





import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class 			__A(    __UpperCAmelCase			):
     __A							=							(KDPMaAncestralDiscreteScheduler,)
     __A							=							10

     def 						_UpperCamelCase  (   self,							**A   ):





            """simple docstring"""

            _UpperCamelCase														=    {
                '''num_train_timesteps''': 1100,
                '''beta_start''': 0.0_001,
                '''beta_end''': 0.02,
                '''beta_schedule''': '''linear''',
            }

            config.update(**A   )
            return config

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

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

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            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 						_UpperCamelCase  (   self   ):





            """simple docstring"""

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

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

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

            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(A   )

            for i, t in enumerate(scheduler.timesteps   ):
                   _UpperCamelCase														=    scheduler.scale_model_input(A,							A   )

                   _UpperCamelCase														=    model(A,							A   )

                   _UpperCamelCase														=    scheduler.step(A,							A,							A,							generator=A   )
                   _UpperCamelCase														=    output.prev_sample

            _UpperCamelCase														=    torch.sum(torch.abs(A   )   )
            _UpperCamelCase														=    torch.mean(torch.abs(A   )   )

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

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

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

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

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

            scheduler.set_timesteps(self.num_inference_steps   )

            _UpperCamelCase														=    self.dummy_model()
            _UpperCamelCase														=    self.dummy_sample_deter * scheduler.init_noise_sigma
            _UpperCamelCase														=    sample.to(A   )

            _UpperCamelCase														=    torch.manual_seed(0   )

            for i, t in enumerate(scheduler.timesteps   ):
                   _UpperCamelCase														=    scheduler.scale_model_input(A,							A   )

                   _UpperCamelCase														=    model(A,							A   )

                   _UpperCamelCase														=    scheduler.step(A,							A,							A,							generator=A   )
                   _UpperCamelCase														=    output.prev_sample

            _UpperCamelCase														=    torch.sum(torch.abs(A   )   )
            _UpperCamelCase														=    torch.mean(torch.abs(A   )   )

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



     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

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

            scheduler.set_timesteps(self.num_inference_steps,							device=A   )
            _UpperCamelCase														=    torch.manual_seed(0   )

            _UpperCamelCase														=    self.dummy_model()
            _UpperCamelCase														=    self.dummy_sample_deter.to(A   ) * scheduler.init_noise_sigma

            for t in scheduler.timesteps:
                   _UpperCamelCase														=    scheduler.scale_model_input(A,							A   )

                   _UpperCamelCase														=    model(A,							A   )

                   _UpperCamelCase														=    scheduler.step(A,							A,							A,							generator=A   )
                   _UpperCamelCase														=    output.prev_sample

            _UpperCamelCase														=    torch.sum(torch.abs(A   )   )
            _UpperCamelCase														=    torch.mean(torch.abs(A   )   )

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













import warnings

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


lowercase     :			str     						=	logging.get_logger(__name__)

class 			__A(    __UpperCAmelCase			):

     def __init__(   self,							*A,							**A   ):





            """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   )






