# 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_1/inductor_cache/2j/c2jpyslnqqzvzecm5aym7d3dkyyo3ywqbq6ta7pdce34bjktk5wx.py
# Topologically Sorted Source Nodes: [mean, y_2, x], Original ATen: [aten.mean, aten.sub, aten.cat]
# Source node to ATen node mapping:
#   mean => mean
#   x => cat
#   y_2 => sub
# Graph fragment:
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%view, [0], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view, %mean), kwargs = {})
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%view_1, %expand], 1), kwargs = {})
#   %copy_ : [num_users=0] = call_function[target=torch.ops.aten.copy_.default](args = (%arg0_1, %view_1), kwargs = {})
triton_poi_fused_cat_mean_sub_0 = async_compile.triton('triton_poi_fused_cat_mean_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: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_mean_sub_0', 'mutated_arg_names': ['in_ptr0', 'out_ptr2'], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_mean_sub_0(in_ptr0, out_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    x1 = (xindex // 64)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (64 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (128 + x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (192 + x0), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = 4.0
    tmp9 = tmp7 / tmp8
    tmp10 = tmp0 - tmp9
    tl.store(out_ptr0 + (x2), tmp10, xmask)
    tl.store(out_ptr1 + (x0 + (80*x1)), tmp10, xmask)
    tl.store(out_ptr2 + (x2), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/lk/clkg6qsda66vnr5yv4figcu6jxv6zjendnzt3sfds4nv4vkwgz7j.py
# Topologically Sorted Source Nodes: [y_5, x], Original ATen: [aten.mean, aten.cat]
# Source node to ATen node mapping:
#   x => cat
#   y_5 => mean_2
# Graph fragment:
#   %mean_2 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%view_3, [-1]), kwargs = {})
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%view_1, %expand], 1), kwargs = {})
triton_per_fused_cat_mean_1 = async_compile.triton('triton_per_fused_cat_mean_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.persistent_reduction(
    size_hints=[1, 64],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_cat_mean_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 1, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_cat_mean_1(in_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 64
    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 = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    r1 = rindex % 16
    r2 = (rindex // 16)
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp2 = tl.load(in_ptr0 + (64 + r0), None)
    tmp5 = tl.load(in_ptr0 + (128 + r0), None)
    tmp8 = tl.load(in_ptr0 + (192 + r0), None)
    tmp1 = tmp0 * tmp0
    tmp3 = tmp2 * tmp2
    tmp4 = tmp1 + tmp3
    tmp6 = tmp5 * tmp5
    tmp7 = tmp4 + tmp6
    tmp9 = tmp8 * tmp8
    tmp10 = tmp7 + tmp9
    tmp11 = 4.0
    tmp12 = tmp10 / tmp11
    tmp13 = 1e-08
    tmp14 = tmp12 + tmp13
    tmp15 = libdevice.sqrt(tmp14)
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK, RBLOCK])
    tmp18 = tl.sum(tmp16, 1)[:, None]
    tmp19 = 64.0
    tmp20 = tmp18 / tmp19
    tl.store(out_ptr1 + (tl.broadcast_to(r1 + (80*r2), [XBLOCK, RBLOCK])), tmp20, None)
''', 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, 1, 4, 4, 4), (64, 256, 16, 4, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 5, 4, 4), (80, 16, 4, 1), torch.float32)
        buf2 = reinterpret_tensor(buf4, (4, 4, 4, 4), (80, 16, 4, 1), 0)  # alias
        # Topologically Sorted Source Nodes: [mean, y_2, x], Original ATen: [aten.mean, aten.sub, aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_mean_sub_0.run(arg0_1, buf0, buf2, arg0_1, 256, grid=grid(256), stream=stream0)
        del arg0_1
        buf3 = reinterpret_tensor(buf4, (4, 1, 4, 4), (80, 16, 4, 1), 64)  # alias
        # Topologically Sorted Source Nodes: [y_5, x], Original ATen: [aten.mean, aten.cat]
        triton_per_fused_cat_mean_1.run(buf0, buf3, 1, 64, grid=grid(1), stream=stream0)
        del buf0
    return (buf4, )


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 torch.nn as nn
import torch.utils.tensorboard
import torch.nn


class MinibatchStd(nn.Module):
    """
    Adds the aveage std of each data point over a
    slice of the minibatch to that slice as a new
    feature map. This gives an output with one extra
    channel.
    Arguments:
        group_size (int): Number of entries in each slice
            of the batch. If <= 0, the entire batch is used.
            Default value is 4.
        eps (float): Epsilon value added for numerical stability.
            Default value is 1e-8.
    """

    def __init__(self, group_size=4, eps=1e-08, *args, **kwargs):
        super(MinibatchStd, self).__init__()
        if group_size is None or group_size <= 0:
            group_size = 0
        assert group_size != 1, 'Can not use 1 as minibatch std group size.'
        self.group_size = group_size
        self.eps = eps

    def forward(self, input, **kwargs):
        """
        Add a new feature map to the input containing the average
        standard deviation for each slice.
        Arguments:
            input (torch.Tensor)
        Returns:
            output (torch.Tensor)
        """
        group_size = self.group_size or input.size(0)
        assert input.size(0
            ) >= group_size, 'Can not use a smaller batch size ' + '({}) than the specified '.format(
            input.size(0)) + 'group size ({}) '.format(group_size
            ) + 'of this minibatch std layer.'
        assert input.size(0
            ) % group_size == 0, 'Can not use a batch of a size ' + '({}) that is not '.format(
            input.size(0)) + 'evenly divisible by the group size ({})'.format(
            group_size)
        x = input
        y = input.view(group_size, -1, *input.size()[1:])
        y = y.float()
        y -= y.mean(dim=0, keepdim=True)
        y = torch.mean(y ** 2, dim=0)
        y = torch.sqrt(y + self.eps)
        y = torch.mean(y.view(y.size(0), -1), dim=-1)
        y = y.view(-1, *([1] * (input.dim() - 1)))
        y = y
        y = y.repeat(group_size, *([1] * (y.dim() - 1)))
        y = y.expand(y.size(0), 1, *x.size()[2:])
        x = torch.cat([x, y], dim=1)
        return x

    def extra_repr(self):
        return 'group_size={}'.format(self.group_size or '-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.utils.tensorboard
import torch.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_cat_mean_sub_0(in_ptr0, out_ptr0, out_ptr1, out_ptr2,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    x1 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (64 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (128 + x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (192 + x0), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = 4.0
    tmp9 = tmp7 / tmp8
    tmp10 = tmp0 - tmp9
    tl.store(out_ptr0 + x2, tmp10, xmask)
    tl.store(out_ptr1 + (x0 + 80 * x1), tmp10, xmask)
    tl.store(out_ptr2 + x2, tmp10, xmask)


@triton.jit
def triton_per_fused_cat_mean_1(in_ptr0, out_ptr1, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    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, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    r1 = rindex % 16
    r2 = rindex // 16
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp2 = tl.load(in_ptr0 + (64 + r0), None)
    tmp5 = tl.load(in_ptr0 + (128 + r0), None)
    tmp8 = tl.load(in_ptr0 + (192 + r0), None)
    tmp1 = tmp0 * tmp0
    tmp3 = tmp2 * tmp2
    tmp4 = tmp1 + tmp3
    tmp6 = tmp5 * tmp5
    tmp7 = tmp4 + tmp6
    tmp9 = tmp8 * tmp8
    tmp10 = tmp7 + tmp9
    tmp11 = 4.0
    tmp12 = tmp10 / tmp11
    tmp13 = 1e-08
    tmp14 = tmp12 + tmp13
    tmp15 = libdevice.sqrt(tmp14)
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK, RBLOCK])
    tmp18 = tl.sum(tmp16, 1)[:, None]
    tmp19 = 64.0
    tmp20 = tmp18 / tmp19
    tl.store(out_ptr1 + tl.broadcast_to(r1 + 80 * r2, [XBLOCK, RBLOCK]),
        tmp20, None)


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, 1, 4, 4, 4), (64, 256, 16, 4, 1),
            torch.float32)
        buf4 = empty_strided_cuda((4, 5, 4, 4), (80, 16, 4, 1), torch.float32)
        buf2 = reinterpret_tensor(buf4, (4, 4, 4, 4), (80, 16, 4, 1), 0)
        get_raw_stream(0)
        triton_poi_fused_cat_mean_sub_0[grid(256)](arg0_1, buf0, buf2,
            arg0_1, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
        buf3 = reinterpret_tensor(buf4, (4, 1, 4, 4), (80, 16, 4, 1), 64)
        triton_per_fused_cat_mean_1[grid(1)](buf0, buf3, 1, 64, XBLOCK=1,
            num_warps=2, num_stages=1)
        del buf0
    return buf4,


class MinibatchStdNew(nn.Module):
    """
    Adds the aveage std of each data point over a
    slice of the minibatch to that slice as a new
    feature map. This gives an output with one extra
    channel.
    Arguments:
        group_size (int): Number of entries in each slice
            of the batch. If <= 0, the entire batch is used.
            Default value is 4.
        eps (float): Epsilon value added for numerical stability.
            Default value is 1e-8.
    """

    def __init__(self, group_size=4, eps=1e-08, *args, **kwargs):
        super(MinibatchStdNew, self).__init__()
        if group_size is None or group_size <= 0:
            group_size = 0
        assert group_size != 1, 'Can not use 1 as minibatch std group size.'
        self.group_size = group_size
        self.eps = eps

    def extra_repr(self):
        return 'group_size={}'.format(self.group_size or '-1')

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