





import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional

from torch.utils.data import ConcatDataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_WITH_LM_HEAD_MAPPING,
    AutoConfig,
    AutoModelWithLMHead,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    PreTrainedTokenizer,
    TextDataset,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


A    :					int =      logging.getLogger(__name__)


A    :					Optional[Any] =      list(MODEL_WITH_LM_HEAD_MAPPING.keys())
A    :					Dict =      tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


@dataclass
class   A			:
			'''simple docstring'''




			__lowerCamelCase	:       Optional[str]							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={
			        '''help''': (
			            '''The model checkpoint for weights initialization. Leave None if you want to train a model from'''
			            ''' scratch.'''
			        )
			    }   ,     )
			__lowerCamelCase	:       Optional[str]							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(SCREAMING_SNAKE_CASE    )}   ,     )
			__lowerCamelCase	:       Optional[str]							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}    )
			__lowerCamelCase	:       Optional[str]							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}    )
			__lowerCamelCase	:       Optional[str]							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}   ,     )


@dataclass
class   A			:
			'''simple docstring'''




			__lowerCamelCase	:       Optional[str]							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''The input training data file (a text file).'''}    )
			__lowerCamelCase	:       Optional[str]							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={
			        '''help''': (
			            '''The input training data files (multiple files in glob format). '''
			            '''Very often splitting large files to smaller files can prevent tokenizer going out of memory'''
			        )
			    }   ,     )
			__lowerCamelCase	:       Optional[str]							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''}   ,     )
			__lowerCamelCase	:       Optional[str]							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''An optional input train ref data file for whole word mask in Chinese.'''}   ,     )
			__lowerCamelCase	:       Optional[str]							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''An optional input eval ref data file for whole word mask in Chinese.'''}   ,     )
			__lowerCamelCase	:       bool							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Whether distinct lines of text in the dataset are to be handled as distinct sequences.'''}   ,     )

			__lowerCamelCase	:       bool							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Train with masked-language modeling loss instead of language modeling.'''}    )
			__lowerCamelCase	:       bool							=    field(default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Whether ot not to use whole word mask.'''}    )
			__lowerCamelCase	:       float							=    field(
			    default=0.15   ,     metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''}    )
			__lowerCamelCase	:       float							=    field(
			    default=1 / 6   ,     metadata={
			        '''help''': (
			            '''Ratio of length of a span of masked tokens to surrounding context length for permutation language'''
			            ''' modeling.'''
			        )
			    }   ,     )
			__lowerCamelCase	:       int							=    field(
			    default=5   ,     metadata={'''help''': '''Maximum length of a span of masked tokens for permutation language modeling.'''}    )

			__lowerCamelCase	:       int							=    field(
			    default=-1   ,     metadata={
			        '''help''': (
			            '''Optional input sequence length after tokenization.'''
			            '''The training dataset will be truncated in block of this size for training.'''
			            '''Default to the model max input length for single sentence inputs (take into account special tokens).'''
			        )
			    }   ,     )
			__lowerCamelCase	:       bool							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}    )







def    __lowerCamelCase					(							__a   :DataTrainingArguments    ,     __a   :PreTrainedTokenizer    ,     __a   :bool = False    ,     __a   :Optional[str] = None    ,     )		->				int:






		"""simple docstring"""

		def _dataset(__a   :Any    ,     __a   :Tuple=None    ):
				if args.line_by_line:
						if ref_path is not None:
								if not args.whole_word_mask or not args.mlm:
										raise ValueError("""You need to set world whole masking and mlm to True for Chinese Whole Word Mask"""    )
								return LineByLineWithRefDataset(
								    tokenizer=__a    ,     file_path=__a    ,     block_size=args.block_size    ,     ref_path=__a    ,     )

						return LineByLineTextDataset(tokenizer=__a    ,     file_path=__a    ,     block_size=args.block_size    )
				else:
						return TextDataset(
						    tokenizer=__a    ,     file_path=__a    ,     block_size=args.block_size    ,     overwrite_cache=args.overwrite_cache    ,     cache_dir=__a    ,     )

		if evaluate:
				return _dataset(args.eval_data_file    ,     args.eval_ref_file    )
		elif args.train_data_files:
				return ConcatDataset([_dataset(__a    ) for f in glob(args.train_data_files    )]    )
		else:
				return _dataset(args.train_data_file    ,     args.train_ref_file    )







def    __lowerCamelCase					(							)		->				str:






		"""simple docstring"""

		A__        =   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
		A__      ,			A__      ,			A__        =   parser.parse_args_into_dataclasses()

		if data_args.eval_data_file is None and training_args.do_eval:
				raise ValueError(
				    """Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file """
				    """or remove the --do_eval argument."""    )
		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 if training_args.local_rank in [-1, 0] else logging.WARN    ,     )
		logger.warning(
		    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""    ,     training_args.local_rank    ,     training_args.device    ,     training_args.n_gpu    ,     bool(training_args.local_rank != -1    )    ,     training_args.fpaa    ,     )
		# Set the verbosity to info of the Transformers logger (on main process only):
		if is_main_process(training_args.local_rank    ):
				transformers.utils.logging.set_verbosity_info()
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()
		logger.info("""Training/evaluation parameters %s"""    ,     __a    )

		# Set seed
		set_seed(training_args.seed    )

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

		if model_args.config_name:
				A__        =   AutoConfig.from_pretrained(model_args.config_name    ,     cache_dir=model_args.cache_dir    )
		elif model_args.model_name_or_path:
				A__        =   AutoConfig.from_pretrained(model_args.model_name_or_path    ,     cache_dir=model_args.cache_dir    )
		else:
				A__        =   CONFIG_MAPPING[model_args.model_type]()
				logger.warning("""You are instantiating a new config instance from scratch."""    )

		if model_args.tokenizer_name:
				A__        =   AutoTokenizer.from_pretrained(model_args.tokenizer_name    ,     cache_dir=model_args.cache_dir    )
		elif model_args.model_name_or_path:
				A__        =   AutoTokenizer.from_pretrained(model_args.model_name_or_path    ,     cache_dir=model_args.cache_dir    )
		else:
				raise ValueError(
				    """You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another"""
				    """ script, save it,and load it from here, using --tokenizer_name"""    )

		if model_args.model_name_or_path:
				A__        =   AutoModelWithLMHead.from_pretrained(
				    model_args.model_name_or_path    ,     from_tf=bool(""".ckpt""" in model_args.model_name_or_path    )    ,     config=__a    ,     cache_dir=model_args.cache_dir    ,     )
		else:
				logger.info("""Training new model from scratch"""    )
				A__        =   AutoModelWithLMHead.from_config(__a    )

		model.resize_token_embeddings(len(__a    )    )

		if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
				raise ValueError(
				    """BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the"""
				    """--mlm flag (masked language modeling)."""    )

		if data_args.block_size <= 0:
				A__        =   tokenizer.max_len
				# Our input block size will be the max possible for the model
		else:
				A__        =   min(data_args.block_size    ,     tokenizer.max_len    )

		# Get datasets

		A__        =   (
		    get_dataset(__a    ,     tokenizer=__a    ,     cache_dir=model_args.cache_dir    ) if training_args.do_train else None
		)
		A__        =   (
		    get_dataset(__a    ,     tokenizer=__a    ,     evaluate=__a    ,     cache_dir=model_args.cache_dir    )
		    if training_args.do_eval
		    else None
		)
		if config.model_type == "xlnet":
				A__        =   DataCollatorForPermutationLanguageModeling(
				    tokenizer=__a    ,     plm_probability=data_args.plm_probability    ,     max_span_length=data_args.max_span_length    ,     )
		else:
				if data_args.mlm and data_args.whole_word_mask:
						A__        =   DataCollatorForWholeWordMask(
						    tokenizer=__a    ,     mlm_probability=data_args.mlm_probability    )
				else:
						A__        =   DataCollatorForLanguageModeling(
						    tokenizer=__a    ,     mlm=data_args.mlm    ,     mlm_probability=data_args.mlm_probability    )

    # Initialize our Trainer
		A__        =   Trainer(
		    model=__a    ,     args=__a    ,     data_collator=__a    ,     train_dataset=__a    ,     eval_dataset=__a    ,     prediction_loss_only=__a    ,     )

		# Training
		if training_args.do_train:
				A__        =   (
				    model_args.model_name_or_path
				    if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path    )
				    else None
				)
				trainer.train(model_path=__a    )
				trainer.save_model()
				# For convenience, we also re-save the tokenizer to the same directory,
				# so that you can share your model easily on huggingface.co/models =)
				if trainer.is_world_master():
						tokenizer.save_pretrained(training_args.output_dir    )

    # Evaluation
		A__        =   {}
		if training_args.do_eval:
				logger.info("""*** Evaluate ***"""    )

				A__        =   trainer.evaluate()

				A__        =   math.exp(eval_output["""eval_loss"""]    )
				A__        =   {"""perplexity""": perplexity}

				A__        =   os.path.join(training_args.output_dir    ,     """eval_results_lm.txt"""    )
				if trainer.is_world_master():
						with open(__a    ,     """w"""    ) as writer:
								logger.info("""***** Eval results *****"""    )
								for key in sorted(result.keys()    ):
										logger.info("""  %s = %s"""    ,     __a    ,     str(result[key]    )    )
										writer.write("""%s = %s\n""" % (key, str(result[key]    ))    )

				results.update(__a    )

		return results







def    __lowerCamelCase					(							__a   :Optional[Any]    )		->				Dict:






		"""simple docstring"""

		main()


if __name__ == "__main__":
					main()










from collections import deque


class   A			:
			'''simple docstring'''




			def __init__(			self   :    List[str]							,	__lowerCAmelCase   :    str							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    int   )	->	None:







					"""simple docstring"""






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


class   A			:
			'''simple docstring'''




			def __init__(			self   :    Tuple							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    list[int]							,	__lowerCAmelCase   :    deque[Process]							,	__lowerCAmelCase   :    int							,	)	->	None:







					"""simple docstring"""






					A__        =   number_of_queues
					# time slice of queues that round robin algorithm applied
					A__        =   time_slices
					# unfinished process is in this ready_queue
					A__        =   queue
					# current time
					A__        =   current_time
					# finished process is in this sequence queue
					A__        =   deque()






			def    a_       (			self   :    Dict   )	->	list[str]:







					"""simple docstring"""






					A__        =   []
					for i in range(len(self.finish_queue   )   ):
							sequence.append(self.finish_queue[i].process_name   )
					return sequence






			def    a_       (			self   :    Tuple							,	__lowerCAmelCase   :    list[Process]   )	->	list[int]:







					"""simple docstring"""






					A__        =   []
					for i in range(len(__lowerCAmelCase   )   ):
							waiting_times.append(queue[i].waiting_time   )
					return waiting_times






			def    a_       (			self   :    Optional[Any]							,	__lowerCAmelCase   :    list[Process]   )	->	list[int]:







					"""simple docstring"""






					A__        =   []
					for i in range(len(__lowerCAmelCase   )   ):
							turnaround_times.append(queue[i].turnaround_time   )
					return turnaround_times






			def    a_       (			self   :    Dict							,	__lowerCAmelCase   :    list[Process]   )	->	list[int]:







					"""simple docstring"""






					A__        =   []
					for i in range(len(__lowerCAmelCase   )   ):
							completion_times.append(queue[i].stop_time   )
					return completion_times






			def    a_       (			self   :    int							,	__lowerCAmelCase   :    deque[Process]   )	->	list[int]:







					"""simple docstring"""






					return [q.burst_time for q in queue]






			def    a_       (			self   :    Any							,	__lowerCAmelCase   :    Process   )	->	int:







					"""simple docstring"""






					process.waiting_time += self.current_time - process.stop_time
					return process.waiting_time






			def    a_       (			self   :    Union[str, Any]							,	__lowerCAmelCase   :    deque[Process]   )	->	deque[Process]:







					"""simple docstring"""






					A__        =   deque()  # sequence deque of finished process
					while len(__lowerCAmelCase   ) != 0:
							A__        =   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
							A__        =   0
							# set the process's turnaround time because it is finished
							A__        =   self.current_time - cp.arrival_time
							# set the completion time
							A__        =   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    a_       (			self   :    Optional[Any]							,	__lowerCAmelCase   :    deque[Process]							,	__lowerCAmelCase   :    int   )	->	tuple[deque[Process], deque[Process]]:







					"""simple docstring"""






					A__        =   deque()  # sequence deque of terminated process
					# just for 1 cycle and unfinished processes will go back to queue
					for _ in range(len(__lowerCAmelCase   )   ):
							A__        =   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
									A__        =   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
									A__        =   0
									# set the finish time
									A__        =   self.current_time
									# update the process' turnaround time because it is finished
									A__        =   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    a_       (			self   :    List[Any]   )	->	deque[Process]:







					"""simple docstring"""






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

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

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



