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.functional as F
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_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
    x0 = xindex % 8
    x1 = xindex // 8
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x2, tmp10, 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 % 64
    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)


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, primals_14) = 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, (64, 8), (8, 1))
    assert_size_stride(primals_4, (64,), (1,))
    assert_size_stride(primals_5, (64, 64), (64, 1))
    assert_size_stride(primals_6, (64,), (1,))
    assert_size_stride(primals_7, (1, 64), (64, 1))
    assert_size_stride(primals_8, (1,), (1,))
    assert_size_stride(primals_9, (64, 8), (8, 1))
    assert_size_stride(primals_10, (64,), (1,))
    assert_size_stride(primals_11, (64, 64), (64, 1))
    assert_size_stride(primals_12, (64,), (1,))
    assert_size_stride(primals_13, (1, 64), (64, 1))
    assert_size_stride(primals_14, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 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((64, 64), (64, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 8), (8, 1), 0),
            reinterpret_tensor(primals_3, (8, 64), (1, 8), 0), out=buf1)
        del primals_3
        buf2 = reinterpret_tensor(buf1, (4, 4, 4, 64), (1024, 256, 64, 1), 0)
        del buf1
        buf16 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch
            .bool)
        triton_poi_fused_relu_threshold_backward_1[grid(4096)](buf2,
            primals_4, buf16, 4096, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_4
        buf3 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (64, 64), (64, 1), 0),
            reinterpret_tensor(primals_5, (64, 64), (1, 64), 0), out=buf3)
        buf4 = reinterpret_tensor(buf3, (4, 4, 4, 64), (1024, 256, 64, 1), 0)
        del buf3
        buf15 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch
            .bool)
        triton_poi_fused_relu_threshold_backward_1[grid(4096)](buf4,
            primals_6, buf15, 4096, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_6
        buf6 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_8, reinterpret_tensor(buf4, (64, 64),
            (64, 1), 0), reinterpret_tensor(primals_7, (64, 1), (1, 64), 0),
            alpha=1, beta=1, out=buf6)
        del primals_8
        buf7 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 8), (8, 1), 0),
            reinterpret_tensor(primals_9, (8, 64), (1, 8), 0), out=buf7)
        del primals_9
        buf8 = reinterpret_tensor(buf7, (4, 4, 4, 64), (1024, 256, 64, 1), 0)
        del buf7
        buf14 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch
            .bool)
        triton_poi_fused_relu_threshold_backward_1[grid(4096)](buf8,
            primals_10, buf14, 4096, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_10
        buf9 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf8, (64, 64), (64, 1), 0),
            reinterpret_tensor(primals_11, (64, 64), (1, 64), 0), out=buf9)
        buf10 = reinterpret_tensor(buf9, (4, 4, 4, 64), (1024, 256, 64, 1), 0)
        del buf9
        buf13 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch
            .bool)
        triton_poi_fused_relu_threshold_backward_1[grid(4096)](buf10,
            primals_12, buf13, 4096, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_12
        buf12 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_14, reinterpret_tensor(buf10, (64, 64),
            (64, 1), 0), reinterpret_tensor(primals_13, (64, 1), (1, 64), 0
            ), alpha=1, beta=1, out=buf12)
        del primals_14
    return reinterpret_tensor(buf6, (4, 4, 4, 1), (16, 4, 1, 1), 0
        ), reinterpret_tensor(buf12, (4, 4, 4, 1), (16, 4, 1, 1), 0
        ), reinterpret_tensor(buf0, (64, 8), (8, 1), 0), reinterpret_tensor(
        buf2, (64, 64), (64, 1), 0), reinterpret_tensor(buf4, (64, 64), (64,
        1), 0), reinterpret_tensor(buf8, (64, 64), (64, 1), 0
        ), reinterpret_tensor(buf10, (64, 64), (64, 1), 0
        ), primals_13, buf13, primals_11, buf14, primals_7, buf15, primals_5, buf16


class MLPNetwork(nn.Module):
    """
    MLP network (can be used as value or policy)
    """

    def __init__(self, input_dim, out_dim, hidden_dim=64, nonlin=F.relu,
        constrain_out=False, norm_in=False, discrete_action=True):
        """
        Inputs:
            input_dim (int): Number of dimensions in input
            out_dim (int): Number of dimensions in output
            hidden_dim (int): Number of hidden dimensions
            nonlin (PyTorch function): Nonlinearity to apply to hidden layers
        """
        super(MLPNetwork, self).__init__()
        if norm_in:
            self.in_fn = nn.BatchNorm1d(input_dim)
            self.in_fn.weight.data.fill_(1)
            self.in_fn.bias.data.fill_(0)
        else:
            self.in_fn = lambda x: x
        self.fc1 = nn.Linear(input_dim, hidden_dim)
        self.fc2 = nn.Linear(hidden_dim, hidden_dim)
        self.fc3 = nn.Linear(hidden_dim, out_dim)
        self.nonlin = nonlin
        if constrain_out and not discrete_action:
            self.fc3.weight.data.uniform_(-0.003, 0.003)
            self.out_fn = torch.tanh
        else:
            self.out_fn = lambda x: x

    def forward(self, X):
        """
        Inputs:
            X (PyTorch Matrix): Batch of observations
        Outputs:
            out (PyTorch Matrix): Output of network (actions, values, etc)
        """
        h1 = self.nonlin(self.fc1(self.in_fn(X)))
        h2 = self.nonlin(self.fc2(h1))
        out = self.out_fn(self.fc3(h2))
        return out


class MLP_QnetNew(nn.Module):
    """
    MLP_Qnet network
    """

    def __init__(self, input_dim, act_dim, hidden_dim=64):
        """
        Inputs:
            input_dim (int): Number of dimensions in input
            out_dim (int): Number of dimensions in output
            hidden_dim (int): Number of hidden dimensions
            nonlin (PyTorch function): Nonlinearity to apply to hidden layers
        """
        super(MLP_QnetNew, self).__init__()
        self.critic1 = MLPNetwork(input_dim + act_dim, 1, hidden_dim=
            hidden_dim, constrain_out=False)
        self.critic2 = MLPNetwork(input_dim + act_dim, 1, hidden_dim=
            hidden_dim, constrain_out=False)

    def forward(self, input_0, input_1):
        primals_3 = self.critic1.fc1.weight
        primals_4 = self.critic1.fc1.bias
        primals_5 = self.critic1.fc2.weight
        primals_6 = self.critic1.fc2.bias
        primals_7 = self.critic1.fc3.weight
        primals_8 = self.critic1.fc3.bias
        primals_9 = self.critic2.fc1.weight
        primals_10 = self.critic2.fc1.bias
        primals_11 = self.critic2.fc2.weight
        primals_12 = self.critic2.fc2.bias
        primals_13 = self.critic2.fc3.weight
        primals_14 = self.critic2.fc3.bias
        primals_1 = input_0
        primals_2 = input_1
        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, primals_14])
        return output[0], output[1]


Measured latency_ms: 0.131389