



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):







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_  ,  a_  ,  a_ = 1.0  ,  a_ = None  ,  ):
											super().__init__()
											a__				      =		initial_learning_rate
											a__				      =		warmup_steps
											a__				      =		power
											a__				      =		decay_schedule_fn
											a__				      =		name


							def __call__(							self  ,  a_		):
											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__				      =		tf.cast(lowerCAmelCase_  ,  tf.floataa		)
															a__				      =		tf.cast(self.warmup_steps  ,  tf.floataa		)
															a__				      =		global_step_float / warmup_steps_float
															a__				      =		self.initial_learning_rate * tf.math.pow(lowerCAmelCase_  ,  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=lowerCAmelCase_  ,  )







							def _a      (							self		):
											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 			A_      (	__a     :				float ,		__a     :				int ,		__a     :				int ,		__a     :				float = 0.0 ,		__a     :				float = 0.9 ,		__a     :				float = 0.9_9_9 ,		__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__				      =		tf.keras.optimizers.schedules.PolynomialDecay(
				    initial_learning_rate=snake_case__ ,		decay_steps=num_train_steps - num_warmup_steps ,		end_learning_rate=init_lr * min_lr_ratio ,		power=snake_case__ ,		)
				if num_warmup_steps:
								a__				      =		WarmUp(
								    initial_learning_rate=snake_case__ ,		decay_schedule_fn=snake_case__ ,		warmup_steps=snake_case__ ,		)
				if weight_decay_rate > 0.0:
								a__				      =		AdamWeightDecay(
								    learning_rate=snake_case__ ,		weight_decay_rate=snake_case__ ,		beta_a=snake_case__ ,		beta_a=snake_case__ ,		epsilon=snake_case__ ,		clipnorm=snake_case__ ,		global_clipnorm=snake_case__ ,		exclude_from_weight_decay=["""LayerNorm""", """layer_norm""", """bias"""] ,		include_in_weight_decay=snake_case__ ,		)
				else:
								a__				      =		tf.keras.optimizers.Adam(
								    learning_rate=snake_case__ ,		beta_a=snake_case__ ,		beta_a=snake_case__ ,		epsilon=snake_case__ ,		clipnorm=snake_case__ ,		global_clipnorm=snake_case__ ,		)
				# 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      (   __lowerCAmelCase):







							'''simple docstring'''






							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_  ,  ):
											super().__init__(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  **lowerCAmelCase_		)
											a__				      =		weight_decay_rate
											a__				      =		include_in_weight_decay
											a__				      =		exclude_from_weight_decay


							@classmethod
							def _a      (							cls  ,  a_		):
											a__				      =		{"""WarmUp""": WarmUp}
											return super(lowerCAmelCase_  ,  cls		).from_config(lowerCAmelCase_  ,  custom_objects=lowerCAmelCase_		)


							def _a      (							self  ,  a_  ,  a_  ,  a_		):
											super(lowerCAmelCase_  ,  self		)._prepare_local(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_		)
											a__				      =		tf.constant(
											    self.weight_decay_rate  ,  name="""adam_weight_decay_rate"""		)


							def _a      (							self  ,  a_  ,  a_  ,  a_		):
											a__				      =		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 _a      (							self  ,  a_  ,  a_=None  ,  **a_		):
											a__      ,			a__				      =		list(zip(*lowerCAmelCase_		)		)
											return super(lowerCAmelCase_  ,  self		).apply_gradients(zip(lowerCAmelCase_  ,  lowerCAmelCase_		)  ,  name=lowerCAmelCase_  ,  **lowerCAmelCase_		)


							def _a      (							self  ,  a_  ,  a_  ,  a_		):
											if apply_state is None:
															return self._decayed_lr_t[var_dtype], {}

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

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


							def _a      (							self  ,  a_  ,  a_  ,  a_=None		):
											a__      ,			a__				      =		self._get_lr(var.device  ,  var.dtype.base_dtype  ,  lowerCAmelCase_		)
											a__				      =		self._decay_weights_op(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_		)
											with tf.control_dependencies([decay]		):
															return super(lowerCAmelCase_  ,  self		)._resource_apply_dense(lowerCAmelCase_  ,  lowerCAmelCase_  ,  **lowerCAmelCase_		)


							def _a      (							self  ,  a_  ,  a_  ,  a_  ,  a_=None		):
											a__      ,			a__				      =		self._get_lr(var.device  ,  var.dtype.base_dtype  ,  lowerCAmelCase_		)
											a__				      =		self._decay_weights_op(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_		)
											with tf.control_dependencies([decay]		):
															return super(lowerCAmelCase_  ,  self		)._resource_apply_sparse(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  **lowerCAmelCase_		)


							def _a      (							self		):
											a__				      =		super().get_config()
											config.update({"""weight_decay_rate""": self.weight_decay_rate}		)
											return config







							def _a      (							self  ,  a_		):
											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(lowerCAmelCase_  ,  lowerCAmelCase_		) is not None:
																							return True

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




class  __snake_case      (   __lowerCAmelCase):







							'''simple docstring'''






							def __init__(							self		):
											a__				      =		[]
											a__				      =		None


							@property
							def _a      (							self		):
											if self._accum_steps is None:
															a__				      =		tf.Variable(
															    tf.constant(0  ,  dtype=tf.intaa		)  ,  trainable=lowerCAmelCase_  ,  synchronization=tf.VariableSynchronization.ON_READ  ,  aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA  ,  )

											return self._accum_steps.value()


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

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

											self._accum_steps.assign_add(1		)







							def _a      (							self		):
											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(lowerCAmelCase_		)		)











from __future__ import annotations

UpperCAmelCase							 =					8.988E9  # units = N * m^s * C^-2

def 			A_      (	__a     :				float ,		__a     :				float ,		__a     :				float ,		__a     :				float		):

				"""simple docstring"""
				a__				      =		abs(chargea * chargea		)

				if (force, chargea, chargea, distance).count(0		) != 1:
								raise ValueError("""One and only one argument must be 0"""		)
				if distance < 0:
								raise ValueError("""Distance cannot be negative"""		)
				if force == 0:
								a__				      =		COULOMBS_CONSTANT * charge_product / (distance**2)
								return {"force": force}
				elif chargea == 0:
								a__				      =		abs(__a		) * (distance**2) / (COULOMBS_CONSTANT * chargea)
								return {"charge1": chargea}
				elif chargea == 0:
								a__				      =		abs(__a		) * (distance**2) / (COULOMBS_CONSTANT * chargea)
								return {"charge2": chargea}
				elif distance == 0:
								a__				      =		(COULOMBS_CONSTANT * charge_product / abs(__a		)) ** 0.5
								return {"distance": distance}
				raise ValueError("""Exactly one argument must be 0"""		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

