
import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class UpperCAmelCase     (		lowerCAmelCase_     ):





   '''simple docstring'''
   __UpperCamelCase	:					Any				       =					(LMSDiscreteScheduler,)
   __UpperCamelCase	:					List[str]				       =					10


   def 				__magic_name__       (      self  :							List[str]    ,    **lowerCAmelCase_  :							str  ):

      """simple docstring"""
      _A:						Optional[int]	    = {
          'num_train_timesteps': 1_1_0_0,
          'beta_start': 0.0001,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
      }

      config.update(**__a  )
      return config


   def 				__magic_name__       (      self  :							Dict  ):

      """simple docstring"""





      for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
         self.check_over_configs(num_train_timesteps=__a  )


   def 				__magic_name__       (      self  :							Optional[Any]  ):

      """simple docstring"""





      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=__a    ,    beta_end=__a  )


   def 				__magic_name__       (      self  :							str  ):

      """simple docstring"""





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


   def 				__magic_name__       (      self  :							str  ):

      """simple docstring"""





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


   def 				__magic_name__       (      self  :							int  ):

      """simple docstring"""





      for t in [0, 5_0_0, 8_0_0]:
         self.check_over_forward(time_step=__a  )


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

      """simple docstring"""
      _A:						Any	    = self.scheduler_classes[0]
      _A:						Optional[Any]	    = self.get_scheduler_config()
      _A:						Dict	    = scheduler_class(**__a  )

      scheduler.set_timesteps(self.num_inference_steps  )

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

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

         _A:						Tuple	    = model(__a    ,    __a  )

         _A:						Union[str, Any]	    = scheduler.step(__a    ,    __a    ,    __a  )
         _A:						Any	    = output.prev_sample

      _A:						List[str]	    = torch.sum(torch.abs(__a  )  )
      _A:						List[Any]	    = torch.mean(torch.abs(__a  )  )

      assert abs(result_sum.item() - 1006.388  ) < 1e-2
      assert abs(result_mean.item() - 1.31  ) < 1e-3


   def 				__magic_name__       (      self  :							str  ):

      """simple docstring"""
      _A:						List[Any]	    = self.scheduler_classes[0]
      _A:						str	    = self.get_scheduler_config(prediction_type='''v_prediction'''  )
      _A:						Any	    = scheduler_class(**__a  )

      scheduler.set_timesteps(self.num_inference_steps  )

      _A:						Dict	    = self.dummy_model()
      _A:						Optional[Any]	    = self.dummy_sample_deter * scheduler.init_noise_sigma

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

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

         _A:						Dict	    = scheduler.step(__a    ,    __a    ,    __a  )
         _A:						Any	    = output.prev_sample

      _A:						Optional[int]	    = torch.sum(torch.abs(__a  )  )
      _A:						Union[str, Any]	    = torch.mean(torch.abs(__a  )  )

      assert abs(result_sum.item() - 0.0017  ) < 1e-2
      assert abs(result_mean.item() - 2.2676e-06  ) < 1e-3


   def 				__magic_name__       (      self  :							Optional[int]  ):

      """simple docstring"""
      _A:						List[Any]	    = self.scheduler_classes[0]
      _A:						Dict	    = self.get_scheduler_config()
      _A:						Tuple	    = scheduler_class(**__a  )

      scheduler.set_timesteps(self.num_inference_steps    ,    device=__a  )

      _A:						List[Any]	    = self.dummy_model()
      _A:						List[Any]	    = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      _A:						int	    = sample.to(__a  )

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

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

         _A:						Dict	    = scheduler.step(__a    ,    __a    ,    __a  )
         _A:						Union[str, Any]	    = output.prev_sample

      _A:						Optional[Any]	    = torch.sum(torch.abs(__a  )  )
      _A:						Optional[int]	    = torch.mean(torch.abs(__a  )  )

      assert abs(result_sum.item() - 1006.388  ) < 1e-2
      assert abs(result_mean.item() - 1.31  ) < 1e-3


   def 				__magic_name__       (      self  :							List[str]  ):

      """simple docstring"""
      _A:						Dict	    = self.scheduler_classes[0]
      _A:						str	    = self.get_scheduler_config()
      _A:						int	    = scheduler_class(**__a    ,    use_karras_sigmas=__a  )

      scheduler.set_timesteps(self.num_inference_steps    ,    device=__a  )

      _A:						Any	    = self.dummy_model()
      _A:						Optional[Any]	    = self.dummy_sample_deter.to(__a  ) * scheduler.init_noise_sigma
      _A:						Union[str, Any]	    = sample.to(__a  )

      for t in scheduler.timesteps:
         _A:						Optional[Any]	    = scheduler.scale_model_input(__a    ,    __a  )

         _A:						Dict	    = model(__a    ,    __a  )

         _A:						List[Any]	    = scheduler.step(__a    ,    __a    ,    __a  )
         _A:						int	    = output.prev_sample

      _A:						Dict	    = torch.sum(torch.abs(__a  )  )
      _A:						Optional[Any]	    = torch.mean(torch.abs(__a  )  )

      assert abs(result_sum.item() - 3812.9927  ) < 2e-2
      assert abs(result_mean.item() - 4.9648  ) < 1e-3





import warnings

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


UpperCAmelCase__	:  Optional[int]      =   logging.get_logger(__name__)






class UpperCAmelCase     (		SCREAMING_SNAKE_CASE__     ):





   '''simple docstring'''


   def __init__(      self  :							str    ,    *lowerCAmelCase_  :							Union[str, Any]    ,    **lowerCAmelCase_  :							Union[str, Any]  ):

      """simple docstring"""
      warnings.warn(
          '''The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
          ''' Please use ConvNextImageProcessor instead.'''    ,    lowerCAmelCase_    ,    )
      super().__init__(*lowerCAmelCase_    ,    **lowerCAmelCase_  )



