'''simple docstring'''





from __future__ import annotations

from statistics import mean





def 	lowerCAmelCase_				(					snake_case_       : list[int]					,    snake_case_       : list[int]					,    snake_case_       : int			)    ->      list[int]:


     '''simple docstring'''




     UpperCAmelCase_           =   [0] * no_of_processes
     UpperCAmelCase_           =   [0] * no_of_processes

     # Initialize remaining_time to waiting_time.

     for i in range(snake_case_			):
          UpperCAmelCase_           =   burst_time[i]
     UpperCAmelCase_           =   []

     UpperCAmelCase_           =   0
     UpperCAmelCase_           =   0

     # When processes are not completed,
     # A process whose arrival time has passed \
     # and has remaining execution time is put into the ready_process.
     # The shortest process in the ready_process, target_process is executed.

     while completed != no_of_processes:
          UpperCAmelCase_           =   []
          UpperCAmelCase_           =   -1

          for i in range(snake_case_			):
               if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
                    ready_process.append(snake_case_			)

          if len(snake_case_			) > 0:
               UpperCAmelCase_           =   ready_process[0]
               for i in ready_process:
                    if remaining_time[i] < remaining_time[target_process]:
                         UpperCAmelCase_           =   i
               total_time += burst_time[target_process]
               completed += 1
               UpperCAmelCase_           =   0
               UpperCAmelCase_           =   (
                   total_time - arrival_time[target_process] - burst_time[target_process]
               )
          else:
               total_time += 1

     return waiting_time





def 	lowerCAmelCase_				(					snake_case_       : list[int]					,    snake_case_       : int					,    snake_case_       : list[int]			)    ->      list[int]:


     '''simple docstring'''




     UpperCAmelCase_           =   [0] * no_of_processes
     for i in range(snake_case_			):
          UpperCAmelCase_           =   burst_time[i] + waiting_time[i]
     return turn_around_time


if __name__ == "__main__":
    print('[TEST CASE 01]')

    SCREAMING_SNAKE_CASE_:     List[str]  =4
    SCREAMING_SNAKE_CASE_:     List[str]  =[2, 5, 3, 7]
    SCREAMING_SNAKE_CASE_:     Tuple  =[0, 0, 0, 0]
    SCREAMING_SNAKE_CASE_:     str  =calculate_waitingtime(arrival_time, burst_time, no_of_processes)
    SCREAMING_SNAKE_CASE_:     Dict  =calculate_turnaroundtime(
        burst_time, no_of_processes, waiting_time
    )

    # Printing the Result
    print('PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time')
    for i, process_id in enumerate(list(range(1, 5))):
        print(
            f"{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t"
            f"{waiting_time[i]}\t\t\t\t{turn_around_time[i]}"
        )
    print(f"\nAverage waiting time = {mean(waiting_time):.5f}")
    print(f"Average turnaround time = {mean(turn_around_time):.5f}")






"""simple docstring"""





import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case,     __snake_case=5	)							->		Union[str, Any]:


					"""simple docstring"""





					assert masked_input.count('''<mask>'''	) == 1
					_UpperCamelCase							 =  torch.tensor(tokenizer.encode(__snake_case,     add_special_tokens=__snake_case	)	).unsqueeze(0	)  # Batch size 1
					_UpperCamelCase							 =  model(__snake_case	)[0]  # The last hidden-state is the first element of the output tuple
					_UpperCamelCase							 =  (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
					_UpperCamelCase							 =  logits[0, masked_index, :]
					_UpperCamelCase							 =  logits.softmax(dim=0	)
					_UpperCamelCase ,  _UpperCamelCase							 =  prob.topk(k=__snake_case,     dim=0	)
					_UpperCamelCase							 =  ''' '''.join(
					    [tokenizer.convert_ids_to_tokens(indices[i].item()	) for i in range(len(__snake_case	)	)]	)
					_UpperCamelCase							 =  tokenizer.mask_token
					_UpperCamelCase							 =  []
					for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' '''	)	):
										_UpperCamelCase							 =  predicted_token_bpe.replace('''\u2581''',     ''' '''	)
										if " {0}".format(__snake_case	) in masked_input:
															topk_filled_outputs.append(
															    (
															        masked_input.replace(''' {0}'''.format(__snake_case	),     __snake_case	),
															        values[index].item(),
															        predicted_token,
															    )	)
										else:
															topk_filled_outputs.append(
															    (
															        masked_input.replace(__snake_case,     __snake_case	),
															        values[index].item(),
															        predicted_token,
															    )	)
					return topk_filled_outputs


_a														=		CamembertTokenizer.from_pretrained("""camembert-base""")
_a														=		CamembertForMaskedLM.from_pretrained("""camembert-base""")
model.eval()

_a														=		"""Le camembert est <mask> :)"""
print(fill_mask(masked_input, model, tokenizer, topk=3))






