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



		def __init__(					self				,		__a				,		__a				,		__a				,		__a = 1.0				,		__a = None				,		)       ->       Union[str, Any]:






							'''simple docstring'''
							super().__init__()
							_UpperCamelCase							 =  initial_learning_rate
							_UpperCamelCase							 =  warmup_steps
							_UpperCamelCase							 =  power
							_UpperCamelCase							 =  decay_schedule_fn
							_UpperCamelCase							 =  name



		def __call__(					self				,		__a)       ->       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`.
												_UpperCamelCase							 =  tf.cast(__a				,		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(__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 						UpperCAmelCase							(					self)       ->       Optional[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__		(			__snake_case,     __snake_case,     __snake_case,     __snake_case = 0.0,     __snake_case = 0.9,     __snake_case = 0.999,     __snake_case = 1e-8,     __snake_case = None,     __snake_case = None,     __snake_case = 0.0,     __snake_case = 1.0,     __snake_case = None,     )							->		List[Any]:


					"""simple docstring"""





					_UpperCamelCase							 =  tf.keras.optimizers.schedules.PolynomialDecay(
					    initial_learning_rate=__lowerCAmelCase,     decay_steps=num_train_steps - num_warmup_steps,     end_learning_rate=init_lr * min_lr_ratio,     power=__lowerCAmelCase,     )
					if num_warmup_steps:
										_UpperCamelCase							 =  WarmUp(
										    initial_learning_rate=__lowerCAmelCase,     decay_schedule_fn=__lowerCAmelCase,     warmup_steps=__lowerCAmelCase,     )
					if weight_decay_rate > 0.0:
										_UpperCamelCase							 =  AdamWeightDecay(
										    learning_rate=__lowerCAmelCase,     weight_decay_rate=__lowerCAmelCase,     beta_a=__lowerCAmelCase,     beta_a=__lowerCAmelCase,     epsilon=__lowerCAmelCase,     clipnorm=__lowerCAmelCase,     global_clipnorm=__lowerCAmelCase,     exclude_from_weight_decay=['''LayerNorm''', '''layer_norm''', '''bias'''],     include_in_weight_decay=__lowerCAmelCase,     )
					else:
										_UpperCamelCase							 =  tf.keras.optimizers.Adam(
										    learning_rate=__lowerCAmelCase,     beta_a=__lowerCAmelCase,     beta_a=__lowerCAmelCase,     epsilon=__lowerCAmelCase,     clipnorm=__lowerCAmelCase,     global_clipnorm=__lowerCAmelCase,     )
					# 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     _UpperCAmelCase(    snake_case__	):



		def __init__(					self				,		__a = 0.001				,		__a = 0.9				,		__a = 0.999				,		__a = 1e-7				,		__a = False				,		__a = 0.0				,		__a = None				,		__a = None				,		__a = "AdamWeightDecay"				,		**__a				,		)       ->       Dict:






							'''simple docstring'''
							super().__init__(__a				,		__a				,		__a				,		__a				,		__a				,		__a				,		**__a)
							_UpperCamelCase							 =  weight_decay_rate
							_UpperCamelCase							 =  include_in_weight_decay
							_UpperCamelCase							 =  exclude_from_weight_decay



		@classmethod
		def 						UpperCAmelCase							(					cls				,		__a)       ->       Tuple:






							'''simple docstring'''
							_UpperCamelCase							 =  {'''WarmUp''': WarmUp}
							return super(__a				,		cls).from_config(__a				,		custom_objects=__a)



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a)       ->       Union[str, Any]:






							'''simple docstring'''
							super(__a				,		self)._prepare_local(__a				,		__a				,		__a)
							_UpperCamelCase							 =  tf.constant(
							    self.weight_decay_rate				,		name='''adam_weight_decay_rate''')



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a)       ->       Any:






							'''simple docstring'''
							_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 						UpperCAmelCase							(					self				,		__a				,		__a=None				,		**__a)       ->       Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase ,  _UpperCamelCase							 =  list(zip(*__a))
							return super(__a				,		self).apply_gradients(zip(__a				,		__a)				,		name=__a				,		**__a)



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a)       ->       Tuple:






							'''simple docstring'''







							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(__a				,		__a)
												_UpperCamelCase							 =  coefficients

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



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a=None)       ->       str:






							'''simple docstring'''
							_UpperCamelCase ,  _UpperCamelCase							 =  self._get_lr(var.device				,		var.dtype.base_dtype				,		__a)
							_UpperCamelCase							 =  self._decay_weights_op(__a				,		__a				,		__a)
							with tf.control_dependencies([decay]):
												return super(__a				,		self)._resource_apply_dense(__a				,		__a				,		**__a)



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a				,		__a=None)       ->       Optional[int]:






							'''simple docstring'''
							_UpperCamelCase ,  _UpperCamelCase							 =  self._get_lr(var.device				,		var.dtype.base_dtype				,		__a)
							_UpperCamelCase							 =  self._decay_weights_op(__a				,		__a				,		__a)
							with tf.control_dependencies([decay]):
												return super(__a				,		self)._resource_apply_sparse(__a				,		__a				,		__a				,		**__a)



		def 						UpperCAmelCase							(					self)       ->       str:






							'''simple docstring'''
							_UpperCamelCase							 =  super().get_config()
							config.update({'''weight_decay_rate''': self.weight_decay_rate})
							return config




		def 						UpperCAmelCase							(					self				,		__a)       ->       Tuple:






							'''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     _UpperCAmelCase(    snake_case__	):



		def __init__(					self)       ->       Optional[int]:






							'''simple docstring'''
							_UpperCamelCase							 =  []
							_UpperCamelCase							 =  None



		@property
		def 						UpperCAmelCase							(					self)       ->       Dict:






							'''simple docstring'''







							if self._accum_steps is None:
												_UpperCamelCase							 =  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 						UpperCAmelCase							(					self)       ->       List[str]:






							'''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				,		__a)       ->       Dict:






							'''simple docstring'''







							if not self._gradients:
												_UpperCamelCase							 =  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 						UpperCAmelCase							(					self)       ->       Union[str, 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))














from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat


def __A    (      )->  tuple[list[int], int]:





						"""simple docstring"""



						_UpperCAmelCase									=			[randint(-1_000 ,		1_000	) for i in range(10	)]
						_UpperCAmelCase									=			randint(-5_000 ,		5_000	)
						return (arr, r)


_a      	=  make_dataset()


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  tuple[int, ...]:





						"""simple docstring"""



						for triplet in permutations(__lowerCAmelCase ,		3	):
												if sum(__lowerCAmelCase	) == target:
																		return tuple(sorted(__lowerCAmelCase	)	)
						return (0, 0, 0)


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  tuple[int, int, int]:





						"""simple docstring"""



						arr.sort()
						_UpperCAmelCase									=			len(__lowerCAmelCase	)
						for i in range(n - 1	):
												_UpperCAmelCase					, _UpperCAmelCase									=			i + 1, n - 1
												while left < right:
																		if arr[i] + arr[left] + arr[right] == target:
																								return (arr[i], arr[left], arr[right])
																		elif arr[i] + arr[left] + arr[right] < target:
																								left += 1
																		elif arr[i] + arr[left] + arr[right] > target:
																								right -= 1
						return (0, 0, 0)


def __A    (      )->  tuple[float, float]:





						"""simple docstring"""



						_UpperCAmelCase									=			'\nfrom __main__ import dataset, triplet_sum1, triplet_sum2\n'
						_UpperCAmelCase									=			'\ntriplet_sum1(*dataset)\n'
						_UpperCAmelCase									=			'\ntriplet_sum2(*dataset)\n'
						_UpperCAmelCase									=			repeat(setup=__lowerCAmelCase ,		stmt=__lowerCAmelCase ,		repeat=5 ,		number=10_000	)
						_UpperCAmelCase									=			repeat(setup=__lowerCAmelCase ,		stmt=__lowerCAmelCase ,		repeat=5 ,		number=10_000	)
						return (min(__lowerCAmelCase	), min(__lowerCAmelCase	))


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		_a      	=  solution_times()
		print(F'''The time for naive implementation is {times[0]}.''')
		print(F'''The time for optimized implementation is {times[1]}.''')
