# Path: init2winit/model_lib/base_model.py
# def _evaluate_batch(flax_module, params, batch_stats, batch, metrics_bundle,
#                     apply_one_hot_in_loss):
# def _predict_batch(flax_module,
#                    params,
#                    batch_stats,
#                    batch,
#                    output_activation_fn=None):
#   def __init__(self, hps, dataset_meta_data, loss_name, metrics_name):
#   def evaluate_batch(self, params, batch_stats, batch):
#   def predict_batch(self,
#                     params,
#                     batch_stats,
#                     batch,
#                     apply_output_activation_fn=False):
#   def training_cost(self, params, batch, batch_stats=None, dropout_rng=None):
#   def get_fake_batch(self, hps):
#   def build_flax_module(self):
# class BaseModel(object):
# 
# Path: init2winit/model_lib/model_utils.py
# ACTIVATIONS = {
#     'relu': jax.nn.relu,
#     'id': lambda x: x,
#     'tanh': jnp.tanh,
#     'sigmoid': jax.nn.sigmoid,
#     'silu': jax.nn.silu,
#     'leaky_relu': jax.nn.leaky_relu,
# }
# INITIALIZERS = {
#     'delta_orthogonal': initializers.delta_orthogonal,
#     'orthogonal': initializers.orthogonal,
#     'lecun_normal': lecun_normal,
#     'xavier_uniform': initializers.xavier_uniform,
# }
# class ScalarMultiply(nn.Module):
#   def __call__(self, x):
# def get_normalizer(normalizer,
#                    train,
#                    batch_size=None,
#                    virtual_batch_size=None,
#                    total_batch_size=None,
#                    dtype=jnp.float32,
#                    data_format='NHWC'):
#     def identity_wrapper(*args, **kwargs):
#       def identity(x, *args, **kwargs):
# def apply_label_smoothing(one_hot_targets, label_smoothing):
# def sync_batchnorm_stats(state):
# def cross_device_avg(pytree):
# def l2_regularization(params, l2_decay_rank_threshold):
# def flatten_dict(nested_dict, sep='/'):
# def rescale_layers(params, layer_rescale_factors):


import functools
import jax.numpy as jnp
from flax import linen as nn
from init2winit.model_lib import base_model
from init2winit.model_lib import model_utils
from ml_collections.config_dict import config_dict
        },
        batch_size=128,
        l2_decay_factor=0.0001,
        l2_decay_rank_threshold=2,
        label_smoothing=None,
        rng_seed=-1,
        use_shallue_label_smoothing=False,
        model_dtype='float32',
        grad_clip=None,
    ))


def classifier(x, num_outputs, dropout_rate, deterministic):
  """Implements the classification portion of the network."""

  x = nn.Dropout(rate=dropout_rate, deterministic=deterministic)(x)
  x = nn.Dense(512)(x)
  x = nn.relu(x)
  x = nn.Dropout(rate=dropout_rate, deterministic=deterministic)(x)
  x = nn.Dense(512)(x)
  x = nn.relu(x)
  x = nn.Dense(num_outputs)(x)
  return x


def features(x, num_layers, normalizer, dtype, train):
  """Implements the feature extraction portion of the network."""

  layers = _layer_size_options[num_layers]
  conv = functools.partial(nn.Conv, use_bias=False, dtype=dtype)

# Path: init2winit/model_lib/base_model.py
# def _evaluate_batch(flax_module, params, batch_stats, batch, metrics_bundle,
#                     apply_one_hot_in_loss):
# def _predict_batch(flax_module,
#                    params,
#                    batch_stats,
#                    batch,
#                    output_activation_fn=None):
#   def __init__(self, hps, dataset_meta_data, loss_name, metrics_name):
#   def evaluate_batch(self, params, batch_stats, batch):
#   def predict_batch(self,
#                     params,
#                     batch_stats,
#                     batch,
#                     apply_output_activation_fn=False):
#   def training_cost(self, params, batch, batch_stats=None, dropout_rng=None):
#   def get_fake_batch(self, hps):
#   def build_flax_module(self):
# class BaseModel(object):
# 
# Path: init2winit/model_lib/model_utils.py
# ACTIVATIONS = {
#     'relu': jax.nn.relu,
#     'id': lambda x: x,
#     'tanh': jnp.tanh,
#     'sigmoid': jax.nn.sigmoid,
#     'silu': jax.nn.silu,
#     'leaky_relu': jax.nn.leaky_relu,
# }
# INITIALIZERS = {
#     'delta_orthogonal': initializers.delta_orthogonal,
#     'orthogonal': initializers.orthogonal,
#     'lecun_normal': lecun_normal,
#     'xavier_uniform': initializers.xavier_uniform,
# }
# class ScalarMultiply(nn.Module):
#   def __call__(self, x):
# def get_normalizer(normalizer,
#                    train,
#                    batch_size=None,
#                    virtual_batch_size=None,
#                    total_batch_size=None,
#                    dtype=jnp.float32,
#                    data_format='NHWC'):
#     def identity_wrapper(*args, **kwargs):
#       def identity(x, *args, **kwargs):
# def apply_label_smoothing(one_hot_targets, label_smoothing):
# def sync_batchnorm_stats(state):
# def cross_device_avg(pytree):
# def l2_regularization(params, l2_decay_rank_threshold):
# def flatten_dict(nested_dict, sep='/'):
# def rescale_layers(params, layer_rescale_factors):


# Path: init2winit/model_lib/adabelief_vgg.py
import functools
import jax.numpy as jnp
from flax import linen as nn
from init2winit.model_lib import base_model
from init2winit.model_lib import model_utils
from ml_collections.config_dict import config_dict

        },
        batch_size=128,
        l2_decay_factor=0.0001,
        l2_decay_rank_threshold=2,
        label_smoothing=None,
        rng_seed=-1,
        use_shallue_label_smoothing=False,
        model_dtype='float32',
        grad_clip=None,
    ))


def classifier(x, num_outputs, dropout_rate, deterministic):
  """Implements the classification portion of the network."""

  x = nn.Dropout(rate=dropout_rate, deterministic=deterministic)(x)
  x = nn.Dense(512)(x)
  x = nn.relu(x)
  x = nn.Dropout(rate=dropout_rate, deterministic=deterministic)(x)
  x = nn.Dense(512)(x)
  x = nn.relu(x)
  x = nn.Dense(num_outputs)(x)
  return x


def features(x, num_layers, normalizer, dtype, train):
  """Implements the feature extraction portion of the network."""

  layers = _layer_size_options[num_layers]
  conv = functools.partial(nn.Conv, use_bias=False, dtype=dtype)

  maybe_normalize = model_utils.get_normalizer(normalizer, train)