





import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


A    :					int =      '''scheduler_config.json'''


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       Union[str, Any]							=    1
			__lowerCamelCase	:       Optional[Any]							=    2
			__lowerCamelCase	:       Dict							=    3
			__lowerCamelCase	:       Any							=    4
			__lowerCamelCase	:       Union[str, Any]							=    5


@dataclass
class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       jnp.ndarray


class   A			:
			'''simple docstring'''




			__lowerCamelCase	:       List[str]							=    SCHEDULER_CONFIG_NAME
			__lowerCamelCase	:       Optional[int]							=    ['''dtype''']
			__lowerCamelCase	:       List[Any]							=    []
			__lowerCamelCase	:       Dict							=    True






			@classmethod
			def    a_       (			cls   :    List[Any]							,	__lowerCAmelCase   :    Dict[str, Any] = None							,	__lowerCAmelCase   :    Optional[str] = None							,	__lowerCAmelCase   :    Optional[Any]=False							,	**__lowerCAmelCase   :    Dict							,	)	->	Optional[int]:







					"""simple docstring"""






					A__      ,			A__        =   cls.load_config(
					    pretrained_model_name_or_path=__lowerCAmelCase							,	subfolder=__lowerCAmelCase							,	return_unused_kwargs=__lowerCAmelCase							,	**__lowerCAmelCase							,	)
					A__      ,			A__        =   cls.from_config(__lowerCAmelCase							,	return_unused_kwargs=__lowerCAmelCase							,	**__lowerCAmelCase   )

					if hasattr(__lowerCAmelCase							,	"""create_state"""   ) and getattr(__lowerCAmelCase							,	"""has_state"""							,	__lowerCAmelCase   ):
							A__        =   scheduler.create_state()

					if return_unused_kwargs:
							return scheduler, state, unused_kwargs

					return scheduler, state






			def    a_       (			self   :    Dict							,	__lowerCAmelCase   :    Union[str, os.PathLike]							,	__lowerCAmelCase   :    bool = False							,	**__lowerCAmelCase   :    Union[str, Any]   )	->	Any:







					"""simple docstring"""






					self.save_config(save_directory=__lowerCAmelCase							,	push_to_hub=__lowerCAmelCase							,	**__lowerCAmelCase   )






			@property
			def    a_       (			self   :    Union[str, Any]   )	->	Optional[int]:







					"""simple docstring"""






					return self._get_compatibles()







			@classmethod
			def    a_       (			cls   :    List[Any]   )	->	Any:







					"""simple docstring"""






					A__        =   list(set([cls.__name__] + cls._compatibles   )   )
					A__        =   importlib.import_module(__name__.split("""."""   )[0]   )
					A__        =   [
					    getattr(__lowerCAmelCase							,	__lowerCAmelCase   ) for c in compatible_classes_str if hasattr(__lowerCAmelCase							,	__lowerCAmelCase   )
					]
					return compatible_classes







def    __lowerCamelCase					(							__a   :jnp.ndarray    ,     __a   :Tuple[int]    )		->				jnp.ndarray:






		"""simple docstring"""

		assert len(__a    ) >= x.ndim
		return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(__a    ) - x.ndim)    )    ,     __a    )







def    __lowerCamelCase					(							__a   :int    ,     __a   :Optional[Any]=0.999    ,     __a   :Any=jnp.floataa    )		->				jnp.ndarray:






		"""simple docstring"""

		def alpha_bar(__a   :Optional[Any]    ):
				return math.cos((time_step + 0.008) / 1.008 * math.pi / 2    ) ** 2

		A__        =   []
		for i in range(__a    ):
				A__        =   i / num_diffusion_timesteps
				A__        =   (i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar(__a    ) / alpha_bar(__a    )    ,     __a    )    )
		return jnp.array(__a    ,     dtype=__a    )


@flax.struct.dataclass
class   A			:
			'''simple docstring'''




			__lowerCamelCase	:       jnp.ndarray
			__lowerCamelCase	:       jnp.ndarray
			__lowerCamelCase	:       jnp.ndarray






			@classmethod
			def    a_       (			cls   :    str							,	__lowerCAmelCase   :    Optional[Any]   )	->	Dict:







					"""simple docstring"""






					A__        =   scheduler.config

					if config.trained_betas is not None:
							A__        =   jnp.asarray(config.trained_betas							,	dtype=scheduler.dtype   )
					elif config.beta_schedule == "linear":
							A__        =   jnp.linspace(config.beta_start							,	config.beta_end							,	config.num_train_timesteps							,	dtype=scheduler.dtype   )
					elif config.beta_schedule == "scaled_linear":
							# this schedule is very specific to the latent diffusion model.
							A__        =   (
							    jnp.linspace(
							        config.beta_start**0.5							,	config.beta_end**0.5							,	config.num_train_timesteps							,	dtype=scheduler.dtype   )
							    ** 2
							)
					elif config.beta_schedule == "squaredcos_cap_v2":
							# Glide cosine schedule
							A__        =   betas_for_alpha_bar(config.num_train_timesteps							,	dtype=scheduler.dtype   )
					else:
							raise NotImplementedError(
							    f'beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'   )

					A__        =   1.0 - betas

					A__        =   jnp.cumprod(__lowerCAmelCase							,	axis=0   )

					return cls(
					    alphas=__lowerCAmelCase							,	betas=__lowerCAmelCase							,	alphas_cumprod=__lowerCAmelCase							,	)







def    __lowerCamelCase					(							__a   :CommonSchedulerState    ,     __a   :jnp.ndarray    ,     __a   :jnp.ndarray    ,     __a   :jnp.ndarray    )		->				Optional[Any]:






		"""simple docstring"""

		A__        =   state.alphas_cumprod

		A__        =   alphas_cumprod[timesteps] ** 0.5
		A__        =   sqrt_alpha_prod.flatten()
		A__        =   broadcast_to_shape_from_left(__a    ,     original_samples.shape    )

		A__        =   (1 - alphas_cumprod[timesteps]) ** 0.5
		A__        =   sqrt_one_minus_alpha_prod.flatten()
		A__        =   broadcast_to_shape_from_left(__a    ,     original_samples.shape    )

		return sqrt_alpha_prod, sqrt_one_minus_alpha_prod







def    __lowerCamelCase					(							__a   :CommonSchedulerState    ,     __a   :jnp.ndarray    ,     __a   :jnp.ndarray    ,     __a   :jnp.ndarray    )		->				str:






		"""simple docstring"""

		A__      ,			A__        =   get_sqrt_alpha_prod(__a    ,     __a    ,     __a    ,     __a    )
		A__        =   sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
		return noisy_samples







def    __lowerCamelCase					(							__a   :CommonSchedulerState    ,     __a   :jnp.ndarray    ,     __a   :jnp.ndarray    ,     __a   :jnp.ndarray    )		->				str:






		"""simple docstring"""

		A__      ,			A__        =   get_sqrt_alpha_prod(__a    ,     __a    ,     __a    ,     __a    )
		A__        =   sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
		return velocity










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()}'''
					)



