# 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/k3/ck374ifm7xqzq3zf5anifcsi7am3vfp7stlut7cdp24o6ynemmw7.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.tanh]
# Source node to ATen node mapping:
#   x_1 => tanh
# Graph fragment:
#   %tanh : [num_users=3] = call_function[target=torch.ops.aten.tanh.default](args = (%convolution,), kwargs = {})
triton_poi_fused_tanh_0 = async_compile.triton('triton_poi_fused_tanh_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=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_tanh_0', 'mutated_arg_names': ['in_out_ptr0'], '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_tanh_0(in_out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 262144
    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_out_ptr0 + (x0), None)
    tmp1 = libdevice.tanh(tmp0)
    tl.store(in_out_ptr0 + (x0), tmp1, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/7n/c7nc4ei5jbiegylt7zvksagtawnvef3e2su2lrpmlcszha2eurvs.py
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.avg_pool2d]
# Source node to ATen node mapping:
#   x_2 => avg_pool2d
# Graph fragment:
#   %avg_pool2d : [num_users=2] = call_function[target=torch.ops.aten.avg_pool2d.default](args = (%tanh, [2, 2], [2, 2]), kwargs = {})
triton_poi_fused_avg_pool2d_1 = async_compile.triton('triton_poi_fused_avg_pool2d_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_avg_pool2d_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_avg_pool2d_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 65536
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 32
    x1 = (xindex // 32)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (128*x1)), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (128*x1)), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (64 + (2*x0) + (128*x1)), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (65 + (2*x0) + (128*x1)), None, eviction_policy='evict_last')
    tmp2 = tmp1 + tmp0
    tmp4 = tmp3 + tmp2
    tmp6 = tmp5 + tmp4
    tmp7 = 0.25
    tmp8 = tmp6 * tmp7
    tl.store(out_ptr0 + (x2), tmp8, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/2c/c2c3m5nrhajaww3lqpfl7wqtykeubavfo2gvixofujckkogirtwf.py
# Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.tanh]
# Source node to ATen node mapping:
#   x_4 => tanh_1
# Graph fragment:
#   %tanh_1 : [num_users=3] = call_function[target=torch.ops.aten.tanh.default](args = (%convolution_1,), kwargs = {})
triton_poi_fused_tanh_2 = async_compile.triton('triton_poi_fused_tanh_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: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_tanh_2', 'mutated_arg_names': ['in_out_ptr0'], '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_tanh_2(in_out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 131072
    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_out_ptr0 + (x0), None)
    tmp1 = libdevice.tanh(tmp0)
    tl.store(in_out_ptr0 + (x0), tmp1, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/yi/cyilci5iwl6uzduxauto3l4jxvzubv2m5kukyyq6y2lxk4chrq3w.py
# Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.avg_pool2d]
# Source node to ATen node mapping:
#   x_5 => avg_pool2d_1
# Graph fragment:
#   %avg_pool2d_1 : [num_users=2] = call_function[target=torch.ops.aten.avg_pool2d.default](args = (%tanh_1, [2, 2], [2, 2]), kwargs = {})
triton_poi_fused_avg_pool2d_3 = async_compile.triton('triton_poi_fused_avg_pool2d_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=[32768], 
    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_avg_pool2d_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_avg_pool2d_3(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 16
    x1 = (xindex // 16)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (64*x1)), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (64*x1)), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (32 + (2*x0) + (64*x1)), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (33 + (2*x0) + (64*x1)), None, eviction_policy='evict_last')
    tmp2 = tmp1 + tmp0
    tmp4 = tmp3 + tmp2
    tmp6 = tmp5 + tmp4
    tmp7 = 0.25
    tmp8 = tmp6 * tmp7
    tl.store(out_ptr0 + (x2), tmp8, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/l3/cl3nar3gqmqdhh6m54jscnzlcqhsijuz7be4zw3xzraofrjxmkjx.py
# Topologically Sorted Source Nodes: [x_7], Original ATen: [aten.tanh]
# Source node to ATen node mapping:
#   x_7 => tanh_2
# Graph fragment:
#   %tanh_2 : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%convolution_3,), kwargs = {})
triton_poi_fused_tanh_4 = async_compile.triton('triton_poi_fused_tanh_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=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_tanh_4', 'mutated_arg_names': ['in_out_ptr0'], '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_tanh_4(in_out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 65536
    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_out_ptr0 + (x0), None)
    tmp1 = libdevice.tanh(tmp0)
    tl.store(in_out_ptr0 + (x0), tmp1, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/6t/c6typ6iqbxb52aakqwbsrda2tdivcrgu6pok5ukd2qozrj5gs4tj.py
# Topologically Sorted Source Nodes: [x_8], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x_8 => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%tanh, %convolution_2, %convolution_4], 1), kwargs = {})
triton_poi_fused_cat_5 = async_compile.triton('triton_poi_fused_cat_5', '''
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: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_5(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1835008
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 4096) % 112
    x0 = xindex % 4096
    x2 = (xindex // 458752)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 16, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (4096*x1) + (65536*x2)), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 48, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + (4096*((-16) + x1)) + (131072*x2)), tmp9, other=0.0)
    tmp11 = tl.load(in_ptr2 + ((-16) + x1), tmp9, eviction_policy='evict_last', other=0.0)
    tmp12 = tmp10 + tmp11
    tmp13 = tl.full(tmp12.shape, 0.0, tmp12.dtype)
    tmp14 = tl.where(tmp9, tmp12, tmp13)
    tmp15 = tmp0 >= tmp7
    tmp16 = tl.full([1], 112, tl.int64)
    tmp17 = tmp0 < tmp16
    tmp18 = tl.load(in_ptr3 + (x0 + (4096*((-48) + x1)) + (262144*x2)), tmp15, other=0.0)
    tmp19 = tl.where(tmp9, tmp14, tmp18)
    tmp20 = tl.where(tmp4, tmp5, tmp19)
    tl.store(out_ptr0 + (x3), tmp20, 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, primals_7, primals_8 = args
    args.clear()
    assert_size_stride(primals_1, (16, 1, 3, 7), (21, 21, 7, 1))
    assert_size_stride(primals_2, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(primals_3, (32, 16, 3, 5), (240, 15, 5, 1))
    assert_size_stride(primals_4, (32, 1, 4, 4), (16, 16, 4, 1))
    assert_size_stride(primals_5, (32, ), (1, ))
    assert_size_stride(primals_6, (64, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_7, (64, 1, 8, 8), (64, 64, 8, 1))
    assert_size_stride(primals_8, (1, 112, 3, 3), (1008, 9, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(1, 1), padding=(1, 3), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.tanh]
        stream0 = get_raw_stream(0)
        triton_poi_fused_tanh_0.run(buf1, 262144, grid=grid(262144), stream=stream0)
        buf2 = empty_strided_cuda((4, 16, 32, 32), (16384, 1024, 32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.avg_pool2d]
        triton_poi_fused_avg_pool2d_1.run(buf1, buf2, 65536, grid=grid(65536), stream=stream0)
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(buf2, primals_3, stride=(1, 1), padding=(1, 2), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 32, 32, 32), (32768, 1024, 32, 1))
        buf4 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.tanh]
        triton_poi_fused_tanh_2.run(buf4, 131072, grid=grid(131072), stream=stream0)
        # Topologically Sorted Source Nodes: [stack2], Original ATen: [aten.convolution]
        buf5 = extern_kernels.convolution(buf4, primals_4, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=32, bias=None)
        assert_size_stride(buf5, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf6 = empty_strided_cuda((4, 32, 16, 16), (8192, 256, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.avg_pool2d]
        triton_poi_fused_avg_pool2d_3.run(buf4, buf6, 32768, grid=grid(32768), stream=stream0)
        # Topologically Sorted Source Nodes: [x_6], Original ATen: [aten.convolution]
        buf7 = extern_kernels.convolution(buf6, primals_6, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (4, 64, 16, 16), (16384, 256, 16, 1))
        buf8 = buf7; del buf7  # reuse
        # Topologically Sorted Source Nodes: [x_7], Original ATen: [aten.tanh]
        triton_poi_fused_tanh_4.run(buf8, 65536, grid=grid(65536), stream=stream0)
        # Topologically Sorted Source Nodes: [stack3], Original ATen: [aten.convolution]
        buf9 = extern_kernels.convolution(buf8, primals_7, stride=(4, 4), padding=(2, 2), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=64, bias=None)
        assert_size_stride(buf9, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf10 = empty_strided_cuda((4, 112, 64, 64), (458752, 4096, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_8], Original ATen: [aten.cat]
        triton_poi_fused_cat_5.run(buf1, buf5, primals_5, buf9, buf10, 1835008, grid=grid(1835008), stream=stream0)
        del buf5
        del buf9
        del primals_5
        # Topologically Sorted Source Nodes: [output], Original ATen: [aten.convolution]
        buf11 = extern_kernels.convolution(buf10, primals_8, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf11, (4, 1, 64, 64), (4096, 4096, 64, 1))
    return (buf11, primals_1, primals_2, primals_3, primals_4, primals_6, primals_7, primals_8, buf1, buf2, buf4, buf6, buf8, buf10, )


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((16, 1, 3, 7), (21, 21, 7, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 1, 64, 64), (4096, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((32, 16, 3, 5), (240, 15, 5, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((32, 1, 4, 4), (16, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((64, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((64, 1, 8, 8), (64, 64, 8, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((1, 112, 3, 3), (1008, 9, 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])
    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
from itertools import product as product


class FeatNet(nn.Module):

    def __init__(self):
        super(FeatNet, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=
            (3, 7), stride=1, padding=(1, 3), bias=False)
        self.tanh1 = nn.Tanh()
        self.Pool1 = nn.AvgPool2d(kernel_size=(2, 2), stride=2)
        self.conv2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size
            =(3, 5), stride=1, padding=(1, 2), bias=False)
        self.tanh2 = nn.Tanh()
        self.Upsample2 = nn.ConvTranspose2d(in_channels=32, out_channels=32,
            kernel_size=4, stride=2, padding=1, groups=32)
        self.Pool2 = nn.AvgPool2d(kernel_size=(2, 2), stride=2)
        self.conv3 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size
            =(3, 3), stride=1, padding=(1, 1), bias=False)
        self.tanh3 = nn.Tanh()
        self.Upsample3 = nn.ConvTranspose2d(in_channels=64, out_channels=64,
            kernel_size=8, stride=4, padding=2, bias=False, groups=64)
        self.conv4 = nn.Conv2d(in_channels=112, out_channels=1, kernel_size
            =3, stride=1, padding=1, bias=False)

    def forward(self, x):
        x = self.conv1(x)
        x = self.tanh1(x)
        stack1 = x
        x = self.Pool1(x)
        x = self.conv2(x)
        x = self.tanh2(x)
        stack2 = self.Upsample2(x)
        x = self.Pool2(x)
        x = self.conv3(x)
        x = self.tanh3(x)
        stack3 = self.Upsample3(x)
        x = torch.concat((stack1, stack2, stack3), 1)
        output = self.conv4(x)
        return output


def get_inputs():
    return [torch.rand([4, 1, 64, 64])]


def get_init_inputs():
    return [[], {}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from itertools import product as product
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_tanh_0(in_out_ptr0, 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_out_ptr0 + x0, None)
    tmp1 = libdevice.tanh(tmp0)
    tl.store(in_out_ptr0 + x0, tmp1, None)


@triton.jit
def triton_poi_fused_avg_pool2d_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 32
    x1 = xindex // 32
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 128 * x1), None, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 128 * x1), None, eviction_policy
        ='evict_last')
    tmp3 = tl.load(in_ptr0 + (64 + 2 * x0 + 128 * x1), None,
        eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (65 + 2 * x0 + 128 * x1), None,
        eviction_policy='evict_last')
    tmp2 = tmp1 + tmp0
    tmp4 = tmp3 + tmp2
    tmp6 = tmp5 + tmp4
    tmp7 = 0.25
    tmp8 = tmp6 * tmp7
    tl.store(out_ptr0 + x2, tmp8, None)


@triton.jit
def triton_poi_fused_tanh_2(in_out_ptr0, 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_out_ptr0 + x0, None)
    tmp1 = libdevice.tanh(tmp0)
    tl.store(in_out_ptr0 + x0, tmp1, None)


@triton.jit
def triton_poi_fused_avg_pool2d_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 64 * x1), None, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 64 * x1), None, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (32 + 2 * x0 + 64 * x1), None, eviction_policy
        ='evict_last')
    tmp5 = tl.load(in_ptr0 + (33 + 2 * x0 + 64 * x1), None, eviction_policy
        ='evict_last')
    tmp2 = tmp1 + tmp0
    tmp4 = tmp3 + tmp2
    tmp6 = tmp5 + tmp4
    tmp7 = 0.25
    tmp8 = tmp6 * tmp7
    tl.store(out_ptr0 + x2, tmp8, None)


@triton.jit
def triton_poi_fused_tanh_4(in_out_ptr0, 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_out_ptr0 + x0, None)
    tmp1 = libdevice.tanh(tmp0)
    tl.store(in_out_ptr0 + x0, tmp1, None)


@triton.jit
def triton_poi_fused_cat_5(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 4096 % 112
    x0 = xindex % 4096
    x2 = xindex // 458752
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 16, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 4096 * x1 + 65536 * x2), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 48, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + 4096 * (-16 + x1) + 131072 * x2), tmp9,
        other=0.0)
    tmp11 = tl.load(in_ptr2 + (-16 + x1), tmp9, eviction_policy=
        'evict_last', other=0.0)
    tmp12 = tmp10 + tmp11
    tmp13 = tl.full(tmp12.shape, 0.0, tmp12.dtype)
    tmp14 = tl.where(tmp9, tmp12, tmp13)
    tmp15 = tmp0 >= tmp7
    tl.full([1], 112, tl.int64)
    tmp18 = tl.load(in_ptr3 + (x0 + 4096 * (-48 + x1) + 262144 * x2), tmp15,
        other=0.0)
    tmp19 = tl.where(tmp9, tmp14, tmp18)
    tmp20 = tl.where(tmp4, tmp5, tmp19)
    tl.store(out_ptr0 + x3, tmp20, None)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8) = args
    args.clear()
    assert_size_stride(primals_1, (16, 1, 3, 7), (21, 21, 7, 1))
    assert_size_stride(primals_2, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(primals_3, (32, 16, 3, 5), (240, 15, 5, 1))
    assert_size_stride(primals_4, (32, 1, 4, 4), (16, 16, 4, 1))
    assert_size_stride(primals_5, (32,), (1,))
    assert_size_stride(primals_6, (64, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_7, (64, 1, 8, 8), (64, 64, 8, 1))
    assert_size_stride(primals_8, (1, 112, 3, 3), (1008, 9, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(1, 
            1), padding=(1, 3), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_tanh_0[grid(262144)](buf1, 262144, XBLOCK=512,
            num_warps=8, num_stages=1)
        buf2 = empty_strided_cuda((4, 16, 32, 32), (16384, 1024, 32, 1),
            torch.float32)
        triton_poi_fused_avg_pool2d_1[grid(65536)](buf1, buf2, 65536,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf3 = extern_kernels.convolution(buf2, primals_3, stride=(1, 1),
            padding=(1, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 32, 32, 32), (32768, 1024, 32, 1))
        buf4 = buf3
        del buf3
        triton_poi_fused_tanh_2[grid(131072)](buf4, 131072, XBLOCK=512,
            num_warps=8, num_stages=1)
        buf5 = extern_kernels.convolution(buf4, primals_4, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=32, bias=None)
        assert_size_stride(buf5, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf6 = empty_strided_cuda((4, 32, 16, 16), (8192, 256, 16, 1),
            torch.float32)
        triton_poi_fused_avg_pool2d_3[grid(32768)](buf4, buf6, 32768,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf7 = extern_kernels.convolution(buf6, primals_6, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (4, 64, 16, 16), (16384, 256, 16, 1))
        buf8 = buf7
        del buf7
        triton_poi_fused_tanh_4[grid(65536)](buf8, 65536, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf9 = extern_kernels.convolution(buf8, primals_7, stride=(4, 4),
            padding=(2, 2), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=64, bias=None)
        assert_size_stride(buf9, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf10 = empty_strided_cuda((4, 112, 64, 64), (458752, 4096, 64, 1),
            torch.float32)
        triton_poi_fused_cat_5[grid(1835008)](buf1, buf5, primals_5, buf9,
            buf10, 1835008, XBLOCK=1024, num_warps=4, num_stages=1)
        del buf5
        del buf9
        del primals_5
        buf11 = extern_kernels.convolution(buf10, primals_8, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf11, (4, 1, 64, 64), (4096, 4096, 64, 1))
    return (buf11, primals_1, primals_2, primals_3, primals_4, primals_6,
        primals_7, primals_8, buf1, buf2, buf4, buf6, buf8, buf10)


class FeatNetNew(nn.Module):

    def __init__(self):
        super(FeatNetNew, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=
            (3, 7), stride=1, padding=(1, 3), bias=False)
        self.tanh1 = nn.Tanh()
        self.Pool1 = nn.AvgPool2d(kernel_size=(2, 2), stride=2)
        self.conv2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size
            =(3, 5), stride=1, padding=(1, 2), bias=False)
        self.tanh2 = nn.Tanh()
        self.Upsample2 = nn.ConvTranspose2d(in_channels=32, out_channels=32,
            kernel_size=4, stride=2, padding=1, groups=32)
        self.Pool2 = nn.AvgPool2d(kernel_size=(2, 2), stride=2)
        self.conv3 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size
            =(3, 3), stride=1, padding=(1, 1), bias=False)
        self.tanh3 = nn.Tanh()
        self.Upsample3 = nn.ConvTranspose2d(in_channels=64, out_channels=64,
            kernel_size=8, stride=4, padding=2, bias=False, groups=64)
        self.conv4 = nn.Conv2d(in_channels=112, out_channels=1, kernel_size
            =3, stride=1, padding=1, bias=False)

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_3 = self.conv2.weight
        primals_4 = self.Upsample2.weight
        primals_5 = self.Upsample2.bias
        primals_6 = self.conv3.weight
        primals_7 = self.Upsample3.weight
        primals_8 = self.conv4.weight
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8])
        return output[0]
