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"""Config for use with the custom training DeepVariant Loop."""

import ml_collections


def get_exome_config(
    config: ml_collections.ConfigDict,
) -> ml_collections.ConfigDict:
  """Config parameters for exome training."""

  # Exome Dataset
  config.train_dataset_pbtxt = '/path/to/your/train.dataset_config.pbtxt'
  config.tune_dataset_pbtxt = '/path/to/your/tune.dataset_config.pbtxt'
  # If set to 0, use full validation dataset.
  config.num_validation_examples = 0

  config.num_epochs = 80
  config.learning_rate = 0.01
  config.learning_rate_num_epochs_per_decay = 2.0
  config.learning_rate_decay_rate = 0.9999
  config.rho = 0.9763046740422171
  config.momentum = 0.9848544529312561
  config.epsilon = 0.8696723762650027
  config.warmup_steps = 718
  config.weight_decay = 0.1
  config.backbone_dropout_rate = 0.22517227651098964

  config.init_checkpoint = ''

  return config


def get_config(config_name: str) -> ml_collections.ConfigDict:
  """Training parameters."""
  config = ml_collections.ConfigDict()

  config.model_type = 'inception_v3'
  config.trial = 0  # Used to allow for replicates during training.

  # Default Dataset
  config.train_dataset_pbtxt = '/path/to/your/train.dataset_config.pbtxt'
  config.tune_dataset_pbtxt = '/path/to/your/tune.dataset_config.pbtxt'

  config.best_checkpoint_metric = 'tune/f1_weighted'
  config.batch_size = 16384
  config.num_epochs = 10
  config.num_validation_examples = 1500000
  config.optimizer = 'rmsprop'

  # Training hyperparameters
  config.learning_rate = 0.001
  config.learning_rate_num_epochs_per_decay = 2.0
  config.learning_rate_decay_rate = 0.947
  config.average_decay = 0.999
  config.label_smoothing = 1e-6
  config.rho = 0.9
  config.momentum = 0.9
  config.epsilon = 1.0
  config.warmup_steps = 10_000
  config.init_checkpoint = ''
  config.init_backbone_with_imagenet = False
  config.best_metrics = 'tune/f1_weighted'
  config.weight_decay = 0.00004
  config.backbone_dropout_rate = 0.2
  # Stop training when this many consecutive evaluations yield no improvement.
  config.early_stopping_patience = 10

  # TensorBoard Options
  config.log_every_steps = 100
  # Tuning happens at every epoch. The frequency can be increased here.
  config.tune_every_steps = 100_000

  # Data Pipeline Options
  config.prefetch_buffer_bytes = 16 * 1000 * 1000
  config.shuffle_buffer_elements = 10_000
  config.input_read_threads = 32

  # Placeholder value for limiting training examples. 0=No limit.
  config.limit = 0

  if config_name == 'exome':
    config = get_exome_config(config)
  elif config_name == 'base':
    # Use the base config.
    pass
  else:
    raise ValueError(f'Unknown config_name: {config_name}')

  return config