import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class 			UpperCAmelCase__    (		snake_case					):



  """simple docstring"""






  lowerCAmelCase__		:	Union[str, Any]    		= (DDPMScheduler,)






  def 	_UpperCAmelCase				(				self:			Dict				,				**__lowerCAmelCase:			Union[str, Any]   ) -> Union[str, Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       {
        "num_train_timesteps": 1_000,
        "beta_start": 0.0001,
        "beta_end": 0.02,
        "beta_schedule": "linear",
        "variance_type": "fixed_small",
        "clip_sample": True,
    }

    config.update(**__lowerCAmelCase   )
    return config






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




    '''simple docstring'''




    for timesteps in [1, 5, 100, 1_000]:
      self.check_over_configs(num_train_timesteps=__lowerCAmelCase   )






  def 	_UpperCAmelCase				(				self:			Optional[int]   ) -> str:




    '''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=__lowerCAmelCase				,				beta_end=__lowerCAmelCase   )






  def 	_UpperCAmelCase				(				self:			int   ) -> Optional[Any]:




    '''simple docstring'''




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






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




    '''simple docstring'''




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






  def 	_UpperCAmelCase				(				self:			Optional[int]   ) -> str:




    '''simple docstring'''




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






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




    '''simple docstring'''




    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 	_UpperCAmelCase				(				self:			Any   ) -> Tuple:




    '''simple docstring'''




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






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




    '''simple docstring'''




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






  def 	_UpperCAmelCase				(				self:			Optional[Any]   ) -> List[str]:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.scheduler_classes[0]
    __UpperCAmelCase						    =       self.get_scheduler_config()
    __UpperCAmelCase						    =       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 	_UpperCAmelCase				(				self:			int   ) -> Optional[Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.scheduler_classes[0]
    __UpperCAmelCase						    =       self.get_scheduler_config()
    __UpperCAmelCase						    =       scheduler_class(**__lowerCAmelCase   )

    __UpperCAmelCase						    =       len(__lowerCAmelCase   )

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

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

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

    __UpperCAmelCase						    =       torch.sum(torch.abs(__lowerCAmelCase   )   )
    __UpperCAmelCase						    =       torch.mean(torch.abs(__lowerCAmelCase   )   )

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






  def 	_UpperCAmelCase				(				self:			Any   ) -> List[str]:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.scheduler_classes[0]
    __UpperCAmelCase						    =       self.get_scheduler_config(prediction_type="v_prediction"   )
    __UpperCAmelCase						    =       scheduler_class(**__lowerCAmelCase   )

    __UpperCAmelCase						    =       len(__lowerCAmelCase   )

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

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

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

    __UpperCAmelCase						    =       torch.sum(torch.abs(__lowerCAmelCase   )   )
    __UpperCAmelCase						    =       torch.mean(torch.abs(__lowerCAmelCase   )   )

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






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




    '''simple docstring'''




    __UpperCAmelCase						    =       self.scheduler_classes[0]
    __UpperCAmelCase						    =       self.get_scheduler_config()
    __UpperCAmelCase						    =       scheduler_class(**__lowerCAmelCase   )

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

    scheduler.set_timesteps(timesteps=__lowerCAmelCase   )

    __UpperCAmelCase						    =       scheduler.timesteps

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

      __UpperCAmelCase						    =       scheduler.previous_timestep(__lowerCAmelCase   )
      __UpperCAmelCase						    =       prev_t.item()

      self.assertEqual(__lowerCAmelCase				,				__lowerCAmelCase   )






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




    '''simple docstring'''




    __UpperCAmelCase						    =       self.scheduler_classes[0]
    __UpperCAmelCase						    =       self.get_scheduler_config()
    __UpperCAmelCase						    =       scheduler_class(**__lowerCAmelCase   )

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

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






  def 	_UpperCAmelCase				(				self:			Any   ) -> Dict:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.scheduler_classes[0]
    __UpperCAmelCase						    =       self.get_scheduler_config()
    __UpperCAmelCase						    =       scheduler_class(**__lowerCAmelCase   )

    __UpperCAmelCase						    =       [100, 87, 50, 1, 0]
    __UpperCAmelCase						    =       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 	_UpperCAmelCase				(				self:			Any   ) -> Dict:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.scheduler_classes[0]
    __UpperCAmelCase						    =       self.get_scheduler_config()
    __UpperCAmelCase						    =       scheduler_class(**__lowerCAmelCase   )

    __UpperCAmelCase						    =       [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   )




import inspect
import unittest


class 			UpperCAmelCase__    (		unittest.TestCase					):



  """simple docstring"""






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




    '''simple docstring'''




    try:
      import diffusers  # noqa: F401
    except ImportError:
      assert False






  def 	_UpperCAmelCase				(				self:			Dict   ) -> Dict:




    '''simple docstring'''




    import diffusers
    from diffusers.dependency_versions_table import deps

    __UpperCAmelCase						    =       inspect.getmembers(__lowerCAmelCase				,				inspect.isclass   )

    for cls_name, cls_module in all_classes:
      if "dummy_" in cls_module.__module__:
        for backend in cls_module._backends:
          if backend == "k_diffusion":
            __UpperCAmelCase						    =       "k-diffusion"
          elif backend == "invisible_watermark":
            __UpperCAmelCase						    =       "invisible-watermark"
          assert backend in deps, F'''{backend} is not in the deps table!'''



