# 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_0/inductor_cache/x5/cx5x3ctvlxfngzqxjhckinfe5o47op62tt3oe6cizqbvhu2jadcg.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
#   x => gt, mul, where
# Graph fragment:
#   %gt : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution, 0), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution, 0.2), kwargs = {})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %convolution, %mul), kwargs = {})
triton_poi_fused_leaky_relu_0 = async_compile.triton('triton_poi_fused_leaky_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: '*i1', 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_leaky_relu_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_leaky_relu_0(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 278784
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(out_ptr0 + (x0), tmp2, xmask)
    tl.store(out_ptr1 + (x0), tmp5, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_0/inductor_cache/j6/cj6preby6v25qpri36zdc64okaiku4g3n2g3ntdegoyuutlffwkx.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
#   x_1 => gt_1, mul_1, where_1
# Graph fragment:
#   %gt_1 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_1, 0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_1, 0.2), kwargs = {})
#   %where_1 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %convolution_1, %mul_1), kwargs = {})
triton_poi_fused_leaky_relu_1 = async_compile.triton('triton_poi_fused_leaky_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=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i1', 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_leaky_relu_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_leaky_relu_1(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 147968
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(out_ptr0 + (x0), tmp2, xmask)
    tl.store(out_ptr1 + (x0), tmp5, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_0/inductor_cache/24/c24d6loxuicc53yfdbmizoqhkrh5m2bpnt3a4ffeuex7f6y3vnth.py
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
#   x_2 => gt_2, mul_2, where_2
# Graph fragment:
#   %gt_2 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_2, 0), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_2, 0.2), kwargs = {})
#   %where_2 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_2, %convolution_2, %mul_2), kwargs = {})
triton_poi_fused_leaky_relu_2 = async_compile.triton('triton_poi_fused_leaky_relu_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=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i1', 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_leaky_relu_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_leaky_relu_2(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 82944
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(out_ptr0 + (x0), tmp2, xmask)
    tl.store(out_ptr1 + (x0), tmp5, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_0/inductor_cache/f7/cf7lcprivgof4fqsrblqrk334iqoref57wus34pdpmrc65sotzf7.py
# Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
#   x_3 => gt_3, mul_3, where_3
# Graph fragment:
#   %gt_3 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_3, 0), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_3, 0.2), kwargs = {})
#   %where_3 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_3, %convolution_3, %mul_3), kwargs = {})
triton_poi_fused_leaky_relu_3 = async_compile.triton('triton_poi_fused_leaky_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=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i1', 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_leaky_relu_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_leaky_relu_3(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 51200
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), None)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(out_ptr0 + (x0), tmp2, None)
    tl.store(out_ptr1 + (x0), tmp5, None)
''', 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 = args
    args.clear()
    assert_size_stride(primals_1, (64, 2, 4, 4), (32, 16, 4, 1))
    assert_size_stride(primals_2, (4, 2, 64, 64), (8192, 4096, 64, 1))
    assert_size_stride(primals_3, (128, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_4, (256, 128, 4, 4), (2048, 16, 4, 1))
    assert_size_stride(primals_5, (512, 256, 4, 4), (4096, 16, 4, 1))
    assert_size_stride(primals_6, (1, 512, 4, 4), (8192, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(2, 2), padding=(2, 2), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 64, 33, 33), (69696, 1089, 33, 1))
        buf1 = empty_strided_cuda((4, 64, 33, 33), (69696, 1089, 33, 1), torch.bool)
        buf2 = empty_strided_cuda((4, 64, 33, 33), (69696, 1089, 33, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.leaky_relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_leaky_relu_0.run(buf0, buf1, buf2, 278784, grid=grid(278784), stream=stream0)
        del buf0
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(buf2, primals_3, stride=(2, 2), padding=(2, 2), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 128, 17, 17), (36992, 289, 17, 1))
        buf4 = empty_strided_cuda((4, 128, 17, 17), (36992, 289, 17, 1), torch.bool)
        buf5 = empty_strided_cuda((4, 128, 17, 17), (36992, 289, 17, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.leaky_relu]
        triton_poi_fused_leaky_relu_1.run(buf3, buf4, buf5, 147968, grid=grid(147968), stream=stream0)
        del buf3
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(buf5, primals_4, stride=(2, 2), padding=(2, 2), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 256, 9, 9), (20736, 81, 9, 1))
        buf7 = empty_strided_cuda((4, 256, 9, 9), (20736, 81, 9, 1), torch.bool)
        buf8 = empty_strided_cuda((4, 256, 9, 9), (20736, 81, 9, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.leaky_relu]
        triton_poi_fused_leaky_relu_2.run(buf6, buf7, buf8, 82944, grid=grid(82944), stream=stream0)
        del buf6
        # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution]
        buf9 = extern_kernels.convolution(buf8, primals_5, stride=(2, 2), padding=(2, 2), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf9, (4, 512, 5, 5), (12800, 25, 5, 1))
        buf10 = empty_strided_cuda((4, 512, 5, 5), (12800, 25, 5, 1), torch.bool)
        buf11 = empty_strided_cuda((4, 512, 5, 5), (12800, 25, 5, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.leaky_relu]
        triton_poi_fused_leaky_relu_3.run(buf9, buf10, buf11, 51200, grid=grid(51200), stream=stream0)
        del buf9
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.convolution]
        buf12 = extern_kernels.convolution(buf11, primals_6, stride=(2, 2), padding=(2, 2), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 1, 3, 3), (9, 9, 3, 1))
    return (buf12, primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, buf1, buf2, buf4, buf5, buf7, buf8, buf10, buf11, )


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((64, 2, 4, 4), (32, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 2, 64, 64), (8192, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((128, 64, 4, 4), (1024, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((256, 128, 4, 4), (2048, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((512, 256, 4, 4), (4096, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((1, 512, 4, 4), (8192, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6])
    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._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 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_leaky_relu_0(in_ptr0, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 278784
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(out_ptr0 + x0, tmp2, xmask)
    tl.store(out_ptr1 + x0, tmp5, xmask)


@triton.jit
def triton_poi_fused_leaky_relu_1(in_ptr0, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 147968
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(out_ptr0 + x0, tmp2, xmask)
    tl.store(out_ptr1 + x0, tmp5, xmask)


@triton.jit
def triton_poi_fused_leaky_relu_2(in_ptr0, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 82944
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(out_ptr0 + x0, tmp2, xmask)
    tl.store(out_ptr1 + x0, tmp5, xmask)


@triton.jit
def triton_poi_fused_leaky_relu_3(in_ptr0, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(out_ptr0 + x0, tmp2, None)
    tl.store(out_ptr1 + x0, tmp5, None)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (64, 2, 4, 4), (32, 16, 4, 1))
    assert_size_stride(primals_2, (4, 2, 64, 64), (8192, 4096, 64, 1))
    assert_size_stride(primals_3, (128, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_4, (256, 128, 4, 4), (2048, 16, 4, 1))
    assert_size_stride(primals_5, (512, 256, 4, 4), (4096, 16, 4, 1))
    assert_size_stride(primals_6, (1, 512, 4, 4), (8192, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(2, 
            2), padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 64, 33, 33), (69696, 1089, 33, 1))
        buf1 = empty_strided_cuda((4, 64, 33, 33), (69696, 1089, 33, 1),
            torch.bool)
        buf2 = empty_strided_cuda((4, 64, 33, 33), (69696, 1089, 33, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_leaky_relu_0[grid(278784)](buf0, buf1, buf2, 
            278784, XBLOCK=512, num_warps=8, num_stages=1)
        del buf0
        buf3 = extern_kernels.convolution(buf2, primals_3, stride=(2, 2),
            padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 128, 17, 17), (36992, 289, 17, 1))
        buf4 = empty_strided_cuda((4, 128, 17, 17), (36992, 289, 17, 1),
            torch.bool)
        buf5 = empty_strided_cuda((4, 128, 17, 17), (36992, 289, 17, 1),
            torch.float32)
        triton_poi_fused_leaky_relu_1[grid(147968)](buf3, buf4, buf5, 
            147968, XBLOCK=512, num_warps=8, num_stages=1)
        del buf3
        buf6 = extern_kernels.convolution(buf5, primals_4, stride=(2, 2),
            padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 256, 9, 9), (20736, 81, 9, 1))
        buf7 = empty_strided_cuda((4, 256, 9, 9), (20736, 81, 9, 1), torch.bool
            )
        buf8 = empty_strided_cuda((4, 256, 9, 9), (20736, 81, 9, 1), torch.
            float32)
        triton_poi_fused_leaky_relu_2[grid(82944)](buf6, buf7, buf8, 82944,
            XBLOCK=512, num_warps=8, num_stages=1)
        del buf6
        buf9 = extern_kernels.convolution(buf8, primals_5, stride=(2, 2),
            padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf9, (4, 512, 5, 5), (12800, 25, 5, 1))
        buf10 = empty_strided_cuda((4, 512, 5, 5), (12800, 25, 5, 1), torch
            .bool)
        buf11 = empty_strided_cuda((4, 512, 5, 5), (12800, 25, 5, 1), torch
            .float32)
        triton_poi_fused_leaky_relu_3[grid(51200)](buf9, buf10, buf11, 
            51200, XBLOCK=256, num_warps=4, num_stages=1)
        del buf9
        buf12 = extern_kernels.convolution(buf11, primals_6, stride=(2, 2),
            padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 1, 3, 3), (9, 9, 3, 1))
    return (buf12, primals_1, primals_2, primals_3, primals_4, primals_5,
        primals_6, buf1, buf2, buf4, buf5, buf7, buf8, buf10, buf11)


class OutputDiscriminatorNew(nn.Module):

    def __init__(self):
        super(OutputDiscriminatorNew, self).__init__()
        filter_num_list = [64, 128, 256, 512, 1]
        self.conv1 = nn.Conv2d(2, filter_num_list[0], kernel_size=4, stride
            =2, padding=2, bias=False)
        self.conv2 = nn.Conv2d(filter_num_list[0], filter_num_list[1],
            kernel_size=4, stride=2, padding=2, bias=False)
        self.conv3 = nn.Conv2d(filter_num_list[1], filter_num_list[2],
            kernel_size=4, stride=2, padding=2, bias=False)
        self.conv4 = nn.Conv2d(filter_num_list[2], filter_num_list[3],
            kernel_size=4, stride=2, padding=2, bias=False)
        self.conv5 = nn.Conv2d(filter_num_list[3], filter_num_list[4],
            kernel_size=4, stride=2, padding=2, bias=False)
        self.leakyrelu = nn.LeakyReLU(negative_slope=0.2)
        self._initialize_weights()

    def _initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                m.weight.data.normal_(0.0, 0.02)
                if m.bias is not None:
                    m.bias.data.zero_()

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