text: # Copyright 2021 The TensorFlow Authors. All Rights Reserved.
  #
  # 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
  """Learning rate utilities for vision tasks."""
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from typing import Any, Mapping, Optional
  
  import numpy as np
  import tensorflow as tf
  
  BASE_LEARNING_RATE = 0.1
  
  
  class WarmupDecaySchedule(tf.keras.optimizers.schedules.LearningRateSchedule):
    """A wrapper for LearningRateSchedule that includes warmup steps."""
  
    def __init__(self,
                 lr_schedule: tf.keras.optimizers.schedules.LearningRateSchedule,
                 warmup_steps: int,
                 warmup_lr: Optional[float] = None):
      """Add warmup decay to a learning rate schedule.
  
      Args:
        lr_schedule: base learning rate scheduler
        warmup_steps: number of warmup steps
        warmup_lr: an optional field for the final warmup learning rate. This
          should be provided if the base `lr_schedule` does not contain this
          field.
      """
      super(WarmupDecaySchedule, self).__init__()
      self._lr_schedule = lr_schedule
      self._warmup_steps = warmup_steps
      self._warmup_lr = warmup_lr
  
    def __call__(self, step: int):
      lr = self._lr_schedule(step)
      if self._warmup_steps:
        if self._warmup_lr is not None:
          initial_learning_rate = tf.convert_to_tensor(
              self._warmup_lr, name="initial_learning_rate")
        else:
          initial_learning_rate = tf.convert_to_tensor(
              self._lr_schedule.initial_learning_rate,
              name="initial_learning_rate")
        dtype = initial_learning_rate.dtype
        global_step_recomp = tf.cast(step, dtype)
        warmup_steps = tf.cast(self._warmup_steps, dtype)
        warmup_lr = initial_learning_rate * global_step_recomp / warmup_steps
        lr = tf.cond(global_step_recomp < warmup_steps, lambda: warmup_lr,
                     lambda: lr)
      return lr
  
    def get_config(self) -> Mapping[str, Any]:
      config = self._lr_schedule.get_config()
      config.update({
          "warmup_steps": self._warmup_steps,
          "warmup_lr": self._warmup_lr,
      })
      return config
  
  
  class CosineDecayWithWarmup(tf.keras.optimizers.schedules.LearningRateSchedule):
    """Class to generate learning rate tensor."""
  
    def __init__(self, batch_size: int, total_steps: int, warmup_steps: int):
      """Creates the consine learning rate tensor with linear warmup.
  
      Args:
        batch_size: The training batch size used in the experiment.
        total_steps: Total training steps.
        warmup_steps: Steps for the warm up period.
      """
      super(CosineDecayWithWarmup, self).__init__()
      base_lr_batch_size = 256
      self._total_steps = total_steps
      self._init_learning_rate = BASE_LEARNING_RATE * batch_size / base_lr_batch_size
      self._warmup_steps = warmup_steps
  
    def __call__(self, global_step: int):
      global_step = tf.cast(global_step, dtype=tf.float32)
      warmup_steps = self._warmup_steps
      init_lr = self._init_learning_rate
      total_steps = self._total_steps
  
      linear_warmup = global_step / warmup_steps * init_lr
  
      cosine_learning_rate = init_lr * (tf.cos(np.pi *
                                               (global_step - warmup_steps) /
                                               (total_steps - warmup_steps)) +
                                        1.0) / 2.0
  
      learning_rate = tf.where(global_step < warmup_steps, linear_warmup,
                               cosine_learning_rate)
      return learning_rate
  
    def get_config(self):
      return {
          "total_steps": self._total_steps,
          "warmup_learning_rate": self._warmup_learning_rate,
          "warmup_steps": self._warmup_steps,
          "init_learning_rate": self._init_learning_rate,
      }
  