >= tmp13
    tmp26 = tl.full([1], 12, tl.int64)
    tmp27 = tmp0 < tmp26
    tmp28 = tmp25 & tmp27
    tmp29 = tl.load(in_ptr0 + (x0 + 4 * (-8 + x1) + 64 * x2), tmp28 & xmask,
        other=0.0)
    tmp30 = tl.load(in_ptr0 + (16 + x0 + 4 * (-8 + x1) + 64 * x2), tmp28 &
        xmask, other=0.0)
    tmp31 = tmp29 + tmp30
    tmp32 = tl.load(in_ptr0 + (32 + x0 + 4 * (-8 + x1) + 64 * x2), tmp28 &
        xmask, other=0.0)
    tmp33 = tmp31 + tmp32
    tmp34 = tl.load(in_ptr0 + (48 + x0 + 4 * (-8 + x1) + 64 * x2), tmp28 &
        xmask, other=0.0)
    tmp35 = tmp33 + tmp34
    tmp36 = 0.25
    tmp37 = tmp35 * tmp36
    tmp38 = tl.full(tmp37.shape, 0.0, tmp37.dtype)
    tmp39 = tl.where(tmp28, tmp37, tmp38)
    tmp40 = tmp0 >= tmp26
    tl.full([1], 16, tl.int64)
    tmp43 = tl.load(in_ptr0 + (16 + x0 + 4 * (-12 + x1) + 64 * x2), tmp40 &
        xmask, other=0.0)
    tmp44 = tl.load(in_ptr0 + (32 + x0 + 4 * (-12 + x1) + 64 * x2), tmp40 &
        xmask, other=0.0)
    tmp45 = tmp43 + tmp44
    tmp46 = tl.load(in_ptr0 + (48 + x0 + 4 * (-12 + x1) + 64 * x2), tmp40 &
        xmask, other=0.0)
    tmp47 = tmp45 + tmp46
    tmp48 = tmp47 * tmp21
    tmp49 = tl.full(tmp48.shape, 0.0, tmp48.dtype)
    tmp50 = tl.where(tmp40, tmp48, tmp49)
    tmp51 = tl.where(tmp28, tmp39, tmp50)
    tmp52 = tl.where(tmp15, tmp24, tmp51)
    tmp53 = tl.where(tmp4, tmp11, tmp52)
    tmp55 = tmp54 - tmp53
    tl.store(out_ptr0 + x3, tmp53, xmask)
    tl.store(out_ptr1 + x3, tmp55, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 16, 4), (64, 4, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_stack_sub_0[grid(256)](arg0_1, buf0, buf1, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
    return reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0), buf1


class MovingAverageNew(nn.Module):

    def __init__(self, cond_size: 'int', pred_size: 'int') ->None:
        super().__init__()
        self.left_window = cond_size // 2
        self.right_window = cond_size - self.left_window

    def forward(self, input_0):
        arg0_1 = input_0
        output = call([arg0_1])
        return output[0], output[1]
