



'''simple docstring'''

import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


UpperCAmelCase     		=   logging.getLogger(__name__)

class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						UpperCAmelCase							:   Dict									=		"sequence-classification"


						def __init__( self     ,	A_				)     ->     List[str]:

							if type(_lowerCAmelCase				) == dict:
								lowerCAmelCase   =     Namespace(**_lowerCAmelCase				)
							lowerCAmelCase   =     glue_output_modes[hparams.task]
							lowerCAmelCase   =     glue_tasks_num_labels[hparams.task]

							super().__init__(_lowerCAmelCase     ,	_lowerCAmelCase     ,	self.mode				)


						def     __snake_case   ( self     ,	**A_				)     ->     Optional[int]:
							return self.model(**_lowerCAmelCase				)


						def     __snake_case   ( self     ,	A_     ,	A_				)     ->     Dict:
							lowerCAmelCase   =     {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}

							if self.config.model_type not in ["distilbert", "bart"]:
								lowerCAmelCase   =     batch[2] if self.config.model_type in ["""bert""", """xlnet""", """albert"""] else None

							lowerCAmelCase   =     self(**_lowerCAmelCase				)
							lowerCAmelCase   =     outputs[0]

							lowerCAmelCase   =     self.trainer.lr_schedulers[0]["""scheduler"""]
							lowerCAmelCase   =     {"""loss""": loss, """rate""": lr_scheduler.get_last_lr()[-1]}
							return {"loss": loss, "log": tensorboard_logs}


						def     __snake_case   ( self				)     ->     Tuple:
							lowerCAmelCase   =     self.hparams
							lowerCAmelCase   =     processors[args.task]()
							lowerCAmelCase   =     processor.get_labels()

							for mode in ["train", "dev"]:
								lowerCAmelCase   =     self._feature_file(_lowerCAmelCase				)
								if os.path.exists(_lowerCAmelCase				) and not args.overwrite_cache:
									logger.info("""Loading features from cached file %s"""     ,	_lowerCAmelCase				)
								else:
									logger.info("""Creating features from dataset file at %s"""     ,	args.data_dir				)
									lowerCAmelCase   =     (
									    processor.get_dev_examples(args.data_dir				)
									    if mode == """dev"""
									    else processor.get_train_examples(args.data_dir				)
									)
									lowerCAmelCase   =     convert_examples_to_features(
									    _lowerCAmelCase     ,	self.tokenizer     ,	max_length=args.max_seq_length     ,	label_list=self.labels     ,	output_mode=args.glue_output_mode     ,	)
									logger.info("""Saving features into cached file %s"""     ,	_lowerCAmelCase				)
									torch.save(_lowerCAmelCase     ,	_lowerCAmelCase				)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_ = False				)     ->     Any:
							lowerCAmelCase   =     """dev""" if mode == """test""" else mode

							lowerCAmelCase   =     self._feature_file(_lowerCAmelCase				)
							logger.info("""Loading features from cached file %s"""     ,	_lowerCAmelCase				)
							lowerCAmelCase   =     torch.load(_lowerCAmelCase				)
							lowerCAmelCase   =     torch.tensor([f.input_ids for f in features]     ,	dtype=torch.long				)
							lowerCAmelCase   =     torch.tensor([f.attention_mask for f in features]     ,	dtype=torch.long				)
							lowerCAmelCase   =     torch.tensor([f.token_type_ids for f in features]     ,	dtype=torch.long				)
							if self.hparams.glue_output_mode == "classification":
								lowerCAmelCase   =     torch.tensor([f.label for f in features]     ,	dtype=torch.long				)
							elif self.hparams.glue_output_mode == "regression":
								lowerCAmelCase   =     torch.tensor([f.label for f in features]     ,	dtype=torch.float				)

							return DataLoader(
							    TensorDataset(_lowerCAmelCase     ,	_lowerCAmelCase     ,	_lowerCAmelCase     ,	_lowerCAmelCase				)     ,	batch_size=_lowerCAmelCase     ,	shuffle=_lowerCAmelCase     ,	)


						def     __snake_case   ( self     ,	A_     ,	A_				)     ->     Optional[int]:
							lowerCAmelCase   =     {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}

							if self.config.model_type not in ["distilbert", "bart"]:
								lowerCAmelCase   =     batch[2] if self.config.model_type in ["""bert""", """xlnet""", """albert"""] else None

							lowerCAmelCase   =     self(**_lowerCAmelCase				)
							lowerCAmelCase,       lowerCAmelCase   =     outputs[:2]
							lowerCAmelCase   =     logits.detach().cpu().numpy()
							lowerCAmelCase   =     inputs["""labels"""].detach().cpu().numpy()

							return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}


						def     __snake_case   ( self     ,	A_				)     ->     int:
							lowerCAmelCase   =     torch.stack([x["""val_loss"""] for x in outputs]				).mean().detach().cpu().item()
							lowerCAmelCase   =     np.concatenate([x["""pred"""] for x in outputs]     ,	axis=0				)

							if self.hparams.glue_output_mode == "classification":
								lowerCAmelCase   =     np.argmax(_lowerCAmelCase     ,	axis=1				)
							elif self.hparams.glue_output_mode == "regression":
								lowerCAmelCase   =     np.squeeze(_lowerCAmelCase				)

							lowerCAmelCase   =     np.concatenate([x["""target"""] for x in outputs]     ,	axis=0				)
							lowerCAmelCase   =     [[] for _ in range(out_label_ids.shape[0]				)]
							lowerCAmelCase   =     [[] for _ in range(out_label_ids.shape[0]				)]

							lowerCAmelCase   =     {**{"""val_loss""": val_loss_mean}, **compute_metrics(self.hparams.task     ,	_lowerCAmelCase     ,	_lowerCAmelCase				)}

							lowerCAmelCase   =     dict(results.items()				)
							lowerCAmelCase   =     results
							return ret, preds_list, out_label_list


						def     __snake_case   ( self     ,	A_				)     ->     str:
							lowerCAmelCase,       lowerCAmelCase,       lowerCAmelCase   =     self._eval_end(_lowerCAmelCase				)
							lowerCAmelCase   =     ret["""log"""]
							return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}


						def     __snake_case   ( self     ,	A_				)     ->     List[str]:
							lowerCAmelCase,       lowerCAmelCase,       lowerCAmelCase   =     self._eval_end(_lowerCAmelCase				)
							lowerCAmelCase   =     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     __snake_case   ( A_     ,	A_				)     ->     Optional[Any]:
							BaseTransformer.add_model_specific_args(_lowerCAmelCase     ,	_lowerCAmelCase				)
							parser.add_argument(
							    """--max_seq_length"""     ,	default=128     ,	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(
							    """--task"""     ,	default=""""""     ,	type=_lowerCAmelCase     ,	required=_lowerCAmelCase     ,	help="""The GLUE task to run"""     ,	)
							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







def 		_snake_case		( )      ->						int:
	lowerCAmelCase   =     argparse.ArgumentParser()
	add_generic_args(_lowerCAmelCase							,		os.getcwd()							)
	lowerCAmelCase   =     GLUETransformer.add_model_specific_args(_lowerCAmelCase							,		os.getcwd()							)
	lowerCAmelCase   =     parser.parse_args()

	# If output_dir not provided, a folder will be generated in pwd
	if args.output_dir is None:
		lowerCAmelCase   =     os.path.join(
		    """./results"""							,		f'{args.task}_{time.strftime("%Y%m%d_%H%M%S"							)}'							,		)
		os.makedirs(args.output_dir							)

	lowerCAmelCase   =     GLUETransformer(_lowerCAmelCase							)
	lowerCAmelCase   =     generic_train(_lowerCAmelCase							,		_lowerCAmelCase							)

	# Optionally, predict on dev set and write to output_dir
	if args.do_predict:
		lowerCAmelCase   =     sorted(glob.glob(os.path.join(args.output_dir							,		"""checkpoint-epoch=*.ckpt"""							)							,		recursive=_lowerCAmelCase							)							)
		lowerCAmelCase   =     model.load_from_checkpoint(checkpoints[-1]							)
		return trainer.test(_lowerCAmelCase							)


if __name__ == "__main__":
				main()




'''simple docstring'''

from __future__ import annotations

import pandas as pd






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list[int]							,		_SCREAMING_SNAKE_CASE					:						list[int]							,		_SCREAMING_SNAKE_CASE					:						int							)      ->						list[int]:







	"""simple docstring"""
	lowerCAmelCase   =     [0] * no_of_processes
	lowerCAmelCase   =     [0] * no_of_processes
	# Copy the burst time into remaining_time[]
	for i in range(_SCREAMING_SNAKE_CASE							):
		lowerCAmelCase   =     burst_time[i]

	lowerCAmelCase   =     0
	lowerCAmelCase   =     0
	lowerCAmelCase   =     999_999_999
	lowerCAmelCase   =     0
	lowerCAmelCase   =     False

	# Process until all processes are completed
	while complete != no_of_processes:
		for j in range(_SCREAMING_SNAKE_CASE							):
			if arrival_time[j] <= increment_time and remaining_time[j] > 0:
				if remaining_time[j] < minm:
					lowerCAmelCase   =     remaining_time[j]
					lowerCAmelCase   =     j
					lowerCAmelCase   =     True

		if not check:
			increment_time += 1
			continue
		remaining_time[short] -= 1

		lowerCAmelCase   =     remaining_time[short]
		if minm == 0:
			lowerCAmelCase   =     999_999_999

		if remaining_time[short] == 0:
			complete += 1
			lowerCAmelCase   =     False

			# Find finish time of current process
			lowerCAmelCase   =     increment_time + 1

			# Calculate waiting time
			lowerCAmelCase   =     finish_time - arrival_time[short]
			lowerCAmelCase   =     finar - burst_time[short]

			if waiting_time[short] < 0:
				lowerCAmelCase   =     0

        # Increment time
		increment_time += 1
	return waiting_time






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list[int]							,		_SCREAMING_SNAKE_CASE					:						int							,		_SCREAMING_SNAKE_CASE					:						list[int]							)      ->						list[int]:







	"""simple docstring"""
	lowerCAmelCase   =     [0] * no_of_processes
	for i in range(_SCREAMING_SNAKE_CASE							):
		lowerCAmelCase   =     burst_time[i] + waiting_time[i]
	return turn_around_time






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list[int]							,		_SCREAMING_SNAKE_CASE					:						list[int]							,		_SCREAMING_SNAKE_CASE					:						int							)      ->						None:







	"""simple docstring"""
	lowerCAmelCase   =     0
	lowerCAmelCase   =     0
	for i in range(_SCREAMING_SNAKE_CASE							):
		lowerCAmelCase   =     total_waiting_time + waiting_time[i]
		lowerCAmelCase   =     total_turn_around_time + turn_around_time[i]
	print(f'Average waiting time = {total_waiting_time / no_of_processes:.5f}'							)
	print("""Average turn around time ="""							,		total_turn_around_time / no_of_processes							)


if __name__ == "__main__":
				print('Enter how many process you want to analyze')
				UpperCAmelCase     		=   int(input())
				UpperCAmelCase     		=   [0] * no_of_processes
				UpperCAmelCase     		=   [0] * no_of_processes
				UpperCAmelCase     		=   list(range(1, no_of_processes + 1))

				for i in range(no_of_processes):
								print('Enter the arrival time and burst time for process:--' + str(i + 1))
								UpperCAmelCase		,       UpperCAmelCase     		=   map(int, input().split())

				UpperCAmelCase     		=   calculate_waitingtime(arrival_time, burst_time, no_of_processes)

				UpperCAmelCase     		=   burst_time
				UpperCAmelCase     		=   no_of_processes
				UpperCAmelCase     		=   waiting_time
				UpperCAmelCase     		=   calculate_turnaroundtime(bt, n, wt)

				calculate_average_times(waiting_time, turn_around_time, no_of_processes)

				UpperCAmelCase     		=   pd.DataFrame(
				    list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
				    columns=[
				        'Process',
				        'BurstTime',
				        'ArrivalTime',
				        'WaitingTime',
				        'TurnAroundTime',
				    ],
				)

				# Printing the dataFrame
				pd.set_option('display.max_rows', fcfs.shape[0] + 1)
				print(fcfs)