# 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.
import functools
import os

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

from fixmatch import FixMatch
from libml import data, utils

FLAGS = flags.FLAGS


class AB_FixMatch_SharpenConditionalConsistency(FixMatch):
    def model(self, batch, lr, wd, wu, uratio, T, ema=0.999, confidence=0.95, **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 * uratio, 2] + hwc, 'y')
        l_in = tf.placeholder(tf.int32, [batch], 'labels')

        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
        skip_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
        x = utils.interleave(tf.concat([xt_in, y_in[:, 0], y_in[:, 1]], 0), 2 * uratio + 1)
        logits = utils.para_cat(lambda x: classifier(x, training=True), x)
        logits = utils.de_interleave(logits, 2 * uratio+1)
        post_ops = [v for v in tf.get_collection(tf.GraphKeys.UPDATE_OPS) if v not in skip_ops]
        logits_x = logits[:batch]
        logits_weak, logits_strong = tf.split(logits[batch:], 2)
        del logits, skip_ops

        loss_xe = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=l_in, logits=logits_x)
        loss_xe = tf.reduce_mean(loss_xe)
        tf.summary.scalar('losses/xe', loss_xe)

        pseudo_labels = tf.stop_gradient(tf.nn.softmax(logits_weak))
        pseudo_mask = tf.to_float(tf.reduce_max(pseudo_labels, axis=1) >= confidence)
        sharp_labels = tf.stop_gradient(tf.nn.softmax(logits_weak / T))
        loss_xeu = tf.nn.softmax_cross_entropy_with_logits_v2(labels=sharp_labels, logits=logits_strong)
        tf.summary.scalar('monitors/mask', tf.reduce_mean(pseudo_mask))
        loss_xeu = tf.reduce_mean(loss_xeu * pseudo_mask)
        tf.summary.scalar('losses/xeu', loss_xeu)

        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_xe + wu * loss_xeu + wd * loss_wd, colocate_gradients_with_ops=True)
        with tf.control_dependencies([train_op]):
            train_op = tf.group(*post_ops)

        return utils.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.PAIR_DATASETS()[FLAGS.dataset]()
    log_width = utils.ilog2(dataset.width)
    model = AB_FixMatch_SharpenConditionalConsistency(
        os.path.join(FLAGS.train_dir, dataset.name, AB_FixMatch_SharpenConditionalConsistency.cta_name()),
        dataset,
        lr=FLAGS.lr,
        wd=FLAGS.wd,
        arch=FLAGS.arch,
        batch=FLAGS.batch,
        nclass=dataset.nclass,
        confidence=FLAGS.confidence,
        T=FLAGS.T,
        wu=FLAGS.wu,
        uratio=FLAGS.uratio,
        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('confidence', 0.95, 'Confidence threshold.')
    flags.DEFINE_float('T', 1, 'Temperature for sharpening.')
    flags.DEFINE_float('wd', 0.0005, 'Weight decay.')
    flags.DEFINE_float('wu', 1, 'Pseudo label loss weight.')
    flags.DEFINE_integer('filters', 32, 'Filter size of convolutions.')
    flags.DEFINE_integer('repeat', 4, 'Number of residual layers per stage.')
    flags.DEFINE_integer('scales', 0, 'Number of 2x2 downscalings in the classifier.')
    flags.DEFINE_integer('uratio', 7, 'Unlabeled batch size ratio.')
    FLAGS.set_default('augment', 'd.d.d')
    FLAGS.set_default('dataset', 'cifar10.3@250-1')
    FLAGS.set_default('batch', 64)
    FLAGS.set_default('lr', 0.03)
    FLAGS.set_default('train_kimg', 1 << 16)
    app.run(main)