


"""simple docstring"""


import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class snake_case					(     _UpperCamelCase):
		__UpperCamelCase							=	(HeunDiscreteScheduler,)
		__UpperCamelCase							=	10







		def        a_       (    self					:				str			,      **a__					:				Any						)				->  Dict:





						'''simple docstring'''







						_A         =	{
						    "num_train_timesteps": 11_00,
						    "beta_start": 0.0_0_0_1,
						    "beta_end": 0.0_2,
						    "beta_schedule": "linear",
						}

						config.update(**a__						)
						return config







		def        a_       (    self					:				Dict						)				->  Tuple:





						'''simple docstring'''







						for timesteps in [10, 50, 1_00, 10_00]:
										self.check_over_configs(num_train_timesteps=a__						)







		def        a_       (    self					:				Any						)				->  Union[str, Any]:





						'''simple docstring'''







						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        a_       (    self					:				List[Any]						)				->  Dict:





						'''simple docstring'''







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







		def        a_       (    self					:				Tuple						)				->  int:





						'''simple docstring'''







						for clip_sample_range in [1.0, 2.0, 3.0]:
										self.check_over_configs(clip_sample_range=a__			,      clip_sample=a__						)







		def        a_       (    self					:				Union[str, Any]						)				->  Union[str, Any]:





						'''simple docstring'''







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







		def        a_       (    self					:				Union[str, Any]						)				->  List[str]:





						'''simple docstring'''







						_A         =	self.scheduler_classes[0]
						_A         =	self.get_scheduler_config()
						_A         =	scheduler_class(**a__						)

						scheduler.set_timesteps(self.num_inference_steps						)

						_A         =	self.dummy_model()
						_A         =	self.dummy_sample_deter * scheduler.init_noise_sigma
						_A         =	sample.to(a__						)

						for i, t in enumerate(scheduler.timesteps						):
										_A         =	scheduler.scale_model_input(a__			,      a__						)

										_A         =	model(a__			,      a__						)

										_A         =	scheduler.step(a__			,      a__			,      a__						)
										_A         =	output.prev_sample

						_A         =	torch.sum(torch.abs(a__						)						)
						_A         =	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        a_       (    self					:				str						)				->  int:





						'''simple docstring'''







						_A         =	self.scheduler_classes[0]
						_A         =	self.get_scheduler_config(prediction_type="v_prediction"						)
						_A         =	scheduler_class(**a__						)

						scheduler.set_timesteps(self.num_inference_steps						)

						_A         =	self.dummy_model()
						_A         =	self.dummy_sample_deter * scheduler.init_noise_sigma
						_A         =	sample.to(a__						)

						for i, t in enumerate(scheduler.timesteps						):
										_A         =	scheduler.scale_model_input(a__			,      a__						)

										_A         =	model(a__			,      a__						)

										_A         =	scheduler.step(a__			,      a__			,      a__						)
										_A         =	output.prev_sample

						_A         =	torch.sum(torch.abs(a__						)						)
						_A         =	torch.mean(torch.abs(a__						)						)

						if torch_device in ["cpu", "mps"]:
										assert abs(result_sum.item() - 4.6_9_3_4E-0_7						) < 1E-2
										assert abs(result_mean.item() - 6.1_1_1_2E-1_0						) < 1E-3
						else:
										# CUDA
										assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2E-0_7						) < 1E-2
										assert abs(result_mean.item() - 0.0_0_0_2						) < 1E-3







		def        a_       (    self					:				Dict						)				->  Optional[int]:





						'''simple docstring'''







						_A         =	self.scheduler_classes[0]
						_A         =	self.get_scheduler_config()
						_A         =	scheduler_class(**a__						)

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

						_A         =	self.dummy_model()
						_A         =	self.dummy_sample_deter.to(a__						) * scheduler.init_noise_sigma

						for t in scheduler.timesteps:
										_A         =	scheduler.scale_model_input(a__			,      a__						)

										_A         =	model(a__			,      a__						)

										_A         =	scheduler.step(a__			,      a__			,      a__						)
										_A         =	output.prev_sample

						_A         =	torch.sum(torch.abs(a__						)						)
						_A         =	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        a_       (    self					:				str						)				->  Dict:





						'''simple docstring'''







						_A         =	self.scheduler_classes[0]
						_A         =	self.get_scheduler_config()
						_A         =	scheduler_class(**a__			,      use_karras_sigmas=a__						)

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

						_A         =	self.dummy_model()
						_A         =	self.dummy_sample_deter.to(a__						) * scheduler.init_noise_sigma
						_A         =	sample.to(a__						)

						for t in scheduler.timesteps:
										_A         =	scheduler.scale_model_input(a__			,      a__						)

										_A         =	model(a__			,      a__						)

										_A         =	scheduler.step(a__			,      a__			,      a__						)
										_A         =	output.prev_sample

						_A         =	torch.sum(torch.abs(a__						)						)
						_A         =	torch.mean(torch.abs(a__						)						)

						assert abs(result_sum.item() - 0.0_0_0_1_5						) < 1E-2
						assert abs(result_mean.item() - 1.9_8_6_9_5_5_4_5_3_5_0_3_4_6_9_5E-0_7						) < 1E-2



"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


a_											=			logging.get_logger(__name__)
class snake_case					(     _UpperCamelCase):







		def __init__(    self					:				str			,      *a__					:				Union[str, Any]			,      **a__					:				Optional[Any]						)				->  None:





						'''simple docstring'''







						warnings.warn(
						    "The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
						    " Please use MobileNetV2ImageProcessor instead."			,      a__			,      )
						super().__init__(*a__			,      **a__						)