


'''simple docstring'''


from __future__ import annotations

import time

import numpy as np

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



class 	__lowerCAmelCase		:


  '''simple docstring'''







  def __init__(							self :       List[str]	,_a :       list[int]	,_a :       list[list[int]]	,_a :       list[list[int]]	,):

        '''simple docstring'''




        A_						:  List[Any]          =				claim_vector
        A_						:  int          =				allocated_resources_table
        A_						:  Dict          =				maximum_claim_table






  def        _a							(							self :       Tuple ):

        '''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        _a							(							self :       int ):

        '''simple docstring'''




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






  def        _a							(							self :       Dict ):

        '''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        _a							(							self :       Optional[int] ):

        '''simple docstring'''




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






  def        _a							(							self :       Optional[int]	,**_a :       int ):

        '''simple docstring'''




        A_						:  Dict          =				self.__need()
        A_						:  List[str]          =				self.__allocated_resources_table
        A_						:  Optional[Any]          =				self.__available_resources()
        A_						:  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_						:  List[str]          =				False
              for each_need in need_list:
                    A_						:  Tuple          =				True
                    for index, need in enumerate(_a ):
                          if need > available_resources[index]:
                                A_						:  Optional[Any]          =				False
                                break
                    if execution:
                          A_						:  Optional[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:
                                      A_						:  List[Any]          =				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_						:  Optional[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        _a							(							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()




'''simple docstring'''


from ... import PretrainedConfig


__magic_name__			=      {
    'sijunhe/nezha-cn-base': 'https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json',
}



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
  a_  					=							"""nezha"""






  def __init__(							self :       int	,_a :       Union[str, Any]=21128	,_a :       int=768	,_a :       Any=12	,_a :       List[str]=12	,_a :       str=3072	,_a :       int="gelu"	,_a :       int=0.1	,_a :       str=0.1	,_a :       Tuple=512	,_a :       List[Any]=64	,_a :       Dict=2	,_a :       List[Any]=0.02	,_a :       Optional[Any]=1e-12	,_a :       List[Any]=0.1	,_a :       Union[str, Any]=0	,_a :       Any=2	,_a :       Union[str, Any]=3	,_a :       int=True	,**_a :       int	,):

        '''simple docstring'''




        super().__init__(pad_token_id=_a	,bos_token_id=_a	,eos_token_id=_a	,**_a )

        A_						:  Tuple          =				vocab_size
        A_						:  int          =				hidden_size
        A_						:  Any          =				num_hidden_layers
        A_						:  List[Any]          =				num_attention_heads
        A_						:  Tuple          =				hidden_act
        A_						:  List[Any]          =				intermediate_size
        A_						:  List[str]          =				hidden_dropout_prob
        A_						:  Tuple          =				attention_probs_dropout_prob
        A_						:  Dict          =				max_position_embeddings
        A_						:  Optional[Any]          =				max_relative_position
        A_						:  List[Any]          =				type_vocab_size
        A_						:  int          =				initializer_range
        A_						:  Tuple          =				layer_norm_eps
        A_						:  Dict          =				classifier_dropout
        A_						:  int          =				use_cache
