






'''simple docstring'''







import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest

class _lowercase			(       _lowercase	):
							a          = (IPNDMScheduler,)
							a          = (("""num_inference_steps""", 50),)




							def 	lowerCamelCase_       (							self:       int    ,							**UpperCamelCase__:       Optional[int]  ):
														lowerCamelCase__      :			Optional[int] 						= {"""num_train_timesteps""": 1_000}
														config.update(**UpperCamelCase__  )
														return config




							def 	lowerCamelCase_       (							self:       List[Any]    ,							UpperCamelCase__:       Optional[int]=0    ,							**UpperCamelCase__:       List[Any]  ):
														lowerCamelCase__      :			Optional[Any] 						= dict(self.forward_default_kwargs  )
														lowerCamelCase__      :			List[str] 						= kwargs.pop("""num_inference_steps"""    ,							UpperCamelCase__  )
														lowerCamelCase__      :			List[str] 						= self.dummy_sample
														lowerCamelCase__      :			List[str] 						= 0.1 * sample
														lowerCamelCase__      :			int 						= [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

																					with tempfile.TemporaryDirectory() as tmpdirname:
																												scheduler.save_config(UpperCamelCase__  )
																												lowerCamelCase__      :			Optional[int] 						= scheduler_class.from_pretrained(UpperCamelCase__  )
																												new_scheduler.set_timesteps(UpperCamelCase__  )
																												# copy over dummy past residuals
																												lowerCamelCase__      :			str 						= dummy_past_residuals[:]

																					lowerCamelCase__      :			Optional[Any] 						= scheduler.step(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							**UpperCamelCase__  ).prev_sample
																					lowerCamelCase__      :			Any 						= new_scheduler.step(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							**UpperCamelCase__  ).prev_sample

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

																					lowerCamelCase__      :			Optional[Any] 						= scheduler.step(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							**UpperCamelCase__  ).prev_sample
																					lowerCamelCase__      :			List[Any] 						= new_scheduler.step(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							**UpperCamelCase__  ).prev_sample

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




							def 	lowerCamelCase_       (							self:       Any  ):
														pass




							def 	lowerCamelCase_       (							self:       Union[str, Any]    ,							UpperCamelCase__:       Dict=0    ,							**UpperCamelCase__:       List[str]  ):
														lowerCamelCase__      :			List[str] 						= dict(self.forward_default_kwargs  )
														lowerCamelCase__      :			Union[str, Any] 						= kwargs.pop("""num_inference_steps"""    ,							UpperCamelCase__  )
														lowerCamelCase__      :			List[Any] 						= self.dummy_sample
														lowerCamelCase__      :			int 						= 0.1 * sample
														lowerCamelCase__      :			Any 						= [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

														for scheduler_class in self.scheduler_classes:
																					lowerCamelCase__      :			int 						= self.get_scheduler_config()
																					lowerCamelCase__      :			Optional[int] 						= scheduler_class(**UpperCamelCase__  )
																					scheduler.set_timesteps(UpperCamelCase__  )

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

																					if time_step is None:
																												lowerCamelCase__      :			Union[str, Any] 						= scheduler.timesteps[len(scheduler.timesteps  ) // 2]

																					with tempfile.TemporaryDirectory() as tmpdirname:
																												scheduler.save_config(UpperCamelCase__  )
																												lowerCamelCase__      :			Optional[Any] 						= scheduler_class.from_pretrained(UpperCamelCase__  )
																												# copy over dummy past residuals
																												new_scheduler.set_timesteps(UpperCamelCase__  )

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

																					lowerCamelCase__      :			Any 						= scheduler.step(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							**UpperCamelCase__  ).prev_sample
																					lowerCamelCase__      :			Any 						= new_scheduler.step(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							**UpperCamelCase__  ).prev_sample

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

																					lowerCamelCase__      :			Optional[Any] 						= scheduler.step(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							**UpperCamelCase__  ).prev_sample
																					lowerCamelCase__      :			List[Any] 						= new_scheduler.step(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							**UpperCamelCase__  ).prev_sample

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




							def 	lowerCamelCase_       (							self:       Optional[int]    ,							**UpperCamelCase__:       Any  ):
														lowerCamelCase__      :			Optional[int] 						= self.scheduler_classes[0]
														lowerCamelCase__      :			Optional[Any] 						= self.get_scheduler_config(**UpperCamelCase__  )
														lowerCamelCase__      :			Optional[Any] 						= scheduler_class(**UpperCamelCase__  )

														lowerCamelCase__      :			Union[str, Any] 						= 10
														lowerCamelCase__      :			int 						= self.dummy_model()
														lowerCamelCase__      :			List[Any] 						= self.dummy_sample_deter
														scheduler.set_timesteps(UpperCamelCase__  )

														for i, t in enumerate(scheduler.timesteps  ):
																					lowerCamelCase__      :			int 						= model(UpperCamelCase__    ,							UpperCamelCase__  )
																					lowerCamelCase__      :			List[str] 						= scheduler.step(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__  ).prev_sample

														for i, t in enumerate(scheduler.timesteps  ):
																					lowerCamelCase__      :			Optional[Any] 						= model(UpperCamelCase__    ,							UpperCamelCase__  )
																					lowerCamelCase__      :			Dict 						= scheduler.step(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__  ).prev_sample

														return sample




							def 	lowerCamelCase_       (							self:       int  ):
														lowerCamelCase__      :			Tuple 						= dict(self.forward_default_kwargs  )

														lowerCamelCase__      :			List[Any] 						= kwargs.pop("""num_inference_steps"""    ,							UpperCamelCase__  )

														for scheduler_class in self.scheduler_classes:
																					lowerCamelCase__      :			int 						= self.get_scheduler_config()
																					lowerCamelCase__      :			Optional[int] 						= scheduler_class(**UpperCamelCase__  )

																					lowerCamelCase__      :			str 						= self.dummy_sample
																					lowerCamelCase__      :			Any 						= 0.1 * sample

																					if num_inference_steps is not None and hasattr(UpperCamelCase__    ,							"""set_timesteps"""  ):
																												scheduler.set_timesteps(UpperCamelCase__  )
																					elif num_inference_steps is not None and not hasattr(UpperCamelCase__    ,							"""set_timesteps"""  ):
																												lowerCamelCase__      :			Tuple 						= num_inference_steps

																					# copy over dummy past residuals (must be done after set_timesteps)
																					lowerCamelCase__      :			int 						= [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
																					lowerCamelCase__      :			Tuple 						= dummy_past_residuals[:]

																					lowerCamelCase__      :			int 						= scheduler.timesteps[5]
																					lowerCamelCase__      :			Tuple 						= scheduler.timesteps[6]

																					lowerCamelCase__      :			Optional[int] 						= scheduler.step(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							**UpperCamelCase__  ).prev_sample
																					lowerCamelCase__      :			Union[str, Any] 						= scheduler.step(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							**UpperCamelCase__  ).prev_sample

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

																					lowerCamelCase__      :			Tuple 						= scheduler.step(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							**UpperCamelCase__  ).prev_sample
																					lowerCamelCase__      :			List[Any] 						= scheduler.step(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							**UpperCamelCase__  ).prev_sample

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




							def 	lowerCamelCase_       (							self:       Tuple  ):
														for timesteps in [100, 1_000]:
																					self.check_over_configs(num_train_timesteps=UpperCamelCase__    ,							time_step=UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Optional[Any]  ):
														for t, num_inference_steps in zip([1, 5, 10]    ,							[10, 50, 100]  ):
																					self.check_over_forward(num_inference_steps=UpperCamelCase__    ,							time_step=UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Optional[Any]  ):
														lowerCamelCase__      :			List[Any] 						= self.full_loop()
														lowerCamelCase__      :			List[str] 						= torch.mean(torch.abs(UpperCamelCase__  )  )

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









'''simple docstring'''







import sys
import turtle





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase   )   -> tuple[float, float]:
							return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    )   -> None:
							my_pen.up()
							my_pen.goto(vertexa[0]       ,    vertexa[1]   )
							my_pen.down()
							my_pen.goto(vertexa[0]       ,    vertexa[1]   )
							my_pen.goto(vertexa[0]       ,    vertexa[1]   )
							my_pen.goto(vertexa[0]       ,    vertexa[1]   )

							if depth == 0:
														return

							triangle(UpperCamelCase       ,    get_mid(UpperCamelCase       ,    UpperCamelCase   )       ,    get_mid(UpperCamelCase       ,    UpperCamelCase   )       ,    depth - 1   )
							triangle(UpperCamelCase       ,    get_mid(UpperCamelCase       ,    UpperCamelCase   )       ,    get_mid(UpperCamelCase       ,    UpperCamelCase   )       ,    depth - 1   )
							triangle(UpperCamelCase       ,    get_mid(UpperCamelCase       ,    UpperCamelCase   )       ,    get_mid(UpperCamelCase       ,    UpperCamelCase   )       ,    depth - 1   )


if __name__ == "__main__":
			if len(sys.argv) != 2:
						raise ValueError(
						    '''Correct format for using this script: '''
						    '''python fractals.py <int:depth_for_fractal>'''
						)
			_A			:    Any					  =turtle.Turtle()
			my_pen.ht()
			my_pen.speed(5)
			my_pen.pencolor('''red''')

			_A			:    Dict					  =[(-175, -125), (0, 175), (175, -125)]  # vertices of triangle
			triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))

