



'''simple docstring'''



import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest





class _UpperCamelCase		(  a__		):





				'''simple docstring'''





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

				def 					__lowerCamelCase  (				self	:	str	,							**_lowerCAmelCase	:	List[Any]):







											'''simple docstring'''

											__lowercase													={
											    "num_train_timesteps": 1_0_0_0,
											    "beta_start": 0.0001,
											    "beta_end": 0.02,
											    "beta_schedule": "linear",
											    "solver_order": 2,
											    "solver_type": "bh2",
											}

											config.update(**lowerCAmelCase__)
											return config

				def 					__lowerCamelCase  (				self	:	int	,							_lowerCAmelCase	:	Optional[int]=0	,							**_lowerCAmelCase	:	Tuple):







											'''simple docstring'''

											__lowercase													=dict(self.forward_default_kwargs)
											__lowercase													=kwargs.pop('num_inference_steps'	,							lowerCAmelCase__)
											__lowercase													=self.dummy_sample
											__lowercase													=0.1 * sample
											__lowercase													=[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

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

				def 					__lowerCamelCase  (				self	:	Optional[int]	,							_lowerCAmelCase	:	Dict=0	,							**_lowerCAmelCase	:	List[str]):







											'''simple docstring'''

											__lowercase													=dict(self.forward_default_kwargs)
											__lowercase													=kwargs.pop('num_inference_steps'	,							lowerCAmelCase__)
											__lowercase													=self.dummy_sample
											__lowercase													=0.1 * sample
											__lowercase													=[residual + 0.2, residual + 0.15, residual + 0.10]

											for scheduler_class in self.scheduler_classes:
																		__lowercase													=self.get_scheduler_config()
																		__lowercase													=scheduler_class(**lowerCAmelCase__)
																		scheduler.set_timesteps(lowerCAmelCase__)

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

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

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

																		__lowercase													=scheduler.step(lowerCAmelCase__	,							lowerCAmelCase__	,							lowerCAmelCase__	,							**lowerCAmelCase__).prev_sample
																		__lowercase													=new_scheduler.step(lowerCAmelCase__	,							lowerCAmelCase__	,							lowerCAmelCase__	,							**lowerCAmelCase__).prev_sample

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

				def 					__lowerCamelCase  (				self	:	List[str]	,							_lowerCAmelCase	:	Union[str, Any]=None	,							**_lowerCAmelCase	:	int):







											'''simple docstring'''

											if scheduler is None:
																		__lowercase													=self.scheduler_classes[0]
																		__lowercase													=self.get_scheduler_config(**lowerCAmelCase__)
																		__lowercase													=scheduler_class(**lowerCAmelCase__)

											__lowercase													=self.scheduler_classes[0]
											__lowercase													=self.get_scheduler_config(**lowerCAmelCase__)
											__lowercase													=scheduler_class(**lowerCAmelCase__)

											__lowercase													=1_0
											__lowercase													=self.dummy_model()
											__lowercase													=self.dummy_sample_deter
											scheduler.set_timesteps(lowerCAmelCase__)

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

											return sample

				def 					__lowerCamelCase  (				self	:	int):







											'''simple docstring'''

											__lowercase													=dict(self.forward_default_kwargs)

											__lowercase													=kwargs.pop('num_inference_steps'	,							lowerCAmelCase__)

											for scheduler_class in self.scheduler_classes:
																		__lowercase													=self.get_scheduler_config()
																		__lowercase													=scheduler_class(**lowerCAmelCase__)

																		__lowercase													=self.dummy_sample
																		__lowercase													=0.1 * sample

																		if num_inference_steps is not None and hasattr(lowerCAmelCase__	,							'set_timesteps'):
																									scheduler.set_timesteps(lowerCAmelCase__)
																		elif num_inference_steps is not None and not hasattr(lowerCAmelCase__	,							'set_timesteps'):
																									__lowercase													=num_inference_steps

																		# copy over dummy past residuals (must be done after set_timesteps)
																		__lowercase													=[residual + 0.2, residual + 0.15, residual + 0.10]
																		__lowercase													=dummy_past_residuals[: scheduler.config.solver_order]

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

																		__lowercase													=scheduler.step(lowerCAmelCase__	,							lowerCAmelCase__	,							lowerCAmelCase__	,							**lowerCAmelCase__).prev_sample
																		__lowercase													=scheduler.step(lowerCAmelCase__	,							lowerCAmelCase__	,							lowerCAmelCase__	,							**lowerCAmelCase__).prev_sample

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

				def 					__lowerCamelCase  (				self	:	List[Any]):







											'''simple docstring'''

											__lowercase													=UniPCMultistepScheduler(**self.get_scheduler_config())
											__lowercase													=self.full_loop(scheduler=lowerCAmelCase__)
											__lowercase													=torch.mean(torch.abs(lowerCAmelCase__))

											assert abs(result_mean.item() - 0.2464) < 1e-3

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

											__lowercase													=self.full_loop(scheduler=lowerCAmelCase__)
											__lowercase													=torch.mean(torch.abs(lowerCAmelCase__))

											assert abs(result_mean.item() - 0.2464) < 1e-3

				def 					__lowerCamelCase  (				self	:	Any):







											'''simple docstring'''

											for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
																		self.check_over_configs(num_train_timesteps=lowerCAmelCase__)

				def 					__lowerCamelCase  (				self	:	List[str]):







											'''simple docstring'''

											self.check_over_configs(thresholding=lowerCAmelCase__)
											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=lowerCAmelCase__	,							prediction_type=lowerCAmelCase__	,							sample_max_value=lowerCAmelCase__	,							solver_order=lowerCAmelCase__	,							solver_type=lowerCAmelCase__	,							)

				def 					__lowerCamelCase  (				self	:	Optional[int]):







											'''simple docstring'''

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

				def 					__lowerCamelCase  (				self	:	Dict):







											'''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=lowerCAmelCase__	,							solver_type=lowerCAmelCase__	,							prediction_type=lowerCAmelCase__	,							)
																																__lowercase													=self.full_loop(
																																    solver_order=lowerCAmelCase__	,							solver_type=lowerCAmelCase__	,							prediction_type=lowerCAmelCase__	,							)
																																assert not torch.isnan(lowerCAmelCase__).any(), "Samples have nan numbers"

				def 					__lowerCamelCase  (				self	:	int):







											'''simple docstring'''

											self.check_over_configs(lower_order_final=lowerCAmelCase__)
											self.check_over_configs(lower_order_final=lowerCAmelCase__)

				def 					__lowerCamelCase  (				self	:	int):







											'''simple docstring'''

											for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
																		self.check_over_forward(num_inference_steps=lowerCAmelCase__	,							time_step=0)

				def 					__lowerCamelCase  (				self	:	Dict):







											'''simple docstring'''

											__lowercase													=self.full_loop()
											__lowercase													=torch.mean(torch.abs(lowerCAmelCase__))

											assert abs(result_mean.item() - 0.2464) < 1e-3

				def 					__lowerCamelCase  (				self	:	List[Any]):







											'''simple docstring'''

											__lowercase													=self.full_loop(prediction_type='v_prediction')
											__lowercase													=torch.mean(torch.abs(lowerCAmelCase__))

											assert abs(result_mean.item() - 0.1014) < 1e-3

				def 					__lowerCamelCase  (				self	:	List[Any]):







											'''simple docstring'''

											__lowercase													=self.scheduler_classes[0]
											__lowercase													=self.get_scheduler_config(thresholding=lowerCAmelCase__	,							dynamic_thresholding_ratio=0)
											__lowercase													=scheduler_class(**lowerCAmelCase__)

											__lowercase													=1_0
											__lowercase													=self.dummy_model()
											__lowercase													=self.dummy_sample_deter.half()
											scheduler.set_timesteps(lowerCAmelCase__)

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

											assert sample.dtype == torch.floataa

				def 					__lowerCamelCase  (				self	:	str	,							**_lowerCAmelCase	:	List[Any]):







											'''simple docstring'''

											for scheduler_class in self.scheduler_classes:
																		__lowercase													=self.get_scheduler_config(**lowerCAmelCase__)
																		__lowercase													=scheduler_class(**lowerCAmelCase__)

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










"""simple docstring"""




from __future__ import annotations

from statistics import mean






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






	'''simple docstring'''






	a :						Union[str, Any]      =							[0] * no_of_processes
	a :						int      =							[0] * no_of_processes

	# Initialize remaining_time to waiting_time.

	for i in range(_lowercase   ):
		a :						List[Any]      =							burst_time[i]
	a :						list[int]      =							[]

	a :						List[Any]      =							0
	a :						Any      =							0

	# When processes are not completed,
	# A process whose arrival time has passed \
	# and has remaining execution time is put into the ready_process.
	# The shortest process in the ready_process, target_process is executed.

	while completed != no_of_processes:
		a :						str      =							[]
		a :						Tuple      =							-1

		for i in range(_lowercase   ):
			if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
				ready_process.append(_lowercase   )

		if len(_lowercase   ) > 0:
			a :						List[Any]      =							ready_process[0]
			for i in ready_process:
				if remaining_time[i] < remaining_time[target_process]:
					a :						Optional[Any]      =							i
			total_time += burst_time[target_process]
			completed += 1
			a :						str      =							0
			a :						Union[str, Any]      =							(
			    total_time - arrival_time[target_process] - burst_time[target_process]
			)
		else:
			total_time += 1

	return waiting_time






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






	'''simple docstring'''






	a :						List[str]      =							[0] * no_of_processes
	for i in range(_lowercase   ):
		a :						Optional[int]      =							burst_time[i] + waiting_time[i]
	return turn_around_time


if __name__ == "__main__":
		print('''[TEST CASE 01]''')

		a		:					Dict	      =     4
		a		:					Tuple	      =     [2, 5, 3, 7]
		a		:					List[Any]	      =     [0, 0, 0, 0]
		a		:					int	      =     calculate_waitingtime(arrival_time, burst_time, no_of_processes)
		a		:					List[Any]	      =     calculate_turnaroundtime(
		    burst_time, no_of_processes, waiting_time
		)

		# Printing the Result
		print('''PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time''')
		for i, process_id in enumerate(list(range(1, 5))):
				print(
				    F'''{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t'''
				    F'''{waiting_time[i]}\t\t\t\t{turn_around_time[i]}'''
				)
		print(F'''\nAverage waiting time = {mean(waiting_time):.5f}''')
		print(F'''Average turnaround time = {mean(turn_around_time):.5f}''')


