# Copyright (c) 2021, NVIDIA CORPORATION. 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.
# ==============================================================================
"""Defining common flags used across all BERT models/applications."""

from absl import flags
import tensorflow as tf

from official.utils.flags import core as flags_core


def define_common_bert_flags():
  """Define common flags for BERT tasks."""
  flags_core.define_base(
      data_dir=False,
      model_dir=True,
      clean=False,
      train_epochs=False,
      epochs_between_evals=False,
      stop_threshold=False,
      batch_size=False,
      num_gpu=True,
      hooks=False,
      export_dir=False,
      distribution_strategy=True,
      run_eagerly=True)
  flags.DEFINE_string('bert_config_file', None,
                      'Bert configuration file to define core bert layers.')
  flags.DEFINE_string(
      'model_export_path', None,
      'Path to the directory, where trainined model will be '
      'exported.')
  flags.DEFINE_string('tpu', '', 'TPU address to connect to.')
  flags.DEFINE_string(
      'init_checkpoint', None,
      'Initial checkpoint (usually from a pre-trained BERT model).')
  flags.DEFINE_bool('use_horovod', False, 'Whether to use horovod.')
  flags.DEFINE_integer('num_accumulation_steps', 1,
                       'Number of accumulation steps before gradient update.')
  flags.DEFINE_integer('num_train_epochs', 3,
                       'Total number of training epochs to perform.')
  flags.DEFINE_integer(
      'steps_per_loop', 200,
      'Number of steps per graph-mode loop. Only training step '
      'happens inside the loop. Callbacks will not be called '
      'inside.')
  flags.DEFINE_float('learning_rate', 5e-5,
                     'The initial learning rate for Adam.')
  flags.DEFINE_boolean(
      'scale_loss', False,
      'Whether to divide the loss by number of replica inside the per-replica '
      'loss function.')
  flags.DEFINE_boolean(
    'use_keras_compile_fit', False,
    'If True, uses Keras compile/fit() API for training logic. Otherwise '
    'use custom training loop.')
  flags.DEFINE_string(
    'hub_module_url', None, 'TF-Hub path/url to Bert module. '
    'If specified, init_checkpoint flag should not be used.')
  flags.DEFINE_enum(
    'model_type', 'bert', ['bert', 'albert'],
    'Specifies the type of the model. '
    'If "bert", will use canonical BERT; if "albert", will use ALBERT model.')
  flags.DEFINE_boolean(
      'use_fp16', False,
      'Whether to use fp32 or fp16 arithmetic on GPU.')
  flags.DEFINE_string("optimizer_type", "adam",
    "Optimizer used for training - LAMB or ADAM")
  flags.DEFINE_integer(
    'save_checkpoint_steps', 1000,
    'save checkpoint for every n steps')
  flags.DEFINE_string(
    'dllog_path', 'bert_dllogger.json', 'filename where dllogger writes to')
  flags.DEFINE_boolean(
      'benchmark', False,
      'Benchmark mode.')

  # Adds flags for mixed precision training.
  flags_core.define_performance(
      num_parallel_calls=False,
      inter_op=False,
      intra_op=False,
      synthetic_data=False,
      max_train_steps=False,
      dtype=True,
      dynamic_loss_scale=True,
      loss_scale=True,
      all_reduce_alg=False,
      num_packs=False,
      enable_xla=True,
      fp16_implementation=True,
  )


def use_float16():
  return flags_core.get_tf_dtype(flags.FLAGS) == tf.float16


def get_loss_scale():
  return flags_core.get_loss_scale(flags.FLAGS, default_for_fp16='dynamic')
