content: # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
  # except for the third-party components listed below.
  # Hunyuan 3D does not impose any additional limitations beyond what is outlined
  # in the repsective licenses of these third-party components.
  # Users must comply with all terms and conditions of original licenses of these third-party
  # components and must ensure that the usage of the third party components adheres to
  # all relevant laws and regulations.
  
  # For avoidance of doubts, Hunyuan 3D means the large language models and
  # their software and algorithms, including trained model weights, parameters (including
  # optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
  # fine-tuning enabling code and other elements of the foregoing made publicly available
  # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
  
  import numpy as np
  
  
  class BaseScheduler(object):
  
      def schedule(self, n, **kwargs):
          raise NotImplementedError
  
  
  class LambdaWarmUpCosineFactorScheduler(BaseScheduler):
      """
      note: use with a base_lr of 1.0
      """
      def __init__(self, warm_up_steps, f_min, f_max, f_start, max_decay_steps, verbosity_interval=0, **ignore_kwargs):
          self.lr_warm_up_steps = warm_up_steps
          self.f_start = f_start
          self.f_min = f_min
          self.f_max = f_max
          self.lr_max_decay_steps = max_decay_steps
          self.last_f = 0.
          self.verbosity_interval = verbosity_interval
  
      def schedule(self, n, **kwargs):
          if self.verbosity_interval > 0:
              if n % self.verbosity_interval == 0:
                  print(f"current step: {n}, recent lr-multiplier: {self.f_start}")
          if n < self.lr_warm_up_steps:
              f = (self.f_max - self.f_start) / self.lr_warm_up_steps * n + self.f_start
              self.last_f = f
              return f
          else:
              t = (n - self.lr_warm_up_steps) / (self.lr_max_decay_steps - self.lr_warm_up_steps)
              t = min(t, 1.0)
              f = self.f_min + 0.5 * (self.f_max - self.f_min) * (1 + np.cos(t * np.pi))
              self.last_f = f
              return f
  
      def __call__(self, n, **kwargs):
          return self.schedule(n, **kwargs)
  