from __future__ import annotations

import time

import numpy as np

_lowercase							:      Optional[Any]      							=[8, 5, 9, 7]
_lowercase							:      List[str]      							=[
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
_lowercase							:      Any      							=[
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]



class  SCREAMING_SNAKE_CASE_ :
  '''simple docstring'''


  def __init__(				self    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					int      ,	)    ->				Tuple:
         A						:     List[Any]         =claim_vector
         A						:     List[str]         =allocated_resources_table
         A						:     Any         =maximum_claim_table







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				Tuple:
         return [
             sum(p_item[i] for p_item in self.__allocated_resources_table     )
             for i in range(len(self.__allocated_resources_table[0]     )     )
         ]







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				Tuple:
         return np.array(self.__claim_vector     ) - np.array(
             self.__processes_resource_summation()     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					str     )    ->				Tuple:
         return [
             list(np.array(self.__maximum_claim_table[i]     ) - np.array(_A     )     )
             for i, allocated_resource in enumerate(self.__allocated_resources_table     )
         ]







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				Union[str, Any]:
         return {self.__need().index(_A     ): i for i in self.__need()}







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int      ,	**SCREAMING_SNAKE_CASE__    :					Union[str, Any]     )    ->				int:
         A						:     Any         =self.__need()
         A						:     Union[str, Any]         =self.__allocated_resources_table
         A						:     Optional[Any]         =self.__available_resources()
         A						:     Union[str, 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:
                A						:     int         =False
                for each_need in need_list:
                       A						:     Optional[int]         =True
                       for index, need in enumerate(_A     ):
                              if need > available_resources[index]:
                                     A						:     Optional[Any]         =False
                                     break
                       if execution:
                              A						:     List[str]         =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:
                                            A						:     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
                              A						:     Any         =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 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]     )    ->				str:
         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()







import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


_lowercase							:      Any      							=logging.getLogger(__name__)



@dataclass
class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     Optional[float]            =				field(
      default=0.0   ,						metadata={"help": "The label smoothing epsilon to apply (if not zero)."}			)
  lowercase		:     bool            =				field(default=lowerCAmelCase_   ,						metadata={"help": "Whether to SortishSamler or not."}			)
  lowercase		:     bool            =				field(
      default=lowerCAmelCase_   ,						metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}			)
  lowercase		:     bool            =				field(default=lowerCAmelCase_   ,						metadata={"help": "whether to use adafactor"}			)
  lowercase		:     Optional[float]            =				field(
      default=lowerCAmelCase_   ,						metadata={"help": "Encoder layer dropout probability. Goes into model.config."}			)
  lowercase		:     Optional[float]            =				field(
      default=lowerCAmelCase_   ,						metadata={"help": "Decoder layer dropout probability. Goes into model.config."}			)
  lowercase		:     Optional[float]            =				field(default=lowerCAmelCase_   ,						metadata={"help": "Dropout probability. Goes into model.config."}			)
  lowercase		:     Optional[float]            =				field(
      default=lowerCAmelCase_   ,						metadata={"help": "Attention dropout probability. Goes into model.config."}			)
  lowercase		:     Optional[str]            =				field(
      default="linear"   ,						metadata={"help": f'Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()			)}'}   ,						)






