# SPDX-FileCopyrightText: Copyright 2022, Arm Limited and/or its affiliates.
# SPDX-License-Identifier: Apache-2.0
"""Test for module optimizations/pruning."""
from pathlib import Path
from typing import List
from typing import Optional

import pytest
import tensorflow as tf
from numpy.core.numeric import isclose

from mlia.nn.tensorflow.optimizations.pruning import Pruner
from mlia.nn.tensorflow.optimizations.pruning import PruningConfiguration
from mlia.nn.tensorflow.tflite_metrics import TFLiteMetrics
from mlia.nn.tensorflow.utils import convert_to_tflite
from mlia.nn.tensorflow.utils import save_tflite_model
from tests.mlia.utils.common import get_dataset
from tests.mlia.utils.common import train_model


def _test_sparsity(
    metrics: TFLiteMetrics,
    target_sparsity: float,
    layers_to_prune: Optional[List[str]],
) -> None:
    pruned_sparsity_dict = metrics.sparsity_per_layer()
    num_sparse_layers = 0
    num_optimizable_layers = len(pruned_sparsity_dict)
    error_margin = 0.03
    if layers_to_prune:
        expected_num_sparse_layers = len(layers_to_prune)
    else:
        expected_num_sparse_layers = num_optimizable_layers
    for layer_name in pruned_sparsity_dict:
        if abs(pruned_sparsity_dict[layer_name] - target_sparsity) < error_margin:
            num_sparse_layers = num_sparse_layers + 1
    # make sure we are having exactly as many sparse layers as we wanted
    assert num_sparse_layers == expected_num_sparse_layers


def _test_check_sparsity(base_tflite_metrics: TFLiteMetrics) -> None:
    """Assert the sparsity of a model is zero."""
    base_sparsity_dict = base_tflite_metrics.sparsity_per_layer()
    for layer_name, sparsity in base_sparsity_dict.items():
        assert isclose(
            sparsity, 0, atol=1e-2
        ), f"Sparsity for layer '{layer_name}' is {sparsity}, but should be zero."


def _get_tflite_metrics(
    path: Path, tflite_fn: str, model: tf.keras.Model
) -> TFLiteMetrics:
    """Save model as TFLiteModel and return metrics."""
    temp_file = path / tflite_fn
    save_tflite_model(convert_to_tflite(model), temp_file)
    return TFLiteMetrics(str(temp_file))


@pytest.mark.parametrize("target_sparsity", (0.5, 0.9))
@pytest.mark.parametrize("mock_data", (False, True))
@pytest.mark.parametrize("layers_to_prune", (["conv1"], ["conv1", "conv2"], None))
def test_prune_simple_model_fully(
    target_sparsity: float,
    mock_data: bool,
    layers_to_prune: Optional[List[str]],
    tmp_path: Path,
    test_keras_model: Path,
) -> None:
    """Simple MNIST test to see if pruning works correctly."""
    x_train, y_train = get_dataset()
    batch_size = 1
    num_epochs = 1

    base_model = tf.keras.models.load_model(str(test_keras_model))
    train_model(base_model)

    base_tflite_metrics = _get_tflite_metrics(
        path=tmp_path,
        tflite_fn="test_prune_simple_model_fully_before.tflite",
        model=base_model,
    )

    # Make sure sparsity is zero before pruning
    _test_check_sparsity(base_tflite_metrics)

    if mock_data:
        pruner = Pruner(
            base_model,
            PruningConfiguration(
                target_sparsity,
                layers_to_prune,
            ),
        )

    else:
        pruner = Pruner(
            base_model,
            PruningConfiguration(
                target_sparsity,
                layers_to_prune,
                x_train,
                y_train,
                batch_size,
                num_epochs,
            ),
        )

    pruner.apply_optimization()
    pruned_model = pruner.get_model()

    pruned_tflite_metrics = _get_tflite_metrics(
        path=tmp_path,
        tflite_fn="test_prune_simple_model_fully_after.tflite",
        model=pruned_model,
    )

    _test_sparsity(pruned_tflite_metrics, target_sparsity, layers_to_prune)