

import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= (DDPMScheduler,)






  def 				lowercase_		(							self						:		int     ,       **__lowerCamelCase						:		Tuple )		->							Any:
       SCREAMING_SNAKE_CASE__   =   {
           '''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 				lowercase_		(							self						:		Tuple )		->							str:
       for timesteps in [1, 5, 100, 1000]:
            self.check_over_configs(num_train_timesteps=__lowerCamelCase )






  def 				lowercase_		(							self						:		Any )		->							Tuple:
       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 				lowercase_		(							self						:		Dict )		->							int:
       for schedule in ["linear", "squaredcos_cap_v2"]:
            self.check_over_configs(beta_schedule=__lowerCamelCase )






  def 				lowercase_		(							self						:		Union[str, Any] )		->							List[str]:
       for variance in ["fixed_small", "fixed_large", "other"]:
            self.check_over_configs(variance_type=__lowerCamelCase )






  def 				lowercase_		(							self						:		str )		->							int:
       for clip_sample in [True, False]:
            self.check_over_configs(clip_sample=__lowerCamelCase )






  def 				lowercase_		(							self						:		Optional[int] )		->							Optional[Any]:
       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 				lowercase_		(							self						:		Dict )		->							int:
       for prediction_type in ["epsilon", "sample", "v_prediction"]:
            self.check_over_configs(prediction_type=__lowerCamelCase )






  def 				lowercase_		(							self						:		str )		->							Optional[int]:
       for t in [0, 500, 999]:
            self.check_over_forward(time_step=__lowerCamelCase )






  def 				lowercase_		(							self						:		int )		->							int:
       SCREAMING_SNAKE_CASE__   =   self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__   =   self.get_scheduler_config()
       SCREAMING_SNAKE_CASE__   =   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 				lowercase_		(							self						:		Union[str, Any] )		->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__   =   self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__   =   self.get_scheduler_config()
       SCREAMING_SNAKE_CASE__   =   scheduler_class(**__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   len(__lowerCamelCase )

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

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

            # 2. predict previous mean of sample x_t-1
            SCREAMING_SNAKE_CASE__   =   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
            SCREAMING_SNAKE_CASE__   =   pred_prev_sample

       SCREAMING_SNAKE_CASE__   =   torch.sum(torch.abs(__lowerCamelCase ) )
       SCREAMING_SNAKE_CASE__   =   torch.mean(torch.abs(__lowerCamelCase ) )

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






  def 				lowercase_		(							self						:		Union[str, Any] )		->							Optional[int]:
       SCREAMING_SNAKE_CASE__   =   self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__   =   self.get_scheduler_config(prediction_type='''v_prediction''' )
       SCREAMING_SNAKE_CASE__   =   scheduler_class(**__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   len(__lowerCamelCase )

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

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

            # 2. predict previous mean of sample x_t-1
            SCREAMING_SNAKE_CASE__   =   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
            SCREAMING_SNAKE_CASE__   =   pred_prev_sample

       SCREAMING_SNAKE_CASE__   =   torch.sum(torch.abs(__lowerCamelCase ) )
       SCREAMING_SNAKE_CASE__   =   torch.mean(torch.abs(__lowerCamelCase ) )

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






  def 				lowercase_		(							self						:		Any )		->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__   =   self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__   =   self.get_scheduler_config()
       SCREAMING_SNAKE_CASE__   =   scheduler_class(**__lowerCamelCase )

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

       scheduler.set_timesteps(timesteps=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   scheduler.timesteps

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

            SCREAMING_SNAKE_CASE__   =   scheduler.previous_timestep(__lowerCamelCase )
            SCREAMING_SNAKE_CASE__   =   prev_t.item()

            self.assertEqual(__lowerCamelCase     ,       __lowerCamelCase )






  def 				lowercase_		(							self						:		Dict )		->							Optional[int]:
       SCREAMING_SNAKE_CASE__   =   self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__   =   self.get_scheduler_config()
       SCREAMING_SNAKE_CASE__   =   scheduler_class(**__lowerCamelCase )

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

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






  def 				lowercase_		(							self						:		List[Any] )		->							Dict:
       SCREAMING_SNAKE_CASE__   =   self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__   =   self.get_scheduler_config()
       SCREAMING_SNAKE_CASE__   =   scheduler_class(**__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   [100, 87, 50, 1, 0]
       SCREAMING_SNAKE_CASE__   =   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 				lowercase_		(							self						:		List[Any] )		->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__   =   self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__   =   self.get_scheduler_config()
       SCREAMING_SNAKE_CASE__   =   scheduler_class(**__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   [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 is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
       from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline


