






"""simple docstring"""
import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       (IPNDMScheduler,)
	__magic_name__			   =       (('''num_inference_steps''', 50),)





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]			,			**lowerCAmelCase_      :				Optional[Any]		)		->    Optional[Any]:
				UpperCAmelCase_  :				Tuple      =      {"num_train_timesteps": 1_000}
				config.update(**lowerCAmelCase_		)
				return config





	def   _SCREAMING_SNAKE_CASE   (   self      :				int			,			lowerCAmelCase_      :				Dict=0			,			**lowerCAmelCase_      :				int		)		->    Dict:
				UpperCAmelCase_  :				Tuple      =      dict(self.forward_default_kwargs		)
				UpperCAmelCase_  :				Tuple      =      kwargs.pop("num_inference_steps"			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				Any      =      self.dummy_sample
				UpperCAmelCase_  :				int      =      0.1 * sample
				UpperCAmelCase_  :				Any      =      [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

				for scheduler_class in self.scheduler_classes:
							UpperCAmelCase_  :				int      =      self.get_scheduler_config(**lowerCAmelCase_		)
							UpperCAmelCase_  :				List[str]      =      scheduler_class(**lowerCAmelCase_		)
							scheduler.set_timesteps(lowerCAmelCase_		)
							# copy over dummy past residuals
							UpperCAmelCase_  :				List[str]      =      dummy_past_residuals[:]

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

							with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(lowerCAmelCase_		)
										UpperCAmelCase_  :				Tuple      =      scheduler_class.from_pretrained(lowerCAmelCase_		)
										new_scheduler.set_timesteps(lowerCAmelCase_		)
										# copy over dummy past residuals
										UpperCAmelCase_  :				List[str]      =      dummy_past_residuals[:]

							UpperCAmelCase_  :				Optional[Any]      =      scheduler.step(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			**lowerCAmelCase_		).prev_sample
							UpperCAmelCase_  :				Union[str, Any]      =      new_scheduler.step(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			**lowerCAmelCase_		).prev_sample

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

							UpperCAmelCase_  :				int      =      scheduler.step(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			**lowerCAmelCase_		).prev_sample
							UpperCAmelCase_  :				Dict      =      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   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]		)		->    Dict:
				pass





	def   _SCREAMING_SNAKE_CASE   (   self      :				str			,			lowerCAmelCase_      :				Optional[int]=0			,			**lowerCAmelCase_      :				Optional[Any]		)		->    Any:
				UpperCAmelCase_  :				str      =      dict(self.forward_default_kwargs		)
				UpperCAmelCase_  :				int      =      kwargs.pop("num_inference_steps"			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				str      =      self.dummy_sample
				UpperCAmelCase_  :				Dict      =      0.1 * sample
				UpperCAmelCase_  :				Dict      =      [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

				for scheduler_class in self.scheduler_classes:
							UpperCAmelCase_  :				int      =      self.get_scheduler_config()
							UpperCAmelCase_  :				Union[str, Any]      =      scheduler_class(**lowerCAmelCase_		)
							scheduler.set_timesteps(lowerCAmelCase_		)

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

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

							with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(lowerCAmelCase_		)
										UpperCAmelCase_  :				int      =      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)
										UpperCAmelCase_  :				List[str]      =      dummy_past_residuals[:]

							UpperCAmelCase_  :				Dict      =      scheduler.step(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			**lowerCAmelCase_		).prev_sample
							UpperCAmelCase_  :				str      =      new_scheduler.step(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			**lowerCAmelCase_		).prev_sample

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

							UpperCAmelCase_  :				List[str]      =      scheduler.step(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			**lowerCAmelCase_		).prev_sample
							UpperCAmelCase_  :				Union[str, Any]      =      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   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]			,			**lowerCAmelCase_      :				Optional[int]		)		->    Union[str, Any]:
				UpperCAmelCase_  :				Optional[Any]      =      self.scheduler_classes[0]
				UpperCAmelCase_  :				int      =      self.get_scheduler_config(**lowerCAmelCase_		)
				UpperCAmelCase_  :				Dict      =      scheduler_class(**lowerCAmelCase_		)

				UpperCAmelCase_  :				Optional[Any]      =      10
				UpperCAmelCase_  :				Union[str, Any]      =      self.dummy_model()
				UpperCAmelCase_  :				int      =      self.dummy_sample_deter
				scheduler.set_timesteps(lowerCAmelCase_		)

				for i, t in enumerate(scheduler.timesteps		):
							UpperCAmelCase_  :				Any      =      model(lowerCAmelCase_			,			lowerCAmelCase_		)
							UpperCAmelCase_  :				List[Any]      =      scheduler.step(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_		).prev_sample

				for i, t in enumerate(scheduler.timesteps		):
							UpperCAmelCase_  :				Optional[int]      =      model(lowerCAmelCase_			,			lowerCAmelCase_		)
							UpperCAmelCase_  :				Optional[int]      =      scheduler.step(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_		).prev_sample

				return sample





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    List[str]:
				UpperCAmelCase_  :				Optional[Any]      =      dict(self.forward_default_kwargs		)

				UpperCAmelCase_  :				List[str]      =      kwargs.pop("num_inference_steps"			,			lowerCAmelCase_		)

				for scheduler_class in self.scheduler_classes:
							UpperCAmelCase_  :				List[str]      =      self.get_scheduler_config()
							UpperCAmelCase_  :				Tuple      =      scheduler_class(**lowerCAmelCase_		)

							UpperCAmelCase_  :				Any      =      self.dummy_sample
							UpperCAmelCase_  :				Tuple      =      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"		):
										UpperCAmelCase_  :				int      =      num_inference_steps

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

							UpperCAmelCase_  :				Any      =      scheduler.timesteps[5]
							UpperCAmelCase_  :				Optional[int]      =      scheduler.timesteps[6]

							UpperCAmelCase_  :				Dict      =      scheduler.step(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			**lowerCAmelCase_		).prev_sample
							UpperCAmelCase_  :				List[str]      =      scheduler.step(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			**lowerCAmelCase_		).prev_sample

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

							UpperCAmelCase_  :				Optional[int]      =      scheduler.step(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			**lowerCAmelCase_		).prev_sample
							UpperCAmelCase_  :				Dict      =      scheduler.step(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			**lowerCAmelCase_		).prev_sample

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





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]		)		->    Any:
				for timesteps in [100, 1_000]:
							self.check_over_configs(num_train_timesteps=lowerCAmelCase_			,			time_step=lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    Optional[Any]:
				for t, num_inference_steps in zip([1, 5, 10]			,			[10, 50, 100]		):
							self.check_over_forward(num_inference_steps=lowerCAmelCase_			,			time_step=lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    List[str]:
				UpperCAmelCase_  :				Optional[Any]      =      self.full_loop()
				UpperCAmelCase_  :				List[str]      =      torch.mean(torch.abs(lowerCAmelCase_		)		)

				assert abs(result_mean.item() - 2_540_529		) < 10











"""simple docstring"""
from __future__ import annotations

import math

def        snake_case   (     A__		):

			if 1 < number < 4:
						# 2 and 3 are primes
						return True
			elif number < 2 or number % 2 == 0 or number % 3 == 0:
						# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
						return False

			# All primes number are in format of 6k +/- 1
			for i in range(5			,int(math.sqrt(A__		) + 1		)			,6		):
						if number % i == 0 or number % (i + 2) == 0:
									return False
			return True


lowerCamelCase_       			=   [num for num in range(3, 10_0001, 2) if not is_prime(num)]

def        snake_case   (     A__		):
			if not isinstance(A__			,A__		):
						raise ValueError("n must be an integer"		)
			if n <= 0:
						raise ValueError("n must be >= 0"		)

			UpperCAmelCase_  :				List[str]      =      []
			for num in range(len(A__		)		):
						UpperCAmelCase_  :				Dict      =      0
						while 2 * i * i <= odd_composites[num]:
									UpperCAmelCase_  :				List[str]      =      odd_composites[num] - 2 * i * i
									if is_prime(A__		):
												break
									i += 1
						else:
									list_nums.append(odd_composites[num]		)
									if len(A__		) == n:
												return list_nums

			return []

def        snake_case   (     ):
			return compute_nums(1		)[0]


if __name__ == "__main__":
			print(f'{solution() = }')



