

from __future__ import annotations

import time

import numpy as np

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





class 					__lowerCAmelCase	:




  def __init__(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  )    ->					None:
        '''simple docstring'''






        _lowercase      			=claim_vector
        _lowercase      			=allocated_resources_table
        _lowercase      			=maximum_claim_table




  def     A__	(							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     A__	(							self   )    ->					list[int]:
        '''simple docstring'''






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




  def     A__	(							self   )    ->					list[list[int]]:
        '''simple docstring'''






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




  def     A__	(							self   )    ->					dict[int, list[int]]:
        '''simple docstring'''






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




  def     A__	(							self				,  **lowerCAmelCase   )    ->					None:
        '''simple docstring'''






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






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


if __name__ == "__main__":
  import doctest

  doctest.testmod()





from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
lowercase_   							=  HfArgumentParser(InitializationArguments)
lowercase_   							=  parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
lowercase_   							=  AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
lowercase_   							=  {
    'vocab_size': len(tokenizer),
    'scale_attn_by_inverse_layer_idx': True,
    'reorder_and_upcast_attn': True,
}

# Load model config (GPT-2 large in this case)
lowercase_   							=  AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
lowercase_   							=  AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)


