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
import torch.nn as nn
from torch.distributions import Normal
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 = 89600
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 1400
    x1 = xindex // 1400
    tmp0 = tl.load(in_out_ptr0 + (x0 + 1408 * x1), 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 + (x0 + 1408 * x1), tmp4, xmask)
    tl.store(out_ptr0 + (x0 + 1408 * x1), tmp6, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, 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)
    x2 = xindex
    x0 = xindex % 1024
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, 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, None)
    tl.store(out_ptr0 + x2, tmp6, None)


@triton.jit
def triton_poi_fused_relu_threshold_backward_2(in_out_ptr0, 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)
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, 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, None)
    tl.store(out_ptr0 + x2, tmp6, None)


@triton.jit
def triton_poi_fused_clamp_ge_le_logical_and_3(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)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = -20.0
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = 2.0
    tmp6 = triton_helpers.minimum(tmp4, tmp5)
    tmp7 = tmp2 >= tmp3
    tmp8 = tmp2 <= tmp5
    tmp9 = tmp7 & tmp8
    tl.store(out_ptr0 + x2, tmp6, xmask)
    tl.store(out_ptr1 + x2, tmp9, 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) = args
    args.clear()
    assert_size_stride(primals_1, (1400, 4), (4, 1))
    assert_size_stride(primals_2, (1400,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (1024, 1400), (1400, 1))
    assert_size_stride(primals_5, (1024,), (1,))
    assert_size_stride(primals_6, (256, 1024), (1024, 1))
    assert_size_stride(primals_7, (256,), (1,))
    assert_size_stride(primals_8, (4, 256), (256, 1))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (4, 256), (256, 1))
    assert_size_stride(primals_11, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 1400), (1408, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 1400), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 1400), (22528, 5632, 1408,
            1), 0)
        del buf0
        buf12 = empty_strided_cuda((4, 4, 4, 1400), (22528, 5632, 1408, 1),
            torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(89600)](buf1,
            primals_2, buf12, 89600, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((64, 1024), (1024, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 1400), (1408, 1), 0
            ), reinterpret_tensor(primals_4, (1400, 1024), (1, 1400), 0),
            out=buf2)
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 1024), (16384, 4096, 1024,
            1), 0)
        del buf2
        buf11 = empty_strided_cuda((4, 4, 4, 1024), (16384, 4096, 1024, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(65536)](buf3,
            primals_5, buf11, 65536, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((64, 256), (256, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (64, 1024), (1024, 1), 0
            ), reinterpret_tensor(primals_6, (1024, 256), (1, 1024), 0),
            out=buf4)
        buf5 = reinterpret_tensor(buf4, (4, 4, 4, 256), (4096, 1024, 256, 1), 0
            )
        del buf4
        buf10 = empty_strided_cuda((4, 4, 4, 256), (4096, 1024, 256, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_2[grid(16384)](buf5,
            primals_7, buf10, 16384, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_7
        buf6 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_9, reinterpret_tensor(buf5, (64, 256),
            (256, 1), 0), reinterpret_tensor(primals_8, (256, 4), (1, 256),
            0), alpha=1, beta=1, out=buf6)
        del primals_9
        buf7 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf5, (64, 256), (256, 1), 0),
            reinterpret_tensor(primals_10, (256, 4), (1, 256), 0), out=buf7)
        buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_clamp_ge_le_logical_and_3[grid(256)](buf7,
            primals_11, buf8, buf9, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf7
        del primals_11
    return reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), buf8, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf1, (64, 1400), (1408, 1), 0
        ), reinterpret_tensor(buf3, (64, 1024), (1024, 1), 0
        ), reinterpret_tensor(buf5, (64, 256), (256, 1), 0
        ), buf9, primals_10, primals_8, buf10, primals_6, buf11, primals_4, buf12


class MLPPolicyNetworkNew(nn.Module):

    def __init__(self, num_inputs, num_actions, hidden_size1=1400,
        hidden_size2=1024, hidden_size3=256, init_w=0.003, log_std_min=-20,
        log_std_max=2):
        super(MLPPolicyNetworkNew, self).__init__()
        self.log_std_min = log_std_min
        self.log_std_max = log_std_max
        self.num_inputs = num_inputs
        self.num_actions = num_actions
        self.linear1 = nn.Linear(num_inputs, hidden_size1)
        self.linear2 = nn.Linear(hidden_size1, hidden_size2)
        self.linear3 = nn.Linear(hidden_size2, hidden_size3)
        self.mean_linear = nn.Linear(hidden_size3, num_actions)
        self.mean_linear.weight.data.uniform_(-init_w, init_w)
        self.mean_linear.bias.data.uniform_(-init_w, init_w)
        self.log_std_linear = nn.Linear(hidden_size3, num_actions)
        self.log_std_linear.weight.data.uniform_(-init_w, init_w)
        self.log_std_linear.bias.data.uniform_(-init_w, init_w)

    def sample(self, state, scale, epsilon=1e-06):
        mean, log_std = self.forward(state)
        std = log_std.exp()
        normal = Normal(mean, std)
        z = normal.rsample()
        action = torch.tanh(z)
        log_pi = normal.log_prob(z) - torch.log(scale * (1 - action.pow(2)) +
            epsilon)
        log_pi = log_pi.sum(1, keepdim=True)
        return action, log_pi, mean, std

    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_8 = self.mean_linear.weight
        primals_9 = self.mean_linear.bias
        primals_10 = self.log_std_linear.weight
        primals_11 = self.log_std_linear.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])
        return output[0], output[1]


Measured latency_ms: 0.01525