




"""simple docstring"""



from __future__ import annotations

import time

import numpy as np

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




class 	UpperCAmelCase_  :




    def __init__(    self						:       List[Any]	, snake_case_						:       list[int]	, snake_case_						:       list[list[int]]	, snake_case_						:       list[list[int]]	, )      ->							None:




     '''simple docstring'''

     A__       =				claim_vector
     A__       =				allocated_resources_table
     A__       =				maximum_claim_table




    def 							__magic_name__							(    self						:       Tuple      )      ->							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 							__magic_name__							(    self						:       int      )      ->							list[int]:




     '''simple docstring'''

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




    def 							__magic_name__							(    self						:       List[str]      )      ->							list[list[int]]:




     '''simple docstring'''

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




    def 							__magic_name__							(    self						:       Optional[Any]      )      ->							dict[int, list[int]]:




     '''simple docstring'''

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




    def 							__magic_name__							(    self						:       Dict	, **snake_case_						:       Optional[Any]      )      ->							None:




     '''simple docstring'''

     A__       =				self.__need()
     A__       =				self.__allocated_resources_table
     A__       =				self.__available_resources()
     A__       =				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__       =				False
      for each_need in need_list:
       A__       =				True
       for index, need in enumerate(UpperCAmelCase__      ):
        if need > available_resources[index]:
         A__       =				False
         break
       if execution:
        A__       =				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__       =				original_need_index
        print(F"""Process {process_number + 1} is executing."""      )
        # remove the process run from stack
        need_list.remove(UpperCAmelCase__      )
        # update available/freed resources stack
        A__       =				np.array(UpperCAmelCase__      ) + np.array(
            alloc_resources_table[process_number]      )
        print(
            "Updated available resource stack for processes: "
            + " ".join([str(UpperCAmelCase__      ) 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 							__magic_name__							(    self						:       Any      )      ->							Optional[int]:




     '''simple docstring'''

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


if __name__ == "__main__":
      import doctest

      doctest.testmod()













"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_snake_case          =   {
    'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
    'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _snake_case          =   [
     'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'AdaptiveEmbedding',
     'TransfoXLForSequenceClassification',
     'TransfoXLLMHeadModel',
     'TransfoXLModel',
     'TransfoXLPreTrainedModel',
     'load_tf_weights_in_transfo_xl',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _snake_case          =   [
     'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFAdaptiveEmbedding',
     'TFTransfoXLForSequenceClassification',
     'TFTransfoXLLMHeadModel',
     'TFTransfoXLMainLayer',
     'TFTransfoXLModel',
     'TFTransfoXLPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
 from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_transfo_xl import (
      TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
      AdaptiveEmbedding,
      TransfoXLForSequenceClassification,
      TransfoXLLMHeadModel,
      TransfoXLModel,
      TransfoXLPreTrainedModel,
      load_tf_weights_in_transfo_xl,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_transfo_xl import (
      TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFAdaptiveEmbedding,
      TFTransfoXLForSequenceClassification,
      TFTransfoXLLMHeadModel,
      TFTransfoXLMainLayer,
      TFTransfoXLModel,
      TFTransfoXLPreTrainedModel,
  )

else:
 import sys

 _snake_case          =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

