# 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/ie/ciettq2a3562jfpgfe75iig4ki2hbm6pmbwujlvp6mw26i2odufm.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_1, %primals_2], 1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_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=[512], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_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_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 16) % 8
    x0 = xindex % 16
    x2 = (xindex // 128)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (16*x1) + (64*x2)), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + (x0 + (16*((-4) + x1)) + (64*x2)), tmp6 & xmask, other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + (x3), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/vn/cvnpex5pi5isev3fnvj6qndrzy76i24cv6txnyr654zq7d6c6srb.py
# Topologically Sorted Source Nodes: [output_1, output_2], Original ATen: [aten.clamp, aten.div]
# Source node to ATen node mapping:
#   output_1 => clamp_max, clamp_min
#   output_2 => div
# Graph fragment:
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%view_1, 0), kwargs = {})
#   %clamp_max : [num_users=2] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 1), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%clamp_max, %expand), kwargs = {})
triton_poi_fused_clamp_div_1 = async_compile.triton('triton_poi_fused_clamp_div_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=[512], 
    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_clamp_div_1', '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_clamp_div_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp5 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (128 + x0), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (256 + x0), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr0 + (384 + x0), xmask, eviction_policy='evict_last')
    tmp1 = 0.0
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = 1.0
    tmp4 = triton_helpers.minimum(tmp2, tmp3)
    tmp6 = triton_helpers.maximum(tmp5, tmp1)
    tmp7 = triton_helpers.minimum(tmp6, tmp3)
    tmp8 = tmp7 * tmp7
    tmp10 = triton_helpers.maximum(tmp9, tmp1)
    tmp11 = triton_helpers.minimum(tmp10, tmp3)
    tmp12 = tmp11 * tmp11
    tmp13 = tmp8 + tmp12
    tmp15 = triton_helpers.maximum(tmp14, tmp1)
    tmp16 = triton_helpers.minimum(tmp15, tmp3)
    tmp17 = tmp16 * tmp16
    tmp18 = tmp13 + tmp17
    tmp20 = triton_helpers.maximum(tmp19, tmp1)
    tmp21 = triton_helpers.minimum(tmp20, tmp3)
    tmp22 = tmp21 * tmp21
    tmp23 = tmp18 + tmp22
    tmp24 = libdevice.sqrt(tmp23)
    tmp25 = 1e-12
    tmp26 = triton_helpers.maximum(tmp24, tmp25)
    tmp27 = tmp4 / tmp26
    tl.store(out_ptr0 + (x2), tmp27, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(primals_1, primals_2, buf0, 512, grid=grid(512), stream=stream0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((128, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [output], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_4, reinterpret_tensor(buf0, (128, 4), (4, 1), 0), reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf1)
        del primals_3
        del primals_4
        buf2 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [output_1, output_2], Original ATen: [aten.clamp, aten.div]
        triton_poi_fused_clamp_div_1.run(buf1, buf2, 512, grid=grid(512), stream=stream0)
    return (buf2, reinterpret_tensor(buf0, (128, 4), (4, 1), 0), buf1, )


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((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    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 numpy as np
from torch import nn
from torch.nn import functional as F


class Actor(nn.Module):

    def __init__(self, input_dim, output_dim):
        super(Actor, self).__init__()
        self.layer1 = nn.Linear(input_dim, output_dim, bias=True)

    def _format(self, state):
        x = state
        if not isinstance(x, torch.Tensor):
            x = torch.tensor(x, dtype=torch.float32)
            x = x.unsqueeze(0)
        return x

    def forward(self, a, p):
        input = self._format(torch.cat((a, p), dim=1))
        output = self.layer1(input)
        output = torch.clamp(output, 0, 1)
        output = F.normalize(output, dim=0)
        return output

    def full_pass(self, a, p):
        logits = self.forward(a, p)
        dist = torch.distributions.Categorical(logits=logits)
        action = dist.sample()
        logpa = dist.log_prob(action).unsqueeze(-1)
        entropy = dist.entropy().unsqueeze(-1)
        is_exploratory = action != np.argmax(logits.detach().numpy())
        return action.item(), is_exploratory.item(), logpa, entropy

    def select_action(self, a, p):
        logits = self.forward(a, p)
        dist = torch.distributions.Categorical(logits=logits)
        action = dist.sample()
        return action.item()

    def select_greedy_action(self, a, p):
        logits = self.forward(a, p)
        return np.argmax(logits.detach().numpy())


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


def get_init_inputs():
    return [[], {'input_dim': 4, 'output_dim': 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 libdevice
import numpy as np
from torch import 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_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 8
    x0 = xindex % 16
    x2 = xindex // 128
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp6 & xmask,
        other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x3, tmp10, xmask)


@triton.jit
def triton_poi_fused_clamp_div_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp5 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (128 + x0), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (256 + x0), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr0 + (384 + x0), xmask, eviction_policy='evict_last')
    tmp1 = 0.0
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = 1.0
    tmp4 = triton_helpers.minimum(tmp2, tmp3)
    tmp6 = triton_helpers.maximum(tmp5, tmp1)
    tmp7 = triton_helpers.minimum(tmp6, tmp3)
    tmp8 = tmp7 * tmp7
    tmp10 = triton_helpers.maximum(tmp9, tmp1)
    tmp11 = triton_helpers.minimum(tmp10, tmp3)
    tmp12 = tmp11 * tmp11
    tmp13 = tmp8 + tmp12
    tmp15 = triton_helpers.maximum(tmp14, tmp1)
    tmp16 = triton_helpers.minimum(tmp15, tmp3)
    tmp17 = tmp16 * tmp16
    tmp18 = tmp13 + tmp17
    tmp20 = triton_helpers.maximum(tmp19, tmp1)
    tmp21 = triton_helpers.minimum(tmp20, tmp3)
    tmp22 = tmp21 * tmp21
    tmp23 = tmp18 + tmp22
    tmp24 = libdevice.sqrt(tmp23)
    tmp25 = 1e-12
    tmp26 = triton_helpers.maximum(tmp24, tmp25)
    tmp27 = tmp4 / tmp26
    tl.store(out_ptr0 + x2, tmp27, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(512)](primals_1, primals_2, buf0, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((128, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_4, reinterpret_tensor(buf0, (128, 4),
            (4, 1), 0), reinterpret_tensor(primals_3, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf1)
        del primals_3
        del primals_4
        buf2 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
        triton_poi_fused_clamp_div_1[grid(512)](buf1, buf2, 512, XBLOCK=256,
            num_warps=4, num_stages=1)
    return buf2, reinterpret_tensor(buf0, (128, 4), (4, 1), 0), buf1


class ActorNew(nn.Module):

    def __init__(self, input_dim, output_dim):
        super(ActorNew, self).__init__()
        self.layer1 = nn.Linear(input_dim, output_dim, bias=True)

    def _format(self, state):
        x = state
        if not isinstance(x, torch.Tensor):
            x = torch.tensor(x, dtype=torch.float32)
            x = x.unsqueeze(0)
        return x

    def full_pass(self, a, p):
        logits = self.forward(a, p)
        dist = torch.distributions.Categorical(logits=logits)
        action = dist.sample()
        logpa = dist.log_prob(action).unsqueeze(-1)
        entropy = dist.entropy().unsqueeze(-1)
        is_exploratory = action != np.argmax(logits.detach().numpy())
        return action.item(), is_exploratory.item(), logpa, entropy

    def select_action(self, a, p):
        logits = self.forward(a, p)
        dist = torch.distributions.Categorical(logits=logits)
        action = dist.sample()
        return action.item()

    def select_greedy_action(self, a, p):
        logits = self.forward(a, p)
        return np.argmax(logits.detach().numpy())

    def forward(self, input_0, input_1):
        primals_3 = self.layer1.weight
        primals_4 = self.layer1.bias
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
