# Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Pseudo-label: The simple and efficient semi-supervised learning method fordeep neural networks.

Reimplementation of http://deeplearning.net/wp-content/uploads/2013/03/pseudo_label_final.pdf
"""

import functools
import os

import numpy as np
import tensorflow as tf
from absl import app
from absl import flags

from libml import utils, data, models
from libml.utils import EasyDict

FLAGS = flags.FLAGS


class PseudoLabel(models.MultiModel):

    def model(self, batch, lr, wd, ema, warmup_pos, consistency_weight, threshold, **kwargs):
        hwc = [self.dataset.height, self.dataset.width, self.dataset.colors]
        xt_in = tf.placeholder(tf.float32, [batch] + hwc, 'xt')  # For training
        x_in = tf.placeholder(tf.float32, [None] + hwc, 'x')
        y_in = tf.placeholder(tf.float32, [batch] + hwc, 'y')
        l_in = tf.placeholder(tf.int32, [batch], 'labels')
        l = tf.one_hot(l_in, self.nclass)

        warmup = tf.clip_by_value(tf.to_float(self.step) / (warmup_pos * (FLAGS.train_kimg << 10)), 0, 1)
        lrate = tf.clip_by_value(tf.to_float(self.step) / (FLAGS.train_kimg << 10), 0, 1)
        lr *= tf.cos(lrate * (7 * np.pi) / (2 * 8))
        tf.summary.scalar('monitors/lr', lr)

        classifier = lambda x, **kw: self.classifier(x, **kw, **kwargs).logits
        logits_x = classifier(xt_in, training=True)
        post_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)  # Take only first call to update batch norm.
        logits_y = classifier(y_in, training=True)
        # Get the pseudo-label loss
        loss_pl = tf.nn.sparse_softmax_cross_entropy_with_logits(
            labels=tf.argmax(logits_y, axis=-1), logits=logits_y
        )
        # Masks denoting which data points have high-confidence predictions
        greater_than_thresh = tf.reduce_any(
            tf.greater(tf.nn.softmax(logits_y), threshold),
            axis=-1,
            keepdims=True,
        )
        greater_than_thresh = tf.cast(greater_than_thresh, loss_pl.dtype)
        # Only enforce the loss when the model is confident
        loss_pl *= greater_than_thresh
        # Note that we also average over examples without confident outputs;
        # this is consistent with the realistic evaluation codebase
        loss_pl = tf.reduce_mean(loss_pl)

        loss = tf.nn.softmax_cross_entropy_with_logits_v2(labels=l, logits=logits_x)
        loss = tf.reduce_mean(loss)
        tf.summary.scalar('losses/xe', loss)
        tf.summary.scalar('losses/pl', loss_pl)

        # L2 regularization
        loss_wd = sum(tf.nn.l2_loss(v) for v in utils.model_vars('classify') if 'kernel' in v.name)
        tf.summary.scalar('losses/wd', loss_wd)

        ema = tf.train.ExponentialMovingAverage(decay=ema)
        ema_op = ema.apply(utils.model_vars())
        ema_getter = functools.partial(utils.getter_ema, ema)
        post_ops.append(ema_op)

        train_op = tf.train.MomentumOptimizer(lr, 0.9, use_nesterov=True).minimize(
            loss + loss_pl * warmup * consistency_weight + wd * loss_wd, colocate_gradients_with_ops=True)
        with tf.control_dependencies([train_op]):
            train_op = tf.group(*post_ops)

        return EasyDict(
            xt=xt_in, x=x_in, y=y_in, label=l_in, train_op=train_op,
            classify_raw=tf.nn.softmax(classifier(x_in, training=False)),  # No EMA, for debugging.
            classify_op=tf.nn.softmax(classifier(x_in, getter=ema_getter, training=False)))


def main(argv):
    utils.setup_main()
    del argv  # Unused.
    dataset = data.DATASETS()[FLAGS.dataset]()
    log_width = utils.ilog2(dataset.width)
    model = PseudoLabel(
        os.path.join(FLAGS.train_dir, dataset.name),
        dataset,
        lr=FLAGS.lr,
        wd=FLAGS.wd,
        arch=FLAGS.arch,
        warmup_pos=FLAGS.warmup_pos,
        batch=FLAGS.batch,
        nclass=dataset.nclass,
        ema=FLAGS.ema,
        smoothing=FLAGS.smoothing,
        consistency_weight=FLAGS.consistency_weight,
        threshold=FLAGS.threshold,

        scales=FLAGS.scales or (log_width - 2),
        filters=FLAGS.filters,
        repeat=FLAGS.repeat)
    model.train(FLAGS.train_kimg << 10, FLAGS.report_kimg << 10)


if __name__ == '__main__':
    utils.setup_tf()
    flags.DEFINE_float('wd', 0.0005, 'Weight decay.')
    flags.DEFINE_float('consistency_weight', 1., 'Consistency weight.')
    flags.DEFINE_float('threshold', 0.95, 'Pseudo-label threshold.')
    flags.DEFINE_float('warmup_pos', 0.4, 'Relative position at which constraint loss warmup ends.')
    flags.DEFINE_float('ema', 0.999, 'Exponential moving average of params.')
    flags.DEFINE_float('smoothing', 0.1, 'Label smoothing.')
    flags.DEFINE_integer('scales', 0, 'Number of 2x2 downscalings in the classifier.')
    flags.DEFINE_integer('filters', 32, 'Filter size of convolutions.')
    flags.DEFINE_integer('repeat', 4, 'Number of residual layers per stage.')
    FLAGS.set_default('dataset', 'cifar10.3@250-5000')
    FLAGS.set_default('batch', 64)
    FLAGS.set_default('lr', 0.03)
    FLAGS.set_default('train_kimg', 1 << 16)
    app.run(main)