'''simple docstring'''


import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class 						A   (   UpperCAmelCase   ):
							a_        =      (IPNDMScheduler,)
							a_        =      (('''num_inference_steps''', 5_0),)





							def       snake_case__						(      self				:							Optional[int]     ,		**__a				:							str	)							->	Tuple:
								__UpperCAmelCase							=			{'''num_train_timesteps''': 1_0_0_0}
								config.update(**__a	)
								return config





							def       snake_case__						(      self				:							str     ,		__a				:							Optional[Any]=0     ,		**__a				:							List[str]	)							->	Dict:
								__UpperCAmelCase							=			dict(self.forward_default_kwargs	)
								__UpperCAmelCase							=			kwargs.pop('''num_inference_steps'''     ,		__a	)
								__UpperCAmelCase							=			self.dummy_sample
								__UpperCAmelCase							=			0.1 * sample
								__UpperCAmelCase							=			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

								for scheduler_class in self.scheduler_classes:
									__UpperCAmelCase							=			self.get_scheduler_config(**__a	)
									__UpperCAmelCase							=			scheduler_class(**__a	)
									scheduler.set_timesteps(__a	)
									# copy over dummy past residuals
									__UpperCAmelCase							=			dummy_past_residuals[:]

									if time_step is None:
										__UpperCAmelCase							=			scheduler.timesteps[len(scheduler.timesteps	) // 2]

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

									__UpperCAmelCase							=			scheduler.step(__a     ,		__a     ,		__a     ,		**__a	).prev_sample
									__UpperCAmelCase							=			new_scheduler.step(__a     ,		__a     ,		__a     ,		**__a	).prev_sample

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

									__UpperCAmelCase							=			scheduler.step(__a     ,		__a     ,		__a     ,		**__a	).prev_sample
									__UpperCAmelCase							=			new_scheduler.step(__a     ,		__a     ,		__a     ,		**__a	).prev_sample

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





							def       snake_case__						(      self				:							Dict	)							->	List[str]:
								pass





							def       snake_case__						(      self				:							List[str]     ,		__a				:							Union[str, Any]=0     ,		**__a				:							int	)							->	Tuple:
								__UpperCAmelCase							=			dict(self.forward_default_kwargs	)
								__UpperCAmelCase							=			kwargs.pop('''num_inference_steps'''     ,		__a	)
								__UpperCAmelCase							=			self.dummy_sample
								__UpperCAmelCase							=			0.1 * sample
								__UpperCAmelCase							=			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

								for scheduler_class in self.scheduler_classes:
									__UpperCAmelCase							=			self.get_scheduler_config()
									__UpperCAmelCase							=			scheduler_class(**__a	)
									scheduler.set_timesteps(__a	)

									# copy over dummy past residuals (must be after setting timesteps)
									__UpperCAmelCase							=			dummy_past_residuals[:]

									if time_step is None:
										__UpperCAmelCase							=			scheduler.timesteps[len(scheduler.timesteps	) // 2]

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

										# copy over dummy past residual (must be after setting timesteps)
										__UpperCAmelCase							=			dummy_past_residuals[:]

									__UpperCAmelCase							=			scheduler.step(__a     ,		__a     ,		__a     ,		**__a	).prev_sample
									__UpperCAmelCase							=			new_scheduler.step(__a     ,		__a     ,		__a     ,		**__a	).prev_sample

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

									__UpperCAmelCase							=			scheduler.step(__a     ,		__a     ,		__a     ,		**__a	).prev_sample
									__UpperCAmelCase							=			new_scheduler.step(__a     ,		__a     ,		__a     ,		**__a	).prev_sample

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





							def       snake_case__						(      self				:							str     ,		**__a				:							Any	)							->	str:
								__UpperCAmelCase							=			self.scheduler_classes[0]
								__UpperCAmelCase							=			self.get_scheduler_config(**__a	)
								__UpperCAmelCase							=			scheduler_class(**__a	)

								__UpperCAmelCase							=			1_0
								__UpperCAmelCase							=			self.dummy_model()
								__UpperCAmelCase							=			self.dummy_sample_deter
								scheduler.set_timesteps(__a	)

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

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

								return sample





							def       snake_case__						(      self				:							str	)							->	Any:
								__UpperCAmelCase							=			dict(self.forward_default_kwargs	)

								__UpperCAmelCase							=			kwargs.pop('''num_inference_steps'''     ,		__a	)

								for scheduler_class in self.scheduler_classes:
									__UpperCAmelCase							=			self.get_scheduler_config()
									__UpperCAmelCase							=			scheduler_class(**__a	)

									__UpperCAmelCase							=			self.dummy_sample
									__UpperCAmelCase							=			0.1 * sample

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

									# copy over dummy past residuals (must be done after set_timesteps)
									__UpperCAmelCase							=			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
									__UpperCAmelCase							=			dummy_past_residuals[:]

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

									__UpperCAmelCase							=			scheduler.step(__a     ,		__a     ,		__a     ,		**__a	).prev_sample
									__UpperCAmelCase							=			scheduler.step(__a     ,		__a     ,		__a     ,		**__a	).prev_sample

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

									__UpperCAmelCase							=			scheduler.step(__a     ,		__a     ,		__a     ,		**__a	).prev_sample
									__UpperCAmelCase							=			scheduler.step(__a     ,		__a     ,		__a     ,		**__a	).prev_sample

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





							def       snake_case__						(      self				:							Dict	)							->	Dict:
								for timesteps in [1_0_0, 1_0_0_0]:
									self.check_over_configs(num_train_timesteps=__a     ,		time_step=__a	)





							def       snake_case__						(      self				:							Dict	)							->	Optional[Any]:
								for t, num_inference_steps in zip([1, 5, 1_0]     ,		[1_0, 5_0, 1_0_0]	):
									self.check_over_forward(num_inference_steps=__a     ,		time_step=__a	)






							def       snake_case__						(      self				:							Union[str, Any]	)							->	List[Any]:
								__UpperCAmelCase							=			self.full_loop()
								__UpperCAmelCase							=			torch.mean(torch.abs(__a	)	)

								assert abs(result_mean.item() - 2_5_4_0_5_2_9	) < 1_0

'''simple docstring'''


from __future__ import annotations

from statistics import mean





def       lowerCAmelCase					(		UpperCamelCase__				:   list[int]    ,					UpperCamelCase__				:   list[int]    ,					UpperCamelCase__				:   int       ):


	"""simple docstring"""
	__UpperCAmelCase							=			[0] * no_of_processes
	__UpperCAmelCase							=			[0] * no_of_processes

	# Initialize remaining_time to waiting_time.

	for i in range(UpperCamelCase__       ):
		__UpperCAmelCase							=			burst_time[i]
	__UpperCAmelCase							=			[]

	__UpperCAmelCase							=			0
	__UpperCAmelCase							=			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:
		__UpperCAmelCase							=			[]
		__UpperCAmelCase							=			-1

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

		if len(UpperCamelCase__       ) > 0:
			__UpperCAmelCase							=			ready_process[0]
			for i in ready_process:
				if remaining_time[i] < remaining_time[target_process]:
					__UpperCAmelCase							=			i
			total_time += burst_time[target_process]
			completed += 1
			__UpperCAmelCase							=			0
			__UpperCAmelCase							=			(
			    total_time - arrival_time[target_process] - burst_time[target_process]
			)
		else:
			total_time += 1

	return waiting_time





def       lowerCAmelCase					(		UpperCamelCase__				:   list[int]    ,					UpperCamelCase__				:   int    ,					UpperCamelCase__				:   list[int]       ):


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


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

					__lowerCAmelCase	:       List[Any]						       =	4
					__lowerCAmelCase	:       List[Any]						       =	[2, 5, 3, 7]
					__lowerCAmelCase	:       Tuple						       =	[0, 0, 0, 0]
					__lowerCAmelCase	:       Optional[int]						       =	calculate_waitingtime(arrival_time, burst_time, no_of_processes)
					__lowerCAmelCase	:       Dict						       =	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}""")
