# Copyright 2020 The Orbit Authors. All Rights Reserved.
#
# 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
#
#     http://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.
# ==============================================================================
"""Tests for orbit.controller."""

import os
from absl import logging
from absl.testing import parameterized
import numpy as np
from orbit import controller
from orbit import standard_runner

import tensorflow as tf


def create_model():
  x = tf.keras.layers.Input(shape=(3,), name="input")
  y = tf.keras.layers.Dense(4, name="dense")(x)
  model = tf.keras.Model(x, y)
  return model


def summaries_with_matching_keyword(keyword, summary_dir):
  """Returns summary protos matching given keyword from event file."""
  matches = []
  event_paths = tf.io.gfile.glob(os.path.join(summary_dir, "events*"))
  for event in tf.compat.v1.train.summary_iterator(event_paths[-1]):
    if event.summary is not None:
      for value in event.summary.value:
        if keyword in value.tag:
          matches.append(event.summary)
  return matches


def dataset_fn(ctx):
  del ctx
  inputs = np.zeros((10, 3), dtype=np.float32)
  targets = np.ones((10, 4), dtype=np.float32)
  dataset = tf.data.Dataset.from_tensor_slices((inputs, targets))
  dataset = dataset.repeat(100)
  dataset = dataset.batch(10, drop_remainder=True)
  return dataset


class TestRunner(standard_runner.StandardTrainer,
                 standard_runner.StandardEvaluator):
  """Implements the training and evaluation APIs for the test model."""

  def __init__(self, return_numpy=False):
    self.strategy = tf.distribute.get_strategy()
    self.model = create_model()
    self.optimizer = tf.keras.optimizers.RMSprop(learning_rate=0.1)
    self.global_step = self.optimizer.iterations
    self.train_loss = tf.keras.metrics.Mean("train_loss", dtype=tf.float32)
    self.eval_loss = tf.keras.metrics.Mean("eval_loss", dtype=tf.float32)
    self.return_numpy = return_numpy
    train_dataset = (
        self.strategy.experimental_distribute_datasets_from_function(dataset_fn)
    )
    eval_dataset = (
        self.strategy.experimental_distribute_datasets_from_function(dataset_fn)
    )
    standard_runner.StandardTrainer.__init__(self, train_dataset)
    standard_runner.StandardEvaluator.__init__(self, eval_dataset)

  def train_step(self, iterator):

    def _replicated_step(inputs):
      """Replicated training step."""
      inputs, targets = inputs
      with tf.GradientTape() as tape:
        outputs = self.model(inputs)
        loss = tf.reduce_mean(tf.keras.losses.MSE(targets, outputs))
      grads = tape.gradient(loss, self.model.variables)
      self.optimizer.apply_gradients(zip(grads, self.model.variables))
      self.train_loss.update_state(loss)

    self.strategy.run(_replicated_step, args=(next(iterator),))

  def train_loop_end(self):
    train_loss = self.train_loss.result()
    return {
        "loss": train_loss.numpy() if self.return_numpy else train_loss,
    }

  def build_eval_dataset(self):
    return self.strategy.experimental_distribute_datasets_from_function(
        dataset_fn)

  def eval_begin(self):
    self.eval_loss.reset_states()

  def eval_step(self, iterator):

    def _replicated_step(inputs):
      """Replicated evaluation step."""
      inputs, targets = inputs
      outputs = self.model(inputs)
      loss = tf.reduce_mean(tf.keras.losses.MSE(targets, outputs))
      self.eval_loss.update_state(loss)

    self.strategy.run(_replicated_step, args=(next(iterator),))

  def eval_end(self):
    eval_loss = self.eval_loss.result()
    return {
        "eval_loss": eval_loss.numpy() if self.return_numpy else eval_loss,
    }


class TestEvaluator(standard_runner.StandardEvaluator):
  """Implements the training and evaluation APIs for the test model."""

  def __init__(self):
    self.strategy = tf.distribute.get_strategy()
    self.model = create_model()
    eval_dataset = self.strategy.experimental_distribute_datasets_from_function(
        dataset_fn)
    standard_runner.StandardEvaluator.__init__(self, eval_dataset)

  def eval_reduce(self, state, output):
    state.append(output)
    return state

  def eval_begin(self):
    return []

  def eval_step(self, iterator):

    def _replicated_step(inputs):
      """Replicated evaluation step."""
      inputs, targets = inputs
      outputs = self.model(inputs)
      loss = tf.reduce_mean(tf.keras.losses.MSE(targets, outputs))
      return loss

    per_replica_losses = self.strategy.run(
        _replicated_step, args=(next(iterator),))
    mean_loss = self.strategy.reduce(
        tf.distribute.ReduceOp.MEAN, per_replica_losses, axis=None)
    return mean_loss

  def eval_end(self, outputs):
    return {
        "eval_loss": tf.reduce_mean(outputs),
    }


class TestEvaluatorWithNestedSummary(standard_runner.StandardEvaluator):
  """Implements the training and evaluation APIs for the test model."""

  def __init__(self):
    self.strategy = tf.distribute.get_strategy()
    self.model = create_model()
    dataset = self.strategy.experimental_distribute_datasets_from_function(
        dataset_fn)
    dataset2 = self.strategy.experimental_distribute_datasets_from_function(
        dataset_fn)
    self.loss = tf.keras.metrics.Mean("loss", dtype=tf.float32)
    self.accuracy = tf.keras.metrics.CategoricalAccuracy(
        "accuracy", dtype=tf.float32)
    self.loss2 = tf.keras.metrics.Mean("loss", dtype=tf.float32)
    self.accuracy2 = tf.keras.metrics.CategoricalAccuracy(
        "accuracy", dtype=tf.float32)
    standard_runner.StandardEvaluator.__init__(
        self, eval_dataset={
            "dataset": dataset,
            "dataset2": dataset2
        })

  def eval_step(self, iterator):

    def _replicated_step(loss, accuracy, inputs):
      """Replicated evaluation step."""
      inputs, targets = inputs
      outputs = self.model(inputs)
      loss.update_state(tf.keras.losses.MSE(targets, outputs))
      accuracy.update_state(targets, outputs)

    self.strategy.run(
        lambda inputs: _replicated_step(self.loss, self.accuracy, inputs),
        args=(next(iterator["dataset"]),))
    self.strategy.run(
        lambda inputs: _replicated_step(self.loss2, self.accuracy2, inputs),
        args=(next(iterator["dataset2"]),))

  def eval_end(self):
    return {
        "dataset": {
            "loss": self.loss.result(),
            "accuracy": self.accuracy.result()
        },
        "dataset2": {
            "loss": self.loss2.result(),
            "accuracy": self.accuracy2.result()
        },
    }


class TestTrainerWithSummaries(standard_runner.StandardTrainer):
  """A Trainer model with summaries for testing purposes."""

  def __init__(self):
    self.strategy = tf.distribute.get_strategy()
    self.model = create_model()
    self.optimizer = tf.keras.optimizers.RMSprop(learning_rate=0.1)
    self.global_step = self.optimizer.iterations
    self.train_loss = tf.keras.metrics.Mean("train_loss", dtype=tf.float32)
    train_dataset = (
        self.strategy.experimental_distribute_datasets_from_function(dataset_fn)
    )
    standard_runner.StandardTrainer.__init__(
        self, train_dataset, use_tpu_summary_optimization=True)

  def build_train_dataset(self):
    return self.strategy.experimental_distribute_datasets_from_function(
        dataset_fn)

  def train_step(self, iterator):

    def _replicated_step(inputs):
      """Replicated training step."""
      inputs, targets = inputs
      with tf.GradientTape() as tape:
        outputs = self.model(inputs)
        loss = tf.reduce_mean(tf.keras.losses.MSE(targets, outputs))
      tf.summary.scalar("loss", loss)
      grads = tape.gradient(loss, self.model.variables)
      self.optimizer.apply_gradients(zip(grads, self.model.variables))
      self.train_loss.update_state(loss)

    self.strategy.run(_replicated_step, args=(next(iterator),))


class ControllerTest(tf.test.TestCase, parameterized.TestCase):

  def setUp(self):
    super().setUp()
    self.model_dir = self.get_temp_dir()

  def test_no_checkpoint(self):
    test_runner = TestRunner()
    # No checkpoint manager and no strategy.
    test_controller = controller.Controller(
        trainer=test_runner,
        evaluator=test_runner,
        global_step=test_runner.global_step,
        steps_per_loop=2,
        summary_dir=os.path.join(self.model_dir, "summaries/train"),
        eval_summary_dir=os.path.join(self.model_dir, "summaries/eval"))
    test_controller.train_and_evaluate(
        train_steps=10, eval_steps=2, eval_interval=6)
    self.assertEqual(test_runner.global_step, 10)
    # Loss and accuracy values should be written into summaries.
    self.assertNotEmpty(
        tf.io.gfile.listdir(os.path.join(self.model_dir, "summaries/train")))
    self.assertNotEmpty(
        summaries_with_matching_keyword(
            "loss", os.path.join(self.model_dir, "summaries/train")))
    self.assertNotEmpty(
        tf.io.gfile.listdir(os.path.join(self.model_dir, "summaries/eval")))
    self.assertNotEmpty(
        summaries_with_matching_keyword(
            "eval_loss", os.path.join(self.model_dir, "summaries/eval")))
    # No checkpoint, so global step starts from 0.
    test_runner.global_step.assign(0)
    test_controller.train_and_evaluate(
        train_steps=10, eval_steps=2, eval_interval=6)
    self.assertEqual(test_runner.global_step, 10)

  def test_no_checkpoint_and_summaries(self):
    test_runner = TestRunner()
    # No checkpoint + summary directories.
    test_controller = controller.Controller(
        trainer=test_runner,
        evaluator=test_runner,
        global_step=test_runner.global_step,
        steps_per_loop=2)
    test_controller.train_and_evaluate(
        train_steps=10, eval_steps=2, eval_interval=6)
    self.assertEqual(test_runner.global_step, 10)

  @parameterized.named_parameters(("return_numpy", True),
                                  ("return_tensor", False))
  def test_train_and_evaluate(self, return_numpy):
    test_runner = TestRunner(return_numpy=return_numpy)

    checkpoint = tf.train.Checkpoint(
        model=test_runner.model, optimizer=test_runner.optimizer)
    checkpoint_manager = tf.train.CheckpointManager(
        checkpoint,
        self.model_dir,
        max_to_keep=None,
        step_counter=test_runner.global_step,
        checkpoint_interval=10)
    test_controller = controller.Controller(
        trainer=test_runner,
        evaluator=test_runner,
        global_step=test_runner.global_step,
        steps_per_loop=2,
        summary_dir=os.path.join(self.model_dir, "summaries/train"),
        checkpoint_manager=checkpoint_manager,
        eval_summary_dir=os.path.join(self.model_dir, "summaries/eval"))
    test_controller.train_and_evaluate(
        train_steps=10, eval_steps=2, eval_interval=6)

    # Checkpoints are saved.
    self.assertNotEmpty(tf.io.gfile.glob(os.path.join(self.model_dir, "ckpt*")))

    # Loss and accuracy values should be written into summaries.
    self.assertNotEmpty(
        tf.io.gfile.listdir(os.path.join(self.model_dir, "summaries/train")))
    self.assertNotEmpty(
        summaries_with_matching_keyword(
            "loss", os.path.join(self.model_dir, "summaries/train")))
    self.assertNotEmpty(
        tf.io.gfile.listdir(os.path.join(self.model_dir, "summaries/eval")))
    self.assertNotEmpty(
        summaries_with_matching_keyword(
            "eval_loss", os.path.join(self.model_dir, "summaries/eval")))

  def test_train_only(self):
    test_runner = TestRunner()

    checkpoint = tf.train.Checkpoint(
        model=test_runner.model, optimizer=test_runner.optimizer)
    checkpoint_manager = tf.train.CheckpointManager(
        checkpoint,
        self.model_dir,
        max_to_keep=None,
        step_counter=test_runner.global_step,
        checkpoint_interval=10)
    test_controller = controller.Controller(
        trainer=test_runner,
        global_step=test_runner.global_step,
        steps_per_loop=2,
        summary_dir=os.path.join(self.model_dir, "summaries/train"),
        checkpoint_manager=checkpoint_manager,
        eval_summary_dir=os.path.join(self.model_dir, "summaries/eval"),
    )
    test_controller.train(steps=10)

    # Checkpoints are saved.
    self.assertNotEmpty(tf.io.gfile.glob(os.path.join(self.model_dir, "ckpt*")))

    # Only train summaries are written.
    self.assertNotEmpty(
        tf.io.gfile.listdir(os.path.join(self.model_dir, "summaries/train")))
    self.assertNotEmpty(
        summaries_with_matching_keyword(
            "loss", os.path.join(self.model_dir, "summaries/train")))
    self.assertFalse(
        tf.io.gfile.exists(os.path.join(self.model_dir, "summaries/eval")))

  def test_evaluate_only(self):
    test_runner = TestRunner()

    checkpoint = tf.train.Checkpoint(model=test_runner.model)
    checkpoint.save(os.path.join(self.model_dir, "ckpt"))
    checkpoint_manager = tf.train.CheckpointManager(
        checkpoint,
        self.model_dir,
        max_to_keep=None,
        step_counter=test_runner.global_step)
    test_controller = controller.Controller(
        evaluator=test_runner,
        global_step=test_runner.global_step,
        checkpoint_manager=checkpoint_manager,
        summary_dir=os.path.join(self.model_dir, "summaries/train"),
        eval_summary_dir=os.path.join(self.model_dir, "summaries/eval"))
    eval_results = test_controller.evaluate(steps=2)

    # Only eval summaries are written
    self.assertFalse(
        tf.io.gfile.exists(os.path.join(self.model_dir, "summaries/train")))
    self.assertNotEmpty(
        tf.io.gfile.listdir(os.path.join(self.model_dir, "summaries/eval")))
    self.assertNotEmpty(
        summaries_with_matching_keyword(
            "eval_loss", os.path.join(self.model_dir, "summaries/eval")))
    self.assertIn("eval_loss", eval_results)

    # Tests continuous eval with timeout and timeout_fn.
    done_file = os.path.join(self.model_dir, "summaries/eval/Done")

    def timeout_fn():
      with tf.io.gfile.GFile(done_file, "w") as f:
        f.write("DONE")
        return True

    test_controller = controller.Controller(
        evaluator=test_runner,
        global_step=test_runner.global_step,
        checkpoint_manager=checkpoint_manager,
        eval_summary_dir=os.path.join(self.model_dir, "summaries/eval"))
    test_controller.evaluate_continuously(
        timeout=1, timeout_fn=timeout_fn, steps=2)
    self.assertNotEmpty(tf.io.gfile.glob(done_file))

  def test_no_eval_steps(self):
    test_runner = TestRunner()

    checkpoint = tf.train.Checkpoint(model=test_runner.model)
    checkpoint.save(os.path.join(self.model_dir, "ckpt"))
    checkpoint_manager = tf.train.CheckpointManager(
        checkpoint,
        self.model_dir,
        max_to_keep=None,
        step_counter=test_runner.global_step)
    test_controller = controller.Controller(
        evaluator=test_runner,
        global_step=test_runner.global_step,
        checkpoint_manager=checkpoint_manager)
    test_controller.evaluate()

  def test_already_trained_model(self):
    test_runner = TestRunner()
    test_runner.global_step.assign(10)

    checkpoint = tf.train.Checkpoint(
        model=test_runner.model, optimizer=test_runner.optimizer)
    checkpoint_manager = tf.train.CheckpointManager(
        checkpoint,
        self.model_dir,
        max_to_keep=None,
        step_counter=test_runner.global_step,
        checkpoint_interval=10)
    test_controller = controller.Controller(
        trainer=test_runner,
        global_step=test_runner.global_step,
        steps_per_loop=2,
        checkpoint_manager=checkpoint_manager)
    # `global_step` is already `train_steps`.
    test_controller.train(steps=10)

  def test_summaries_inside_train_fn(self):
    test_runner = TestTrainerWithSummaries()

    checkpoint = tf.train.Checkpoint(
        model=test_runner.model, optimizer=test_runner.optimizer)
    checkpoint_manager = tf.train.CheckpointManager(
        checkpoint,
        self.model_dir,
        max_to_keep=None,
        step_counter=test_runner.global_step)
    test_controller = controller.Controller(
        trainer=test_runner,
        global_step=test_runner.global_step,
        steps_per_loop=2,
        summary_dir=os.path.join(self.model_dir, "summaries/train"),
        summary_interval=2,
        checkpoint_manager=checkpoint_manager,
    )
    test_controller.train(steps=10)

    # Checkpoints are saved.
    self.assertEmpty(tf.io.gfile.glob(os.path.join(self.model_dir, "ckpt*")))

    # Only train summaries are written.
    self.assertNotEmpty(
        tf.io.gfile.listdir(os.path.join(self.model_dir, "summaries/train")))
    self.assertNotEmpty(
        summaries_with_matching_keyword(
            "loss", os.path.join(self.model_dir, "summaries/train")))
    self.assertFalse(
        tf.io.gfile.exists(os.path.join(self.model_dir, "summaries/eval")))

  def test_train_and_evaluate_with_same_summary_dir(self):
    test_runner = TestRunner()

    checkpoint = tf.train.Checkpoint(
        model=test_runner.model, optimizer=test_runner.optimizer)
    checkpoint_manager = tf.train.CheckpointManager(
        checkpoint,
        self.model_dir,
        max_to_keep=None,
        step_counter=test_runner.global_step)
    test_controller = controller.Controller(
        trainer=test_runner,
        evaluator=test_runner,
        global_step=test_runner.global_step,
        steps_per_loop=2,
        summary_dir=os.path.join(self.model_dir, "summaries"),
        checkpoint_manager=checkpoint_manager,
        eval_summary_dir=os.path.join(self.model_dir, "summaries"))
    test_controller.train_and_evaluate(
        train_steps=10, eval_steps=2, eval_interval=6)

    # Loss and accuracy values should be written into summaries.
    self.assertNotEmpty(
        tf.io.gfile.listdir(os.path.join(self.model_dir, "summaries")))
    self.assertNotEmpty(
        summaries_with_matching_keyword(
            "loss", os.path.join(self.model_dir, "summaries")))
    self.assertNotEmpty(
        summaries_with_matching_keyword(
            "eval_loss", os.path.join(self.model_dir, "summaries")))

  def test_early_stop_on_eval_loss(self):
    test_runner = TestRunner()

    class EarlyStopController(controller.Controller):
      """A subclass of Controller supports early stopping."""

      def train_and_evaluate(self,
                             train_steps: int = None,
                             eval_steps: int = None,
                             eval_interval: int = None):
        while self.global_step.numpy() < train_steps:
          interval = min(train_steps - self.global_step.numpy(), eval_interval)
          num_steps = self.global_step.numpy() + interval
          self.train(steps=num_steps, checkpoint_at_completion=False)
          self.evaluate(steps=eval_steps)
          # Early stop condition.
          if test_runner.eval_loss.result() < 0.1:
            logging.info(
                "Training early stopped as eval_loss %s is less than 0.1",
                test_runner.eval_loss.result())
            return

    checkpoint = tf.train.Checkpoint(
        model=test_runner.model, optimizer=test_runner.optimizer)
    checkpoint_manager = tf.train.CheckpointManager(
        checkpoint,
        self.model_dir,
        max_to_keep=None,
        step_counter=test_runner.global_step,
        checkpoint_interval=10)
    test_controller = EarlyStopController(
        trainer=test_runner,
        evaluator=test_runner,
        global_step=test_runner.global_step,
        steps_per_loop=2,
        checkpoint_manager=checkpoint_manager)
    test_controller.train_and_evaluate(
        train_steps=10, eval_steps=6, eval_interval=2)

    self.assertLess(test_runner.global_step, 10)

  def test_evaluate_with_loss_outputs(self):
    test_evaluator = TestEvaluator()

    checkpoint = tf.train.Checkpoint(model=test_evaluator.model)
    checkpoint.save(os.path.join(self.model_dir, "ckpt"))
    checkpoint_manager = tf.train.CheckpointManager(
        checkpoint, self.model_dir, max_to_keep=None)
    test_controller = controller.Controller(
        evaluator=test_evaluator,
        global_step=tf.Variable(0, dtype=tf.int64),
        checkpoint_manager=checkpoint_manager,
        eval_summary_dir=os.path.join(self.model_dir, "summaries/eval"))
    test_controller.evaluate(steps=5)

    # Only eval summaries are written
    self.assertNotEmpty(
        tf.io.gfile.listdir(os.path.join(self.model_dir, "summaries/eval")))
    self.assertNotEmpty(
        summaries_with_matching_keyword(
            "eval_loss", os.path.join(self.model_dir, "summaries/eval")))

  def test_train_and_evaluate_reset_datasets(self):
    test_runner = TestRunner()

    test_controller = controller.Controller(
        trainer=test_runner,
        evaluator=test_runner,
        global_step=test_runner.global_step,
        steps_per_loop=2)

    test_controller.train_and_evaluate(
        train_steps=10, eval_steps=2, eval_interval=6)

    train_dataset = (
        test_runner.strategy.experimental_distribute_datasets_from_function(
            dataset_fn))
    eval_dataset = (
        test_runner.strategy.experimental_distribute_datasets_from_function(
            dataset_fn))
    test_runner.train_dataset = train_dataset
    test_runner.eval_dataset = eval_dataset

    test_controller.train_and_evaluate(
        train_steps=10, eval_steps=2, eval_interval=6)

  def test_eval_and_checkpoint_interval(self):
    test_runner = TestRunner()

    checkpoint = tf.train.Checkpoint(
        model=test_runner.model, optimizer=test_runner.optimizer)
    checkpoint_manager = tf.train.CheckpointManager(
        checkpoint,
        self.model_dir,
        max_to_keep=None,
        step_counter=test_runner.global_step,
        checkpoint_interval=5)
    test_controller = controller.Controller(
        trainer=test_runner,
        evaluator=test_runner,
        global_step=test_runner.global_step,
        steps_per_loop=10,
        checkpoint_manager=checkpoint_manager)
    test_controller.train_and_evaluate(
        train_steps=10, eval_steps=2, eval_interval=5)

    # Expect 3 checkpoints to be saved at step: 0, 5, 10.
    self.assertLen(
        tf.io.gfile.glob(os.path.join(self.model_dir, "ckpt-*.data*")), 3)
    # Expect evaluation is performed 2 times at step: 5, 10.
    self.assertLen(
        summaries_with_matching_keyword("eval_loss", self.model_dir), 2)

  def test_evaluate_with_nested_summaries(self):
    test_evaluator = TestEvaluatorWithNestedSummary()
    test_controller = controller.Controller(
        evaluator=test_evaluator,
        global_step=tf.Variable(0, dtype=tf.int64),
        eval_summary_dir=self.model_dir)
    test_controller.evaluate(steps=5)

    self.assertNotEmpty(
        tf.io.gfile.listdir(os.path.join(self.model_dir, "dataset")))
    self.assertNotEmpty(
        summaries_with_matching_keyword(
            "loss", os.path.join(self.model_dir, "dataset")))
    self.assertNotEmpty(
        summaries_with_matching_keyword(
            "accuracy", os.path.join(self.model_dir, "dataset")))

    self.assertNotEmpty(
        tf.io.gfile.listdir(os.path.join(self.model_dir, "dataset2")))
    self.assertNotEmpty(
        summaries_with_matching_keyword(
            "loss", os.path.join(self.model_dir, "dataset2")))
    self.assertNotEmpty(
        summaries_with_matching_keyword(
            "accuracy", os.path.join(self.model_dir, "dataset2")))

if __name__ == "__main__":
  tf.test.main()
