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


class ActorMARL(nn.Module):

    def __init__(self, dim_observation, dim_action):
        super(ActorMARL, self).__init__()
        self.FC1 = nn.Linear(dim_observation, 500)
        self.FC2 = nn.Linear(500, 128)
        self.FC3 = nn.Linear(128, dim_action)

    def forward(self, obs):
        result = F.relu(self.FC1(obs))
        result = F.relu(self.FC2(result))
        result = F.tanh(self.FC3(result))
        return result


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


def get_init_inputs():
    return [[], {'dim_observation': 4, 'dim_action': 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
from torch._inductor.runtime.triton_helpers import libdevice
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_threshold_backward_0(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 32000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x0 = xindex % 500
    x2 = xindex // 2000
    x3 = xindex % 2000
    tmp0 = tl.load(in_ptr0 + x4, 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 + (x3 + 2016 * x2), tmp4, xmask)
    tl.store(out_ptr1 + (x3 + 2048 * x2), tmp6, xmask)


@triton.jit
def triton_poi_fused_relu_view_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 32000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 500
    x1 = xindex // 500
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 500 * (x1 % 4) + 2016 * (x1 // 4)), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


@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 % 128
    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_tanh_3(in_out_ptr0, in_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')
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.tanh(tmp2)
    tl.store(in_out_ptr0 + x2, tmp3, 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, (500, 4), (4, 1))
    assert_size_stride(primals_2, (500,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (128, 500), (500, 1))
    assert_size_stride(primals_5, (128,), (1,))
    assert_size_stride(primals_6, (4, 128), (128, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 500), (500, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 500), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4, 500), (8064, 2016, 500, 1),
            torch.float32)
        buf8 = empty_strided_cuda((4, 4, 4, 500), (8192, 2048, 500, 1),
            torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(32000)](buf0,
            primals_2, buf1, buf8, 32000, XBLOCK=256, num_warps=4, num_stages=1
            )
        del primals_2
        buf2 = buf0
        del buf0
        triton_poi_fused_relu_view_1[grid(32000)](buf1, buf2, 32000, XBLOCK
            =256, num_warps=4, num_stages=1)
        del buf1
        buf3 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
        extern_kernels.mm(buf2, reinterpret_tensor(primals_4, (500, 128), (
            1, 500), 0), out=buf3)
        buf4 = reinterpret_tensor(buf3, (4, 4, 4, 128), (2048, 512, 128, 1), 0)
        del buf3
        buf7 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_2[grid(8192)](buf4,
            primals_5, buf7, 8192, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_5
        buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf4, (64, 128), (128, 1), 0),
            reinterpret_tensor(primals_6, (128, 4), (1, 128), 0), out=buf5)
        buf6 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf5
        triton_poi_fused_tanh_3[grid(256)](buf6, primals_7, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_7
    return buf6, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), buf2, reinterpret_tensor(buf4, (64, 128), (128, 1), 0
        ), buf6, primals_6, buf7, primals_4, buf8


class ActorMARLNew(nn.Module):

    def __init__(self, dim_observation, dim_action):
        super(ActorMARLNew, self).__init__()
        self.FC1 = nn.Linear(dim_observation, 500)
        self.FC2 = nn.Linear(500, 128)
        self.FC3 = nn.Linear(128, dim_action)

    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]
