


'''simple docstring'''






import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										=(DDPMParallelScheduler,)






       def       lowerCAmelCase		(      self  ,							**snake_case)						->  Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       str							   ={
              'num_train_timesteps': 1_0_0_0,
              'beta_start': 0.00_01,
              'beta_end': 0.02,
              'beta_schedule': 'linear',
              'variance_type': 'fixed_small',
              'clip_sample': True,
          }

          config.update(**snake_case)
          return config






       def       lowerCAmelCase		(      self)						->  Optional[Any]:




          '''simple docstring'''
          for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
             self.check_over_configs(num_train_timesteps=snake_case)






       def       lowerCAmelCase		(      self)						->  int:




          '''simple docstring'''
          for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]  ,							[0.0_02, 0.02, 0.2, 2]):
             self.check_over_configs(beta_start=snake_case  ,							beta_end=snake_case)






       def       lowerCAmelCase		(      self)						->  Any:




          '''simple docstring'''
          for schedule in ["linear", "squaredcos_cap_v2"]:
             self.check_over_configs(beta_schedule=snake_case)






       def       lowerCAmelCase		(      self)						->  int:




          '''simple docstring'''
          for variance in ["fixed_small", "fixed_large", "other"]:
             self.check_over_configs(variance_type=snake_case)






       def       lowerCAmelCase		(      self)						->  Optional[Any]:




          '''simple docstring'''
          for clip_sample in [True, False]:
             self.check_over_configs(clip_sample=snake_case)






       def       lowerCAmelCase		(      self)						->  int:




          '''simple docstring'''
          self.check_over_configs(thresholding=snake_case)
          for threshold in [0.5, 1.0, 2.0]:
             for prediction_type in ["epsilon", "sample", "v_prediction"]:
                self.check_over_configs(
                    thresholding=snake_case  ,							prediction_type=snake_case  ,							sample_max_value=snake_case  ,							)






       def       lowerCAmelCase		(      self)						->  Tuple:




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






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




          '''simple docstring'''
          for t in [0, 5_0_0, 9_9_9]:
             self.check_over_forward(time_step=snake_case)






       def       lowerCAmelCase		(      self)						->  str:




          '''simple docstring'''
          _UpperCAmelCase       :       List[str]							   =self.scheduler_classes[0]
          _UpperCAmelCase       :       List[str]							   =self.get_scheduler_config()
          _UpperCAmelCase       :       int							   =scheduler_class(**snake_case)

          assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(4_8_7) - 0.0_09_79)) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(9_9_9) - 0.02)) < 1E-5






       def       lowerCAmelCase		(      self)						->  Dict:




          '''simple docstring'''
          _UpperCAmelCase       :       Tuple							   =self.scheduler_classes[0]
          _UpperCAmelCase       :       Optional[int]							   =self.get_scheduler_config()
          _UpperCAmelCase       :       int							   =scheduler_class(**snake_case)

          _UpperCAmelCase       :       Optional[Any]							   =len(snake_case)

          _UpperCAmelCase       :       str							   =self.dummy_model()
          _UpperCAmelCase       :       Tuple							   =self.dummy_sample_deter
          _UpperCAmelCase       :       Any							   =self.dummy_sample_deter + 0.1
          _UpperCAmelCase       :       List[str]							   =self.dummy_sample_deter - 0.1

          _UpperCAmelCase       :       Dict							   =samplea.shape[0]
          _UpperCAmelCase       :       List[Any]							   =torch.stack([samplea, samplea, samplea]  ,							dim=0)
          _UpperCAmelCase       :       Any							   =torch.arange(snake_case)[0:3, None].repeat(1  ,							snake_case)

          _UpperCAmelCase       :       Tuple							   =model(samples.flatten(0  ,							1)  ,							timesteps.flatten(0  ,							1))
          _UpperCAmelCase       :       Optional[Any]							   =scheduler.batch_step_no_noise(snake_case  ,							timesteps.flatten(0  ,							1)  ,							samples.flatten(0  ,							1))

          _UpperCAmelCase       :       Optional[int]							   =torch.sum(torch.abs(snake_case))
          _UpperCAmelCase       :       str							   =torch.mean(torch.abs(snake_case))

          assert abs(result_sum.item() - 11_53.18_33) < 1E-2
          assert abs(result_mean.item() - 0.50_05) < 1E-3






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




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[Any]							   =self.scheduler_classes[0]
          _UpperCAmelCase       :       List[Any]							   =self.get_scheduler_config()
          _UpperCAmelCase       :       Union[str, Any]							   =scheduler_class(**snake_case)

          _UpperCAmelCase       :       Tuple							   =len(snake_case)

          _UpperCAmelCase       :       Dict							   =self.dummy_model()
          _UpperCAmelCase       :       Any							   =self.dummy_sample_deter
          _UpperCAmelCase       :       str							   =torch.manual_seed(0)

          for t in reversed(range(snake_case)):
             # 1. predict noise residual
             _UpperCAmelCase       :       Union[str, Any]							   =model(snake_case  ,							snake_case)

             # 2. predict previous mean of sample x_t-1
             _UpperCAmelCase       :       Any							   =scheduler.step(snake_case  ,							snake_case  ,							snake_case  ,							generator=snake_case).prev_sample

             _UpperCAmelCase       :       Optional[Any]							   =pred_prev_sample

          _UpperCAmelCase       :       str							   =torch.sum(torch.abs(snake_case))
          _UpperCAmelCase       :       List[Any]							   =torch.mean(torch.abs(snake_case))

          assert abs(result_sum.item() - 2_58.96_06) < 1E-2
          assert abs(result_mean.item() - 0.33_72) < 1E-3






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




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[Any]							   =self.scheduler_classes[0]
          _UpperCAmelCase       :       Optional[int]							   =self.get_scheduler_config(prediction_type='v_prediction')
          _UpperCAmelCase       :       List[str]							   =scheduler_class(**snake_case)

          _UpperCAmelCase       :       List[str]							   =len(snake_case)

          _UpperCAmelCase       :       str							   =self.dummy_model()
          _UpperCAmelCase       :       str							   =self.dummy_sample_deter
          _UpperCAmelCase       :       Optional[Any]							   =torch.manual_seed(0)

          for t in reversed(range(snake_case)):
             # 1. predict noise residual
             _UpperCAmelCase       :       Any							   =model(snake_case  ,							snake_case)

             # 2. predict previous mean of sample x_t-1
             _UpperCAmelCase       :       List[str]							   =scheduler.step(snake_case  ,							snake_case  ,							snake_case  ,							generator=snake_case).prev_sample

             _UpperCAmelCase       :       Optional[Any]							   =pred_prev_sample

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

          assert abs(result_sum.item() - 2_02.02_96) < 1E-2
          assert abs(result_mean.item() - 0.26_31) < 1E-3






       def       lowerCAmelCase		(      self)						->  Dict:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[Any]							   =self.scheduler_classes[0]
          _UpperCAmelCase       :       int							   =self.get_scheduler_config()
          _UpperCAmelCase       :       List[Any]							   =scheduler_class(**snake_case)

          _UpperCAmelCase       :       Union[str, Any]							   =[1_0_0, 8_7, 5_0, 1, 0]

          scheduler.set_timesteps(timesteps=snake_case)

          _UpperCAmelCase       :       Union[str, Any]							   =scheduler.timesteps

          for i, timestep in enumerate(snake_case):
             if i == len(snake_case) - 1:
                _UpperCAmelCase       :       int							   =-1
             else:
                _UpperCAmelCase       :       str							   =timesteps[i + 1]

             _UpperCAmelCase       :       Union[str, Any]							   =scheduler.previous_timestep(snake_case)
             _UpperCAmelCase       :       Tuple							   =prev_t.item()

             self.assertEqual(snake_case  ,							snake_case)






       def       lowerCAmelCase		(      self)						->  int:




          '''simple docstring'''
          _UpperCAmelCase       :       Tuple							   =self.scheduler_classes[0]
          _UpperCAmelCase       :       str							   =self.get_scheduler_config()
          _UpperCAmelCase       :       List[str]							   =scheduler_class(**snake_case)

          _UpperCAmelCase       :       Any							   =[1_0_0, 8_7, 5_0, 5_1, 0]

          with self.assertRaises(snake_case  ,							msg='`custom_timesteps` must be in descending order.'):
             scheduler.set_timesteps(timesteps=snake_case)






       def       lowerCAmelCase		(      self)						->  Any:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[int]							   =self.scheduler_classes[0]
          _UpperCAmelCase       :       str							   =self.get_scheduler_config()
          _UpperCAmelCase       :       Tuple							   =scheduler_class(**snake_case)

          _UpperCAmelCase       :       int							   =[1_0_0, 8_7, 5_0, 1, 0]
          _UpperCAmelCase       :       str							   =len(snake_case)

          with self.assertRaises(snake_case  ,							msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'):
             scheduler.set_timesteps(num_inference_steps=snake_case  ,							timesteps=snake_case)






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




          '''simple docstring'''
          _UpperCAmelCase       :       List[str]							   =self.scheduler_classes[0]
          _UpperCAmelCase       :       int							   =self.get_scheduler_config()
          _UpperCAmelCase       :       int							   =scheduler_class(**snake_case)

          _UpperCAmelCase       :       Union[str, Any]							   =[scheduler.config.num_train_timesteps]

          with self.assertRaises(
              snake_case  ,							msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'  ,							):
             scheduler.set_timesteps(timesteps=snake_case)





'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


lowercase							=logging.get_logger(__name__)






class  __magic_name__							(		lowerCAmelCase    ):






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




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

