

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			):
  """simple docstring"""






  def __init__(      self			:   List[Any]	,							lowerCAmelCase			:   List[str]	,							lowerCAmelCase			:   Optional[Any]	,							lowerCAmelCase			:   Optional[int]	,							lowerCAmelCase			:   Optional[Any] = 1.0	,							lowerCAmelCase			:   Any = None	,							)							->     Dict:






     """simple docstring"""







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







  def __call__(      self			:   Tuple	,							lowerCAmelCase			:   Tuple   )							->     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`.
        __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 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     Optional[int]:






     """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      snake_case_				(__A					:						int ,       __A					:						Optional[int] ,       __A					:						Union[str, Any] ,       __A					:						List[str] = 0.0 ,       __A					:						Union[str, Any] = 0.9 ,       __A					:						Union[str, Any] = 0.999 ,       __A					:						str = 1e-8 ,       __A					:						Union[str, Any] = None ,       __A					:						Optional[Any] = None ,       __A					:						Any = 0.0 ,       __A					:						Union[str, Any] = 1.0 ,       __A					:						Dict = None ,       )      ->     Dict:
   __lowerCAmelCase    =						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:
      __lowerCAmelCase    =						WarmUp(
          initial_learning_rate=lowerCAmelCase__ ,       decay_schedule_fn=lowerCAmelCase__ ,       warmup_steps=lowerCAmelCase__ ,       )
   if weight_decay_rate > 0.0:
      __lowerCAmelCase    =						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:
      __lowerCAmelCase    =						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 SCREAMING_SNAKE_CASE     (  SCREAMING_SNAKE_CASE__			):
  """simple docstring"""






  def __init__(      self			:   int	,							lowerCAmelCase			:   List[Any] = 0.001	,							lowerCAmelCase			:   Optional[int] = 0.9	,							lowerCAmelCase			:   List[Any] = 0.999	,							lowerCAmelCase			:   Optional[int] = 1e-7	,							lowerCAmelCase			:   Tuple = False	,							lowerCAmelCase			:   List[Any] = 0.0	,							lowerCAmelCase			:   Optional[Any] = None	,							lowerCAmelCase			:   Any = None	,							lowerCAmelCase			:   List[str] = "AdamWeightDecay"	,							**lowerCAmelCase			:   List[str]	,							)							->     Any:






     """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 	SCREAMING_SNAKE_CASE   (      cls			:   Tuple	,							lowerCAmelCase			:   Dict   )							->     str:






     """simple docstring"""







     __lowerCAmelCase    =						{"""WarmUp""": WarmUp}
     return super(A__	,							cls   ).from_config(A__	,							custom_objects=A__   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]	,							lowerCAmelCase			:   Dict	,							lowerCAmelCase			:   List[str]	,							lowerCAmelCase			:   Optional[int]   )							->     List[str]:






     """simple docstring"""







     super(A__	,							self   )._prepare_local(A__	,							A__	,							A__   )
     __lowerCAmelCase    =						tf.constant(
         self.weight_decay_rate	,							name="""adam_weight_decay_rate"""   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   List[Any]	,							lowerCAmelCase			:   List[str]   )							->     Optional[Any]:






     """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 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   Optional[Any]=None	,							**lowerCAmelCase			:   Optional[int]   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase   ,__lowerCAmelCase    =						list(zip(*A__   )   )
     return super(A__	,							self   ).apply_gradients(zip(A__	,							A__   )	,							name=A__	,							**A__   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any	,							lowerCAmelCase			:   Optional[int]	,							lowerCAmelCase			:   Optional[Any]	,							lowerCAmelCase			:   List[Any]   )							->     str:






     """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 	SCREAMING_SNAKE_CASE   (      self			:   Tuple	,							lowerCAmelCase			:   str	,							lowerCAmelCase			:   List[str]	,							lowerCAmelCase			:   Optional[int]=None   )							->     Dict:






     """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 	SCREAMING_SNAKE_CASE   (      self			:   Dict	,							lowerCAmelCase			:   Dict	,							lowerCAmelCase			:   Optional[int]	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   int=None   )							->     List[Any]:






     """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 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     int:






     """simple docstring"""







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







  def 	SCREAMING_SNAKE_CASE   (      self			:   str	,							lowerCAmelCase			:   Any   )							->     List[Any]:






     """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 SCREAMING_SNAKE_CASE     (  SCREAMING_SNAKE_CASE__			):
  """simple docstring"""






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






     """simple docstring"""







     __lowerCAmelCase    =						[]
     __lowerCAmelCase    =						None







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     int:






     """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 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     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			:   Optional[int]	,							lowerCAmelCase			:   Any   )							->     int:






     """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 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     str:






     """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





def      snake_case_				(__A					:						list[float] ,       __A					:						list[float]					)      ->     float:
   __lowerCAmelCase  :      Union[str, Any]    =						sorted(numsa + numsa					)
   __lowerCAmelCase   ,__lowerCAmelCase  :      Optional[int]    =						divmod(len(__A					) ,       2					)
   if mod == 1:
      return all_numbers[div]
   else:
      return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   __UpperCAmelCase								=			[float(x) for x in input("""Enter the elements of first array: """).split()]
   __UpperCAmelCase								=			[float(x) for x in input("""Enter the elements of second array: """).split()]
   print(F'The median of two arrays is: {median_of_two_arrays(array_a, array_a)}')


