import torch
import numpy as np
import torch as tor
from torch import nn


class SaveableModel(object):

    def save(self, path):
        tor.save(self, path)

    @classmethod
    def load(cls, path):
        return tor.load(path)

    @classmethod
    def load_best(cls, path):
        assert os.path.isdir(path)
        best_models = glob.glob(os.path.join(path, '*best*'))
        assert not len(best_models > 1)
        return tor.load(os.path.join(path, best_models[0]))


class NeuralNet(nn.Module, SaveableModel):

    def __init__(self):
        super(NeuralNet, self).__init__()
        self.relu = nn.ReLU()
        self.tanh = nn.Tanh()
        self.softmax = nn.Softmax()

    def action(self, x):
        pass


class StochasticNeuralNet(NeuralNet):

    def __init__(self):
        super(StochasticNeuralNet, self).__init__()

    def sample_action(self, action_distribution=None):
        if not action_distribution:
            action_distribution = self.out
        action_distribution = action_distribution.cpu().data.numpy()
        action = np.random.choice(action_distribution.squeeze(), p=
            action_distribution.squeeze())
        action = np.argmax(action_distribution == action)
        return action


class PolicyAHG(StochasticNeuralNet):

    def __init__(self, input_size, output_size):
        super(PolicyAHG, self).__init__()
        self.f1 = nn.Linear(input_size, 32)
        self.f2 = nn.Linear(32, output_size)

    def forward(self, x):
        out = self.f1(x)
        out = self.tanh(out)
        out = self.f2(out)
        out = self.softmax(out)
        self.out = out
        return out


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


def get_init_inputs():
    return [[], {'input_size': 4, 'output_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
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import numpy as np
import torch as tor
from torch import 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_tanh_0(in_out_ptr0, in_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 % 32
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.tanh(tmp2)
    tl.store(in_out_ptr0 + x2, tmp3, None)


@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x3, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x3, tmp8, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (32, 4), (4, 1))
    assert_size_stride(primals_2, (32,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 32), (32, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 32), (32, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 32), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 32), (512, 128, 32, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_tanh_0[grid(2048)](buf1, primals_2, 2048, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 32),
            (32, 1), 0), reinterpret_tensor(primals_4, (32, 4), (1, 32), 0),
            alpha=1, beta=1, out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(256)](buf2, buf3, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf4 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf2
        triton_poi_fused__softmax_2[grid(256)](buf3, buf4, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf3
    return buf4, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), buf1, buf4, primals_4


class SaveableModel(object):

    def save(self, path):
        tor.save(self, path)

    @classmethod
    def load(cls, path):
        return tor.load(path)

    @classmethod
    def load_best(cls, path):
        assert os.path.isdir(path)
        best_models = glob.glob(os.path.join(path, '*best*'))
        assert not len(best_models > 1)
        return tor.load(os.path.join(path, best_models[0]))


class NeuralNet(nn.Module, SaveableModel):

    def __init__(self):
        super(NeuralNet, self).__init__()
        self.relu = nn.ReLU()
        self.tanh = nn.Tanh()
        self.softmax = nn.Softmax()

    def action(self, x):
        pass


class StochasticNeuralNet(NeuralNet):

    def __init__(self):
        super(StochasticNeuralNet, self).__init__()

    def sample_action(self, action_distribution=None):
        if not action_distribution:
            action_distribution = self.out
        action_distribution = action_distribution.cpu().data.numpy()
        action = np.random.choice(action_distribution.squeeze(), p=
            action_distribution.squeeze())
        action = np.argmax(action_distribution == action)
        return action


class PolicyAHGNew(StochasticNeuralNet):

    def __init__(self, input_size, output_size):
        super(PolicyAHGNew, self).__init__()
        self.f1 = nn.Linear(input_size, 32)
        self.f2 = nn.Linear(32, output_size)

    def forward(self, input_0):
        primals_1 = self.f1.weight
        primals_2 = self.f1.bias
        primals_4 = self.f2.weight
        primals_5 = self.f2.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
