# 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, Optional
from flax import linen as nn
from init2winit.model_lib import base_model
from init2winit.model_lib import model_utils
from jax import lax
from jax.nn import initializers
from jax.nn import one_hot
from ml_collections.config_dict import config_dict
import jax.numpy as jnp
import numpy as np
    x = nn.relu(x)
    x = nn.Dropout(rate=self.dropout_rate, deterministic=not train)(x)
    output = nn.Dense(
        actual_out_dim,
        dtype=self.dtype,
        kernel_init=self.kernel_init,
        bias_init=self.bias_init)(x)
    output = nn.Dropout(
        rate=self.dropout_rate, deterministic=not train)(output)
    return output


class Encoder1DBlock(nn.Module):
  """Transformer encoder layer.

  Attributes:
    qkv_dim: <int> Dimension of the query/key/value.
    mlp_dim: <int> Dimension of the mlp on top of attention block.
    num_heads: <int> Number of heads.
    dtype: Dtype of the computation (default: float32).
    dropout_rate: <float> Dropout rate.
    attention_dropout_rate: <float> Dropout rate for attention weights.
    normalizer: One of 'batch_norm', 'layer_norm', 'post_layer_norm',
      'pre_layer_norm', 'none'
    enc_self_attn_kernel_init_fn: initializer for encoder's
      self attention matrices.
  """
  qkv_dim: int
  mlp_dim: int
  num_heads: int

# 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/xformer_translate.py
from typing import Any, Optional
from flax import linen as nn
from init2winit.model_lib import base_model
from init2winit.model_lib import model_utils
from jax import lax
from jax.nn import initializers
from jax.nn import one_hot
from ml_collections.config_dict import config_dict
import jax.numpy as jnp
import numpy as np

    x = nn.relu(x)
    x = nn.Dropout(rate=self.dropout_rate, deterministic=not train)(x)
    output = nn.Dense(
        actual_out_dim,
        dtype=self.dtype,
        kernel_init=self.kernel_init,
        bias_init=self.bias_init)(x)
    output = nn.Dropout(
        rate=self.dropout_rate, deterministic=not train)(output)
    return output


class Encoder1DBlock(nn.Module):
  """Transformer encoder layer.

  Attributes:
    qkv_dim: <int> Dimension of the query/key/value.
    mlp_dim: <int> Dimension of the mlp on top of attention block.
    num_heads: <int> Number of heads.
    dtype: Dtype of the computation (default: float32).
    dropout_rate: <float> Dropout rate.
    attention_dropout_rate: <float> Dropout rate for attention weights.
    normalizer: One of 'batch_norm', 'layer_norm', 'post_layer_norm',
      'pre_layer_norm', 'none'
    enc_self_attn_kernel_init_fn: initializer for encoder's
      self attention matrices.
  """
  qkv_dim: int
  mlp_dim: int
  num_heads: int

  dtype: model_utils.Dtype = jnp.float32