




'''simple docstring'''


from __future__ import annotations

from statistics import mean
def 				SCREAMING_SNAKE_CASE(      __lowercase  ) ->   list[int]:
     A:		Optional[int]        =		2
     A:		Union[str, Any]        =		list(__lowercase  )
     A:		str        =		[0] * len(__lowercase  )
     A:		int        =		0
     while True:
          A:		Dict        =		True
          for i, burst_time in enumerate(__lowercase  ):
               if rem_burst_times[i] > 0:
                    A:		Dict        =		False
                    if rem_burst_times[i] > quantum:
                         t += quantum
                         rem_burst_times[i] -= quantum
                    else:
                         t += rem_burst_times[i]
                         A:		int        =		t - burst_time
                         A:		int        =		0
          if done is True:
               return waiting_times
def 				SCREAMING_SNAKE_CASE(      __lowercase		,							__lowercase  ) ->   list[int]:
     return [burst + waiting for burst, waiting in zip(__lowercase		,							__lowercase  )]


if __name__ == "__main__":
     UpperCamelCase     =				[3, 5, 7]
     UpperCamelCase     =				calculate_waiting_times(burst_times)
     UpperCamelCase     =				calculate_turn_around_times(burst_times, waiting_times)
     print('''Process ID \tBurst Time \tWaiting Time \tTurnaround Time''')
     for i, burst_time in enumerate(burst_times):
          print(
              f'  {i + 1}\t\t  {burst_time}\t\t  {waiting_times[i]}\t\t  '
              f'{turn_around_times[i]}'
          )
     print(f'\nAverage waiting time = {mean(waiting_times):.5f}')
     print(f'Average turn around time = {mean(turn_around_times):.5f}')












'''simple docstring'''


from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest






@custom_tokenizers
class 	lowerCAmelCase_  (					UpperCAmelCase_       ):







  '''simple docstring'''

  def 				_snake_case    (   self							:       Optional[int]				,     SCREAMING_SNAKE_CASE_							:       Dict				,     SCREAMING_SNAKE_CASE_							:       Union[str, Any]				,     SCREAMING_SNAKE_CASE_							:       List[Any]				,     SCREAMING_SNAKE_CASE_							:       Dict				,     SCREAMING_SNAKE_CASE_							:       List[Any]  ) ->		int:







       '''simple docstring'''



       return True




  def 				_snake_case    (   self							:       Optional[int]  ) ->		List[str]:







       '''simple docstring'''



       A:		Optional[Any]        =		CpmTokenizer.from_pretrained('''TsinghuaAI/CPM-Generate'''  )
       A:		Optional[int]        =		'''Hugging Face大法好，谁用谁知道。'''
       A:		str        =		'''Hugging Face大法好,谁用谁知道。<unk>'''
       A:		Dict        =		'''▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。'''.split()

       A:		int        =		tokenizer.tokenize(SCREAMING_SNAKE_CASE_  )
       self.assertListEqual(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  )

       A:		Optional[int]        =		tokens + [tokenizer.unk_token]

       A:		str        =		[1_37_89, 1_32_83, 14_21, 8, 10, 11_64, 1_36_08, 1_65_28, 63, 8, 9, 4_40, 1_08, 4_40, 1_21, 90, 8, 12, 0]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_  )				,     SCREAMING_SNAKE_CASE_  )

       A:		Any        =		tokenizer.decode(SCREAMING_SNAKE_CASE_  )
       self.assertEqual(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  )






