
"""simple docstring"""
import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class      lowerCAmelCase_						(							a_				):



							'''simple docstring'''





							_snake_case		     =						(KDPMaAncestralDiscreteScheduler,)
							_snake_case		     =						1_0




							def  A__		(				self  ,							**snake_case_							)   ->       Optional[Any]:
													__lowerCAmelCase        =       {
													    """num_train_timesteps""": 1_100,
													    """beta_start""": 0.0_001,
													    """beta_end""": 0.02,
													    """beta_schedule""": """linear""",
													}

													config.update(**snake_case__							)
													return config




							def  A__		(				self							)   ->       Tuple:


													for timesteps in [10, 50, 100, 1_000]:
																			self.check_over_configs(num_train_timesteps=snake_case__							)




							def  A__		(				self							)   ->       str:


													for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]  ,							[0.0_002, 0.002, 0.02]							):
																			self.check_over_configs(beta_start=snake_case__  ,							beta_end=snake_case__							)




							def  A__		(				self							)   ->       Tuple:


													for schedule in ["linear", "scaled_linear"]:
																			self.check_over_configs(beta_schedule=snake_case__							)




							def  A__		(				self							)   ->       Tuple:


													if torch_device == "mps":
																			return
													__lowerCAmelCase        =       self.scheduler_classes[0]
													__lowerCAmelCase        =       self.get_scheduler_config()
													__lowerCAmelCase        =       scheduler_class(**snake_case__							)

													scheduler.set_timesteps(self.num_inference_steps							)

													__lowerCAmelCase        =       torch.manual_seed(0							)

													__lowerCAmelCase        =       self.dummy_model()
													__lowerCAmelCase        =       self.dummy_sample_deter * scheduler.init_noise_sigma
													__lowerCAmelCase        =       sample.to(snake_case__							)

													for i, t in enumerate(scheduler.timesteps							):
																			__lowerCAmelCase        =       scheduler.scale_model_input(snake_case__  ,							snake_case__							)

																			__lowerCAmelCase        =       model(snake_case__  ,							snake_case__							)

																			__lowerCAmelCase        =       scheduler.step(snake_case__  ,							snake_case__  ,							snake_case__  ,							generator=snake_case__							)
																			__lowerCAmelCase        =       output.prev_sample

													__lowerCAmelCase        =       torch.sum(torch.abs(snake_case__							)							)
													__lowerCAmelCase        =       torch.mean(torch.abs(snake_case__							)							)

													assert abs(result_sum.item() - 13_849.3_877							) < 1e-2
													assert abs(result_mean.item() - 18.0_331							) < 5e-3




							def  A__		(				self							)   ->       Any:


													for prediction_type in ["epsilon", "v_prediction"]:
																			self.check_over_configs(prediction_type=snake_case__							)




							def  A__		(				self							)   ->       Tuple:


													if torch_device == "mps":
																			return
													__lowerCAmelCase        =       self.scheduler_classes[0]
													__lowerCAmelCase        =       self.get_scheduler_config(prediction_type="""v_prediction"""							)
													__lowerCAmelCase        =       scheduler_class(**snake_case__							)

													scheduler.set_timesteps(self.num_inference_steps							)

													__lowerCAmelCase        =       self.dummy_model()
													__lowerCAmelCase        =       self.dummy_sample_deter * scheduler.init_noise_sigma
													__lowerCAmelCase        =       sample.to(snake_case__							)

													__lowerCAmelCase        =       torch.manual_seed(0							)

													for i, t in enumerate(scheduler.timesteps							):
																			__lowerCAmelCase        =       scheduler.scale_model_input(snake_case__  ,							snake_case__							)

																			__lowerCAmelCase        =       model(snake_case__  ,							snake_case__							)

																			__lowerCAmelCase        =       scheduler.step(snake_case__  ,							snake_case__  ,							snake_case__  ,							generator=snake_case__							)
																			__lowerCAmelCase        =       output.prev_sample

													__lowerCAmelCase        =       torch.sum(torch.abs(snake_case__							)							)
													__lowerCAmelCase        =       torch.mean(torch.abs(snake_case__							)							)

													assert abs(result_sum.item() - 328.9_970							) < 1e-2
													assert abs(result_mean.item() - 0.4_284							) < 1e-3




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


													if torch_device == "mps":
																			return
													__lowerCAmelCase        =       self.scheduler_classes[0]
													__lowerCAmelCase        =       self.get_scheduler_config()
													__lowerCAmelCase        =       scheduler_class(**snake_case__							)

													scheduler.set_timesteps(self.num_inference_steps  ,							device=snake_case__							)
													__lowerCAmelCase        =       torch.manual_seed(0							)

													__lowerCAmelCase        =       self.dummy_model()
													__lowerCAmelCase        =       self.dummy_sample_deter.to(snake_case__							) * scheduler.init_noise_sigma

													for t in scheduler.timesteps:
																			__lowerCAmelCase        =       scheduler.scale_model_input(snake_case__  ,							snake_case__							)

																			__lowerCAmelCase        =       model(snake_case__  ,							snake_case__							)

																			__lowerCAmelCase        =       scheduler.step(snake_case__  ,							snake_case__  ,							snake_case__  ,							generator=snake_case__							)
																			__lowerCAmelCase        =       output.prev_sample

													__lowerCAmelCase        =       torch.sum(torch.abs(snake_case__							)							)
													__lowerCAmelCase        =       torch.mean(torch.abs(snake_case__							)							)

													assert abs(result_sum.item() - 13_849.3_818							) < 1e-1
													assert abs(result_mean.item() - 18.0_331							) < 1e-3



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

from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE_			      =					TypeVar('''T''')
class      lowerCAmelCase_						(							A__				):



							'''simple docstring'''





							pass
class      lowerCAmelCase_						(							A__				):



							'''simple docstring'''





							pass
class      lowerCAmelCase_						(							Generic[T]				):



							'''simple docstring'''





							def __init__(				self  ,							snake_case_ = 10							)   ->       Tuple:
													__lowerCAmelCase        =       []
													__lowerCAmelCase        =       limit




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




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




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




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




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




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




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




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




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

def 				lowercase      ():
						__lowerCAmelCase        =       Stack(10		)
						assert bool(_lowerCAmelCase		) is False
						assert stack.is_empty() is True
						assert stack.is_full() is False
						assert str(_lowerCAmelCase		) == "[]"

						try:
												__lowerCAmelCase        =       stack.pop()
												raise AssertionError  # This should not happen
						except StackUnderflowError:
												assert True  # This should happen

						try:
												__lowerCAmelCase        =       stack.peek()
												raise AssertionError  # This should not happen
						except StackUnderflowError:
												assert True  # This should happen

						for i in range(10		):
												assert stack.size() == i
												stack.push(_lowerCAmelCase		)

						assert bool(_lowerCAmelCase		)
						assert not stack.is_empty()
						assert stack.is_full()
						assert str(_lowerCAmelCase		) == str(list(range(10		)		)		)
						assert stack.pop() == 9
						assert stack.peek() == 8

						stack.push(100		)
						assert str(_lowerCAmelCase		) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 100]		)

						try:
												stack.push(200		)
												raise AssertionError  # This should not happen
						except StackOverflowError:
												assert True  # This should happen

						assert not stack.is_empty()
						assert stack.size() == 10

						assert 5 in stack
						assert 55 not in stack


if __name__ == "__main__":
			test_stack()
