import numpy as np
import pytest
from PIL import Image

from image_prediction.estimator.adapter.adapters.keras import KerasEstimatorAdapter
from image_prediction.estimator.adapter.adapters.mock import DummyEstimator, EstimatorAdapterMock
from image_prediction.estimator.estimator import Estimator
from image_prediction.exceptions import UnknownEstimatorAdapter
from image_prediction.predictor.predictor import Predictor


@pytest.fixture
def predictor(estimator):
    return Predictor(estimator)


@pytest.fixture
def estimator(estimator_adapter, classes):
    service_estimator = Estimator(estimator_adapter, classes)
    return service_estimator


@pytest.fixture
def estimator_adapter(estimator_type, keras_model, output_batch, monkeypatch):
    if estimator_type == "mock":
        estimator = EstimatorAdapterMock(DummyEstimator())
    elif estimator_type == "keras":
        estimator = KerasEstimatorAdapter(keras_model)
    else:
        raise UnknownEstimatorAdapter(f"No adapter for estimator type {estimator_type} was specified.")

    def mock_predict(batch):
        _predict(batch)
        return output_batch

    _predict = estimator.predict
    monkeypatch.setattr(estimator, "predict", mock_predict)

    return estimator


@pytest.fixture
def keras_model(input_size):
    import warnings
    warnings.filterwarnings("ignore", category=DeprecationWarning)

    import os
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'

    import tensorflow as tf

    tf.keras.backend.set_image_data_format('channels_last')

    inputs = tf.keras.Input(shape=input_size)
    conv = tf.keras.layers.Conv2D(3, 3)
    dense = tf.keras.layers.Dense(10, activation="relu")

    outputs = tf.keras.layers.Dense(10)(dense(conv(inputs)))
    model = tf.keras.Model(inputs=inputs, outputs=outputs)
    model.compile()

    return model


@pytest.fixture
def batch_size():
    return 4


@pytest.fixture
def images(input_batch):
    return list(map(array_to_image, input_batch))


@pytest.fixture
def input_batch(batch_size, input_size):
    return np.random.random_sample(size=(batch_size, *input_size))


def array_to_image(array):
    assert np.all(array <= 1)
    assert np.all(array >= 0)
    return Image.fromarray(np.uint8(array * 255), mode="RGB")


@pytest.fixture
def input_size(depth=3, width=10, height=15):
    return width, height, depth


@pytest.fixture
def expected_predictions(output_batch, classes):
    return map_labels(output_batch, classes)


@pytest.fixture
def output_batch(batch_size, classes):
    return np.random.randint(low=0, high=len(classes), size=batch_size)


@pytest.fixture
def classes():
    return ["A", "B", "C"]


def map_labels(numeric_labels, classes):
    return [classes[nl] for nl in numeric_labels]