






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

       '''simple docstring'''



       def __init__(							self			,						a__			,						a__			,						a__			,						a__ = 1.0			,						a__ = None			,						) ->      Optional[Any]:






            '''simple docstring'''





            super().__init__()
            __snake_case			:Dict          =						initial_learning_rate
            __snake_case			:Union[str, Any]          =						warmup_steps
            __snake_case			:int          =						power
            __snake_case			:Optional[Any]          =						decay_schedule_fn
            __snake_case			:Any          =						name



       def __call__(							self			,						a__    ) ->      Tuple:






            '''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`.
                 __snake_case			:Optional[Any]          =						tf.cast(a__			,						tf.floataa    )
                 __snake_case			:List[str]          =						tf.cast(self.warmup_steps			,						tf.floataa    )
                 __snake_case			:Optional[Any]          =						global_step_float / warmup_steps_float
                 __snake_case			:int          =						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 					__lowercase						(							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  (       snake_case__     :   float	,snake_case__     :   int	,snake_case__     :   int	,snake_case__     :   float = 0.0	,snake_case__     :   float = 0.9	,snake_case__     :   float = 0.9_9_9	,snake_case__     :   float = 1e-8	,snake_case__     :   Optional[float] = None	,snake_case__     :   Optional[float] = None	,snake_case__     :   float = 0.0	,snake_case__     :   float = 1.0	,snake_case__     :   Optional[List[str]] = None	,):




     '''simple docstring'''






     __snake_case			:Tuple          =						tf.keras.optimizers.schedules.PolynomialDecay(
         initial_learning_rate=snake_case__	,decay_steps=num_train_steps - num_warmup_steps	,end_learning_rate=init_lr * min_lr_ratio	,power=snake_case__	,)
     if num_warmup_steps:
          __snake_case			:List[Any]          =						WarmUp(
              initial_learning_rate=snake_case__	,decay_schedule_fn=snake_case__	,warmup_steps=snake_case__	,)
     if weight_decay_rate > 0.0:
          __snake_case			:Any          =						AdamWeightDecay(
              learning_rate=snake_case__	,weight_decay_rate=snake_case__	,beta_a=snake_case__	,beta_a=snake_case__	,epsilon=snake_case__	,clipnorm=snake_case__	,global_clipnorm=snake_case__	,exclude_from_weight_decay=["""LayerNorm""", """layer_norm""", """bias"""]	,include_in_weight_decay=snake_case__	,)
     else:
          __snake_case			:str          =						tf.keras.optimizers.Adam(
              learning_rate=snake_case__	,beta_a=snake_case__	,beta_a=snake_case__	,epsilon=snake_case__	,clipnorm=snake_case__	,global_clipnorm=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 			snake_case__   (							lowercase_):

       '''simple docstring'''



       def __init__(							self			,						a__ = 0.0_01			,						a__ = 0.9			,						a__ = 0.9_99			,						a__ = 1e-7			,						a__ = False			,						a__ = 0.0			,						a__ = None			,						a__ = None			,						a__ = "AdamWeightDecay"			,						**a__			,						) ->      Optional[int]:






            '''simple docstring'''





            super().__init__(a__			,						a__			,						a__			,						a__			,						a__			,						a__			,						**a__    )
            __snake_case			:Dict          =						weight_decay_rate
            __snake_case			:List[Any]          =						include_in_weight_decay
            __snake_case			:Any          =						exclude_from_weight_decay



       @classmethod
       def 					__lowercase						(							cls			,						a__    ) ->      Tuple:






            '''simple docstring'''





            __snake_case			:str          =						{"""WarmUp""": WarmUp}
            return super(a__			,						cls    ).from_config(a__			,						custom_objects=a__    )



       def 					__lowercase						(							self			,						a__			,						a__			,						a__    ) ->      str:






            '''simple docstring'''





            super(a__			,						self    )._prepare_local(a__			,						a__			,						a__    )
            __snake_case			:str          =						tf.constant(
                self.weight_decay_rate			,						name="""adam_weight_decay_rate"""    )



       def 					__lowercase						(							self			,						a__			,						a__			,						a__    ) ->      Any:






            '''simple docstring'''





            __snake_case			:Dict          =						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 					__lowercase						(							self			,						a__			,						a__=None			,						**a__    ) ->      Any:






            '''simple docstring'''





            __snake_case			:Dict          =						list(zip(*a__    )    )
            return super(a__			,						self    ).apply_gradients(zip(a__			,						a__    )			,						name=a__			,						**a__    )



       def 					__lowercase						(							self			,						a__			,						a__			,						a__    ) ->      List[Any]:






            '''simple docstring'''





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

            __snake_case			:Union[str, Any]          =						apply_state or {}
            __snake_case			:Any          =						apply_state.get((var_device, var_dtype)    )
            if coefficients is None:
                 __snake_case			:str          =						self._fallback_apply_state(a__			,						a__    )
                 __snake_case			:Tuple          =						coefficients

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



       def 					__lowercase						(							self			,						a__			,						a__			,						a__=None    ) ->      Union[str, Any]:






            '''simple docstring'''





            __snake_case			:str          =						self._get_lr(var.device			,						var.dtype.base_dtype			,						a__    )
            __snake_case			:List[Any]          =						self._decay_weights_op(a__			,						a__			,						a__    )
            with tf.control_dependencies([decay]    ):
                 return super(a__			,						self    )._resource_apply_dense(a__			,						a__			,						**a__    )



       def 					__lowercase						(							self			,						a__			,						a__			,						a__			,						a__=None    ) ->      Union[str, Any]:






            '''simple docstring'''





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



       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





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



       def 					__lowercase						(							self			,						a__    ) ->      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(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 			snake_case__   (							lowercase_):

       '''simple docstring'''



       def __init__(							self    ) ->      List[Any]:






            '''simple docstring'''





            __snake_case			:List[str]          =						[]
            __snake_case			:List[str]          =						None



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






            '''simple docstring'''





            if self._accum_steps is None:
                 __snake_case			: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 					__lowercase						(							self    ) ->      Tuple:






            '''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__    ) ->      Tuple:






            '''simple docstring'''





            if not self._gradients:
                 __snake_case			:str          =						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 					__lowercase						(							self    ) ->      Union[str, Any]:






            '''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

import time

import numpy as np

lowerCamelCase__							=     [8, 5, 9, 7]
lowerCamelCase__							=     [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
lowerCamelCase__							=     [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]




class 			snake_case__   :

       '''simple docstring'''



       def __init__(							self			,						a__			,						a__			,						a__			,						) ->      None:






            '''simple docstring'''





            __snake_case			:Dict          =						claim_vector
            __snake_case			:Optional[int]          =						allocated_resources_table
            __snake_case			:Optional[int]          =						maximum_claim_table



       def 					__lowercase						(							self    ) ->      list[int]:






            '''simple docstring'''





            return [
                sum(p_item[i] for p_item in self.__allocated_resources_table    )
                for i in range(len(self.__allocated_resources_table[0]    )    )
            ]



       def 					__lowercase						(							self    ) ->      list[int]:






            '''simple docstring'''





            return np.array(self.__claim_vector    ) - np.array(
                self.__processes_resource_summation()    )



       def 					__lowercase						(							self    ) ->      list[list[int]]:






            '''simple docstring'''





            return [
                list(np.array(self.__maximum_claim_table[i]    ) - np.array(a__    )    )
                for i, allocated_resource in enumerate(self.__allocated_resources_table    )
            ]



       def 					__lowercase						(							self    ) ->      dict[int, list[int]]:






            '''simple docstring'''





            return {self.__need().index(a__    ): i for i in self.__need()}



       def 					__lowercase						(							self			,						**a__    ) ->      None:






            '''simple docstring'''





            __snake_case			:Optional[int]          =						self.__need()
            __snake_case			:List[Any]          =						self.__allocated_resources_table
            __snake_case			:str          =						self.__available_resources()
            __snake_case			:List[Any]          =						self.__need_index_manager()
            for kw, val in kwargs.items():
                 if kw and val is True:
                      self.__pretty_data()
            print("""_""" * 50 + """\n"""    )
            while need_list:
                 __snake_case			:Dict          =						False
                 for each_need in need_list:
                      __snake_case			:Dict          =						True
                      for index, need in enumerate(a__    ):
                           if need > available_resources[index]:
                                __snake_case			:Dict          =						False
                                break
                      if execution:
                           __snake_case			:Any          =						True
                           # get the original index of the process from ind_ctrl db
                           for original_need_index, need_clone in need_index_manager.items():
                                if each_need == need_clone:
                                     __snake_case			:List[str]          =						original_need_index
                           print(F'''Process {process_number + 1} is executing.'''    )
                           # remove the process run from stack
                           need_list.remove(a__    )
                           # update available/freed resources stack
                           __snake_case			:Tuple          =						np.array(a__    ) + np.array(
                               alloc_resources_table[process_number]    )
                           print(
                               """Updated available resource stack for processes: """
                               + """ """.join([str(a__    ) for x in available_resources]    )    )
                           break
                 if safe:
                      print("""The process is in a safe state.\n"""    )
                 else:
                      print("""System in unsafe state. Aborting...\n"""    )
                      break



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            print(""" """ * 9 + """Allocated Resource Table"""    )
            for item in self.__allocated_resources_table:
                 print(
                     F'''P{self.__allocated_resources_table.index(a__    ) + 1}'''
                     + """ """.join(F'''{it:>8}''' for it in item    )
                     + """\n"""    )
            print(""" """ * 9 + """System Resource Table"""    )
            for item in self.__maximum_claim_table:
                 print(
                     F'''P{self.__maximum_claim_table.index(a__    ) + 1}'''
                     + """ """.join(F'''{it:>8}''' for it in item    )
                     + """\n"""    )
            print(
                """Current Usage by Active Processes: """
                + """ """.join(str(a__    ) for x in self.__claim_vector    )    )
            print(
                """Initial Available Resources:       """
                + """ """.join(str(a__    ) for x in self.__available_resources()    )    )
            time.sleep(1    )


if __name__ == "__main__":
     import doctest

     doctest.testmod()


