


'''simple docstring'''







import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    (DDPMScheduler,)







     def a	( self      ,	**snake_case__   ):

        '''simple docstring'''

        _lowerCAmelCase    :							Union[str, Any]   			=       {
            'num_train_timesteps': 1000,
            'beta_start': 0.0001,
            'beta_end': 0.02,
            'beta_schedule': 'linear',
            'variance_type': 'fixed_small',
            'clip_sample': True,
        }

        config.update(**snake_case__   )
        return config







     def a	( self   ):

        '''simple docstring'''

        for timesteps in [1, 5, 100, 1000]:
           self.check_over_configs(num_train_timesteps=snake_case__   )







     def a	( self   ):

        '''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=snake_case__      ,	beta_end=snake_case__   )







     def a	( self   ):

        '''simple docstring'''

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







     def a	( self   ):

        '''simple docstring'''

        for variance in ["fixed_small", "fixed_large", "other"]:
           self.check_over_configs(variance_type=snake_case__   )







     def a	( self   ):

        '''simple docstring'''

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







     def a	( self   ):

        '''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 a	( self   ):

        '''simple docstring'''

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







     def a	( self   ):

        '''simple docstring'''

        for t in [0, 500, 999]:
           self.check_over_forward(time_step=snake_case__   )







     def a	( self   ):

        '''simple docstring'''

        _lowerCAmelCase    :							List[Any]   			=       self.scheduler_classes[0]
        _lowerCAmelCase    :							Optional[Any]   			=       self.get_scheduler_config()
        _lowerCAmelCase    :							List[Any]   			=       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(487   ) - 0.0_0979   )   ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(999   ) - 0.02   )   ) < 1E-5







     def a	( self   ):

        '''simple docstring'''

        _lowerCAmelCase    :							Optional[Any]   			=       self.scheduler_classes[0]
        _lowerCAmelCase    :							Optional[Any]   			=       self.get_scheduler_config()
        _lowerCAmelCase    :							List[Any]   			=       scheduler_class(**snake_case__   )

        _lowerCAmelCase    :							Optional[int]   			=       len(snake_case__   )

        _lowerCAmelCase    :							str   			=       self.dummy_model()
        _lowerCAmelCase    :							str   			=       self.dummy_sample_deter
        _lowerCAmelCase    :							Union[str, Any]   			=       torch.manual_seed(0   )

        for t in reversed(range(snake_case__   )   ):
           # 1. predict noise residual
           _lowerCAmelCase    :							List[Any]   			=       model(snake_case__      ,	snake_case__   )

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

           # if t > 0:
           #     noise = self.dummy_sample_deter
           #     variance = scheduler.get_variance(t) ** (0.5) * noise
           #
           # sample = pred_prev_sample + variance
           _lowerCAmelCase    :							Dict   			=       pred_prev_sample

        _lowerCAmelCase    :							Dict   			=       torch.sum(torch.abs(snake_case__   )   )
        _lowerCAmelCase    :							List[str]   			=       torch.mean(torch.abs(snake_case__   )   )

        assert abs(result_sum.item() - 258.9606   ) < 1E-2
        assert abs(result_mean.item() - 0.3372   ) < 1E-3







     def a	( self   ):

        '''simple docstring'''

        _lowerCAmelCase    :							int   			=       self.scheduler_classes[0]
        _lowerCAmelCase    :							Any   			=       self.get_scheduler_config(prediction_type='v_prediction'   )
        _lowerCAmelCase    :							Union[str, Any]   			=       scheduler_class(**snake_case__   )

        _lowerCAmelCase    :							Optional[int]   			=       len(snake_case__   )

        _lowerCAmelCase    :							Any   			=       self.dummy_model()
        _lowerCAmelCase    :							Tuple   			=       self.dummy_sample_deter
        _lowerCAmelCase    :							Optional[int]   			=       torch.manual_seed(0   )

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

           # 2. predict previous mean of sample x_t-1
           _lowerCAmelCase    :							Dict   			=       scheduler.step(snake_case__      ,	snake_case__      ,	snake_case__      ,	generator=snake_case__   ).prev_sample

           # if t > 0:
           #     noise = self.dummy_sample_deter
           #     variance = scheduler.get_variance(t) ** (0.5) * noise
           #
           # sample = pred_prev_sample + variance
           _lowerCAmelCase    :							Tuple   			=       pred_prev_sample

        _lowerCAmelCase    :							Any   			=       torch.sum(torch.abs(snake_case__   )   )
        _lowerCAmelCase    :							Dict   			=       torch.mean(torch.abs(snake_case__   )   )

        assert abs(result_sum.item() - 202.0296   ) < 1E-2
        assert abs(result_mean.item() - 0.2631   ) < 1E-3







     def a	( self   ):

        '''simple docstring'''

        _lowerCAmelCase    :							List[Any]   			=       self.scheduler_classes[0]
        _lowerCAmelCase    :							Optional[int]   			=       self.get_scheduler_config()
        _lowerCAmelCase    :							Union[str, Any]   			=       scheduler_class(**snake_case__   )

        _lowerCAmelCase    :							Union[str, Any]   			=       [100, 87, 50, 1, 0]

        scheduler.set_timesteps(timesteps=snake_case__   )

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

        for i, timestep in enumerate(snake_case__   ):
           if i == len(snake_case__   ) - 1:
              _lowerCAmelCase    :							str   			=       -1
           else:
              _lowerCAmelCase    :							Optional[Any]   			=       timesteps[i + 1]

           _lowerCAmelCase    :							int   			=       scheduler.previous_timestep(snake_case__   )
           _lowerCAmelCase    :							int   			=       prev_t.item()

           self.assertEqual(snake_case__      ,	snake_case__   )







     def a	( self   ):

        '''simple docstring'''

        _lowerCAmelCase    :							Optional[int]   			=       self.scheduler_classes[0]
        _lowerCAmelCase    :							Tuple   			=       self.get_scheduler_config()
        _lowerCAmelCase    :							List[Any]   			=       scheduler_class(**snake_case__   )

        _lowerCAmelCase    :							Optional[int]   			=       [100, 87, 50, 51, 0]

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







     def a	( self   ):

        '''simple docstring'''

        _lowerCAmelCase    :							Optional[int]   			=       self.scheduler_classes[0]
        _lowerCAmelCase    :							List[str]   			=       self.get_scheduler_config()
        _lowerCAmelCase    :							Union[str, Any]   			=       scheduler_class(**snake_case__   )

        _lowerCAmelCase    :							Optional[int]   			=       [100, 87, 50, 1, 0]
        _lowerCAmelCase    :							int   			=       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 a	( self   ):

        '''simple docstring'''

        _lowerCAmelCase    :							Optional[int]   			=       self.scheduler_classes[0]
        _lowerCAmelCase    :							int   			=       self.get_scheduler_config()
        _lowerCAmelCase    :							Any   			=       scheduler_class(**snake_case__   )

        _lowerCAmelCase    :							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'''







from typing import Any






def 			lowercase				(_A					):







       """simple docstring"""







       if not input_list:
              return []
       _lowerCAmelCase    :							Optional[int]   			=       [input_list.count(_A					) for value in input_list]
       _lowerCAmelCase    :							int   			=       max(_A					)  # Gets the maximum count in the input list.
       # Gets values of modes
       return sorted({input_list[i] for i, value in enumerate(_A					) if value == y}					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()

