import random

import numpy as np
import pytest
from PIL import Image

from image_prediction.classifier.classifier import Classifier
from image_prediction.classifier.image_classifier import ImageClassifier
from image_prediction.estimator.adapter.adapters.keras import KerasEstimatorAdapter
from image_prediction.estimator.adapter.adapters.mock import DummyEstimator, EstimatorAdapterMock
from image_prediction.exceptions import UnknownEstimatorAdapter


@pytest.fixture
def predictor(classifier, monkeypatch, expected_predictions):
    return ImageClassifier(classifier)


@pytest.fixture
def classifier(estimator_adapter, classes):
    classifier = Classifier(estimator_adapter, classes)
    return classifier


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

    def mock_predict(batch):
        # Run real predict function to test for mechanical issues, but return externally defined
        # predictions to test the callers of the estimator adapter against the expected predictions
        return [next(output_batch_generator) for _ in _predict(batch)]

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

    return estimator_adapter


@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)

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

    return model


@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(input_batch, classes):
    return random.choices(range(len(classes)), k=len(input_batch))


@pytest.fixture
def output_batch_generator(output_batch):
    return iter(output_batch)


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


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