import torch
import torch.nn as nn


class DQN(nn.Module):

    def __init__(self, state_dim, nb_actions, hidden1=50, hidden2=50):
        super(DQN, self).__init__()
        self.fc1 = nn.Linear(state_dim, hidden1)
        self.fc2 = nn.Linear(hidden1, hidden2)
        self.fc3 = nn.Linear(hidden2, nb_actions)
        self.relu = nn.ReLU()
        self.tanh = nn.Tanh()

    def forward(self, x):
        out = self.fc1(x)
        out = self.relu(out)
        out = self.fc2(out)
        out = self.relu(out)
        return self.fc3(out.view(out.size(0), -1))


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


def get_init_inputs():
    return [[], {'state_dim': 4, 'nb_actions': 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
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_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 200
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 50
    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)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_view_1(in_ptr0, in_ptr1,
    out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 200
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 50
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + 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(out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr1 + x2, tmp6, 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, (50, 4), (4, 1))
    assert_size_stride(primals_2, (50,), (1,))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (50, 50), (50, 1))
    assert_size_stride(primals_5, (50,), (1,))
    assert_size_stride(primals_6, (4, 50), (50, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 50), (50, 1), torch.float32)
        extern_kernels.mm(primals_3, reinterpret_tensor(primals_1, (4, 50),
            (1, 4), 0), out=buf0)
        del primals_1
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(200)](buf1, primals_2, 200, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((4, 50), (50, 1), torch.float32)
        extern_kernels.mm(buf1, reinterpret_tensor(primals_4, (50, 50), (1,
            50), 0), out=buf2)
        buf3 = empty_strided_cuda((4, 50), (50, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 50), (50, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_view_1[grid(200)](buf2,
            primals_5, buf3, buf5, 200, XBLOCK=256, num_warps=4, num_stages=1)
        del buf2
        del primals_5
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, buf3, reinterpret_tensor(primals_6,
            (50, 4), (1, 50), 0), alpha=1, beta=1, out=buf4)
        del primals_7
    return buf4, primals_3, buf1, buf3, primals_6, buf5, primals_4


class DQNNew(nn.Module):

    def __init__(self, state_dim, nb_actions, hidden1=50, hidden2=50):
        super(DQNNew, self).__init__()
        self.fc1 = nn.Linear(state_dim, hidden1)
        self.fc2 = nn.Linear(hidden1, hidden2)
        self.fc3 = nn.Linear(hidden2, nb_actions)
        self.relu = nn.ReLU()
        self.tanh = nn.Tanh()

    def forward(self, input_0):
        primals_1 = self.fc1.weight
        primals_2 = self.fc1.bias
        primals_4 = self.fc2.weight
        primals_5 = self.fc2.bias
        primals_6 = self.fc3.weight
        primals_7 = self.fc3.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]
