





'''simple docstring'''

import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class lowerCamelCase    (lowerCAmelCase__       ):
  _lowercase	:						str							       =					(KDPMaAncestralDiscreteScheduler,)
  _lowercase	:						List[str]							       =					10







  def 					UpperCAmelCase_					(		self  ,  **lowercase__		)		->    int:






        """simple docstring"""





        _snake_case		:						Tuple         =  {
            '''num_train_timesteps''': 1_100,
            '''beta_start''': 0.0_001,
            '''beta_end''': 0.02,
            '''beta_schedule''': '''linear''',
        }

        config.update(**_A		)
        return config







  def 					UpperCAmelCase_					(		self		)		->    int:






        """simple docstring"""





        for timesteps in [10, 50, 100, 1_000]:
              self.check_over_configs(num_train_timesteps=_A		)







  def 					UpperCAmelCase_					(		self		)		->    Optional[int]:






        """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		)		->    List[Any]:






        """simple docstring"""





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







  def 					UpperCAmelCase_					(		self		)		->    List[str]:






        """simple docstring"""





        if torch_device == "mps":
              return
        _snake_case		:						Dict         =  self.scheduler_classes[0]
        _snake_case		:						Union[str, Any]         =  self.get_scheduler_config()
        _snake_case		:						int         =  scheduler_class(**_A		)

        scheduler.set_timesteps(self.num_inference_steps		)

        _snake_case		:						Optional[Any]         =  torch.manual_seed(0		)

        _snake_case		:						Dict         =  self.dummy_model()
        _snake_case		:						Dict         =  self.dummy_sample_deter * scheduler.init_noise_sigma
        _snake_case		:						Tuple         =  sample.to(_A		)

        for i, t in enumerate(scheduler.timesteps		):
              _snake_case		:						Union[str, Any]         =  scheduler.scale_model_input(_A  ,  _A		)

              _snake_case		:						Union[str, Any]         =  model(_A  ,  _A		)

              _snake_case		:						Any         =  scheduler.step(_A  ,  _A  ,  _A  ,  generator=_A		)
              _snake_case		:						Optional[int]         =  output.prev_sample

        _snake_case		:						Union[str, Any]         =  torch.sum(torch.abs(_A		)		)
        _snake_case		:						Tuple         =  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		)		->    Tuple:






        """simple docstring"""





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







  def 					UpperCAmelCase_					(		self		)		->    Optional[int]:






        """simple docstring"""





        if torch_device == "mps":
              return
        _snake_case		:						str         =  self.scheduler_classes[0]
        _snake_case		:						List[Any]         =  self.get_scheduler_config(prediction_type='''v_prediction'''		)
        _snake_case		:						Tuple         =  scheduler_class(**_A		)

        scheduler.set_timesteps(self.num_inference_steps		)

        _snake_case		:						Optional[Any]         =  self.dummy_model()
        _snake_case		:						Optional[int]         =  self.dummy_sample_deter * scheduler.init_noise_sigma
        _snake_case		:						Tuple         =  sample.to(_A		)

        _snake_case		:						Dict         =  torch.manual_seed(0		)

        for i, t in enumerate(scheduler.timesteps		):
              _snake_case		:						Optional[int]         =  scheduler.scale_model_input(_A  ,  _A		)

              _snake_case		:						str         =  model(_A  ,  _A		)

              _snake_case		:						Tuple         =  scheduler.step(_A  ,  _A  ,  _A  ,  generator=_A		)
              _snake_case		:						int         =  output.prev_sample

        _snake_case		:						List[str]         =  torch.sum(torch.abs(_A		)		)
        _snake_case		:						Optional[int]         =  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		)		->    int:






        """simple docstring"""





        if torch_device == "mps":
              return
        _snake_case		:						str         =  self.scheduler_classes[0]
        _snake_case		:						Optional[Any]         =  self.get_scheduler_config()
        _snake_case		:						Dict         =  scheduler_class(**_A		)

        scheduler.set_timesteps(self.num_inference_steps  ,  device=_A		)
        _snake_case		:						str         =  torch.manual_seed(0		)

        _snake_case		:						str         =  self.dummy_model()
        _snake_case		:						List[str]         =  self.dummy_sample_deter.to(_A		) * scheduler.init_noise_sigma

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

              _snake_case		:						List[str]         =  model(_A  ,  _A		)

              _snake_case		:						str         =  scheduler.step(_A  ,  _A  ,  _A  ,  generator=_A		)
              _snake_case		:						Any         =  output.prev_sample

        _snake_case		:						Optional[Any]         =  torch.sum(torch.abs(_A		)		)
        _snake_case		:						Union[str, Any]         =  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_vilt import ViltImageProcessor


UpperCamelCase__	:   Optional[Any]								=     logging.get_logger(__name__)



class    __snake_case	(     lowerCAmelCase__							):

  def __init__(       self    ,		*_A    ,		**_A):
      warnings.warn(
          'The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
          ' use ViltImageProcessor instead.'    ,		_A    ,		)
      super().__init__(*_A    ,		**_A)


