



'''simple docstring'''



import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


lowerCAmelCase_     :       str								=						logging.getLogger(__name__)
class 	lowerCamelCase_							(					snake_case_			):
						_lowerCAmelCase     :   Any	     =				'token-classification'







						def __init__(  self   :      Any				,    lowerCAmelCase__   :      Optional[Any]				):

													"""simple docstring"""
													if type(lowerCAmelCase__				) == dict:
																				SCREAMING_SNAKE_CASE					:      Dict     					=			Namespace(**lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      str     					=			import_module('''tasks'''				)
													try:
																				SCREAMING_SNAKE_CASE					:      int     					=			getattr(lowerCAmelCase__				,    hparams.task_type				)
																				SCREAMING_SNAKE_CASE					:      TokenClassificationTask     					=			token_classification_task_clazz()
													except AttributeError:
																				raise ValueError(
																				    F"""Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
																				    F"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""				)
													SCREAMING_SNAKE_CASE					:      int     					=			self.token_classification_task.get_labels(hparams.labels				)
													SCREAMING_SNAKE_CASE					:      int     					=			CrossEntropyLoss().ignore_index
													super().__init__(lowerCAmelCase__				,    len(self.labels				)				,    self.mode				)







						def 					__lowercase    (  self   :      Optional[int]				,    **lowerCAmelCase__   :      List[Any]				):

													"""simple docstring"""
													return self.model(**lowerCAmelCase__				)







						def 					__lowercase    (  self   :      str				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      Tuple				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
													if self.config.model_type != "distilbert":
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			(
																				    batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
																				)  # XLM and RoBERTa don"t use token_type_ids

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self(**lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			outputs[0]
													# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
													return {"loss": loss}







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			self.hparams
													for mode in ["train", "dev", "test"]:
																				SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self._feature_file(lowerCAmelCase__				)
																				if os.path.exists(lowerCAmelCase__				) and not args.overwrite_cache:
																											logger.info('''Loading features from cached file %s'''				,    lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      Tuple     					=			torch.load(lowerCAmelCase__				)
																				else:
																											logger.info('''Creating features from dataset file at %s'''				,    args.data_dir				)
																											SCREAMING_SNAKE_CASE					:      Dict     					=			self.token_classification_task.read_examples_from_file(args.data_dir				,    lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.token_classification_task.convert_examples_to_features(
																											    lowerCAmelCase__				,    self.labels				,    args.max_seq_length				,    self.tokenizer				,    cls_token_at_end=bool(self.config.model_type in ['''xlnet''']				)				,    cls_token=self.tokenizer.cls_token				,    cls_token_segment_id=2 if self.config.model_type in ['''xlnet'''] else 0				,    sep_token=self.tokenizer.sep_token				,    sep_token_extra=lowerCAmelCase__				,    pad_on_left=bool(self.config.model_type in ['''xlnet''']				)				,    pad_token=self.tokenizer.pad_token_id				,    pad_token_segment_id=self.tokenizer.pad_token_type_id				,    pad_token_label_id=self.pad_token_label_id				,    )
																											logger.info('''Saving features into cached file %s'''				,    lowerCAmelCase__				)
																											torch.save(lowerCAmelCase__				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      int				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      bool = False				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			self._feature_file(lowerCAmelCase__				)
													logger.info('''Loading features from cached file %s'''				,    lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			torch.load(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			torch.tensor([f.input_ids for f in features]				,    dtype=torch.long				)
													SCREAMING_SNAKE_CASE					:      int     					=			torch.tensor([f.attention_mask for f in features]				,    dtype=torch.long				)
													if features[0].token_type_ids is not None:
																				SCREAMING_SNAKE_CASE					:      int     					=			torch.tensor([f.token_type_ids for f in features]				,    dtype=torch.long				)
													else:
																				SCREAMING_SNAKE_CASE					:      int     					=			torch.tensor([0 for f in features]				,    dtype=torch.long				)
																				# HACK(we will not use this anymore soon)
													SCREAMING_SNAKE_CASE					:      Dict     					=			torch.tensor([f.label_ids for f in features]				,    dtype=torch.long				)
													return DataLoader(
													    TensorDataset(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				)				,    batch_size=lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Optional[Any]				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      List[Any]				):

													"""simple docstring"""
													"""Compute validation""" ""
													SCREAMING_SNAKE_CASE					:      Any     					=			{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
													if self.config.model_type != "distilbert":
																				SCREAMING_SNAKE_CASE					:      Tuple     					=			(
																				    batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
																				)  # XLM and RoBERTa don"t use token_type_ids
													SCREAMING_SNAKE_CASE					:      List[str]     					=			self(**lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			outputs[:2]
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			logits.detach().cpu().numpy()
													SCREAMING_SNAKE_CASE					:      List[str]     					=			inputs['''labels'''].detach().cpu().numpy()
													return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}







						def 					__lowercase    (  self   :      int				,    lowerCAmelCase__   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			torch.stack([x['''val_loss'''] for x in outputs]				).mean()
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			np.concatenate([x['''pred'''] for x in outputs]				,    axis=0				)
													SCREAMING_SNAKE_CASE					:      str     					=			np.argmax(lowerCAmelCase__				,    axis=2				)
													SCREAMING_SNAKE_CASE					:      Any     					=			np.concatenate([x['''target'''] for x in outputs]				,    axis=0				)

													SCREAMING_SNAKE_CASE					:      Dict     					=			dict(enumerate(self.labels				)				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			[[] for _ in range(out_label_ids.shape[0]				)]
													SCREAMING_SNAKE_CASE					:      Tuple     					=			[[] for _ in range(out_label_ids.shape[0]				)]

													for i in range(out_label_ids.shape[0]				):
																				for j in range(out_label_ids.shape[1]				):
																											if out_label_ids[i, j] != self.pad_token_label_id:
																																		out_label_list[i].append(label_map[out_label_ids[i][j]]				)
																																		preds_list[i].append(label_map[preds[i][j]]				)

													SCREAMING_SNAKE_CASE					:      Dict     					=			{
													    '''val_loss''': val_loss_mean,
													    '''accuracy_score''': accuracy_score(lowerCAmelCase__				,    lowerCAmelCase__				),
													    '''precision''': precision_score(lowerCAmelCase__				,    lowerCAmelCase__				),
													    '''recall''': recall_score(lowerCAmelCase__				,    lowerCAmelCase__				),
													    '''f1''': fa_score(lowerCAmelCase__				,    lowerCAmelCase__				),
													}

													SCREAMING_SNAKE_CASE					:      Tuple     					=			dict(results.items()				)
													SCREAMING_SNAKE_CASE					:      str     					=			results
													return ret, preds_list, out_label_list







						def 					__lowercase    (  self   :      Optional[Any]				,    lowerCAmelCase__   :      Tuple				):

													"""simple docstring"""
													# when stable
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Optional[int]     					=			self._eval_end(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Dict     					=			ret['''log''']
													return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







						def 					__lowercase    (  self   :      Optional[Any]				,    lowerCAmelCase__   :      Optional[Any]				):

													"""simple docstring"""
													# updating to test_epoch_end instead of deprecated test_end
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      str     					=			self._eval_end(lowerCAmelCase__				)

													# Converting to the dict required by pl
													# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
													# pytorch_lightning/trainer/logging.py#L139
													SCREAMING_SNAKE_CASE					:      int     					=			ret['''log''']
													# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
													return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







						@staticmethod
						def 					__lowercase    (  lowerCAmelCase__   :      Dict				,    lowerCAmelCase__   :      List[str]				):

													"""simple docstring"""
													# Add NER specific options
													BaseTransformer.add_model_specific_args(lowerCAmelCase__				,    lowerCAmelCase__				)
													parser.add_argument(
													    '''--task_type'''				,    default='''NER'''				,    type=lowerCAmelCase__				,    help='''Task type to fine tune in training (e.g. NER, POS, etc)'''				)
													parser.add_argument(
													    '''--max_seq_length'''				,    default=1_28				,    type=lowerCAmelCase__				,    help=(
													        '''The maximum total input sequence length after tokenization. Sequences longer '''
													        '''than this will be truncated, sequences shorter will be padded.'''
													    )				,    )

													parser.add_argument(
													    '''--labels'''				,    default=''''''				,    type=lowerCAmelCase__				,    help='''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''				,    )
													parser.add_argument(
													    '''--gpus'''				,    default=0				,    type=lowerCAmelCase__				,    help='''The number of GPUs allocated for this, it is by default 0 meaning none'''				,    )

													parser.add_argument(
													    '''--overwrite_cache'''				,    action='''store_true'''				,    help='''Overwrite the cached training and evaluation sets'''				)

													return parser


if __name__ == "__main__":
							lowerCAmelCase_     :       Optional[int]								=						argparse.ArgumentParser()
							add_generic_args(parser, os.getcwd())
							lowerCAmelCase_     :       Optional[Any]								=						NERTransformer.add_model_specific_args(parser, os.getcwd())
							lowerCAmelCase_     :       Union[str, Any]								=						parser.parse_args()
							lowerCAmelCase_     :       Any								=						NERTransformer(args)
							lowerCAmelCase_     :       Tuple								=						generic_train(model, args)

							if args.do_predict:
														# See https://github.com/huggingface/transformers/issues/3159
														# pl use this default format to create a checkpoint:
														# https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
														# /pytorch_lightning/callbacks/model_checkpoint.py#L322
														lowerCAmelCase_     :       Union[str, Any]								=						sorted(glob.glob(os.path.join(args.output_dir, 'checkpoint-epoch=*.ckpt'), recursive=True))
														lowerCAmelCase_     :       str								=						model.load_from_checkpoint(checkpoints[-1])
														trainer.test(model)









'''simple docstring'''



from collections import deque
class 	lowerCamelCase_							:







						def __init__(  self   :      Tuple				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			process_name  # process name
													SCREAMING_SNAKE_CASE					:      int     					=			arrival_time  # arrival time of the process
													# completion time of finished process or last interrupted time
													SCREAMING_SNAKE_CASE					:      List[str]     					=			arrival_time
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			burst_time  # remaining burst time
													SCREAMING_SNAKE_CASE					:      List[str]     					=			0  # total time of the process wait in ready queue
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			0  # time from arrival time to completion time


class 	lowerCamelCase_							:







						def __init__(  self   :      str				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      list[int]				,    lowerCAmelCase__   :      deque[Process]				,    lowerCAmelCase__   :      int				,    ):

													"""simple docstring"""
													# total number of mlfq's queues
													SCREAMING_SNAKE_CASE					:      str     					=			number_of_queues
													# time slice of queues that round robin algorithm applied
													SCREAMING_SNAKE_CASE					:      int     					=			time_slices
													# unfinished process is in this ready_queue
													SCREAMING_SNAKE_CASE					:      str     					=			queue
													# current time
													SCREAMING_SNAKE_CASE					:      Any     					=			current_time
													# finished process is in this sequence queue
													SCREAMING_SNAKE_CASE					:      deque[Process]     					=			deque()







						def 					__lowercase    (  self   :      Optional[int]				):

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







						def 					__lowercase    (  self   :      int				,    lowerCAmelCase__   :      list[Process]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			[]
													for i in range(len(lowerCAmelCase__				)				):
																				waiting_times.append(queue[i].waiting_time				)
													return waiting_times







						def 					__lowercase    (  self   :      List[Any]				,    lowerCAmelCase__   :      list[Process]				):

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







						def 					__lowercase    (  self   :      Dict				,    lowerCAmelCase__   :      list[Process]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			[]
													for i in range(len(lowerCAmelCase__				)				):
																				completion_times.append(queue[i].stop_time				)
													return completion_times







						def 					__lowercase    (  self   :      Union[str, Any]				,    lowerCAmelCase__   :      deque[Process]				):

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







						def 					__lowercase    (  self   :      Optional[int]				,    lowerCAmelCase__   :      Process				):

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







						def 					__lowercase    (  self   :      Dict				,    lowerCAmelCase__   :      deque[Process]				):

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

													"""simple docstring"""
													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					:      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 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					:      Union[str, Any]     					=			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					:      Optional[Any]     					=			0
																											# set the finish time
																											SCREAMING_SNAKE_CASE					:      Tuple     					=			self.current_time
																											# update the process' turnaround time because it is finished
																											SCREAMING_SNAKE_CASE					:      List[Any]     					=			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 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""

													#  all queues except last one have round_robin algorithm
													for i in range(self.number_of_queues - 1				):
																				SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Optional[int]     					=			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

							lowerCAmelCase_     :       str								=						Process('P1', 0, 53)
							lowerCAmelCase_     :       Optional[int]								=						Process('P2', 0, 17)
							lowerCAmelCase_     :       Optional[Any]								=						Process('P3', 0, 68)
							lowerCAmelCase_     :       Optional[int]								=						Process('P4', 0, 24)
							lowerCAmelCase_     :       List[str]								=						3
							lowerCAmelCase_     :       str								=						[17, 25]
							lowerCAmelCase_     :       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])})

							lowerCAmelCase_     :       Tuple								=						Process('P1', 0, 53)
							lowerCAmelCase_     :       int								=						Process('P2', 0, 17)
							lowerCAmelCase_     :       Union[str, Any]								=						Process('P3', 0, 68)
							lowerCAmelCase_     :       Any								=						Process('P4', 0, 24)
							lowerCAmelCase_     :       int								=						3
							lowerCAmelCase_     :       Optional[Any]								=						[17, 25]
							lowerCAmelCase_     :       Dict								=						deque([Pa, Pa, Pa, Pa])
							lowerCAmelCase_     :       Union[str, Any]								=						MLFQ(number_of_queues, time_slices, queue, 0)
							lowerCAmelCase_     :       List[Any]								=						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()}'
							)




