# AOT ID: ['0_inference']
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_6/inductor_cache/up/cup6o5cbzbunxrgsjove7ghgc4dkxlfpf5zncfpxjlrgo5zjrjpe.py
# Topologically Sorted Source Nodes: [mean, sub_2, exp, var, truediv_1, mul_1, sub_3, sub, truediv, pow_2, mul, alpha, beta], Original ATen: [aten.sigmoid, aten.rsub, aten.exp, aten.pow, aten.div, aten.mul, aten.sub]
# Source node to ATen node mapping:
#   alpha => sub_1
#   beta => sub_4
#   exp => exp
#   mean => sigmoid
#   mul => mul
#   mul_1 => mul_1
#   pow_2 => pow_2
#   sub => sub
#   sub_2 => sub_2
#   sub_3 => sub_3
#   truediv => div
#   truediv_1 => div_1
#   var => pow_1
# Graph fragment:
#   %sigmoid : [num_users=5] = call_function[target=torch.ops.aten.sigmoid.default](args = (%arg0_1,), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sigmoid), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%arg1_1,), kwargs = {})
#   %pow_1 : [num_users=2] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%exp, 2), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub_2, %pow_1), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div_1, %sigmoid), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_1, 1), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sigmoid), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub, %pow_1), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sigmoid, 2), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div, %pow_2), kwargs = {})
#   %sub_1 : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul, %sigmoid), kwargs = {})
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub_3, %sub_1), kwargs = {})
triton_poi_fused_div_exp_mul_pow_rsub_sigmoid_sub_0 = async_compile.triton('triton_poi_fused_div_exp_mul_pow_rsub_sigmoid_sub_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=[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_div_exp_mul_pow_rsub_sigmoid_sub_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_div_exp_mul_pow_rsub_sigmoid_sub_0(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
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp4 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.sigmoid(tmp0)
    tmp2 = 1.0
    tmp3 = tmp2 - tmp1
    tmp5 = tl_math.exp(tmp4)
    tmp6 = tmp5 * tmp5
    tmp7 = tmp3 / tmp6
    tmp8 = tmp1 * tmp1
    tmp9 = tmp7 * tmp8
    tmp10 = tmp9 - tmp1
    tmp11 = tmp7 * tmp1
    tmp12 = tmp11 - tmp2
    tmp13 = tmp12 - tmp10
    tl.store(out_ptr0 + (x2), tmp10, xmask)
    tl.store(out_ptr1 + (x2), tmp13, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (1, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mean, sub_2, exp, var, truediv_1, mul_1, sub_3, sub, truediv, pow_2, mul, alpha, beta], Original ATen: [aten.sigmoid, aten.rsub, aten.exp, aten.pow, aten.div, aten.mul, aten.sub]
        stream0 = get_raw_stream(0)
        triton_poi_fused_div_exp_mul_pow_rsub_sigmoid_sub_0.run(arg0_1, arg1_1, buf0, buf1, 256, grid=grid(256), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf0, buf1, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1])
    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
import torch.nn as nn


class BoundedBeta(torch.distributions.Beta):

    def log_prob(self, x):
        return super().log_prob((x + 1) / 2)


class Beta2(nn.Module):

    def __init__(self, action_dim, init_std=0.25, learn_std=False):
        super(Beta2, self).__init__()
        assert init_std < 0.5, 'Beta distribution has a max std dev of 0.5'
        self.action_dim = action_dim
        self.logstd = nn.Parameter(torch.ones(1, action_dim) * np.log(
            init_std), requires_grad=learn_std)
        self.learn_std = learn_std

    def forward(self, x):
        mean = torch.sigmoid(x)
        var = self.logstd.exp().pow(2)
        """
        alpha = ((1 - mu) / sigma^2 - 1 / mu) * mu^2
        beta  = alpha * (1 / mu - 1)

        Implemented slightly differently for numerical stability.
        """
        alpha = (1 - mean) / var * mean.pow(2) - mean
        beta = (1 - mean) / var * mean - 1 - alpha
        return alpha, beta

    def sample(self, x, deterministic):
        if deterministic is False:
            action = self.evaluate(x).sample()
        else:
            return self.evaluate(x).mean
        return 2 * action - 1

    def evaluate(self, x):
        alpha, beta = self(x)
        return BoundedBeta(alpha, beta)


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


def get_init_inputs():
    return [[], {'action_dim': 4}]

import torch
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 math as tl_math
import numpy as np
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


@triton.jit
def triton_poi_fused_div_exp_mul_pow_rsub_sigmoid_sub_0(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
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp4 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.sigmoid(tmp0)
    tmp2 = 1.0
    tmp3 = tmp2 - tmp1
    tmp5 = tl_math.exp(tmp4)
    tmp6 = tmp5 * tmp5
    tmp7 = tmp3 / tmp6
    tmp8 = tmp1 * tmp1
    tmp9 = tmp7 * tmp8
    tmp10 = tmp9 - tmp1
    tmp11 = tmp7 * tmp1
    tmp12 = tmp11 - tmp2
    tmp13 = tmp12 - tmp10
    tl.store(out_ptr0 + x2, tmp10, xmask)
    tl.store(out_ptr1 + x2, tmp13, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (1, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_div_exp_mul_pow_rsub_sigmoid_sub_0[grid(256)](arg0_1,
            arg1_1, buf0, buf1, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
    return buf0, buf1


class BoundedBeta(torch.distributions.Beta):

    def log_prob(self, x):
        return super().log_prob((x + 1) / 2)


class Beta2New(nn.Module):

    def __init__(self, action_dim, init_std=0.25, learn_std=False):
        super(Beta2New, self).__init__()
        assert init_std < 0.5, 'Beta distribution has a max std dev of 0.5'
        self.action_dim = action_dim
        self.logstd = nn.Parameter(torch.ones(1, action_dim) * np.log(
            init_std), requires_grad=learn_std)
        self.learn_std = learn_std

    def sample(self, x, deterministic):
        if deterministic is False:
            action = self.evaluate(x).sample()
        else:
            return self.evaluate(x).mean
        return 2 * action - 1

    def evaluate(self, x):
        alpha, beta = self(x)
        return BoundedBeta(alpha, beta)

    def forward(self, input_0):
        arg1_1 = self.logstd
        arg0_1 = input_0
        output = call([arg0_1, arg1_1])
        return output[0], output[1]
