# 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
                     'Dataset shuffle buffer size.')
flags.DEFINE_string(
    'experiment_dir', '/tmp/experiments',
    'Path to store experiment output in, i.e. models, snapshots.')
flags.DEFINE_integer(
    'update_iterations',
    1000,
    'Epoch interval after which to evaluate test error.',
    lower_bound=1)
flags.DEFINE_integer(
    'update_epoch', 10, 'Epoch interval after which to evaluate test error.',
    lower_bound=1)


def run_training():
  """Trains a model."""
  print('Logging to {}'.format(FLAGS.log_dir))
  work_unit_id = uuid.uuid4()
  experiment_dir = path.join(FLAGS.experiment_dir, str(work_unit_id))

  logging.info('Saving experimental results to %s', experiment_dir)

  host_count = jax.host_count()
  local_device_count = jax.local_device_count()
  logging.info('Device count: %d, host count: %d, local device count: %d',
               jax.device_count(), host_count, local_device_count)

  if jax.host_id() == 0:
    summary_writer = tensorboard.SummaryWriter(experiment_dir)


# 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

                     'Dataset shuffle buffer size.')
flags.DEFINE_string(
    'experiment_dir', '/tmp/experiments',
    'Path to store experiment output in, i.e. models, snapshots.')
flags.DEFINE_integer(
    'update_iterations',
    1000,
    'Epoch interval after which to evaluate test error.',
    lower_bound=1)
flags.DEFINE_integer(
    'update_epoch', 10, 'Epoch interval after which to evaluate test error.',
    lower_bound=1)


def run_training():
  """Trains a model."""
  print('Logging to {}'.format(FLAGS.log_dir))
  work_unit_id = uuid.uuid4()
  experiment_dir = path.join(FLAGS.experiment_dir, str(work_unit_id))

  logging.info('Saving experimental results to %s', experiment_dir)

  host_count = jax.host_count()
  local_device_count = jax.local_device_count()
  logging.info('Device count: %d, host count: %d, local device count: %d',
               jax.device_count(), host_count, local_device_count)

  if jax.host_id() == 0:
    summary_writer = tensorboard.SummaryWriter(experiment_dir)


  dataset = dataset_factory.create_dataset(