



'''simple docstring'''


import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest







class 	UpperCAmelCase	(		__a):


 '''simple docstring'''




 __magic_name__					: Dict  =      (DDPMScheduler,)




 def       lowercase_  (   self							,  **lowerCAmelCase_) ->      Any:



      """simple docstring"""







      a_       		={
          "num_train_timesteps": 1_0_0_0,
          "beta_start": 0.0_0_0_1,
          "beta_end": 0.0_2,
          "beta_schedule": "linear",
          "variance_type": "fixed_small",
          "clip_sample": True,
      }

      config.update(**lowerCAmelCase_)
      return config




 def       lowercase_  (   self) ->      Dict:



      """simple docstring"""







      for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
           self.check_over_configs(num_train_timesteps=lowerCAmelCase_)




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



      """simple docstring"""







      for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]							,  [0.0_0_2, 0.0_2, 0.2, 2]):
           self.check_over_configs(beta_start=lowerCAmelCase_							,  beta_end=lowerCAmelCase_)




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



      """simple docstring"""







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




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



      """simple docstring"""







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




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



      """simple docstring"""







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




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



      """simple docstring"""







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




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



      """simple docstring"""







      for t in [0, 5_0_0, 9_9_9]:
           self.check_over_forward(time_step=lowerCAmelCase_)




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



      """simple docstring"""







      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(4_8_7) - 0.0_0_9_7_9)) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(9_9_9) - 0.0_2)) < 1e-5




 def       lowercase_  (   self) ->      Tuple:



      """simple docstring"""







      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() - 2_5_8.9_6_0_6) < 1e-2
      assert abs(result_mean.item() - 0.3_3_7_2) < 1e-3




 def       lowercase_  (   self) ->      Tuple:



      """simple docstring"""







      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() - 2_0_2.0_2_9_6) < 1e-2
      assert abs(result_mean.item() - 0.2_6_3_1) < 1e-3




 def       lowercase_  (   self) ->      Any:



      """simple docstring"""







      a_       		=self.scheduler_classes[0]
      a_       		=self.get_scheduler_config()
      a_       		=scheduler_class(**lowerCAmelCase_)

      a_       		=[1_0_0, 8_7, 5_0, 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       lowercase_  (   self) ->      Dict:



      """simple docstring"""







      a_       		=self.scheduler_classes[0]
      a_       		=self.get_scheduler_config()
      a_       		=scheduler_class(**lowerCAmelCase_)

      a_       		=[1_0_0, 8_7, 5_0, 5_1, 0]

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




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



      """simple docstring"""







      a_       		=self.scheduler_classes[0]
      a_       		=self.get_scheduler_config()
      a_       		=scheduler_class(**lowerCAmelCase_)

      a_       		=[1_0_0, 8_7, 5_0, 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       lowercase_  (   self) ->      List[str]:



      """simple docstring"""







      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_)







'''simple docstring'''


import torch

from diffusers import StableDiffusionPipeline


lowercase							       =			'''path-to-your-trained-model'''
lowercase							       =			StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')

lowercase							       =			'''A photo of sks dog in a bucket'''
lowercase							       =			pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save('''dog-bucket.png''')


