# 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_4/inductor_cache/3g/c3g7yskclh2aowyp3b5zcgjfwm6xukp4hlxv7nsxf2sknfzb76ze.py
# Topologically Sorted Source Nodes: [x, neg], Original ATen: [aten.convolution, aten.neg]
# Source node to ATen node mapping:
#   neg => neg
#   x => convolution
# Graph fragment:
#   %convolution : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_1, %primals_2, %primals_3, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %neg : [num_users=2] = call_function[target=torch.ops.aten.neg.default](args = (%convolution,), kwargs = {})
triton_poi_fused_convolution_neg_0 = async_compile.triton('triton_poi_fused_convolution_neg_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: '*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_convolution_neg_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_convolution_neg_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 14400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    x1 = xindex % 3600
    x2 = (xindex // 3600)
    tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp1 = tl.load(in_ptr0 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = -tmp3
    tl.store(in_out_ptr0 + (x0), tmp3, xmask)
    tl.store(out_ptr0 + (x1 + (3616*x2)), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/qk/cqkww2degwhrkqxgkcseanp2gga76j4krckkwaxggodrquxmunkg.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x_1 => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%neg_1, %getitem_2], 1), kwargs = {})
triton_poi_fused_cat_1 = async_compile.triton('triton_poi_fused_cat_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=[8], 
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2
    x1 = (xindex // 2)
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x1), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = -tmp5
    tmp7 = tl.full(tmp6.shape, 0.0, tmp6.dtype)
    tmp8 = tl.where(tmp4, tmp6, tmp7)
    tmp9 = tmp0 >= tmp3
    tmp10 = tl.full([1], 2, tl.int64)
    tmp11 = tmp0 < tmp10
    tmp12 = tl.load(in_ptr1 + (x1), tmp9 & xmask, eviction_policy='evict_last', other=0.0)
    tmp13 = tl.where(tmp4, tmp8, tmp12)
    tl.store(out_ptr0 + (x2), tmp13, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/k4/ck4rjvij26siqskt4g6lhmch5h2qz73i3c4fbzncexeyuzwgqgxb.py
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   x_2 => convolution_1
# Graph fragment:
#   %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%cat, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_2 = async_compile.triton('triton_poi_fused_convolution_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.pointwise(
    size_hints=[4], 
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp1 = tl.load(in_ptr0 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tl.store(in_out_ptr0 + (x0), tmp3, 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, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_2, (1, 3, 7, 7), (147, 49, 7, 1))
    assert_size_stride(primals_3, (1, ), (1, ))
    assert_size_stride(primals_4, (1, 2, 1, 1), (2, 1, 1, 1))
    assert_size_stride(primals_5, (1, ), (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_1, primals_2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 1, 60, 60), (3600, 3600, 60, 1))
        buf1 = buf0; del buf0  # reuse
        buf2 = empty_strided_cuda((4, 1, 60, 60), (3616, 3616, 60, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x, neg], Original ATen: [aten.convolution, aten.neg]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_neg_0.run(buf1, primals_3, buf2, 14400, grid=grid(14400), stream=stream0)
        del primals_3
        # Topologically Sorted Source Nodes: [max_pool2d], Original ATen: [aten.max_pool2d_with_indices]
        buf3 = torch.ops.aten.max_pool2d_with_indices.default(buf2, [60, 60])
        buf4 = buf3[0]
        buf5 = buf3[1]
        del buf3
        # Topologically Sorted Source Nodes: [x2], Original ATen: [aten.max_pool2d_with_indices]
        buf6 = torch.ops.aten.max_pool2d_with_indices.default(buf1, [60, 60])
        buf7 = buf6[0]
        buf8 = buf6[1]
        del buf6
        buf9 = empty_strided_cuda((4, 2, 1, 1), (2, 1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.cat]
        triton_poi_fused_cat_1.run(buf4, buf7, buf9, 8, grid=grid(8), stream=stream0)
        del buf4
        del buf7
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.convolution]
        buf10 = extern_kernels.convolution(buf9, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 1, 1, 1), (1, 1, 1, 1))
        buf11 = reinterpret_tensor(buf10, (4, 1, 1, 1), (1, 4, 4, 4), 0); del buf10  # reuse
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_2.run(buf11, primals_5, 4, grid=grid(4), stream=stream0)
        del primals_5
    return (reinterpret_tensor(buf11, (4, 1), (1, 1), 0), primals_1, primals_2, primals_4, buf1, buf2, buf5, 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((4, 3, 64, 64), (12288, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((1, 3, 7, 7), (147, 49, 7, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((1, 2, 1, 1), (2, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((1, ), (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 math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils


class FocusLiteNNMinMax(nn.Module):

    def __init__(self, num_channel=1):
        super(FocusLiteNNMinMax, self).__init__()
        self.num_channel = num_channel
        self.conv = nn.Conv2d(3, self.num_channel, 7, stride=1, padding=1)
        self.fc = nn.Conv2d(self.num_channel * 2, 1, 1, stride=1, padding=0)
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2.0 / n))

    def forward(self, x):
        batch_size = x.size()[0]
        x = self.conv(x)
        pool_size = x.shape[2:4]
        x1 = -F.max_pool2d(-x, pool_size)
        x2 = F.max_pool2d(x, pool_size)
        x = torch.cat((x1, x2), 1)
        x = self.fc(x)
        x = x.view(batch_size, -1)
        return x


def get_inputs():
    return [torch.rand([4, 3, 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 math
import torch.nn as nn
import torch.utils
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_convolution_neg_0(in_out_ptr0, in_ptr0, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 14400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    x1 = xindex % 3600
    x2 = xindex // 3600
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = -tmp3
    tl.store(in_out_ptr0 + x0, tmp3, xmask)
    tl.store(out_ptr0 + (x1 + 3616 * x2), tmp4, xmask)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2
    x1 = xindex // 2
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + x1, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp6 = -tmp5
    tmp7 = tl.full(tmp6.shape, 0.0, tmp6.dtype)
    tmp8 = tl.where(tmp4, tmp6, tmp7)
    tmp9 = tmp0 >= tmp3
    tl.full([1], 2, tl.int64)
    tmp12 = tl.load(in_ptr1 + x1, tmp9 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp13 = tl.where(tmp4, tmp8, tmp12)
    tl.store(out_ptr0 + x2, tmp13, xmask)


@triton.jit
def triton_poi_fused_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tl.store(in_out_ptr0 + x0, tmp3, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_2, (1, 3, 7, 7), (147, 49, 7, 1))
    assert_size_stride(primals_3, (1,), (1,))
    assert_size_stride(primals_4, (1, 2, 1, 1), (2, 1, 1, 1))
    assert_size_stride(primals_5, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 1, 60, 60), (3600, 3600, 60, 1))
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 1, 60, 60), (3616, 3616, 60, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_neg_0[grid(14400)](buf1, primals_3,
            buf2, 14400, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_3
        buf3 = torch.ops.aten.max_pool2d_with_indices.default(buf2, [60, 60])
        buf4 = buf3[0]
        buf5 = buf3[1]
        del buf3
        buf6 = torch.ops.aten.max_pool2d_with_indices.default(buf1, [60, 60])
        buf7 = buf6[0]
        buf8 = buf6[1]
        del buf6
        buf9 = empty_strided_cuda((4, 2, 1, 1), (2, 1, 1, 1), torch.float32)
        triton_poi_fused_cat_1[grid(8)](buf4, buf7, buf9, 8, XBLOCK=8,
            num_warps=1, num_stages=1)
        del buf4
        del buf7
        buf10 = extern_kernels.convolution(buf9, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 1, 1, 1), (1, 1, 1, 1))
        buf11 = reinterpret_tensor(buf10, (4, 1, 1, 1), (1, 4, 4, 4), 0)
        del buf10
        triton_poi_fused_convolution_2[grid(4)](buf11, primals_5, 4, XBLOCK
            =4, num_warps=1, num_stages=1)
        del primals_5
    return reinterpret_tensor(buf11, (4, 1), (1, 1), 0
        ), primals_1, primals_2, primals_4, buf1, buf2, buf5, buf8, buf9


class FocusLiteNNMinMaxNew(nn.Module):

    def __init__(self, num_channel=1):
        super(FocusLiteNNMinMaxNew, self).__init__()
        self.num_channel = num_channel
        self.conv = nn.Conv2d(3, self.num_channel, 7, stride=1, padding=1)
        self.fc = nn.Conv2d(self.num_channel * 2, 1, 1, stride=1, padding=0)
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2.0 / n))

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