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


class CA1(nn.Module):
    """Reconstructs the inputs that originated from EC network.

    Consists of 2 fully connected layers, recieving inputs from CA3
    and outputs to EC. 
    """

    def __init__(self, N, D_in, D_out, resize_dim):
        super(CA1, self).__init__()
        self.N, self.resize_dim = N, resize_dim
        self.fc1 = nn.Linear(D_in, 100)
        self.fc2 = nn.Linear(100, D_out)
        nn.init.xavier_uniform_(self.fc1.weight)

    def forward(self, x):
        x = F.leaky_relu(self.fc1(x), 0.1)
        x = F.leaky_relu(self.fc2(x), 0.1)
        x = x.view(self.N, 1, self.resize_dim, self.resize_dim)
        return x


def get_inputs():
    return [torch.rand([4, 1, 4, 4])]


def get_init_inputs():
    return [[], {'N': 4, 'D_in': 4, 'D_out': 4, 'resize_dim': 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
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_leaky_relu_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 1600
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 100
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.1
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr1 + x2, tmp7, xmask)


@triton.jit
def triton_poi_fused_leaky_relu_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.1
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr1 + x2, tmp7, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (100, 4), (4, 1))
    assert_size_stride(primals_2, (100,), (1,))
    assert_size_stride(primals_3, (4, 1, 4, 4), (16, 16, 4, 1))
    assert_size_stride(primals_4, (4, 100), (100, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 100), (100, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 100), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 1, 4, 100), (400, 400, 100, 1), torch
            .bool)
        buf2 = empty_strided_cuda((4, 1, 4, 100), (400, 1, 100, 1), torch.
            float32)
        get_raw_stream(0)
        triton_poi_fused_leaky_relu_0[grid(1600)](buf0, primals_2, buf1,
            buf2, 1600, XBLOCK=256, num_warps=4, num_stages=1)
        del buf0
        del primals_2
        buf3 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 100), (100, 1), 0),
            reinterpret_tensor(primals_4, (100, 4), (1, 100), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 1, 4, 4), (16, 16, 4, 1), torch.bool)
        buf5 = empty_strided_cuda((4, 1, 4, 4), (16, 16, 4, 1), torch.float32)
        triton_poi_fused_leaky_relu_1[grid(64)](buf3, primals_5, buf4, buf5,
            64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf3
        del primals_5
    return buf5, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0
        ), buf1, reinterpret_tensor(buf2, (16, 100), (100, 1), 0
        ), buf4, primals_4


class CA1New(nn.Module):
    """Reconstructs the inputs that originated from EC network.

    Consists of 2 fully connected layers, recieving inputs from CA3
    and outputs to EC. 
    """

    def __init__(self, N, D_in, D_out, resize_dim):
        super(CA1New, self).__init__()
        self.N, self.resize_dim = N, resize_dim
        self.fc1 = nn.Linear(D_in, 100)
        self.fc2 = nn.Linear(100, D_out)
        nn.init.xavier_uniform_(self.fc1.weight)

    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_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
