


'''simple docstring'''



import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest





class 							__UpperCamelCase    (		lowerCAmelCase_ ):
	A_           =   (IPNDMScheduler,)
	A_           =   (("num_inference_steps", 50),)




	def 				__UpperCAmelCase							(       self						,    **__a							):

						'''simple docstring'''





						__a      :	List[Any]    =  {'num_train_timesteps': 1000}
						config.update(**__a							)
						return config




	def 				__UpperCAmelCase							(       self						,    __a=0						,    **__a							):

						'''simple docstring'''





						__a      :	Any    =  dict(self.forward_default_kwargs							)
						__a      :	Optional[Any]    =  kwargs.pop('num_inference_steps'						,    __a							)
						__a      :	str    =  self.dummy_sample
						__a      :	List[Any]    =  0.1 * sample
						__a      :	Tuple    =  [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

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

											__a      :	str    =  scheduler.step(__a						,    __a						,    __a						,    **__a							).prev_sample
											__a      :	int    =  new_scheduler.step(__a						,    __a						,    __a						,    **__a							).prev_sample

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

											__a      :	List[str]    =  scheduler.step(__a						,    __a						,    __a						,    **__a							).prev_sample
											__a      :	Tuple    =  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 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						pass




	def 				__UpperCAmelCase							(       self						,    __a=0						,    **__a							):

						'''simple docstring'''





						__a      :	Any    =  dict(self.forward_default_kwargs							)
						__a      :	Union[str, Any]    =  kwargs.pop('num_inference_steps'						,    __a							)
						__a      :	Any    =  self.dummy_sample
						__a      :	List[Any]    =  0.1 * sample
						__a      :	str    =  [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

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

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

											__a      :	Optional[Any]    =  scheduler.step(__a						,    __a						,    __a						,    **__a							).prev_sample
											__a      :	int    =  new_scheduler.step(__a						,    __a						,    __a						,    **__a							).prev_sample

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

											__a      :	Dict    =  scheduler.step(__a						,    __a						,    __a						,    **__a							).prev_sample
											__a      :	Optional[int]    =  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 				__UpperCAmelCase							(       self						,    **__a							):

						'''simple docstring'''





						__a      :	int    =  self.scheduler_classes[0]
						__a      :	List[Any]    =  self.get_scheduler_config(**__a							)
						__a      :	List[str]    =  scheduler_class(**__a							)

						__a      :	int    =  10
						__a      :	Any    =  self.dummy_model()
						__a      :	int    =  self.dummy_sample_deter
						scheduler.set_timesteps(__a							)

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

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

						return sample




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[int]    =  dict(self.forward_default_kwargs							)

						__a      :	Optional[Any]    =  kwargs.pop('num_inference_steps'						,    __a							)

						for scheduler_class in self.scheduler_classes:
											__a      :	Union[str, Any]    =  self.get_scheduler_config()
											__a      :	Tuple    =  scheduler_class(**__a							)

											__a      :	List[Any]    =  self.dummy_sample
											__a      :	Optional[Any]    =  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'							):
																__a      :	Optional[int]    =  num_inference_steps

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

											__a      :	Optional[Any]    =  scheduler.timesteps[5]
											__a      :	Union[str, Any]    =  scheduler.timesteps[6]

											__a      :	Optional[int]    =  scheduler.step(__a						,    __a						,    __a						,    **__a							).prev_sample
											__a      :	Optional[int]    =  scheduler.step(__a						,    __a						,    __a						,    **__a							).prev_sample

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

											__a      :	List[str]    =  scheduler.step(__a						,    __a						,    __a						,    **__a							).prev_sample
											__a      :	Tuple    =  scheduler.step(__a						,    __a						,    __a						,    **__a							).prev_sample

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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						for timesteps in [100, 1000]:
											self.check_over_configs(num_train_timesteps=__a						,    time_step=__a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						for t, num_inference_steps in zip([1, 5, 10]						,    [10, 50, 100]							):
											self.check_over_forward(num_inference_steps=__a						,    time_step=__a							)





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	int    =  self.full_loop()
						__a      :	List[str]    =  torch.mean(torch.abs(__a							)							)

						assert abs(result_mean.item() - 254_0529							) < 10







'''simple docstring'''



def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       int						,				_SCREAMING_SNAKE_CASE				:       int       ):
					if not isinstance(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       ):
										raise ValueError('iterations must be defined as integers'       )
					if not isinstance(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       ) or not number >= 1:
										raise ValueError(
										    'starting number must be\n                         and integer and be more than 0'       )
					if not iterations >= 1:
										raise ValueError('Iterations must be done more than 0 times to play FizzBuzz'       )

					__a      :	Dict    =  ''
					while number <= iterations:
										if number % 3 == 0:
															out += "Fizz"
										if number % 5 == 0:
															out += "Buzz"
										if 0 not in (number % 3, number % 5):
															out += str(_SCREAMING_SNAKE_CASE       )

										# print(out)
										number += 1
										out += " "
					return out


if __name__ == "__main__":
	import doctest

	doctest.testmod()



