# 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_1/inductor_cache/vk/cvkfkcxx2eq6evt22oohystebz2yozj4mjiqnsskws4gbjhocgvw.py
# Topologically Sorted Source Nodes: [input_1, input_2], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   input_1 => convolution
#   input_2 => relu
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [0, 0], [1, 1], True, [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=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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 = 495616
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 121) % 1024
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, 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, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/ol/colybmpnaokx67ittdzvagjpdwvsnv2xyytilrk4tza6wuqaj4mi.py
# Topologically Sorted Source Nodes: [input_3, input_4], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   input_3 => convolution_1
#   input_4 => 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], [1, 1], [1, 1], True, [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=[1048576], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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 = 991232
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 484) % 512
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, 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, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/ih/cih4pste5qxfm3mjsemycqwwbafvlkkalo5mq4wlkyr4ucvqegu7.py
# Topologically Sorted Source Nodes: [input_5, input_6], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   input_5 => convolution_2
#   input_6 => relu_2
# Graph fragment:
#   %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_1, %primals_6, %primals_7, [2, 2], [1, 1], [1, 1], True, [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_2 = async_compile.triton('triton_poi_fused_convolution_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=[2097152], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1982464
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 1936) % 256
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, 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, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/ph/cph3b64ryhg5hvukx5u6fmmqptyw5iuisa7uadoo4w6u2saxwgs7.py
# Topologically Sorted Source Nodes: [input_7, input_8], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   input_7 => convolution_3
#   input_8 => relu_3
# Graph fragment:
#   %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_2, %primals_8, %primals_9, [2, 2], [1, 1], [1, 1], True, [0, 0], 1), kwargs = {})
#   %relu_3 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_3,), 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=[4194304], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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 = 3964928
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 7744) % 128
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, 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, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/fp/cfprvosswwsmsfodeidueehvqzc4q3bj7yyxhuzjwrua6n3gypnc.py
# Topologically Sorted Source Nodes: [input_9], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   input_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], True, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_4 = async_compile.triton('triton_poi_fused_convolution_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=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_4', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 92928
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 7744) % 3
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, 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, (64, 1024, 8, 8), (65536, 64, 8, 1))
    assert_size_stride(primals_2, (1024, ), (1, ))
    assert_size_stride(primals_3, (4, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_4, (1024, 512, 4, 4), (8192, 16, 4, 1))
    assert_size_stride(primals_5, (512, ), (1, ))
    assert_size_stride(primals_6, (512, 256, 4, 4), (4096, 16, 4, 1))
    assert_size_stride(primals_7, (256, ), (1, ))
    assert_size_stride(primals_8, (256, 128, 4, 4), (2048, 16, 4, 1))
    assert_size_stride(primals_9, (128, ), (1, ))
    assert_size_stride(primals_10, (128, 3, 1, 1), (3, 1, 1, 1))
    assert_size_stride(primals_11, (3, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [input_1], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 1024, 11, 11), (123904, 121, 11, 1))
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [input_1, input_2], Original ATen: [aten.convolution, aten.relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_relu_0.run(buf1, primals_2, 495616, grid=grid(495616), stream=stream0)
        del primals_2
        # Topologically Sorted Source Nodes: [input_3], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 512, 22, 22), (247808, 484, 22, 1))
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [input_3, input_4], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_1.run(buf3, primals_5, 991232, grid=grid(991232), stream=stream0)
        del primals_5
        # Topologically Sorted Source Nodes: [input_5], Original ATen: [aten.convolution]
        buf4 = extern_kernels.convolution(buf3, primals_6, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 256, 44, 44), (495616, 1936, 44, 1))
        buf5 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [input_5, input_6], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_2.run(buf5, primals_7, 1982464, grid=grid(1982464), stream=stream0)
        del primals_7
        # Topologically Sorted Source Nodes: [input_7], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(buf5, primals_8, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 128, 88, 88), (991232, 7744, 88, 1))
        buf7 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [input_7, input_8], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_3.run(buf7, primals_9, 3964928, grid=grid(3964928), stream=stream0)
        del primals_9
        # Topologically Sorted Source Nodes: [input_9], Original ATen: [aten.convolution]
        buf8 = extern_kernels.convolution(buf7, primals_10, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 3, 88, 88), (23232, 7744, 88, 1))
        buf9 = buf8; del buf8  # reuse
        # Topologically Sorted Source Nodes: [input_9], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_4.run(buf9, primals_11, 92928, grid=grid(92928), stream=stream0)
        del primals_11
    return (buf9, primals_1, primals_3, primals_4, primals_6, primals_8, primals_10, buf1, buf3, buf5, buf7, )


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, 1024, 8, 8), (65536, 64, 8, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((1024, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 64, 4, 4), (1024, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((1024, 512, 4, 4), (8192, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((512, 256, 4, 4), (4096, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((256, 128, 4, 4), (2048, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((128, 3, 1, 1), (3, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((3, ), (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
import torch.nn as nn


def get_activation(s_act):
    if s_act == 'relu':
        return nn.ReLU(inplace=True)
    elif s_act == 'sigmoid':
        return nn.Sigmoid()
    elif s_act == 'softplus':
        return nn.Softplus()
    elif s_act == 'linear':
        return None
    elif s_act == 'tanh':
        return nn.Tanh()
    elif s_act == 'leakyrelu':
        return nn.LeakyReLU(0.2, inplace=True)
    elif s_act == 'softmax':
        return nn.Softmax(dim=1)
    elif s_act == 'spherical':
        return SphericalActivation()
    else:
        raise ValueError(f'Unexpected activation: {s_act}')


class SphericalActivation(nn.Module):

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

    def forward(self, x):
        return x / x.norm(p=2, dim=1, keepdim=True)


class DeConvNet64(nn.Module):
    """ConvNet architecture for CelebA64 following Ghosh et al., 2019"""

    def __init__(self, in_chan=64, out_chan=3, nh=32, out_activation=
        'linear', activation='relu', num_groups=None, use_bn=False):
        super().__init__()
        self.fc1 = nn.ConvTranspose2d(in_chan, nh * 32, kernel_size=8, bias
            =True)
        self.conv1 = nn.ConvTranspose2d(nh * 32, nh * 16, kernel_size=4,
            stride=2, padding=1, bias=True)
        self.conv2 = nn.ConvTranspose2d(nh * 16, nh * 8, kernel_size=4,
            stride=2, padding=1, bias=True)
        self.conv3 = nn.ConvTranspose2d(nh * 8, nh * 4, kernel_size=4,
            stride=2, padding=1, bias=True)
        self.conv4 = nn.ConvTranspose2d(nh * 4, out_chan, kernel_size=1,
            bias=True)
        self.in_chan, self.out_chan = in_chan, out_chan
        self.num_groups = num_groups
        self.use_bn = use_bn
        layers = []
        layers.append(self.fc1)
        if num_groups is not None:
            layers.append(self.get_norm_layer(num_channels=nh * 32))
        layers.append(get_activation(activation))
        layers.append(self.conv1)
        if num_groups is not None:
            layers.append(self.get_norm_layer(num_channels=nh * 16))
        layers.append(get_activation(activation))
        layers.append(self.conv2)
        if num_groups is not None:
            layers.append(self.get_norm_layer(num_channels=nh * 8))
        layers.append(get_activation(activation))
        layers.append(self.conv3)
        if num_groups is not None:
            layers.append(self.get_norm_layer(num_channels=nh * 4))
        layers.append(get_activation(activation))
        layers.append(self.conv4)
        out_activation = get_activation(out_activation)
        if out_activation is not None:
            layers.append(out_activation)
        self.net = nn.Sequential(*layers)

    def forward(self, x):
        return self.net(x)

    def get_norm_layer(self, num_channels):
        if self.num_groups is not None:
            return nn.GroupNorm(num_groups=self.num_groups, num_channels=
                num_channels)
        elif self.use_bn:
            return nn.BatchNorm2d(num_channels)


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


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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride


@triton.jit
def triton_poi_fused_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 121 % 1024
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, 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, None)


@triton.jit
def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 484 % 512
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, 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, None)


@triton.jit
def triton_poi_fused_convolution_relu_2(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 1936 % 256
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, 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, None)


@triton.jit
def triton_poi_fused_convolution_relu_3(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 7744 % 128
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, 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, None)


@triton.jit
def triton_poi_fused_convolution_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 92928
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 7744 % 3
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, 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, (64, 1024, 8, 8), (65536, 64, 8, 1))
    assert_size_stride(primals_2, (1024,), (1,))
    assert_size_stride(primals_3, (4, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_4, (1024, 512, 4, 4), (8192, 16, 4, 1))
    assert_size_stride(primals_5, (512,), (1,))
    assert_size_stride(primals_6, (512, 256, 4, 4), (4096, 16, 4, 1))
    assert_size_stride(primals_7, (256,), (1,))
    assert_size_stride(primals_8, (256, 128, 4, 4), (2048, 16, 4, 1))
    assert_size_stride(primals_9, (128,), (1,))
    assert_size_stride(primals_10, (128, 3, 1, 1), (3, 1, 1, 1))
    assert_size_stride(primals_11, (3,), (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=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 1024, 11, 11), (123904, 121, 11, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(495616)](buf1, primals_2, 
            495616, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_2
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 512, 22, 22), (247808, 484, 22, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_relu_1[grid(991232)](buf3, primals_5, 
            991232, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_5
        buf4 = extern_kernels.convolution(buf3, primals_6, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 256, 44, 44), (495616, 1936, 44, 1))
        buf5 = buf4
        del buf4
        triton_poi_fused_convolution_relu_2[grid(1982464)](buf5, primals_7,
            1982464, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_7
        buf6 = extern_kernels.convolution(buf5, primals_8, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 128, 88, 88), (991232, 7744, 88, 1))
        buf7 = buf6
        del buf6
        triton_poi_fused_convolution_relu_3[grid(3964928)](buf7, primals_9,
            3964928, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_9
        buf8 = extern_kernels.convolution(buf7, primals_10, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 3, 88, 88), (23232, 7744, 88, 1))
        buf9 = buf8
        del buf8
        triton_poi_fused_convolution_4[grid(92928)](buf9, primals_11, 92928,
            XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_11
    return (buf9, primals_1, primals_3, primals_4, primals_6, primals_8,
        primals_10, buf1, buf3, buf5, buf7)


def get_activation(s_act):
    if s_act == 'relu':
        return nn.ReLU(inplace=True)
    elif s_act == 'sigmoid':
        return nn.Sigmoid()
    elif s_act == 'softplus':
        return nn.Softplus()
    elif s_act == 'linear':
        return None
    elif s_act == 'tanh':
        return nn.Tanh()
    elif s_act == 'leakyrelu':
        return nn.LeakyReLU(0.2, inplace=True)
    elif s_act == 'softmax':
        return nn.Softmax(dim=1)
    elif s_act == 'spherical':
        return SphericalActivation()
    else:
        raise ValueError(f'Unexpected activation: {s_act}')


class SphericalActivation(nn.Module):

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

    def forward(self, x):
        return x / x.norm(p=2, dim=1, keepdim=True)


class DeConvNet64New(nn.Module):
    """ConvNet architecture for CelebA64 following Ghosh et al., 2019"""

    def __init__(self, in_chan=64, out_chan=3, nh=32, out_activation=
        'linear', activation='relu', num_groups=None, use_bn=False):
        super().__init__()
        self.fc1 = nn.ConvTranspose2d(in_chan, nh * 32, kernel_size=8, bias
            =True)
        self.conv1 = nn.ConvTranspose2d(nh * 32, nh * 16, kernel_size=4,
            stride=2, padding=1, bias=True)
        self.conv2 = nn.ConvTranspose2d(nh * 16, nh * 8, kernel_size=4,
            stride=2, padding=1, bias=True)
        self.conv3 = nn.ConvTranspose2d(nh * 8, nh * 4, kernel_size=4,
            stride=2, padding=1, bias=True)
        self.conv4 = nn.ConvTranspose2d(nh * 4, out_chan, kernel_size=1,
            bias=True)
        self.in_chan, self.out_chan = in_chan, out_chan
        self.num_groups = num_groups
        self.use_bn = use_bn
        layers = []
        layers.append(self.fc1)
        if num_groups is not None:
            layers.append(self.get_norm_layer(num_channels=nh * 32))
        layers.append(get_activation(activation))
        layers.append(self.conv1)
        if num_groups is not None:
            layers.append(self.get_norm_layer(num_channels=nh * 16))
        layers.append(get_activation(activation))
        layers.append(self.conv2)
        if num_groups is not None:
            layers.append(self.get_norm_layer(num_channels=nh * 8))
        layers.append(get_activation(activation))
        layers.append(self.conv3)
        if num_groups is not None:
            layers.append(self.get_norm_layer(num_channels=nh * 4))
        layers.append(get_activation(activation))
        layers.append(self.conv4)
        out_activation = get_activation(out_activation)
        if out_activation is not None:
            layers.append(out_activation)
        self.net = nn.Sequential(*layers)

    def get_norm_layer(self, num_channels):
        if self.num_groups is not None:
            return nn.GroupNorm(num_groups=self.num_groups, num_channels=
                num_channels)
        elif self.use_bn:
            return nn.BatchNorm2d(num_channels)

    def forward(self, input_0):
        primals_1 = self.fc1.weight
        primals_2 = self.fc1.bias
        primals_4 = self.conv1.weight
        primals_5 = self.conv1.bias
        primals_6 = self.conv2.weight
        primals_7 = self.conv2.bias
        primals_8 = self.conv3.weight
        primals_9 = self.conv3.bias
        primals_10 = self.conv4.weight
        primals_11 = self.conv4.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]
