# 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):


from typing import Any, Dict, Sequence
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
    decoder_keys = [
        'filter_sizes',
        'kernel_sizes',
        'window_sizes',
        'paddings',
        'activations',
    ]
    if len(set(len(self.decoder[k]) for k in decoder_keys)) > 1:
      raise ValueError(
          'The elements in decoder dict do not have the same length.')

    # encoder
    for i in range(len(self.encoder['filter_sizes'])):
      x = nn.Conv(
          self.encoder['filter_sizes'][i],
          self.encoder['kernel_sizes'][i],
          padding=self.encoder['kernel_paddings'][i])(x)
      x = model_utils.ACTIVATIONS[self.encoder['activations'][i]](x)
      x = nn.max_pool(
          x, self.encoder['window_sizes'][i],
          strides=self.encoder['strides'][i],
          padding=self.encoder['window_paddings'][i])

    # decoder
    for i in range(len(self.decoder['filter_sizes'])):
      x = nn.ConvTranspose(
          self.decoder['filter_sizes'][i],
          self.decoder['kernel_sizes'][i],
          self.decoder['window_sizes'][i],
          padding=self.decoder['paddings'][i])(x)

# 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/convolutional_autoencoder.py
from typing import Any, Dict, Sequence
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

    decoder_keys = [
        'filter_sizes',
        'kernel_sizes',
        'window_sizes',
        'paddings',
        'activations',
    ]
    if len(set(len(self.decoder[k]) for k in decoder_keys)) > 1:
      raise ValueError(
          'The elements in decoder dict do not have the same length.')

    # encoder
    for i in range(len(self.encoder['filter_sizes'])):
      x = nn.Conv(
          self.encoder['filter_sizes'][i],
          self.encoder['kernel_sizes'][i],
          padding=self.encoder['kernel_paddings'][i])(x)
      x = model_utils.ACTIVATIONS[self.encoder['activations'][i]](x)
      x = nn.max_pool(
          x, self.encoder['window_sizes'][i],
          strides=self.encoder['strides'][i],
          padding=self.encoder['window_paddings'][i])

    # decoder
    for i in range(len(self.decoder['filter_sizes'])):
      x = nn.ConvTranspose(
          self.decoder['filter_sizes'][i],
          self.decoder['kernel_sizes'][i],
          self.decoder['window_sizes'][i],
          padding=self.decoder['paddings'][i])(x)

      x = model_utils.ACTIVATIONS[self.decoder['activations'][i]](x)