import torch
import torch.nn as nn
import torch.nn.functional as F


class CoreNetwork(nn.Module):
    """The core network.

    An RNN that maintains an internal state by integrating
    information extracted from the history of past observations.
    It encodes the agent's knowledge of the environment through
    a state vector `h_t` that gets updated at every time step `t`.

    Concretely, it takes the glimpse representation `g_t` as input,
    and combines it with its internal state `h_t_prev` at the previous
    time step, to produce the new internal state `h_t` at the current
    time step.

    In other words:

        `h_t = relu( fc(h_t_prev) + fc(g_t) )`

    Args:
        input_size: input size of the rnn.
        hidden_size: hidden size of the rnn.
        g_t: a 2D tensor of shape (B, hidden_size). The glimpse
            representation returned by the glimpse network for the
            current timestep `t`.
        h_t_prev: a 2D tensor of shape (B, hidden_size). The
            hidden state vector for the previous timestep `t-1`.

    Returns:
        h_t: a 2D tensor of shape (B, hidden_size). The hidden
            state vector for the current timestep `t`.
    """

    def __init__(self, input_size, hidden_size):
        super().__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.i2h = nn.Linear(input_size, hidden_size)
        self.h2h = nn.Linear(hidden_size, hidden_size)

    def forward(self, g_t, h_t_prev):
        h1 = self.i2h(g_t)
        h2 = self.h2h(h_t_prev)
        h_t = F.relu(h1 + h2)
        return h_t


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


def get_init_inputs():
    return [[], {'input_size': 4, 'hidden_size': 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_add_relu_threshold_backward_0(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, 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_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x2, xmask)
    tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = tl.full([1], 0, tl.int32)
    tmp8 = triton_helpers.maximum(tmp7, tmp6)
    tmp9 = 0.0
    tmp10 = tmp8 <= tmp9
    tl.store(in_out_ptr0 + x2, tmp8, xmask)
    tl.store(out_ptr0 + x2, tmp10, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_6, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1)
        del primals_4
        buf2 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_add_relu_threshold_backward_0[grid(256)](buf2,
            primals_2, buf1, primals_5, buf3, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del buf1
        del primals_2
        del primals_5
    return buf2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), reinterpret_tensor(primals_6, (64, 4), (4, 1), 0), buf3


class CoreNetworkNew(nn.Module):
    """The core network.

    An RNN that maintains an internal state by integrating
    information extracted from the history of past observations.
    It encodes the agent's knowledge of the environment through
    a state vector `h_t` that gets updated at every time step `t`.

    Concretely, it takes the glimpse representation `g_t` as input,
    and combines it with its internal state `h_t_prev` at the previous
    time step, to produce the new internal state `h_t` at the current
    time step.

    In other words:

        `h_t = relu( fc(h_t_prev) + fc(g_t) )`

    Args:
        input_size: input size of the rnn.
        hidden_size: hidden size of the rnn.
        g_t: a 2D tensor of shape (B, hidden_size). The glimpse
            representation returned by the glimpse network for the
            current timestep `t`.
        h_t_prev: a 2D tensor of shape (B, hidden_size). The
            hidden state vector for the previous timestep `t-1`.

    Returns:
        h_t: a 2D tensor of shape (B, hidden_size). The hidden
            state vector for the current timestep `t`.
    """

    def __init__(self, input_size, hidden_size):
        super().__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.i2h = nn.Linear(input_size, hidden_size)
        self.h2h = nn.Linear(hidden_size, hidden_size)

    def forward(self, input_0, input_1):
        primals_1 = self.i2h.weight
        primals_2 = self.i2h.bias
        primals_4 = self.h2h.weight
        primals_5 = self.h2h.bias
        primals_3 = input_0
        primals_6 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]
