from collections import deque






class 		_lowerCamelCase		:
    def __init__(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )       ->					None:
     SCREAMING_SNAKE_CASE__:   str=      process_name  # process name
     SCREAMING_SNAKE_CASE__:   Tuple=      arrival_time  # arrival time of the process
     # completion time of finished process or last interrupted time
     SCREAMING_SNAKE_CASE__:   Any=      arrival_time
     SCREAMING_SNAKE_CASE__:   Dict=      burst_time  # remaining burst time
     SCREAMING_SNAKE_CASE__:   int=      0  # total time of the process wait in ready queue
     SCREAMING_SNAKE_CASE__:   str=      0  # time from arrival time to completion time






class 		_lowerCamelCase		:
    def __init__(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , )       ->					None:
     # total number of mlfq's queues
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      number_of_queues
     # time slice of queues that round robin algorithm applied
     SCREAMING_SNAKE_CASE__:   Optional[int]=      time_slices
     # unfinished process is in this ready_queue
     SCREAMING_SNAKE_CASE__:   List[str]=      queue
     # current time
     SCREAMING_SNAKE_CASE__:   List[Any]=      current_time
     # finished process is in this sequence queue
     SCREAMING_SNAKE_CASE__:   deque[Process]=      deque()
    def 						UpperCamelCase_				(  self   )       ->					list[str]:
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      []
     for i in range(len(self.finish_queue   )   ):
      sequence.append(self.finish_queue[i].process_name   )
     return sequence
    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					list[int]:
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      []
     for i in range(len(lowerCAmelCase   )   ):
      waiting_times.append(queue[i].waiting_time   )
     return waiting_times
    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					list[int]:
     SCREAMING_SNAKE_CASE__:   Tuple=      []
     for i in range(len(lowerCAmelCase   )   ):
      turnaround_times.append(queue[i].turnaround_time   )
     return turnaround_times
    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					list[int]:
     SCREAMING_SNAKE_CASE__:   str=      []
     for i in range(len(lowerCAmelCase   )   ):
      completion_times.append(queue[i].stop_time   )
     return completion_times
    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					list[int]:
     return [q.burst_time for q in queue]
    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					int:
     process.waiting_time += self.current_time - process.stop_time
     return process.waiting_time
    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					deque[Process]:
     SCREAMING_SNAKE_CASE__:   deque[Process]=      deque()  # sequence deque of finished process
     while len(lowerCAmelCase   ) != 0:
      SCREAMING_SNAKE_CASE__:   Optional[int]=      ready_queue.popleft()  # current process

      # if process's arrival time is later than current time, update current time
      if self.current_time < cp.arrival_time:
       self.current_time += cp.arrival_time

      # update waiting time of current process
      self.update_waiting_time(lowerCAmelCase   )
      # update current time
      self.current_time += cp.burst_time
      # finish the process and set the process's burst-time 0
      SCREAMING_SNAKE_CASE__:   List[Any]=      0
      # set the process's turnaround time because it is finished
      SCREAMING_SNAKE_CASE__:   List[str]=      self.current_time - cp.arrival_time
      # set the completion time
      SCREAMING_SNAKE_CASE__:   Tuple=      self.current_time
      # add the process to queue that has finished queue
      finished.append(lowerCAmelCase   )

     self.finish_queue.extend(lowerCAmelCase   )  # add finished process to finish queue
     # FCFS will finish all remaining processes
     return finished
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase   )       ->					tuple[deque[Process], deque[Process]]:
     SCREAMING_SNAKE_CASE__:   deque[Process]=      deque()  # sequence deque of terminated process
     # just for 1 cycle and unfinished processes will go back to queue
     for _ in range(len(lowerCAmelCase   )   ):
      SCREAMING_SNAKE_CASE__:   Tuple=      ready_queue.popleft()  # current process

      # if process's arrival time is later than current time, update current time
      if self.current_time < cp.arrival_time:
       self.current_time += cp.arrival_time

      # update waiting time of unfinished processes
      self.update_waiting_time(lowerCAmelCase   )
      # if the burst time of process is bigger than time-slice
      if cp.burst_time > time_slice:
       # use CPU for only time-slice
       self.current_time += time_slice
       # update remaining burst time
       cp.burst_time -= time_slice
       # update end point time
       SCREAMING_SNAKE_CASE__:   List[str]=      self.current_time
       # locate the process behind the queue because it is not finished
       ready_queue.append(lowerCAmelCase   )
      else:
       # use CPU for remaining burst time
       self.current_time += cp.burst_time
       # set burst time 0 because the process is finished
       SCREAMING_SNAKE_CASE__:   str=      0
       # set the finish time
       SCREAMING_SNAKE_CASE__:   Tuple=      self.current_time
       # update the process' turnaround time because it is finished
       SCREAMING_SNAKE_CASE__:   str=      self.current_time - cp.arrival_time
       # add the process to queue that has finished queue
       finished.append(lowerCAmelCase   )

     self.finish_queue.extend(lowerCAmelCase   )  # add finished process to finish queue
     # return finished processes queue and remaining processes queue
     return finished, ready_queue



    def 						UpperCamelCase_				(  self   )       ->					deque[Process]:

     #  all queues except last one have round_robin algorithm
     for i in range(self.number_of_queues - 1   ):
      SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   str=      self.round_robin(
          self.ready_queue , self.time_slices[i]   )
     #  the last queue has first_come_first_served algorithm
     self.first_come_first_served(self.ready_queue   )

     return self.finish_queue


if __name__ == "__main__":
      import doctest

      lowercase_     :   int 				=				Process('P1', 0, 5_3)
      lowercase_     :   str 				=				Process('P2', 0, 1_7)
      lowercase_     :   List[Any] 				=				Process('P3', 0, 6_8)
      lowercase_     :   int 				=				Process('P4', 0, 2_4)
      lowercase_     :   Optional[int] 				=				3
      lowercase_     :   List[Any] 				=				[1_7, 2_5]
      lowercase_     :   Union[str, Any] 				=				deque([Pa, Pa, Pa, Pa])

      if len(time_slices) != number_of_queues - 1:
            raise SystemExit(0)

      doctest.testmod(extraglobs={'queue': deque([Pa, Pa, Pa, Pa])})

      lowercase_     :   int 				=				Process('P1', 0, 5_3)
      lowercase_     :   Optional[int] 				=				Process('P2', 0, 1_7)
      lowercase_     :   str 				=				Process('P3', 0, 6_8)
      lowercase_     :   List[str] 				=				Process('P4', 0, 2_4)
      lowercase_     :   str 				=				3
      lowercase_     :   Union[str, Any] 				=				[1_7, 2_5]
      lowercase_     :   int 				=				deque([Pa, Pa, Pa, Pa])
      lowercase_     :   Dict 				=				MLFQ(number_of_queues, time_slices, queue, 0)
      lowercase_     :   Optional[int] 				=				mlfq.multi_level_feedback_queue()

      # print total waiting times of processes(P1, P2, P3, P4)
      print(
          f'''waiting time:\
        \t\t\t{MLFQ.calculate_waiting_time(mlfq, [Pa, Pa, Pa, Pa])}'''
      )
      # print completion times of processes(P1, P2, P3, P4)
      print(
          f'''completion time:\
        \t\t{MLFQ.calculate_completion_time(mlfq, [Pa, Pa, Pa, Pa])}'''
      )
      # print total turnaround times of processes(P1, P2, P3, P4)
      print(
          f'''turnaround time:\
        \t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}'''
      )
      # print sequence of finished processes
      print(
          f'''sequence of finished processes:\
        {mlfq.calculate_sequence_of_finish_queue()}'''
      )


import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


lowercase_     :   Tuple 				=				getLogger(__name__)







def 			A__      (	snake_case_ :		Optional[int]       ,				snake_case_ :		str       ,				snake_case_ :		str       ,				snake_case_ :		int = 8       ,				snake_case_ :		int = 1_024       ,				snake_case_ :		Union[str, Any]="val"       ,				snake_case_ :		Tuple=None       ,				snake_case_ :		int=False       ,				snake_case_ :		Optional[Any]="summarization"       ,				snake_case_ :		Optional[Any]=None       ,				snake_case_ :		List[Any]=1       ,				snake_case_ :		Dict = None       ,				snake_case_ :		int=""       ,				**snake_case_ :		Any       ,				):
 SCREAMING_SNAKE_CASE__:   List[str]=      str(snake_case_      )
 assert local_rank is not None
 torch.distributed.init_process_group(backend='''nccl'''       ,				rank=snake_case_      )

 SCREAMING_SNAKE_CASE__:   str=      Path(snake_case_      )
 SCREAMING_SNAKE_CASE__:   Tuple=      save_dir.joinpath(F'rank_{local_rank}_output.json'      )
 torch.cuda.set_device(snake_case_      )
 SCREAMING_SNAKE_CASE__:   Tuple=      AutoModelForSeqaSeqLM.from_pretrained(snake_case_      ).cuda()
 if fpaa:
  SCREAMING_SNAKE_CASE__:   Dict=      model.half()
 # determine if we need to increase num_beams
 use_task_specific_params(snake_case_       ,				snake_case_      )  # update config with task specific params
 SCREAMING_SNAKE_CASE__:   List[Any]=      generate_kwargs.pop('''num_beams'''       ,				model.config.num_beams      )  # AttributeError risk?
 if num_return_sequences > num_beams:
  SCREAMING_SNAKE_CASE__:   str=      num_return_sequences

 SCREAMING_SNAKE_CASE__:   Optional[int]=      AutoTokenizer.from_pretrained(snake_case_      )
 logger.info(F'Inferred tokenizer type: {tokenizer.__class__}'      )  # if this is wrong, check config.model_type.

 if max_source_length is None:
  SCREAMING_SNAKE_CASE__:   Dict=      tokenizer.model_max_length
 if prefix is None:
  SCREAMING_SNAKE_CASE__:   List[Any]=      prefix or getattr(model.config       ,				'''prefix'''       ,				''''''      ) or ''''''
 SCREAMING_SNAKE_CASE__:   Optional[int]=      SeqaSeqDataset(
     snake_case_       ,				snake_case_       ,				snake_case_       ,				max_target_length=1_024       ,				type_path=snake_case_       ,				n_obs=snake_case_       ,				prefix=snake_case_       ,				**snake_case_       ,				)
 # I set shuffle=True for a more accurate progress bar.
 # If all the longest samples are first, the prog bar estimate is too high at the beginning.
 SCREAMING_SNAKE_CASE__:   Union[str, Any]=      ds.make_sortish_sampler(snake_case_       ,				distributed=snake_case_       ,				add_extra_examples=snake_case_       ,				shuffle=snake_case_      )
 SCREAMING_SNAKE_CASE__:   List[Any]=      DataLoader(snake_case_       ,				sampler=snake_case_       ,				batch_size=snake_case_       ,				collate_fn=ds.collate_fn      )
 SCREAMING_SNAKE_CASE__:   List[Any]=      []
 for batch in tqdm(snake_case_      ):
  SCREAMING_SNAKE_CASE__:   List[Any]=      model.generate(
      input_ids=batch['''input_ids'''].to(model.device      )       ,				attention_mask=batch['''attention_mask'''].to(model.device      )       ,				num_return_sequences=snake_case_       ,				num_beams=snake_case_       ,				**snake_case_       ,				)
  SCREAMING_SNAKE_CASE__:   int=      tokenizer.batch_decode(snake_case_       ,				skip_special_tokens=snake_case_       ,				clean_up_tokenization_spaces=snake_case_      )
  SCREAMING_SNAKE_CASE__:   int=      batch['''ids''']
  if num_return_sequences > 1:
   SCREAMING_SNAKE_CASE__:   List[Any]=      chunks(snake_case_       ,				snake_case_      )  # batch size chunks, each of size num_return_seq
  for i, pred in enumerate(snake_case_      ):
   results.append({'''pred''': pred, '''id''': ids[i].item()}      )
 save_json(snake_case_       ,				snake_case_      )
 return results, sampler.num_replicas







def 			A__      (	):
 SCREAMING_SNAKE_CASE__:   List[Any]=      argparse.ArgumentParser(
     epilog='''Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate'''      )
 parser.add_argument('''--data_dir'''       ,				type=snake_case_       ,				help='''like cnn_dm/test.source'''      )
 parser.add_argument(
     '''--model_name'''       ,				type=snake_case_       ,				help='''like facebook/bart-large-cnn,t5-base, etc.'''       ,				default='''sshleifer/distilbart-xsum-12-3'''       ,				)
 parser.add_argument('''--save_dir'''       ,				type=snake_case_       ,				help='''where to save'''       ,				default='''tmp_gen'''      )
 parser.add_argument('''--max_source_length'''       ,				type=snake_case_       ,				default=snake_case_      )
 parser.add_argument(
     '''--type_path'''       ,				type=snake_case_       ,				default='''test'''       ,				help='''which subset to evaluate typically train/val/test'''      )
 parser.add_argument('''--task'''       ,				type=snake_case_       ,				default='''summarization'''       ,				help='''used for task_specific_params + metrics'''      )
 parser.add_argument('''--bs'''       ,				type=snake_case_       ,				default=8       ,				required=snake_case_       ,				help='''batch size'''      )
 parser.add_argument(
     '''--local_rank'''       ,				type=snake_case_       ,				default=-1       ,				required=snake_case_       ,				help='''should be passed by distributed.launch'''      )

 parser.add_argument(
     '''--n_obs'''       ,				type=snake_case_       ,				default=snake_case_       ,				required=snake_case_       ,				help='''How many observations. Defaults to all.'''      )
 parser.add_argument(
     '''--num_return_sequences'''       ,				type=snake_case_       ,				default=1       ,				required=snake_case_       ,				help='''How many sequences to return'''      )
 parser.add_argument(
     '''--sync_timeout'''       ,				type=snake_case_       ,				default=600       ,				required=snake_case_       ,				help='''How long should master process wait for other processes to finish.'''       ,				)
 parser.add_argument('''--src_lang'''       ,				type=snake_case_       ,				default=snake_case_       ,				required=snake_case_      )
 parser.add_argument('''--tgt_lang'''       ,				type=snake_case_       ,				default=snake_case_       ,				required=snake_case_      )
 parser.add_argument(
     '''--prefix'''       ,				type=snake_case_       ,				required=snake_case_       ,				default=snake_case_       ,				help='''will be added to the begininng of src examples'''      )
 parser.add_argument('''--fp16'''       ,				action='''store_true'''      )
 parser.add_argument('''--debug'''       ,				action='''store_true'''      )
 SCREAMING_SNAKE_CASE__:   Dict=      time.time()
 SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Dict=      parser.parse_known_args()
 SCREAMING_SNAKE_CASE__:   Tuple=      parse_numeric_n_bool_cl_kwargs(snake_case_      )
 if generate_kwargs and args.local_rank <= 0:
  print(F'parsed the following generate kwargs: {generate_kwargs}'      )
 SCREAMING_SNAKE_CASE__:   List[Any]=      Path(args.save_dir + '''_tmp'''      )
 Path(snake_case_      ).mkdir(exist_ok=snake_case_      )  # this handles locking.
 SCREAMING_SNAKE_CASE__:   Dict=      list(json_save_dir.glob('''rank_*.json'''      )      )
 if intermediate_files:
  raise ValueError(F'Found files at {json_save_dir} please move or remove them.'      )
  # In theory, a node could finish and save before another node hits this. If this happens, we can address later.
 SCREAMING_SNAKE_CASE__:   List[str]=      {}
 if args.src_lang is not None:
  SCREAMING_SNAKE_CASE__:   str=      args.src_lang
 if args.tgt_lang is not None:
  SCREAMING_SNAKE_CASE__:   Any=      args.tgt_lang

 Path(args.save_dir      ).mkdir(exist_ok=snake_case_      )
 SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   str=      eval_data_dir(
     args.data_dir       ,				snake_case_       ,				args.model_name       ,				type_path=args.type_path       ,				bs=args.bs       ,				fpaa=args.fpaa       ,				task=args.task       ,				local_rank=args.local_rank       ,				n_obs=args.n_obs       ,				max_source_length=args.max_source_length       ,				num_return_sequences=args.num_return_sequences       ,				prefix=args.prefix       ,				dataset_kwargs=snake_case_       ,				**snake_case_       ,				)

 if args.local_rank <= 0:
  SCREAMING_SNAKE_CASE__:   str=      Path(args.save_dir      )
  save_dir.mkdir(exist_ok=snake_case_      )
  SCREAMING_SNAKE_CASE__:   Optional[Any]=      gather_results_from_each_node(snake_case_       ,				snake_case_       ,				args.sync_timeout      )
  SCREAMING_SNAKE_CASE__:   Union[str, Any]=      combine_partial_results(snake_case_      )
  if args.num_return_sequences > 1:
   SCREAMING_SNAKE_CASE__:   Optional[Any]=      save_dir.joinpath('''pseudolabel_results.json'''      )
   print(F'Saving aggregated results at {save_path}, intermediate in {json_save_dir}/'      )
   save_json(snake_case_       ,				snake_case_      )
   return
  SCREAMING_SNAKE_CASE__:   Union[str, Any]=      Path(args.data_dir      ).joinpath(args.type_path + '''.target'''      )
  with open(snake_case_      ) as f:
   SCREAMING_SNAKE_CASE__:   List[str]=      [x.rstrip() for x in f.readlines()][: len(snake_case_      )]

  # Calculate metrics, save metrics,  and save _generations.txt
  SCREAMING_SNAKE_CASE__:   Optional[int]=      '''translation''' in args.task
  SCREAMING_SNAKE_CASE__:   str=      calculate_bleu if calc_bleu else calculate_rouge
  SCREAMING_SNAKE_CASE__:   Dict=      '''bleu''' if calc_bleu else '''rouge'''
  SCREAMING_SNAKE_CASE__:   Dict=      score_fn(snake_case_       ,				snake_case_      )
  SCREAMING_SNAKE_CASE__:   List[str]=      len(snake_case_      )
  SCREAMING_SNAKE_CASE__:   Dict=      time.time() - start_time
  SCREAMING_SNAKE_CASE__:   List[str]=      round(runtime / metrics['''n_obs''']       ,				4      )
  SCREAMING_SNAKE_CASE__:   str=      num_replicas
  # TODO(@stas00): add whatever metadata to metrics
  SCREAMING_SNAKE_CASE__:   str=      save_dir.joinpath(F'{args.type_path}_{metric_name}.json'      )
  save_json(snake_case_       ,				snake_case_       ,				indent=snake_case_      )
  print(snake_case_      )
  write_txt_file(snake_case_       ,				save_dir.joinpath(F'{args.type_path}_generations.txt'      )      )
  if args.debug:
   write_txt_file(snake_case_       ,				save_dir.joinpath(F'{args.type_path}.target'      )      )
  else:
   shutil.rmtree(snake_case_      )







def 			A__      (	snake_case_ :		Optional[int]      ):
 SCREAMING_SNAKE_CASE__:   int=      []
 for partial_result in partial_results:
  records.extend(snake_case_      )
 SCREAMING_SNAKE_CASE__:   str=      sorted(snake_case_       ,				key=lambda snake_case_      : x["id"]      )
 SCREAMING_SNAKE_CASE__:   int=      [x['''pred'''] for x in records]
 return preds







def 			A__      (	snake_case_ :		Union[str, Any]       ,				snake_case_ :		List[Any]       ,				snake_case_ :		int      ):
 # WAIT FOR lots of .json files
 SCREAMING_SNAKE_CASE__:   Optional[int]=      time.time()
 logger.info('''waiting for all nodes to finish'''      )
 SCREAMING_SNAKE_CASE__:   Optional[Any]=      None
 while (time.time() - start_wait) < timeout:
  SCREAMING_SNAKE_CASE__:   List[Any]=      list(save_dir.glob('''rank_*.json'''      )      )
  if len(snake_case_      ) < num_replicas:
   continue
  try:
   # make sure all json files are fully saved
   SCREAMING_SNAKE_CASE__:   Optional[int]=      lmap(snake_case_       ,				snake_case_      )
   return json_data
  except JSONDecodeError:
   continue
 else:
  raise TimeoutError('''Rank 0 gave up on waiting for other processes'''      )
 # Unreachable


if __name__ == "__main__":
      # Usage for MT:
      run_generate()

