# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_4/inductor_cache/xs/cxsx2jlz7qi3eerscrmq6daj5pyplc3rcuj6cilxq2zofbhntrow.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
#   x_1 => gt, mul, where
# Graph fragment:
#   %gt : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_3, 0), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_3, 0.01), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %view_3, %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=[4096], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: '*fp32', 4: '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), 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': 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_leaky_relu_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2560
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 40
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + (x2), tmp4, xmask)
    tl.store(out_ptr1 + (x2), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ui/cui6sxfzttgecmgtvnw2gevr54jsmp7y3t6fwxqjhor3rpwdmuui.py
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
#   x_2 => gt_1, mul_1, where_1
# Graph fragment:
#   %gt_1 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_5, 0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_5, 0.01), kwargs = {})
#   %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %view_5, %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=[4096], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: '*fp32', 4: '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), 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': 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_leaky_relu_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 3200
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 50
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + (x2), tmp4, xmask)
    tl.store(out_ptr1 + (x2), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/2o/c2osggm77pqxxx5r32gyjemu3ryhpfi3fx5zzrm7y43ln3qjxt2k.py
# Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
#   x_3 => gt_2, mul_2, where_2
# Graph fragment:
#   %gt_2 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_7, 0), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_7, 0.01), kwargs = {})
#   %where_2 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_2, %view_7, %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=[2048], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: '*fp32', 4: '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), 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': 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_leaky_relu_2(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1920
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 30
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + (x2), tmp4, xmask)
    tl.store(out_ptr1 + (x2), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/gc/cgcgqaqngs7z7n2pbstvkxhguyvvx7auk4jaz3wlw25y3neeo5vn.py
# Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
#   x_4 => gt_3, mul_3, where_3
# Graph fragment:
#   %gt_3 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_9, 0), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_9, 0.01), kwargs = {})
#   %where_3 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_3, %view_9, %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=[1024], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: '*fp32', 4: '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), 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': 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_leaky_relu_3(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 12
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + (x2), tmp4, xmask)
    tl.store(out_ptr1 + (x2), tmp7, 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, primals_12, primals_13 = args
    args.clear()
    assert_size_stride(primals_1, (20, 4), (4, 1))
    assert_size_stride(primals_2, (20, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (40, 20), (20, 1))
    assert_size_stride(primals_5, (40, ), (1, ))
    assert_size_stride(primals_6, (50, 40), (40, 1))
    assert_size_stride(primals_7, (50, ), (1, ))
    assert_size_stride(primals_8, (30, 50), (50, 1))
    assert_size_stride(primals_9, (30, ), (1, ))
    assert_size_stride(primals_10, (12, 30), (30, 1))
    assert_size_stride(primals_11, (12, ), (1, ))
    assert_size_stride(primals_12, (2, 12), (12, 1))
    assert_size_stride(primals_13, (2, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 20), (20, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 20), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((64, 40), (40, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf0, reinterpret_tensor(primals_4, (20, 40), (1, 20), 0), out=buf1)
        buf2 = empty_strided_cuda((4, 4, 4, 40), (640, 160, 40, 1), torch.bool)
        buf3 = empty_strided_cuda((4, 4, 4, 40), (640, 160, 40, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.leaky_relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_leaky_relu_0.run(buf1, primals_5, buf2, buf3, 2560, grid=grid(2560), stream=stream0)
        del buf1
        del primals_5
        buf4 = empty_strided_cuda((64, 50), (50, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf3, (64, 40), (40, 1), 0), reinterpret_tensor(primals_6, (40, 50), (1, 40), 0), out=buf4)
        buf5 = empty_strided_cuda((4, 4, 4, 50), (800, 200, 50, 1), torch.bool)
        buf6 = empty_strided_cuda((4, 4, 4, 50), (800, 200, 50, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.leaky_relu]
        triton_poi_fused_leaky_relu_1.run(buf4, primals_7, buf5, buf6, 3200, grid=grid(3200), stream=stream0)
        del buf4
        del primals_7
        buf7 = empty_strided_cuda((64, 30), (30, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf6, (64, 50), (50, 1), 0), reinterpret_tensor(primals_8, (50, 30), (1, 50), 0), out=buf7)
        buf8 = empty_strided_cuda((4, 4, 4, 30), (480, 120, 30, 1), torch.bool)
        buf9 = empty_strided_cuda((4, 4, 4, 30), (480, 120, 30, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.leaky_relu]
        triton_poi_fused_leaky_relu_2.run(buf7, primals_9, buf8, buf9, 1920, grid=grid(1920), stream=stream0)
        del buf7
        del primals_9
        buf10 = empty_strided_cuda((64, 12), (12, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf9, (64, 30), (30, 1), 0), reinterpret_tensor(primals_10, (30, 12), (1, 30), 0), out=buf10)
        buf11 = empty_strided_cuda((4, 4, 4, 12), (192, 48, 12, 1), torch.bool)
        buf12 = empty_strided_cuda((4, 4, 4, 12), (192, 48, 12, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.leaky_relu]
        triton_poi_fused_leaky_relu_3.run(buf10, primals_11, buf11, buf12, 768, grid=grid(768), stream=stream0)
        del buf10
        del primals_11
        buf13 = empty_strided_cuda((64, 2), (2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_13, reinterpret_tensor(buf12, (64, 12), (12, 1), 0), reinterpret_tensor(primals_12, (12, 2), (1, 12), 0), alpha=1, beta=1, out=buf13)
        del primals_13
    return (reinterpret_tensor(buf13, (4, 4, 4, 2), (32, 8, 2, 1), 0), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf0, buf2, reinterpret_tensor(buf3, (64, 40), (40, 1), 0), buf5, reinterpret_tensor(buf6, (64, 50), (50, 1), 0), buf8, reinterpret_tensor(buf9, (64, 30), (30, 1), 0), buf11, reinterpret_tensor(buf12, (64, 12), (12, 1), 0), primals_12, primals_10, primals_8, primals_6, primals_4, )


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((20, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((20, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((40, 20), (20, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((40, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((50, 40), (40, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((50, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((30, 50), (50, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((30, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((12, 30), (30, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((12, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((2, 12), (12, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = 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, primals_12, primals_13])
    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
import torch.nn.functional as F


class ActorNet(nn.Module):

    def __init__(self):
        super(ActorNet, self).__init__()
        self.fc1 = nn.Linear(4, 20)
        self.fc2 = nn.Linear(20, 40)
        self.fc3 = nn.Linear(40, 50)
        self.fc4 = nn.Linear(50, 30)
        self.fc5 = nn.Linear(30, 12)
        self.fc6 = nn.Linear(12, 2)

    def forward(self, x):
        x = self.fc1(x)
        x = F.leaky_relu(self.fc2(x))
        x = F.leaky_relu(self.fc3(x))
        x = F.leaky_relu(self.fc4(x))
        x = F.leaky_relu(self.fc5(x))
        x = self.fc6(x)
        return x


def get_inputs():
    return [torch.rand([4, 4, 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
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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_leaky_relu_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 2560
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 40
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr1 + x2, tmp7, xmask)


@triton.jit
def triton_poi_fused_leaky_relu_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 3200
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 50
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr1 + x2, tmp7, xmask)


@triton.jit
def triton_poi_fused_leaky_relu_2(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 1920
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 30
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr1 + x2, tmp7, xmask)


@triton.jit
def triton_poi_fused_leaky_relu_3(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 12
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr1 + x2, tmp7, 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, primals_12,
        primals_13) = args
    args.clear()
    assert_size_stride(primals_1, (20, 4), (4, 1))
    assert_size_stride(primals_2, (20,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (40, 20), (20, 1))
    assert_size_stride(primals_5, (40,), (1,))
    assert_size_stride(primals_6, (50, 40), (40, 1))
    assert_size_stride(primals_7, (50,), (1,))
    assert_size_stride(primals_8, (30, 50), (50, 1))
    assert_size_stride(primals_9, (30,), (1,))
    assert_size_stride(primals_10, (12, 30), (30, 1))
    assert_size_stride(primals_11, (12,), (1,))
    assert_size_stride(primals_12, (2, 12), (12, 1))
    assert_size_stride(primals_13, (2,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 20), (20, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 20), (1, 4), 
            0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((64, 40), (40, 1), torch.float32)
        extern_kernels.mm(buf0, reinterpret_tensor(primals_4, (20, 40), (1,
            20), 0), out=buf1)
        buf2 = empty_strided_cuda((4, 4, 4, 40), (640, 160, 40, 1), torch.bool)
        buf3 = empty_strided_cuda((4, 4, 4, 40), (640, 160, 40, 1), torch.
            float32)
        get_raw_stream(0)
        triton_poi_fused_leaky_relu_0[grid(2560)](buf1, primals_5, buf2,
            buf3, 2560, XBLOCK=128, num_warps=4, num_stages=1)
        del buf1
        del primals_5
        buf4 = empty_strided_cuda((64, 50), (50, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (64, 40), (40, 1), 0),
            reinterpret_tensor(primals_6, (40, 50), (1, 40), 0), out=buf4)
        buf5 = empty_strided_cuda((4, 4, 4, 50), (800, 200, 50, 1), torch.bool)
        buf6 = empty_strided_cuda((4, 4, 4, 50), (800, 200, 50, 1), torch.
            float32)
        triton_poi_fused_leaky_relu_1[grid(3200)](buf4, primals_7, buf5,
            buf6, 3200, XBLOCK=256, num_warps=4, num_stages=1)
        del buf4
        del primals_7
        buf7 = empty_strided_cuda((64, 30), (30, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf6, (64, 50), (50, 1), 0),
            reinterpret_tensor(primals_8, (50, 30), (1, 50), 0), out=buf7)
        buf8 = empty_strided_cuda((4, 4, 4, 30), (480, 120, 30, 1), torch.bool)
        buf9 = empty_strided_cuda((4, 4, 4, 30), (480, 120, 30, 1), torch.
            float32)
        triton_poi_fused_leaky_relu_2[grid(1920)](buf7, primals_9, buf8,
            buf9, 1920, XBLOCK=128, num_warps=4, num_stages=1)
        del buf7
        del primals_9
        buf10 = empty_strided_cuda((64, 12), (12, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf9, (64, 30), (30, 1), 0),
            reinterpret_tensor(primals_10, (30, 12), (1, 30), 0), out=buf10)
        buf11 = empty_strided_cuda((4, 4, 4, 12), (192, 48, 12, 1), torch.bool)
        buf12 = empty_strided_cuda((4, 4, 4, 12), (192, 48, 12, 1), torch.
            float32)
        triton_poi_fused_leaky_relu_3[grid(768)](buf10, primals_11, buf11,
            buf12, 768, XBLOCK=128, num_warps=4, num_stages=1)
        del buf10
        del primals_11
        buf13 = empty_strided_cuda((64, 2), (2, 1), torch.float32)
        extern_kernels.addmm(primals_13, reinterpret_tensor(buf12, (64, 12),
            (12, 1), 0), reinterpret_tensor(primals_12, (12, 2), (1, 12), 0
            ), alpha=1, beta=1, out=buf13)
        del primals_13
    return reinterpret_tensor(buf13, (4, 4, 4, 2), (32, 8, 2, 1), 0
        ), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), buf0, buf2, reinterpret_tensor(buf3, (64, 40), (40, 1), 0
        ), buf5, reinterpret_tensor(buf6, (64, 50), (50, 1), 0
        ), buf8, reinterpret_tensor(buf9, (64, 30), (30, 1), 0
        ), buf11, reinterpret_tensor(buf12, (64, 12), (12, 1), 0
        ), primals_12, primals_10, primals_8, primals_6, primals_4


class ActorNetNew(nn.Module):

    def __init__(self):
        super(ActorNetNew, self).__init__()
        self.fc1 = nn.Linear(4, 20)
        self.fc2 = nn.Linear(20, 40)
        self.fc3 = nn.Linear(40, 50)
        self.fc4 = nn.Linear(50, 30)
        self.fc5 = nn.Linear(30, 12)
        self.fc6 = nn.Linear(12, 2)

    def forward(self, input_0):
        primals_1 = self.fc1.weight
        primals_2 = self.fc1.bias
        primals_4 = self.fc2.weight
        primals_5 = self.fc2.bias
        primals_6 = self.fc3.weight
        primals_7 = self.fc3.bias
        primals_8 = self.fc4.weight
        primals_9 = self.fc4.bias
        primals_10 = self.fc5.weight
        primals_11 = self.fc5.bias
        primals_12 = self.fc6.weight
        primals_13 = self.fc6.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, primals_12, primals_13])
        return output[0]
