'''simple docstring'''





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     Dict		=(DDPMScheduler,)



     def 			a__  (					self				,      **lowerCAmelCase__       ):
            _A=	{
                'num_train_timesteps': 1000,
                'beta_start': 0.0001,
                'beta_end': 0.02,
                'beta_schedule': 'linear',
                'variance_type': 'fixed_small',
                'clip_sample': True,
            }

            config.update(**lowerCAmelCase__       )
            return config



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



     def 			a__  (					self       ):
            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=lowerCAmelCase__				,      beta_end=lowerCAmelCase__       )



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



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



     def 			a__  (					self       ):
            for clip_sample in [True, False]:
                   self.check_over_configs(clip_sample=lowerCAmelCase__       )



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



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



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



     def 			a__  (					self       ):
            _A=	self.scheduler_classes[0]
            _A=	self.get_scheduler_config()
            _A=	scheduler_class(**lowerCAmelCase__       )

            assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 0.0       )       ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(487       ) - 0.00979       )       ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(999       ) - 0.02       )       ) < 1E-5



     def 			a__  (					self       ):
            _A=	self.scheduler_classes[0]
            _A=	self.get_scheduler_config()
            _A=	scheduler_class(**lowerCAmelCase__       )

            _A=	len(lowerCAmelCase__       )

            _A=	self.dummy_model()
            _A=	self.dummy_sample_deter
            _A=	torch.manual_seed(0       )

            for t in reversed(range(lowerCAmelCase__       )       ):
                   # 1. predict noise residual
                   _A=	model(lowerCAmelCase__				,      lowerCAmelCase__       )

                   # 2. predict previous mean of sample x_t-1
                   _A=	scheduler.step(lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      generator=lowerCAmelCase__       ).prev_sample

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

            _A=	torch.sum(torch.abs(lowerCAmelCase__       )       )
            _A=	torch.mean(torch.abs(lowerCAmelCase__       )       )

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



     def 			a__  (					self       ):
            _A=	self.scheduler_classes[0]
            _A=	self.get_scheduler_config(prediction_type='v_prediction'       )
            _A=	scheduler_class(**lowerCAmelCase__       )

            _A=	len(lowerCAmelCase__       )

            _A=	self.dummy_model()
            _A=	self.dummy_sample_deter
            _A=	torch.manual_seed(0       )

            for t in reversed(range(lowerCAmelCase__       )       ):
                   # 1. predict noise residual
                   _A=	model(lowerCAmelCase__				,      lowerCAmelCase__       )

                   # 2. predict previous mean of sample x_t-1
                   _A=	scheduler.step(lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      generator=lowerCAmelCase__       ).prev_sample

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

            _A=	torch.sum(torch.abs(lowerCAmelCase__       )       )
            _A=	torch.mean(torch.abs(lowerCAmelCase__       )       )

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



     def 			a__  (					self       ):
            _A=	self.scheduler_classes[0]
            _A=	self.get_scheduler_config()
            _A=	scheduler_class(**lowerCAmelCase__       )

            _A=	[100, 87, 50, 1, 0]

            scheduler.set_timesteps(timesteps=lowerCAmelCase__       )

            _A=	scheduler.timesteps

            for i, timestep in enumerate(lowerCAmelCase__       ):
                   if i == len(lowerCAmelCase__       ) - 1:
                          _A=	-1
                   else:
                          _A=	timesteps[i + 1]

                   _A=	scheduler.previous_timestep(lowerCAmelCase__       )
                   _A=	prev_t.item()

                   self.assertEqual(lowerCAmelCase__				,      lowerCAmelCase__       )



     def 			a__  (					self       ):
            _A=	self.scheduler_classes[0]
            _A=	self.get_scheduler_config()
            _A=	scheduler_class(**lowerCAmelCase__       )

            _A=	[100, 87, 50, 51, 0]

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



     def 			a__  (					self       ):
            _A=	self.scheduler_classes[0]
            _A=	self.get_scheduler_config()
            _A=	scheduler_class(**lowerCAmelCase__       )

            _A=	[100, 87, 50, 1, 0]
            _A=	len(lowerCAmelCase__       )

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





     def 			a__  (					self       ):
            _A=	self.scheduler_classes[0]
            _A=	self.get_scheduler_config()
            _A=	scheduler_class(**lowerCAmelCase__       )

            _A=	[scheduler.config.num_train_timesteps]

            with self.assertRaises(
                lowerCAmelCase__				,      msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'				,      ):
                   scheduler.set_timesteps(timesteps=lowerCAmelCase__       )
from ..utils import DummyObject, requires_backends





class      lowerCAmelCase						(					metaclass=_a			):
     _SCREAMING_SNAKE_CASE     :     Optional[int]		=["""transformers""", """torch""", """note_seq"""]



     def __init__(					self				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            requires_backends(self				,      ['transformers', 'torch', 'note_seq']       )



     @classmethod
     def 			a__  (					cls				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            requires_backends(cls				,      ['transformers', 'torch', 'note_seq']       )





     @classmethod
     def 			a__  (					cls				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            requires_backends(cls				,      ['transformers', 'torch', 'note_seq']       )