

import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
				__lowerCamelCase						:	Optional[int]														=					(EulerDiscreteScheduler,)
				__lowerCamelCase						:	Optional[Any]														=					10





				def 				snake_case_    (				self						,       **a):
									lowercase__   :       Optional[Any]				=	{
									    'num_train_timesteps': 1100,
									    'beta_start': 0.0_001,
									    'beta_end': 0.02,
									    'beta_schedule': 'linear',
									}

									config.update(**a)
									return config





				def 				snake_case_    (				self):
									for timesteps in [10, 50, 100, 1000]:
														self.check_over_configs(num_train_timesteps=a)





				def 				snake_case_    (				self):
									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=a						,       beta_end=a)





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





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





				def 				snake_case_    (				self):
									lowercase__   :       List[str]				=	self.scheduler_classes[0]
									lowercase__   :       int				=	self.get_scheduler_config()
									lowercase__   :       int				=	scheduler_class(**a)

									scheduler.set_timesteps(self.num_inference_steps)

									lowercase__   :       Any				=	torch.manual_seed(0)

									lowercase__   :       int				=	self.dummy_model()
									lowercase__   :       int				=	self.dummy_sample_deter * scheduler.init_noise_sigma
									lowercase__   :       Optional[int]				=	sample.to(a)

									for i, t in enumerate(scheduler.timesteps):
														lowercase__   :       Any				=	scheduler.scale_model_input(a						,       a)

														lowercase__   :       str				=	model(a						,       a)

														lowercase__   :       Tuple				=	scheduler.step(a						,       a						,       a						,       generator=a)
														lowercase__   :       List[Any]				=	output.prev_sample

									lowercase__   :       Any				=	torch.sum(torch.abs(a))
									lowercase__   :       Optional[Any]				=	torch.mean(torch.abs(a))

									assert abs(result_sum.item() - 10.0_807) < 1e-2
									assert abs(result_mean.item() - 0.0_131) < 1e-3





				def 				snake_case_    (				self):
									lowercase__   :       Union[str, Any]				=	self.scheduler_classes[0]
									lowercase__   :       Dict				=	self.get_scheduler_config(prediction_type='v_prediction')
									lowercase__   :       str				=	scheduler_class(**a)

									scheduler.set_timesteps(self.num_inference_steps)

									lowercase__   :       Optional[int]				=	torch.manual_seed(0)

									lowercase__   :       str				=	self.dummy_model()
									lowercase__   :       List[Any]				=	self.dummy_sample_deter * scheduler.init_noise_sigma
									lowercase__   :       Any				=	sample.to(a)

									for i, t in enumerate(scheduler.timesteps):
														lowercase__   :       Any				=	scheduler.scale_model_input(a						,       a)

														lowercase__   :       Union[str, Any]				=	model(a						,       a)

														lowercase__   :       str				=	scheduler.step(a						,       a						,       a						,       generator=a)
														lowercase__   :       List[str]				=	output.prev_sample

									lowercase__   :       str				=	torch.sum(torch.abs(a))
									lowercase__   :       str				=	torch.mean(torch.abs(a))

									assert abs(result_sum.item() - 0.0_002) < 1e-2
									assert abs(result_mean.item() - 2.2676e-06) < 1e-3





				def 				snake_case_    (				self):
									lowercase__   :       Optional[Any]				=	self.scheduler_classes[0]
									lowercase__   :       int				=	self.get_scheduler_config()
									lowercase__   :       List[Any]				=	scheduler_class(**a)

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

									lowercase__   :       Optional[int]				=	torch.manual_seed(0)

									lowercase__   :       Optional[int]				=	self.dummy_model()
									lowercase__   :       Optional[Any]				=	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
									lowercase__   :       Optional[int]				=	sample.to(a)

									for t in scheduler.timesteps:
														lowercase__   :       str				=	scheduler.scale_model_input(a						,       a)

														lowercase__   :       Dict				=	model(a						,       a)

														lowercase__   :       List[str]				=	scheduler.step(a						,       a						,       a						,       generator=a)
														lowercase__   :       Any				=	output.prev_sample

									lowercase__   :       Optional[int]				=	torch.sum(torch.abs(a))
									lowercase__   :       str				=	torch.mean(torch.abs(a))

									assert abs(result_sum.item() - 10.0_807) < 1e-2
									assert abs(result_mean.item() - 0.0_131) < 1e-3





				def 				snake_case_    (				self):
									lowercase__   :       Tuple				=	self.scheduler_classes[0]
									lowercase__   :       Optional[Any]				=	self.get_scheduler_config()
									lowercase__   :       Any				=	scheduler_class(**a						,       use_karras_sigmas=a)

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

									lowercase__   :       Tuple				=	torch.manual_seed(0)

									lowercase__   :       Any				=	self.dummy_model()
									lowercase__   :       Union[str, Any]				=	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
									lowercase__   :       Optional[Any]				=	sample.to(a)

									for t in scheduler.timesteps:
														lowercase__   :       Any				=	scheduler.scale_model_input(a						,       a)

														lowercase__   :       int				=	model(a						,       a)

														lowercase__   :       Optional[int]				=	scheduler.step(a						,       a						,       a						,       generator=a)
														lowercase__   :       Union[str, Any]				=	output.prev_sample

									lowercase__   :       int				=	torch.sum(torch.abs(a))
									lowercase__   :       Optional[int]				=	torch.mean(torch.abs(a))

									assert abs(result_sum.item() - 124.52_299_499_511_719) < 1e-2
									assert abs(result_mean.item() - 0.16_213_932_633_399_963) < 1e-3




from __future__ import annotations

from collections.abc import Callable

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Callable[[int | float], int | float]     ,					SCREAMING_SNAKE_CASE_		:   int | float     ,					SCREAMING_SNAKE_CASE_		:   int | float     ,					SCREAMING_SNAKE_CASE_		:   int = 100     ,					):



     '''simple docstring'''





     lowercase__   :       Tuple				=	x_start
     lowercase__   :       Tuple				=	fnc(SCREAMING_SNAKE_CASE_      )
     lowercase__   :       List[Any]				=	0.0
     for _ in range(SCREAMING_SNAKE_CASE_      ):
          # Approximates small segments of curve as linear and solve
          # for trapezoidal area
          lowercase__   :       Any				=	(x_end - x_start) / steps + xa
          lowercase__   :       Optional[Any]				=	fnc(SCREAMING_SNAKE_CASE_      )
          area += abs(fxa + fxa      ) * (xa - xa) / 2
          # Increment step
          lowercase__   :       Any				=	xa
          lowercase__   :       str				=	fxa
     return area


if __name__ == "__main__":

       def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Tuple      ):



            '''simple docstring'''





            return x**3 + x**2

       print('''f(x) = x^3 + x^2''')
       print('''The area between the curve, x = -5, x = 5 and the x axis is:''')
       snake_case_        =			10
       while i <= 100_000:
              print(F'''with {i} steps: {trapezoidal_area(f, -5, 5, i)}''')
              i *= 10

