# Copyright 2022 The TensorFlow 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.
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"""Multitask training driver library."""
# pytype: disable=attribute-error
import os
from typing import Any, List, Optional, Tuple
from absl import logging
import orbit
import tensorflow as tf
from official.core import base_task
from official.core import base_trainer as core_lib
from official.core import train_utils
from official.modeling.multitask import base_model
from official.modeling.multitask import base_trainer
from official.modeling.multitask import configs
from official.modeling.multitask import evaluator as evaluator_lib
from official.modeling.multitask import interleaving_trainer
from official.modeling.multitask import multitask
from official.modeling.multitask import task_sampler

TRAINERS = {
    'interleaving': interleaving_trainer.MultiTaskInterleavingTrainer,
    'joint': base_trainer.MultiTaskBaseTrainer
}


def run_experiment(
    *,
    distribution_strategy: tf.distribute.Strategy,
    task: multitask.MultiTask,
    model: base_model.MultiTaskBaseModel,
    mode: str,
    params: configs.MultiTaskExperimentConfig,
    model_dir: str,
    trainer: base_trainer.MultiTaskBaseTrainer = None
) -> base_model.MultiTaskBaseModel:
  """Runs train/eval configured by the experiment params.

  Args:
    distribution_strategy: A distribution distribution_strategy.
    task: A MultiTaskTask instance.
    model: A MultiTaskBaseModel instance.
    mode: A 'str', specifying the mode. Can be 'train', 'eval', 'train_and_eval'
      or 'continuous_eval'.
    params: ExperimentConfig instance.
    model_dir: A 'str', a path to store model checkpoints and summaries.
    trainer: (optional) A multi-task trainer to use. If none is provided, a
      default one will be created based on `params`.

  Returns:
      model: `base_model.MultiTaskBaseModel` instance.
  """

  is_training = 'train' in mode
  is_eval = 'eval' in mode
  with distribution_strategy.scope():
    optimizer = task.create_optimizer(params.trainer.optimizer_config,
                                      params.runtime)
    kwargs = dict(multi_task=task, multi_task_model=model, optimizer=optimizer)
    if params.trainer.trainer_type == 'interleaving':
      sampler = task_sampler.get_task_sampler(params.trainer.task_sampler,
                                              task.task_weights)
      kwargs.update(dict(task_sampler=sampler))
    if trainer is None:
      trainer = TRAINERS[params.trainer.trainer_type](
          **kwargs) if is_training else None
    if is_eval:
      eval_steps = task.task_eval_steps
      evaluator = evaluator_lib.MultiTaskEvaluator(
          eval_tasks=task.tasks.values(),
          model=model,
          eval_steps=eval_steps,
          global_step=trainer.global_step if is_training else None,
          checkpoint_exporter=train_utils.maybe_create_best_ckpt_exporter(
              params, model_dir))
    else:
      evaluator = None

  if trainer:
    checkpoint = trainer.checkpoint
    global_step = trainer.global_step
  else:
    checkpoint = evaluator.checkpoint
    global_step = evaluator.global_step

  # TODO(hongkuny,haozhangthu): Revisit initialization method.
  checkpoint_manager = tf.train.CheckpointManager(
      checkpoint,
      directory=model_dir,
      max_to_keep=params.trainer.max_to_keep,
      step_counter=global_step,
      checkpoint_interval=params.trainer.checkpoint_interval,
      init_fn=model.initialize)

  controller = orbit.Controller(
      strategy=distribution_strategy,
      trainer=trainer,
      evaluator=evaluator,
      global_step=global_step,
      steps_per_loop=params.trainer.steps_per_loop,
      checkpoint_manager=checkpoint_manager,
      summary_dir=os.path.join(model_dir, 'train'),
      eval_summary_dir=os.path.join(model_dir, 'validation'),
      summary_interval=params.trainer.summary_interval)

  logging.info('Starts to execute mode: %s', mode)
  with distribution_strategy.scope():
    if mode == 'train':
      controller.train(steps=params.trainer.train_steps)
    elif mode == 'train_and_eval':
      controller.train_and_evaluate(
          train_steps=params.trainer.train_steps,
          eval_steps=params.trainer.validation_steps,
          eval_interval=params.trainer.validation_interval)
    elif mode == 'eval':
      controller.evaluate(steps=params.trainer.validation_steps)
    elif mode == 'continuous_eval':

      def timeout_fn():
        if evaluator.global_step.numpy() >= params.trainer.train_steps:
          return True
        return False

      controller.evaluate_continuously(
          steps=params.trainer.validation_steps,
          timeout=params.trainer.continuous_eval_timeout,
          timeout_fn=timeout_fn)
    else:
      raise NotImplementedError('The mode is not implemented: %s' % mode)

    return model


def run_experiment_with_multitask_eval(
    *,
    distribution_strategy: tf.distribute.Strategy,
    train_task: base_task.Task,
    eval_tasks: List[base_task.Task],
    mode: str,
    params: configs.MultiEvalExperimentConfig,
    model_dir: str,
    run_post_eval: bool = False,
    save_summary: bool = True,
    trainer: Optional[core_lib.Trainer] = None) -> Tuple[Any, Any]:
  """Runs train/eval configured by the experiment params.

  Args:
    distribution_strategy: A distribution distribution_strategy.
    train_task: A base_task.Task instance.
    eval_tasks: A list of evaluation tasks.
    mode: A 'str', specifying the mode. Can be 'train', 'eval', 'train_and_eval'
      or 'continuous_eval'.
    params: MultiEvalExperimentConfig instance.
    model_dir: A 'str', a path to store model checkpoints and summaries.
    run_post_eval: Whether to run post eval once after training, metrics logs
      are returned.
    save_summary: Whether to save train and validation summary.
    trainer: the core_lib.Trainer instance. It should be created within the
      strategy.scope(). If not provided, an instance will be created by default
      if `mode` contains 'train'.

  Returns:
      model: `tf.keras.Model` instance.
  """

  is_training = 'train' in mode
  is_eval = 'eval' in mode
  with distribution_strategy.scope():
    if is_training:
      trainer = trainer or core_lib.Trainer(
          config=params,
          task=train_task,
          model=train_task.build_model(),
          optimizer=train_task.create_optimizer(params.trainer.optimizer_config,
                                                params.runtime),
          train=True,
          evaluate=False)
    else:
      trainer = None
    model = trainer.model if trainer else train_task.build_model()

    if is_eval:
      eval_steps = dict([(task_routine.task_config.name,
                          task_routine.eval_steps)
                         for task_routine in params.eval_tasks])
      evaluator = evaluator_lib.MultiTaskEvaluator(
          eval_tasks=eval_tasks,
          model=model,
          global_step=trainer.global_step if is_training else None,
          eval_steps=eval_steps,
          checkpoint_exporter=train_utils.maybe_create_best_ckpt_exporter(
              params, model_dir))
    else:
      evaluator = None

  if trainer:
    checkpoint = trainer.checkpoint
    global_step = trainer.global_step
  else:
    checkpoint = evaluator.checkpoint
    global_step = evaluator.global_step

  checkpoint_manager = tf.train.CheckpointManager(
      checkpoint,
      directory=model_dir,
      max_to_keep=params.trainer.max_to_keep,
      step_counter=global_step,
      checkpoint_interval=params.trainer.checkpoint_interval,
      init_fn=trainer.initialize if trainer else None)

  controller = orbit.Controller(
      strategy=distribution_strategy,
      trainer=trainer,
      evaluator=evaluator,
      global_step=global_step,
      steps_per_loop=params.trainer.steps_per_loop,
      checkpoint_manager=checkpoint_manager,
      summary_dir=os.path.join(model_dir, 'train') if save_summary else None,
      eval_summary_dir=os.path.join(model_dir, 'validation') if
      (save_summary) else None,
      summary_interval=params.trainer.summary_interval if
      (save_summary) else None)

  logging.info('Starts to execute mode: %s', mode)
  with distribution_strategy.scope():
    if mode == 'train':
      controller.train(steps=params.trainer.train_steps)
    elif mode == 'train_and_eval':
      controller.train_and_evaluate(
          train_steps=params.trainer.train_steps,
          eval_steps=params.trainer.validation_steps,
          eval_interval=params.trainer.validation_interval)
    elif mode == 'eval':
      controller.evaluate(steps=params.trainer.validation_steps)
    elif mode == 'continuous_eval':

      def timeout_fn():
        if evaluator.global_step.numpy() >= params.trainer.train_steps:
          return True
        return False

      controller.evaluate_continuously(
          steps=params.trainer.validation_steps,
          timeout=params.trainer.continuous_eval_timeout,
          timeout_fn=timeout_fn)
    else:
      raise NotImplementedError('The mode is not implemented: %s' % mode)

    if run_post_eval:
      return model, evaluator.evaluate(
          tf.convert_to_tensor(params.trainer.validation_steps))  # pytype: disable=bad-return-type  # typed-keras
    else:
      return model, {}  # pytype: disable=bad-return-type  # typed-keras
