# 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_6/inductor_cache/az/cazxolgp2ne6vc522yhqcdzkhjb6btel7txdrpwzpkcc5t6sm46x.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.maximum, aten.eq, aten.gt, aten.lt]
# Source node to ATen node mapping:
#   x_1 => maximum
# Graph fragment:
#   %maximum : [num_users=2] = call_function[target=torch.ops.aten.maximum.default](args = (%getitem, %getitem_1), kwargs = {})
#   %eq_2 : [num_users=1] = call_function[target=torch.ops.aten.eq.Tensor](args = (%getitem, %getitem_1), kwargs = {})
#   %gt_1 : [num_users=1] = call_function[target=torch.ops.aten.gt.Tensor](args = (%getitem, %getitem_1), kwargs = {})
#   %lt_1 : [num_users=1] = call_function[target=torch.ops.aten.lt.Tensor](args = (%getitem, %getitem_1), kwargs = {})
triton_poi_fused_eq_gt_lt_maximum_0 = async_compile.triton('triton_poi_fused_eq_gt_lt_maximum_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: '*i1', 4: '*i1', 5: '*i1', 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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_eq_gt_lt_maximum_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_eq_gt_lt_maximum_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, out_ptr2, out_ptr3, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = (xindex // 64)
    x3 = xindex % 64
    x1 = (xindex // 16) % 4
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3 + (128*x2)), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (64 + x3 + (128*x2)), xmask)
    tmp4 = tl.load(in_ptr1 + (4 + x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = triton_helpers.maximum(tmp2, tmp5)
    tmp7 = tmp2 == tmp5
    tmp8 = tmp2 > tmp5
    tmp9 = tmp2 < tmp5
    tl.store(out_ptr0 + (x4), tmp6, xmask)
    tl.store(out_ptr1 + (x4), tmp7, xmask)
    tl.store(out_ptr2 + (x4), tmp8, xmask)
    tl.store(out_ptr3 + (x4), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/dx/cdxsiauqixxznc5upksv4k5qv54fs7gz2sgvr4qfd5yyu72syijl.py
# Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.maximum, aten.eq, aten.gt, aten.lt]
# Source node to ATen node mapping:
#   x_3 => maximum_1
# Graph fragment:
#   %maximum_1 : [num_users=1] = call_function[target=torch.ops.aten.maximum.default](args = (%getitem_2, %getitem_3), kwargs = {})
#   %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Tensor](args = (%getitem_2, %getitem_3), kwargs = {})
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Tensor](args = (%getitem_2, %getitem_3), kwargs = {})
#   %lt : [num_users=1] = call_function[target=torch.ops.aten.lt.Tensor](args = (%getitem_2, %getitem_3), kwargs = {})
triton_poi_fused_eq_gt_lt_maximum_1 = async_compile.triton('triton_poi_fused_eq_gt_lt_maximum_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.pointwise(
    size_hints=[2048], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 4: '*i1', 5: '*i1', 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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_eq_gt_lt_maximum_1', '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_eq_gt_lt_maximum_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, out_ptr2, out_ptr3, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1296
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = (xindex // 324)
    x3 = xindex % 324
    x1 = (xindex // 81) % 4
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3 + (648*x2)), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (324 + x3 + (648*x2)), xmask)
    tmp4 = tl.load(in_ptr1 + (4 + x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = triton_helpers.maximum(tmp2, tmp5)
    tmp7 = tmp2 == tmp5
    tmp8 = tmp2 > tmp5
    tmp9 = tmp2 < tmp5
    tl.store(out_ptr0 + (x4), tmp6, xmask)
    tl.store(out_ptr1 + (x4), tmp7, xmask)
    tl.store(out_ptr2 + (x4), tmp8, xmask)
    tl.store(out_ptr3 + (x4), tmp9, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (8, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_2, (8, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (8, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (8, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 8, 4, 4), (128, 16, 4, 1))
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.maximum, aten.eq, aten.gt, aten.lt]
        stream0 = get_raw_stream(0)
        triton_poi_fused_eq_gt_lt_maximum_0.run(buf0, primals_2, buf1, buf7, buf8, buf9, 256, grid=grid(256), stream=stream0)
        del buf0
        del primals_2
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(4, 4), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 8, 9, 9), (648, 81, 9, 1))
        buf3 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.bool)
        buf5 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.bool)
        buf6 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.maximum, aten.eq, aten.gt, aten.lt]
        triton_poi_fused_eq_gt_lt_maximum_1.run(buf2, primals_5, buf3, buf4, buf5, buf6, 1296, grid=grid(1296), stream=stream0)
        del buf2
        del primals_5
    return (buf3, primals_1, primals_3, primals_4, buf1, buf4, buf5, buf6, buf7, buf8, buf9, )


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((8, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((8, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((8, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((8, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
    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 nn


class mfm(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,
        padding=1, type=1):
        super(mfm, self).__init__()
        self.out_channels = out_channels
        if type == 1:
            self.filter = nn.Conv2d(in_channels, 2 * out_channels,
                kernel_size=kernel_size, stride=stride, padding=padding)
        else:
            self.filter = nn.Linear(in_channels, 2 * out_channels)

    def forward(self, x):
        x = self.filter(x)
        out = torch.split(x, self.out_channels, 1)
        return torch.max(out[0], out[1])


class group(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride, padding
        ):
        super(group, self).__init__()
        self.conv_a = mfm(in_channels, in_channels, 1, 1, 0)
        self.conv = mfm(in_channels, out_channels, kernel_size, stride, padding
            )

    def forward(self, x):
        x = self.conv_a(x)
        x = self.conv(x)
        return x


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_channels': 4, 'kernel_size': 4,
        'stride': 1, 'padding': 4}]

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
from torch._inductor.runtime import triton_helpers
from torch import 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_eq_gt_lt_maximum_0(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, out_ptr2, out_ptr3, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 64
    x3 = xindex % 64
    x1 = xindex // 16 % 4
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3 + 128 * x2), xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (64 + x3 + 128 * x2), xmask)
    tmp4 = tl.load(in_ptr1 + (4 + x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = triton_helpers.maximum(tmp2, tmp5)
    tmp7 = tmp2 == tmp5
    tmp8 = tmp2 > tmp5
    tmp9 = tmp2 < tmp5
    tl.store(out_ptr0 + x4, tmp6, xmask)
    tl.store(out_ptr1 + x4, tmp7, xmask)
    tl.store(out_ptr2 + x4, tmp8, xmask)
    tl.store(out_ptr3 + x4, tmp9, xmask)


@triton.jit
def triton_poi_fused_eq_gt_lt_maximum_1(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, out_ptr2, out_ptr3, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1296
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 324
    x3 = xindex % 324
    x1 = xindex // 81 % 4
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3 + 648 * x2), xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (324 + x3 + 648 * x2), xmask)
    tmp4 = tl.load(in_ptr1 + (4 + x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = triton_helpers.maximum(tmp2, tmp5)
    tmp7 = tmp2 == tmp5
    tmp8 = tmp2 > tmp5
    tmp9 = tmp2 < tmp5
    tl.store(out_ptr0 + x4, tmp6, xmask)
    tl.store(out_ptr1 + x4, tmp7, xmask)
    tl.store(out_ptr2 + x4, tmp8, xmask)
    tl.store(out_ptr3 + x4, tmp9, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (8, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_2, (8,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (8, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (8,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 8, 4, 4), (128, 16, 4, 1))
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_eq_gt_lt_maximum_0[grid(256)](buf0, primals_2,
            buf1, buf7, buf8, buf9, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf0
        del primals_2
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1),
            padding=(4, 4), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 8, 9, 9), (648, 81, 9, 1))
        buf3 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.bool)
        buf5 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.bool)
        buf6 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.bool)
        triton_poi_fused_eq_gt_lt_maximum_1[grid(1296)](buf2, primals_5,
            buf3, buf4, buf5, buf6, 1296, XBLOCK=128, num_warps=4, num_stages=1
            )
        del buf2
        del primals_5
    return (buf3, primals_1, primals_3, primals_4, buf1, buf4, buf5, buf6,
        buf7, buf8, buf9)


class mfm(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,
        padding=1, type=1):
        super(mfm, self).__init__()
        self.out_channels = out_channels
        if type == 1:
            self.filter = nn.Conv2d(in_channels, 2 * out_channels,
                kernel_size=kernel_size, stride=stride, padding=padding)
        else:
            self.filter = nn.Linear(in_channels, 2 * out_channels)

    def forward(self, x):
        x = self.filter(x)
        out = torch.split(x, self.out_channels, 1)
        return torch.max(out[0], out[1])


class groupNew(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride, padding
        ):
        super(groupNew, self).__init__()
        self.conv_a = mfm(in_channels, in_channels, 1, 1, 0)
        self.conv = mfm(in_channels, out_channels, kernel_size, stride, padding
            )

    def forward(self, input_0):
        primals_1 = self.conv_a.filter.weight
        primals_2 = self.conv_a.filter.bias
        primals_4 = self.conv.filter.weight
        primals_5 = self.conv.filter.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
