# Path: rigl/experimental/jax/datasets/dataset_factory.py
# DATASETS: Mapping[str, Type[dataset_base.Dataset]] = {
#     'MNIST': mnist.MNISTDataset,
#     'CIFAR10': cifar10.CIFAR10Dataset,
# }
# def create_dataset(name, *args, **kwargs):
# 
# Path: rigl/experimental/jax/models/model_factory.py
# MODELS: Mapping[str, Type[flax.deprecated.nn.Model]] = {
#     'MNIST_CNN': mnist_cnn.MNISTCNN,
#     'MNIST_FC': mnist_fc.MNISTFC,
#     'CIFAR10_CNN': cifar10_cnn.CIFAR10CNN,
# }
# def create_model(
#     name, rng,
#     input_specs, **kwargs
# ):
# def update_model(model,
#                  **kwargs):
# 
# Path: rigl/experimental/jax/training/training.py
# LABELKEY = dataset_base.ImageDataset.LABELKEY
# DATAKEY = dataset_base.ImageDataset.DATAKEY
# def _shard_batch(xs):
#   def _prepare(x):
# def train_step(
#     optimizer: flax.optim.Optimizer, batch: Mapping[str, jnp.array],
#     rng: Callable[[int], jnp.array], state: flax.deprecated.nn.Collection,
#     learning_rate_fn: Callable[[int], float]
# ) -> Tuple[flax.optim.Optimizer, flax.deprecated.nn.Collection, float, float]:
#   def loss_fn(
#       model: flax.deprecated.nn.Model
#   ) -> Tuple[float, Tuple[flax.deprecated.nn.Collection, jnp.array]]:
#   def __init__(
#       self,
#       optimizer_def: flax.optim.OptimizerDef,
#       initial_model: flax.deprecated.nn.Model,
#       initial_state: flax.deprecated.nn.Collection,
#       dataset: jnp.array,
#       rng: Callable[[int], jnp.array] = None,
#       summary_writer: Optional[tf.summary.SummaryWriter] = None,
#   ):
# class Trainer:


import ast
import uuid
import flax
import jax
import jax.numpy as np
from os import path
from typing import List, Sequence
from absl import app
from absl import flags
from absl import logging
from flax.metrics import tensorboard
from flax.training import lr_schedule
from rigl.experimental.jax.datasets import dataset_factory
from rigl.experimental.jax.models import model_factory
from rigl.experimental.jax.training import training
        learning_rate=FLAGS.lr,
        beta=FLAGS.momentum,
        weight_decay=FLAGS.weight_decay,
        nesterov=False)

  steps_per_epoch = dataset.get_train_len() // FLAGS.batch_size

  if FLAGS.lr_schedule == 'constant':
    lr_fn = lr_schedule.create_constant_learning_rate_schedule(
        FLAGS.lr, steps_per_epoch)
  elif FLAGS.lr_schedule == 'stepped':
    lr_schedule_steps = ast.literal_eval(FLAGS.lr_schedule_steps)
    lr_fn = lr_schedule.create_stepped_learning_rate_schedule(
        FLAGS.lr, steps_per_epoch, lr_schedule_steps)
  elif FLAGS.lr_schedule == 'cosine':
    lr_fn = lr_schedule.create_cosine_learning_rate_schedule(
        FLAGS.lr, steps_per_epoch, FLAGS.epochs)
  else:
    raise ValueError('Unknown LR schedule type {}'.format(FLAGS.lr_schedule))

  if jax.host_id() == 0:
    trainer = training.Trainer(
        optimizer,
        model,
        initial_state,
        dataset,
        rng,
        summary_writer=summary_writer,
    )
  else:

# Path: rigl/experimental/jax/datasets/dataset_factory.py
# DATASETS: Mapping[str, Type[dataset_base.Dataset]] = {
#     'MNIST': mnist.MNISTDataset,
#     'CIFAR10': cifar10.CIFAR10Dataset,
# }
# def create_dataset(name, *args, **kwargs):
# 
# Path: rigl/experimental/jax/models/model_factory.py
# MODELS: Mapping[str, Type[flax.deprecated.nn.Model]] = {
#     'MNIST_CNN': mnist_cnn.MNISTCNN,
#     'MNIST_FC': mnist_fc.MNISTFC,
#     'CIFAR10_CNN': cifar10_cnn.CIFAR10CNN,
# }
# def create_model(
#     name, rng,
#     input_specs, **kwargs
# ):
# def update_model(model,
#                  **kwargs):
# 
# Path: rigl/experimental/jax/training/training.py
# LABELKEY = dataset_base.ImageDataset.LABELKEY
# DATAKEY = dataset_base.ImageDataset.DATAKEY
# def _shard_batch(xs):
#   def _prepare(x):
# def train_step(
#     optimizer: flax.optim.Optimizer, batch: Mapping[str, jnp.array],
#     rng: Callable[[int], jnp.array], state: flax.deprecated.nn.Collection,
#     learning_rate_fn: Callable[[int], float]
# ) -> Tuple[flax.optim.Optimizer, flax.deprecated.nn.Collection, float, float]:
#   def loss_fn(
#       model: flax.deprecated.nn.Model
#   ) -> Tuple[float, Tuple[flax.deprecated.nn.Collection, jnp.array]]:
#   def __init__(
#       self,
#       optimizer_def: flax.optim.OptimizerDef,
#       initial_model: flax.deprecated.nn.Model,
#       initial_state: flax.deprecated.nn.Collection,
#       dataset: jnp.array,
#       rng: Callable[[int], jnp.array] = None,
#       summary_writer: Optional[tf.summary.SummaryWriter] = None,
#   ):
# class Trainer:


# Path: rigl/experimental/jax/train.py
import ast
import uuid
import flax
import jax
import jax.numpy as np
from os import path
from typing import List, Sequence
from absl import app
from absl import flags
from absl import logging
from flax.metrics import tensorboard
from flax.training import lr_schedule
from rigl.experimental.jax.datasets import dataset_factory
from rigl.experimental.jax.models import model_factory
from rigl.experimental.jax.training import training

        learning_rate=FLAGS.lr,
        beta=FLAGS.momentum,
        weight_decay=FLAGS.weight_decay,
        nesterov=False)

  steps_per_epoch = dataset.get_train_len() // FLAGS.batch_size

  if FLAGS.lr_schedule == 'constant':
    lr_fn = lr_schedule.create_constant_learning_rate_schedule(
        FLAGS.lr, steps_per_epoch)
  elif FLAGS.lr_schedule == 'stepped':
    lr_schedule_steps = ast.literal_eval(FLAGS.lr_schedule_steps)
    lr_fn = lr_schedule.create_stepped_learning_rate_schedule(
        FLAGS.lr, steps_per_epoch, lr_schedule_steps)
  elif FLAGS.lr_schedule == 'cosine':
    lr_fn = lr_schedule.create_cosine_learning_rate_schedule(
        FLAGS.lr, steps_per_epoch, FLAGS.epochs)
  else:
    raise ValueError('Unknown LR schedule type {}'.format(FLAGS.lr_schedule))

  if jax.host_id() == 0:
    trainer = training.Trainer(
        optimizer,
        model,
        initial_state,
        dataset,
        rng,
        summary_writer=summary_writer,
    )
  else:

    trainer = training.Trainer(optimizer, model, initial_state, dataset, rng)