

import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest


class 			lowerCamelCase_    (  A__		):
							__lowercase				:	Union[str, Any]        =				(IPNDMScheduler,)
							__lowercase				:	Dict        =				(("num_inference_steps", 50),)


							def  lowercase   ( self      ,				**lowerCamelCase_						)							->   Any:



								"""simple docstring"""

								_UpperCamelCase			=   {"num_train_timesteps": 10_00}
								config.update(**lowerCamelCase_						)
								return config


							def  lowercase   ( self      ,				lowerCamelCase_=0      ,				**lowerCamelCase_						)							->   List[str]:



								"""simple docstring"""

								_UpperCamelCase			=   dict(self.forward_default_kwargs						)
								_UpperCamelCase			=   kwargs.pop("num_inference_steps"      ,				lowerCamelCase_						)
								_UpperCamelCase			=   self.dummy_sample
								_UpperCamelCase			=   0.1 * sample
								_UpperCamelCase			=   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
									_UpperCamelCase			=   self.get_scheduler_config(**lowerCamelCase_						)
									_UpperCamelCase			=   scheduler_class(**lowerCamelCase_						)
									scheduler.set_timesteps(lowerCamelCase_						)
									# 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(lowerCamelCase_						)
										_UpperCamelCase			=   scheduler_class.from_pretrained(lowerCamelCase_						)
										new_scheduler.set_timesteps(lowerCamelCase_						)
										# copy over dummy past residuals
										_UpperCamelCase			=   dummy_past_residuals[:]

									_UpperCamelCase			=   scheduler.step(lowerCamelCase_      ,				lowerCamelCase_      ,				lowerCamelCase_      ,				**lowerCamelCase_						).prev_sample
									_UpperCamelCase			=   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			=   scheduler.step(lowerCamelCase_      ,				lowerCamelCase_      ,				lowerCamelCase_      ,				**lowerCamelCase_						).prev_sample
									_UpperCamelCase			=   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  lowercase   ( self						)							->   int:



								"""simple docstring"""

								pass


							def  lowercase   ( self      ,				lowerCamelCase_=0      ,				**lowerCamelCase_						)							->   Tuple:



								"""simple docstring"""

								_UpperCamelCase			=   dict(self.forward_default_kwargs						)
								_UpperCamelCase			=   kwargs.pop("num_inference_steps"      ,				lowerCamelCase_						)
								_UpperCamelCase			=   self.dummy_sample
								_UpperCamelCase			=   0.1 * sample
								_UpperCamelCase			=   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
									_UpperCamelCase			=   self.get_scheduler_config()
									_UpperCamelCase			=   scheduler_class(**lowerCamelCase_						)
									scheduler.set_timesteps(lowerCamelCase_						)

									# 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(lowerCamelCase_						)
										_UpperCamelCase			=   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			=   dummy_past_residuals[:]

									_UpperCamelCase			=   scheduler.step(lowerCamelCase_      ,				lowerCamelCase_      ,				lowerCamelCase_      ,				**lowerCamelCase_						).prev_sample
									_UpperCamelCase			=   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			=   scheduler.step(lowerCamelCase_      ,				lowerCamelCase_      ,				lowerCamelCase_      ,				**lowerCamelCase_						).prev_sample
									_UpperCamelCase			=   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  lowercase   ( self      ,				**lowerCamelCase_						)							->   Any:



								"""simple docstring"""

								_UpperCamelCase			=   self.scheduler_classes[0]
								_UpperCamelCase			=   self.get_scheduler_config(**lowerCamelCase_						)
								_UpperCamelCase			=   scheduler_class(**lowerCamelCase_						)

								_UpperCamelCase			=   10
								_UpperCamelCase			=   self.dummy_model()
								_UpperCamelCase			=   self.dummy_sample_deter
								scheduler.set_timesteps(lowerCamelCase_						)

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

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

								return sample


							def  lowercase   ( self						)							->   List[str]:



								"""simple docstring"""

								_UpperCamelCase			=   dict(self.forward_default_kwargs						)

								_UpperCamelCase			=   kwargs.pop("num_inference_steps"      ,				lowerCamelCase_						)

								for scheduler_class in self.scheduler_classes:
									_UpperCamelCase			=   self.get_scheduler_config()
									_UpperCamelCase			=   scheduler_class(**lowerCamelCase_						)

									_UpperCamelCase			=   self.dummy_sample
									_UpperCamelCase			=   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			=   num_inference_steps

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

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

									_UpperCamelCase			=   scheduler.step(lowerCamelCase_      ,				lowerCamelCase_      ,				lowerCamelCase_      ,				**lowerCamelCase_						).prev_sample
									_UpperCamelCase			=   scheduler.step(lowerCamelCase_      ,				lowerCamelCase_      ,				lowerCamelCase_      ,				**lowerCamelCase_						).prev_sample

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

									_UpperCamelCase			=   scheduler.step(lowerCamelCase_      ,				lowerCamelCase_      ,				lowerCamelCase_      ,				**lowerCamelCase_						).prev_sample
									_UpperCamelCase			=   scheduler.step(lowerCamelCase_      ,				lowerCamelCase_      ,				lowerCamelCase_      ,				**lowerCamelCase_						).prev_sample

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


							def  lowercase   ( self						)							->   str:



								"""simple docstring"""

								for timesteps in [1_00, 10_00]:
									self.check_over_configs(num_train_timesteps=lowerCamelCase_      ,				time_step=lowerCamelCase_						)


							def  lowercase   ( self						)							->   Optional[int]:



								"""simple docstring"""

								for t, num_inference_steps in zip([1, 5, 10]      ,				[10, 50, 1_00]						):
									self.check_over_forward(num_inference_steps=lowerCamelCase_      ,				time_step=lowerCamelCase_						)







							def  lowercase   ( self						)							->   Optional[Any]:



								"""simple docstring"""

								_UpperCamelCase			=   self.full_loop()
								_UpperCamelCase			=   torch.mean(torch.abs(lowerCamelCase_						)						)

								assert abs(result_mean.item() - 2_54_05_29						) < 10












import sys
import turtle






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2






def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  ):






				'''simple docstring'''
				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(lowerCamelCase_						,  get_mid(lowerCamelCase_						,  lowerCamelCase_							)						,  get_mid(lowerCamelCase_						,  lowerCamelCase_							)						,  depth - 1							)
				triangle(lowerCamelCase_						,  get_mid(lowerCamelCase_						,  lowerCamelCase_							)						,  get_mid(lowerCamelCase_						,  lowerCamelCase_							)						,  depth - 1							)
				triangle(lowerCamelCase_						,  get_mid(lowerCamelCase_						,  lowerCamelCase_							)						,  get_mid(lowerCamelCase_						,  lowerCamelCase_							)						,  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__      :     Dict       =					turtle.Turtle()
							my_pen.ht()
							my_pen.speed(5)
							my_pen.pencolor('red')

							A__      :     Union[str, Any]       =					[(-1_75, -1_25), (0, 1_75), (1_75, -1_25)]  # vertices of triangle
							triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))




