# 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_7/inductor_cache/c7/cc7ew5e5macgqb56lqlcg7icjy35237zalpxaiyszf6jy5fcpfuv.py
# Topologically Sorted Source Nodes: [out, out_1], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   out => convolution
#   out_1 => relu
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [4, 4], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
triton_poi_fused_convolution_relu_0 = async_compile.triton('triton_poi_fused_convolution_relu_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=[524288], 
    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_convolution_relu_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_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 369024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 961) % 96
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x3), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/xk/cxkvyfvspf73ksgokcqqswzdbo3mm6zcwn3oysmga7t3dttjw46i.py
# Topologically Sorted Source Nodes: [out_2, out_3], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   out_2 => convolution_1
#   out_3 => relu_1
# Graph fragment:
#   %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu, %primals_4, %primals_5, [2, 2], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {})
triton_poi_fused_convolution_relu_1 = async_compile.triton('triton_poi_fused_convolution_relu_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=[65536], 
    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_convolution_relu_1', '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_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 50176
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 196) % 64
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x3), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/bz/cbzvzpyfx3oivvtdguuwq32yrfatzdn6j7z2x5yvazqpwq5w6suy.py
# Topologically Sorted Source Nodes: [out_4], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   out_4 => getitem, getitem_1
# Graph fragment:
#   %getitem : [num_users=2] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 0), kwargs = {})
#   %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_2 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_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=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i8', 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_max_pool2d_with_indices_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 9, '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_max_pool2d_with_indices_2(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 9216
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 6
    x1 = (xindex // 6) % 6
    x2 = (xindex // 36)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (28*x1) + (196*x2)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (28*x1) + (196*x2)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + (2*x0) + (28*x1) + (196*x2)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (14 + (2*x0) + (28*x1) + (196*x2)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (15 + (2*x0) + (28*x1) + (196*x2)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (16 + (2*x0) + (28*x1) + (196*x2)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (28 + (2*x0) + (28*x1) + (196*x2)), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (29 + (2*x0) + (28*x1) + (196*x2)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (30 + (2*x0) + (28*x1) + (196*x2)), xmask, eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp8 = triton_helpers.maximum(tmp7, tmp6)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tmp12 = triton_helpers.maximum(tmp11, tmp10)
    tmp14 = triton_helpers.maximum(tmp13, tmp12)
    tmp16 = triton_helpers.maximum(tmp15, tmp14)
    tmp17 = tmp1 > tmp0
    tmp18 = tl.full([1], 1, tl.int8)
    tmp19 = tl.full([1], 0, tl.int8)
    tmp20 = tl.where(tmp17, tmp18, tmp19)
    tmp21 = tmp3 > tmp2
    tmp22 = tl.full([1], 2, tl.int8)
    tmp23 = tl.where(tmp21, tmp22, tmp20)
    tmp24 = tmp5 > tmp4
    tmp25 = tl.full([1], 3, tl.int8)
    tmp26 = tl.where(tmp24, tmp25, tmp23)
    tmp27 = tmp7 > tmp6
    tmp28 = tl.full([1], 4, tl.int8)
    tmp29 = tl.where(tmp27, tmp28, tmp26)
    tmp30 = tmp9 > tmp8
    tmp31 = tl.full([1], 5, tl.int8)
    tmp32 = tl.where(tmp30, tmp31, tmp29)
    tmp33 = tmp11 > tmp10
    tmp34 = tl.full([1], 6, tl.int8)
    tmp35 = tl.where(tmp33, tmp34, tmp32)
    tmp36 = tmp13 > tmp12
    tmp37 = tl.full([1], 7, tl.int8)
    tmp38 = tl.where(tmp36, tmp37, tmp35)
    tmp39 = tmp15 > tmp14
    tmp40 = tl.full([1], 8, tl.int8)
    tmp41 = tl.where(tmp39, tmp40, tmp38)
    tl.store(out_ptr0 + (x3), tmp16, xmask)
    tl.store(out_ptr1 + (x3), tmp41, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/v6/cv65cney566y6h5klpmakajve33zudsdblkqxhrcx26ofxbsvp5p.py
# Topologically Sorted Source Nodes: [out_5, out_6], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   out_5 => convolution_2
#   out_6 => relu_2
# Graph fragment:
#   %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem, %primals_6, %primals_7, [2, 2], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_2,), kwargs = {})
triton_poi_fused_convolution_relu_3 = async_compile.triton('triton_poi_fused_convolution_relu_3', '''
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=[512], 
    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_convolution_relu_3', '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_relu_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 4) % 32
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x3), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/dr/cdrfa4j4sg4tuquz2onfpx2tmwrrqsxoubj47ovzcbet5hovoncx.py
# Topologically Sorted Source Nodes: [out_9, out_10], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   out_10 => relu_4
#   out_9 => convolution_4
# Graph fragment:
#   %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_3, %primals_10, %primals_11, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_4,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_4, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_4 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_4', '''
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=[32], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_relu_threshold_backward_4', '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_relu_threshold_backward_4(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 4) % 2
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + (x3), tmp4, xmask)
    tl.store(out_ptr0 + (x3), tmp6, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11 = args
    args.clear()
    assert_size_stride(primals_1, (96, 1, 7, 7), (49, 49, 7, 1))
    assert_size_stride(primals_2, (96, ), (1, ))
    assert_size_stride(primals_3, (4, 1, 128, 128), (16384, 16384, 128, 1))
    assert_size_stride(primals_4, (64, 96, 5, 5), (2400, 25, 5, 1))
    assert_size_stride(primals_5, (64, ), (1, ))
    assert_size_stride(primals_6, (32, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_7, (32, ), (1, ))
    assert_size_stride(primals_8, (32, 32, 1, 1), (32, 1, 1, 1))
    assert_size_stride(primals_9, (32, ), (1, ))
    assert_size_stride(primals_10, (2, 32, 1, 1), (32, 1, 1, 1))
    assert_size_stride(primals_11, (2, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(4, 4), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 96, 31, 31), (92256, 961, 31, 1))
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [out, out_1], Original ATen: [aten.convolution, aten.relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_relu_0.run(buf1, primals_2, 369024, grid=grid(369024), stream=stream0)
        del primals_2
        # Topologically Sorted Source Nodes: [out_2], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 64, 14, 14), (12544, 196, 14, 1))
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [out_2, out_3], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_1.run(buf3, primals_5, 50176, grid=grid(50176), stream=stream0)
        del primals_5
        buf4 = empty_strided_cuda((4, 64, 6, 6), (2304, 36, 6, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 64, 6, 6), (2304, 36, 6, 1), torch.int8)
        # Topologically Sorted Source Nodes: [out_4], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_2.run(buf3, buf4, buf5, 9216, grid=grid(9216), stream=stream0)
        # Topologically Sorted Source Nodes: [out_5], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(buf4, primals_6, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 32, 2, 2), (128, 4, 2, 1))
        buf7 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [out_5, out_6], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_3.run(buf7, primals_7, 512, grid=grid(512), stream=stream0)
        del primals_7
        # Topologically Sorted Source Nodes: [out_7], Original ATen: [aten.convolution]
        buf8 = extern_kernels.convolution(buf7, primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 32, 2, 2), (128, 4, 2, 1))
        buf9 = buf8; del buf8  # reuse
        # Topologically Sorted Source Nodes: [out_7, out_8], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_3.run(buf9, primals_9, 512, grid=grid(512), stream=stream0)
        del primals_9
        # Topologically Sorted Source Nodes: [out_9], Original ATen: [aten.convolution]
        buf10 = extern_kernels.convolution(buf9, primals_10, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 2, 2, 2), (8, 4, 2, 1))
        buf11 = buf10; del buf10  # reuse
        buf12 = empty_strided_cuda((4, 2, 2, 2), (8, 4, 2, 1), torch.bool)
        # Topologically Sorted Source Nodes: [out_9, out_10], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_4.run(buf11, primals_11, buf12, 32, grid=grid(32), stream=stream0)
        del primals_11
    return (reinterpret_tensor(buf11, (4, 4, 2), (8, 2, 1), 0), primals_1, primals_3, primals_4, primals_6, primals_8, primals_10, buf1, buf3, buf4, buf5, buf7, buf9, buf12, )


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((96, 1, 7, 7), (49, 49, 7, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((96, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 1, 128, 128), (16384, 16384, 128, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((64, 96, 5, 5), (2400, 25, 5, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((32, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((32, 32, 1, 1), (32, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((2, 32, 1, 1), (32, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((2, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11])
    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 Discriminator(nn.Module):

    def __init__(self, in_features=1):
        super(Discriminator, self).__init__()
        self.conv1 = nn.Conv2d(in_features, 96, kernel_size=7, stride=4)
        self.relu = nn.ReLU()
        self.conv2 = nn.Conv2d(96, 64, kernel_size=5, stride=2)
        self.max_pool = nn.MaxPool2d(kernel_size=3, stride=2)
        self.conv3 = nn.Conv2d(64, 32, kernel_size=3, stride=2)
        self.conv4 = nn.Conv2d(32, 32, kernel_size=1, stride=1)
        self.conv5 = nn.Conv2d(32, 2, kernel_size=1, stride=1)

    def forward(self, x):
        out = self.conv1(x)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.relu(out)
        out = self.max_pool(out)
        out = self.conv3(out)
        out = self.relu(out)
        out = self.conv4(out)
        out = self.relu(out)
        out = self.conv5(out)
        out = self.relu(out)
        return out.view(out.size(0), -1, 2)


def get_inputs():
    return [torch.rand([4, 1, 128, 128])]


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
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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 369024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 961 % 96
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 50176
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 196 % 64
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_2(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 9216
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 6
    x1 = xindex // 6 % 6
    x2 = xindex // 36
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 28 * x1 + 196 * x2), xmask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 28 * x1 + 196 * x2), xmask,
        eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 2 * x0 + 28 * x1 + 196 * x2), xmask,
        eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (14 + 2 * x0 + 28 * x1 + 196 * x2), xmask,
        eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (15 + 2 * x0 + 28 * x1 + 196 * x2), xmask,
        eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (16 + 2 * x0 + 28 * x1 + 196 * x2), xmask,
        eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (28 + 2 * x0 + 28 * x1 + 196 * x2), xmask,
        eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (29 + 2 * x0 + 28 * x1 + 196 * x2), xmask,
        eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (30 + 2 * x0 + 28 * x1 + 196 * x2), xmask,
        eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp8 = triton_helpers.maximum(tmp7, tmp6)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tmp12 = triton_helpers.maximum(tmp11, tmp10)
    tmp14 = triton_helpers.maximum(tmp13, tmp12)
    tmp16 = triton_helpers.maximum(tmp15, tmp14)
    tmp17 = tmp1 > tmp0
    tmp18 = tl.full([1], 1, tl.int8)
    tmp19 = tl.full([1], 0, tl.int8)
    tmp20 = tl.where(tmp17, tmp18, tmp19)
    tmp21 = tmp3 > tmp2
    tmp22 = tl.full([1], 2, tl.int8)
    tmp23 = tl.where(tmp21, tmp22, tmp20)
    tmp24 = tmp5 > tmp4
    tmp25 = tl.full([1], 3, tl.int8)
    tmp26 = tl.where(tmp24, tmp25, tmp23)
    tmp27 = tmp7 > tmp6
    tmp28 = tl.full([1], 4, tl.int8)
    tmp29 = tl.where(tmp27, tmp28, tmp26)
    tmp30 = tmp9 > tmp8
    tmp31 = tl.full([1], 5, tl.int8)
    tmp32 = tl.where(tmp30, tmp31, tmp29)
    tmp33 = tmp11 > tmp10
    tmp34 = tl.full([1], 6, tl.int8)
    tmp35 = tl.where(tmp33, tmp34, tmp32)
    tmp36 = tmp13 > tmp12
    tmp37 = tl.full([1], 7, tl.int8)
    tmp38 = tl.where(tmp36, tmp37, tmp35)
    tmp39 = tmp15 > tmp14
    tmp40 = tl.full([1], 8, tl.int8)
    tmp41 = tl.where(tmp39, tmp40, tmp38)
    tl.store(out_ptr0 + x3, tmp16, xmask)
    tl.store(out_ptr1 + x3, tmp41, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_3(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 32
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_4(in_out_ptr0,
    in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 2
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x3, tmp4, xmask)
    tl.store(out_ptr0 + x3, tmp6, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11) = args
    args.clear()
    assert_size_stride(primals_1, (96, 1, 7, 7), (49, 49, 7, 1))
    assert_size_stride(primals_2, (96,), (1,))
    assert_size_stride(primals_3, (4, 1, 128, 128), (16384, 16384, 128, 1))
    assert_size_stride(primals_4, (64, 96, 5, 5), (2400, 25, 5, 1))
    assert_size_stride(primals_5, (64,), (1,))
    assert_size_stride(primals_6, (32, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_7, (32,), (1,))
    assert_size_stride(primals_8, (32, 32, 1, 1), (32, 1, 1, 1))
    assert_size_stride(primals_9, (32,), (1,))
    assert_size_stride(primals_10, (2, 32, 1, 1), (32, 1, 1, 1))
    assert_size_stride(primals_11, (2,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(4, 
            4), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 96, 31, 31), (92256, 961, 31, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(369024)](buf1, primals_2, 
            369024, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_2
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 64, 14, 14), (12544, 196, 14, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_relu_1[grid(50176)](buf3, primals_5, 
            50176, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((4, 64, 6, 6), (2304, 36, 6, 1), torch.
            float32)
        buf5 = empty_strided_cuda((4, 64, 6, 6), (2304, 36, 6, 1), torch.int8)
        triton_poi_fused_max_pool2d_with_indices_2[grid(9216)](buf3, buf4,
            buf5, 9216, XBLOCK=128, num_warps=4, num_stages=1)
        buf6 = extern_kernels.convolution(buf4, primals_6, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 32, 2, 2), (128, 4, 2, 1))
        buf7 = buf6
        del buf6
        triton_poi_fused_convolution_relu_3[grid(512)](buf7, primals_7, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_7
        buf8 = extern_kernels.convolution(buf7, primals_8, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 32, 2, 2), (128, 4, 2, 1))
        buf9 = buf8
        del buf8
        triton_poi_fused_convolution_relu_3[grid(512)](buf9, primals_9, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_9
        buf10 = extern_kernels.convolution(buf9, primals_10, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 2, 2, 2), (8, 4, 2, 1))
        buf11 = buf10
        del buf10
        buf12 = empty_strided_cuda((4, 2, 2, 2), (8, 4, 2, 1), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_4[grid(32)](buf11,
            primals_11, buf12, 32, XBLOCK=32, num_warps=1, num_stages=1)
        del primals_11
    return (reinterpret_tensor(buf11, (4, 4, 2), (8, 2, 1), 0), primals_1,
        primals_3, primals_4, primals_6, primals_8, primals_10, buf1, buf3,
        buf4, buf5, buf7, buf9, buf12)


class DiscriminatorNew(nn.Module):

    def __init__(self, in_features=1):
        super(DiscriminatorNew, self).__init__()
        self.conv1 = nn.Conv2d(in_features, 96, kernel_size=7, stride=4)
        self.relu = nn.ReLU()
        self.conv2 = nn.Conv2d(96, 64, kernel_size=5, stride=2)
        self.max_pool = nn.MaxPool2d(kernel_size=3, stride=2)
        self.conv3 = nn.Conv2d(64, 32, kernel_size=3, stride=2)
        self.conv4 = nn.Conv2d(32, 32, kernel_size=1, stride=1)
        self.conv5 = nn.Conv2d(32, 2, kernel_size=1, stride=1)

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_2 = self.conv1.bias
        primals_4 = self.conv2.weight
        primals_5 = self.conv2.bias
        primals_6 = self.conv3.weight
        primals_7 = self.conv3.bias
        primals_8 = self.conv4.weight
        primals_9 = self.conv4.bias
        primals_10 = self.conv5.weight
        primals_11 = self.conv5.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11])
        return output[0]
