
"""simple docstring"""





import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest



class __lowerCAmelCase				(					_lowercase      ):




			"""simple docstring"""







			snake_case	      =				(UniPCMultistepScheduler,)
			snake_case	      =				(("num_inference_steps", 25),)




			def      lowerCamelCase__     (				self						:					Optional[int]       ,   **_snake_case						:					Optional[int]	)      -> str:







								"""simple docstring"""
								A_				 =						{
								    "num_train_timesteps": 1_000,
								    "beta_start": 0.0_0_0_1,
								    "beta_end": 0.0_2,
								    "beta_schedule": "linear",
								    "solver_order": 2,
								    "solver_type": "bh2",
								}

								config.update(**_snake_case	)
								return config




			def      lowerCamelCase__     (				self						:					Optional[int]       ,   _snake_case						:					Dict=0       ,   **_snake_case						:					int	)      -> Any:







								"""simple docstring"""
								A_				 =						dict(self.forward_default_kwargs	)
								A_				 =						kwargs.pop("num_inference_steps"       ,   _snake_case	)
								A_				 =						self.dummy_sample
								A_				 =						0.1 * sample
								A_				 =						[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

								for scheduler_class in self.scheduler_classes:
													A_				 =						self.get_scheduler_config(**_snake_case	)
													A_				 =						scheduler_class(**_snake_case	)
													scheduler.set_timesteps(_snake_case	)
													# copy over dummy past residuals
													A_				 =						dummy_past_residuals[: scheduler.config.solver_order]

													with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(_snake_case	)
																		A_				 =						scheduler_class.from_pretrained(_snake_case	)
																		new_scheduler.set_timesteps(_snake_case	)
																		# copy over dummy past residuals
																		A_				 =						dummy_past_residuals[: new_scheduler.config.solver_order]

													A_ ,    A_				 =						sample, sample
													for t in range(_snake_case       ,   time_step + scheduler.config.solver_order + 1	):
																		A_				 =						scheduler.step(_snake_case       ,   _snake_case       ,   _snake_case       ,   **_snake_case	).prev_sample
																		A_				 =						new_scheduler.step(_snake_case       ,   _snake_case       ,   _snake_case       ,   **_snake_case	).prev_sample

																		assert torch.sum(torch.abs(output - new_output	)	) < 1e-5, "Scheduler outputs are not identical"




			def      lowerCamelCase__     (				self						:					int       ,   _snake_case						:					Dict=0       ,   **_snake_case						:					int	)      -> int:







								"""simple docstring"""
								A_				 =						dict(self.forward_default_kwargs	)
								A_				 =						kwargs.pop("num_inference_steps"       ,   _snake_case	)
								A_				 =						self.dummy_sample
								A_				 =						0.1 * sample
								A_				 =						[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

								for scheduler_class in self.scheduler_classes:
													A_				 =						self.get_scheduler_config()
													A_				 =						scheduler_class(**_snake_case	)
													scheduler.set_timesteps(_snake_case	)

													# copy over dummy past residuals (must be after setting timesteps)
													A_				 =						dummy_past_residuals[: scheduler.config.solver_order]

													with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(_snake_case	)
																		A_				 =						scheduler_class.from_pretrained(_snake_case	)
																		# copy over dummy past residuals
																		new_scheduler.set_timesteps(_snake_case	)

																		# copy over dummy past residual (must be after setting timesteps)
																		A_				 =						dummy_past_residuals[: new_scheduler.config.solver_order]

													A_				 =						scheduler.step(_snake_case       ,   _snake_case       ,   _snake_case       ,   **_snake_case	).prev_sample
													A_				 =						new_scheduler.step(_snake_case       ,   _snake_case       ,   _snake_case       ,   **_snake_case	).prev_sample

													assert torch.sum(torch.abs(output - new_output	)	) < 1e-5, "Scheduler outputs are not identical"




			def      lowerCamelCase__     (				self						:					List[Any]       ,   _snake_case						:					Tuple=None       ,   **_snake_case						:					Union[str, Any]	)      -> str:







								"""simple docstring"""
								if scheduler is None:
													A_				 =						self.scheduler_classes[0]
													A_				 =						self.get_scheduler_config(**_snake_case	)
													A_				 =						scheduler_class(**_snake_case	)

								A_				 =						self.scheduler_classes[0]
								A_				 =						self.get_scheduler_config(**_snake_case	)
								A_				 =						scheduler_class(**_snake_case	)

								A_				 =						10
								A_				 =						self.dummy_model()
								A_				 =						self.dummy_sample_deter
								scheduler.set_timesteps(_snake_case	)

								for i, t in enumerate(scheduler.timesteps	):
													A_				 =						model(_snake_case       ,   _snake_case	)
													A_				 =						scheduler.step(_snake_case       ,   _snake_case       ,   _snake_case	).prev_sample

								return sample




			def      lowerCamelCase__     (				self						:					Any	)      -> List[str]:







								"""simple docstring"""
								A_				 =						dict(self.forward_default_kwargs	)

								A_				 =						kwargs.pop("num_inference_steps"       ,   _snake_case	)

								for scheduler_class in self.scheduler_classes:
													A_				 =						self.get_scheduler_config()
													A_				 =						scheduler_class(**_snake_case	)

													A_				 =						self.dummy_sample
													A_				 =						0.1 * sample

													if num_inference_steps is not None and hasattr(_snake_case       ,   "set_timesteps"	):
																		scheduler.set_timesteps(_snake_case	)
													elif num_inference_steps is not None and not hasattr(_snake_case       ,   "set_timesteps"	):
																		A_				 =						num_inference_steps

													# copy over dummy past residuals (must be done after set_timesteps)
													A_				 =						[residual + 0.2, residual + 0.1_5, residual + 0.1_0]
													A_				 =						dummy_past_residuals[: scheduler.config.solver_order]

													A_				 =						scheduler.timesteps[5]
													A_				 =						scheduler.timesteps[6]

													A_				 =						scheduler.step(_snake_case       ,   _snake_case       ,   _snake_case       ,   **_snake_case	).prev_sample
													A_				 =						scheduler.step(_snake_case       ,   _snake_case       ,   _snake_case       ,   **_snake_case	).prev_sample

													self.assertEqual(output_a.shape       ,   sample.shape	)
													self.assertEqual(output_a.shape       ,   output_a.shape	)




			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> str:







								"""simple docstring"""
								# make sure that iterating over schedulers with same config names gives same results
								# for defaults
								A_				 =						UniPCMultistepScheduler(**self.get_scheduler_config()	)
								A_				 =						self.full_loop(scheduler=_snake_case	)
								A_				 =						torch.mean(torch.abs(_snake_case	)	)

								assert abs(result_mean.item() - 0.2_4_6_4	) < 1e-3

								A_				 =						DPMSolverSinglestepScheduler.from_config(scheduler.config	)
								A_				 =						DEISMultistepScheduler.from_config(scheduler.config	)
								A_				 =						DPMSolverMultistepScheduler.from_config(scheduler.config	)
								A_				 =						UniPCMultistepScheduler.from_config(scheduler.config	)

								A_				 =						self.full_loop(scheduler=_snake_case	)
								A_				 =						torch.mean(torch.abs(_snake_case	)	)

								assert abs(result_mean.item() - 0.2_4_6_4	) < 1e-3




			def      lowerCamelCase__     (				self						:					int	)      -> Any:







								"""simple docstring"""
								for timesteps in [25, 50, 100, 999, 1_000]:
													self.check_over_configs(num_train_timesteps=_snake_case	)




			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> Any:







								"""simple docstring"""
								self.check_over_configs(thresholding=_snake_case	)
								for order in [1, 2, 3]:
													for solver_type in ["bh1", "bh2"]:
																		for threshold in [0.5, 1.0, 2.0]:
																							for prediction_type in ["epsilon", "sample"]:
																												self.check_over_configs(
																												    thresholding=_snake_case       ,   prediction_type=_snake_case       ,   sample_max_value=_snake_case       ,   solver_order=_snake_case       ,   solver_type=_snake_case       ,   )




			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> Optional[Any]:







								"""simple docstring"""
								for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=_snake_case	)




			def      lowerCamelCase__     (				self						:					Any	)      -> Tuple:







								"""simple docstring"""
								for solver_type in ["bh1", "bh2"]:
													for order in [1, 2, 3]:
																		for prediction_type in ["epsilon", "sample"]:
																							self.check_over_configs(
																							    solver_order=_snake_case       ,   solver_type=_snake_case       ,   prediction_type=_snake_case       ,   )
																							A_				 =						self.full_loop(
																							    solver_order=_snake_case       ,   solver_type=_snake_case       ,   prediction_type=_snake_case       ,   )
																							assert not torch.isnan(_snake_case	).any(), "Samples have nan numbers"




			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> Optional[int]:







								"""simple docstring"""
								self.check_over_configs(lower_order_final=_snake_case	)
								self.check_over_configs(lower_order_final=_snake_case	)




			def      lowerCamelCase__     (				self						:					List[str]	)      -> int:







								"""simple docstring"""
								for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
													self.check_over_forward(num_inference_steps=_snake_case       ,   time_step=0	)




			def      lowerCamelCase__     (				self						:					Optional[Any]	)      -> List[str]:







								"""simple docstring"""
								A_				 =						self.full_loop()
								A_				 =						torch.mean(torch.abs(_snake_case	)	)

								assert abs(result_mean.item() - 0.2_4_6_4	) < 1e-3




			def      lowerCamelCase__     (				self						:					List[Any]	)      -> str:







								"""simple docstring"""
								A_				 =						self.full_loop(prediction_type="v_prediction"	)
								A_				 =						torch.mean(torch.abs(_snake_case	)	)

								assert abs(result_mean.item() - 0.1_0_1_4	) < 1e-3




			def      lowerCamelCase__     (				self						:					Tuple	)      -> Optional[Any]:







								"""simple docstring"""
								A_				 =						self.scheduler_classes[0]
								A_				 =						self.get_scheduler_config(thresholding=_snake_case       ,   dynamic_thresholding_ratio=0	)
								A_				 =						scheduler_class(**_snake_case	)

								A_				 =						10
								A_				 =						self.dummy_model()
								A_				 =						self.dummy_sample_deter.half()
								scheduler.set_timesteps(_snake_case	)

								for i, t in enumerate(scheduler.timesteps	):
													A_				 =						model(_snake_case       ,   _snake_case	)
													A_				 =						scheduler.step(_snake_case       ,   _snake_case       ,   _snake_case	).prev_sample

								assert sample.dtype == torch.floataa






			def      lowerCamelCase__     (				self						:					List[Any]       ,   **_snake_case						:					Union[str, Any]	)      -> Optional[Any]:







								"""simple docstring"""
								for scheduler_class in self.scheduler_classes:
													A_				 =						self.get_scheduler_config(**_snake_case	)
													A_				 =						scheduler_class(**_snake_case	)

													scheduler.set_timesteps(scheduler.config.num_train_timesteps	)
													assert len(scheduler.timesteps.unique()	) == scheduler.num_inference_steps






"""simple docstring"""





from collections import deque



class __lowerCAmelCase				:




			"""simple docstring"""







			def __init__(				self						:					Optional[Any]       ,   _snake_case						:					str       ,   _snake_case						:					int       ,   _snake_case						:					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 __lowerCAmelCase				:




			"""simple docstring"""







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







								"""simple docstring"""
								# total number of mlfq's queues
								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						:					List[str]	)      -> list[str]:







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




			def      lowerCamelCase__     (				self						:					str       ,   _snake_case						:					list[Process]	)      -> list[int]:







								"""simple docstring"""
								A_				 =						[]
								for i in range(len(_snake_case	)	):
													waiting_times.append(queue[i].waiting_time	)
								return waiting_times




			def      lowerCamelCase__     (				self						:					Tuple       ,   _snake_case						:					list[Process]	)      -> list[int]:







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




			def      lowerCamelCase__     (				self						:					Tuple       ,   _snake_case						:					list[Process]	)      -> list[int]:







								"""simple docstring"""
								A_				 =						[]
								for i in range(len(_snake_case	)	):
													completion_times.append(queue[i].stop_time	)
								return completion_times




			def      lowerCamelCase__     (				self						:					Optional[int]       ,   _snake_case						:					deque[Process]	)      -> list[int]:







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




			def      lowerCamelCase__     (				self						:					int       ,   _snake_case						:					Process	)      -> int:







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




			def      lowerCamelCase__     (				self						:					Dict       ,   _snake_case						:					deque[Process]	)      -> deque[Process]:







								"""simple docstring"""
								A_				 =						deque()  # sequence deque of finished process
								while len(_snake_case	) != 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(_snake_case	)
													# 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(_snake_case	)

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




			def      lowerCamelCase__     (				self						:					List[str]       ,   _snake_case						:					deque[Process]       ,   _snake_case						:					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(_snake_case	)	):
													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(_snake_case	)
													# 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(_snake_case	)
													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(_snake_case	)

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






			def      lowerCamelCase__     (				self						:					Any	)      -> deque[Process]:







								"""simple docstring"""

								#  all queues except last one have round_robin algorithm
								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

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

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




