
"""simple docstring"""






import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class      _SCREAMING_SNAKE_CASE   (     A__				):
			UpperCAmelCase_    :Tuple												=		(HeunDiscreteScheduler,)
			UpperCAmelCase_    :List[Any]												=		10






			def 					__lowerCAmelCase					(   self			,  **__A			)     ->	Optional[Any]:
						lowerCAmelCase_       :Dict     	=    {
						    """num_train_timesteps""": 1100,
						    """beta_start""": 0.0_0_0_1,
						    """beta_end""": 0.0_2,
						    """beta_schedule""": """linear""",
						}

						config.update(**__A			)
						return config






			def 					__lowerCAmelCase					(   self			)     ->	Dict:
						for timesteps in [10, 50, 100, 1000]:
									self.check_over_configs(num_train_timesteps=__A			)






			def 					__lowerCAmelCase					(   self			)     ->	Optional[int]:
						for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]			,  [0.0_0_0_2, 0.0_0_2, 0.0_2]			):
									self.check_over_configs(beta_start=__A			,  beta_end=__A			)






			def 					__lowerCAmelCase					(   self			)     ->	Optional[int]:
						for schedule in ["linear", "scaled_linear", "exp"]:
									self.check_over_configs(beta_schedule=__A			)






			def 					__lowerCAmelCase					(   self			)     ->	Union[str, Any]:
						for clip_sample_range in [1.0, 2.0, 3.0]:
									self.check_over_configs(clip_sample_range=__A			,  clip_sample=__A			)






			def 					__lowerCAmelCase					(   self			)     ->	List[str]:
						for prediction_type in ["epsilon", "v_prediction", "sample"]:
									self.check_over_configs(prediction_type=__A			)






			def 					__lowerCAmelCase					(   self			)     ->	int:
						lowerCAmelCase_       :Tuple     	=    self.scheduler_classes[0]
						lowerCAmelCase_       :Optional[int]     	=    self.get_scheduler_config()
						lowerCAmelCase_       :Tuple     	=    scheduler_class(**__A			)

						scheduler.set_timesteps(self.num_inference_steps			)

						lowerCAmelCase_       :List[Any]     	=    self.dummy_model()
						lowerCAmelCase_       :Union[str, Any]     	=    self.dummy_sample_deter * scheduler.init_noise_sigma
						lowerCAmelCase_       :Dict     	=    sample.to(__A			)

						for i, t in enumerate(scheduler.timesteps			):
									lowerCAmelCase_       :Any     	=    scheduler.scale_model_input(__A			,  __A			)

									lowerCAmelCase_       :Union[str, Any]     	=    model(__A			,  __A			)

									lowerCAmelCase_       :Tuple     	=    scheduler.step(__A			,  __A			,  __A			)
									lowerCAmelCase_       :Any     	=    output.prev_sample

						lowerCAmelCase_       :List[Any]     	=    torch.sum(torch.abs(__A			)			)
						lowerCAmelCase_       :Union[str, Any]     	=    torch.mean(torch.abs(__A			)			)

						if torch_device in ["cpu", "mps"]:
									assert abs(result_sum.item() - 0.1_2_3_3			) < 1E-2
									assert abs(result_mean.item() - 0.0_0_0_2			) < 1E-3
						else:
									# CUDA
									assert abs(result_sum.item() - 0.1_2_3_3			) < 1E-2
									assert abs(result_mean.item() - 0.0_0_0_2			) < 1E-3






			def 					__lowerCAmelCase					(   self			)     ->	Dict:
						lowerCAmelCase_       :Optional[Any]     	=    self.scheduler_classes[0]
						lowerCAmelCase_       :Tuple     	=    self.get_scheduler_config(prediction_type="""v_prediction"""			)
						lowerCAmelCase_       :int     	=    scheduler_class(**__A			)

						scheduler.set_timesteps(self.num_inference_steps			)

						lowerCAmelCase_       :List[str]     	=    self.dummy_model()
						lowerCAmelCase_       :Union[str, Any]     	=    self.dummy_sample_deter * scheduler.init_noise_sigma
						lowerCAmelCase_       :Optional[Any]     	=    sample.to(__A			)

						for i, t in enumerate(scheduler.timesteps			):
									lowerCAmelCase_       :str     	=    scheduler.scale_model_input(__A			,  __A			)

									lowerCAmelCase_       :int     	=    model(__A			,  __A			)

									lowerCAmelCase_       :Dict     	=    scheduler.step(__A			,  __A			,  __A			)
									lowerCAmelCase_       :Any     	=    output.prev_sample

						lowerCAmelCase_       :Optional[int]     	=    torch.sum(torch.abs(__A			)			)
						lowerCAmelCase_       :Tuple     	=    torch.mean(torch.abs(__A			)			)

						if torch_device in ["cpu", "mps"]:
									assert abs(result_sum.item() - 4.69_34E-07			) < 1E-2
									assert abs(result_mean.item() - 6.11_12E-10			) < 1E-3
						else:
									# CUDA
									assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07			) < 1E-2
									assert abs(result_mean.item() - 0.0_0_0_2			) < 1E-3






			def 					__lowerCAmelCase					(   self			)     ->	int:
						lowerCAmelCase_       :Tuple     	=    self.scheduler_classes[0]
						lowerCAmelCase_       :Union[str, Any]     	=    self.get_scheduler_config()
						lowerCAmelCase_       :List[Any]     	=    scheduler_class(**__A			)

						scheduler.set_timesteps(self.num_inference_steps			,  device=__A			)

						lowerCAmelCase_       :Any     	=    self.dummy_model()
						lowerCAmelCase_       :Dict     	=    self.dummy_sample_deter.to(__A			) * scheduler.init_noise_sigma

						for t in scheduler.timesteps:
									lowerCAmelCase_       :Optional[Any]     	=    scheduler.scale_model_input(__A			,  __A			)

									lowerCAmelCase_       :List[Any]     	=    model(__A			,  __A			)

									lowerCAmelCase_       :str     	=    scheduler.step(__A			,  __A			,  __A			)
									lowerCAmelCase_       :List[Any]     	=    output.prev_sample

						lowerCAmelCase_       :Dict     	=    torch.sum(torch.abs(__A			)			)
						lowerCAmelCase_       :Optional[int]     	=    torch.mean(torch.abs(__A			)			)

						if str(__A			).startswith("""cpu"""			):
									# The following sum varies between 148 and 156 on mps. Why?
									assert abs(result_sum.item() - 0.1_2_3_3			) < 1E-2
									assert abs(result_mean.item() - 0.0_0_0_2			) < 1E-3
						elif str(__A			).startswith("""mps"""			):
									# Larger tolerance on mps
									assert abs(result_mean.item() - 0.0_0_0_2			) < 1E-2
						else:
									# CUDA
									assert abs(result_sum.item() - 0.1_2_3_3			) < 1E-2
									assert abs(result_mean.item() - 0.0_0_0_2			) < 1E-3






			def 					__lowerCAmelCase					(   self			)     ->	str:
						lowerCAmelCase_       :Optional[int]     	=    self.scheduler_classes[0]
						lowerCAmelCase_       :Optional[int]     	=    self.get_scheduler_config()
						lowerCAmelCase_       :Tuple     	=    scheduler_class(**__A			,  use_karras_sigmas=__A			)

						scheduler.set_timesteps(self.num_inference_steps			,  device=__A			)

						lowerCAmelCase_       :List[Any]     	=    self.dummy_model()
						lowerCAmelCase_       :Dict     	=    self.dummy_sample_deter.to(__A			) * scheduler.init_noise_sigma
						lowerCAmelCase_       :Union[str, Any]     	=    sample.to(__A			)

						for t in scheduler.timesteps:
									lowerCAmelCase_       :List[str]     	=    scheduler.scale_model_input(__A			,  __A			)

									lowerCAmelCase_       :List[str]     	=    model(__A			,  __A			)

									lowerCAmelCase_       :Union[str, Any]     	=    scheduler.step(__A			,  __A			,  __A			)
									lowerCAmelCase_       :List[str]     	=    output.prev_sample

						lowerCAmelCase_       :Tuple     	=    torch.sum(torch.abs(__A			)			)
						lowerCAmelCase_       :Optional[Any]     	=    torch.mean(torch.abs(__A			)			)

						assert abs(result_sum.item() - 0.0_0_0_1_5			) < 1E-2
						assert abs(result_mean.item() - 1.98_69_55_45_35_03_46_95E-07			) < 1E-2








"""simple docstring"""






from __future__ import annotations

from typing import Generic, TypeVar

__UpperCAmelCase 					=    TypeVar('T')




class      _SCREAMING_SNAKE_CASE   (     A__				):
			pass




class      _SCREAMING_SNAKE_CASE   (     A__				):
			pass




class      _SCREAMING_SNAKE_CASE   (     Generic[T]				):






			def __init__(   self			,  __A = 10			)     ->	Optional[int]:
						lowerCAmelCase_       :list[T]     	=    []
						lowerCAmelCase_       :int     	=    limit






			def __bool__(   self			)     ->	bool:
						return bool(self.stack			)






			def __str__(   self			)     ->	str:
						return str(self.stack			)






			def 					__lowerCAmelCase					(   self			,  __A			)     ->	None:
						if len(self.stack			) >= self.limit:
									raise StackOverflowError
						self.stack.append(__A			)






			def 					__lowerCAmelCase					(   self			)     ->	T:
						if not self.stack:
									raise StackUnderflowError
						return self.stack.pop()






			def 					__lowerCAmelCase					(   self			)     ->	T:
						if not self.stack:
									raise StackUnderflowError
						return self.stack[-1]






			def 					__lowerCAmelCase					(   self			)     ->	bool:
						return not bool(self.stack			)






			def 					__lowerCAmelCase					(   self			)     ->	bool:
						return self.size() == self.limit






			def 					__lowerCAmelCase					(   self			)     ->	int:
						return len(self.stack			)






			def __contains__(   self			,  __A			)     ->	bool:
						return item in self.stack






def 		_snake_case			(      )    ->							None:


			'''simple docstring'''





			lowerCAmelCase_       :Stack[int]     	=    Stack(1_0						)
			assert bool(lowercase__						) is False
			assert stack.is_empty() is True
			assert stack.is_full() is False
			assert str(lowercase__						) == "[]"

			try:
						lowerCAmelCase_       :Optional[Any]     	=    stack.pop()
						raise AssertionError  # This should not happen
			except StackUnderflowError:
						assert True  # This should happen

			try:
						lowerCAmelCase_       :Optional[int]     	=    stack.peek()
						raise AssertionError  # This should not happen
			except StackUnderflowError:
						assert True  # This should happen

			for i in range(1_0						):
						assert stack.size() == i
						stack.push(lowercase__						)

			assert bool(lowercase__						)
			assert not stack.is_empty()
			assert stack.is_full()
			assert str(lowercase__						) == str(list(range(1_0						)						)						)
			assert stack.pop() == 9
			assert stack.peek() == 8

			stack.push(1_0_0						)
			assert str(lowercase__						) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 1_0_0]						)

			try:
						stack.push(2_0_0						)
						raise AssertionError  # This should not happen
			except StackOverflowError:
						assert True  # This should happen

			assert not stack.is_empty()
			assert stack.size() == 1_0

			assert 5 in stack
			assert 5_5 not in stack


if __name__ == "__main__":
				test_stack()






