



"""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 			lowercase__			(      tf.keras.optimizers.schedules.LearningRateSchedule    ):
							'''simple docstring'''







							def __init__(  self	,       snake_case	,       snake_case	,       snake_case	,       snake_case = 1.0	,       snake_case = None	,       )			->							List[Any]:
									super().__init__()
									_UpperCAmelCase							 =		initial_learning_rate
									_UpperCAmelCase							 =		warmup_steps
									_UpperCAmelCase							 =		power
									_UpperCAmelCase							 =		decay_schedule_fn
									_UpperCAmelCase							 =		name






							def __call__(  self	,       snake_case						)			->							Dict:
									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`.
											_UpperCAmelCase							 =		tf.cast(snake_case	,       tf.floataa						)
											_UpperCAmelCase							 =		tf.cast(self.warmup_steps	,       tf.floataa						)
											_UpperCAmelCase							 =		global_step_float / warmup_steps_float
											_UpperCAmelCase							 =		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        lowerCamelCase_   (  self						)			->							Optional[Any]:
									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     (		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'''


		_UpperCAmelCase							 =		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:
				_UpperCAmelCase							 =		WarmUp(
				    initial_learning_rate=A     ,				decay_schedule_fn=A     ,				warmup_steps=A     ,				)
		if weight_decay_rate > 0.0:
				_UpperCAmelCase							 =		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:
				_UpperCAmelCase							 =		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 			lowercase__			(      A    ):
							'''simple docstring'''







							def __init__(  self	,       snake_case = 0.001	,       snake_case = 0.9	,       snake_case = 0.999	,       snake_case = 1E-7	,       snake_case = False	,       snake_case = 0.0	,       snake_case = None	,       snake_case = None	,       snake_case = "AdamWeightDecay"	,       **snake_case	,       )			->							Any:
									super().__init__(snake_case	,       snake_case	,       snake_case	,       snake_case	,       snake_case	,       snake_case	,       **snake_case						)
									_UpperCAmelCase							 =		weight_decay_rate
									_UpperCAmelCase							 =		include_in_weight_decay
									_UpperCAmelCase							 =		exclude_from_weight_decay






							@classmethod
							def        lowerCamelCase_   (  cls	,       snake_case						)			->							List[Any]:
									_UpperCAmelCase							 =		{'WarmUp': WarmUp}
									return super(snake_case	,       cls						).from_config(snake_case	,       custom_objects=snake_case						)






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case						)			->							int:
									super(snake_case	,       self						)._prepare_local(snake_case	,       snake_case	,       snake_case						)
									_UpperCAmelCase							 =		tf.constant(
									    self.weight_decay_rate	,       name='adam_weight_decay_rate'						)






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case						)			->							Optional[int]:
									_UpperCAmelCase							 =		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	,       snake_case	,       snake_case=None	,       **snake_case						)			->							Any:
									_UpperCAmelCase ,					_UpperCAmelCase							 =		list(zip(*snake_case						)						)
									return super(snake_case	,       self						).apply_gradients(zip(snake_case	,       snake_case						)	,       name=snake_case	,       **snake_case						)






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case						)			->							Tuple:
									if apply_state is None:
											return self._decayed_lr_t[var_dtype], {}

									_UpperCAmelCase							 =		apply_state or {}
									_UpperCAmelCase							 =		apply_state.get((var_device, var_dtype)						)
									if coefficients is None:
											_UpperCAmelCase							 =		self._fallback_apply_state(snake_case	,       snake_case						)
											_UpperCAmelCase							 =		coefficients

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






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case=None						)			->							int:
									_UpperCAmelCase ,					_UpperCAmelCase							 =		self._get_lr(var.device	,       var.dtype.base_dtype	,       snake_case						)
									_UpperCAmelCase							 =		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        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case	,       snake_case=None						)			->							List[str]:
									_UpperCAmelCase ,					_UpperCAmelCase							 =		self._get_lr(var.device	,       var.dtype.base_dtype	,       snake_case						)
									_UpperCAmelCase							 =		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        lowerCamelCase_   (  self						)			->							List[str]:
									_UpperCAmelCase							 =		super().get_config()
									config.update({'weight_decay_rate': self.weight_decay_rate}						)
									return config






							def        lowerCamelCase_   (  self	,       snake_case						)			->							str:
									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 			lowercase__			(      A    ):
							'''simple docstring'''







							def __init__(  self						)			->							Optional[int]:
									_UpperCAmelCase							 =		[]
									_UpperCAmelCase							 =		None






							@property
							def        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									if self._accum_steps is None:
											_UpperCAmelCase							 =		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        lowerCamelCase_   (  self						)			->							Optional[int]:
									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	,       snake_case						)			->							List[Any]:
									if not self._gradients:
											_UpperCAmelCase							 =		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        lowerCamelCase_   (  self						)			->							Tuple:
									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"""





def 	UpperCAmelCase     (		A	:						int       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		abs(A       )
		_UpperCAmelCase							 =		0
		while n > 0:
				res += n % 10
				n //= 10
		return res




def 	UpperCAmelCase     (		A	:						int       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		abs(A       )
		return n if n < 10 else n % 10 + sum_of_digits(n // 10       )




def 	UpperCAmelCase     (		A	:						int       ):

		'''simple docstring'''


		return sum(int(A       ) for c in str(abs(A       )       )       )




def 	UpperCAmelCase     (		):

		'''simple docstring'''


		from collections.abc import Callable
		from timeit import timeit

		def benchmark_a_function(A	:						Callable     ,				A	:						int       ) -> None:
				_UpperCAmelCase							 =		f'{func.__name__}({value})'
				_UpperCAmelCase							 =		timeit(f'__main__.{call}'     ,				setup='import __main__'       )
				print(f'{call:56} = {func(A       )} -- {timing:.4f} seconds'       )

		for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
				for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
						benchmark_a_function(A     ,				A       )
				print()


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					benchmark()


