#!/usr/bin/env python
# vim: set fileencoding=utf-8 :
# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>

import tensorflow as tf

from bob.learn.tensorflow.network import dummy
from bob.learn.tensorflow.estimators import Siamese, Logits

from bob.learn.tensorflow.dataset.siamese_image import shuffle_data_and_labels_image_augmentation as siamese_batch
from bob.learn.tensorflow.dataset.image import shuffle_data_and_labels_image_augmentation as single_batch

from bob.learn.tensorflow.loss import contrastive_loss, mean_cross_entropy_loss
from bob.learn.tensorflow.utils.hooks import LoggerHookEstimator
from .test_estimator_transfer import dummy_adapted
from bob.learn.tensorflow.utils import reproducible

import pkg_resources
import shutil


# Fixing problem with MAC https://github.com/dmlc/xgboost/issues/1715
import os
os.environ['KMP_DUPLICATE_LIB_OK']='True'

tfrecord_train = "./train_mnist.tfrecord"
tfrecord_validation = "./validation_mnist.tfrecord"
model_dir = "./temp"
model_dir_adapted = "./temp2"

learning_rate = 0.0001
data_shape = (250, 250, 3)  # size of atnt images
output_shape = (50, 50)
data_type = tf.float32
batch_size = 4
validation_batch_size = 2
epochs = 1
steps = 5000

# Data
filenames = [
    pkg_resources.resource_filename(
        __name__, 'data/dummy_image_database/m301_01_p01_i0_0.png'),
    pkg_resources.resource_filename(
        __name__, 'data/dummy_image_database/m301_01_p02_i0_0.png'),
    pkg_resources.resource_filename(
        __name__, 'data/dummy_image_database/m301_01_p01_i0_0.png'),
    pkg_resources.resource_filename(
        __name__, 'data/dummy_image_database/m301_01_p02_i0_0.png'),
    pkg_resources.resource_filename(
        __name__, 'data/dummy_image_database/m301_01_p01_i0_0.png'),
    pkg_resources.resource_filename(
        __name__, 'data/dummy_image_database/m301_01_p02_i0_0.png'),
    pkg_resources.resource_filename(
        __name__, 'data/dummy_image_database/m301_01_p02_i0_0.png'),
    pkg_resources.resource_filename(
        __name__, 'data/dummy_image_database/m301_01_p01_i0_0.png'),
    pkg_resources.resource_filename(
        __name__, 'data/dummy_image_database/m301_01_p02_i0_0.png'),
    pkg_resources.resource_filename(
        __name__, 'data/dummy_image_database/m304_01_p01_i0_0.png'),
    pkg_resources.resource_filename(
        __name__, 'data/dummy_image_database/m304_02_f12_i0_0.png'),
    pkg_resources.resource_filename(
        __name__, 'data/dummy_image_database/m304_01_p01_i0_0.png'),
    pkg_resources.resource_filename(
        __name__, 'data/dummy_image_database/m304_02_f12_i0_0.png'),
    pkg_resources.resource_filename(
        __name__, 'data/dummy_image_database/m304_01_p01_i0_0.png'),
    pkg_resources.resource_filename(
        __name__, 'data/dummy_image_database/m304_02_f12_i0_0.png'),
    pkg_resources.resource_filename(
        __name__, 'data/dummy_image_database/m304_01_p01_i0_0.png'),
    pkg_resources.resource_filename(
        __name__, 'data/dummy_image_database/m304_02_f12_i0_0.png'),
    pkg_resources.resource_filename(
        __name__, 'data/dummy_image_database/m304_02_f12_i0_0.png'),
]
labels = [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]


def test_siamesetrainer():
    # Trainer logits
    try:

        # Setting seed
        session_config, run_config, _, _, _ = reproducible.set_seed()
        run_config = run_config.replace(save_checkpoints_steps=500)

        trainer = Siamese(
            model_dir=model_dir,
            architecture=dummy,
            config=run_config,
            optimizer=tf.train.GradientDescentOptimizer(learning_rate),
            loss_op=contrastive_loss,
            validation_batch_size=validation_batch_size)
        run_siamesetrainer(trainer)
    finally:
        try:
            shutil.rmtree(model_dir, ignore_errors=True)
            # pass
        except Exception:
            pass


def test_siamesetrainer_transfer():
    def logits_input_fn():
        return single_batch(
            filenames,
            labels,
            data_shape,
            data_type,
            batch_size,
            epochs=epochs,
            output_shape=output_shape)

    # Trainer logits first than siamese
    try:
        # Setting seed
        session_config, run_config, _, _, _ = reproducible.set_seed()
        run_config = run_config.replace(save_checkpoints_steps=500)

        extra_checkpoint = {
            "checkpoint_path": model_dir,
            "scopes": dict({
                "Dummy/": "Dummy/"
            }),
            "trainable_variables": []
        }

        # LOGISTS
        logits_trainer = Logits(
            model_dir=model_dir,
            architecture=dummy,
            optimizer=tf.train.GradientDescentOptimizer(learning_rate),
            n_classes=2,
            config=run_config,
            loss_op=mean_cross_entropy_loss,
            embedding_validation=False,
            validation_batch_size=validation_batch_size)
        logits_trainer.train(logits_input_fn, steps=steps)

        # NOW THE FUCKING SIAMESE
        trainer = Siamese(
            model_dir=model_dir_adapted,
            architecture=dummy_adapted,
            optimizer=tf.train.GradientDescentOptimizer(learning_rate),
            config=run_config,
            loss_op=contrastive_loss,
            validation_batch_size=validation_batch_size,
            extra_checkpoint=extra_checkpoint)
        run_siamesetrainer(trainer)
    finally:
        try:
            shutil.rmtree(model_dir, ignore_errors=True)
            shutil.rmtree(model_dir_adapted, ignore_errors=True)
        except Exception:
            pass


def test_siamesetrainer_transfer_extraparams():
    def logits_input_fn():
        return single_batch(
            filenames,
            labels,
            data_shape,
            data_type,
            batch_size,
            epochs=epochs,
            output_shape=output_shape)

    # Trainer logits first than siamese
    try:

        extra_checkpoint = {
            "checkpoint_path": model_dir,
            "scopes": dict({
                "Dummy/": "Dummy/"
            }),
            "trainable_variables": ["Dummy"]
        }

        # Setting seed
        session_config, run_config, _, _, _ = reproducible.set_seed()
        run_config = run_config.replace(save_checkpoints_steps=500)

        # LOGISTS
        logits_trainer = Logits(
            model_dir=model_dir,
            architecture=dummy,
            optimizer=tf.train.GradientDescentOptimizer(learning_rate),
            n_classes=2,
            config=run_config,
            loss_op=mean_cross_entropy_loss,
            embedding_validation=False,
            validation_batch_size=validation_batch_size)

        logits_trainer.train(logits_input_fn, steps=steps)

        # NOW THE FUCKING SIAMESE
        trainer = Siamese(
            model_dir=model_dir_adapted,
            architecture=dummy_adapted,
            optimizer=tf.train.GradientDescentOptimizer(learning_rate),
            loss_op=contrastive_loss,
            config=run_config,
            validation_batch_size=validation_batch_size,
            extra_checkpoint=extra_checkpoint)
        run_siamesetrainer(trainer)
    finally:
        try:
            shutil.rmtree(model_dir, ignore_errors=True)
            shutil.rmtree(model_dir_adapted, ignore_errors=True)
        except Exception:
            pass


def run_siamesetrainer(trainer):
    # Cleaning up
    tf.reset_default_graph()
    assert len(tf.global_variables()) == 0

    def input_fn():
        return siamese_batch(
            filenames,
            labels,
            data_shape,
            data_type,
            batch_size,
            epochs=epochs,
            output_shape=output_shape,
            random_flip=True,
            random_brightness=True,
            random_contrast=True,
            random_saturation=True)

    def input_validation_fn():
        return single_batch(
            filenames,
            labels,
            data_shape,
            data_type,
            validation_batch_size,
            epochs=10,
            output_shape=output_shape)

    hooks = [
        LoggerHookEstimator(trainer, batch_size, 300),
        tf.train.SummarySaverHook(
            save_steps=1000,
            output_dir=model_dir,
            scaffold=tf.train.Scaffold(),
            summary_writer=tf.summary.FileWriter(model_dir))
    ]

    trainer.train(input_fn, steps=1, hooks=hooks)

    acc = trainer.evaluate(input_validation_fn)
    assert acc['accuracy'] > 0.3

    # Cleaning up
    tf.reset_default_graph()
    assert len(tf.global_variables()) == 0
