

import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class     lowercase						(       _UpperCamelCase					):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE												=				(KDPMaAncestralDiscreteScheduler,)
 __SCREAMING_SNAKE_CASE												=				10




 def   UpperCamelCase__							(self       ,       **__a ) ->					Tuple:






  """simple docstring"""





  UpperCAmelCase__							=				{
      'num_train_timesteps': 1100,
      'beta_start': 0.00_01,
      'beta_end': 0.02,
      'beta_schedule': 'linear',
  }

  config.update(**__a )
  return config




 def   UpperCamelCase__							(self ) ->					Any:






  """simple docstring"""





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




 def   UpperCamelCase__							(self ) ->					Any:






  """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=__a       ,       beta_end=__a )




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






  """simple docstring"""





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




 def   UpperCamelCase__							(self ) ->					List[Any]:






  """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() - 1_38_49.38_77 ) < 1E-2
  assert abs(result_mean.item() - 18.03_31 ) < 5E-3




 def   UpperCamelCase__							(self ) ->					Union[str, Any]:






  """simple docstring"""





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




 def   UpperCamelCase__							(self ) ->					Tuple:






  """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() - 3_28.99_70 ) < 1E-2
  assert abs(result_mean.item() - 0.42_84 ) < 1E-3




 def   UpperCamelCase__							(self ) ->					Any:






  """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() - 1_38_49.38_18 ) < 1E-1
  assert abs(result_mean.item() - 18.03_31 ) < 1E-3









'''simple docstring'''



import warnings

from ...utils import logging
from .image_processing_detr import DetrImageProcessor


UpperCAmelCase_				:     Dict     			=						logging.get_logger(__name__)





class 			lowerCAmelCase      (       __lowerCAmelCase):


       def __init__(      self			,     *__SCREAMING_SNAKE_CASE			,     **__SCREAMING_SNAKE_CASE     )					->     None:



            '''simple docstring'''




            warnings.warn(
                '''The class DetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
                ''' Please use DetrImageProcessor instead.'''			,     __SCREAMING_SNAKE_CASE			,     )
            super().__init__(*__SCREAMING_SNAKE_CASE			,     **__SCREAMING_SNAKE_CASE     )






