


'''simple docstring'''




from __future__ import annotations

from typing import Generic, TypeVar

_a      :      Union[str, Any]       				=      TypeVar("T")




class  _lowercase   (  __lowercase       ):
   pass




class  _lowercase   (  __lowercase       ):
   pass




class  _lowercase   (  Generic[T]       ):
   def __init__(	self					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			int = 10			)   ->       Tuple:
        __snake_case    			=	[]
        __snake_case    			=	limit
   def __bool__(	self					:			Optional[int]			)   ->       bool:
        return bool(self.stack			)
   def __str__(	self					:			List[Any]			)   ->       str:
        return str(self.stack			)
   def a     (	self					:			str					,      SCREAMING_SNAKE_CASE_					:			T			)   ->       None:
        if len(self.stack			) >= self.limit:
             raise StackOverflowError
        self.stack.append(SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			List[str]			)   ->       T:
        if not self.stack:
             raise StackUnderflowError
        return self.stack.pop()
   def a     (	self					:			Dict			)   ->       T:
        if not self.stack:
             raise StackUnderflowError
        return self.stack[-1]
   def a     (	self					:			Tuple			)   ->       bool:
        return not bool(self.stack			)
   def a     (	self					:			Tuple			)   ->       bool:
        return self.size() == self.limit
   def a     (	self					:			Optional[int]			)   ->       int:
        return len(self.stack			)





   def __contains__(	self					:			str					,      SCREAMING_SNAKE_CASE_					:			T			)   ->       bool:
        return item in self.stack





def      _a		() -> None:






     """simple docstring"""
     __snake_case    			=	Stack(1_0	)
     assert bool(lowercase__	) is False
     assert stack.is_empty() is True
     assert stack.is_full() is False
     assert str(lowercase__	) == "[]"

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

     try:
          __snake_case    			=	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()








'''simple docstring'''




import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class  _lowercase   (  __lowercase       ):
   _SCREAMING_SNAKE_CASE     :     List[str]									=  (EulerAncestralDiscreteScheduler,)
   _SCREAMING_SNAKE_CASE     :     Dict									=  1_0
   def a     (	self					:			List[str]					,      **SCREAMING_SNAKE_CASE_					:			Union[str, Any]			)   ->       List[str]:
        __snake_case    			=	{
            'num_train_timesteps': 1100,
            'beta_start': 0.0_0_0_1,
            'beta_end': 0.0_2,
            'beta_schedule': 'linear',
        }

        config.update(**SCREAMING_SNAKE_CASE_			)
        return config
   def a     (	self					:			Optional[int]			)   ->       int:
        for timesteps in [10, 50, 100, 1000]:
             self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			Any			)   ->       List[Any]:
        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=SCREAMING_SNAKE_CASE_					,      beta_end=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			List[str]			)   ->       int:
        for schedule in ["linear", "scaled_linear"]:
             self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			Dict			)   ->       Optional[Any]:
        for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			List[Any]			)   ->       str:
        __snake_case    			=	self.scheduler_classes[0]
        __snake_case    			=	self.get_scheduler_config()
        __snake_case    			=	scheduler_class(**SCREAMING_SNAKE_CASE_			)

        scheduler.set_timesteps(self.num_inference_steps			)

        __snake_case    			=	torch.manual_seed(0			)

        __snake_case    			=	self.dummy_model()
        __snake_case    			=	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        __snake_case    			=	sample.to(SCREAMING_SNAKE_CASE_			)

        for i, t in enumerate(scheduler.timesteps			):
             __snake_case    			=	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	model(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	scheduler.step(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      generator=SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	output.prev_sample

        __snake_case    			=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
        __snake_case    			=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

        assert abs(result_sum.item() - 1_5_2.3_1_9_2			) < 1e-2
        assert abs(result_mean.item() - 0.1_9_8_3			) < 1e-3
   def a     (	self					:			List[Any]			)   ->       Optional[Any]:
        __snake_case    			=	self.scheduler_classes[0]
        __snake_case    			=	self.get_scheduler_config(prediction_type='v_prediction'			)
        __snake_case    			=	scheduler_class(**SCREAMING_SNAKE_CASE_			)

        scheduler.set_timesteps(self.num_inference_steps			)

        __snake_case    			=	torch.manual_seed(0			)

        __snake_case    			=	self.dummy_model()
        __snake_case    			=	self.dummy_sample_deter * scheduler.init_noise_sigma
        __snake_case    			=	sample.to(SCREAMING_SNAKE_CASE_			)

        for i, t in enumerate(scheduler.timesteps			):
             __snake_case    			=	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	model(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	scheduler.step(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      generator=SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	output.prev_sample

        __snake_case    			=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
        __snake_case    			=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

        assert abs(result_sum.item() - 1_0_8.4_4_3_9			) < 1e-2
        assert abs(result_mean.item() - 0.1_4_1_2			) < 1e-3





   def a     (	self					:			Optional[int]			)   ->       Dict:
        __snake_case    			=	self.scheduler_classes[0]
        __snake_case    			=	self.get_scheduler_config()
        __snake_case    			=	scheduler_class(**SCREAMING_SNAKE_CASE_			)

        scheduler.set_timesteps(self.num_inference_steps					,      device=SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	torch.manual_seed(0			)

        __snake_case    			=	self.dummy_model()
        __snake_case    			=	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        __snake_case    			=	sample.to(SCREAMING_SNAKE_CASE_			)

        for t in scheduler.timesteps:
             __snake_case    			=	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	model(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	scheduler.step(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      generator=SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	output.prev_sample

        __snake_case    			=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
        __snake_case    			=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

        assert abs(result_sum.item() - 1_5_2.3_1_9_2			) < 1e-2
        assert abs(result_mean.item() - 0.1_9_8_3			) < 1e-3




