



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



     def __init__(self					,      __a					,      __a					,      __a					,      __a = 1.0					,      __a = None					,      ):






           '''simple docstring'''



           super().__init__()
           lowerCamelCase             =		initial_learning_rate
           lowerCamelCase             =		warmup_steps
           lowerCamelCase             =		power
           lowerCamelCase             =		decay_schedule_fn
           lowerCamelCase             =		name







     def __call__(self					,      __a     ):






           '''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             =		tf.cast(__a					,      tf.floataa     )
                 lowerCamelCase             =		tf.cast(self.warmup_steps					,      tf.floataa     )
                 lowerCamelCase             =		global_step_float / warmup_steps_float
                 lowerCamelCase             =		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     _a   (self     ):






           '''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      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = 0.0 , UpperCAmelCase__ = 0.9 , UpperCAmelCase__ = 0.9_9_9 , UpperCAmelCase__ = 1E-8 , UpperCAmelCase__ = None , UpperCAmelCase__ = None , UpperCAmelCase__ = 0.0 , UpperCAmelCase__ = 1.0 , UpperCAmelCase__ = None , ):


      """simple docstring"""



      lowerCamelCase             =		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             =		WarmUp(
                initial_learning_rate=UpperCAmelCase__ , decay_schedule_fn=UpperCAmelCase__ , warmup_steps=UpperCAmelCase__ , )
      if weight_decay_rate > 0.0:
            lowerCamelCase             =		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             =		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  lowerCamelCase__	(    UpperCAmelCase_):
     """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					,      ):






           '''simple docstring'''



           super().__init__(__a					,      __a					,      __a					,      __a					,      __a					,      __a					,      **__a     )
           lowerCamelCase             =		weight_decay_rate
           lowerCamelCase             =		include_in_weight_decay
           lowerCamelCase             =		exclude_from_weight_decay







     @classmethod
     def     _a   (cls					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		{"WarmUp": WarmUp}
           return super(__a					,      cls     ).from_config(__a					,      custom_objects=__a     )







     def     _a   (self					,      __a					,      __a					,      __a     ):






           '''simple docstring'''



           super(__a					,      self     )._prepare_local(__a					,      __a					,      __a     )
           lowerCamelCase             =		tf.constant(
               self.weight_decay_rate					,      name="adam_weight_decay_rate"     )







     def     _a   (self					,      __a					,      __a					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		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     ):






           '''simple docstring'''



           lowerCamelCase      ,				lowerCamelCase             =		list(zip(*__a     )     )
           return super(__a					,      self     ).apply_gradients(zip(__a					,      __a     )					,      name=__a					,      **__a     )







     def     _a   (self					,      __a					,      __a					,      __a     ):






           '''simple docstring'''



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

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

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







     def     _a   (self					,      __a					,      __a					,      __a=None     ):






           '''simple docstring'''



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







     def     _a   (self					,      __a					,      __a					,      __a					,      __a=None     ):






           '''simple docstring'''



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







     def     _a   (self     ):






           '''simple docstring'''



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







     def     _a   (self					,      __a     ):






           '''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  lowerCamelCase__	(    UpperCAmelCase_):
     """simple docstring"""



     def __init__(self     ):






           '''simple docstring'''



           lowerCamelCase             =		[]
           lowerCamelCase             =		None







     @property
     def     _a   (self     ):






           '''simple docstring'''



           if self._accum_steps is None:
                 lowerCamelCase             =		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     _a   (self     ):






           '''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     ):






           '''simple docstring'''



           if not self._gradients:
                 lowerCamelCase             =		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     _a   (self     ):






           '''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 math import pow, sqrt

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      if (resistance, reactance, impedance).count(0			) != 1:
            raise ValueError("One and only one argument must be 0"			)
      if resistance == 0:
            return {"resistance": sqrt(pow(UpperCAmelCase__ , 2			) - pow(UpperCAmelCase__ , 2			)			)}
      elif reactance == 0:
            return {"reactance": sqrt(pow(UpperCAmelCase__ , 2			) - pow(UpperCAmelCase__ , 2			)			)}
      elif impedance == 0:
            return {"impedance": sqrt(pow(UpperCAmelCase__ , 2			) + pow(UpperCAmelCase__ , 2			)			)}
      else:
            raise ValueError("Exactly one argument must be 0"			)


if __name__ == "__main__":
     import doctest

     doctest.testmod()