import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	int						=(DDIMParallelScheduler,)
  SCREAMING_SNAKE_CASE_	:	Optional[Any]						=(("eta", 0.0), ("num_inference_steps", 50))






  def 		_a					(self   ,				**SCREAMING_SNAKE_CASE_	)		->      List[str]:





       '''simple docstring'''





       UpperCamelCase__										=      {
           '''num_train_timesteps''': 1000,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
           '''clip_sample''': True,
       }

       config.update(**SCREAMING_SNAKE_CASE_	)
       return config






  def 		_a					(self   ,				**SCREAMING_SNAKE_CASE_	)		->      List[str]:





       '''simple docstring'''





       UpperCamelCase__										=      self.scheduler_classes[0]
       UpperCamelCase__										=      self.get_scheduler_config(**SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      scheduler_class(**SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__						,       UpperCamelCase__										=      10, 0.0

       UpperCamelCase__										=      self.dummy_model()
       UpperCamelCase__										=      self.dummy_sample_deter

       scheduler.set_timesteps(SCREAMING_SNAKE_CASE_	)

       for t in scheduler.timesteps:
            UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)
            UpperCamelCase__										=      scheduler.step(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	).prev_sample

       return sample






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





       '''simple docstring'''





       for timesteps in [100, 500, 1000]:
            self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      int:





       '''simple docstring'''





       for steps_offset in [0, 1]:
            self.check_over_configs(steps_offset=SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      self.scheduler_classes[0]
       UpperCamelCase__										=      self.get_scheduler_config(steps_offset=1	)
       UpperCamelCase__										=      scheduler_class(**SCREAMING_SNAKE_CASE_	)
       scheduler.set_timesteps(5	)
       assert torch.equal(scheduler.timesteps   ,				torch.LongTensor([801, 601, 401, 201, 1]	)	)






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





       '''simple docstring'''





       for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]   ,				[0.002, 0.02, 0.2, 2]	):
            self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_   ,				beta_end=SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      str:





       '''simple docstring'''





       for schedule in ["linear", "squaredcos_cap_v2"]:
            self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





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






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





       '''simple docstring'''





       for clip_sample in [True, False]:
            self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE_	)






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





       '''simple docstring'''





       for timestep_spacing in ["trailing", "leading"]:
            self.check_over_configs(timestep_spacing=SCREAMING_SNAKE_CASE_	)






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





       '''simple docstring'''





       for rescale_betas_zero_snr in [True, False]:
            self.check_over_configs(rescale_betas_zero_snr=SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      int:





       '''simple docstring'''





       self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE_	)
       for threshold in [0.5, 1.0, 2.0]:
            for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(
                     thresholding=SCREAMING_SNAKE_CASE_   ,				prediction_type=SCREAMING_SNAKE_CASE_   ,				sample_max_value=SCREAMING_SNAKE_CASE_   ,				)






  def 		_a					(self	)		->      int:





       '''simple docstring'''





       for t in [1, 10, 49]:
            self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_	)






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





       '''simple docstring'''





       for t, num_inference_steps in zip([1, 10, 50]   ,				[10, 50, 500]	):
            self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_   ,				num_inference_steps=SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      int:





       '''simple docstring'''





       for t, eta in zip([1, 10, 49]   ,				[0.0, 0.5, 1.0]	):
            self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_   ,				eta=SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      self.scheduler_classes[0]
       UpperCamelCase__										=      self.get_scheduler_config()
       UpperCamelCase__										=      scheduler_class(**SCREAMING_SNAKE_CASE_	)

       assert torch.sum(torch.abs(scheduler._get_variance(0   ,				0	) - 0.0	)	) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(420   ,				400	) - 0.14771	)	) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(980   ,				960	) - 0.32460	)	) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(0   ,				0	) - 0.0	)	) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(487   ,				486	) - 0.00979	)	) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(999   ,				998	) - 0.02	)	) < 1E-5






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





       '''simple docstring'''





       UpperCamelCase__										=      self.scheduler_classes[0]
       UpperCamelCase__										=      self.get_scheduler_config()
       UpperCamelCase__										=      scheduler_class(**SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__						,       UpperCamelCase__										=      10, 0.0
       scheduler.set_timesteps(SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      self.dummy_model()
       UpperCamelCase__										=      self.dummy_sample_deter
       UpperCamelCase__										=      self.dummy_sample_deter + 0.1
       UpperCamelCase__										=      self.dummy_sample_deter - 0.1

       UpperCamelCase__										=      samplea.shape[0]
       UpperCamelCase__										=      torch.stack([samplea, samplea, samplea]   ,				dim=0	)
       UpperCamelCase__										=      torch.arange(SCREAMING_SNAKE_CASE_	)[0:3, None].repeat(1   ,				SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      model(samples.flatten(0   ,				1	)   ,				timesteps.flatten(0   ,				1	)	)
       UpperCamelCase__										=      scheduler.batch_step_no_noise(SCREAMING_SNAKE_CASE_   ,				timesteps.flatten(0   ,				1	)   ,				samples.flatten(0   ,				1	)   ,				SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      torch.sum(torch.abs(SCREAMING_SNAKE_CASE_	)	)
       UpperCamelCase__										=      torch.mean(torch.abs(SCREAMING_SNAKE_CASE_	)	)

       assert abs(result_sum.item() - 1147.7904	) < 1E-2
       assert abs(result_mean.item() - 0.4982	) < 1E-3






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





       '''simple docstring'''





       UpperCamelCase__										=      self.full_loop()

       UpperCamelCase__										=      torch.sum(torch.abs(SCREAMING_SNAKE_CASE_	)	)
       UpperCamelCase__										=      torch.mean(torch.abs(SCREAMING_SNAKE_CASE_	)	)

       assert abs(result_sum.item() - 172.0067	) < 1E-2
       assert abs(result_mean.item() - 0.223967	) < 1E-3






  def 		_a					(self	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      self.full_loop(prediction_type='''v_prediction'''	)

       UpperCamelCase__										=      torch.sum(torch.abs(SCREAMING_SNAKE_CASE_	)	)
       UpperCamelCase__										=      torch.mean(torch.abs(SCREAMING_SNAKE_CASE_	)	)

       assert abs(result_sum.item() - 52.5302	) < 1E-2
       assert abs(result_mean.item() - 0.0684	) < 1E-3






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE_   ,				beta_start=0.01	)
       UpperCamelCase__										=      torch.sum(torch.abs(SCREAMING_SNAKE_CASE_	)	)
       UpperCamelCase__										=      torch.mean(torch.abs(SCREAMING_SNAKE_CASE_	)	)

       assert abs(result_sum.item() - 149.8295	) < 1E-2
       assert abs(result_mean.item() - 0.1951	) < 1E-3






  def 		_a					(self	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE_   ,				beta_start=0.01	)
       UpperCamelCase__										=      torch.sum(torch.abs(SCREAMING_SNAKE_CASE_	)	)
       UpperCamelCase__										=      torch.mean(torch.abs(SCREAMING_SNAKE_CASE_	)	)

       assert abs(result_sum.item() - 149.0784	) < 1E-2
       assert abs(result_mean.item() - 0.1941	) < 1E-3




import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


__magic_name__								=logging.get_logger(__name__)



class     _A   (    __UpperCamelCase							):






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





       '''simple docstring'''





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



