

'''simple docstring'''






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 			lowercase_	:




  """simple docstring"""




  def __init__(      self   :     Optional[Any]					,lowercase__   :     list[int]					,lowercase__   :     list[list[int]]					,lowercase__   :     list[list[int]]					,):
   __lowercase							  = claim_vector
   __lowercase							  = allocated_resources_table
   __lowercase							  = maximum_claim_table






  def 					SCREAMING_SNAKE_CASE      (      self   :     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   :     str			):
   return np.array(self.__claim_vector			) - np.array(
       self.__processes_resource_summation()			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]			):
   return [
       list(np.array(self.__maximum_claim_table[i]			) - np.array(lowercase__			)			)
       for i, allocated_resource in enumerate(self.__allocated_resources_table			)
   ]






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   return {self.__need().index(lowercase__			): i for i in self.__need()}






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]					,**lowercase__   :     List[Any]			):
   __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('''_''' * 5_0 + '''\n'''			)
   while need_list:
    __lowercase							  = False
    for each_need in need_list:
     __lowercase							  = True
     for index, need in enumerate(lowercase__			):
      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(lowercase__			)
      # update available/freed resources stack
      __lowercase							  = np.array(lowercase__			) + np.array(
          alloc_resources_table[process_number]			)
      print(
          '''Updated available resource stack for processes: '''
          + ''' '''.join([str(lowercase__			) 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[int]			):
   print(''' ''' * 9 + '''Allocated Resource Table'''			)
   for item in self.__allocated_resources_table:
    print(
        F"P{self.__allocated_resources_table.index(lowercase__			) + 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(lowercase__			) + 1}"
        + ''' '''.join(F"{it:>8}" for it in item			)
        + '''\n'''			)
   print(
       '''Current Usage by Active Processes: '''
       + ''' '''.join(str(lowercase__			) for x in self.__claim_vector			)			)
   print(
       '''Initial Available Resources:       '''
       + ''' '''.join(str(lowercase__			) for x in self.__available_resources()			)			)
   time.sleep(1			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()








'''simple docstring'''


import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

lowerCAmelCase					:     Optional[int]												=logging.getLogger()
def 							UpperCAmelCase_  (						):
   lowercase_			:Union[str, Any]    			= argparse.ArgumentParser()
   parser.add_argument("-f" )
   lowercase_			:Any    			= parser.parse_args()
   return args.f


class       a_   (				_lowerCAmelCase							):





     def 							lowercase__      ( self		:					List[Any]						):





        """simple docstring"""




        lowercase_			:List[Any]    			= logging.StreamHandler(sys.stdout						)
        logger.addHandler(lowercase						)





     def 							lowercase__      ( self		:					str	,      lowercase		:					str						):





        """simple docstring"""




        lowercase_			:Dict    			= get_gpu_count()

        if n_gpu > 1:
           pass
           # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
           # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
           # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
           # cmd = [sys.executable] + distributed_args + args
           # execute_subprocess_async(cmd, env=self.get_env())
           # XXX: test the results - need to save them first into .json file
        else:
           args.insert(0	,      "run_glue_deebert.py"						)
           with patch.object(lowercase	,      "argv"	,      lowercase						):
              lowercase_			:int    			= run_glue_deebert.main()
              for value in result.values():
                 self.assertGreaterEqual(lowercase	,      0.6_66						)







     @slow
     @require_torch_non_multi_gpu
     def 							lowercase__      ( self		:					Tuple						):





        """simple docstring"""




        lowercase_			:List[Any]    			= "\n            --model_type roberta\n            --model_name_or_path roberta-base\n            --task_name MRPC\n            --do_train\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --max_seq_length 128\n            --per_gpu_eval_batch_size=1\n            --per_gpu_train_batch_size=8\n            --learning_rate 2e-4\n            --num_train_epochs 3\n            --overwrite_output_dir\n            --seed 42\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --save_steps 0\n            --overwrite_cache\n            --eval_after_first_stage\n            ".split()
        self.run_and_check(lowercase						)

        lowercase_			:List[str]    			= "\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --eval_each_highway\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            ".split()
        self.run_and_check(lowercase						)

        lowercase_			:Any    			= "\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --early_exit_entropy 0.1\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            ".split()
        self.run_and_check(lowercase						)


