

import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class    A_				( _a  ):


    '''simple docstring'''






    a__       							=    (KDPMaAncestralDiscreteScheduler,)
    a__       							=    10


    def        lowerCAmelCase_  (self       , **lowercase__					)       -> Dict:
        __UpperCAmelCase			   =     {
            '''num_train_timesteps''': 1_100,
            '''beta_start''': 0.0001,
            '''beta_end''': 0.02,
            '''beta_schedule''': '''linear''',
        }

        config.update(**lowercase__					)
        return config


    def        lowerCAmelCase_  (self					)       -> str:
        for timesteps in [10, 50, 100, 1_000]:
            self.check_over_configs(num_train_timesteps=lowercase__					)


    def        lowerCAmelCase_  (self					)       -> Union[str, Any]:
        for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]       , [0.0002, 0.002, 0.02]					):
            self.check_over_configs(beta_start=lowercase__       , beta_end=lowercase__					)


    def        lowerCAmelCase_  (self					)       -> Optional[int]:
        for schedule in ["linear", "scaled_linear"]:
            self.check_over_configs(beta_schedule=lowercase__					)


    def        lowerCAmelCase_  (self					)       -> Optional[int]:
        if torch_device == "mps":
            return
        __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
        __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() - 13849.3877					) < 1E-2
        assert abs(result_mean.item() - 18.0331					) < 5E-3


    def        lowerCAmelCase_  (self					)       -> List[Any]:
        for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=lowercase__					)


    def        lowerCAmelCase_  (self					)       -> Optional[Any]:
        if torch_device == "mps":
            return
        __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			   =     self.dummy_model()
        __UpperCAmelCase			   =     self.dummy_sample_deter * scheduler.init_noise_sigma
        __UpperCAmelCase			   =     sample.to(lowercase__					)

        __UpperCAmelCase			   =     torch.manual_seed(0					)

        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() - 328.9970					) < 1E-2
        assert abs(result_mean.item() - 0.4284					) < 1E-3






    def        lowerCAmelCase_  (self					)       -> Optional[Any]:
        if torch_device == "mps":
            return
        __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.to(lowercase__					) * scheduler.init_noise_sigma

        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() - 13849.3818					) < 1E-1
        assert abs(result_mean.item() - 18.0331					) < 1E-3







import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


A_						:     int			  =				logging.get_logger(__name__)
class    A_				( _a  ):


    '''simple docstring'''






    def __init__(self       , *lowercase__       , **lowercase__					)       -> None:
        warnings.warn(
            '''The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
            ''' Please use ConvNextImageProcessor instead.'''       , lowercase__       , )
        super().__init__(*lowercase__       , **lowercase__					)




