






"""simple docstring"""


import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      Any							=					(EulerAncestralDiscreteScheduler,)
   A_		:      Any							=					10


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict,			**_lowerCamelCase					:	Union[str, Any]    ):

     '''simple docstring'''
     __A		     =					{
         '''num_train_timesteps''': 11_00,
         '''beta_start''': 0.00_01,
         '''beta_end''': 0.02,
         '''beta_schedule''': '''linear''',
     }

     config.update(**_lowerCamelCase    )
     return config


   def 							_SCREAMING_SNAKE_CASE							(      self					:	str    ):

     '''simple docstring'''
     for timesteps in [10, 50, 1_00, 10_00]:
       self.check_over_configs(num_train_timesteps=_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict    ):

     '''simple docstring'''
     for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01],			[0.00_02, 0.0_02, 0.02]    ):
       self.check_over_configs(beta_start=_lowerCamelCase,			beta_end=_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Union[str, Any]    ):

     '''simple docstring'''
     for schedule in ["linear", "scaled_linear"]:
       self.check_over_configs(beta_schedule=_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	str    ):

     '''simple docstring'''
     for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(prediction_type=_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str]    ):

     '''simple docstring'''
     __A		     =					self.scheduler_classes[0]
     __A		     =					self.get_scheduler_config()
     __A		     =					scheduler_class(**_lowerCamelCase    )

     scheduler.set_timesteps(self.num_inference_steps    )

     __A		     =					torch.manual_seed(0    )

     __A		     =					self.dummy_model()
     __A		     =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
     __A		     =					sample.to(_lowerCamelCase    )

     for i, t in enumerate(scheduler.timesteps    ):
       __A		     =					scheduler.scale_model_input(_lowerCamelCase,			_lowerCamelCase    )

       __A		     =					model(_lowerCamelCase,			_lowerCamelCase    )

       __A		     =					scheduler.step(_lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase,			generator=_lowerCamelCase    )
       __A		     =					output.prev_sample

     __A		     =					torch.sum(torch.abs(_lowerCamelCase    )    )
     __A		     =					torch.mean(torch.abs(_lowerCamelCase    )    )

     assert abs(result_sum.item() - 1_52.31_92    ) < 1e-2
     assert abs(result_mean.item() - 0.19_83    ) < 1e-3


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any]    ):

     '''simple docstring'''
     __A		     =					self.scheduler_classes[0]
     __A		     =					self.get_scheduler_config(prediction_type='''v_prediction'''    )
     __A		     =					scheduler_class(**_lowerCamelCase    )

     scheduler.set_timesteps(self.num_inference_steps    )

     __A		     =					torch.manual_seed(0    )

     __A		     =					self.dummy_model()
     __A		     =					self.dummy_sample_deter * scheduler.init_noise_sigma
     __A		     =					sample.to(_lowerCamelCase    )

     for i, t in enumerate(scheduler.timesteps    ):
       __A		     =					scheduler.scale_model_input(_lowerCamelCase,			_lowerCamelCase    )

       __A		     =					model(_lowerCamelCase,			_lowerCamelCase    )

       __A		     =					scheduler.step(_lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase,			generator=_lowerCamelCase    )
       __A		     =					output.prev_sample

     __A		     =					torch.sum(torch.abs(_lowerCamelCase    )    )
     __A		     =					torch.mean(torch.abs(_lowerCamelCase    )    )

     assert abs(result_sum.item() - 1_08.44_39    ) < 1e-2
     assert abs(result_mean.item() - 0.14_12    ) < 1e-3






   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple    ):

     '''simple docstring'''
     __A		     =					self.scheduler_classes[0]
     __A		     =					self.get_scheduler_config()
     __A		     =					scheduler_class(**_lowerCamelCase    )

     scheduler.set_timesteps(self.num_inference_steps,			device=_lowerCamelCase    )
     __A		     =					torch.manual_seed(0    )

     __A		     =					self.dummy_model()
     __A		     =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
     __A		     =					sample.to(_lowerCamelCase    )

     for t in scheduler.timesteps:
       __A		     =					scheduler.scale_model_input(_lowerCamelCase,			_lowerCamelCase    )

       __A		     =					model(_lowerCamelCase,			_lowerCamelCase    )

       __A		     =					scheduler.step(_lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase,			generator=_lowerCamelCase    )
       __A		     =					output.prev_sample

     __A		     =					torch.sum(torch.abs(_lowerCamelCase    )    )
     __A		     =					torch.mean(torch.abs(_lowerCamelCase    )    )

     assert abs(result_sum.item() - 1_52.31_92    ) < 1e-2
     assert abs(result_mean.item() - 0.19_83    ) < 1e-3








"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_levit import LevitImageProcessor


__lowercase          = logging.get_logger(__name__)






class 		_A    (    _a    ):







    """simple docstring"""




    def __init__(   self							:       Optional[int]							,					*__UpperCAmelCase							:       Dict							,					**__UpperCAmelCase							:       Any):
      warnings.warn(
          "The class LevitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
          " use LevitImageProcessor instead."							,					__UpperCAmelCase							,					)
      super().__init__(*__UpperCAmelCase							,					**__UpperCAmelCase)






