



"""simple docstring"""






from collections import deque



class     UpperCAmelCase    :



   def __init__(       self    :	Dict							,       __lowerCamelCase    :	str							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     _snake_case   	=      process_name  # process name
     _snake_case   	=      arrival_time  # arrival time of the process
     # completion time of finished process or last interrupted time
     _snake_case   	=      arrival_time
     _snake_case   	=      burst_time  # remaining burst time
     _snake_case   	=      0  # total time of the process wait in ready queue
     _snake_case   	=      0  # time from arrival time to completion time



class     UpperCAmelCase    :



   def __init__(       self    :	Dict							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	list[int]							,       __lowerCamelCase    :	deque[Process]							,       __lowerCamelCase    :	int							,       ):



     """simple docstring"""
     # total number of mlfq's queues
     _snake_case   	=      number_of_queues
     # time slice of queues that round robin algorithm applied
     _snake_case   	=      time_slices
     # unfinished process is in this ready_queue
     _snake_case   	=      queue
     # current time
     _snake_case   	=      current_time
     # finished process is in this sequence queue
     _snake_case   	=      deque()



   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     _snake_case   	=      []
     for i in range(len(self.finish_queue				)				):
       sequence.append(self.finish_queue[i].process_name				)
     return sequence



   def    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	list[Process]				):



     """simple docstring"""
     _snake_case   	=      []
     for i in range(len(__lowerCamelCase				)				):
       waiting_times.append(queue[i].waiting_time				)
     return waiting_times



   def    __UpperCAmelCase    (       self    :	Optional[int]							,       __lowerCamelCase    :	list[Process]				):



     """simple docstring"""
     _snake_case   	=      []
     for i in range(len(__lowerCamelCase				)				):
       turnaround_times.append(queue[i].turnaround_time				)
     return turnaround_times



   def    __UpperCAmelCase    (       self    :	Optional[int]							,       __lowerCamelCase    :	list[Process]				):



     """simple docstring"""
     _snake_case   	=      []
     for i in range(len(__lowerCamelCase				)				):
       completion_times.append(queue[i].stop_time				)
     return completion_times



   def    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	deque[Process]				):



     """simple docstring"""
     return [q.burst_time for q in queue]



   def    __UpperCAmelCase    (       self    :	List[Any]							,       __lowerCamelCase    :	Process				):



     """simple docstring"""
     process.waiting_time += self.current_time - process.stop_time
     return process.waiting_time



   def    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	deque[Process]				):



     """simple docstring"""
     _snake_case   	=      deque()  # sequence deque of finished process
     while len(__lowerCamelCase				) != 0:
       _snake_case   	=      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
       _snake_case   	=      0
       # set the process's turnaround time because it is finished
       _snake_case   	=      self.current_time - cp.arrival_time
       # set the completion time
       _snake_case   	=      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    :	Union[str, Any]							,       __lowerCamelCase    :	deque[Process]							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     _snake_case   	=      deque()  # sequence deque of terminated process
     # just for 1 cycle and unfinished processes will go back to queue
     for _ in range(len(__lowerCamelCase				)				):
       _snake_case   	=      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
         _snake_case   	=      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
         _snake_case   	=      0
         # set the finish time
         _snake_case   	=      self.current_time
         # update the process' turnaround time because it is finished
         _snake_case   	=      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    :	List[str]				):



     """simple docstring"""

     #  all queues except last one have round_robin algorithm
     for i in range(self.number_of_queues - 1				):
       _snake_case	,     _snake_case   	=      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

     snake_case   					=				Process('''P1''', 0, 5_3)
     snake_case   					=				Process('''P2''', 0, 1_7)
     snake_case   					=				Process('''P3''', 0, 6_8)
     snake_case   					=				Process('''P4''', 0, 2_4)
     snake_case   					=				3
     snake_case   					=				[1_7, 2_5]
     snake_case   					=				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])})

     snake_case   					=				Process('''P1''', 0, 5_3)
     snake_case   					=				Process('''P2''', 0, 1_7)
     snake_case   					=				Process('''P3''', 0, 6_8)
     snake_case   					=				Process('''P4''', 0, 2_4)
     snake_case   					=				3
     snake_case   					=				[1_7, 2_5]
     snake_case   					=				deque([Pa, Pa, Pa, Pa])
     snake_case   					=				MLFQ(number_of_queues, time_slices, queue, 0)
     snake_case   					=				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 os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features


__snake_case     :Optional[Any]			       =logging.get_logger(__name__)

__snake_case     :int			       =list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
__snake_case     :Optional[int]			       =tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



@dataclass
class        lowerCAmelCase__					:
				A_   :     str								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'Model type selected in the list: ' + ', '.join(_lowerCamelCase       )}       )
				A_   :     str								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'The input data dir. Should contain the .json files for the SQuAD task.'}       )
				A_   :     int								=     field(
				    default=1_2_8 ,				metadata={
				        'help': (
				            'The maximum total input sequence length after tokenization. Sequences longer '
				            'than this will be truncated, sequences shorter will be padded.'
				        )
				    } ,				)
				A_   :     int								=     field(
				    default=1_2_8 ,				metadata={'help': 'When splitting up a long document into chunks, how much stride to take between chunks.'} ,				)
				A_   :     int								=     field(
				    default=6_4 ,				metadata={
				        'help': (
				            'The maximum number of tokens for the question. Questions longer than this will '
				            'be truncated to this length.'
				        )
				    } ,				)
				A_   :     int								=     field(
				    default=3_0 ,				metadata={
				        'help': (
				            'The maximum length of an answer that can be generated. This is needed because the start '
				            'and end predictions are not conditioned on one another.'
				        )
				    } ,				)
				A_   :     bool								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'Overwrite the cached training and evaluation sets'}       )
				A_   :     bool								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'If true, the SQuAD examples contain some that do not have an answer.'}       )
				A_   :     float								=     field(
				    default=0.0 ,				metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'}       )
				A_   :     int								=     field(
				    default=2_0 ,				metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'}       )
				A_   :     int								=     field(
				    default=0 ,				metadata={
				        'help': (
				            'language id of input for language-specific xlm models (see'
				            ' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'
				        )
				    } ,				)
				A_   :     int								=     field(default=1 ,				metadata={'help': 'multiple threads for converting example to features'}       )





class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     str								=     'train'
				A_   :     str								=     'dev'





class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     SquadDataTrainingArguments
				A_   :     List[SquadFeatures]
				A_   :     Split
				A_   :     bool


				def __init__(							self      :  Optional[int]						, __UpperCamelCase      :  SquadDataTrainingArguments						, __UpperCamelCase      :  PreTrainedTokenizer						, __UpperCamelCase      :  Optional[int] = None						, __UpperCamelCase      :  Union[str, Split] = Split.train						, __UpperCamelCase      :  Optional[bool] = False						, __UpperCamelCase      :  Optional[str] = None						, __UpperCamelCase      :  Optional[str] = "pt"						, )		->     Any:
											A      						=    args
											A      						=    is_language_sensitive
											A      						=    SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
											if isinstance(__UpperCamelCase						, __UpperCamelCase       ):
																		try:
																									A      						=    Split[mode]
																		except KeyError:
																									raise KeyError('mode is not a valid split name'       )
											A      						=    mode
											# Load data features from cache or dataset file
											A      						=    'v2' if args.version_2_with_negative else 'v1'
											A      						=    os.path.join(
											    cache_dir if cache_dir is not None else args.data_dir						, f'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}'''						, )

											# Make sure only the first process in distributed training processes the dataset,
											# and the others will use the cache.
											A      						=    cached_features_file + '.lock'
											with FileLock(__UpperCamelCase       ):
																		if os.path.exists(__UpperCamelCase       ) and not args.overwrite_cache:
																									A      						=    time.time()
																									A      						=    torch.load(__UpperCamelCase       )

																									# Legacy cache files have only features, while new cache files
																									# will have dataset and examples also.
																									A      						=    self.old_features['features']
																									A      						=    self.old_features.get('dataset'						, __UpperCamelCase       )
																									A      						=    self.old_features.get('examples'						, __UpperCamelCase       )
																									logger.info(
																									    f'''Loading features from cached file {cached_features_file} [took %.3f s]'''						, time.time() - start       )

																									if self.dataset is None or self.examples is None:
																																logger.warning(
																																    f'''Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'''
																																    ' future run'       )
																		else:
																									if mode == Split.dev:
																																A      						=    self.processor.get_dev_examples(args.data_dir       )
																									else:
																																A      						=    self.processor.get_train_examples(args.data_dir       )

																									A						,   A      						=    squad_convert_examples_to_features(
																									    examples=self.examples						, tokenizer=__UpperCamelCase						, max_seq_length=args.max_seq_length						, doc_stride=args.doc_stride						, max_query_length=args.max_query_length						, is_training=mode == Split.train						, threads=args.threads						, return_dataset=__UpperCamelCase						, )

																									A      						=    time.time()
																									torch.save(
																									    {'features': self.features, 'dataset': self.dataset, 'examples': self.examples}						, __UpperCamelCase						, )
																									# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
																									logger.info(
																									    f'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'''       )


				def __len__(							self      :  Optional[Any]       )		->     Tuple:
											return len(self.features       )


				def __getitem__(							self      :  Tuple						, __UpperCamelCase      :  List[Any]       )		->     Dict[str, torch.Tensor]:
											# Convert to Tensors and build dataset
											A      						=    self.features[i]

											A      						=    torch.tensor(feature.input_ids						, dtype=torch.long       )
											A      						=    torch.tensor(feature.attention_mask						, dtype=torch.long       )
											A      						=    torch.tensor(feature.token_type_ids						, dtype=torch.long       )
											A      						=    torch.tensor(feature.cls_index						, dtype=torch.long       )
											A      						=    torch.tensor(feature.p_mask						, dtype=torch.float       )
											A      						=    torch.tensor(feature.is_impossible						, dtype=torch.float       )

											A      						=    {
											    'input_ids': input_ids,
											    'attention_mask': attention_mask,
											    'token_type_ids': token_type_ids,
											}

											if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
																		del inputs["token_type_ids"]

											if self.args.model_type in ["xlnet", "xlm"]:
																		inputs.update({'cls_index': cls_index, 'p_mask': p_mask}       )
																		if self.args.version_2_with_negative:
																									inputs.update({'is_impossible': is_impossible}       )
																		if self.is_language_sensitive:
																									inputs.update({'langs': (torch.ones(input_ids.shape						, dtype=torch.intaa       ) * self.args.lang_id)}       )

											if self.mode == Split.train:
																		A      						=    torch.tensor(feature.start_position						, dtype=torch.long       )
																		A      						=    torch.tensor(feature.end_position						, dtype=torch.long       )
																		inputs.update({'start_positions': start_positions, 'end_positions': end_positions}       )

											return inputs