# 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_4/inductor_cache/hq/chq4plj7tbuetf5zmibkuldzziostnlcjhe3drbka65quxufxuxe.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.mean]
# Source node to ATen node mapping:
#   x => mean
# Graph fragment:
#   %mean : [num_users=2] = call_function[target=torch.ops.aten.mean.dim](args = (%arg0_1, [1], True), kwargs = {})
triton_poi_fused_mean_0 = async_compile.triton('triton_poi_fused_mean_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=[16384], 
    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_mean_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_mean_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 4096
    x1 = (xindex // 4096)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (16384*x1)), None)
    tmp1 = tl.load(in_ptr0 + (4096 + x0 + (16384*x1)), None)
    tmp3 = tl.load(in_ptr0 + (8192 + x0 + (16384*x1)), None)
    tmp5 = tl.load(in_ptr0 + (12288 + x0 + (16384*x1)), None)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tl.store(out_ptr0 + (x2), tmp8, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/zd/czdxgfio3jmll4rqu726gnzbmgiahmjn3qwokkx4h54u6pkmalqk.py
# Topologically Sorted Source Nodes: [mse_loss], Original ATen: [aten.mse_loss]
# Source node to ATen node mapping:
#   mse_loss => mean_1, pow_1, sub
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%convolution, %convolution_1), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_1,), kwargs = {})
triton_red_fused_mse_loss_1 = async_compile.triton('triton_red_fused_mse_loss_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.reduction(
    size_hints=[2, 8192],
    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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused_mse_loss_1', 'mutated_arg_names': [], 'no_x_dim': False, '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_red_fused_mse_loss_1(in_ptr0, in_ptr1, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
    xnumel = 2
    rnumel = 7200
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex
    _tmp5 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp0 = tl.load(in_ptr0 + (r1 + (7200*x0)), rmask & xmask, eviction_policy='evict_first', other=0.0)
        tmp1 = tl.load(in_ptr1 + (r1 + (7200*x0)), rmask & xmask, eviction_policy='evict_first', other=0.0)
        tmp2 = tmp0 - tmp1
        tmp3 = tmp2 * tmp2
        tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
        tmp6 = _tmp5 + tmp4
        _tmp5 = tl.where(rmask & xmask, tmp6, _tmp5)
    tmp5 = tl.sum(_tmp5, 1)[:, None]
    tl.store(out_ptr0 + (x0), tmp5, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/yt/cyt66nidibjarohykffujnl3sybfab37ubarj4ctm2mnmbk63gql.py
# Topologically Sorted Source Nodes: [mse_loss], Original ATen: [aten.mse_loss]
# Source node to ATen node mapping:
#   mse_loss => mean_1, pow_1, sub
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%convolution, %convolution_1), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_1,), kwargs = {})
triton_per_fused_mse_loss_2 = async_compile.triton('triton_per_fused_mse_loss_2', '''
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, 2],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_mse_loss_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, '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_mse_loss_2(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 2
    RBLOCK: tl.constexpr = 2
    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
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.sum(tmp1, 1)[:, None]
    tmp4 = 14400.0
    tmp5 = tmp3 / tmp4
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp5, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 64, 64), (16384, 4096, 64, 1))
    assert_size_stride(arg1_1, (1, 1, 5, 5), (25, 25, 5, 1))
    assert_size_stride(arg2_1, (1, 1, 5, 5), (25, 25, 5, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 64, 64), (4096, 4096, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.mean]
        stream0 = get_raw_stream(0)
        triton_poi_fused_mean_0.run(arg0_1, buf0, 16384, grid=grid(16384), stream=stream0)
        del arg0_1
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(buf0, arg1_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 1, 60, 60), (3600, 3600, 60, 1))
        del arg1_1
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(buf0, arg2_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 1, 60, 60), (3600, 3600, 60, 1))
        del arg2_1
        del buf0
        buf3 = empty_strided_cuda((2, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [mse_loss], Original ATen: [aten.mse_loss]
        triton_red_fused_mse_loss_1.run(buf1, buf2, buf3, 2, 7200, grid=grid(2), stream=stream0)
        del buf1
        del buf2
        buf4 = empty_strided_cuda((), (), torch.float32)
        buf5 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [mse_loss], Original ATen: [aten.mse_loss]
        triton_per_fused_mse_loss_2.run(buf5, buf3, 1, 2, grid=grid(1), stream=stream0)
        del buf3
    return (buf5, )


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, 64, 64), (16384, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((1, 1, 5, 5), (25, 25, 5, 1), device='cuda:0', dtype=torch.float32)
    arg2_1 = rand_strided((1, 1, 5, 5), (25, 25, 5, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_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 numpy as np
import torch.nn as nn
from torch.nn import functional


class GrayscaleLayer(nn.Module):

    def __init__(self):
        super(GrayscaleLayer, self).__init__()

    def forward(self, x):
        return torch.mean(x, 1, keepdim=True)


class StdLoss(nn.Module):

    def __init__(self):
        """
        Loss on the variance of the image.
        Works in the grayscale.
        If the image is smooth, gets zero
        """
        super(StdLoss, self).__init__()
        blur = 1 / 25 * np.ones((5, 5))
        blur = blur.reshape(1, 1, blur.shape[0], blur.shape[1])
        self.mse = nn.MSELoss()
        self.blur = nn.Parameter(data=torch.FloatTensor(blur),
            requires_grad=False)
        image = np.zeros((5, 5))
        image[2, 2] = 1
        image = image.reshape(1, 1, image.shape[0], image.shape[1])
        self.image = nn.Parameter(data=torch.FloatTensor(image),
            requires_grad=False)
        self.gray_scale = GrayscaleLayer()

    def forward(self, x):
        x = self.gray_scale(x)
        return self.mse(functional.conv2d(x, self.image), functional.conv2d
            (x, self.blur))


def get_inputs():
    return [torch.rand([4, 4, 64, 64])]


def get_init_inputs():
    return [[], {}]

import torch
from torch._inductor.select_algorithm import extern_kernels
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 numpy as np
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_poi_fused_mean_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 4096
    x1 = xindex // 4096
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16384 * x1), None)
    tmp1 = tl.load(in_ptr0 + (4096 + x0 + 16384 * x1), None)
    tmp3 = tl.load(in_ptr0 + (8192 + x0 + 16384 * x1), None)
    tmp5 = tl.load(in_ptr0 + (12288 + x0 + 16384 * x1), None)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tl.store(out_ptr0 + x2, tmp8, None)


@triton.jit
def triton_red_fused_mse_loss_1(in_ptr0, in_ptr1, out_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    xnumel = 2
    rnumel = 7200
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex
    _tmp5 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp0 = tl.load(in_ptr0 + (r1 + 7200 * x0), rmask & xmask,
            eviction_policy='evict_first', other=0.0)
        tmp1 = tl.load(in_ptr1 + (r1 + 7200 * x0), rmask & xmask,
            eviction_policy='evict_first', other=0.0)
        tmp2 = tmp0 - tmp1
        tmp3 = tmp2 * tmp2
        tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
        tmp6 = _tmp5 + tmp4
        _tmp5 = tl.where(rmask & xmask, tmp6, _tmp5)
    tmp5 = tl.sum(_tmp5, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp5, xmask)


@triton.jit
def triton_per_fused_mse_loss_2(in_out_ptr0, in_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 2
    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
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.sum(tmp1, 1)[:, None]
    tmp4 = 14400.0
    tmp5 = tmp3 / tmp4
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp5, None)


def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 64, 64), (16384, 4096, 64, 1))
    assert_size_stride(arg1_1, (1, 1, 5, 5), (25, 25, 5, 1))
    assert_size_stride(arg2_1, (1, 1, 5, 5), (25, 25, 5, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 64, 64), (4096, 4096, 64, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mean_0[grid(16384)](arg0_1, buf0, 16384, XBLOCK=
            256, num_warps=4, num_stages=1)
        del arg0_1
        buf1 = extern_kernels.convolution(buf0, arg1_1, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 1, 60, 60), (3600, 3600, 60, 1))
        del arg1_1
        buf2 = extern_kernels.convolution(buf0, arg2_1, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 1, 60, 60), (3600, 3600, 60, 1))
        del arg2_1
        del buf0
        buf3 = empty_strided_cuda((2,), (1,), torch.float32)
        triton_red_fused_mse_loss_1[grid(2)](buf1, buf2, buf3, 2, 7200,
            XBLOCK=1, RBLOCK=2048, num_warps=16, num_stages=1)
        del buf1
        del buf2
        buf4 = empty_strided_cuda((), (), torch.float32)
        buf5 = buf4
        del buf4
        triton_per_fused_mse_loss_2[grid(1)](buf5, buf3, 1, 2, XBLOCK=1,
            num_warps=2, num_stages=1)
        del buf3
    return buf5,


class GrayscaleLayer(nn.Module):

    def __init__(self):
        super(GrayscaleLayer, self).__init__()

    def forward(self, x):
        return torch.mean(x, 1, keepdim=True)


class StdLossNew(nn.Module):

    def __init__(self):
        """
        Loss on the variance of the image.
        Works in the grayscale.
        If the image is smooth, gets zero
        """
        super(StdLossNew, self).__init__()
        blur = 1 / 25 * np.ones((5, 5))
        blur = blur.reshape(1, 1, blur.shape[0], blur.shape[1])
        self.mse = nn.MSELoss()
        self.blur = nn.Parameter(data=torch.FloatTensor(blur),
            requires_grad=False)
        image = np.zeros((5, 5))
        image[2, 2] = 1
        image = image.reshape(1, 1, image.shape[0], image.shape[1])
        self.image = nn.Parameter(data=torch.FloatTensor(image),
            requires_grad=False)
        self.gray_scale = GrayscaleLayer()

    def forward(self, input_0):
        arg1_1 = self.blur
        arg2_1 = self.image
        arg0_1 = input_0
        output = call([arg0_1, arg1_1, arg2_1])
        return output[0]
