

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     ):





    def __init__(      self						,   a						,   a						,   a						,   a = 1.0						,   a = None						,   ):
         super().__init__()
         lowercase__    :   Union[str, Any]      =       initial_learning_rate
         lowercase__    :   Optional[Any]      =       warmup_steps
         lowercase__    :   Dict      =       power
         lowercase__    :   List[str]      =       decay_schedule_fn
         lowercase__    :   Optional[Any]      =       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`.
              lowercase__    :   Any      =       tf.cast(a						,   tf.floataa)
              lowercase__    :   Optional[int]      =       tf.cast(self.warmup_steps						,   tf.floataa)
              lowercase__    :   Tuple      =       global_step_float / warmup_steps_float
              lowercase__    :   List[str]      =       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       snake_case_				(      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 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    float	,     SCREAMING_SNAKE_CASE_		:    int	,     SCREAMING_SNAKE_CASE_		:    int	,     SCREAMING_SNAKE_CASE_		:    float = 0.0	,     SCREAMING_SNAKE_CASE_		:    float = 0.9	,     SCREAMING_SNAKE_CASE_		:    float = 0.999	,     SCREAMING_SNAKE_CASE_		:    float = 1E-8	,     SCREAMING_SNAKE_CASE_		:    Optional[float] = None	,     SCREAMING_SNAKE_CASE_		:    Optional[float] = None	,     SCREAMING_SNAKE_CASE_		:    float = 0.0	,     SCREAMING_SNAKE_CASE_		:    float = 1.0	,     SCREAMING_SNAKE_CASE_		:    Optional[List[str]] = None	,     ):



     '''simple docstring'''





     lowercase__    :   Optional[Any]      =       tf.keras.optimizers.schedules.PolynomialDecay(
         initial_learning_rate=SCREAMING_SNAKE_CASE_	,     decay_steps=num_train_steps - num_warmup_steps	,     end_learning_rate=init_lr * min_lr_ratio	,     power=SCREAMING_SNAKE_CASE_	,     )
     if num_warmup_steps:
          lowercase__    :   Any      =       WarmUp(
              initial_learning_rate=SCREAMING_SNAKE_CASE_	,     decay_schedule_fn=SCREAMING_SNAKE_CASE_	,     warmup_steps=SCREAMING_SNAKE_CASE_	,     )
     if weight_decay_rate > 0.0:
          lowercase__    :   Tuple      =       AdamWeightDecay(
              learning_rate=SCREAMING_SNAKE_CASE_	,     weight_decay_rate=SCREAMING_SNAKE_CASE_	,     beta_a=SCREAMING_SNAKE_CASE_	,     beta_a=SCREAMING_SNAKE_CASE_	,     epsilon=SCREAMING_SNAKE_CASE_	,     clipnorm=SCREAMING_SNAKE_CASE_	,     global_clipnorm=SCREAMING_SNAKE_CASE_	,     exclude_from_weight_decay=['LayerNorm', 'layer_norm', 'bias']	,     include_in_weight_decay=SCREAMING_SNAKE_CASE_	,     )
     else:
          lowercase__    :   List[Any]      =       tf.keras.optimizers.Adam(
              learning_rate=SCREAMING_SNAKE_CASE_	,     beta_a=SCREAMING_SNAKE_CASE_	,     beta_a=SCREAMING_SNAKE_CASE_	,     epsilon=SCREAMING_SNAKE_CASE_	,     clipnorm=SCREAMING_SNAKE_CASE_	,     global_clipnorm=SCREAMING_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       SCREAMING_SNAKE_CASE__					(__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						,   ):
         super().__init__(a						,   a						,   a						,   a						,   a						,   a						,   **a)
         lowercase__    :   Union[str, Any]      =       weight_decay_rate
         lowercase__    :   Any      =       include_in_weight_decay
         lowercase__    :   Tuple      =       exclude_from_weight_decay





    @classmethod
    def       snake_case_				(      cls						,   a):
         lowercase__    :   Optional[int]      =       {'WarmUp': WarmUp}
         return super(a						,   cls).from_config(a						,   custom_objects=a)





    def       snake_case_				(      self						,   a						,   a						,   a):
         super(a						,   self)._prepare_local(a						,   a						,   a)
         lowercase__    :   Any      =       tf.constant(
             self.weight_decay_rate						,   name='adam_weight_decay_rate')





    def       snake_case_				(      self						,   a						,   a						,   a):
         lowercase__    :   int      =       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       snake_case_				(      self						,   a						,   a=None						,   **a):
         lowercase__      , lowercase__    :   Optional[Any]      =       list(zip(*a))
         return super(a						,   self).apply_gradients(zip(a						,   a)						,   name=a						,   **a)





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

         lowercase__    :   Union[str, Any]      =       apply_state or {}
         lowercase__    :   Optional[int]      =       apply_state.get((var_device, var_dtype))
         if coefficients is None:
              lowercase__    :   str      =       self._fallback_apply_state(a						,   a)
              lowercase__    :   Optional[Any]      =       coefficients

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





    def       snake_case_				(      self						,   a						,   a						,   a=None):
         lowercase__      , lowercase__    :   Tuple      =       self._get_lr(var.device						,   var.dtype.base_dtype						,   a)
         lowercase__    :   Optional[int]      =       self._decay_weights_op(a						,   a						,   a)
         with tf.control_dependencies([decay]):
              return super(a						,   self)._resource_apply_dense(a						,   a						,   **a)





    def       snake_case_				(      self						,   a						,   a						,   a						,   a=None):
         lowercase__      , lowercase__    :   List[Any]      =       self._get_lr(var.device						,   var.dtype.base_dtype						,   a)
         lowercase__    :   str      =       self._decay_weights_op(a						,   a						,   a)
         with tf.control_dependencies([decay]):
              return super(a						,   self)._resource_apply_sparse(a						,   a						,   a						,   **a)





    def       snake_case_				(      self):
         lowercase__    :   Optional[int]      =       super().get_config()
         config.update({'weight_decay_rate': self.weight_decay_rate})
         return config





    def       snake_case_				(      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(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__					(__snake_case     ):





    def __init__(      self):
         lowercase__    :   Dict      =       []
         lowercase__    :   Dict      =       None





    @property
    def       snake_case_				(      self):
         if self._accum_steps is None:
              lowercase__    :   str      =       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       snake_case_				(      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:
              lowercase__    :   Union[str, Any]      =       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       snake_case_				(      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(a))




from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

snake_case_			  = TypeVar('''T''')
snake_case_			  = TypeVar('''U''')


class       SCREAMING_SNAKE_CASE__					(Generic[T, U]     ):





    def __init__(      self						,   a						,   a):
         lowercase__    :   List[Any]      =       key
         lowercase__    :   List[Any]      =       val
         lowercase__    :   DoubleLinkedListNode[T, U] | None      =       None
         lowercase__    :   DoubleLinkedListNode[T, U] | None      =       None





    def __repr__(      self):
         return (
             f"""Node: key: {self.key}, val: {self.val}, """
             f"""has next: {bool(self.next)}, has prev: {bool(self.prev)}"""
         )



class       SCREAMING_SNAKE_CASE__					(Generic[T, U]     ):





    def __init__(      self):
         lowercase__    :   DoubleLinkedListNode[T, U]      =       DoubleLinkedListNode(a						,   a)
         lowercase__    :   DoubleLinkedListNode[T, U]      =       DoubleLinkedListNode(a						,   a)
         lowercase__      , lowercase__    :   Union[str, Any]      =       self.rear, self.head





    def __repr__(      self):
         lowercase__    :   Any      =       ['DoubleLinkedList']
         lowercase__    :   List[str]      =       self.head
         while node.next is not None:
              rep.append(str(a))
              lowercase__    :   Tuple      =       node.next
         rep.append(str(self.rear))
         return ",\n    ".join(a)





    def       snake_case_				(      self						,   a):
         lowercase__    :   Optional[Any]      =       self.rear.prev

         # All nodes other than self.head are guaranteed to have non-None previous
         assert previous is not None

         lowercase__    :   Dict      =       node
         lowercase__    :   int      =       previous
         lowercase__    :   Union[str, Any]      =       node
         lowercase__    :   Optional[int]      =       self.rear





    def       snake_case_				(      self						,   a):

         if node.prev is None or node.next is None:
              return None

         lowercase__    :   Union[str, Any]      =       node.next
         lowercase__    :   Tuple      =       node.prev
         lowercase__    :   Union[str, Any]      =       None
         lowercase__    :   List[Any]      =       None
         return node



class       SCREAMING_SNAKE_CASE__					(Generic[T, U]     ):
    __lowerCamelCase						:       dict[Callable[[T], U], LRUCache[T, U]]														=     {}





    def __init__(      self						,   a):
         lowercase__    :   DoubleLinkedList[T, U]      =       DoubleLinkedList()
         lowercase__    :   Optional[Any]      =       capacity
         lowercase__    :   Union[str, Any]      =       0
         lowercase__    :   Tuple      =       0
         lowercase__    :   int      =       0
         lowercase__    :   dict[T, DoubleLinkedListNode[T, U]]      =       {}





    def __repr__(      self):
         return (
             f"""CacheInfo(hits={self.hits}, misses={self.miss}, """
             f"""capacity={self.capacity}, current size={self.num_keys})"""
         )





    def __contains__(      self						,   a):
         return key in self.cache





    def       snake_case_				(      self						,   a):
         # Note: pythonic interface would throw KeyError rather than return None

         if key in self.cache:
              self.hits += 1
              lowercase__    :   DoubleLinkedListNode[T, U]      =       self.cache[key]
              lowercase__    :   str      =       self.list.remove(self.cache[key])
              assert node == value_node

              # node is guaranteed not None because it is in self.cache
              assert node is not None
              self.list.add(a)
              return node.val
         self.miss += 1
         return None





    def       snake_case_				(      self						,   a						,   a):

         if key not in self.cache:
              if self.num_keys >= self.capacity:
                   # delete first node (oldest) when over capacity
                   lowercase__    :   Optional[int]      =       self.list.head.next

                   # guaranteed to have a non-None first node when num_keys > 0
                   # explain to type checker via assertions
                   assert first_node is not None
                   assert first_node.key is not None
                   assert (
                       self.list.remove(a) is not None
                   )  # node guaranteed to be in list assert node.key is not None

                   del self.cache[first_node.key]
                   self.num_keys -= 1
              lowercase__    :   Optional[Any]      =       DoubleLinkedListNode(a						,   a)
              self.list.add(self.cache[key])
              self.num_keys += 1

         else:
              # bump node to the end of the list, update value
              lowercase__    :   Any      =       self.list.remove(self.cache[key])
              assert node is not None  # node guaranteed to be in list
              lowercase__    :   Union[str, Any]      =       value
              self.list.add(a)





    @classmethod
    def       snake_case_				(      cls						,   a = 128):

         def cache_decorator_inner(a) -> Callable[..., U]:
              def cache_decorator_wrapper(*a) -> U:
                   if func not in cls.decorator_function_to_instance_map:
                        lowercase__    :   Dict      =       LRUCache(a)

                   lowercase__    :   str      =       cls.decorator_function_to_instance_map[func].get(args[0])
                   if result is None:
                        lowercase__    :   str      =       func(*a)
                        cls.decorator_function_to_instance_map[func].put(args[0]						,   a)
                   return result

              def cache_info() -> LRUCache[T, U]:
                   return cls.decorator_function_to_instance_map[func]

              setattr(a						,   'cache_info'						,   a)  # noqa: B010

              return cache_decorator_wrapper

         return cache_decorator_inner


if __name__ == "__main__":
       import doctest

       doctest.testmod()

