






from collections import deque
class 						lowerCamelCase_				:







				def __init__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      process_name  # process name
								__magic_name__		:Optional[int]        =      arrival_time  # arrival time of the process
								# completion time of finished process or last interrupted time
								__magic_name__		:Union[str, Any]        =      arrival_time
								__magic_name__		:Dict        =      burst_time  # remaining burst time
								__magic_name__		:Tuple        =      0  # total time of the process wait in ready queue
								__magic_name__		:Dict        =      0  # time from arrival time to completion time







class 						lowerCamelCase_				:







				def __init__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      number_of_queues
								# time slice of queues that round robin algorithm applied
								__magic_name__		:List[Any]        =      time_slices
								# unfinished process is in this ready_queue
								__magic_name__		:Optional[int]        =      queue
								# current time
								__magic_name__		:Dict        =      current_time
								# finished process is in this sequence queue
								__magic_name__		:Optional[int]        =      deque()







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







				def 			A	(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]        =      []
								for i in range(len(lowercase_     )     ):
												waiting_times.append(queue[i].waiting_time     )
								return waiting_times







				def 			A	(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      []
								for i in range(len(lowercase_     )     ):
												turnaround_times.append(queue[i].turnaround_time     )
								return turnaround_times







				def 			A	(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      []
								for i in range(len(lowercase_     )     ):
												completion_times.append(queue[i].stop_time     )
								return completion_times







				def 			A	(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								return [q.burst_time for q in queue]







				def 			A	(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								process.waiting_time += self.current_time - process.stop_time
								return process.waiting_time







				def 			A	(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      deque()  # sequence deque of finished process
								while len(lowercase_     ) != 0:
												__magic_name__		:List[Any]        =      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(lowercase_     )
												# update current time
												self.current_time += cp.burst_time
												# finish the process and set the process's burst-time 0
												__magic_name__		:str        =      0
												# set the process's turnaround time because it is finished
												__magic_name__		:int        =      self.current_time - cp.arrival_time
												# set the completion time
												__magic_name__		:List[str]        =      self.current_time
												# add the process to queue that has finished queue
												finished.append(lowercase_     )

								self.finish_queue.extend(lowercase_     )  # add finished process to finish queue
								# FCFS will finish all remaining processes
								return finished







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      deque()  # sequence deque of terminated process
								# just for 1 cycle and unfinished processes will go back to queue
								for _ in range(len(lowercase_     )     ):
												__magic_name__		: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 unfinished processes
												self.update_waiting_time(lowercase_     )
												# 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
																__magic_name__		:List[Any]        =      self.current_time
																# locate the process behind the queue because it is not finished
																ready_queue.append(lowercase_     )
												else:
																# use CPU for remaining burst time
																self.current_time += cp.burst_time
																# set burst time 0 because the process is finished
																__magic_name__		:str        =      0
																# set the finish time
																__magic_name__		:Dict        =      self.current_time
																# update the process' turnaround time because it is finished
																__magic_name__		:List[str]        =      self.current_time - cp.arrival_time
																# add the process to queue that has finished queue
																finished.append(lowercase_     )

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







				def 			A	(						self     ):
								"""simple docstring"""







								for i in range(self.number_of_queues - 1     ):
												__magic_name__     ,				__magic_name__		:Any        =      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

					SCREAMING_SNAKE_CASE__      :			int							=		Process("""P1""", 0, 53)
					SCREAMING_SNAKE_CASE__      :			List[Any]							=		Process("""P2""", 0, 17)
					SCREAMING_SNAKE_CASE__      :			List[Any]							=		Process("""P3""", 0, 68)
					SCREAMING_SNAKE_CASE__      :			Any							=		Process("""P4""", 0, 24)
					SCREAMING_SNAKE_CASE__      :			Dict							=		3
					SCREAMING_SNAKE_CASE__      :			Tuple							=		[17, 25]
					SCREAMING_SNAKE_CASE__      :			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])})

					SCREAMING_SNAKE_CASE__      :			Any							=		Process("""P1""", 0, 53)
					SCREAMING_SNAKE_CASE__      :			Optional[int]							=		Process("""P2""", 0, 17)
					SCREAMING_SNAKE_CASE__      :			Tuple							=		Process("""P3""", 0, 68)
					SCREAMING_SNAKE_CASE__      :			Dict							=		Process("""P4""", 0, 24)
					SCREAMING_SNAKE_CASE__      :			str							=		3
					SCREAMING_SNAKE_CASE__      :			int							=		[17, 25]
					SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		deque([Pa, Pa, Pa, Pa])
					SCREAMING_SNAKE_CASE__      :			List[Any]							=		MLFQ(number_of_queues, time_slices, queue, 0)
					SCREAMING_SNAKE_CASE__      :			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 logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()

def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case,  snake_case,  snake_case = None,  ):







				"""simple docstring"""






				__magic_name__		:List[Any]        =      {}

				if train_file is not None:
								__magic_name__		:List[Any]        =      [train_file]
				if eval_file is not None:
								__magic_name__		:int        =      [eval_file]
				if test_file is not None:
								__magic_name__		:Any        =      [test_file]

				__magic_name__		:Optional[Any]        =      datasets.load_dataset('''csv''',  data_files=snake_case )
				__magic_name__		:List[Any]        =      list(ds[list(files.keys() )[0]].features.keys() )
				__magic_name__		:Optional[int]        =      features_name.pop(snake_case )
				__magic_name__		:List[str]        =      list(set(ds[list(files.keys() )[0]][label_name] ) )
				__magic_name__		:List[Any]        =      {label: i for i, label in enumerate(snake_case )}
				__magic_name__		:Optional[Any]        =      tokenizer.model_input_names
				__magic_name__		:Any        =      {}

				if len(snake_case ) == 1:
								for k in files.keys():
												__magic_name__		:Union[str, Any]        =      ds[k].map(
												    lambda snake_case : tokenizer.batch_encode_plus(
												        example[features_name[0]],  truncation=snake_case,  max_length=snake_case,  padding='''max_length''' ),  batched=snake_case,  )
				elif len(snake_case ) == 2:
								for k in files.keys():
												__magic_name__		:Optional[Any]        =      ds[k].map(
												    lambda snake_case : tokenizer.batch_encode_plus(
												        (example[features_name[0]], example[features_name[1]]),  truncation=snake_case,  max_length=snake_case,  padding='''max_length''',  ),  batched=snake_case,  )

				def gen_train():
								for ex in transformed_ds[datasets.Split.TRAIN]:
												__magic_name__		:Union[str, Any]        =      {k: v for k, v in ex.items() if k in input_names}
												__magic_name__		:int        =      labelaid[ex[label_name]]
												yield (d, label)

				def gen_val():
								for ex in transformed_ds[datasets.Split.VALIDATION]:
												__magic_name__		:Optional[Any]        =      {k: v for k, v in ex.items() if k in input_names}
												__magic_name__		:Dict        =      labelaid[ex[label_name]]
												yield (d, label)

				def gen_test():
								for ex in transformed_ds[datasets.Split.TEST]:
												__magic_name__		:Any        =      {k: v for k, v in ex.items() if k in input_names}
												__magic_name__		:List[Any]        =      labelaid[ex[label_name]]
												yield (d, label)

				__magic_name__		:Optional[Any]        =      (
				    tf.data.Dataset.from_generator(
				        snake_case,  ({k: tf.intaa for k in input_names}, tf.intaa),  ({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )),  )
				    if datasets.Split.TRAIN in transformed_ds
				    else None
				)

				if train_ds is not None:
								__magic_name__		:Optional[int]        =      train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN] ) ) )

				__magic_name__		:Any        =      (
				    tf.data.Dataset.from_generator(
				        snake_case,  ({k: tf.intaa for k in input_names}, tf.intaa),  ({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )),  )
				    if datasets.Split.VALIDATION in transformed_ds
				    else None
				)

				if val_ds is not None:
								__magic_name__		:str        =      val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION] ) ) )

				__magic_name__		:Optional[int]        =      (
				    tf.data.Dataset.from_generator(
				        snake_case,  ({k: tf.intaa for k in input_names}, tf.intaa),  ({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )),  )
				    if datasets.Split.TEST in transformed_ds
				    else None
				)

				if test_ds is not None:
								__magic_name__		:Dict        =      test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST] ) ) )

				return train_ds, val_ds, test_ds, labelaid


SCREAMING_SNAKE_CASE__      :			List[Any]							=		logging.getLogger(__name__)
@dataclass
class 						lowerCamelCase_				:
				a__					       =  field(metadata={'''help''': '''Which column contains the label'''} )
				a__					       =  field(default=lowerCamelCase			,				metadata={'''help''': '''The path of the training file'''} )
				a__					       =  field(default=lowerCamelCase			,				metadata={'''help''': '''The path of the development file'''} )
				a__					       =  field(default=lowerCamelCase			,				metadata={'''help''': '''The path of the test file'''} )
				a__					       =  field(
				    default=1_28			,				metadata={
				        '''help''': (
				            '''The maximum total input sequence length after tokenization. Sequences longer '''
				            '''than this will be truncated, sequences shorter will be padded.'''
				        )
				    }			,				)
				a__					       =  field(
				    default=lowerCamelCase			,				metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )







@dataclass
class 						lowerCamelCase_				:
				a__					       =  field(
				    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
				a__					       =  field(
				    default=lowerCamelCase			,				metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
				a__					       =  field(
				    default=lowerCamelCase			,				metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
				a__					       =  field(default=lowerCamelCase			,				metadata={'''help''': '''Set this flag to use fast tokenization.'''} )
				# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
				# or just modify its tokenizer_config.json.
				a__					       =  field(
				    default=lowerCamelCase			,				metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}			,				)







def      __lowercase					( ):







				"""simple docstring"""






				__magic_name__		:str        =      HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
				__magic_name__     ,				__magic_name__     ,				__magic_name__		:Any        =      parser.parse_args_into_dataclasses()

				if (
				    os.path.exists(training_args.output_dir )
				    and os.listdir(training_args.output_dir )
				    and training_args.do_train
				    and not training_args.overwrite_output_dir
				):
								raise ValueError(
								    f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
								    ''' --overwrite_output_dir to overcome.''' )

				# Setup logging
				logging.basicConfig(
				    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',  datefmt='''%m/%d/%Y %H:%M:%S''',  level=logging.INFO,  )
				logger.info(
				    f'''n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1 )}, '''
				    f'''16-bits training: {training_args.fpaa}''' )
				logger.info(f'''Training/evaluation parameters {training_args}''' )

				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.

				__magic_name__		:Union[str, Any]        =      AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,  cache_dir=model_args.cache_dir,  )

				__magic_name__     ,				__magic_name__     ,				__magic_name__     ,				__magic_name__		:int        =      get_tfds(
				    train_file=data_args.train_file,  eval_file=data_args.dev_file,  test_file=data_args.test_file,  tokenizer=snake_case,  label_column_id=data_args.label_column_id,  max_seq_length=data_args.max_seq_length,  )

				__magic_name__		:Optional[Any]        =      AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path,  num_labels=len(snake_case ),  labelaid=snake_case,  idalabel={id: label for label, id in labelaid.items()},  finetuning_task='''text-classification''',  cache_dir=model_args.cache_dir,  )

				with training_args.strategy.scope():
								__magic_name__		:Dict        =      TFAutoModelForSequenceClassification.from_pretrained(
								    model_args.model_name_or_path,  from_pt=bool('''.bin''' in model_args.model_name_or_path ),  config=snake_case,  cache_dir=model_args.cache_dir,  )

				def compute_metrics(snake_case ) -> Dict:
								__magic_name__		:str        =      np.argmax(p.predictions,  axis=1 )

								return {"acc": (preds == p.label_ids).mean()}

				# Initialize our Trainer
				__magic_name__		:str        =      TFTrainer(
				    model=snake_case,  args=snake_case,  train_dataset=snake_case,  eval_dataset=snake_case,  compute_metrics=snake_case,  )

				# Training
				if training_args.do_train:
								trainer.train()
								trainer.save_model()
								tokenizer.save_pretrained(training_args.output_dir )

				# Evaluation
				__magic_name__		:Optional[Any]        =      {}
				if training_args.do_eval:
								logger.info('''*** Evaluate ***''' )
								__magic_name__		:int        =      trainer.evaluate()
								__magic_name__		:Dict        =      os.path.join(training_args.output_dir,  '''eval_results.txt''' )

								with open(snake_case,  '''w''' ) as writer:
												logger.info('''***** Eval results *****''' )

												for key, value in result.items():
																logger.info(f'''  {key} = {value}''' )
																writer.write(f'''{key} = {value}\n''' )

												results.update(snake_case )

				return results


if __name__ == "__main__":
					main()






