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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
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# pytest ./tests/test_super_container.py -s         #
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import random
import unittest
import pytest

import torch
from xautodl import spaces
from xautodl.xlayers import super_core


"""Test the super container layers."""


def _internal_func(inputs, model):
    outputs = model(inputs)
    abstract_space = model.abstract_search_space
    print(
        "The abstract search space for SuperAttention is:\n{:}".format(abstract_space)
    )
    abstract_space.clean_last()
    abstract_child = abstract_space.random(reuse_last=True)
    print("The abstract child program is:\n{:}".format(abstract_child))
    model.enable_candidate()
    model.set_super_run_type(super_core.SuperRunMode.Candidate)
    model.apply_candidate(abstract_child)
    outputs = model(inputs)
    return abstract_child, outputs


def _create_stel(input_dim, output_dim, order):
    return super_core.SuperSequential(
        super_core.SuperLinear(input_dim, output_dim),
        super_core.SuperTransformerEncoderLayer(
            output_dim,
            num_heads=spaces.Categorical(2, 4, 6),
            mlp_hidden_multiplier=spaces.Categorical(1, 2, 4),
            order=order,
        ),
    )


@pytest.mark.parametrize("batch", (1, 2, 4))
@pytest.mark.parametrize("seq_dim", (1, 10, 30))
@pytest.mark.parametrize("input_dim", (6, 12, 24, 27))
@pytest.mark.parametrize(
    "order", (super_core.LayerOrder.PreNorm, super_core.LayerOrder.PostNorm)
)
def test_super_sequential(batch, seq_dim, input_dim, order):
    out1_dim = spaces.Categorical(12, 24, 36)
    out2_dim = spaces.Categorical(24, 36, 48)
    out3_dim = spaces.Categorical(36, 72, 100)
    layer1 = _create_stel(input_dim, out1_dim, order)
    layer2 = _create_stel(out1_dim, out2_dim, order)
    layer3 = _create_stel(out2_dim, out3_dim, order)
    model = super_core.SuperSequential(layer1, layer2, layer3)
    print(model)
    model.apply_verbose(True)
    inputs = torch.rand(batch, seq_dim, input_dim)
    abstract_child, outputs = _internal_func(inputs, model)
    output_shape = (
        batch,
        seq_dim,
        out3_dim.abstract(reuse_last=True).random(reuse_last=True).value,
    )
    assert tuple(outputs.shape) == output_shape


def test_super_sequential_v1():
    model = super_core.SuperSequential(
        super_core.SuperSimpleNorm(1, 1),
        torch.nn.ReLU(),
        super_core.SuperLeakyReLU(),
        super_core.SuperLinear(10, 10),
        super_core.SuperReLU(),
    )
    inputs = torch.rand(10, 10)
    print(model)
    outputs = model(inputs)

    abstract_search_space = model.abstract_search_space
    print(abstract_search_space)