import torch
import numpy as np
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import torch.optim


class BasicNet:

    def __init__(self, optimizer_fn, gpu, LSTM=False):
        self.gpu = gpu and torch.cuda.is_available()
        self.LSTM = LSTM
        if self.gpu:
            self
            self.FloatTensor = torch.FloatTensor
        else:
            self.FloatTensor = torch.FloatTensor

    def to_torch_variable(self, x, dtype='float32'):
        if isinstance(x, Variable):
            return x
        if not isinstance(x, torch.FloatTensor):
            x = torch.from_numpy(np.asarray(x, dtype=dtype))
        if self.gpu:
            x = x
        return Variable(x)

    def reset(self, terminal):
        if not self.LSTM:
            return
        if terminal:
            self.h.data.zero_()
            self.c.data.zero_()
        self.h = Variable(self.h.data)
        self.c = Variable(self.c.data)


class VanillaNet(BasicNet):

    def predict(self, x, to_numpy=False):
        y = self.forward(x)
        if to_numpy:
            if type(y) is list:
                y = [y_.cpu().data.numpy() for y_ in y]
            else:
                y = y.cpu().data.numpy()
        return y


class FCNet(nn.Module, VanillaNet):

    def __init__(self, dims, optimizer_fn=None, gpu=True):
        super(FCNet, self).__init__()
        self.fc1 = nn.Linear(dims[0], dims[1])
        self.fc2 = nn.Linear(dims[1], dims[2])
        self.fc3 = nn.Linear(dims[2], dims[3])
        BasicNet.__init__(self, optimizer_fn, gpu)

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


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


def get_init_inputs():
    return [[], {'dims': [4, 4, 4, 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 numpy as np
from torch.autograd import Variable
import torch.nn as nn
import torch.optim
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 = 16
    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 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, 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, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (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, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (4, 4),
            (1, 4), 0), out=buf0)
        del primals_2
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(16)](buf1, primals_3, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf1, reinterpret_tensor(primals_4, (4, 4), (1, 4
            ), 0), out=buf2)
        buf3 = buf2
        del buf2
        triton_poi_fused_relu_0[grid(16)](buf3, primals_5, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, buf3, reinterpret_tensor(primals_6,
            (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf4)
        del primals_7
    return buf4, primals_1, buf1, buf3, primals_6, primals_4


class BasicNet:

    def __init__(self, optimizer_fn, gpu, LSTM=False):
        self.gpu = gpu and torch.cuda.is_available()
        self.LSTM = LSTM
        if self.gpu:
            self
            self.FloatTensor = torch.FloatTensor
        else:
            self.FloatTensor = torch.FloatTensor

    def to_torch_variable(self, x, dtype='float32'):
        if isinstance(x, Variable):
            return x
        if not isinstance(x, torch.FloatTensor):
            x = torch.from_numpy(np.asarray(x, dtype=dtype))
        if self.gpu:
            x = x
        return Variable(x)

    def reset(self, terminal):
        if not self.LSTM:
            return
        if terminal:
            self.h.data.zero_()
            self.c.data.zero_()
        self.h = Variable(self.h.data)
        self.c = Variable(self.c.data)


class VanillaNet(BasicNet):

    def predict(self, x, to_numpy=False):
        y = self.forward(x)
        if to_numpy:
            if type(y) is list:
                y = [y_.cpu().data.numpy() for y_ in y]
            else:
                y = y.cpu().data.numpy()
        return y


class FCNetNew(nn.Module, VanillaNet):

    def __init__(self, dims, optimizer_fn=None, gpu=True):
        super(FCNetNew, self).__init__()
        self.fc1 = nn.Linear(dims[0], dims[1])
        self.fc2 = nn.Linear(dims[1], dims[2])
        self.fc3 = nn.Linear(dims[2], dims[3])
        BasicNet.__init__(self, optimizer_fn, gpu)

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