# 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/pr/cprthrqz6iotcmrjfcrj7taqntzxisdcjtr54gsuz2ck2kf6kbsr.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   x => relu
# Graph fragment:
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {})
#   %le_2 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp1 = tl.load(in_ptr0 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tl.full([1], 0, tl.int32)
    tmp5 = triton_helpers.maximum(tmp4, tmp3)
    tmp6 = 0.0
    tmp7 = tmp5 <= tmp6
    tl.store(in_out_ptr0 + (x0), tmp5, xmask)
    tl.store(out_ptr0 + (x0), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/mp/cmpdsbnpgfsr7uwb7env74mojrq3nlzieqot6rnnkfpbzkkensbi.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   x_1 => relu_1
# Graph fragment:
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_3,), kwargs = {})
#   %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_1 = async_compile.triton('triton_poi_fused_relu_threshold_backward_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
    tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/d4/cd432jugyi4ssarhot7h6csa752vfkvgs6yoqdcnthsxxlmc5wk4.py
# Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   softmax => amax, exp, sub, sum_1
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_6, [1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_6, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
triton_poi_fused__softmax_2 = async_compile.triton('triton_poi_fused__softmax_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 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__softmax_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, '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__softmax_2(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + ((4*x0) % 16), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + ((1 + (4*x0)) % 16), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr1 + ((2 + (4*x0)) % 16), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + ((3 + (4*x0)) % 16), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp7 = tmp5 + tmp6
    tmp8 = triton_helpers.maximum(tmp3, tmp7)
    tmp9 = triton_helpers.maximum(tmp4, tmp8)
    tmp12 = tmp10 + tmp11
    tmp13 = triton_helpers.maximum(tmp3, tmp12)
    tmp14 = triton_helpers.maximum(tmp9, tmp13)
    tmp17 = tmp15 + tmp16
    tmp18 = triton_helpers.maximum(tmp3, tmp17)
    tmp19 = triton_helpers.maximum(tmp14, tmp18)
    tmp20 = tmp4 - tmp19
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp8 - tmp19
    tmp23 = tl_math.exp(tmp22)
    tmp24 = tmp21 + tmp23
    tmp25 = tmp13 - tmp19
    tmp26 = tl_math.exp(tmp25)
    tmp27 = tmp24 + tmp26
    tmp28 = tmp18 - tmp19
    tmp29 = tl_math.exp(tmp28)
    tmp30 = tmp27 + tmp29
    tl.store(out_ptr0 + (x0), tmp19, xmask)
    tl.store(out_ptr1 + (x0), tmp30, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/nj/cnj3sc5sqh53qv3ve6jdrvn2ieksonjrw2xwfaef4ffuhqxauz6u.py
# Topologically Sorted Source Nodes: [x_2, softmax], Original ATen: [aten.relu, aten._softmax, aten.threshold_backward]
# Source node to ATen node mapping:
#   softmax => amax, div, exp, sub
#   x_2 => relu_2
# Graph fragment:
#   %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_5,), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_6, [1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_6, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_2, 0), kwargs = {})
triton_poi_fused__softmax_relu_threshold_backward_3 = async_compile.triton('triton_poi_fused__softmax_relu_threshold_backward_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: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*i1', 6: '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, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_relu_threshold_backward_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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__softmax_relu_threshold_backward_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x1 = (xindex // 4)
    x2 = xindex % 16
    tmp0 = tl.load(in_ptr0 + (x4), xmask)
    tmp1 = tl.load(in_ptr1 + (x4 % 16), xmask)
    tmp5 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = tmp4 - tmp5
    tmp7 = tl_math.exp(tmp6)
    tmp9 = tmp7 / tmp8
    tmp11 = tmp0 + tmp10
    tmp12 = triton_helpers.maximum(tmp3, tmp11)
    tmp13 = 0.0
    tmp14 = tmp12 <= tmp13
    tl.store(out_ptr0 + (x4), tmp9, xmask)
    tl.store(out_ptr1 + (x4), tmp14, 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 = args
    args.clear()
    assert_size_stride(primals_1, (1, 4), (4, 1))
    assert_size_stride(primals_2, (1, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 1), (1, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (16, 4), (4, 1))
    assert_size_stride(primals_7, (16, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 1), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 1), (16, 4, 1, 1), 0); del buf0  # reuse
        buf10 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.relu, aten.threshold_backward]
        stream0 = get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0.run(buf1, primals_2, buf10, 64, grid=grid(64), stream=stream0)
        del primals_2
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 1), (1, 0), 0), reinterpret_tensor(primals_4, (1, 4), (1, 1), 0), out=buf2)
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf2  # reuse
        buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_1.run(buf3, primals_5, buf9, 256, grid=grid(256), stream=stream0)
        del primals_5
        buf4 = empty_strided_cuda((64, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 16), (1, 4), 0), out=buf4)
        buf5 = empty_strided_cuda((256, 1), (1, 256), torch.float32)
        buf6 = empty_strided_cuda((256, 1), (1, 256), torch.float32)
        # Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_2.run(buf4, primals_7, buf5, buf6, 256, grid=grid(256), stream=stream0)
        buf7 = empty_strided_cuda((256, 4), (4, 1), torch.float32)
        buf8 = empty_strided_cuda((4, 4, 4, 16), (256, 64, 16, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_2, softmax], Original ATen: [aten.relu, aten._softmax, aten.threshold_backward]
        triton_poi_fused__softmax_relu_threshold_backward_3.run(buf4, primals_7, buf5, buf6, buf7, buf8, 1024, grid=grid(1024), stream=stream0)
        del buf4
        del buf5
        del buf6
        del primals_7
    return (reinterpret_tensor(buf7, (64, 4, 4), (16, 4, 1), 0), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(buf1, (64, 1), (1, 1), 0), reinterpret_tensor(buf3, (64, 4), (4, 1), 0), buf7, buf8, primals_6, buf9, primals_4, 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((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((1, ), (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((4, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((16, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7])
    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)

from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F


class CategoricalDQN(nn.Module):

    def __init__(self, num_inputs, num_actions, args):
        super(CategoricalDQN, self).__init__()
        self.num_inputs = num_inputs
        self.num_actions = num_actions
        self.num_atoms = args.atom
        self.vmax = args.vmax
        self.vmin = args.vmin
        self.linear1 = nn.Linear(num_inputs, args.hidden_size // 4)
        self.linear2 = nn.Linear(args.hidden_size // 4, args.hidden_size)
        self.linear3 = nn.Linear(args.hidden_size, num_actions * args.atom)

    def forward(self, input):
        x = F.relu(self.linear1(input))
        x = F.relu(self.linear2(x))
        x = F.relu(self.linear3(x))
        x = F.softmax(x.view(-1, self.num_atoms)).view(-1, self.num_actions,
            self.num_atoms)
        return x

    def act(self, state):
        with torch.no_grad():
            state = torch.tensor(state, dtype=torch.float).unsqueeze(0)
        dist = self.forward(state).data.cpu()
        dist = dist * torch.linspace(self.vmin, self.vmax, self.num_atoms)
        action = dist.sum(2).max(1)[1].numpy()[0]
        return action


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


def get_init_inputs():
    return [[], {'num_inputs': 4, 'num_actions': 4, 'args': _mock_config(
        atom=4, vmax=4, vmin=4, hidden_size=4)}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
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_relu_threshold_backward_0(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tl.full([1], 0, tl.int32)
    tmp5 = triton_helpers.maximum(tmp4, tmp3)
    tmp6 = 0.0
    tmp7 = tmp5 <= tmp6
    tl.store(in_out_ptr0 + x0, tmp5, xmask)
    tl.store(out_ptr0 + x0, tmp7, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * x0 % 16, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (1 + 4 * x0) % 16, xmask, eviction_policy=
        'evict_last')
    tmp10 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp11 = tl.load(in_ptr1 + (2 + 4 * x0) % 16, xmask, eviction_policy=
        'evict_last')
    tmp15 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp16 = tl.load(in_ptr1 + (3 + 4 * x0) % 16, xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp7 = tmp5 + tmp6
    tmp8 = triton_helpers.maximum(tmp3, tmp7)
    tmp9 = triton_helpers.maximum(tmp4, tmp8)
    tmp12 = tmp10 + tmp11
    tmp13 = triton_helpers.maximum(tmp3, tmp12)
    tmp14 = triton_helpers.maximum(tmp9, tmp13)
    tmp17 = tmp15 + tmp16
    tmp18 = triton_helpers.maximum(tmp3, tmp17)
    tmp19 = triton_helpers.maximum(tmp14, tmp18)
    tmp20 = tmp4 - tmp19
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp8 - tmp19
    tmp23 = tl_math.exp(tmp22)
    tmp24 = tmp21 + tmp23
    tmp25 = tmp13 - tmp19
    tmp26 = tl_math.exp(tmp25)
    tmp27 = tmp24 + tmp26
    tmp28 = tmp18 - tmp19
    tmp29 = tl_math.exp(tmp28)
    tmp30 = tmp27 + tmp29
    tl.store(out_ptr0 + x0, tmp19, xmask)
    tl.store(out_ptr1 + x0, tmp30, xmask)


@triton.jit
def triton_poi_fused__softmax_relu_threshold_backward_3(in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x1 = xindex // 4
    x2 = xindex % 16
    tmp0 = tl.load(in_ptr0 + x4, xmask)
    tmp1 = tl.load(in_ptr1 + x4 % 16, xmask)
    tmp5 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = tmp4 - tmp5
    tmp7 = tl_math.exp(tmp6)
    tmp9 = tmp7 / tmp8
    tmp11 = tmp0 + tmp10
    tmp12 = triton_helpers.maximum(tmp3, tmp11)
    tmp13 = 0.0
    tmp14 = tmp12 <= tmp13
    tl.store(out_ptr0 + x4, tmp9, xmask)
    tl.store(out_ptr1 + x4, tmp14, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (1, 4), (4, 1))
    assert_size_stride(primals_2, (1,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 1), (1, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (16, 4), (4, 1))
    assert_size_stride(primals_7, (16,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 1), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf0
        buf10 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(64)](buf1,
            primals_2, buf10, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 1), (1, 0), 0),
            reinterpret_tensor(primals_4, (1, 4), (1, 1), 0), out=buf2)
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf2
        buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(256)](buf3,
            primals_5, buf9, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((64, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_6, (4, 16), (1, 4), 0), out=buf4)
        buf5 = empty_strided_cuda((256, 1), (1, 256), torch.float32)
        buf6 = empty_strided_cuda((256, 1), (1, 256), torch.float32)
        triton_poi_fused__softmax_2[grid(256)](buf4, primals_7, buf5, buf6,
            256, XBLOCK=256, num_warps=4, num_stages=1)
        buf7 = empty_strided_cuda((256, 4), (4, 1), torch.float32)
        buf8 = empty_strided_cuda((4, 4, 4, 16), (256, 64, 16, 1), torch.bool)
        triton_poi_fused__softmax_relu_threshold_backward_3[grid(1024)](buf4,
            primals_7, buf5, buf6, buf7, buf8, 1024, XBLOCK=256, num_warps=
            4, num_stages=1)
        del buf4
        del buf5
        del buf6
        del primals_7
    return reinterpret_tensor(buf7, (64, 4, 4), (16, 4, 1), 0
        ), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf1, (64, 1), (1, 1), 0), reinterpret_tensor(
        buf3, (64, 4), (4, 1), 0
        ), buf7, buf8, primals_6, buf9, primals_4, buf10


class CategoricalDQNNew(nn.Module):

    def __init__(self, num_inputs, num_actions, args):
        super(CategoricalDQNNew, self).__init__()
        self.num_inputs = num_inputs
        self.num_actions = num_actions
        self.num_atoms = args.atom
        self.vmax = args.vmax
        self.vmin = args.vmin
        self.linear1 = nn.Linear(num_inputs, args.hidden_size // 4)
        self.linear2 = nn.Linear(args.hidden_size // 4, args.hidden_size)
        self.linear3 = nn.Linear(args.hidden_size, num_actions * args.atom)

    def act(self, state):
        with torch.no_grad():
            state = torch.tensor(state, dtype=torch.float).unsqueeze(0)
        dist = self.forward(state).data.cpu()
        dist = dist * torch.linspace(self.vmin, self.vmax, self.num_atoms)
        action = dist.sum(2).max(1)[1].numpy()[0]
        return action

    def forward(self, input_0):
        primals_1 = self.linear1.weight
        primals_2 = self.linear1.bias
        primals_4 = self.linear2.weight
        primals_5 = self.linear2.bias
        primals_6 = self.linear3.weight
        primals_7 = self.linear3.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]
