old_contents: # Copyright 2020 The Flax 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
  import jax.numpy as jnp
  import numpy as onp
  from .. import struct
  from .base import OptimizerDef
  
  
  @struct.dataclass
  class _AdagradHyperParams:
    """Adagrad hyper parameters"""
  
    learning_rate: float
  
  
  @struct.dataclass
  class _AdagradParamState:
    """Adagrad parameter state"""
  
    G: onp.ndarray
  
  
  class Adagrad(OptimizerDef):
    """Adagrad optimizer"""
    def __init__(self, learning_rate: float = None):
      """Constructor for the Adagrad optimizer.
          
          Args:
              learning_rate: the step size used to update the parameters.
  
          """
      hyper_params = _AdagradHyperParams(learning_rate)
      super().__init__(hyper_params)
  
    def init_param_state(self, param):
      """Initialize parameter state"""
      return _AdagradParamState(jnp.zeros_like(param))
  
    def apply_param_gradient(self, step, hyper_params, param, state, grad):
      """Apply per-parameter gradients"""
  
      assert hyper_params.learning_rate is not None, 'no learning rate provided.'
      new_G = state.G + jnp.square(grad)
      new_param = param - hyper_params.learning_rate * grad / jnp.sqrt(new_G)
      new_state = _AdagradParamState(new_G)
  
      return new_param, new_state
  
new_contents: # Copyright 2020 The Flax 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
  import jax.numpy as jnp
  import numpy as onp
  from .. import struct
  from .base import OptimizerDef
  
  
  @struct.dataclass
  class _AdagradHyperParams:
    """Adagrad hyper parameters"""
  
    learning_rate: float
  
  
  @struct.dataclass
  class _AdagradParamState:
    """Adagrad parameter state"""
  
    G: onp.ndarray
  
  
  class Adagrad(OptimizerDef):
    """Adagrad optimizer"""
    def __init__(self, learning_rate: float = None):
      """Constructor for the Adagrad optimizer.
          
      Args:
        learning_rate: the step size used to update the parameters.
      """
      hyper_params = _AdagradHyperParams(learning_rate)
      super().__init__(hyper_params)
  
    def init_param_state(self, param):
      """Initialize parameter state"""
      return _AdagradParamState(jnp.zeros_like(param))
  
    def apply_param_gradient(self, step, hyper_params, param, state, grad):
      """Apply per-parameter gradients"""
  
      assert hyper_params.learning_rate is not None, 'no learning rate provided.'
      new_G = state.G + jnp.square(grad)
      new_param = param - hyper_params.learning_rate * grad / jnp.sqrt(new_G)
      new_state = _AdagradParamState(new_G)
  
      return new_param, new_state
  
subject: Fix spacing