






"""simple docstring"""


import re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
   from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
   from tensorflow.keras.optimizers import Adam






class 						snake_case  (  tf.keras.optimizers.schedules.LearningRateSchedule	):





    def __init__(							self     : Union[str, Any]    ,		A     : float    ,		A     : Callable    ,		A     : int    ,		A     : float = 1.0    ,		A     : str = None    ,		):


      '''simple docstring'''




      super().__init__()
      a					:      List[str]    =       initial_learning_rate
      a					:      int    =       warmup_steps
      a					:      Tuple    =       power
      a					:      List[str]    =       decay_schedule_fn
      a					:      int    =       name





    def __call__(							self     : int    ,		A     : List[Any]      ):


      '''simple docstring'''




      with tf.name_scope(self.name or 'WarmUp'      ) as name:
        # Implements polynomial warmup. i.e., if global_step < warmup_steps, the
        # learning rate will be `global_step/num_warmup_steps * init_lr`.
        a					:      Any    =       tf.cast(A    ,		tf.floataa      )
        a					:      List[str]    =       tf.cast(self.warmup_steps    ,		tf.floataa      )
        a					:      Dict    =       global_step_float / warmup_steps_float
        a					:      Tuple    =       self.initial_learning_rate * tf.math.pow(A    ,		self.power      )
        return tf.cond(
            global_step_float < warmup_steps_float    ,		lambda: warmup_learning_rate    ,		lambda: self.decay_schedule_fn(step - self.warmup_steps      )    ,		name=A    ,		)





    def     lowerCamelCase__				(							self     : Optional[int]      ):


      '''simple docstring'''




      return {
          "initial_learning_rate": self.initial_learning_rate,
          "decay_schedule_fn": self.decay_schedule_fn,
          "warmup_steps": self.warmup_steps,
          "power": self.power,
          "name": self.name,
      }




def       snake_case  (A_				:float     ,      A_				:int     ,      A_				:int     ,      A_				:float = 0.0     ,      A_				:float = 0.9     ,      A_				:float = 0.999     ,      A_				:float = 1E-8     ,      A_				:Optional[float] = None     ,      A_				:Optional[float] = None     ,      A_				:float = 0.0     ,      A_				:float = 1.0     ,      A_				:Optional[List[str]] = None     ,      ):







  '''simple docstring'''

  a					:      Any    =       tf.keras.optimizers.schedules.PolynomialDecay(
      initial_learning_rate=A_     ,      decay_steps=num_train_steps - num_warmup_steps     ,      end_learning_rate=init_lr * min_lr_ratio     ,      power=A_     ,      )
  if num_warmup_steps:
    a					:      str    =       WarmUp(
        initial_learning_rate=A_     ,      decay_schedule_fn=A_     ,      warmup_steps=A_     ,      )
  if weight_decay_rate > 0.0:
    a					:      Any    =       AdamWeightDecay(
        learning_rate=A_     ,      weight_decay_rate=A_     ,      beta_a=A_     ,      beta_a=A_     ,      epsilon=A_     ,      clipnorm=A_     ,      global_clipnorm=A_     ,      exclude_from_weight_decay=['LayerNorm', 'layer_norm', 'bias']     ,      include_in_weight_decay=A_     ,      )
  else:
    a					:      Dict    =       tf.keras.optimizers.Adam(
        learning_rate=A_     ,      beta_a=A_     ,      beta_a=A_     ,      epsilon=A_     ,      clipnorm=A_     ,      global_clipnorm=A_     ,      )
  # We return the optimizer and the LR scheduler in order to better track the
  # evolution of the LR independently of the optimizer.
  return optimizer, lr_schedule






class 						snake_case  (  UpperCAmelCase	):





    def __init__(							self     : int    ,		A     : Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.0_01    ,		A     : float = 0.9    ,		A     : float = 0.9_99    ,		A     : float = 1E-7    ,		A     : bool = False    ,		A     : float = 0.0    ,		A     : Optional[List[str]] = None    ,		A     : Optional[List[str]] = None    ,		A     : str = "AdamWeightDecay"    ,		**A     : List[Any]    ,		):


      '''simple docstring'''




      super().__init__(A    ,		A    ,		A    ,		A    ,		A    ,		A    ,		**A      )
      a					:      Union[str, Any]    =       weight_decay_rate
      a					:      Tuple    =       include_in_weight_decay
      a					:      List[Any]    =       exclude_from_weight_decay





    @classmethod
    def     lowerCamelCase__				(							cls     : int    ,		A     : Dict      ):


      '''simple docstring'''




      a					:      List[str]    =       {'WarmUp': WarmUp}
      return super(A    ,		cls      ).from_config(A    ,		custom_objects=A      )





    def     lowerCamelCase__				(							self     : Union[str, Any]    ,		A     : Union[str, Any]    ,		A     : Dict    ,		A     : Optional[Any]      ):


      '''simple docstring'''




      super(A    ,		self      )._prepare_local(A    ,		A    ,		A      )
      a					:      str    =       tf.constant(
          self.weight_decay_rate    ,		name='adam_weight_decay_rate'      )





    def     lowerCamelCase__				(							self     : Optional[int]    ,		A     : Dict    ,		A     : List[str]    ,		A     : Dict      ):


      '''simple docstring'''




      a					:      Dict    =       self._do_use_weight_decay(var.name      )
      if do_decay:
        return var.assign_sub(
            learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['weight_decay_rate']    ,		use_locking=self._use_locking    ,		)
      return tf.no_op()





    def     lowerCamelCase__				(							self     : int    ,		A     : Tuple    ,		A     : Any=None    ,		**A     : List[Any]      ):


      '''simple docstring'''




      a,					a					:      Dict    =       list(zip(*A      )      )
      return super(A    ,		self      ).apply_gradients(zip(A    ,		A      )    ,		name=A    ,		**A      )





    def     lowerCamelCase__				(							self     : List[Any]    ,		A     : Optional[Any]    ,		A     : List[Any]    ,		A     : int      ):


      '''simple docstring'''




      if apply_state is None:
        return self._decayed_lr_t[var_dtype], {}

      a					:      str    =       apply_state or {}
      a					:      List[str]    =       apply_state.get((var_device, var_dtype)      )
      if coefficients is None:
        a					:      Optional[int]    =       self._fallback_apply_state(A    ,		A      )
        a					:      Dict    =       coefficients

      return coefficients["lr_t"], {"apply_state": apply_state}





    def     lowerCamelCase__				(							self     : List[str]    ,		A     : Dict    ,		A     : Union[str, Any]    ,		A     : Any=None      ):


      '''simple docstring'''




      a,					a					:      Union[str, Any]    =       self._get_lr(var.device    ,		var.dtype.base_dtype    ,		A      )
      a					:      Any    =       self._decay_weights_op(A    ,		A    ,		A      )
      with tf.control_dependencies([decay]      ):
        return super(A    ,		self      )._resource_apply_dense(A    ,		A    ,		**A      )





    def     lowerCamelCase__				(							self     : str    ,		A     : Union[str, Any]    ,		A     : str    ,		A     : Dict    ,		A     : Any=None      ):


      '''simple docstring'''




      a,					a					:      List[str]    =       self._get_lr(var.device    ,		var.dtype.base_dtype    ,		A      )
      a					:      str    =       self._decay_weights_op(A    ,		A    ,		A      )
      with tf.control_dependencies([decay]      ):
        return super(A    ,		self      )._resource_apply_sparse(A    ,		A    ,		A    ,		**A      )





    def     lowerCamelCase__				(							self     : Dict      ):


      '''simple docstring'''




      a					:      Tuple    =       super().get_config()
      config.update({'weight_decay_rate': self.weight_decay_rate}      )
      return config





    def     lowerCamelCase__				(							self     : Optional[Any]    ,		A     : Dict      ):


      '''simple docstring'''




      if self.weight_decay_rate == 0:
        return False

      if self._include_in_weight_decay:
        for r in self._include_in_weight_decay:
          if re.search(A    ,		A      ) is not None:
            return True

      if self._exclude_from_weight_decay:
        for r in self._exclude_from_weight_decay:
          if re.search(A    ,		A      ) is not None:
            return False
      return True






class 						snake_case  (  UpperCAmelCase	):





    def __init__(							self     : List[Any]      ):


      '''simple docstring'''




      a					:      List[Any]    =       []
      a					:      Dict    =       None





    @property
    def     lowerCamelCase__				(							self     : Optional[int]      ):


      '''simple docstring'''




      if self._accum_steps is None:
        a					:      Dict    =       tf.Variable(
            tf.constant(0    ,		dtype=tf.intaa      )    ,		trainable=A    ,		synchronization=tf.VariableSynchronization.ON_READ    ,		aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA    ,		)

      return self._accum_steps.value()





    @property
    def     lowerCamelCase__				(							self     : Optional[int]      ):


      '''simple docstring'''




      if not self._gradients:
        raise ValueError('The accumulator should be called first to initialize the gradients'      )
      return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]





    def __call__(							self     : Dict    ,		A     : Tuple      ):


      '''simple docstring'''




      if not self._gradients:
        a					:      Dict    =       self.step  # Create the step variable.
        self._gradients.extend(
            [
                tf.Variable(
                    tf.zeros_like(A      )    ,		trainable=A    ,		synchronization=tf.VariableSynchronization.ON_READ    ,		aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA    ,		)
                if gradient is not None
                else gradient
                for gradient in gradients
            ]      )
      if len(A      ) != len(self._gradients      ):
        raise ValueError(F'''Expected {len(self._gradients      )} gradients, but got {len(A      )}'''      )

      for accum_gradient, gradient in zip(self._gradients    ,		A      ):
        if accum_gradient is not None and gradient is not None:
          accum_gradient.assign_add(A      )

      self._accum_steps.assign_add(1      )





    def     lowerCamelCase__				(							self     : Any      ):


      '''simple docstring'''




      if not self._gradients:
        return
      self._accum_steps.assign(0      )
      for gradient in self._gradients:
        if gradient is not None:
          gradient.assign(tf.zeros_like(A      )      )












"""simple docstring"""


def       snake_case  (A_				:int				):







  '''simple docstring'''

  if isinstance(A_     ,      A_				):
    raise TypeError('\'float\' object cannot be interpreted as an integer'				)
  if isinstance(A_     ,      A_				):
    raise TypeError('\'str\' object cannot be interpreted as an integer'				)

  if num == 0:
    return "0b0"

  a					:      List[Any]    =       False

  if num < 0:
    a					:      Optional[int]    =       True
    a					:      Dict    =       -num

  a					:      list[int]    =       []
  while num > 0:
    binary.insert(0     ,      num % 2				)
    num >>= 1

  if negative:
    return "-0b" + "".join(str(A_				) for e in binary				)

  return "0b" + "".join(str(A_				) for e in binary				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()




