



"""simple docstring"""


import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class  A_     (	_UpperCAmelCase							):



						"""simple docstring"""
						__UpperCamelCase													=				(EulerAncestralDiscreteScheduler,)
						__UpperCamelCase													=				10






						def       UpperCAmelCase__	(   self					:Optional[int]  ,    **lowercase_					:Optional[Any]   )							->     Optional[Any]:
												UpperCAmelCase			 =					{
												    'num_train_timesteps': 11_00,
												    'beta_start': 0.0001,
												    'beta_end': 0.02,
												    'beta_schedule': 'linear',
												}

												config.update(**lowercase_   )
												return config






						def       UpperCAmelCase__	(   self					:str   )							->     Dict:







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






						def       UpperCAmelCase__	(   self					:str   )							->     List[Any]:







												for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]  ,    [0.0002, 0.002, 0.02]   ):
																		self.check_over_configs(beta_start=lowercase_  ,    beta_end=lowercase_   )






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







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






						def       UpperCAmelCase__	(   self					:Dict   )							->     List[str]:







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






						def       UpperCAmelCase__	(   self					:Dict   )							->     List[str]:
												UpperCAmelCase			 =					self.scheduler_classes[0]
												UpperCAmelCase			 =					self.get_scheduler_config()
												UpperCAmelCase			 =					scheduler_class(**lowercase_   )

												scheduler.set_timesteps(self.num_inference_steps   )

												UpperCAmelCase			 =					torch.manual_seed(0   )

												UpperCAmelCase			 =					self.dummy_model()
												UpperCAmelCase			 =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
												UpperCAmelCase			 =					sample.to(lowercase_   )

												for i, t in enumerate(scheduler.timesteps   ):
																		UpperCAmelCase			 =					scheduler.scale_model_input(lowercase_  ,    lowercase_   )

																		UpperCAmelCase			 =					model(lowercase_  ,    lowercase_   )

																		UpperCAmelCase			 =					scheduler.step(lowercase_  ,    lowercase_  ,    lowercase_  ,    generator=lowercase_   )
																		UpperCAmelCase			 =					output.prev_sample

												UpperCAmelCase			 =					torch.sum(torch.abs(lowercase_   )   )
												UpperCAmelCase			 =					torch.mean(torch.abs(lowercase_   )   )

												assert abs(result_sum.item() - 152.3192   ) < 1E-2
												assert abs(result_mean.item() - 0.1983   ) < 1E-3






						def       UpperCAmelCase__	(   self					:Optional[Any]   )							->     List[str]:
												UpperCAmelCase			 =					self.scheduler_classes[0]
												UpperCAmelCase			 =					self.get_scheduler_config(prediction_type='v_prediction'   )
												UpperCAmelCase			 =					scheduler_class(**lowercase_   )

												scheduler.set_timesteps(self.num_inference_steps   )

												UpperCAmelCase			 =					torch.manual_seed(0   )

												UpperCAmelCase			 =					self.dummy_model()
												UpperCAmelCase			 =					self.dummy_sample_deter * scheduler.init_noise_sigma
												UpperCAmelCase			 =					sample.to(lowercase_   )

												for i, t in enumerate(scheduler.timesteps   ):
																		UpperCAmelCase			 =					scheduler.scale_model_input(lowercase_  ,    lowercase_   )

																		UpperCAmelCase			 =					model(lowercase_  ,    lowercase_   )

																		UpperCAmelCase			 =					scheduler.step(lowercase_  ,    lowercase_  ,    lowercase_  ,    generator=lowercase_   )
																		UpperCAmelCase			 =					output.prev_sample

												UpperCAmelCase			 =					torch.sum(torch.abs(lowercase_   )   )
												UpperCAmelCase			 =					torch.mean(torch.abs(lowercase_   )   )

												assert abs(result_sum.item() - 108.4439   ) < 1E-2
												assert abs(result_mean.item() - 0.1412   ) < 1E-3






						def       UpperCAmelCase__	(   self					:Any   )							->     Dict:
												UpperCAmelCase			 =					self.scheduler_classes[0]
												UpperCAmelCase			 =					self.get_scheduler_config()
												UpperCAmelCase			 =					scheduler_class(**lowercase_   )

												scheduler.set_timesteps(self.num_inference_steps  ,    device=lowercase_   )
												UpperCAmelCase			 =					torch.manual_seed(0   )

												UpperCAmelCase			 =					self.dummy_model()
												UpperCAmelCase			 =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
												UpperCAmelCase			 =					sample.to(lowercase_   )

												for t in scheduler.timesteps:
																		UpperCAmelCase			 =					scheduler.scale_model_input(lowercase_  ,    lowercase_   )

																		UpperCAmelCase			 =					model(lowercase_  ,    lowercase_   )

																		UpperCAmelCase			 =					scheduler.step(lowercase_  ,    lowercase_  ,    lowercase_  ,    generator=lowercase_   )
																		UpperCAmelCase			 =					output.prev_sample

												UpperCAmelCase			 =					torch.sum(torch.abs(lowercase_   )   )
												UpperCAmelCase			 =					torch.mean(torch.abs(lowercase_   )   )

												assert abs(result_sum.item() - 152.3192   ) < 1E-2
												assert abs(result_mean.item() - 0.1983   ) < 1E-3








import warnings

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


lowercase_									=    logging.get_logger(__name__)


class 					A		(				_UpperCAmelCase					):







    """simple docstring"""





    def __init__( self					:			str,*lowercase_					:			List[Any],**lowercase_					:			Dict						)->       None:





        '''simple docstring'''







        warnings.warn(
            'The class MobileNetV1FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
            ' Please use MobileNetV1ImageProcessor instead.',lowercase_,)
        super().__init__(*lowercase_,**lowercase_						)

