


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        UpperCamelCase__   (	tf.keras.optimizers.schedules.LearningRateSchedule   ):

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






          '''simple docstring'''






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

     def __call__(     self       :   Dict							,					UpperCamelCase__       :   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`.
               lowercase_  							=			tf.cast(UpperCamelCase__							,					tf.floataa       )
               lowercase_  							=			tf.cast(self.warmup_steps							,					tf.floataa       )
               lowercase_  							=			global_step_float / warmup_steps_float
               lowercase_  							=			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   UpperCAmelCase__       (     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 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__ = 0.0						,							UpperCAmelCase__ = 0.9						,							UpperCAmelCase__ = 0.999						,							UpperCAmelCase__ = 1e-8						,							UpperCAmelCase__ = None						,							UpperCAmelCase__ = None						,							UpperCAmelCase__ = 0.0						,							UpperCAmelCase__ = 1.0						,							UpperCAmelCase__ = None						,							):
     lowercase_  							=			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:
          lowercase_  							=			WarmUp(
              initial_learning_rate=UpperCAmelCase__						,							decay_schedule_fn=UpperCAmelCase__						,							warmup_steps=UpperCAmelCase__						,							)
     if weight_decay_rate > 0.0:
          lowercase_  							=			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:
          lowercase_  							=			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        UpperCamelCase__   (	__magic_name__   ):

     def __init__(     self       :   Union[str, Any]							,					UpperCamelCase__       :   Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.001							,					UpperCamelCase__       :   float = 0.9							,					UpperCamelCase__       :   float = 0.999							,					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__       :   List[str]							,					):






          '''simple docstring'''






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

     @classmethod
     def   UpperCAmelCase__       (     cls       :   Tuple							,					UpperCamelCase__       :   Optional[Any]       ):






          '''simple docstring'''






          lowercase_  							=			{"""WarmUp""": WarmUp}
          return super(UpperCamelCase__							,					cls       ).from_config(UpperCamelCase__							,					custom_objects=UpperCamelCase__       )

     def   UpperCAmelCase__       (     self       :   int							,					UpperCamelCase__       :   Optional[int]							,					UpperCamelCase__       :   Tuple							,					UpperCamelCase__       :   Optional[Any]       ):






          '''simple docstring'''






          super(UpperCamelCase__							,					self       )._prepare_local(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__       )
          lowercase_  							=			tf.constant(
              self.weight_decay_rate							,					name="""adam_weight_decay_rate"""       )

     def   UpperCAmelCase__       (     self       :   Tuple							,					UpperCamelCase__       :   List[str]							,					UpperCamelCase__       :   List[str]							,					UpperCamelCase__       :   Optional[int]       ):






          '''simple docstring'''






          lowercase_  							=			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   UpperCAmelCase__       (     self       :   Tuple							,					UpperCamelCase__       :   Dict							,					UpperCamelCase__       :   int=None							,					**UpperCamelCase__       :   Any       ):






          '''simple docstring'''






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

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






          '''simple docstring'''






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

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

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

     def   UpperCAmelCase__       (     self       :   List[Any]							,					UpperCamelCase__       :   Optional[Any]							,					UpperCamelCase__       :   List[str]							,					UpperCamelCase__       :   str=None       ):






          '''simple docstring'''






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

     def   UpperCAmelCase__       (     self       :   int							,					UpperCamelCase__       :   Optional[Any]							,					UpperCamelCase__       :   Union[str, Any]							,					UpperCamelCase__       :   str							,					UpperCamelCase__       :   int=None       ):






          '''simple docstring'''






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

     def   UpperCAmelCase__       (     self       :   int       ):






          '''simple docstring'''






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







     def   UpperCAmelCase__       (     self       :   str							,					UpperCamelCase__       :   List[str]       ):






          '''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        UpperCamelCase__   (	__magic_name__   ):

     def __init__(     self       :   Optional[Any]       ):






          '''simple docstring'''






          lowercase_  							=			[]
          lowercase_  							=			None

     @property
     def   UpperCAmelCase__       (     self       :   Dict       ):






          '''simple docstring'''






          if self._accum_steps is None:
               lowercase_  							=			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   UpperCAmelCase__       (     self       :   Optional[int]       ):






          '''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__       :   Union[str, Any]       ):






          '''simple docstring'''






          if not self._gradients:
               lowercase_  							=			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   UpperCAmelCase__       (     self       :   int       ):






          '''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__       )       )






from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

a					  =     TypeVar('T')







class        UpperCamelCase__   (	Generic[T]   ):
					__SCREAMING_SNAKE_CASE       :  deque[T]  # Cache store of keys
					__SCREAMING_SNAKE_CASE       :  set[T]  # References of the keys in cache
					__SCREAMING_SNAKE_CASE       :  int						=   10  # Maximum capacity of cache

					def __init__(     self       :   str							,					UpperCamelCase__       :   int       ):






										'''simple docstring'''






										lowercase_  							=			deque()
										lowercase_  							=			set()
										if not n:
															lowercase_  							=			sys.maxsize
										elif n < 0:
															raise ValueError("""n should be an integer greater than 0."""       )
										else:
															lowercase_  							=			n

					def   UpperCAmelCase__       (     self       :   Optional[int]							,					UpperCamelCase__       :   T       ):






										'''simple docstring'''






										if x not in self.key_reference:
															if len(self.dq_store       ) == LRUCache._MAX_CAPACITY:
																				lowercase_  							=			self.dq_store.pop()
																				self.key_reference.remove(UpperCamelCase__       )
										else:
															self.dq_store.remove(UpperCamelCase__       )

										self.dq_store.appendleft(UpperCamelCase__       )
										self.key_reference.add(UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   List[Any]       ):






										'''simple docstring'''






										for k in self.dq_store:
															print(UpperCamelCase__       )







					def __repr__(     self       :   Optional[Any]       ):






										'''simple docstring'''






										return F'''LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store       )}'''


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							a					  =     LRUCache(4)
							lru_cache.refer('A')
							lru_cache.refer(2)
							lru_cache.refer(3)
							lru_cache.refer('A')
							lru_cache.refer(4)
							lru_cache.refer(5)
							lru_cache.display()

							print(lru_cache)
							assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"


