






'''simple docstring'''




from collections import deque

class    _UpperCAmelCase			:




   """simple docstring"""

   def __init__(		self :	List[Any]      ,      __UpperCAmelCase :	str      ,      __UpperCAmelCase :	int      ,      __UpperCAmelCase :	int		):





         '''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    _UpperCAmelCase			:




   """simple docstring"""

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





         '''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 						lowerCAmelCase (		self :	Any		):





         '''simple docstring'''





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

   def 						lowerCAmelCase (		self :	Any      ,      __UpperCAmelCase :	list[Process]		):





         '''simple docstring'''





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

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





         '''simple docstring'''





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

   def 						lowerCAmelCase (		self :	Dict      ,      __UpperCAmelCase :	list[Process]		):





         '''simple docstring'''





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

   def 						lowerCAmelCase (		self :	Any      ,      __UpperCAmelCase :	deque[Process]		):





         '''simple docstring'''





         return [q.burst_time for q in queue]

   def 						lowerCAmelCase (		self :	Optional[Any]      ,      __UpperCAmelCase :	Process		):





         '''simple docstring'''





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

   def 						lowerCAmelCase (		self :	Tuple      ,      __UpperCAmelCase :	deque[Process]		):





         '''simple docstring'''





         _A			     =	deque()  # sequence deque of finished process
         while len(__UpperCAmelCase		) != 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(__UpperCAmelCase		)
               # 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(__UpperCAmelCase		)

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

   def 						lowerCAmelCase (		self :	Tuple      ,      __UpperCAmelCase :	deque[Process]      ,      __UpperCAmelCase :	int		):





         '''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(__UpperCAmelCase		)		):
               _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(__UpperCAmelCase		)
               # 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(__UpperCAmelCase		)
               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(__UpperCAmelCase		)

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

   def 						lowerCAmelCase (		self :	str		):





         '''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

    lowerCamelCase_												=					Process('''P1''', 0, 53)
    lowerCamelCase_												=					Process('''P2''', 0, 17)
    lowerCamelCase_												=					Process('''P3''', 0, 68)
    lowerCamelCase_												=					Process('''P4''', 0, 24)
    lowerCamelCase_												=					3
    lowerCamelCase_												=					[17, 25]
    lowerCamelCase_												=					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_												=					Process('''P1''', 0, 53)
    lowerCamelCase_												=					Process('''P2''', 0, 17)
    lowerCamelCase_												=					Process('''P3''', 0, 68)
    lowerCamelCase_												=					Process('''P4''', 0, 24)
    lowerCamelCase_												=					3
    lowerCamelCase_												=					[17, 25]
    lowerCamelCase_												=					deque([Pa, Pa, Pa, Pa])
    lowerCamelCase_												=					MLFQ(number_of_queues, time_slices, queue, 0)
    lowerCamelCase_												=					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()}"""
    )













"""simple docstring"""



import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable

import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm

from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule

class        a     (   a_	):
    UpperCAmelCase_ :     BigBirdConfig
    UpperCAmelCase_ :     jnp.dtype	     =jnp.floataa
    UpperCAmelCase_ :     bool	     =True







    def 			UpperCamelCase_      (			self       ):
      super().setup()
      lowercase							    =       nn.Dense(5							,		dtype=self.dtype       )







    def __call__(			self							,		*_lowerCamelCase							,		**_lowerCamelCase       ):
      lowercase							    =       super().__call__(*_lowerCamelCase							,		**_lowerCamelCase       )
      lowercase							    =       self.cls(outputs[2]       )
      return outputs[:2] + (cls_out,)


class        a     (   a_	):
    UpperCAmelCase_ :     str	     =FlaxBigBirdForNaturalQuestionsModule


def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Optional[int]		,					__snake_case			:  Dict		,					__snake_case			:  Optional[Any]		,					__snake_case			:  Optional[int]		,					__snake_case			:  Tuple		,					__snake_case			:  Tuple    ):





  '''simple docstring'''





  def cross_entropy(__snake_case			:  Dict		,					__snake_case			:  str		,					__snake_case			:  Any=None    ):
    lowercase							    =       logits.shape[-1]
    lowercase							    =       (labels[..., None] == jnp.arange(__snake_case    )[None]).astype('f4'    )
    lowercase							    =       jax.nn.log_softmax(__snake_case		,					axis=-1    )
    lowercase							    =       -jnp.sum(labels * logits		,					axis=-1    )
    if reduction is not None:
      lowercase							    =       reduction(__snake_case    )
    return loss

  lowercase							    =       partial(__snake_case		,					reduction=jnp.mean    )
  lowercase							    =       cross_entropy(__snake_case		,					__snake_case    )
  lowercase							    =       cross_entropy(__snake_case		,					__snake_case    )
  lowercase							    =       cross_entropy(__snake_case		,					__snake_case    )
  return (start_loss + end_loss + pooled_loss) / 3

@dataclass
class        a     :
    UpperCAmelCase_ :     str	     ="google/bigbird-roberta-base"
    UpperCAmelCase_ :     int	     =3000
    UpperCAmelCase_ :     int	     =1_0500

    UpperCAmelCase_ :     int	     =128
    UpperCAmelCase_ :     int	     =3

    UpperCAmelCase_ :     int	     =1
    UpperCAmelCase_ :     int	     =5

    # tx_args
    UpperCAmelCase_ :     float	     =3e-5
    UpperCAmelCase_ :     float	     =0.0
    UpperCAmelCase_ :     int	     =2_0000
    UpperCAmelCase_ :     float	     =0.00_95

    UpperCAmelCase_ :     str	     ="bigbird-roberta-natural-questions"
    UpperCAmelCase_ :     str	     ="training-expt"
    UpperCAmelCase_ :     str	     ="data/nq-training.jsonl"
    UpperCAmelCase_ :     str	     ="data/nq-validation.jsonl"







    def 			UpperCamelCase_      (			self       ):
      os.makedirs(self.base_dir							,		exist_ok=_lowerCamelCase       )
      lowercase							    =       os.path.join(self.base_dir							,		self.save_dir       )
      lowercase							    =       self.batch_size_per_device * jax.device_count()


@dataclass
class        a     :
    UpperCAmelCase_ :     int
    UpperCAmelCase_ :     int	     =4096  # no dynamic padding on TPUs







    def __call__(			self							,		_lowerCamelCase       ):
      lowercase							    =       self.collate_fn(_lowerCamelCase       )
      lowercase							    =       jax.tree_util.tree_map(_lowerCamelCase							,		_lowerCamelCase       )
      return batch







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase    ,     lowercase							    =       self.fetch_inputs(features['input_ids']       )
      lowercase							    =       {
          'input_ids': jnp.array(_lowerCamelCase							,		dtype=jnp.intaa       ),
          'attention_mask': jnp.array(_lowerCamelCase							,		dtype=jnp.intaa       ),
          'start_labels': jnp.array(features['start_token']							,		dtype=jnp.intaa       ),
          'end_labels': jnp.array(features['end_token']							,		dtype=jnp.intaa       ),
          'pooled_labels': jnp.array(features['category']							,		dtype=jnp.intaa       ),
      }
      return batch







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase							    =       [self._fetch_inputs(_lowerCamelCase       ) for ids in input_ids]
      return zip(*_lowerCamelCase       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase							    =       [1 for _ in range(len(_lowerCamelCase       )       )]
      while len(_lowerCamelCase       ) < self.max_length:
        input_ids.append(self.pad_id       )
        attention_mask.append(0       )
      return input_ids, attention_mask


def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Any		,					__snake_case			:  Tuple		,					__snake_case			:  Optional[Any]=None    ):





  '''simple docstring'''





  if seed is not None:
    lowercase							    =       dataset.shuffle(seed=__snake_case    )
  for i in range(len(__snake_case    ) // batch_size    ):
    lowercase							    =       dataset[i * batch_size : (i + 1) * batch_size]
    yield dict(__snake_case    )
@partial(jax.pmap		,					axis_name='batch'    )
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Dict		,					__snake_case			:  List[Any]		,					**__snake_case			:  List[Any]    ):





  '''simple docstring'''





  def loss_fn(__snake_case			:  str    ):
    lowercase							    =       model_inputs.pop('start_labels'    )
    lowercase							    =       model_inputs.pop('end_labels'    )
    lowercase							    =       model_inputs.pop('pooled_labels'    )

    lowercase							    =       state.apply_fn(**__snake_case		,					params=__snake_case		,					dropout_rng=__snake_case		,					train=__snake_case    )
    lowercase    ,     lowercase    ,     lowercase							    =       outputs

    return state.loss_fn(
        __snake_case		,					__snake_case		,					__snake_case		,					__snake_case		,					__snake_case		,					__snake_case		,					)

  lowercase    ,     lowercase							    =       jax.random.split(__snake_case    )
  lowercase							    =       jax.value_and_grad(__snake_case    )
  lowercase    ,     lowercase							    =       grad_fn(state.params    )
  lowercase							    =       jax.lax.pmean({'loss': loss}		,					axis_name='batch'    )
  lowercase							    =       jax.lax.pmean(__snake_case		,					'batch'    )

  lowercase							    =       state.apply_gradients(grads=__snake_case    )
  return state, metrics, new_drp_rng
@partial(jax.pmap		,					axis_name='batch'    )
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Optional[int]		,					**__snake_case			:  Dict    ):





  '''simple docstring'''





  lowercase							    =       model_inputs.pop('start_labels'    )
  lowercase							    =       model_inputs.pop('end_labels'    )
  lowercase							    =       model_inputs.pop('pooled_labels'    )

  lowercase							    =       state.apply_fn(**__snake_case		,					params=state.params		,					train=__snake_case    )
  lowercase    ,     lowercase    ,     lowercase							    =       outputs

  lowercase							    =       state.loss_fn(__snake_case		,					__snake_case		,					__snake_case		,					__snake_case		,					__snake_case		,					__snake_case    )
  lowercase							    =       jax.lax.pmean({'loss': loss}		,					axis_name='batch'    )
  return metrics

class        a     (   train_state.TrainState	):
    UpperCAmelCase_ :     Callable	     =struct.field(pytree_node=a_	)


@dataclass
class        a     :
    UpperCAmelCase_ :     Args
    UpperCAmelCase_ :     Callable
    UpperCAmelCase_ :     Callable
    UpperCAmelCase_ :     Callable
    UpperCAmelCase_ :     Callable
    UpperCAmelCase_ :     wandb
    UpperCAmelCase_ :     Callable	     =None







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase=None       ):
      lowercase							    =       model.params
      lowercase							    =       TrainState.create(
          apply_fn=model.__call__							,		params=_lowerCamelCase							,		tx=_lowerCamelCase							,		loss_fn=_lowerCamelCase							,		)
      if ckpt_dir is not None:
        lowercase    ,     lowercase    ,     lowercase    ,     lowercase    ,     lowercase							    =       restore_checkpoint(_lowerCamelCase							,		_lowerCamelCase       )
        lowercase							    =       {
            'lr': args.lr,
            'init_lr': args.init_lr,
            'warmup_steps': args.warmup_steps,
            'num_train_steps': num_train_steps,
            'weight_decay': args.weight_decay,
        }
        lowercase    ,     lowercase							    =       build_tx(**_lowerCamelCase       )
        lowercase							    =       train_state.TrainState(
            step=_lowerCamelCase							,		apply_fn=model.__call__							,		params=_lowerCamelCase							,		tx=_lowerCamelCase							,		opt_state=_lowerCamelCase							,		)
        lowercase							    =       args
        lowercase							    =       data_collator
        lowercase							    =       lr
        lowercase							    =       params
      lowercase							    =       jax_utils.replicate(_lowerCamelCase       )
      return state







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase       ):
      lowercase							    =       self.args
      lowercase							    =       len(_lowerCamelCase       ) // args.batch_size

      lowercase							    =       jax.random.PRNGKey(0       )
      lowercase							    =       jax.random.split(_lowerCamelCase							,		jax.device_count()       )
      for epoch in range(args.max_epochs       ):
        lowercase							    =       jnp.array(0							,		dtype=jnp.floataa       )
        lowercase							    =       get_batched_dataset(_lowerCamelCase							,		args.batch_size							,		seed=_lowerCamelCase       )
        lowercase							    =       0
        for batch in tqdm(_lowerCamelCase							,		total=_lowerCamelCase							,		desc=F'Running EPOCH-{epoch}'       ):
          lowercase							    =       self.data_collator(_lowerCamelCase       )
          lowercase    ,     lowercase    ,     lowercase							    =       self.train_step_fn(_lowerCamelCase							,		_lowerCamelCase							,		**_lowerCamelCase       )
          running_loss += jax_utils.unreplicate(metrics['loss']       )
          i += 1
          if i % args.logging_steps == 0:
            lowercase							    =       jax_utils.unreplicate(state.step       )
            lowercase							    =       running_loss.item() / i
            lowercase							    =       self.scheduler_fn(state_step - 1       )

            lowercase							    =       self.evaluate(_lowerCamelCase							,		_lowerCamelCase       )
            lowercase							    =       {
                'step': state_step.item(),
                'eval_loss': eval_loss.item(),
                'tr_loss': tr_loss,
                'lr': lr.item(),
            }
            tqdm.write(str(_lowerCamelCase       )       )
            self.logger.log(_lowerCamelCase							,		commit=_lowerCamelCase       )

          if i % args.save_steps == 0:
            self.save_checkpoint(args.save_dir + F'-e{epoch}-s{i}'							,		state=_lowerCamelCase       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase       ):
      lowercase							    =       get_batched_dataset(_lowerCamelCase							,		self.args.batch_size       )
      lowercase							    =       len(_lowerCamelCase       ) // self.args.batch_size
      lowercase							    =       jnp.array(0							,		dtype=jnp.floataa       )
      lowercase							    =       0
      for batch in tqdm(_lowerCamelCase							,		total=_lowerCamelCase							,		desc='Evaluating ... '       ):
        lowercase							    =       self.data_collator(_lowerCamelCase       )
        lowercase							    =       self.val_step_fn(_lowerCamelCase							,		**_lowerCamelCase       )
        running_loss += jax_utils.unreplicate(metrics['loss']       )
        i += 1
      return running_loss / i







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase       ):
      lowercase							    =       jax_utils.unreplicate(_lowerCamelCase       )
      print(F'SAVING CHECKPOINT IN {save_dir}'							,		end=' ... '       )
      self.model_save_fn(_lowerCamelCase							,		params=state.params       )
      with open(os.path.join(_lowerCamelCase							,		'opt_state.msgpack'       )							,		'wb'       ) as f:
        f.write(to_bytes(state.opt_state       )       )
      joblib.dump(self.args							,		os.path.join(_lowerCamelCase							,		'args.joblib'       )       )
      joblib.dump(self.data_collator							,		os.path.join(_lowerCamelCase							,		'data_collator.joblib'       )       )
      with open(os.path.join(_lowerCamelCase							,		'training_state.json'       )							,		'w'       ) as f:
        json.dump({'step': state.step.item()}							,		_lowerCamelCase       )
      print('DONE'       )


def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  int		,					__snake_case			:  Tuple    ):





  '''simple docstring'''





  print(f'RESTORING CHECKPOINT FROM {save_dir}'		,					end=' ... '    )
  with open(os.path.join(__snake_case		,					'flax_model.msgpack'    )		,					'rb'    ) as f:
    lowercase							    =       from_bytes(state.params		,					f.read()    )

  with open(os.path.join(__snake_case		,					'opt_state.msgpack'    )		,					'rb'    ) as f:
    lowercase							    =       from_bytes(state.opt_state		,					f.read()    )

  lowercase							    =       joblib.load(os.path.join(__snake_case		,					'args.joblib'    )    )
  lowercase							    =       joblib.load(os.path.join(__snake_case		,					'data_collator.joblib'    )    )

  with open(os.path.join(__snake_case		,					'training_state.json'    )		,					'r'    ) as f:
    lowercase							    =       json.load(__snake_case    )
  lowercase							    =       training_state['step']

  print('DONE'    )
  return params, opt_state, step, args, data_collator
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  int		,					__snake_case			:  str		,					__snake_case			:  Any		,					__snake_case			:  Any    ):





  '''simple docstring'''





  lowercase							    =       num_train_steps - warmup_steps
  lowercase							    =       optax.linear_schedule(init_value=__snake_case		,					end_value=__snake_case		,					transition_steps=__snake_case    )
  lowercase							    =       optax.linear_schedule(init_value=__snake_case		,					end_value=1e-7		,					transition_steps=__snake_case    )
  lowercase							    =       optax.join_schedules(schedules=[warmup_fn, decay_fn]		,					boundaries=[warmup_steps]    )
  return lr



def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Union[str, Any]		,					__snake_case			:  Union[str, Any]		,					__snake_case			:  List[str]		,					__snake_case			:  str		,					__snake_case			:  Optional[int]    ):





  '''simple docstring'''





  def weight_decay_mask(__snake_case			:  Tuple    ):
    lowercase							    =       traverse_util.flatten_dict(__snake_case    )
    lowercase							    =       {k: (v[-1] != 'bias' and v[-2:] != ('LayerNorm', 'scale')) for k, v in params.items()}
    return traverse_util.unflatten_dict(__snake_case    )

  lowercase							    =       scheduler_fn(__snake_case		,					__snake_case		,					__snake_case		,					__snake_case    )

  lowercase							    =       optax.adamw(learning_rate=__snake_case		,					weight_decay=__snake_case		,					mask=__snake_case    )
  return tx, lr


