




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      :		int		,	_UpperCAmelCase      :		float		,	_UpperCAmelCase      :		Callable		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		float = 1.0		,	_UpperCAmelCase      :		str = None		,	)  ->    Tuple:





								'''simple docstring'''




								super().__init__()
								_lowerCAmelCase    :    Union[str, Any]										=      initial_learning_rate
								_lowerCAmelCase    :    Tuple										=      warmup_steps
								_lowerCAmelCase    :    Optional[Any]										=      power
								_lowerCAmelCase    :    Optional[Any]										=      decay_schedule_fn
								_lowerCAmelCase    :    List[str]										=      name



				def __call__(      self      :		Union[str, Any]		,	_UpperCAmelCase      :		int      )  ->    Tuple:





								'''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`.
												_lowerCAmelCase    :    Optional[Any]										=      tf.cast(_UpperCAmelCase		,	tf.floataa      )
												_lowerCAmelCase    :    Optional[Any]										=      tf.cast(self.warmup_steps		,	tf.floataa      )
												_lowerCAmelCase    :    int										=      global_step_float / warmup_steps_float
												_lowerCAmelCase    :    List[Any]										=      self.initial_learning_rate * tf.math.pow(_UpperCAmelCase		,	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=_UpperCAmelCase		,	)



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    Dict:





								'''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 							_UpperCAmelCase  (UpperCamelCase_ : float      ,    UpperCamelCase_ : int      ,    UpperCamelCase_ : int      ,    UpperCamelCase_ : float = 0.0      ,    UpperCamelCase_ : float = 0.9      ,    UpperCamelCase_ : float = 0.999      ,    UpperCamelCase_ : float = 1E-8      ,    UpperCamelCase_ : Optional[float] = None      ,    UpperCamelCase_ : Optional[float] = None      ,    UpperCamelCase_ : float = 0.0      ,    UpperCamelCase_ : float = 1.0      ,    UpperCamelCase_ : Optional[List[str]] = None      ,    ):
				'''simple docstring'''
				_lowerCAmelCase    :    Union[str, Any]										=      tf.keras.optimizers.schedules.PolynomialDecay(
				    initial_learning_rate=UpperCamelCase_      ,    decay_steps=num_train_steps - num_warmup_steps      ,    end_learning_rate=init_lr * min_lr_ratio      ,    power=UpperCamelCase_      ,    )
				if num_warmup_steps:
								_lowerCAmelCase    :    Tuple										=      WarmUp(
								    initial_learning_rate=UpperCamelCase_      ,    decay_schedule_fn=UpperCamelCase_      ,    warmup_steps=UpperCamelCase_      ,    )
				if weight_decay_rate > 0.0:
								_lowerCAmelCase    :    List[Any]										=      AdamWeightDecay(
								    learning_rate=UpperCamelCase_      ,    weight_decay_rate=UpperCamelCase_      ,    beta_a=UpperCamelCase_      ,    beta_a=UpperCamelCase_      ,    epsilon=UpperCamelCase_      ,    clipnorm=UpperCamelCase_      ,    global_clipnorm=UpperCamelCase_      ,    exclude_from_weight_decay=["""LayerNorm""", """layer_norm""", """bias"""]      ,    include_in_weight_decay=UpperCamelCase_      ,    )
				else:
								_lowerCAmelCase    :    Tuple										=      tf.keras.optimizers.Adam(
								    learning_rate=UpperCamelCase_      ,    beta_a=UpperCamelCase_      ,    beta_a=UpperCamelCase_      ,    epsilon=UpperCamelCase_      ,    clipnorm=UpperCamelCase_      ,    global_clipnorm=UpperCamelCase_      ,    )
				# 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  (_a		):



				def __init__(      self      :		Tuple		,	_UpperCAmelCase      :		Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.001		,	_UpperCAmelCase      :		float = 0.9		,	_UpperCAmelCase      :		float = 0.999		,	_UpperCAmelCase      :		float = 1E-7		,	_UpperCAmelCase      :		bool = False		,	_UpperCAmelCase      :		float = 0.0		,	_UpperCAmelCase      :		Optional[List[str]] = None		,	_UpperCAmelCase      :		Optional[List[str]] = None		,	_UpperCAmelCase      :		str = "AdamWeightDecay"		,	**_UpperCAmelCase      :		List[Any]		,	)  ->    Optional[int]:





								'''simple docstring'''




								super().__init__(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      )
								_lowerCAmelCase    :    Optional[Any]										=      weight_decay_rate
								_lowerCAmelCase    :    str										=      include_in_weight_decay
								_lowerCAmelCase    :    str										=      exclude_from_weight_decay



				@classmethod
				def       SCREAMING_SNAKE_CASE       (      cls      :		List[Any]		,	_UpperCAmelCase      :		Optional[int]      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      {"""WarmUp""": WarmUp}
								return super(_UpperCAmelCase		,	cls      ).from_config(_UpperCAmelCase		,	custom_objects=_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		str		,	_UpperCAmelCase      :		List[str]		,	_UpperCAmelCase      :		Optional[Any]		,	_UpperCAmelCase      :		Optional[Any]      )  ->    List[str]:





								'''simple docstring'''




								super(_UpperCAmelCase		,	self      )._prepare_local(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      )
								_lowerCAmelCase    :    List[str]										=      tf.constant(
								    self.weight_decay_rate		,	name="""adam_weight_decay_rate"""      )



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]		,	_UpperCAmelCase      :		List[str]		,	_UpperCAmelCase      :		Any		,	_UpperCAmelCase      :		str      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    List[str]										=      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       SCREAMING_SNAKE_CASE       (      self      :		str		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		Any=None		,	**_UpperCAmelCase      :		Dict      )  ->    Dict:





								'''simple docstring'''




								_lowerCAmelCase					,    _lowerCAmelCase    :    Dict										=      list(zip(*_UpperCAmelCase      )      )
								return super(_UpperCAmelCase		,	self      ).apply_gradients(zip(_UpperCAmelCase		,	_UpperCAmelCase      )		,	name=_UpperCAmelCase		,	**_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	_UpperCAmelCase      :		Union[str, Any]		,	_UpperCAmelCase      :		Union[str, Any]		,	_UpperCAmelCase      :		List[str]      )  ->    Optional[Any]:





								'''simple docstring'''




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

								_lowerCAmelCase    :    Union[str, Any]										=      apply_state or {}
								_lowerCAmelCase    :    int										=      apply_state.get((var_device, var_dtype)      )
								if coefficients is None:
												_lowerCAmelCase    :    Any										=      self._fallback_apply_state(_UpperCAmelCase		,	_UpperCAmelCase      )
												_lowerCAmelCase    :    List[Any]										=      coefficients

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



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict		,	_UpperCAmelCase      :		Optional[int]		,	_UpperCAmelCase      :		Dict		,	_UpperCAmelCase      :		str=None      )  ->    Dict:





								'''simple docstring'''




								_lowerCAmelCase					,    _lowerCAmelCase    :    Optional[Any]										=      self._get_lr(var.device		,	var.dtype.base_dtype		,	_UpperCAmelCase      )
								_lowerCAmelCase    :    Tuple										=      self._decay_weights_op(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      )
								with tf.control_dependencies([decay]      ):
												return super(_UpperCAmelCase		,	self      )._resource_apply_dense(_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]		,	_UpperCAmelCase      :		Optional[Any]		,	_UpperCAmelCase      :		Optional[Any]		,	_UpperCAmelCase      :		Any		,	_UpperCAmelCase      :		int=None      )  ->    Union[str, Any]:





								'''simple docstring'''




								_lowerCAmelCase					,    _lowerCAmelCase    :    int										=      self._get_lr(var.device		,	var.dtype.base_dtype		,	_UpperCAmelCase      )
								_lowerCAmelCase    :    Union[str, Any]										=      self._decay_weights_op(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      )
								with tf.control_dependencies([decay]      ):
												return super(_UpperCAmelCase		,	self      )._resource_apply_sparse(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    Tuple:





								'''simple docstring'''




								_lowerCAmelCase    :    str										=      super().get_config()
								config.update({"""weight_decay_rate""": self.weight_decay_rate}      )
								return config



				def       SCREAMING_SNAKE_CASE       (      self      :		str		,	_UpperCAmelCase      :		List[Any]      )  ->    int:





								'''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(_UpperCAmelCase		,	_UpperCAmelCase      ) is not None:
																				return True

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






class        __snake_case  (_a		):



				def __init__(      self      :		Tuple      )  ->    Tuple:





								'''simple docstring'''




								_lowerCAmelCase    :    List[Any]										=      []
								_lowerCAmelCase    :    Any										=      None



				@property
				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]      )  ->    Optional[Any]:





								'''simple docstring'''




								if self._accum_steps is None:
												_lowerCAmelCase    :    List[Any]										=      tf.Variable(
												    tf.constant(0		,	dtype=tf.intaa      )		,	trainable=_UpperCAmelCase		,	synchronization=tf.VariableSynchronization.ON_READ		,	aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA		,	)

								return self._accum_steps.value()



				@property
				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]      )  ->    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      :		Tuple		,	_UpperCAmelCase      :		Union[str, Any]      )  ->    int:





								'''simple docstring'''




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

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

								self._accum_steps.assign_add(1      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    str:





								'''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(_UpperCAmelCase      )      )







from math import loga




def 							_UpperCAmelCase  (UpperCamelCase_ : int       ):
				'''simple docstring'''
				if a < 0:
								raise ValueError("""Input value must be a positive integer"""       )
				elif isinstance(UpperCamelCase_      ,    UpperCamelCase_       ):
								raise TypeError("""Input value must be a 'int' type"""       )
				return 0 if (a == 0) else int(loga(a & -a       )       )


if __name__ == "__main__":
	import doctest

	doctest.testmod()

