# 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/eo/ceol4pvopahhfr62gdumixobymkaolmwvai66o7pjybv57v53l22.py
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%mean, %sqrt], 1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_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=[128], 
    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_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, '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_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4) % 8
    x0 = xindex % 4
    x2 = (xindex // 32)
    x3 = xindex
    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 + (16*x1) + (64*x2)), tmp4 & xmask, other=0.0)
    tmp6 = tl.load(in_ptr0 + (4 + x0 + (16*x1) + (64*x2)), tmp4 & xmask, other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.load(in_ptr0 + (8 + x0 + (16*x1) + (64*x2)), tmp4 & xmask, other=0.0)
    tmp9 = tmp7 + tmp8
    tmp10 = tl.load(in_ptr0 + (12 + x0 + (16*x1) + (64*x2)), tmp4 & xmask, other=0.0)
    tmp11 = tmp9 + tmp10
    tmp12 = 4.0
    tmp13 = tmp11 / tmp12
    tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
    tmp15 = tl.where(tmp4, tmp13, tmp14)
    tmp16 = tmp0 >= tmp3
    tmp17 = tl.full([1], 8, tl.int64)
    tmp18 = tmp0 < tmp17
    tmp19 = tl.load(in_ptr0 + (x0 + (16*((-4) + x1)) + (64*x2)), tmp16 & xmask, other=0.0)
    tmp20 = tl.load(in_ptr0 + (4 + x0 + (16*((-4) + x1)) + (64*x2)), tmp16 & xmask, other=0.0)
    tmp21 = tmp19 + tmp20
    tmp22 = tl.load(in_ptr0 + (8 + x0 + (16*((-4) + x1)) + (64*x2)), tmp16 & xmask, other=0.0)
    tmp23 = tmp21 + tmp22
    tmp24 = tl.load(in_ptr0 + (12 + x0 + (16*((-4) + x1)) + (64*x2)), tmp16 & xmask, other=0.0)
    tmp25 = tmp23 + tmp24
    tmp26 = tmp25 / tmp12
    tmp27 = tmp19 - tmp26
    tmp28 = tmp27 * tmp27
    tmp29 = tmp20 - tmp26
    tmp30 = tmp29 * tmp29
    tmp31 = tmp28 + tmp30
    tmp32 = tmp22 - tmp26
    tmp33 = tmp32 * tmp32
    tmp34 = tmp31 + tmp33
    tmp35 = tmp24 - tmp26
    tmp36 = tmp35 * tmp35
    tmp37 = tmp34 + tmp36
    tmp38 = 3.0
    tmp39 = tmp37 / tmp38
    tmp40 = libdevice.sqrt(tmp39)
    tmp41 = tl.full(tmp40.shape, 0.0, tmp40.dtype)
    tmp42 = tl.where(tmp16, tmp40, tmp41)
    tmp43 = tl.where(tmp4, tmp15, tmp42)
    tl.store(out_ptr0 + (x3), tmp43, 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, 8, 4), (32, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(arg0_1, buf0, 128, grid=grid(128), stream=stream0)
        del arg0_1
    return (buf0, )


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
import warnings
import torch.nn as nn
from typing import Optional
import torch.optim
import torch.nn.functional as F


class StatsPool(nn.Module):
    """Statistics pooling

    Compute temporal mean and (unbiased) standard deviation
    and returns their concatenation.

    Reference
    ---------
    https://en.wikipedia.org/wiki/Weighted_arithmetic_mean

    """

    def forward(self, sequences: 'torch.Tensor', weights:
        'Optional[torch.Tensor]'=None) ->torch.Tensor:
        """Forward pass

        Parameters
        ----------
        sequences : (batch, channel, frames) torch.Tensor
            Sequences.
        weights : (batch, frames) torch.Tensor, optional
            When provided, compute weighted mean and standard deviation.

        Returns
        -------
        output : (batch, 2 * channel) torch.Tensor
            Concatenation of mean and (unbiased) standard deviation.
        """
        if weights is None:
            mean = sequences.mean(dim=2)
            std = sequences.std(dim=2, unbiased=True)
        else:
            weights = weights.unsqueeze(dim=1)
            num_frames = sequences.shape[2]
            num_weights = weights.shape[2]
            if num_frames != num_weights:
                warnings.warn(
                    f'Mismatch between frames ({num_frames}) and weights ({num_weights}) numbers.'
                    )
                weights = F.interpolate(weights, size=num_frames, mode=
                    'linear', align_corners=False)
            v1 = weights.sum(dim=2)
            mean = torch.sum(sequences * weights, dim=2) / v1
            dx2 = torch.square(sequences - mean.unsqueeze(2))
            v2 = torch.square(weights).sum(dim=2)
            var = torch.sum(dx2 * weights, dim=2) / (v1 - v2 / v1)
            std = torch.sqrt(var)
        return torch.cat([mean, std], dim=1)


def get_inputs():
    return [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.triton_helpers import libdevice
import torch.nn as nn
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 8
    x0 = xindex % 4
    x2 = xindex // 32
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tl.load(in_ptr0 + (4 + x0 + 16 * x1 + 64 * x2), tmp4 & xmask,
        other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.load(in_ptr0 + (8 + x0 + 16 * x1 + 64 * x2), tmp4 & xmask,
        other=0.0)
    tmp9 = tmp7 + tmp8
    tmp10 = tl.load(in_ptr0 + (12 + x0 + 16 * x1 + 64 * x2), tmp4 & xmask,
        other=0.0)
    tmp11 = tmp9 + tmp10
    tmp12 = 4.0
    tmp13 = tmp11 / tmp12
    tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
    tmp15 = tl.where(tmp4, tmp13, tmp14)
    tmp16 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp19 = tl.load(in_ptr0 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp16 &
        xmask, other=0.0)
    tmp20 = tl.load(in_ptr0 + (4 + x0 + 16 * (-4 + x1) + 64 * x2), tmp16 &
        xmask, other=0.0)
    tmp21 = tmp19 + tmp20
    tmp22 = tl.load(in_ptr0 + (8 + x0 + 16 * (-4 + x1) + 64 * x2), tmp16 &
        xmask, other=0.0)
    tmp23 = tmp21 + tmp22
    tmp24 = tl.load(in_ptr0 + (12 + x0 + 16 * (-4 + x1) + 64 * x2), tmp16 &
        xmask, other=0.0)
    tmp25 = tmp23 + tmp24
    tmp26 = tmp25 / tmp12
    tmp27 = tmp19 - tmp26
    tmp28 = tmp27 * tmp27
    tmp29 = tmp20 - tmp26
    tmp30 = tmp29 * tmp29
    tmp31 = tmp28 + tmp30
    tmp32 = tmp22 - tmp26
    tmp33 = tmp32 * tmp32
    tmp34 = tmp31 + tmp33
    tmp35 = tmp24 - tmp26
    tmp36 = tmp35 * tmp35
    tmp37 = tmp34 + tmp36
    tmp38 = 3.0
    tmp39 = tmp37 / tmp38
    tmp40 = libdevice.sqrt(tmp39)
    tmp41 = tl.full(tmp40.shape, 0.0, tmp40.dtype)
    tmp42 = tl.where(tmp16, tmp40, tmp41)
    tmp43 = tl.where(tmp4, tmp15, tmp42)
    tl.store(out_ptr0 + x3, tmp43, 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, 8, 4), (32, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(128)](arg0_1, buf0, 128, XBLOCK=128,
            num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class StatsPoolNew(nn.Module):
    """Statistics pooling

    Compute temporal mean and (unbiased) standard deviation
    and returns their concatenation.

    Reference
    ---------
    https://en.wikipedia.org/wiki/Weighted_arithmetic_mean

    """

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