






"""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 							__SCREAMING_SNAKE_CASE						(				tf.keras.optimizers.schedules.LearningRateSchedule		):


							def __init__(			self      :						List[str]			,    snake_case      :						float			,    snake_case      :						Callable			,    snake_case      :						int			,    snake_case      :						float = 1.0			,    snake_case      :						str = None			,    ):
									'''simple docstring'''





									super().__init__()
									A__						:  Dict		  =  initial_learning_rate
									A__						:  int		  =  warmup_steps
									A__						:  Optional[Any]		  =  power
									A__						:  List[Any]		  =  decay_schedule_fn
									A__						:  Tuple		  =  name


							def __call__(			self      :						List[str]			,    snake_case      :						List[str]							):
									'''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__						:  Union[str, Any]		  =  tf.cast(snake_case			,    tf.floataa							)
											A__						:  Optional[int]		  =  tf.cast(self.warmup_steps			,    tf.floataa							)
											A__						:  List[str]		  =  global_step_float / warmup_steps_float
											A__						:  str		  =  self.initial_learning_rate * tf.math.pow(snake_case			,    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=snake_case			,    )


							def 						_UpperCamelCase       (			self      :						Any							):
									'''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      _lowerCAmelCase		(							UpperCAmelCase__       :					int,						UpperCAmelCase__       :					Optional[Any],						UpperCAmelCase__       :					Union[str, Any],						UpperCAmelCase__       :					Dict = 0.0,						UpperCAmelCase__       :					Any = 0.9,						UpperCAmelCase__       :					int = 0.999,						UpperCAmelCase__       :					Union[str, Any] = 1e-8,						UpperCAmelCase__       :					Union[str, Any] = None,						UpperCAmelCase__       :					Any = None,						UpperCAmelCase__       :					Dict = 0.0,						UpperCAmelCase__       :					Any = 1.0,						UpperCAmelCase__       :					Optional[Any] = None,						)		->Tuple:
		A__						:  Optional[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:
				A__						:  Optional[Any]		  =  WarmUp(
				    initial_learning_rate=__UpperCAmelCase,						decay_schedule_fn=__UpperCAmelCase,						warmup_steps=__UpperCAmelCase,						)
		if weight_decay_rate > 0.0:
				A__						:  Optional[int]		  =  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:
				A__						:  Optional[Any]		  =  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 							__SCREAMING_SNAKE_CASE						(				_snake_case		):


							def __init__(			self      :						List[Any]			,    snake_case      :						Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.001			,    snake_case      :						float = 0.9			,    snake_case      :						float = 0.999			,    snake_case      :						float = 1e-7			,    snake_case      :						bool = False			,    snake_case      :						float = 0.0			,    snake_case      :						Optional[List[str]] = None			,    snake_case      :						Optional[List[str]] = None			,    snake_case      :						str = "AdamWeightDecay"			,    **snake_case      :						str			,    ):
									'''simple docstring'''





									super().__init__(snake_case			,    snake_case			,    snake_case			,    snake_case			,    snake_case			,    snake_case			,    **snake_case							)
									A__						:  Tuple		  =  weight_decay_rate
									A__						:  Optional[Any]		  =  include_in_weight_decay
									A__						:  List[Any]		  =  exclude_from_weight_decay


							@classmethod
							def 						_UpperCamelCase       (			cls      :						int			,    snake_case      :						Optional[int]							):
									'''simple docstring'''





									A__						:  Optional[Any]		  =  {"""WarmUp""": WarmUp}
									return super(snake_case			,    cls							).from_config(snake_case			,    custom_objects=snake_case							)


							def 						_UpperCamelCase       (			self      :						Dict			,    snake_case      :						Dict			,    snake_case      :						Dict			,    snake_case      :						Optional[Any]							):
									'''simple docstring'''





									super(snake_case			,    self							)._prepare_local(snake_case			,    snake_case			,    snake_case							)
									A__						:  Any		  =  tf.constant(
									    self.weight_decay_rate			,    name="""adam_weight_decay_rate"""							)


							def 						_UpperCamelCase       (			self      :						List[Any]			,    snake_case      :						Any			,    snake_case      :						Tuple			,    snake_case      :						List[str]							):
									'''simple docstring'''





									A__						:  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 						_UpperCamelCase       (			self      :						List[str]			,    snake_case      :						int			,    snake_case      :						Dict=None			,    **snake_case      :						int							):
									'''simple docstring'''





									A__  ,  A__						:  Optional[int]		  =  list(zip(*snake_case							)							)
									return super(snake_case			,    self							).apply_gradients(zip(snake_case			,    snake_case							)			,    name=snake_case			,    **snake_case							)


							def 						_UpperCamelCase       (			self      :						List[Any]			,    snake_case      :						List[str]			,    snake_case      :						Optional[int]			,    snake_case      :						Any							):
									'''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__						:  int		  =  self._fallback_apply_state(snake_case			,    snake_case							)
											A__						:  Any		  =  coefficients

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


							def 						_UpperCamelCase       (			self      :						List[str]			,    snake_case      :						Any			,    snake_case      :						Optional[Any]			,    snake_case      :						List[str]=None							):
									'''simple docstring'''





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


							def 						_UpperCamelCase       (			self      :						Dict			,    snake_case      :						Dict			,    snake_case      :						int			,    snake_case      :						Tuple			,    snake_case      :						Union[str, Any]=None							):
									'''simple docstring'''





									A__  ,  A__						:  Optional[int]		  =  self._get_lr(var.device			,    var.dtype.base_dtype			,    snake_case							)
									A__						:  Tuple		  =  self._decay_weights_op(snake_case			,    snake_case			,    snake_case							)
									with tf.control_dependencies([decay]							):
											return super(snake_case			,    self							)._resource_apply_sparse(snake_case			,    snake_case			,    snake_case			,    **snake_case							)


							def 						_UpperCamelCase       (			self      :						Union[str, Any]							):
									'''simple docstring'''





									A__						:  Dict		  =  super().get_config()
									config.update({"""weight_decay_rate""": self.weight_decay_rate}							)
									return config


							def 						_UpperCamelCase       (			self      :						Optional[Any]			,    snake_case      :						Optional[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(snake_case			,    snake_case							) is not None:
															return True

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






class 							__SCREAMING_SNAKE_CASE						(				_snake_case		):


							def __init__(			self      :						Any							):
									'''simple docstring'''





									A__						:  Optional[Any]		  =  []
									A__						:  Dict		  =  None


							@property
							def 						_UpperCamelCase       (			self      :						int							):
									'''simple docstring'''





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

									return self._accum_steps.value()


							@property
							def 						_UpperCamelCase       (			self      :						Any							):
									'''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      :						str			,    snake_case      :						Any							):
									'''simple docstring'''





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

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

									self._accum_steps.assign_add(1							)


							def 						_UpperCamelCase       (			self      :						List[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(snake_case							)							)














"""simple docstring"""



import baseaa




def      _lowerCAmelCase		(							UpperCAmelCase__       :					str      )		->bytes:
						return baseaa.baaencode(string.encode("""utf-8"""      )      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					bytes      )		->str:
						return baseaa.baadecode(UpperCAmelCase__      ).decode("""utf-8"""      )


if __name__ == "__main__":
						A_    =							'''Hello World!'''
						A_    =							baseaa_encode(test)
						print(encoded)

						A_    =							baseaa_decode(encoded)
						print(decoded)






