# 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
# coding=utf-8
# Copyright 2022 RigL Authors.
#
# 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.

# Lint as: python3
"""Weight Symmetry: Train Model.

Trains a model from scratch, saving the relevant early weight snapshots.
"""



FLAGS = flags.FLAGS

MODEL_LIST: Sequence[str] = tuple(model_factory.MODELS.keys())

# 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

# coding=utf-8
# Copyright 2022 RigL Authors.
#
# 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.

# Lint as: python3
"""Weight Symmetry: Train Model.

Trains a model from scratch, saving the relevant early weight snapshots.
"""



FLAGS = flags.FLAGS

MODEL_LIST: Sequence[str] = tuple(model_factory.MODELS.keys())

DATASET_LIST: Sequence[str] = tuple(dataset_factory.DATASETS.keys())