



'''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  SCREAMING_SNAKE_CASE (  tf.keras.optimizers.schedules.LearningRateSchedule		):






    """simple docstring"""





    def __init__(  self      :					Optional[int]  ,						UpperCamelCase__      :					float  ,						UpperCamelCase__      :					Callable  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					float = 1.0  ,						UpperCamelCase__      :					str = None  ,						):


       """simple docstring"""







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






    def __call__(  self      :					List[str]  ,						UpperCamelCase__      :					Optional[Any] ):


       """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(UpperCamelCase__  ,						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(UpperCamelCase__  ,						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=UpperCamelCase__  ,						)






    def  A       (  self      :					List[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				(   A__    ,						A__    ,						A__    ,						A__ = 0.0    ,						A__ = 0.9    ,						A__ = 0.999    ,						A__ = 1e-8    ,						A__ = None    ,						A__ = None    ,						A__ = 0.0    ,						A__ = 1.0    ,						A__ = None    ,						)       ->	str:






   """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  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    def __init__(  self      :					str  ,						UpperCamelCase__      :					Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.0_0_1  ,						UpperCamelCase__      :					float = 0.9  ,						UpperCamelCase__      :					float = 0.9_9_9  ,						UpperCamelCase__      :					float = 1E-7  ,						UpperCamelCase__      :					bool = False  ,						UpperCamelCase__      :					float = 0.0  ,						UpperCamelCase__      :					Optional[List[str]] = None  ,						UpperCamelCase__      :					Optional[List[str]] = None  ,						UpperCamelCase__      :					str = "AdamWeightDecay"  ,						**UpperCamelCase__      :					Union[str, Any]  ,						):


       """simple docstring"""







       super().__init__(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						**UpperCamelCase__ )
       UpperCamelCase					=  weight_decay_rate
       UpperCamelCase					=  include_in_weight_decay
       UpperCamelCase					=  exclude_from_weight_decay






    @classmethod
    def  A       (  cls      :					Tuple  ,						UpperCamelCase__      :					Any ):


       """simple docstring"""







       UpperCamelCase					=  {'WarmUp': WarmUp}
       return super(UpperCamelCase__  ,						cls ).from_config(UpperCamelCase__  ,						custom_objects=UpperCamelCase__ )






    def  A       (  self      :					List[str]  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Optional[int]  ,						UpperCamelCase__      :					Optional[Any] ):


       """simple docstring"""







       super(UpperCamelCase__  ,						self )._prepare_local(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
       UpperCamelCase					=  tf.constant(
           self.weight_decay_rate  ,						name='adam_weight_decay_rate' )






    def  A       (  self      :					int  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					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  A       (  self      :					List[str]  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					Optional[int]=None  ,						**UpperCamelCase__      :					Any ):


       """simple docstring"""







       UpperCamelCase				,      UpperCamelCase					=  list(zip(*UpperCamelCase__ ) )
       return super(UpperCamelCase__  ,						self ).apply_gradients(zip(UpperCamelCase__  ,						UpperCamelCase__ )  ,						name=UpperCamelCase__  ,						**UpperCamelCase__ )






    def  A       (  self      :					int  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Dict ):


       """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(UpperCamelCase__  ,						UpperCamelCase__ )
          UpperCamelCase					=  coefficients

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






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					List[str]  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					Union[str, Any]=None ):


       """simple docstring"""







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






    def  A       (  self      :					List[Any]  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					List[str]  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					Any=None ):


       """simple docstring"""







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






    def  A       (  self      :					Any ):


       """simple docstring"""







       UpperCamelCase					=  super().get_config()
       config.update({'weight_decay_rate': self.weight_decay_rate} )
       return config






    def  A       (  self      :					Any  ,						UpperCamelCase__      :					Dict ):


       """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(UpperCamelCase__  ,						UpperCamelCase__ ) is not None:
                return True

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

class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    def __init__(  self      :					int ):


       """simple docstring"""







       UpperCamelCase					=  []
       UpperCamelCase					=  None






    @property
    def  A       (  self      :					List[str] ):


       """simple docstring"""







       if self._accum_steps is None:
          UpperCamelCase					=  tf.Variable(
              tf.constant(0  ,						dtype=tf.intaa )  ,						trainable=UpperCamelCase__  ,						synchronization=tf.VariableSynchronization.ON_READ  ,						aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA  ,						)

       return self._accum_steps.value()






    @property
    def  A       (  self      :					Dict ):


       """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]  ,						UpperCamelCase__      :					List[str] ):


       """simple docstring"""







       if not self._gradients:
          UpperCamelCase					=  self.step  # Create the step variable.
          self._gradients.extend(
              [
                  tf.Variable(
                      tf.zeros_like(UpperCamelCase__ )  ,						trainable=UpperCamelCase__  ,						synchronization=tf.VariableSynchronization.ON_READ  ,						aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA  ,						)
                  if gradient is not None
                  else gradient
                  for gradient in gradients
              ] )
       if len(UpperCamelCase__ ) != len(self._gradients ):
          raise ValueError(f"""Expected {len(self._gradients )} gradients, but got {len(UpperCamelCase__ )}""" )

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

       self._accum_steps.assign_add(1 )






    def  A       (  self      :					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(UpperCamelCase__ ) )








import random




def 		lowerCAmelCase_    (  __UpperCAmelCase:			list		,   __UpperCAmelCase:			Optional[int]      )      ->	tuple:
    UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				List[Any]          =      [], [], []
    for element in data:
        if element < pivot:
            less.append(__UpperCAmelCase      )
        elif element > pivot:
            greater.append(__UpperCAmelCase      )
        else:
            equal.append(__UpperCAmelCase      )
    return less, equal, greater





def 		lowerCAmelCase_    (  __UpperCAmelCase:			list		,   __UpperCAmelCase:			int      )      ->	List[str]:
    # index = len(items) // 2 when trying to find the median
    #   (value of index when items is sorted)

    # invalid input
    if index >= len(__UpperCAmelCase      ) or index < 0:
        return None

    UpperCamelCase__							:				List[str]          =      items[random.randint(0		,   len(__UpperCAmelCase      ) - 1      )]
    UpperCamelCase__							:				List[Any]          =      0
    UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				int          =      _partition(__UpperCAmelCase		,   __UpperCAmelCase      )
    UpperCamelCase__							:				Union[str, Any]          =      len(__UpperCAmelCase      )
    UpperCamelCase__							:				Dict          =      len(__UpperCAmelCase      )

    # index is the pivot
    if m <= index < m + count:
        return pivot
    # must be in smaller
    elif m > index:
        return quick_select(__UpperCAmelCase		,   __UpperCAmelCase      )
    # must be in larger
    else:
        return quick_select(__UpperCAmelCase		,   index - (m + count)      )

